MétaCan
Menu
Back to cohort
Record W3167521026 · doi:10.3389/fphar.2021.664349

Pulmonary Edema in COVID-19 Patients: Mechanisms and Treatment Potential

2021· review· en· W3167521026 on OpenAlexaff
Xinyu Cui, Wuyue Chen, Haoyan Zhou, Yuan Gong, Bowen Zhu, Xiang Lv, Hongbo Guo, Jing Zhou, Edyta Marcon, Hongyue Ma

Bibliographic record

VenueFrontiers in Pharmacology · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of Toronto
FundersJiangsu Collaborative Innovation Center of Chinese Medicinal Resources IndustrializationQinglan Project of Jiangsu Province of ChinaNational Natural Science Foundation of China
KeywordsPulmonary edemaMedicineEdemaLungCoronavirus disease 2019 (COVID-19)CoronavirusImmunologyIntensive care medicineDiseasePathologyInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

COVID-19 mortality is primarily driven by abnormal alveolar fluid metabolism of the lung, leading to fluid accumulation in the alveolar airspace. This condition is generally referred to as pulmonary edema and is a direct consequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. There are multiple potential mechanisms leading to pulmonary edema in severe Coronavirus Disease (COVID-19) patients and understanding of those mechanisms may enable proper management of this condition. Here, we provide a perspective on abnormal lung humoral metabolism of pulmonary edema in COVID-19 patients, review the mechanisms by which pulmonary edema may be induced in COVID-19 patients, and propose putative drug targets that may be of use in treating COVID-19. Among the currently pursued therapeutic strategies against COVID-19, little attention has been paid to abnormal lung humoral metabolism. Perplexingly, successful balance of lung humoral metabolism may lead to the reduction of the number of COVID-19 death limiting the possibility of healthcare services with insufficient capacity to provide ventilator-assisted respiration.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
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 head0.065
GPT teacher head0.465
Teacher spread0.400 · 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

Citations67
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in PharmacologySame topicCOVID-19 Clinical Research StudiesFrench-language works237,207