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Record W4382725546 · doi:10.3390/v15071462

Modulation of the Host Response as a Therapeutic Strategy in Severe Lung Infections

2023· review· en· W4382725546 on OpenAlexafffund
Elyse Latreille, Warren L. Lee

Bibliographic record

VenueViruses · 2023
Typereview
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsARDSPathogenImmunologyLungMedicineRespiratory distressDiffuse alveolar damageImmune systemIntensive care medicineAcute respiratory distressBiologyInternal medicine

Abstract

fetched live from OpenAlex

Respiratory pathogens such as influenza and SARS-CoV-2 can cause severe lung infections leading to acute respiratory distress syndrome (ARDS). The pathophysiology of ARDS includes an excessive host immune response, lung epithelial and endothelial cell death and loss of the epithelial and endothelial barrier integrity, culminating in pulmonary oedema and respiratory failure. Traditional approaches for the treatment of respiratory infections include drugs that exert direct anti-pathogen effects (e.g., antivirals). However, such agents are typically ineffective or insufficient after the development of ARDS. Modulation of the host response has emerged as a promising alternative therapeutic approach to mitigate damage to the host for the treatment of respiratory infections; in principle, this strategy should also be less susceptible to the development of pathogen resistance. In this review, we discuss different host-targeting strategies against pathogen-induced ARDS. Developing therapeutics that enhance the host response is a pathogen-agnostic approach that will help prepare for the next pandemic.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.138
GPT teacher head0.409
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2023
Admission routes2
Has abstractyes

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