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Previous training models in India were short‑term courses predominantly based on didactic teaching ranging from 2 to 5 days and covering only the basics of infectious disease modelling without being deliverable‑driven and devoid of long‑term mentorship. Following the COVID‑19 pandemic, several disease modelling experts have come together to form groups such as the National Disease Modelling Consortium and the Indian Scientists’ Response to CoViD‑19 (ISRC) to develop India‑specific disease models to aid national policy‑makers make informed decisions and improve disease control and elimination efforts . However, training and mentorship have never been at the forefront of their agenda. Recognising this gap in training, the Department of Health Research (DHR), which is the Government of India body for research in India, released a call for applications under the Human Resource Development Scheme to design long‑term capacity‑building programs in key priority areas, including infectious disease modelling. In response to the call, we proposed a 3‑month post‑graduate (PG) certificate course in infectious disease modelling in hybrid mode to the DHR in order to build a team of infectious disease modellers who can prove to be a great asset in tackling future pandemics and emerging threats. Following sanction by the DHR, we developed the course structure and curriculum and delivered the first cycle of the course during July to September 2024, producing the first cohort of 20 infectious disease modeilers in the country. The course curriculum was guided by Kolb’s experiential learning theory, which is an andragogical approach to learning focussing on real‑world experiences and practical applications . This was the first such course on infectious disease modelling in India. The structure, content and key components of the first course, along with the strengths, challenges and way forward from the participants’ and facilitators’ perspective, are discussed in this paper below.
|
PMC11697579_p5
|
PMC11697579
|
Introduction
| 4.075498 |
biomedical
|
Study
|
[
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[
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0.013212282210588455,
0.0005781775107607245
] |
en
| 0.999997 |
Study design: This was a mixed‑methods approach to evaluate a capacity‑building program on infectious disease modelling.
|
PMC11697579_p6
|
PMC11697579
|
Methods
| 2.734646 |
biomedical
|
Study
|
[
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[
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0.0007015233277343214,
0.00025732300127856433
] |
en
| 0.999996 |
Study setting: We describe the design and development of the capacity‑building model below.
|
PMC11697579_p7
|
PMC11697579
|
Methods
| 1.986776 |
biomedical
|
Study
|
[
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0.0029713083058595657,
0.03639649972319603
] |
[
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0.03467649966478348,
0.0007850078982301056,
0.0006444684695452452
] |
en
| 0.999994 |
This is a learning‑by‑doing model of andragogical training conceived and designed by faculty from the All India Institute of Medical Sciences (AIIMS) Nagpur, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, and the Indian Council of Medical Research (ICMR), New Delhi, India. The faculty are experienced epidemiologists with special interest and expertise in infectious disease epidemiology and modelling. The development of the curriculum was guided by Kolb’s experiential learning theory . There are four stages, which begin with having a concrete learning experience, followed by reflective observation and abstract conceptualisation, and ending with them actively experimenting with the knowledge they gained.
|
PMC11697579_p8
|
PMC11697579
|
Course development
| 2.44408 |
biomedical
|
Other
|
[
0.9769784808158875,
0.0036379152443259954,
0.01938359998166561
] |
[
0.016055967658758163,
0.9818453788757324,
0.0014092937344685197,
0.0006894405232742429
] |
en
| 0.999997 |
We delivered concrete learning experiences through a series of online lectures, recorded videos, self‑reading materials and practical exercises with reflections after each practical exercise, open discussion forums and Q&As. We built in opportunities for the participants to conceptualise the process through biweekly assignments which were reviewed, and in‑depth inputs were provided. Every participant was supposed to submit a project by the 11th week involving designing and optimising a specific infectious disease model by applying the knowledge learnt during the course. This provided the participants with the chance to experiment with their newly gained insights in a practice situation in a highly mentored environment.
|
PMC11697579_p9
|
PMC11697579
|
Course development
| 2.323009 |
biomedical
|
Other
|
[
0.886598527431488,
0.005951880477368832,
0.10744954645633698
] |
[
0.0645902007818222,
0.93156898021698,
0.0026427432894706726,
0.0011980881681665778
] |
en
| 0.999996 |
The overall goal of this initiative was to strengthen the capacity in mathematical disease modelling to enhance their use in decision‑making and effective communication of modelling outputs to policy‑makers in India.
|
PMC11697579_p10
|
PMC11697579
|
Course goal
| 1.633285 |
biomedical
|
Other
|
[
0.6851183176040649,
0.0035530978348106146,
0.31132856011390686
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[
0.007369940634816885,
0.9912943243980408,
0.0010280533460900187,
0.00030769853037782013
] |
en
| 0.999996 |
The course participants included regular faculty/scientists/PhD students/post‑doctoral students from medical colleges and research institutes, biostatisticians, veterinarians, public health and clinical researchers from government institutes, non‑governmental organisations (NGOs) or other organisations from India and policy‑makers and disease control professionals with interest and background in infectious disease modelling. Specialist mathematical training was not a prerequisite. However, some familiarity with spreadsheet packages (Microsoft Excel) was desirable.
|
PMC11697579_p11
|
PMC11697579
|
Selection of participants
| 1.8594 |
biomedical
|
Other
|
[
0.9437881708145142,
0.0033832488115876913,
0.05282856523990631
] |
[
0.033275995403528214,
0.9632202386856079,
0.0025568350683897734,
0.0009469028445892036
] |
en
| 0.999997 |
Selection of participants was competitive and individuals with prior experience in the infectious disease domain and those who committed to taking this capacity‑building initiative forward in their respective institutions were preferred. The applications were scored using a structured scoring sheet. The criteria used to score were: highest educational qualification, graduation marks, work experience in the field of infectious diseases, publications and research projects in the domain of infectious diseases and any fellowship/diploma/PG or any equivalent course in infectious diseases. No course fee was charged to the participants.
|
PMC11697579_p12
|
PMC11697579
|
Selection of participants
| 2.04453 |
biomedical
|
Study
|
[
0.956475019454956,
0.003653136780485511,
0.03987192362546921
] |
[
0.6531948447227478,
0.342856764793396,
0.0028501534834504128,
0.0010981784434989095
] |
en
| 0.999997 |
A participant successfully completes the course and gets the certificate if they fulfil all the following criteria: Attends at least 75% of the online sessions Submits all four assignments and the project work to the satisfaction of the facilitators before the deadline Attends all the offline contact sessions Completes the final exit examination scoring at least 50% marks
|
PMC11697579_p13
|
PMC11697579
|
Who is a successful participant?
| 1.142684 |
other
|
Other
|
[
0.018372105434536934,
0.0013067913241684437,
0.9803211688995361
] |
[
0.0026834486052393913,
0.9962934851646423,
0.0006088973605073988,
0.00041409541154280305
] |
en
| 0.999997 |
The details of the course structure and the delivery of the course are described in Table 1 and Figure 1 . Supplementary appendix 1 shows the details of the curriculum including the week‑wise course content and the teaching learning methods. The course was delivered via a six‑step process: 8‑week online course: This was delivered through live online video lectures, online software demonstrations and exercises, once weekly live discussion forums, bi‑weekly assignments and reading materials. The total duration of teaching was around 45 hours per week. Bi‑weekly assignments: After the completion of every 2 weeks, assignments were given. All four assignments had to be submitted within the specified deadlines. (Milestones 1‑4). Project work: A project‑based assignment was given wherein they will be practically applying the principles learnt. The final project report had to be submitted before the completion of the 10th week of the course. The format in which the project report is to be submitted is given in Supplementary appendix 2 (Milestone 5). Online revision and discussion classes in Week 11. 3‑day contact programme: A 3‑day contact programme was held in the 12th week of the course to discuss and revise the key concepts, clarify doubts and give them a mentored hands‑on practice on the key exercises. Exit examination: The contact programme was followed by an exit examination the very next day. Table 1 Details about the course structure and delivery. DOMAIN COURSE DETAILS Approach Deliverable‑driven hands‑on approach to training with intensive mentorship during practicum and in‑person sessions Mode of delivery Hybrid mode (online video lectures, discussions and demonstrations, offline contact programme and exit examination) Target trainees Public health professionals, medical college faculty, biostatisticians, microbiologists and scientists working in the domain of infectious diseases Duration of the course 12 weeks (including 8 weeks of online training, followed by assignments and project submission, face‑to‑face contact session and examination) Deliverables Four assignments and project work Course advertisement Advertised within the priority organisations, professional networks and on social media such as LinkedIn, Facebook and Twitter Trainee selection and number A total of 24 participants were selected on the basis of their previous clinical, programmatic or research experience in the domain of infectious diseases out of 224 applications received. Course format Live online video lectures, online hands‑on practical exercises and software demonstrations, live discussion forums and Q&A, case studies, journal clubs, bi‑weekly assignments and project submission, 3‑day contact session followed by final exit examination Course fee No course fee was charged to the participants. However, the participants had to bear their cost of travel, accommodation and other expenses during the face‑to‑face contact session and the exit examination Assessment Formative assessment: Four assignments (25 marks each) Summative assessment (100 marks): End‑of‑course exit examination (75 marks) Theory (50 marks) Practical (25 marks) End‑of‑course project submission (25 marks) Course feedback and evaluation Participants evaluated the structure and content of the training at the end of each week of training through formal and informal feedback mechanisms to inform subsequent sessions. In addition, trainees provided overall evaluation of the course at the end of the training, including training logistics. Figure 1 Details about the course structure, modes of delivery, milestones, deliverables and assessment methods. Course structure, teaching/learning methods, milestones, deliverables, and assessment methods
|
PMC11697579_p14
|
PMC11697579
|
Capacity‑Building model
| 4.319824 |
biomedical
|
Study
|
[
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0.009145821444690228,
0.011668414808809757
] |
[
0.937449038028717,
0.05459732189774513,
0.007210724987089634,
0.0007429688703268766
] |
en
| 0.999997 |
Table 2 provides details about the week‑wise course topics and milestones.
|
PMC11697579_p15
|
PMC11697579
|
Capacity‑Building model
| 1.44063 |
other
|
Other
|
[
0.1993340700864792,
0.0029422861989587545,
0.797723650932312
] |
[
0.022036023437976837,
0.9730848073959351,
0.00400095758959651,
0.0008782385266385972
] |
en
| 0.999994 |
Study participants: The study population included all participants ( n = 20) who completed all milestones (required online and offline session attendance, submission of assignments and project work) and were eligible for the final exit examination and the course facilitators.
|
PMC11697579_p16
|
PMC11697579
|
Capacity‑Building model
| 2.006724 |
biomedical
|
Study
|
[
0.8129068613052368,
0.003307013539597392,
0.1837860643863678
] |
[
0.9841338992118835,
0.015323645435273647,
0.0002791983133647591,
0.00026323553174734116
] |
en
| 0.999997 |
Self‑administered, semi‑structured questionnaires were emailed to the course participants ( n = 20) via Google form after completion of the face‑to‑face offline sessions. Anonymous feedback was collected to get appropriate responses without any desirability bias. Identifying information and email IDs were not collected. The questionnaire included closed‑ended quantitative and open‑ended qualitative variables. The quantitative variables included feedback on the overall course content, learning objectives, balance between theory and hands‑on, delivery of the course, contribution of the course towards learning and skill and responsiveness of the facilitators. A five‑point Likert scale was used to record the responses. The qualitative variables included open‑ended questions assessing strengths (what worked well?), weaknesses (what did not work well?) and suggestions to improve the delivery of the course in subsequent cycles. Facilitators’ feedback regarding the strengths and weaknesses of the course and suggestions for improvement in the subsequent cycles was also taken in a meeting of the facilitators after the course.
