Treatment of infection and inflammation associated with COVID-19, multi-drug resistant pneumonia and fungal sinusitis by nebulizing a nanosilver solution
Bibliographic record
Abstract
, and higher oxidation state silver, generated from nanocrystalline silver dressings, were anti-inflammatory against porcine skin inflammation. The dressings have clinically-demonstrated broad-spectrum antimicrobial activity, suggesting application of nanosilver solutions in treating pulmonary infection. Nanosilver solutions were tested for antimicrobial efficacy; against HSV-1 and SARS-CoV-2; and nebulized in rats with acute pneumonia. Patients with pneumonia (ventilated), fungal sinusitis, burns plus COVID-19, and two non-hospitalized patients with COVID-19 received nebulized nanosilver solution. Nanosilver solutions demonstrated pH-dependent antimicrobial efficacy; reduced infection and inflammation without evidence of lung toxicity in the rat model; and inactivated HSV-1 and SARS-CoV-2. Pneumonia patients had rapidly reduced pulmonary symptoms, recovering pre-illness respiratory function. Fungal sinusitis-related inflammation decreased immediately with infection clearance within 21 days. Non-hospitalized patients with COVID-19 experienced rapid symptom remission. Nanosilver solutions, due to anti-inflammatory, antiviral, and antimicrobial activity, may be effective for treating respiratory inflammation and infections caused by viruses and/or microbes.
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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) | 0.000 | 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 | 0.001 |
| 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".