Low Serum Erythropoietin Levels are Associated with Fatal COVID‐19 Cases at 4,150 meters above sea level
Bibliographic record
Abstract
Previous studies suggested that erythropoietin (EPO) may protect against severe COVID‐19‐induced injuries, ultimately preventing mortality. This hypothesis is based on the fact that, in addition to promoting the increase in red blood cells, EPO is an anti‐inflammatory, anti‐apoptotic and protective factor in several non‐erythropoietic tissues. Furthermore, EPO promotes nitric oxide production in the hypoxic lung and stimulates ventilation by interacting with the respiratory centers of the brainstem. Given that EPO in the blood is increased at high‐altitude, we evaluated the serum levels of EPO in critical patients with COVID‐19 at “Hospital Agramont” in the city of El Alto (4,150 masl) in Bolivia. A total of 16 patients, 15 men, one woman, with a mean age of 55.8 ± 8.49 years, admitted to the Intensive Care Unit were studied. All patients were permanent residents of El Alto, with no travel history below 3,000 masl for at least one year. Blood samples were collected upon admission to the ICU. Serum EPO concentration was assessed using an ELISA kit, and a standard technique determined hemoglobin concentration. Only half of the observed patients survived the disease. Remarkably, fatal cases showed 2.5 times lower serum EPO than survivors (2.78 ± 0.8643 mU/ml vs 7.06 ± 2.713 mU/ml; p = 0.0096), and 1.24 times lower hemoglobin levels (13.96 ± 2.56 g/dL vs 17.41 ± 1.61 g/dL; p = 0.0159). While the number of cases evaluated in this work is low, our findings strongly warrant further investigation of EPO levels in COVID‐19 patients at high and low altitudes. Our results also support the hypothesis that exogenous EPO administration could help critically ill COVID‐19 patients overcome the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".