|
PMC11697579_p17
|
PMC11697579
|
Data collection and variables
| 3.351123 |
biomedical
|
Study
|
[
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0.001905092503875494,
0.3142591416835785
] |
[
0.9893049001693726,
0.00980960763990879,
0.0006792182102799416,
0.0002062769199255854
] |
en
| 0.999996 |
The data (both quantitative and qualitative) were captured in MS Excel format. Quantitative variables were summarised using proportions. The responses ‘very good’ and ‘excellent’, as well as ‘agree’ and ‘strongly agree’, were combined to form a single category.
|
PMC11697579_p18
|
PMC11697579
|
Data analysis
| 1.956291 |
biomedical
|
Study
|
[
0.9715304970741272,
0.0023468462750315666,
0.02612261474132538
] |
[
0.9738599061965942,
0.024530913680791855,
0.0011212783865630627,
0.00048801262164488435
] |
en
| 0.999997 |
Manual descriptive content analysis of the textual responses to the open‑ended questions was carried out by two authors (J.P.T. and P.D.), who are experienced in qualitative research. Themes were generated in consensus using standard procedures by a deductive approach . Any disagreement between the two authors was resolved by mutual discussion. The participants were contacted again by email or telephone in case any clarification was required. Statements in italics represent direct quotes from the participants.
|
PMC11697579_p19
|
PMC11697579
|
Data analysis
| 3.081191 |
biomedical
|
Study
|
[
0.8756634593009949,
0.0011627718340605497,
0.1231737956404686
] |
[
0.9863014221191406,
0.01259517576545477,
0.0009516236605122685,
0.00015170629194471985
] |
en
| 0.999997 |
Obtaining feedback from the participants was performed as part of routine evaluation of the training mandated by the funding agency. Thus, approval from the ethics committee was not deemed necessary. Feedback was completely anonymous and participants were free not to respond to the questionnaires.
|
PMC11697579_p20
|
PMC11697579
|
Ethics approval
| 1.340062 |
other
|
Other
|
[
0.38478243350982666,
0.00425572507083416,
0.6109617948532104
] |
[
0.08107879012823105,
0.9167501926422119,
0.001269441214390099,
0.0009016150142997503
] |
en
| 0.999997 |
Out of 224 applicants, a total of 24 participants were selected for the first cohort. The mean age of the participants was 37.7 years (standard deviation 4.9), ranging from 29 to 48 years. About 42% ( n = 10) were female. Most of them belonged to a public health background ( n = 16, 66.6%), followed by biostatisticians ( n = 4, 16.7%) and microbiologists ( n = 4, 16.7%). Those from a public health background came from diverse domains including medical college faculty, scientists from ICMR institutes, junior and senior residents, national consultants working with the World Health Organization, state‑level public health administrators, etc. Only one of them (4.2%) had attended a course on mathematical modelling of infectious disease before. Of the 24 selected participants, 20 (83.3%) successfully completed the course; 1 dropped out of the course very early in the first month and the remaining 3 could not attend the contact workshop due to other competing personal or professional commitments, and thus were ineligible for the final exit examination.
|
PMC11697579_p21
|
PMC11697579
|
Background of the selected participants
| 3.010103 |
biomedical
|
Study
|
[
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0.002001193119212985,
0.002746343845501542
] |
[
0.996624231338501,
0.0030469573102891445,
0.00016997956845443696,
0.00015880628779996186
] |
en
| 0.999999 |
Out of 20 participants, about three‑fourths ( n = 15, 75%) felt that the contribution of the course towards enhancing their knowledge was ‘very good’ or ‘excellent’. Most of them felt (‘agree’ or ‘strongly agree’) that the learning objectives were clear ( n = 18, 90%), course content was well organised and delivered ( n = 19, 95%) and the course structure allowed all participants to fully participate ( n = 19, 95%) in the learning process. They believed that the course instructors were effective teachers ( n = 20, 100%), stimulated student interest ( n = 19, 95%) and were available and helpful ( n = 20, 100%). All the participants ( n = 20, 100%) found this course useful and would recommend it to their colleagues enthusiastically.
|
PMC11697579_p22
|
PMC11697579
|
Background of the selected participants
| 1.844651 |
other
|
Study
|
[
0.12958844006061554,
0.0014152004150673747,
0.8689963817596436
] |
[
0.6523330807685852,
0.34384438395500183,
0.0027455314993858337,
0.001077011227607727
] |
en
| 0.999998 |
The following broad themes emerged: strengths of the course, challenges and way forward from a participants’ perspective.
|
PMC11697579_p23
|
PMC11697579
|
Qualitative findings
| 1.658149 |
other
|
Other
|
[
0.3433052897453308,
0.006561306770890951,
0.6501333713531494
] |
[
0.09692233055830002,
0.8923742771148682,
0.008869500830769539,
0.0018339172238484025
] |
en
| 0.999997 |
COURSE CONTENT: THEORY FOLLOWED BY HANDS‑ON SESSIONS
|
PMC11697579_p24
|
PMC11697579
|
Strengths of the course
| 1.351181 |
other
|
Other
|
[
0.16843438148498535,
0.0056912279687821865,
0.8258744478225708
] |
[
0.002695502247661352,
0.9940614104270935,
0.002491852967068553,
0.0007511279545724392
] |
en
| 0.999994 |
Most of the participants felt that practical exercises after the basic theory lecture were extremely helpful in understanding the concepts and their applications.
|
PMC11697579_p25
|
PMC11697579
|
Strengths of the course
| 1.550232 |
other
|
Other
|
[
0.46955281496047974,
0.0034423465840518475,
0.5270048379898071
] |
[
0.07340025901794434,
0.9212785363197327,
0.004367871209979057,
0.0009532299591228366
] |
en
| 0.999997 |
The practical hands‑on sessions and the discussions following that were especially useful in the understanding of complex concepts. The participants suggested more such exercises and discussions in subsequent courses.
|
PMC11697579_p26
|
PMC11697579
|
Strengths of the course
| 1.307872 |
other
|
Other
|
[
0.2328844964504242,
0.004033398814499378,
0.7630821466445923
] |
[
0.006860069930553436,
0.9901396632194519,
0.002283633453771472,
0.000716639740858227
] |
en
| 0.999997 |
Most of the participants found the 3‑day contact workshop very useful as there were many practical hands‑on activities, small group activities, interactive discussions and less theory lectures, which was not possible during the online sessions. It also helped them revise and consolidate the concepts learnt earlier, especially the complex topics during the latter half of the course. Some of them even said that some complex topics such as modelling HIV/sexually transmitted infections (STIs) and mixing of populations were difficult to follow online, but the in‑person sessions were useful in clarifying them.
|
PMC11697579_p27
|
PMC11697579
|
Interactive contact sessions
| 1.799097 |
biomedical
|
Other
|
[
0.6809781789779663,
0.006162571255117655,
0.31285929679870605
] |
[
0.054784633219242096,
0.9381632208824158,
0.005974037107080221,
0.0010780951706692576
] |
en
| 0.999996 |
The participants reported that support from trainees’ institutions to pursue the course and attend all online and offline sessions was important. The application process mandatorily required applicants to submit a letter of support from their employers, which meant that they could focus on the course and devote sufficient time without having to worry about their full‑time work commitments.
|
PMC11697579_p28
|
PMC11697579
|
Support from trainees’ institutions
| 1.149658 |
other
|
Other
|
[
0.010256066918373108,
0.001206474844366312,
0.9885374307632446
] |
[
0.004896052181720734,
0.9938310384750366,
0.0006644497625529766,
0.0006084603373892605
] |
en
| 0.999998 |
The facilitators reported that trainer–trainee communication through various forums and the trainee’s commitment were critical to the success of this cohort.
|
PMC11697579_p29
|
PMC11697579
|
Support from trainees’ institutions
| 1.562834 |
other
|
Other
|
[
0.19179287552833557,
0.01059443037956953,
0.7976126670837402
] |
[
0.025996960699558258,
0.9710536599159241,
0.00189224595669657,
0.001057081506587565
] |
en
| 0.999996 |
A key feature of this program critical for trainees’ success was the regular communication between trainees and trainers through regular online sessions, online discussion forums, Q&As and the practical hands‑on sessions which provided trainees the space to implement the concepts learned and to receive feedback. We created a WhatsApp group to facilitate easier communication between trainers and trainees as well as knowledge sharing and networking among trainees. Additionally, during in‑person sessions, trainers were available for discussions at the end of each training day.
|
PMC11697579_p30
|
PMC11697579
|
Trainer–Trainee communication
| 1.788592 |
other
|
Other
|
[
0.2674652934074402,
0.007206431590020657,
0.725328266620636
] |
[
0.008900472894310951,
0.9899119734764099,
0.0007024411461316049,
0.00048513596993871033
] |
en
| 0.999997 |
Another facilitator of success was the trainees’ commitment, demonstrated by completing assignments and project work on time and attending evening online sessions regularly amidst competing commitments from their full‑time work.
|
PMC11697579_p31
|
PMC11697579
|
Trainees’ commitment
| 1.174409 |
other
|
Other
|
[
0.03840227425098419,
0.0019038589671254158,
0.959693968296051
] |
[
0.003489746479317546,
0.9952652454376221,
0.0007445080555044115,
0.000500619993545115
] |
en
| 0.999997 |
SCANTY COVERAGE OF THE BASICS OF INFECTIOUS DISEASES
|
PMC11697579_p32
|
PMC11697579
|
Challenges and way forward
| 1.417408 |
biomedical
|
Other
|
[
0.9412655830383301,
0.008221922442317009,
0.05051247030496597
] |
[
0.007823063060641289,
0.9613392949104309,
0.027617551386356354,
0.003220199840143323
] |
de
| 0.857139 |
The participants also gave useful feedback on the challenges they encountered during the course. A participant from a non‑medical background commented that the basics of common infectious diseases and their natural history should be discussed thoroughly. More real‑life examples of mathematical models from the literature for common diseases should be discussed.
|
PMC11697579_p33
|
PMC11697579
|
Challenges and way forward
| 1.561064 |
biomedical
|
Other
|
[
0.8820973038673401,
0.005916771944612265,
0.11198582500219345
] |
[
0.024297265335917473,
0.9658898115158081,
0.008335952647030354,
0.0014769426779821515
] |
en
| 0.999997 |
LESS TIME FOR DISEASE‑SPECIFIC MODELLING
|
PMC11697579_p34
|
PMC11697579
|
Challenges and way forward
| 1.695292 |
biomedical
|
Other
|
[
0.9295191168785095,
0.007217036094516516,
0.06326382607221603
] |
[
0.0066869850270450115,
0.9896951913833618,
0.002704192651435733,
0.0009136783191934228
] |
de
| 0.999993 |
Some of the participants felt that the disease‑specific modelling topics such as TB, HIV and sexually transmitted infections (STIs) were difficult to grasp and needed more time.
|
PMC11697579_p35
|
PMC11697579
|
Challenges and way forward
| 1.731018 |
biomedical
|
Other
|
[
0.965086042881012,
0.0056288777850568295,
0.029285022988915443
] |
[
0.4210881292819977,
0.566986083984375,
0.009339682757854462,
0.0025861512403935194
] |
en
| 0.999996 |
TIMING OF ONLINE SESSIONS AND NON‑AVAILABILITY OF RECORDED VIDEOS
|
PMC11697579_p36
|
PMC11697579
|
Challenges and way forward
| 1.231033 |
other
|
Other
|
[
0.33422034978866577,
0.00463704252615571,
0.6611425876617432
] |
[
0.04971172288060188,
0.9452955722808838,
0.0034236335195600986,
0.0015690346481278539
] |
de
| 0.999995 |
Timing of the evening online sessions and non‑availability of recorded videos were also reported by some participants as a challenge.
