Enable, Empower, Succeed: Harnessing Open Science for Antimicrobial Resistance Containment
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) poses a significant threat to global health, particularly in Western sub-Saharan Africa where 27.3 deaths per 100,000 lives are affected, and surveillance and control measures are often limited. Genomics research plays a crucial role in understanding the emergence, spread and containment measures of AMR. However, its implementation in such settings is particularly challenging due to limited human capacity. This manuscript outlines a three-day bioinformatics workshop in Cameroon, highlighting efforts to build human capacity for genomics research to support AMR surveillance using readily accessible and user-friendly web-based tools. The workshop introduced participants to basic next-generation sequencing concepts, data file formats used in bacterial genomics, data sharing procedures and considerations, as well as the use of web-based bioinformatics software to analyse genomic data, including in silico prediction of AMR, phylogenetics analyses, and a quick introduction to Linux© command line. We provide a detailed description of the relevant training approaches used, including workshop structure, the selection and planning, and utilization of freely available web-based tools, and the evaluation methods employed. Our approach aimed to overcome limitations such as inadequate infrastructure, limited access to computational resources, and scarcity of expertise. By leveraging the power of freely available web-based tools, we demonstrated how participants can acquire fundamental bioinformatics skills, enhance their understanding of biological data analysis, and contribute to the field, even in an underprivileged environment. Our findings highlight the effectiveness of this training approach in empowering local researchers and bridging the bioinformatics gap in genomics surveillance of AMR in resource-constrained settings. Building human capacity for genomics research globally, and especially in resource-constrained settings, is imperative for ensuring global health and sustainable containment of AMR. Data summary The authors confirm that all supporting data, code and protocols have been provided within the article or through supplementary data files. Impact Statement Antimicrobial resistance (AMR) is a major global health threat, especially in Western sub-Saharan Africa, where 27.3 deaths per 100,000 lives occur. Genomics research play an instrumental role for understanding AMR’s emergence, spread, and containment measures. However, its implementation in these settings is challenging due to limited human capacity. A three-day bioinformatics workshop in Cameroon aimed to build human capacity for genomics research using web-based tools. Participants were introduced to next-generation sequencing concepts, data file formats, data sharing procedures, and web-based bioinformatics software for analysing genomic data. The workshop aimed to overcome limitations like inadequate infrastructure, computational resources, and expertise scarcity. The findings show the effectiveness of this training approach in empowering local researchers and bridging the bioinformatics gap in genomics surveillance of AMR in resource-constrained settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".