Two-Phase Immune Responses of COVID-19 and Therapeutic Approaches
Bibliographic record
Abstract
The novel coronavirus disease 2019 spread rapidly worldwide and disease was announced a pandemic by the WHO. Standard therapeutic strategy against COVID-19 is lacking. Regarding to the two-phase pathogenesis of the COVID-19 (immune defense-based protective and postinitial inflammation-driven damaging phases, different therapeutic regimens including antiviral agents (e.g., lopinavir/ritonavir, remdesivir, ribavirin, oseltamivir, and sofosbuvir, etc.), some antibiotics and immunomodulatory (chloroquine/hydroxychloroquine) and anti inflammatory agents (corticosteroids) have been considered in hospitals for COVID-19 patients, but balancing theirbenefits and potential risk is of great importance [1-7].Immune-boosting strategies (e.g., anti-sera or pegylated IFNα) and antiviral therapy may be of great importance in the initial phase or non-severe stages, while immunosuppressive or immunomodulatory approaches can be used for halting tissue damage and managing the symptoms in the inflammatory phase. There are conflicting results in prescribing corticosteroids or immunomodulation for COVID-19 patients due to paradoxical negative effects (risk of death, secondary bacterial infections and longer hospitalization) [8-9]. Activation of coagulation pathwaysis also associated with increased proinflammatory cytokines, resulting in multiorgan injury. Severe COVID‐19 had disseminated intravascular coagulation, leading to the risk of venous thromboembolism [10], where naproxen (for antiplatelet and anti-inflammatory and anticoagulative effects) and low molecular weight heparin (LMWH; anti-inflammatory properties) may be considered for patients. It has been suggested that LMWH is contemplated because of concerns because of the presence of thrombi in the pulmonary circulation for those in patients withraised d-dimer.However, effective therapeutic approach requires balancing harmful and beneficial effects of regimens, to be prescribed by precision. Also, two-phase pathophysiology and immune responses of COVID-19 should not be underemphasized for treatment of patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".