|
PMC11697579_p37
|
PMC11697579
|
Challenges and way forward
| 1.334234 |
other
|
Other
|
[
0.14537803828716278,
0.0036931491922587156,
0.850928783416748
] |
[
0.10352548211812973,
0.8930134177207947,
0.0019868069794028997,
0.0014743527863174677
] |
en
| 0.999997 |
Table 3 presents the challenges and recommendations given by the participants and suggested a plan of action for future courses.
|
PMC11697579_p38
|
PMC11697579
|
Challenges and way forward
| 1.573722 |
other
|
Other
|
[
0.2188454121351242,
0.0021945314947515726,
0.7789600491523743
] |
[
0.08365742117166519,
0.9116077423095703,
0.003798627993091941,
0.0009362187120132148
] |
en
| 0.999996 |
This is the first study using a mixed methods approach to evaluate learner’s perceptions of an innovative 3‑month hybrid training program in infectious disease modelling targeting mid‑career professionals in India. This paper describes the structure, curriculum and delivery of the course and also highlights the strengths and challenges in training the first cohort of disease modellers along with recommendations for the subsequent cohort.
|
PMC11697579_p39
|
PMC11697579
|
Discussion
| 3.790903 |
biomedical
|
Study
|
[
0.9969735145568848,
0.0007825935608707368,
0.0022438750602304935
] |
[
0.9992318153381348,
0.0004694616363849491,
0.00023086616420187056,
0.00006785608275095001
] |
en
| 0.999998 |
Some of the participants who belonged to the non‑medical background suggested that more focus should be on the basics of infectious disease epidemiology, disease transmission, natural history of diseases and their prevention and management. Accordingly, we plan to include more recorded lectures and discussions on those topics, including the clinical aspects of these diseases in the first 2 weeks, so that everyone is on the same page irrespective of their educational background before we move into the modelling of these diseases.
|
PMC11697579_p40
|
PMC11697579
|
Discussion
| 1.861111 |
biomedical
|
Other
|
[
0.9586679339408875,
0.003619672730565071,
0.03771239519119263
] |
[
0.0260578952729702,
0.9692699909210205,
0.0038649120833724737,
0.000807252072263509
] |
en
| 0.999998 |
The working professionals reported that attending evening online lectures around 6 PM, 5 days a week was challenging, as it was their commuting time. Non‑availability of recorded videos was also reported by many as it did not allow them to revise the concepts and make up for their missed classes, if any. To offset these challenges, we are designing a web‑based course portal for the subsequent courses wherein lecture videos and other resource materials will be uploaded and the participants can complete the course at their own pace.
|
PMC11697579_p41
|
PMC11697579
|
Discussion
| 1.507054 |
other
|
Other
|
[
0.10903594642877579,
0.004223051480948925,
0.8867409825325012
] |
[
0.010821419768035412,
0.9878292083740234,
0.0007813906413502991,
0.0005679676542058587
] |
en
| 0.999996 |
The number of applications ( n = 224) far exceeded the number anticipated by the team, which demonstrated the demand of the course. These applications were processed objectively using a structured scoring sheet which took longer than planned and required substantial effort from the trainers. Additional personnel support for program coordination might have helped.
|
PMC11697579_p42
|
PMC11697579
|
Discussion
| 1.488008 |
other
|
Other
|
[
0.2021743506193161,
0.003991193138062954,
0.7938344478607178
] |
[
0.02806081250309944,
0.9700196981430054,
0.0011182588059455156,
0.000801236426923424
] |
en
| 0.999996 |
Further, given that most of the content was developed originally for this course, the time and effort required to prepare the course content was substantial. However, we can leverage the course materials of the first cohort for future courses, although the materials need to be tailored to specific trainee populations.
|
PMC11697579_p43
|
PMC11697579
|
Discussion
| 1.246968 |
other
|
Other
|
[
0.07302647829055786,
0.0017392331501469016,
0.925234317779541
] |
[
0.004764803685247898,
0.993334174156189,
0.0013628751039505005,
0.0005380765651352704
] |
en
| 0.999998 |
A major limitation in this study was the self‑reporting of strengths and weaknesses by the authors of the papers, who were also the respondents in this study. Thus, responder bias cannot be ruled out. However, responses from the study participants were completely anonymised to minimise social desirability bias. In addition, responses to the open‑ended questions were obtained online, leaving no scope for probes and further in‑depth exploration, thus affecting the richness of the qualitative data.
|
PMC11697579_p44
|
PMC11697579
|
Discussion
| 2.783584 |
biomedical
|
Study
|
[
0.9734362363815308,
0.0009644786478020251,
0.025599362328648567
] |
[
0.998231828212738,
0.0011950910557061434,
0.0004950510337948799,
0.00007801463652867824
] |
en
| 0.999994 |
This is the first structured 3‑month PG certificate course in India attempting to build the capacity of researchers in the field of infectious disease modelling and its applications. The first cycle of the course yielded 20 trained infectious disease modelers in the country. There were some challenges and recommendations from the first cycle which will feed into the subsequent course cycles. Future courses are planned to be hosted on an online platform to facilitate the completion of the course at the participants’ own pace and be able to access the course materials and online videos at any time. More collaboration with various stakeholders, nationally and internationally, will be sought to improve the content, delivery and robustness of the program.
|
PMC11697579_p45
|
PMC11697579
|
Conclusions
| 1.927987 |
biomedical
|
Other
|
[
0.8954246044158936,
0.003746399190276861,
0.10082902759313583
] |
[
0.01624511554837227,
0.9796717762947083,
0.003313578199595213,
0.0007694831001572311
] |
en
| 0.999995 |
Among severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants, delta (B.1.617.2) and omicron (B.1.1.529) variant viruses caused a worldwide pandemic due to increased transmissibility compared to that of the Wuhan virus ( 1 ). It is urgent to elucidate the molecular mechanisms governing the onset and progression of these variants to develop effective strategies aimed at reducing recurrence rates and improving therapeutic potency.
|
PMC11697598_p0
|
PMC11697598
|
Introduction
| 3.860266 |
biomedical
|
Study
|
[
0.9995582699775696,
0.0001678521657595411,
0.0002738709736149758
] |
[
0.889637291431427,
0.044930197298526764,
0.06488978117704391,
0.0005427257274277508
] |
en
| 0.999996 |
Metabolomics has the potential to enhance our understanding of host−pathogen interactions in infectious diseases. In particular, metabolomics has been widely applied for biomarker discovery and to investigate the immunometabolic response of individuals infected with various viruses, including more recently SARS-CoV-2 ( 2 – 7 ). Notably, targeting specific metabolic pathways that are crucial for viral replication can potentially disrupt virus growth and reduce infection severity ( 8 ). Depletion of GSH due to viral infection leads to disruption of the redox balance in the lungs and results in tissue damage ( 9 ). High kynurenine/tryptophan ratios were observed in the plasma of patients with moderate and severe COVID-19 ( 10 ). L-arginine is metabolized to L-ornithine in the urea cycle by arginase and serves as a substrate for the production of nitric oxide (NO) by nitric oxide synthase (NOS), a signaling molecule involved in inflammatory responses ( 11 ). Interestingly, arginine administration and modulation of nitric oxide (NO) production have emerged as promising therapeutic strategies with high potency in the management of patients with severe coronavirus disease 2019 (COVID-19) ( 12 , 13 ), but molecular mechanistic studies regarding arginine metabolism are still limited. Therefore, deeper metabolic pathway studies based on metabolomics are crucial to fully understand the arginine-NO metabolic pathway and its implications for therapeutic interventions in patients with severe COVID-19.
|
PMC11697598_p1
|
PMC11697598
|
Introduction
| 4.490685 |
biomedical
|
Study
|
[
0.998857855796814,
0.0006696308846585453,
0.00047247743350453675
] |
[
0.6653727889060974,
0.001652096281759441,
0.3322463929653168,
0.0007287628832273185
] |
en
| 0.999996 |
The golden Syrian hamster model is valuable for studying pulmonary pathology during COVID-19 due to the high genetic similarity of these hamsters to humans ( 14 , 15 ). Recent years have seen continuous efforts to explore the pathophysiology of SARS-CoV-2 infection using various animal models such as hamsters, minks, and ferrets infected with the Wuhan virus, shedding light on changes including TCA cycle, purine metabolism, pentose phosphate pathway, kynurenine pathway and triacylglycerol accumulation ( 16 – 18 ). Moreover, multi-omics studies have elucidated the underlying mechanism involved in SARS-CoV-2 pathophysiology such as a shift toward enhanced glycolysis ( 19 ) and significant phospholipid metabolic alterations ( 20 ). However, metabolomic studies focusing on pulmonary pathophysiology in preclinical models of SARS-CoV-2 infection are still lacking. Current research on delta and omicron variant infections focuses on transcriptional changes in inflammatory mediators and specific genes but lacks a comprehensive view of systemic metabolic alterations in host-pathogen interactions ( 21 ).
|
PMC11697598_p2
|
PMC11697598
|
Introduction
| 4.184353 |
biomedical
|
Study
|
[
0.999413013458252,
0.00029294591513462365,
0.00029402069048956037
] |
[
0.9700047373771667,
0.0004518753557931632,
0.0293646901845932,
0.00017862889217212796
] |
en
| 0.999996 |
Here, we performed molecular profiling through metabolomic and transcriptomic analysis to acquire a comprehensive understanding of the systemic effects and metabolic alterations induced by SARS-CoV-2 variants, including delta and omicron. Overall, our study provided insights into how delta and omicron viruses manipulate host’s lung metabolism. We performed metabolomic profiling and integrated transcriptomic analysis, offering valuable insights into potential therapeutic targets for the treatment of SARS-CoV-2 delta and omicron variant infections in hamsters.
|
PMC11697598_p3
|
PMC11697598
|
Introduction
| 4.125031 |
biomedical
|
Study
|
[
0.9995871186256409,
0.00025191091117449105,
0.00016097760817501694
] |
[
0.9992363452911377,
0.00020045605197083205,
0.0004894145531579852,
0.00007372010441031307
] |
en
| 0.999997 |
Golden Syrian hamsters (6 weeks old, male) were purchased from Central Laboratory Animal Inc. (Seoul, South Korea). Our study examined male hamsters because male animals exhibited less variability in phenotype. The animals were maintained under a 12 h light and dark cycle and fed a standard diet and water ad libitum. The hamsters were divided into three groups (n=5/group): the negative control, delta variant and omicron variant groups. The hamsters were anesthetized, and thereafter, the infection was established by intranasally administration of 20 μL (10 5.0 TCID 50 /ml) of SARS-CoV-2 delta variant (B.1.617.2) or SARS-CoV-2 omicron variant (B.1.1.529). The body weights of all infected hamsters were monitored daily until sacrifice. Five hamsters from each group were sacrificed at 0, 4, and 7 days post-infection (dpi), and the lungs were collected to assess the metabolic changes following viral infection . Lung samples were divided for metabolic profiling, transcriptomic analysis, and H&E staining and were stored at -80°C until use. This study adhered to the guidelines of Jeonbuk National University and was approved by the Institutional Animal Care and Use Committee , and the experimental protocols requiring biosafety were approved by the Institutional Biosafety Committee of Jeonbuk National University . All animal experiments were carried out at the Animal Use Biosafety Level-3 (ABL-3) facility at the Korea Zoonosis Research Institute, which is certified by the Korea Disease Control and Prevention Agency of the Ministry of Health and Welfare (certification number: KCDC-16-3-06).
