Demystifying therapeutic potential of medicinal plants against chikungunya virus
Bibliographic record
Abstract
Viral infections are posing a great threat to humanity for the last few years. Among these, Chikungunya which is a mosquito-borne viral infection has produced enormous epidemics around the world after been rebounded. Although this infection shows a low mortality rate, patients suffer from fever, arthralgia, and maculopapular rashes, which reduce the quality of life for several weeks to years. The currently available treatments only provide symptomatic relief based on analgesics, antipyretics, and anti-inflammatory drugs which are nonspecific without satisfactory results. Medicinal plants are a widely accepted source of new molecules for the treatment of infectious diseases including viral infections. The scientific reports, primarily focusing on the anti-chikungunya activity of plant extracts, natural origin pure compounds, and their synthetic analog published from 2011 to 2021, were selected from PubMed, Google Scholar, and Scopus by using related keywords like anti-chikungunya plants, natural antivirals for Chikungunya. The present review decodes scientific reports on medicinal plants against chikungunya virus (CHIKV) infection and demystifies the potential phytoconstituents which reveals that the screening of flavonoids containing plants and phytochemicals showing efficacy against other arbovirus infections, may prove as a potential lead for drug development against CHIKV. The present article also outlines pathogenesis, clinical aspects, molecular virology, and diagnostic approaches of CHIKV infection.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".