Bibliographic record
Abstract
Platelets, small anucleate cells circulating in the blood, are critical mediators in haemostasis and thrombosis. Interestingly, recent studies demonstrated that platelets contain both pro-inflammatory and anti-inflammatory molecules, equipping platelets with immunoregulatory function in both innate and adaptive immunity. In the context of infectious diseases, platelets are involved in early detection of invading microorganisms and are actively recruited to sites of infection. Platelets exert their effects on microbial pathogens either by direct binding to eliminate or restrict dissemination, or by shaping the subsequent host immune response. Reciprocally, many invading microbial pathogens can directly or indirectly target host platelets, altering platelet count or/and function. In addition, microbial pathogens can impact the host auto- and alloimmune responses to platelet antigens in several immune-mediated diseases, such as immune thrombocytopenia, and fetal and neonatal alloimmune thrombocytopenia. In this review, we discuss the mechanisms that contribute to the bidirectional interactions between platelets and various microbial pathogens, and how these interactions hold relevant implications in the pathogenesis of many infectious diseases. The knowledge obtained from "well-studied" microbes may also help us understand the pathogenesis of emerging microbes, such as SARS-CoV-2 coronavirus.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".