|
PMC11697598_p4
|
PMC11697598
|
Experimental model and study design
| 4.053309 |
biomedical
|
Study
|
[
0.9994333386421204,
0.00035505881533026695,
0.00021155581634957343
] |
[
0.999392032623291,
0.000341596343787387,
0.0001994135236600414,
0.00006694535113638267
] |
en
| 0.999998 |
To measure the viral loads of SARS-CoV-2 in lung tissue samples, quantitative real-time PCR was performed to detect the N gene of SARS-CoV-2 using TaqMan Fast Virus 1-Step Master Mix (Thermo Fisher Scientific, MA, USA) as previously described ( 22 , 23 ). One gram of tissue samples from all hamsters were placed into soft tissue homogenizing CK14 tubes (Precellys, Betin Technologies) prefilled with ceramic beads and DMEM and then homogenized using a Bead blaster 24 (Benchmark Scientific, NJ, USA). Viral RNA was extracted from the homogenized tissues using a QIAamp viral RNA Mini Kit (Qiagen) according to the manufacturer’s protocol. Real-time PCR was conducted using a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA).
|
PMC11697598_p5
|
PMC11697598
|
Quantitative real-time PCR to measure the SARS-CoV-2 RNA copy number
| 4.088273 |
biomedical
|
Study
|
[
0.9996262788772583,
0.00020842941012233496,
0.00016523855447303504
] |
[
0.9992457628250122,
0.00041974513442255557,
0.000269080454017967,
0.00006545293581439182
] |
en
| 0.999996 |
All animals were euthanized using an intraperitoneal injection of xylazine and succinyl choline at the end of the experiment. At necropsy, gross lesions in the lung were examined, and then the lung tissues were collected and fixed in 4% neutral-buffered formalin for 1 week. Tissues embedded in paraffin blocks were sectioned at a thickness of 4 μm and then mounted onto glass slides. The slides were deparaffinized in xylene, rehydrated through a series of graded 100% ethanol to distilled water and then stained with hematoxylin and eosin. All tissue samples were assessed by a blinded veterinary anatomic pathologist.
|
PMC11697598_p6
|
PMC11697598
|
Histopathological analysis
| 4.150779 |
biomedical
|
Study
|
[
0.9984643459320068,
0.0013842988992109895,
0.0001513895986136049
] |
[
0.9935590624809265,
0.005366042256355286,
0.0006114388816058636,
0.00046353143989108503
] |
en
| 0.999998 |
To extract metabolites from lung tissue, 100 mg of lung sample was weighed and mixed with 600 μL of methanol/water (1:1, v/v) in a 1.5 mL Eppendorf (EP) tube containing zirconium oxide beads. The mixed sample was homogenized at 5,000 rpm twice using a Precellys 24 tissue grinder (Bertin Technologies, France) and centrifuged after homogenization. After adding 600 μL of chloroform, the sample was vortexed for 1 min and incubated at 4°C for 10 min. The mixture was centrifuged at 12,700 rpm for 20 min at 4°C. For the extraction of serum metabolites, 50 μL of each serum sample were mixed with 550 μL of chloroform/methanol mixture (2:1, v/v) and vortexed for 1 min. Next, 100 μL of water was mixed with the lung and serum samples, respectively and incubated at 4°C for 10 min. The mixture centrifuged at 12,700 rpm for 20 min at 4°C. Then, 150 μL of the upper aqueous supernatant from lung tissue and 50 μL supernatant from the serum were transferred into a new 1.5 mL tube and dried using a speed vac evaporator. The dried lung and serum extracts were redissolved in 200 μL of an acetonitrile/water mixture (75:25, v/v) containing internal standards (0.1 μg/ml betaine-D 11 , 10 μg/ml glutamate- 13 C 5 , 5 μg/ml leucine- 13 C 6 , 2 μg/ml phenylalanine- 13 C 6 , 10 μg/ml succinate- 13 C 4 , 10 μg/ml taurine- 13 C 2 , and 10 μg/ml uridine- 13 C 9 , 15 N 2 ).
|
PMC11697598_p7
|
PMC11697598
|
UPLC-QTOF MS-based metabolomics
| 4.157807 |
biomedical
|
Study
|
[
0.9995230436325073,
0.0003258563519921154,
0.00015113908739294857
] |
[
0.9973815083503723,
0.0019496760796755552,
0.0005253743729554117,
0.00014344525698106736
] |
en
| 0.999998 |
Liquid chromatography (LC)-electrospray ionization (ESI)-mass spectrometry (MS) analyses for metabolomics of lung tissue extracts were performed on a triple TOF™ 5600 MS/MS system (AB Sciex, Canada) combined with a UPLC system (Waters, USA). LC separations were carried out on a ZIC-HILIC column (2.1 mm × 100 mm, 3.5 μm; SeQuant, Germany). The column temperature and flow rate were set to 35°C and 0.4 mL/min, respectively. The mobile phases used were 10 mM ammonium acetate and 0.1% formic acid in water/acetonitrile (10:90, v/v) (A) and water/acetonitrile (50:50, v/v) (B). The linear gradient program was as follows: 1% B from 0 to 2 min, 1–55% B from 2 to 8 min, 55–99% B from 8 to 9 min, 99% B from 9 to 11 min, 99–1% B from 11–11.1 min, and 1% B from 11.1 to 15 min. The injection volume of the sample was 2 µL for both positive and negative ionization polarity modes. Quality control (QC) samples, which were pooled identical aliquots of the samples, were analyzed regularly throughout the run to ensure data reproducibility. The spectral data were analyzed by MarkerView™ (AB Sciex, Canada), which was used to find peaks, perform peak alignment, and generate peak tables of m/z and retention times (min). The data were normalized using the total area of the spectra. To identify reliable peaks and remove instrumental bias, peaks with coefficients of variation below 20 in QC samples were selected. Metabolites were identified by comparing the experimental data against an in-house library and the online database MS-DIAL.
|
PMC11697598_p8
|
PMC11697598
|
UPLC-QTOF MS-based metabolomics
| 4.235083 |
biomedical
|
Study
|
[
0.9994803071022034,
0.0003570180560927838,
0.00016274394874926656
] |
[
0.9990726709365845,
0.00034772726939991117,
0.00047791170072741807,
0.00010160703823203221
] |
en
| 0.999998 |
Total RNA from lung tissues was isolated and prepared using the TRIzol cell RNA extraction protocol. The libraries were prepared for 151 bp paired-end sequencing using a TruSeq Stranded mRNA Sample Preparation Kit (Illumina, CA, USA). Namely, mRNA molecules were purified and fragmented from 1 μg of total RNA using oligo (dT) magnetic beads. The fragmented mRNAs were synthesized as single-stranded cDNAs through random hexamer priming. By applying this single-stranded cDNA as a template for second strand synthesis, double-stranded cDNA was prepared. After the sequential processes of end repair, A-tailing and adapter ligation, cDNA libraries were amplified with polymerase chain reaction (PCR). The quality of these cDNA libraries was evaluated with an Agilent 2100 Bioanalyzer (Agilent, CA, USA). The libraries were quantified with a KAPA library quantification kit (Kapa Biosystems, MA, USA) according to the manufacturer’s library quantification protocol. Following cluster amplification of denatured templates, paired-end sequencing (2×151 bp) was performed using an Illumina NovaSeq 6000 (Illumina, CA, USA).
|
PMC11697598_p9
|
PMC11697598
|
mRNA sequencing and data analysis
| 4.187712 |
biomedical
|
Study
|
[
0.9995402097702026,
0.00026983077987097204,
0.000189931524801068
] |
[
0.9976738095283508,
0.0017287118826061487,
0.0004710389766842127,
0.00012642689398489892
] |
en
| 0.999996 |
The adapter sequences and the ends of the reads with a Phred quality score less than 20 were trimmed, and simultaneously, the reads shorter than 50 bp were removed by using cutadapt v.2.8 ( 24 ). Filtered reads were mapped to the reference genome related to the species using the aligner STAR v.2.7.1a ( 25 ) following ENCODE standard options (refer to “Alignment” of the “Help” section in the html report) with the “-quantMode TranscriptomeSAM” option for estimation of transcriptome expression level. Gene expression estimation was performed by RSEM v.1.3.1 ( 26 ) considering the direction of the reads that correspond to the library protocol using the option –strandedness. To improve the accuracy of the measurement, the “–estimate-rspd” option was applied. All other options were set to default values. To normalize the sequencing depth among samples, FPKM and TPM values were calculated. Based on the estimated read counts in the previous step, differentially expressed genes (DEGs) were identified using the R package TCC v.1.26.0 ( 27 ). The TCC package applies robust normalization strategies to compare tag count data. Normalization factors were calculated using the iterative DESeq2 ( 28 ) method. The Q-value was calculated based on the p value using the p.adjust function of the R package with default parameter settings. The DEGs were identified based on the q-value threshold less than 0.05 for correcting errors caused by multiple testing ( 29 ).
|
PMC11697598_p10
|
PMC11697598
|
mRNA sequencing and data analysis
| 4.243506 |
biomedical
|
Study
|
[
0.9994621872901917,
0.0003004602331202477,
0.00023728587257210165
] |
[
0.998808741569519,
0.0004536628839559853,
0.0006458320422098041,
0.00009179745393339545
] |
en
| 0.999998 |
We constructed a network based on correlation coefficients among the metabolites, transcriptome, and cytokines using Cytoscape v.3.10.1 ( https://cytoscape.org ). In the network graph, the metabolites and transcripts within the three selected metabolic pathways and significantly altered cytokines within the SARS-CoV-2 variant group are represented as nodes. The thickness of the lines connecting each node was determined by the Pearson’s correlation coefficient values.
|
PMC11697598_p11
|
PMC11697598
|
Network analysis
| 4.045454 |
biomedical
|
Study
|
[
0.9996659755706787,
0.00016064659575931728,
0.00017340776685159653
] |
[
0.9992691874504089,
0.00037632585735991597,
0.00029996188823133707,
0.00005454330676002428
] |
en
| 0.999998 |
SIMCA-P+ v.16.0 (Umetrics, Sweden) was used to conduct multivariate analysis. All metabolite levels were scaled to unit variance prior to principal component analysis (PCA). PCA was applied to provide an overview of metabolomic data. All the results were analyzed using the Statistical Package for Social Sciences software, v.28.0 (SPSS Inc., USA) and plotted using GraphPad Prism, v.8 (GraphPad Software, Inc., USA). Statistical significance was assessed using one-way ANOVA with Tukey’s multiple comparisons post hoc test. After performing robust scaling on the metabolomics and transcriptomics data using Google Colab ( colab.research.google.com ), Pearson’s correlation analysis was conducted on the scaled data. Pathway analysis was performed in the MetaboAnalyst computational platform ( www.metaboanalyst.ca ) ( 30 ).
|
PMC11697598_p12
|
PMC11697598
|
Statistics
| 3.963392 |
biomedical
|
Study
|
[
0.9996401071548462,
0.0001499206991866231,
0.00020992945064790547
] |
[
0.9983526468276978,
0.0010961275547742844,
0.0004742938617710024,
0.00007703524170210585
] |
en
| 0.999997 |
To elucidate the immune response and pathogenic molecular mechanisms of SARS-CoV-2 variants, we used the hamster model for delta and omicron variant infection . After intranasal infection with the variants, the body weight of the hamsters was measured daily. In comparison to the non-infected control group, the groups infected with the delta and omicron variants showed significant weight loss, indicating a successful viral infection in the hamster model according to clinical signs . Specifically, the delta variant group demonstrated a more pronounced reduction in body weight than the omicron variant group, indicating a heightened severity of viral infection within the delta group. In addition, SARS-CoV-2 viral RNA copy numbers from lung tissue in both the delta and omicron variants showed significant increases at 4 and 7 dpi compared to those of the control group . However, no statistically significant difference was observed in the viral load between the two variants. Next, histological analysis of lung tissue was performed to evaluate pulmonary lesions . Histopathological changes such as perivascular inflammatory cell infiltration, pneumocyte hyperplasia, alveolar hemorrhages, and septal thickening were observed in the hamsters challenged with the delta or omicron variant at 4 dpi and 7 dpi. These findings indicate that SARS-CoV-2 variant viruses infect hamster lung tissues, with delta variant causing more significant inflammatory pathology compared to omicron variant.
