Advances in Molecular Technologies and Platforms for the Diagnosis of Infectious Diseases
Bibliographic record
Abstract
nfectious microbial pathogens constitute the largest cause of morbidity and mortality worldwide. Early diagnosis and rapid infection control measures can lead to improved outcomes, earlier discharges and reduced nosocomial infections. Conventional diagnostic methods for infectious diseases such as microscopy, culture, and immunological methods, in most cases, are not universally applicable, less sensitive and could take from days to months to complete depending on the pathogen. Molecular assays based on nucleic acids such as polymerase chain reaction (PCR) have improved the sensitivity, specificity and turn-around time in diagnostic microbiology laboratories. These tests are particularly important to detect very low levels of pathogens in clinical samples, and for organisms that have long half-lives or are non-culturable. However, individual molecular tests are available for only a limited number of the more common infectious agents. Moreover, infectious disease events arising from novel pathogens or genetic variants have significantly increased, recently, for which, routine diagnostic methods are not yet available. Therefore, molecular methods and technologies capable of detecting multiple pathogens in a single test have become available over the last few years. Although, these methods are based on the conventional nucleic acid amplification and hybridization chemistry, enhanced multiplexing capability has been achieved through innovations in nucleic acid labeling techniques, and post-amplification analytic methods and instrumentation. The availability of these test kits brought a new level of convenience to the physicians ordering practices, and to the laboratory personnel, as they require very little hands on time. However, these tests are yet unaffordable to many laboratories, and in many cases, the sensitivity is poor compared to that of single-target, real-time PCR assays. Looking into the future, the revolutionary, next generation sequencing (NGS) technology is now being considered as a potential method for rapid identification of hundreds of pathogens, in an unbiased manner, with a single test that could significantly benefit patients who are critically ill with undiagnosed disease.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".