GENETIC DIVERSITY OF PATHOGENIC MICROORGANISMS AND ITS MEDICAL AND PUBLIC HEALTH SIGNIFICANCE
Bibliographic record
Abstract
*JS Virdi, P Gulati, M Pai In the last decade or so, significant amounts of data have of genetic diversity data has been the development of been generated on genetic diversity of a number of pathogens. diagnostics, therapeutics and vaccines. Poorly understood This has been accomplished largely due to the development diversity of a pathogen can lead to development of of genotyping techniques such as multilocus enzyme diagnostics that cannot detect the entire set of pathogen electrophoresis, restriction fragment length polymorphism, genotypes causing a particular disease. For example, the random amplified polymorphic DNA, pulse field gel discovery of highly divergent strains of human electrophoresis, repetitive sequence-based polymerase chain immunodeficiency virus (HIV) that were not reliably detected reaction, multilocus sequence typing and DNA sequencing. by a number of commonly used diagnostic kits emphasized Why is the study of genetic diversity important and what can the need for studies to type HIV variants. Another example is it contribute to medicine and public health? Several the development of new tests for the detection of tuberculosis observations of both fundamental and applied nature have infection. Comparative genomics has allowed the identification been gleaned from studies on diversity. Thanks to these of genomic segments (e.g., region of difference (RD1)) that studies, we now understand that although pathogenic are present in Mycobacterium tuberculosis but deleted from microbes constitute a small proportion of all microbial species, Mycobacterium bovis BCG sub-strains. Antigens encoded by many are characterized by high genetic diversity. For example such regions (e.g., ESAT-6) are currently being used in novel, organisms such as the HIV and influenza virus mutate blood-based interferon-gamma assays and these assays have extensively and pose major challenges to disease control. been shown to be more specific than the tuberculin skin test.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".