|
PMC11697598_p13
|
PMC11697598
|
Acute inflammatory response and lung pathology in hamsters infected with delta and omicron
| 4.213962 |
biomedical
|
Study
|
[
0.9994875192642212,
0.0003532902046572417,
0.00015915244875941426
] |
[
0.9991094470024109,
0.00021836523956153542,
0.0005821967497467995,
0.00008997263648780063
] |
en
| 0.999996 |
To investigate host-pathogen interactions and changes in host’s metabolism infected by delta and omicron variants, LC/MS-based metabolic profiling was conducted on lung tissues, a key target organ in SARS-CoV-2 pathology. A total of 5,427 and 3,110 peak features were detected in positive and negative ion modes, respectively. Tightly scattered quality control (QC) samples in principal component analysis (PCA) score plots indicated good analytical reproducibility during the LC/MS experiment . Regarding metabolic pattern recognition after infection with the delta variant, PCA score plots showed distinct separation between pre- and post-infection in both positive and negative ion modes , while lung tissue samples derived from hamsters infected with omicron were slightly separated between pre- and post-infection in PCA score plots. On the other hand, no significant differences were observed PCA score plots between pre- and post-infection in control group . These results suggest that SARS-CoV-2 variants can modulate lung metabolism, with the delta variant exhibiting a greater impact on lung metabolism reprogramming than the omicron variant.
|
PMC11697598_p14
|
PMC11697598
|
Delta and omicron infection induce metabolic alterations in hamster lung tissues
| 4.162843 |
biomedical
|
Study
|
[
0.9995275735855103,
0.0003021678712684661,
0.00017027879948727787
] |
[
0.9992285966873169,
0.00016825413331389427,
0.0005278991884551942,
0.00007522558007622138
] |
en
| 0.999995 |
A heat map was generated to visualize the changes in the levels of 88 identified metabolite in the lung tissues of hamsters infected with the delta and omicron variants of SARS-CoV-2 . In both the delta and omicron groups, we observed significant elevation of the levels of several amino acids, including arginine, phenylalanine, asparagine, histidine, tryptophan, cystine, lysine, ornithine, serine, threonine and S-adenosyl-L-methionine (SAM), after variant infection. On the other hand, there were lower levels of taurine, allantoin and 1-methyladenosine after delta and omicron infection. Interestingly, the levels of S-adenosyl-L-homocysteine (SAH), cholic acid, glycochenodeoxycholic acid, malate, 3-hydroxy-3-methylglutaric acid, and kynurenine and the ratio of kynurenine to tryptophan were markedly increased only after delta infection.
|
PMC11697598_p15
|
PMC11697598
|
Delta and omicron infection induce metabolic alterations in hamster lung tissues
| 4.121878 |
biomedical
|
Study
|
[
0.9995774626731873,
0.00024113222025334835,
0.00018139267922379076
] |
[
0.9993200302124023,
0.000181259325472638,
0.00043781890417449176,
0.00006096748620620929
] |
en
| 0.999998 |
Next, to identify key metabolic pathways affected by SARS-CoV-2 variant infection at each distinct symptomatic phase (e.g., 7 dpi for delta and 4 dpi for omicron) ( 31 ), metabolic pathway analysis was performed based on differentially regulated metabolites specific to those time points . The results of metabolic pathway analysis revealed distinct changes specific to each variant group. Arginine biosynthesis and taurine and hypotaurine metabolism were important metabolic pathways for both the delta and omicron variants. In the delta variant group, tryptophan metabolism and glutathione (GSH) metabolism were identified as key metabolic pathways. Conversely, the omicron variant group showed arginine and proline metabolism, as well as histidine metabolism, played significant roles following infection. These results demonstrated distinct metabolic changes occurring in the lung tissue of hamsters as a direct consequence of infection with the SARS-CoV-2 variants.
|
PMC11697598_p16
|
PMC11697598
|
Delta and omicron infection induce metabolic alterations in hamster lung tissues
| 4.119111 |
biomedical
|
Study
|
[
0.9995556473731995,
0.0002616831916384399,
0.0001826829247875139
] |
[
0.999345600605011,
0.00016047632379923016,
0.00043338732211850584,
0.000060527410823851824
] |
en
| 0.999995 |
Based on the comprehensive examination of a heat map and pathway analysis, notable metabolic alterations were observed in three pathways: arginine biosynthesis, GSH metabolism, and tryptophan metabolism. The levels of most metabolites involved in arginine biosynthesis showed an increasing trend in both the delta and omicron groups compared to those pre-infection. In particular, significant accumulation of arginine and ornithine was observed after delta and omicron infection. In GSH metabolism, a remarkable increase in cystine and a decrease in GSH levels were observed in the delta variants at 7 dpi compared to those at 0 dpi. The levels of taurine were lower after delta and omicron infection than before infection. Within tryptophan metabolism, a significant increase in kynurenine levels was observed at 4 and 7 dpi, while tryptophan levels showed a decrease specifically in the delta group compared to those at baseline, indicating that tryptophan was being converted to kynurenine. To investigate systemic metabolic changes in response to coronavirus variants infections, we also examined alterations in those three pathways in the serum . Increased levels of citrulline and ornithine were observed in the serum, mirroring the trends identified in lung tissue for both the delta and omicron groups . Arginine levels showed an increasing trend in the serum of delta group, while a contrasting decrease was noted in omicron group. Additionally, a reduction in aspartate was observed in the serum. In the context of glutathione metabolism, a significantly reduction in cystine levels was observed in the serum, in contrast to the lung tissue. Additionally, there was an increase in both glutamine and GSSG levels in two variant groups. In tryptophan metabolism, we observed increase of kynurenine levels in both variant groups, mirroring the findings in lung tissue. The delta group exhibited a reduction in tryptophan whereas the omicron group exhibited an increase in tryptophan. Furthermore, a decline in kynurenic acid was also observed in the serum. Supplementary Figure S2 visually represents the individual trends of metabolite levels in three specific metabolic pathways between pre- and post-infection in lung tissue and serum in both the delta and omicron groups.
|
PMC11697598_p17
|
PMC11697598
|
Metabolic pathway regulation combined with mRNA levels in lungs infected with delta and omicron
| 4.271417 |
biomedical
|
Study
|
[
0.9993595480918884,
0.0004050533752888441,
0.00023540615802630782
] |
[
0.9991990923881531,
0.00019753673404920846,
0.0005128144402988255,
0.00009063041215995327
] |
en
| 0.999996 |
We observed the correlation between the metabolic profiles of lung tissue and serum in the delta group at 7dpi and the omicron group at 4dpi , which exhibited distinct metabolic changes after infection. In the delta group, predominantly positive correlations were observed among various metabolites . Notably, arginine in lung tissue were positively correlated with arginine, glutamine and kynurenine in serum. Cystine in lung tissue were positively correlated with arginine, citrulline, proline, cystine and SAM in serum. Lung kynurenine also showed positive correlation with serum citrulline, proline and cystine. Conversely, in the omicron group, predominantly negative correlations were observed among various metabolites . Particularly, proline in lung tissue showed a significant negative correlation with ornithine, GSSG and SAM in serum. These findings underscore that the delta and omicron variants induce different metabolic alterations in the host’s lung tissue and serum following infection, and imply that a coronavirus infection impacts not only the pulmonary tissue but also has systemic effects throughout the body.
|
PMC11697598_p18
|
PMC11697598
|
Metabolic pathway regulation combined with mRNA levels in lungs infected with delta and omicron
| 4.167764 |
biomedical
|
Study
|
[
0.9995429515838623,
0.0002818849461618811,
0.00017518867389298975
] |
[
0.9991078972816467,
0.00019470846746116877,
0.0006257288623601198,
0.00007161435496527702
] |
en
| 0.999996 |
Next, an RNA-Seq analysis was conducted to investigate the transcriptional alterations in genes linked to each of the three identified metabolic pathways, as outlined in the Kyoto Encyclopedia of Genes and Genomes (KEGG). The genes associated with the three metabolic pathways showed mostly similar trends of changes in transcription for both the delta and omicron variants, especially the magnitude of the significant change, which was much larger in the delta group than in the omicron group . In arginine biosynthesis, the levels of Ass1 were significantly increased at 7 dpi compared to those at 0 dpi in the delta group but not in the omicron group. The transcription level of most genes involved in GSH metabolism, including Gpx1, Ggt1, Gsr, Pgd, Anpep and Lap3, was significantly higher post-infection than pre-infection in the delta group but not in the omicron group. In tryptophan metabolism, increased patterns of transcription of genes related to kynurenine synthesis, including Tdo2 and Ido1, were observed, while the levels of Cyp1a1 were significantly lower at 7 dpi than at 0 dpi in the delta group.
|
PMC11697598_p19
|
PMC11697598
|
Metabolic pathway regulation combined with mRNA levels in lungs infected with delta and omicron
| 4.20345 |
biomedical
|
Study
|
[
0.9994373917579651,
0.000324649183312431,
0.00023790355771780014
] |
[
0.99937903881073,
0.00016764241445343941,
0.00038577214581891894,
0.00006755935464752838
] |
en
| 0.999997 |
Based on metabolomic and transcriptomic analyses, we were able to identify altered metabolic pathways in response to the SARS-CoV-2 variants. By combining the two sets of analyses, the modified metabolic pathways could be depicted in a single figure . Upregulation of arginine biosynthesis and the urea cycle was observed with both the delta and omicron variants . An examination of the integrated metabolic pathway for GSH metabolism revealed distinct alterations in the context of the delta variant, wherein the synthesis of GSH was found to be suppressed, concomitant with an augmented production of cystine . In the context of the metabolic pathway related to tryptophan metabolism, enhancement of the synthesis of kynurenine was observed with only the delta variant . These findings demonstrate that SARS-CoV-2 variants induce alterations in the metabolic pathways of hamster lung tissue. Specifically, it was shown that the delta variant of the virus had a stronger impact on the lung metabolism of hamsters upon infection than the omicron variant.
|
PMC11697598_p20
|
PMC11697598
|
Metabolic pathway regulation combined with mRNA levels in lungs infected with delta and omicron
| 4.184717 |
biomedical
|
Study
|
[
0.9995428323745728,
0.000284632173134014,
0.00017254841804970056
] |
[
0.9991614818572998,
0.00017672132526058704,
0.0005891292239539325,
0.00007266391912708059
] |
en
| 0.999997 |
The levels of cytokines, including IL-6, IL-1β, IL-10, IFN-γ, tumor necrosis factor-α (TNF-α), and colony-stimulating factor (CSF), gradually increase with the severity of COVID-19 and play a crucial role in the immune response to SARS-CoV-2 infection ( 10 , 32 ). Thus, the mRNA levels of cytokines were examined to gain insights into their role in the immune response to SARS-CoV-2 delta and omicron variants . Most cytokine levels within the lung tissue were elevated after infection with the delta and the omicron variants, consistent with previous studies. In particular, we observed increase in the levels of cytokines known to contribute to cytokine storms as IL-1β, IL-6, IL-12A, IL-12B, IFN-γ, and TNF-α as well as various chemokines and CSFs upon infection with the COVID-19 variants . Moreover, these alterations were more notable in the delta group than in the omicron group.
