Heat shock protein 27 in the pathogenesis of COVID-19 and non-COVID acute respiratory distress syndrome
Bibliographic record
Abstract
Acute respiratory distress syndrome (ARDS) is a common cause of hypoxemic respiratory failure in intensive care units that has increased dramatically as a result of the COVID-19 pandemic. In both COVID-19 and non-COVID ARDS, the pathogenesis of lung injury involves local (pulmonary) and systemic inflammation, leading to impaired gas exchange, requirement for mechanical ventilation, and a high risk of mortality. Heat shock protein 27 (HSP27) is a chaperone protein expressed in times of cell stress with roles in modulation of systemic inflammation via the NF-κB pathway. Given its important role as a modulator of inflammation, we sought to investigate the role of HSP27 and its associated auto-antibodies in ARDS caused by both SARS-CoV-2 and non-COVID etiologies. A total of 68 patients admitted to the intensive care unit with ARDS requiring mechanical ventilation were enrolled in a prospective, observational study that included 22 non-COVID-19 and 46 COVID-19 patients. Blood plasma levels of HSP27, anti-HSP27 auto-antibody (AAB), and cytokine profiles were measured on days 1 and 3 of ICU admission along with clinical outcome measures. Patients with COVID-19 ARDS displayed significantly higher levels of HSP27 in plasma, and a higher ratio of HSP27:AAB on both day 1 and day 3 of ICU admission. In patients with COVID-19, higher levels of circulating HSP27 and HSP27:AAB ratio were associated with a more severe systemic inflammatory response and adverse clinical outcomes including more severe hypoxemic respiratory failure. These findings implicate HSP27 as a marker of advanced pathogenesis of disease contributing to the dysregulated systemic inflammation and worse clinical outcomes in COVID-19 ARDS, and therefore may represent a potential therapeutic target.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".