COVID-19: The Disease, the Immunological Challenges, the Treatment with Pharmaceuticals and Low-Dose Ionizing Radiation
Bibliographic record
Abstract
The year 2020 will be carved in the history books-with the proliferation of COVID-19 over the globe and with frontline health workers and basic scientists worldwide diligently fighting to alleviate life-threatening symptoms and curb the spread of the disease. Behind the shocking prevalence of death are countless families who lost loved ones. To these families and to humanity as a whole, the tallies are not irrelevant digits, but a motivation to develop effective strategies to save lives. However, at the onset of the pandemic, not many therapeutic choices were available besides supportive oxygen, anti-inflammatory dexamethasone, and antiviral remdesivir. Low-dose radiation (LDR), at a much lower dosage than applied in cancer treatment, re-emerged after a 75-year silence in its use in unresolved pneumonia, as a scientific interest with surprising effects in soothing the cytokine storm and other symptoms in severe COVID-19 patients. Here, we review the epidemiology, symptoms, immunological alterations, mutations, pharmaceuticals, and vaccine development of COVID-19, summarizing the history of X-ray irradiation in non-COVID diseases (especially pneumonia) and the currently registered clinical trials that apply LDR in treating COVID-19 patients. We discuss concerns, advantages, and disadvantages of LDR treatment and potential avenues that may provide empirical evidence supporting its potential use in defending against the pandemic.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".