The use of selenium-containing drugs in the prevention and treatment of complications in patients with COVID-19
Bibliographic record
Abstract
Objective. To study literature data reflecting the use of selenium (Se) and selenium-containing drugs in the complex prophylaxis and treatment of complications in patients with COVID-19. Material and methods. Data analysis of 37 publications of scientists from Russian Federation, United States of America, People's Republic of China, Great Britain, India, France, Germany, Italy, Sweden, Canada, Brazil, United Arab Emirates, Saudi Arabia, Ireland, Holland, Greece, Australia, Poland, Pakistan, Sudan, Nepal was performed. The authors reflected on the epidemiology, diagnosis, pathogenesis, clinic, risk of acute respiratory distress syndrome, multiple organ failure, cardiovascular complications, mortality in patients with COVID-19, the importance of Se deficiency in the body and the preventive use of selenium-containing drugs in novel coronavirus infection. Results. Low dietary Se intake was associated with the development of acute respiratory distress syndrome in men and women with COVID-19. Deficiencies were associated with increased risk of morbidity and mortality. Organic forms of Se had the best bioavailability. Se had antioxidant, anti-inflammatory, antithrombotic, antiviral, immunomodulatory effects in patients with COVID-19. Conclusions. Thus, control and optimization of the selenium status in population of selenium-deficient areas with addition of Se-enriched food to the diet, as well as SELENBIO for women complex of Russian company "Parapharm" could be one of the directions of prevention and treatment of complications in patients with COVID-19.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".