|
PMC11697598_p21
|
PMC11697598
|
Impaired metabolic pathways were associated with inflammatory cytokines after delta and omicron infection
| 4.128725 |
biomedical
|
Study
|
[
0.9995208978652954,
0.0002753419103100896,
0.00020374867017380893
] |
[
0.9993256330490112,
0.00015958792937453836,
0.0004499581700656563,
0.00006483093602582812
] |
en
| 0.999998 |
Next, a correlation analysis was conducted to explore the association between metabolites and genes involved in infection-induced altered metabolic pathways and all cytokines changed after infection . To visualize and interpret the modulation of metabolic pathways in relation to metabolic and transcriptional changes in the immune response, we created integrated metabolic network diagrams based on the correlation analysis of cytokines, metabolites, and genes for both the delta and omicron variants . For the delta variant, the network showed predominantly strong positive correlations among cytokines, metabolites, and transcripts . In particular, arginine exhibited positive correlations with major proinflammatory cytokines, such as CCL4 and CCL5. And proline and GSH were positively correlated with IL-12B . Additionally, oxidized glutathione (GSSG) and SAM were positively correlated with CCL8, while taurine exhibited a negative correlation with CXCL17 . Regarding transcriptome profiles, strong positive correlations were observed between genes and cytokines, mirroring the correlations between metabolites and cytokines . Genes related to arginine biosynthesis, such as Got1l1, Otc, and Asl, exhibited positive correlations with most cytokines. Notably, Asl showed a significant positive correlation with cytokines from the TNF and transforming growth factor-beta (TGF-β) families, while Arg1 and Otc showed negative and positive correlations with IL-12B, respectively. In GSH metabolism, Gpx1 and Pgd exhibited a negative correlation with IL-1β, while Anpep showed positive correlations with TNFAIP8L2, TNFSF12, TNFSF13b, and TGF-β1. Additionally, Lap3 was positively correlated with CCL12 and IL-18bp, and Gstt3 exhibited a positive correlation with CCL5. In tryptophan metabolism, Ido1 and Kynu showed positive correlations with CXCL10, TNFSF12, and TGF-β1, while Tdo2, Inmt, and Aldh7a1 exhibited positive correlations with IL-12B. For the omicron variant, the network primarily showed negative correlations . Specifically, all metabolites exhibited negative correlations with CCL5, CCL8, TNFAIP8L2, and CSF1, but showed positive correlations with XCL1 . No significant correlations were observed between genes related to arginine biosynthesis and cytokines for the omicron variant .
|
PMC11697598_p22
|
PMC11697598
|
Impaired metabolic pathways were associated with inflammatory cytokines after delta and omicron infection
| 4.316318 |
biomedical
|
Study
|
[
0.9994063377380371,
0.00037499915924854577,
0.00021863498841412365
] |
[
0.9989995360374451,
0.0002419814991299063,
0.0006562594790011644,
0.00010221946286037564
] |
en
| 0.999997 |
In glutathione metabolism, Ggt1 and Pgd were negatively correlated with TNFSF10, while Gpx1 exhibited a positive correlation with TNFAIP8L2. Additionally, Anpep showed negative correlations with CCL5, CCL8, and CSF1. In tryptophan metabolism, Ido1 and Kynu showed negative and positive correlations with TNFSF10, respectively, while Cyp1a1 exhibited negative correlations with CCL5 and CSF1.These results indicate that SARS-CoV-2 variant infection triggers an inflammatory response associated with arginine biosynthesis, glutathione metabolism and tryptophan metabolism in the lungs of hamsters by modulating metabolite and transcript levels, and the delta and omicron variant viruses exert distinct inflammatory responses on hamster lung tissue, as evidenced by different correlations with cytokines.
|
PMC11697598_p23
|
PMC11697598
|
Impaired metabolic pathways were associated with inflammatory cytokines after delta and omicron infection
| 4.197165 |
biomedical
|
Study
|
[
0.9995822310447693,
0.00023844958923291415,
0.00017931598995346576
] |
[
0.9991434812545776,
0.00019829790107905865,
0.0005886724684387445,
0.00006960963946767151
] |
en
| 0.999996 |
In this study, we investigated a comprehensive molecular mechanism in hamster lung tissue infected with delta and omicron SARS-CoV-2 variants by integrating metabolomics and transcriptomics. Following viral infection, arginine biosynthesis, GSH metabolism, and tryptophan metabolism were concurrently regulated at both the metabolic and genetic levels in lung tissue. Importantly, these metabolic pathways were notably associated with the production of inflammatory cytokines. Interestingly, the delta variant induced a stronger impact on lung metabolism and inflammatory responses compared to the omicron variant, according to metabolic profile patterns , levels of metabolites and genes , and changes in cytokine levels ( Supplementary Table S3 ). Additionally, these metabolic alterations were reflected in the serum, emphasizing the systemic impact of the virus on various metabolic processes. Viruses can influence host metabolic processes and induce physiological dysfunction ( 33 ). Understanding the pathophysiology of SASR-CoV-2 through the elucidation of molecular mechanisms via metabolomics and transcriptomics, as well as exploring metabolic interventions as novel therapeutic strategies, may contribute to the prevention and treatment of COVID-19. Hence, this study can provide potential molecular targets for therapeutic exploration in the quest for new drugs targeting the host pulmonary immune response following infection with delta and the omicron variants.
|
PMC11697598_p24
|
PMC11697598
|
Discussion
| 4.364847 |
biomedical
|
Study
|
[
0.999363362789154,
0.0004720887227449566,
0.00016456989396829158
] |
[
0.9977280497550964,
0.0003603312361519784,
0.0017393957823514938,
0.00017229988588951528
] |
en
| 0.999995 |
In this study, an increase in arginine synthesis was observed in both the delta and omicron variant viruses. Arginine serves as a substrate for the generation of nitric oxide (NO), which is a signaling molecule in inflammatory responses. Previous studies reported decreased levels of arginine and a dysregulated urea cycle in plasma from patients with severe COVID-19 ( 34 , 35 ). Within the urea cycle, arginine is converted to ornithine and then recycled back to arginine via the enzymes Otc, Ass1, and Asl ( 36 ). Therefore, the increase in arginine levels can be derived from ornithine, as indicated by the upregulation of enzymes such as Arg1, Ass1, Otc, and Asl within the urea cycle. The alterations in arginine biosynthesis could be attributed to the actions of the urea cycle toward enhancing the reduction of elevated NO levels induced by the inflammation triggered by infection. Interestingly, some studies have suggested that arginine supplementation therapy in COVID-19 patients could improve immune function and reduce inflammation ( 34 , 37 – 39 ). Additionally, targeting arginine depletion by regulating arginine biosynthesis enzymes, aiming to inhibit viral replication may present a potential therapeutic strategy for the treatment of COVID-19 patients ( 36 ).
|
PMC11697598_p25
|
PMC11697598
|
Discussion
| 4.373771 |
biomedical
|
Study
|
[
0.9994681477546692,
0.0003530418616719544,
0.000178816364496015
] |
[
0.9979640245437622,
0.00028098750044591725,
0.0016274636145681143,
0.00012754685303661972
] |
en
| 0.999997 |
Previously, a decrease in the levels of GSH along with an increase in the levels of GSSG was observed after coronavirus infection ( 40 ) indicating enhanced intracellular free radical generation and increased oxidative stress. Lung tissue functions as a reservoir for cellular thiols, primarily in the form of GSH. Viral infections deplete GSH and disrupt the redox balance in lung tissue, inducing cellular stress with lung damage ( 9 ). In patients experiencing hypoxemia due to SARS-CoV-2 infection, a reduction in serum cysteine has been reported, consistent with our research findings ( 41 ). Furthermore, we observed a significant increase in the levels of cystine, Ggt1 and Lap3. When GSH levels within the lung tissue are maintained, cystine from outside the cells enters and undergoes reduction to cysteine inside the cells ( 41 , 42 ). The decrease in GSH levels due to viral infection is anticipated to result from a reduction in serum cysteine levels required for GSH synthesis and the inhibition of the conversion of cystine to cysteine in lung tissue, leading to the accumulation of cystine. Thus, this study suggested that the alteration in GSH metabolism during SARS-CoV-2 variant infection can serve as an indicator of how the coronavirus affects oxidative stress and contributes to lung damage.
|
PMC11697598_p26
|
PMC11697598
|
Discussion
| 4.422777 |
biomedical
|
Study
|
[
0.9993997812271118,
0.0004169617313891649,
0.00018317917420063168
] |
[
0.9988732933998108,
0.0003458135761320591,
0.0006199705530889332,
0.00016079438501037657
] |
en
| 0.999996 |
In tryptophan metabolism, kynurenine, primarily known as an inflammatory marker, was significant enriched, along with a notable decrease in tryptophan levels after delta variant infection. Additionally, increased expression of genes such as tryptophan 2,3-dioxygenase 2 (Tdo2) and indoleamine 2,3-dioxygenase 1 (Ido1) was observed, indicating the enhancement of kynurenine synthesis after delta infection. Previous studies reported that kynurenine and tryptophan are associated with COVID-19 severity ( 35 , 43 , 44 ). Furthermore, Kynu and Ido1, which are involved in tryptophan metabolism, are upregulated during coronavirus infection. In particular, the reduction in tryptophan levels due to the action of Ido1 has long-term immunosuppressive effects ( 45 ). Consequently, our findings suggest that the enhancement of kynurenine synthesis represents a distinct inflammatory response in the lung tissue following infection with the delta variant.
|
PMC11697598_p27
|
PMC11697598
|
Discussion
| 4.255655 |
biomedical
|
Study
|
[
0.9995360374450684,
0.00026209166389890015,
0.00020186482288409024
] |
[
0.9992901086807251,
0.00020724281785078347,
0.00042247443343512714,
0.00008021936082514003
] |
en
| 0.999998 |
Interestingly, our findings are consistent with those of a previous human study. Li et al. found significant up-regulation in arginine metabolism and the urea cycle as well as tryptophan metabolism in plasma samples obtained from omicron patients compared to healthy controls ( 46 ). Notably, disruption of the urea cycle was observed, with a significant increase in ornithine cycle-related metabolites such as N2-acetyl-L-ornithine and asparagine, which were associated with cytokine storm. Additionally, these findings suggested that homoarginine and ornithine play a role in liver detoxification ( 35 ). Therefore, we suggested the potential for clinical application of SARS-CoV-2 research using the hamster model.
