A mini review on the prospects of <i>Fagara zanthoxyloides</i> extract based composites: a remedy for COVID-19 and associated replica?
Bibliographic record
Abstract
Abstract Studies are still being conducted to find a sustainable and long-lasting solution to the lethal consequences of the feared virus characterized as coronavirus disease (Covid-19) and its accompanying pathogenic replication, which pose a serious threat to human survival in the wake of its broad distribution. Since its emergence, researchers have investigated synthetic approaches in search of a dependable vaccine or treatment and curtail the spread of the virus and also enhance the health of a patient who has been affected. Unfortunately, the infection is yet to be entirely eradicated in many parts of the world. Despite the introduction of synthetic pharmaceuticals like remdesivir and derivatives of chloroquine, plant extracts may be an alternative reliable strategy that could successfully combat the operation of the virus. Herein, we investigated the prospects of fagara zanthoxyloides lam. (rutaceae) (syn. zanthoxylum zanthoxyloides), a well-known medicinal tree whose extracts have demonstrated success in treating many microbiological and viral-related infections. The distinctive plant extracts contain several bioactive phytochemicals with promising biological activity with minimal or no side effects and are being researched for a variety of applications, particularly in the pharmaceutical and medicinal industries. Consequently, in this review, we examined the crude extracts from the Fagara species and suggested that careful consideration should be given to its independent use or combination with other bioactive molecules, such as biopolymers and nano-metallic composites, to combat the terrifying Covid-19 virus and its associates.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".