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Record W4313340148 · doi:10.1080/14786419.2022.2162513

Identification and quantitative analysis of bioactive components from <i>Potentilla kleiniana</i> Wight et Arn with anti HIV-1 proteases activity

2022· article· en· W4313340148 on OpenAlex
Yongqiang Zhou, Sumei Li, Xin Wei, Xin Yang, Jun Xiao, Bo Wen Pan, Shou Xia Xie, Ying Zhou, Jian Yang, Ying Wei

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNatural Product Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNatural product bioactivities and synthesis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsProteasesProteaseEllagic acidUrsolic acidChemistryTraditional medicineHIV-1 proteasePharmacologyChromatographyBiochemistryEnzymeBiologyMedicinePolyphenolAntioxidant

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.335
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it