Identification and quantitative analysis of bioactive components from <i>Potentilla kleiniana</i> Wight et Arn with anti HIV-1 proteases activity
Bibliographic record
Abstract
Potentilla kleiniana Wight et Arn（PK, ‘Wu Pi Feng’ in Chinese） was recorded as Miao ethnic medicine for treatment of fever, cough, ulcer, and erysipelas for thousands years. This study aimed to evaluate the antiviral activity of four PK extracts and seven compounds by using HIV-1 protease (HIV-1 PR). In addition, Ultra-High Performance Liquid Chromatography and High Resolution Mass Spectrometry (UPLC-HRMS) was employed to identify the bioactive components. The toxicity assessment of the extracts was done before antiviral screening using a highly specific human aspartyl protease, renin protease by fluorimetric method. As a result, seven compounds and four extracts of PK inhibited HIV-1 PR with IC50 range from 0.009 to 0.36 mg/mL, and did not appreciably inhibit the general human protease renin. This study first demonstrated that four PK extracts, ellagic acid and ursolic acid potent inhibit HIV-1 protease, could be used as an efficacious drug candidate to treat SARS-CoV-2 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".