Bibliographic record
Abstract
Viral infections and outbreaks have become a major concern and are one of the main causes of morbidity and mortality worldwide. The development of successful antiviral therapeutics and vaccines remains a daunting challenge. The discovery of novel antiviral agents is a public health emergency, and extraordinary efforts are underway globally to identify safe and effective treatments for different viral diseases. Alkaloids are natural phytochemicals known for their biological activities, many of which have been intensively studied for their broad-spectrum of antiviral activities against different DNA and RNA viruses. The purpose of this review was to summarize the evidence supporting the efficacy of the antiviral activity of plant alkaloids at half-maximum effective concentration (EC50) or half-maximum inhibitory concentration (IC50) below 10 μM and describe the molecular sites most often targeted by natural alkaloids acting against different virus families. This review highlights that considering the devastating effects of virus pandemics on humans, plants, and animals, the development of high efficiency and low-toxicity antiviral drugs targeting these viruses need to be developed. Furthermore, it summarizes the current research status of alkaloids as the source of antiviral drug development, their structural characteristics, and antiviral targets. Overall, the influence of alkaloids at the molecular level suggests a high degree of specificity which means they could serve as potent and safe antiviral agents waiting for evaluation and exploitation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".