COVID-19 in Africa: an ovarian victory?
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) mainly attacks the respiratory system and is characterized by pneumonia, cytokine storm, coagulation disorders and severe immune downregulation. Although public health experts predicted worst outcomes in Africa, the incidence, hospitalization and mortality rates have been lower in Africa compared to other continents. Interestingly, lower incidence and mortality rates have been observed in women from Africa compared to their cohorts from other continents. Also, in the US non-Hispanic Black females have lower COVID-19 and death rates compared to their white counterparts. It's unclear why this significant difference exists; however, the ovarian function, genetics and immunological statuses could play a major role. Women of African descent have elevated levels of estrogen compared with Caucasians hence we anticipate that estrogen might offer some protection against the SARS-CoV-2 infections. The racial differences in lifestyle, age and inaccessibility to contraceptive usage might also play a role. Here, we provide insight on how the high levels of estrogen in African women might contribute to the lower cases and fatalities in Africa. Specifically, estrogen might offer protection against COVID-19 by suppressing hyper-production of cytokines, promoting anti-inflammatory cytokines, stimulating antibody production and suppressing endoplasmic reticulum (ER) stress. This will as well provide useful information on how future pandemics could be managed using Africa as a case study.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".