Preventive Medicinal Plants and their Phytoconstituents against SARS-CoV-2/COVID-19
Bibliographic record
Abstract
Pandemic coronavirus disease-2019 (COVID-19) is an infectious disease caused by the newly discovered virus "Severe Acute Respiratory Syndrome-CoronaVirus-2 (SARS-CoV-2)" . Considering the present scenario of COVID-19 outbreak and its impact on humankind, holistic remedies with respect to herbal medicine validated from ethnopharmacological rationale are now targeting approaches globally as a preventive care against SARS-CoV-2. Aim: This review is primarily focused on to deliver a concise fact of the coronaviridae family, pathophysiology, mechanism of action, ethnopharmacological validated Indian herbs for inhibiting the virus with possible targets. Experimental procedure: In this study, science mapping tool Bibliometrix R-package was used to perform bibliometric analysis and building data matrices for keywords co-occurrence investigation, country-wise scientific production; collaboration between the countries worldwide, co-word analysis on topic "keywords associated with SARS-CoV-2 and medicinal plants" . Results and Conclusion: Our findings is to deliver a concise knowledge about the coronaviridae family, pathophysiology, possible targets for managing the SARS-CoV-2, in addition to potential medicinal plants and their phytoconstituents against COVID-19. Target-specific inflammatory pathways due to post infection of SARS e.g. NLRP3, p38-MAPK, Metallopeptidase Domain 17; endocytosis pathways e.g. Clathrin, HMGB1 pathways are primarily highlighted along with relevant interleukins and cytokines, which directly/indirectly triggering to immune system and play a significant role. Based on selective pathways and potential lead, the outcome of our elaborated study put forward selected Indian medicinal plants that hold a very high probability as preventive care in this global crisis.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".