|
PMC11697598_p28
|
PMC11697598
|
Discussion
| 4.106429 |
biomedical
|
Study
|
[
0.9996064305305481,
0.00020630531071219593,
0.00018726449343375862
] |
[
0.9992399215698242,
0.0001864105579443276,
0.0005102918948978186,
0.00006334000499919057
] |
en
| 0.999996 |
The networks of cytokines and metabolic pathways suggested the presence of an inflammatory response and immune activation due to delta and omicron infection. Numerous studies have reported increased levels of inflammatory cytokines in COVID-19 patients, which supports our findings ( 32 ). Coronaviruses infect the respiratory tract and trigger a cytokine storm characterized by the production of inflammatory cytokines such as IL-1, IL-6, IL-8, IL-12, TNF-α, and other chemokines. This excessive release of inflammatory cytokines causes a rapid increase in cytokine levels in the bloodstream, leading to systemic inflammation. As a result, it can cause not only lung damage but also multiorgan failure, which is closely related to the severity of the disease. In patients with severe COVID-19, a high correlation was observed between circulating inflammatory cytokines, such as IL-6, CXCL10 (IP-10), and CSF1 (M-CSF), and arginine metabolism as well as tryptophan metabolism ( 43 ). Arginine is closely associated with inflammatory responses due to its essential role in T-cell activation, regulating both innate and adaptive immunity ( 47 ). These results reveal a strong correlation between TNF family cytokines and transcripts related to GSH metabolism, suggesting a potential link between the release of inflammatory cytokines and oxidative stress. The release of these inflammatory cytokines can potentially induce damage to lung tissue ( 48 ). Tryptophan metabolism is known to have the strongest correlation with IL-6 ( 43 , 49 ). Additionally, TNF-α, IL-6, and IL-1β induce elevated Ido1 expression in the context of immunosuppression in lung cancer progression ( 50 ).
|
PMC11697598_p29
|
PMC11697598
|
Discussion
| 4.407121 |
biomedical
|
Study
|
[
0.99936443567276,
0.0004078627098351717,
0.0002276498853461817
] |
[
0.998599112033844,
0.00023828732082620263,
0.0010325588518753648,
0.00013004998618271202
] |
en
| 0.999995 |
In conclusion, this study can be considered a notable advance as it included a comprehensive approach involving metabolic and transcriptomic profiling in animal models, which is relatively unexplored in the context of SARS-CoV-2 and its variants. We suggest that arginine biosynthesis, GSH metabolism and tryptophan metabolism are key metabolic pathways, shedding light on their relationship with the pulmonary immune response to both the delta and omicron infections. Furthermore, these pathways could be potential targets for therapeutic interventions aimed at mitigating the impact of these two SARS-CoV-2 variants. Overall, this study demonstrates that metabolic profiling with transcriptomic profiling is a valuable tool for exploring the immunometabolic responses associated with infectious diseases.
|
PMC11697598_p30
|
PMC11697598
|
Discussion
| 4.126226 |
biomedical
|
Study
|
[
0.999591052532196,
0.0002679016615729779,
0.00014108164759818465
] |
[
0.9985892176628113,
0.00023340416373685002,
0.001083504525013268,
0.00009384034638060257
] |
en
| 0.999999 |
Math anxiety can be defined as an intensive negative emotional experience associated with math-related tasks (for example, manipulation of numbers) . Academic literature dedicated to math anxiety has increased rapidly in recent years . According to bibliometric research , the majority of studies are dedicated to cognitive correlates of math anxiety, psychological effects, and educational contexts.
|
PMC11697699_p0
|
PMC11697699
|
Introduction
| 1.08116 |
other
|
Other
|
[
0.010903012938797474,
0.0006614067242480814,
0.9884356260299683
] |
[
0.01718737557530403,
0.9676942825317383,
0.014049012213945389,
0.0010693215299397707
] |
en
| 0.999997 |
Math anxiety may have a negative impact on math achievements and, as a phenomenon, exists across different cultural contexts .
|
PMC11697699_p1
|
PMC11697699
|
Introduction
| 1.026423 |
other
|
Other
|
[
0.00593959866091609,
0.0006743887206539512,
0.9933859705924988
] |
[
0.004531643353402615,
0.993118405342102,
0.0016173595795407891,
0.0007325592450797558
] |
en
| 0.999995 |
There are several cross-cultural comparative studies on math anxiety, such as a comparison between Russia and the UK , Confucian and European countries , the US and Colombia , Finland, the US, and Korea , and Germany and Brazil . The studies provide evidence for a strong cultural influence on math anxiety. For example, students in Confucian countries report higher math anxiety in comparison with European countries . In the present study, we expect to see differences in math anxiety scores between Chinese and Russian schoolchildren due to cultural and educational system differences.
|
PMC11697699_p2
|
PMC11697699
|
Introduction
| 1.084446 |
other
|
Study
|
[
0.01755567267537117,
0.0005309752887114882,
0.9819133877754211
] |
[
0.9351897239685059,
0.061731692403554916,
0.0020492018666118383,
0.0010293739615008235
] |
en
| 0.999995 |
One of the salient results from studies of math anxiety is that girls are more prone to exhibit high math anxiety in comparison to boys . Several studies highlight an increase in math anxiety with age. We also expect to trace those patterns in the current study.
|
PMC11697699_p3
|
PMC11697699
|
Introduction
| 1.11608 |
other
|
Other
|
[
0.02216053009033203,
0.0004492497246246785,
0.9773902297019958
] |
[
0.4652256369590759,
0.5297744274139404,
0.0033867163583636284,
0.0016131955198943615
] |
en
| 0.999997 |
Abbreviated Math Anxiety Scale (AMAS), developed from Mathematics Anxiety Rating Scale (MARS) by Hopko et al. , was validated for different cultural contexts, including Arabic , Serbian , Turkish , Polish , Spanish , Russian , Italian , German , and Chinese . There is some evidence for age and gender invariance of AMAS , as well as evidence for culture invariance in Italian and English contexts . Among well-established findings from AMAS studies is a higher level of math anxiety for girls , especially in older children . Results regarding age differences are less consistent: while some studies report an increase in math anxiety with age , others highlight an opposite tendency .
|
PMC11697699_p4
|
PMC11697699
|
Introduction
| 1.256462 |
other
|
Other
|
[
0.00684545561671257,
0.00026245054323226213,
0.9928921461105347
] |
[
0.21162721514701843,
0.7609307765960693,
0.02533290907740593,
0.0021091133821755648
] |
en
| 0.999997 |
The purpose of the current study is to compare AMAS math anxiety scores for Chinese and Russian schoolchildren. Additionally, factor structure and invariance for Russian and Chinese cultural contexts of AMAS are measured to justify the applicability of AMAS for cross-cultural comparison.
|
PMC11697699_p5
|
PMC11697699
|
Introduction
| 1.534811 |
other
|
Study
|
[
0.05219344422221184,
0.0008104734588414431,
0.9469960927963257
] |
[
0.9913170337677002,
0.007918617688119411,
0.0004485936660785228,
0.00031574247987009585
] |
en
| 0.999997 |
The total sample consisted of 7,702 participants (52% girls, M = 13.2, SD = 1.36, Me = 13.0). The Russian sample comprised 4,292 participants (54% girls, M = 13.7, SD = 1.21, Me = 14.0). The Chinese sample comprised 3,410 participants (48% girls, M = 12.7, SD = 1.21, Me = 13.0). The socio-demographic data and age distribution of samples are shown in Supplementary Table 1 and Supplementary Figure 1 .
|
PMC11697699_p6
|
PMC11697699
|
Sample
| 2.171521 |
biomedical
|
Study
|
[
0.9534976482391357,
0.0008660461753606796,
0.04563639685511589
] |
[
0.9900806546211243,
0.009478183463215828,
0.00029208522755652666,
0.00014900336100254208
] |
en
| 0.999997 |
The Abbreviated Math Anxiety Scale (AMAS) is a 9-item questionnaire developed by Hopko et al. based on the Mathematics Anxiety Rating Scale (MARS). It comprises two subscales: one addressing learning math anxiety (LMA, consisting of five items, e.g., “I feel anxious listening to a lecture in math class”) and the other focusing on math evaluation anxiety (MEA, consisting of four items, e.g., “I feel anxious taking an examination in a math course”). Respondents are required to assess their anxiety levels for each situation described in the statements on a Likert scale ranging from 1 (low anxiety) to 5 (high anxiety). The Russian version of the AMAS has demonstrated strong psychometric properties for middle and high schoolchildren and for high schoolchildren . Similarly, the Chinese version of the AMAS has been validated for primary and middle schoolchildren .
|
PMC11697699_p7
|
PMC11697699
|
Instruments
| 1.950072 |
other
|
Other
|
[
0.019774653017520905,
0.0002815974585246295,
0.9799436926841736
] |
[
0.12318870425224304,
0.8682194948196411,
0.007733519189059734,
0.0008582887821830809
] |
en
| 0.999997 |
Statistical analysis was performed in Python (v 3.9) and JASP software . Basic descriptive statistics were used to assess differences in math anxiety in Russian and Chinese samples. Confirmatory Factor Analysis (CFA) was performed to assess the factor structure of AMAS for the two samples. The DWLS (Diagonal Weighted Least Squares) estimator was used. One-factor, two-factor, second-order (two factors), and bi-factor (two factors) CFA models were compared. TLI, CFI, RMSEA, and SRMR metrics were used to assess model fit. Structural Equation Modeling (SEM) analysis was performed to measure invariance, and the Cronbach Alpha coefficient was used to assess the internal consistency of AMAS and subscales for two samples.
|
PMC11697699_p8
|
PMC11697699
|
Statistical analysis
| 3.607655 |
biomedical
|
Study
|
[
0.5736798644065857,
0.0007215908262878656,
0.42559853196144104
] |
[
0.9949430823326111,
0.004186057019978762,
0.0007709146593697369,
0.000099885233794339
] |
en
| 0.999997 |
For all samples (the joint, Russian, and Chinese), the bi-factor model with two factors fit the data best (see Table 1 ). Two-factor and second-order models demonstrate acceptable fit statistics in all samples. Factor loadings of the bi-factor model for two samples are presented in Supplementary Tables 3, 4 and Supplementary Figure 2 depict the bi-factor model.
|
PMC11697699_p9
|
PMC11697699
|
Factor structure
| 2.104633 |
biomedical
|
Study
|
[
0.6128101348876953,
0.00111780920997262,
0.386072039604187
] |
[
0.9850934743881226,
0.014099640771746635,
0.0006090440438129008,
0.00019789667567238212
] |
en
| 0.999994 |
Structural equation modeling was used to assess whether AMAS works equally for two samples (e.g., whether it is reasonable to compare their scores). During the first stage, configural invariance was tested (i.e., model parameters were estimated for both countries). Secondly, metric invariance was tested (whether factor loadings are the same in Russian and Chinese schoolchildren). Results are presented in Table 2 . Model 1 shows configural invariance with an acceptable fit to the data (CFI = 0.970, RMSEA = 0.069). Model 2 demonstrates metric invariance and fits the data (CFI = 0.965, RMSEA = 0.069). Thus, the data demonstrate an invariance across countries. This means that AMAS is an instrument that can be used to measure cross-cultural differences between Chinese and Russian schoolchildren.
|
PMC11697699_p10
|
PMC11697699
|
Invariance across countries
| 2.766514 |
other
|
Study
|
[
0.19530721008777618,
0.0007866871310397983,
0.8039060831069946
] |
[
0.9954771399497986,
0.003647939069196582,
0.0007218212704174221,
0.000153011511429213
] |
en
| 0.999997 |
The total AMAS scale and both subscales demonstrate acceptable Cronbach Alpha in both samples (see Table 3 ). There are no differences between Russian and Chinese samples for the total AMAS scale, but subscales, especially the LMA subscale, demonstrate slightly higher internal consistency in the Russian sample.
|
PMC11697699_p11
|
PMC11697699
|
Internal consistency
| 1.797526 |
other
|
Study
|
[
0.38365229964256287,
0.0013127528363838792,
0.6150349974632263
] |
[
0.9721474647521973,
0.02668183483183384,
0.0008710093679837883,
0.00029962416738271713
] |
en
| 0.999997 |
Descriptive statistics for total AMAS and two subscales with frequency histograms across the sample are presented in Supplementary Table 4 and Supplementary Figure 3 . It can be seen that regardless of country, the LMA score distribution is skewed to the low values, while the MEA score distribution is closer to normal distribution. MEA and LMA show moderate Pearson correlations for all samples: r = 0.54, p < 0.001 for the joint sample, r = 0.52 for the Russian sample, and r = 0.66 for the Chinese sample.
