Sex differences in COVID-19 mortality: opportunity to develop HSP27 (HSPB1) immunotherapy to treat hyper-inflammation?
Bibliographic record
Abstract
Since January 10, 2020, when the first death linked to the severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) occurred in Wuhan, China, more than 530,000 people have died (as of July 4, 2020). Globally, there are 11.2 million people infected with the virus, with 2.9 million in the USA alone and other populous countries surging. While there is a charge to develop a vaccine, as well as means of eliminating the virus from those infected (e.g., remdesivir), neither of these therapeutic strategies will directly address the major life-threatening complications that may occur once infected, namely, the profound upregulation of the innate immune system. Like the severe acute respiratory syndrome coronavirus that emerged in 2002 (SARS-1, caused by SARS-CoV) and the Middle East respiratory syndrome-related coronavirus of 2012 (MERS-CoV), COVID-19 (the disease associated with SARS-CoV-2) is associated with a storm of pro-inflammatory cytokines like IL-1β, IL-6, and TNF. Precisely why some patients evolve to this hyper-inflammation state while others do not remains unclear but is likely due to nonviral factors that are specific to the host, including age and comorbidities (Yang et al. 2020). These cytokines play an important role in various tissue complications with acute respiratory distress syndrome (ARDS), a form of acute lung injury that is without tangible therapeutic options apart from supportive care, of principal concern (Nieto-Torres et al. 2015).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".