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Record W4200274941 · doi:10.1093/infdis/jiab604

High SARS-CoV-2 Viral Load and Low <i>CCL5</i> Expression Levels in the Upper Respiratory Tract Are Associated With COVID-19 Severity

2021· article· en· W4200274941 on OpenAlexfundno aff
Felipe Pérez‐García, María Martin‐Vicente, Rosa Lía Rojas-García, Lucía Castilla-García, María José Muñoz‐Gómez, Irene Hervás Fernández, Victoria González Ventosa, Erick Joan Vidal-Alcántara, Juan Cuadros‐González, Jesús F. Bermejo-Martín, Salvador Resino, Isidoro Martínez

Bibliographic record

VenueThe Journal of Infectious Diseases · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIICanadian Institutes of Health ResearchDalhousie Medical Research Foundation
KeywordsViral loadImmune systemRespiratory tractCoronavirusImmunologyCCL5VirologyViral replicationMedicineRespiratory systemInnate immune systemVirusBiologyCoronavirus disease 2019 (COVID-19)DiseaseT cellInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Mucosal immune response in the upper respiratory tract is crucial for initial control of viral replication, clearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and progression of coronavirus disease 2019 (COVID-19). We analyzed SARS-CoV-2 RNA load and expression of selected immune genes in the upper respiratory tract (nasopharynx) of 255 SARS-CoV-2-infected patients and evaluated their association with severe COVID-19. SARS-CoV-2 replication in nasopharyngeal mucosa induces expression of several innate immune genes. High SARS-CoV-2 viral load and low CCL5 expression levels were associated with intensive care unit admission or death, although CCL5 was the best predictor of COVID-19 severity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.382
Teacher spread0.332 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations36
Published2021
Admission routes1
Has abstractyes

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