Treatment of infection and inflammation associated with COVID-19, multi-drug resistant pneumonia and fungal sinusitis by nebulizing a nanosilver solution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it