Possible ameliorative role of <i>Spirulina platensis</i> on coagulation factors, lymphocytopenia, and malnutrition in ICU patients with COVID-19
Bibliographic record
Abstract
Considering the anti-viral effects of Spirulina platensis (Sp), this study investigated the impact of Sp on impaired blood biomarkers of patients hospitalized in the intensive care unit (ICU) with COVID-19. Therefore, 104 patients (aged 48–66; 61.5% male) were randomly assigned to the Sp (daily consumption of 5 g) or placebo group for 2 weeks. Linear regression analysis was employed to assess the differences in blood test results between the control and intervention groups among patients with COVID-19. Our results showed significant differences in certain hematological tests, including a higher level of hematocrit (HCT) and a lower platelet count (PLT) in the intervention group ( p < 0.05). The percentage of lymphocytes (Lym%) in serology testing was significantly different between the control and intervention groups ( p = 0.03). In terms of biochemical test analyses, Sp supplementation was associated with reduced levels of both blood urea nitrogen (BUN) and lactate dehydrogenase (LDH) ( p = 0.01). Furthermore, on day 14, the intervention group displayed significantly higher medians of serum protein, albumin, and zinc compared to the control group ( p < 0.05). Additionally, patients supplemented with Sp had a lower BUN–albumin ratio (BAR) ( p = 0.01). No immunological and hormonal differences were observed between groups following 2 weeks. Our analysis indicates that Sp supplementation may be effective in regulating some blood test abnormalities associated with COVID-19. This study was registered at ISRCTN as IRCT20200720048139N1.
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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) | 0.001 | 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 | 0.000 |
| 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".