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Record W3047881527 · doi:10.3389/fimmu.2020.01962

Crosstalk Between Platelets and Microbial Pathogens

2020· review· en· W3047881527 on OpenAlexafffund
Conglei Li, June Li, Heyu Ni

Bibliographic record

VenueFrontiers in Immunology · 2020
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsSt. Michael's HospitalCanadian Blood ServicesUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanadian Blood Services
KeywordsCrosstalkPlateletMicrobiologyBiologyComputational biologyImmunologyEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.286
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations90
Published2020
Admission routes2
Has abstractyes

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