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Record W4385442640 · doi:10.1080/14786419.2023.2239995

Antiviral activity and active components of the leaves from <i>Sabia parviflora</i> Wall. ex Roxb

2023· article· en· W4385442640 on OpenAlex
Yongqiang Zhou, Sumei Li, Bo‐Wen Pan, Junwei Xiao, Tingting Tang, Shouxia Xie, Xin Yang, G Wu, Jian Yang, Ying Zhou, Yuxin Pang, 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 · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTraditional medicineIC50ProteaseHIV-1 proteaseHuman immunodeficiency virus (HIV)ChemistryBiologyVirologyEnzymeIn vitroMedicineBiochemistry

Abstract

fetched live from OpenAlex

Sabia parviflora (SP, “xiao hua qing feng teng” in Chinese) was recorded as an important ethnic medicine to be used for treating viral hepatitis. The antiviral activity of four SP extracts and potent antiviral compounds evaluated with cathepsin L protease (Cat L PR) and HIV-1 protease (HIV-1 PR). UPLC-HRMS was used for identifying the bioactive components. In addition, the possible inhibitory mechanism of the identified compounds on viral protease was further discussed by molecular docking. As a result, four extracts of SP exhibited inhibitory activity of HIV-1 PR and Cat L PR with IC50 range from 0.015 to 0.80 mg/mL. Meanwhile, six compounds inhibited HIV-1 PR with IC50 range from 0.032 to 0.80 mg/mL. Moreover, procyanidin B2 had good affinity for HIV-1 PR and CatL PR protein, respectively. These findings suggest S. parviflora leaves can be used for treating HIV and procyanidin B2 may play a role in antiviral protease.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.058
GPT teacher head0.345
Teacher spread0.288 · 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