Experimental Analysis of the Potential Validity of Lorenzini’s Hypothesis to Treat COVID-19 Patients
Bibliographic record
Abstract
The present research was aimed at testing the potential validity of Lorenzini’s hypothesis against Covid-19, based on the use of far UV-C rays directly on the patient’s ill lungs, so to significantly reduce the virus presence and with it the interstitial pneumonia, the cause of most deaths. Not having had the chance to experiment directly on the SARS-CoV-2, we shifted to a plant virus called TSWV, which has some common characteristics with SARS-CoV-2, so making our tests significant. The virus was suitably utilized so to affect an opportune range of pepper plants, after a treatment with 2 UV-C lamps, of which just one spreading far UV-C rays. Results showed that the effect of a quick exposition to UV-C rays of TSWV-infected sap extract reduces nearly to zero the viral effect, leaving the plant healthy or infected, but asymptomatic. Due to the test conditions and to the similarities of TSWV to SARS-CoV-2, we can conclude that Lorenzini’s hypothesis against Covid-19 is well posed and, if adopted, could save many lives.
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.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".