|
PMC11697699_p12
|
PMC11697699
|
Descriptive statistics
| 3.876326 |
biomedical
|
Study
|
[
0.9821884036064148,
0.0004032971046399325,
0.01740839332342148
] |
[
0.9994783997535706,
0.0003341531555633992,
0.00015948891814332455,
0.000027937850973103195
] |
en
| 0.999997 |
Table 4 represents the difference in scores between Russian and Chinese schoolchildren. Overall, Russian schoolchildren show significantly higher scores for AMAS and both subscales than Chinese schoolchildren .
|
PMC11697699_p13
|
PMC11697699
|
Descriptive statistics
| 1.547116 |
other
|
Study
|
[
0.17660103738307953,
0.0008661003084853292,
0.8225328922271729
] |
[
0.9223396182060242,
0.07595939934253693,
0.0012835304951295257,
0.0004173702618572861
] |
en
| 0.999997 |
Supplementary Table 5 represents the difference in AMAS, LMA, and MEA scores between two samples for different age groups: 10–11-year-olds, 12–13-year-olds, and 14–15-year-olds. The same pattern is observed: Chinese schoolchildren demonstrate lower math anxiety in all three age groups than Russian schoolchildren. There is an exception for the LMA subscale for the oldest age group, where Chinese schoolchildren show a higher mean score . However, the effect size is relatively small, and statistical significance is subthreshold ( p = 0.014).
|
PMC11697699_p14
|
PMC11697699
|
Descriptive statistics
| 2.073185 |
other
|
Study
|
[
0.2080455720424652,
0.000916130025871098,
0.7910383343696594
] |
[
0.9919875860214233,
0.007357787806540728,
0.00048509376938454807,
0.0001695617538644001
] |
en
| 0.999994 |
Supplementary Tables 6, 7 present age differences in AMAS scores for the two samples. Notably, no significant differences in total AMAS scores are observed across age groups in the Russian sample. However, when LMA and MEA scores are analyzed separately, opposite trends emerge: LMA scores decrease between the ages of 12–13 and 14–15, while MEA scores increase with age, particularly between the ages of 10–11 and 12–13. In contrast, in the Chinese sample, both MEA and LMA scores, as well as total AMAS scores, increase significantly with age.
|
PMC11697699_p15
|
PMC11697699
|
Age differences
| 2.898927 |
biomedical
|
Study
|
[
0.8181242942810059,
0.0007720611174590886,
0.18110372126102448
] |
[
0.9971247315406799,
0.002494974760338664,
0.00031978447805158794,
0.000060575119277928025
] |
en
| 0.999999 |
Due to these opposing trends in LMA scores between the Russian and Chinese samples, Chinese schoolchildren in the 14–15-year-old cohort surpass their Russian peers in mean LMA scores. Supplementary Figure 6 illustrates the age differences in AMAS, LMA, and MEA scores between Russian and Chinese schoolchildren.
|
PMC11697699_p16
|
PMC11697699
|
Age differences
| 1.911456 |
biomedical
|
Study
|
[
0.5354767441749573,
0.0009109169477596879,
0.4636123478412628
] |
[
0.9567551612854004,
0.042354412376880646,
0.0006206883117556572,
0.0002698130556382239
] |
en
| 0.999996 |
Gender differences in the two samples are presented in Supplementary Table 8 and Supplementary Figure 7 . In both countries, girls demonstrate higher scores for AMAS and subscales. For LMA scales, the difference between boys and girls is not significant in the samples of both countries. However, the difference becomes significant in a joint sample.
|
PMC11697699_p17
|
PMC11697699
|
Gender differences
| 1.82628 |
biomedical
|
Study
|
[
0.641148030757904,
0.0012946524657309055,
0.35755738615989685
] |
[
0.9751515984535217,
0.023914575576782227,
0.0006890003569424152,
0.0002448801533319056
] |
en
| 0.999996 |
Supplementary Tables 9, 10 and Supplementary Figure 8 provide information on the interaction between gender and age in the two samples. It can be seen that the youngest Russian schoolchildren (ages 10–11 years) demonstrate no significant differences in mean total and subscale scores. At age 10, Russian girls have slightly lower scores than boys on the AMAS, particularly on the MEA subscale (as indicated by intersecting confidence intervals). However, this gender advantage reverses at age 11.
|
PMC11697699_p18
|
PMC11697699
|
Differences in age-gender interaction
| 2.018993 |
other
|
Study
|
[
0.3951345980167389,
0.0009707052959129214,
0.6038946509361267
] |
[
0.9895290732383728,
0.009932341985404491,
0.00038751785177737474,
0.0001510997099103406
] |
en
| 0.999996 |
For AMAS and MEA, a significant gender difference in Russian schoolchildren can be seen at age 12, and then on, its magnitude remains approximately the same. For LMA, no significant gender differences were observed across all ages. On the contrary, in Chinese schoolchildren, gender differences are significant from the beginning (10–11-year-olds). Interestingly, for LMA scores in 10–11-year-olds, there is a significant gender difference (girls score higher), but it disappears in schoolchildren of older ages. Overall, gender differences in AMAS and MEA scores are more significant in the Russian sample.
|
PMC11697699_p19
|
PMC11697699
|
Differences in age-gender interaction
| 1.532317 |
other
|
Study
|
[
0.06621745228767395,
0.0005435056518763304,
0.9332391023635864
] |
[
0.9164634346961975,
0.0816551223397255,
0.0012646716786548495,
0.0006168007384985685
] |
en
| 0.999996 |
The study aimed to investigate differences in math anxiety scores between Russian and Chinese schoolchildren aged 10–15 years. Gender and age differences were estimated, as well as differences in gender-age interaction. The abbreviated Math Anxiety Scale was used as a measuring tool.
|
PMC11697699_p20
|
PMC11697699
|
Discussion
| 1.713638 |
other
|
Study
|
[
0.1302424669265747,
0.0012951993849128485,
0.8684622645378113
] |
[
0.9950002431869507,
0.004493218846619129,
0.00027943175518885255,
0.0002270737459184602
] |
en
| 0.999996 |
The bi-factor model fits the data best for both Russian and Chinese samples, and it has the lowest RMSEA, lowest SRMR, and highest TLI among all the models tested. A number of studies report the best fit of the bi-factor model as well. Some studies, however, report two-factor and second-factor models that fit the data best. One of the advantages of a bifactor model is that it simultaneously captures the general factor and separate factors for subscales . In the case of AMAS, the bifactor model shows that it makes sense to calculate total math anxiety scores and scores for LMA and MEA separately.
|
PMC11697699_p21
|
PMC11697699
|
Discussion
| 2.213696 |
other
|
Study
|
[
0.1255815625190735,
0.0004158137016929686,
0.8740026354789734
] |
[
0.8369370102882385,
0.1588188111782074,
0.003671212587505579,
0.0005729634431190789
] |
en
| 0.999996 |
It was also shown that there is an invariance across countries (i.e., AMAS measures the same construct in Russian and Chinese schoolchildren), which is consistent with previous studies on culture invariance . In both countries, AMAS demonstrates high internal consistency. However, LMA shows a slightly lower internal consistency for Chinese schoolchildren than the Russian subsample. It may be because the LMA subscale does not capture the specificity of math learning situations in China well.
|
PMC11697699_p22
|
PMC11697699
|
Discussion
| 1.299958 |
other
|
Study
|
[
0.0189537201076746,
0.0004943987587466836,
0.9805518388748169
] |
[
0.9114303588867188,
0.08603294938802719,
0.0015256272163242102,
0.001011009095236659
] |
en
| 0.999996 |
Schoolchildren from both countries demonstrate a similar pattern: low level of learning math anxiety and moderate level of math evaluation anxiety. This pattern is consistent with other studies . Overall, Russian schoolchildren show higher math anxiety in comparison with their Chinese peers. This tendency persists if comparison is made within age groups with one exception: 14–15-year-old Chinese children show higher levels of learning math anxiety in comparison with 14–15-year-old Russian children. The analysis of changes in math anxiety levels as a function of age reveals that in Russia, learning math anxiety decreases with age, while math evaluation anxiety increases, whereas in China, both learning and evaluation anxiety increase as children grow older.
|
PMC11697699_p23
|
PMC11697699
|
Discussion
| 1.235494 |
other
|
Study
|
[
0.015193377621471882,
0.0004433212161529809,
0.9843632578849792
] |
[
0.9394340515136719,
0.05867348983883858,
0.0011210195953026414,
0.0007714495877735317
] |
en
| 0.999997 |
The differing trajectories of learning math anxiety dynamics in Russia and China explain the atypical pattern observed at ages 14–15.
|
PMC11697699_p24
|
PMC11697699
|
Discussion
| 1.10264 |
other
|
Other
|
[
0.015980185940861702,
0.000517392298206687,
0.9835024476051331
] |
[
0.0588696151971817,
0.9376048445701599,
0.0023290489334613085,
0.0011965518351644278
] |
en
| 0.999997 |
Lanfaloni et al. demonstrate that learning math anxiety and math evaluation anxiety, despite being correlated, can exist independently and play different roles in math achievement. The difference in the dynamic of math evaluation anxiety and learning math anxiety observed in the Russian sample is partly consistent with a finding that secondary schoolchildren show higher math evaluation anxiety and similar learning math anxiety to primary schoolchildren . However, some studies show that overall math anxiety increases with age, which is more consistent with our findings for the Chinese sample . Further studies are necessary to understand the origins of different dynamics of learning math anxiety in China and Russia.
|
PMC11697699_p25
|
PMC11697699
|
Discussion
| 1.090465 |
other
|
Study
|
[
0.00721881166100502,
0.00034866653732024133,
0.9924324750900269
] |
[
0.8095009326934814,
0.18198564648628235,
0.006669532507658005,
0.0018439225386828184
] |
en
| 0.999998 |
In both countries, girls show significantly higher levels of math anxiety in comparison with boys on the entire scale and MEA subscale, while for LMA, no significant gender differences are observed. Notably, country differences are more pronounced than gender differences. In previous studies on Chinese , Russian , and other populations, gender differences in math anxiety were also highlighted.
|
PMC11697699_p26
|
PMC11697699
|
Discussion
| 1.211993 |
other
|
Study
|
[
0.030239474028348923,
0.0006239194190129638,
0.9691367149353027
] |
[
0.8694315552711487,
0.1266230195760727,
0.0027721889782696962,
0.0011732144048437476
] |
en
| 0.999996 |
While investigating the changes in math anxiety with age for boys and girls separately (gender-age interaction), some differences between Russian and Chinese schoolchildren were observed. In Russia, there are no significant gender differences in the youngest age group, which is consistent with studies that demonstrate the absence of gender differences in primary schoolchildren . It is worth noting that at age 10 years, Russian girls demonstrate even lower levels of overall math anxiety and evaluation math anxiety, although the difference was not statistically significant. At age 12 years, boys demonstrate a low level of math anxiety, and this pattern is maintained in older ages. In China, that pattern was established earlier: girls score higher than boys in all ages observed in the current study.
|
PMC11697699_p27
|
PMC11697699
|
Discussion
| 1.398169 |
other
|
Study
|
[
0.029985977336764336,
0.0005460043903440237,
0.9694681167602539
] |
[
0.9692055583000183,
0.029473919421434402,
0.000822009053081274,
0.0004984702682122588
] |
en
| 0.999996 |
Subsets and Splits
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