본문으로 건너뛰기
← 뒤로

Discovery of hemostatic component combination from Nelumbinis Receptaculum using dual machine learning spectrum-effect analysis.

1/5 보강
Journal of ethnopharmacology 2026 Vol.358() p. 120995
Retraction 확인
출처

Xu Q, Zou WS, Li H, Wang ZX, Li MN

📝 환자 설명용 한 줄

[ETHNOPHARMACOLOGICAL RELEVANCE] Nelumbinis Receptaculum (NR) were hemostatic herbal medicines for treating metrorrhagia, hematuria or hemorrhoids in ethnopharmacological systems, such as Ayurveda and

이 논문을 인용하기

↓ .bib ↓ .ris
APA Xu Q, Zou WS, et al. (2026). Discovery of hemostatic component combination from Nelumbinis Receptaculum using dual machine learning spectrum-effect analysis.. Journal of ethnopharmacology, 358, 120995. https://doi.org/10.1016/j.jep.2025.120995
MLA Xu Q, et al.. "Discovery of hemostatic component combination from Nelumbinis Receptaculum using dual machine learning spectrum-effect analysis.." Journal of ethnopharmacology, vol. 358, 2026, pp. 120995.
PMID 41344520

Abstract

[ETHNOPHARMACOLOGICAL RELEVANCE] Nelumbinis Receptaculum (NR) were hemostatic herbal medicines for treating metrorrhagia, hematuria or hemorrhoids in ethnopharmacological systems, such as Ayurveda and traditional Chinese medicines.

[AIM OF THE STUDY] The material basis for traditional hemostatic efficacy of NR is unclear. Thus, hemostatic component combination (HCC) of NR were screened by dual machine learning-based spectrum-effect relationships (DML-SER) analysis.

[MATERIALS AND METHODS] Ultra-high performance liquid chromatography tandem with mass spectrometry and simvastatin-induced intestinal bleeding zebrafish models were respectively used for chemical profiling and hemostatic evaluation of NR extract. Grey relational analysis were used to preliminarily correlate quantified compounds with hemostatic efficacy. Dual machine learning of artificial neural network (ANN)/support vector regression (SVR) were used for modeling spectrum-effect relationships between multi-compound contents in NR extract and zebrafish intestinal bleeding incidences. Potential HCCs were designed by compounds with top-10 permutation importance (PI) and verified by comparing hemostatic indicators with NR extract on zebrafish.

[RESULTS] Totally, 266 compounds were characterized in NR extract, among which 42 were quantified. All NR extract showed hemostasis with decreased intestinal bleeding incidences on zebrafish. Grey correlation degrees of quantified compounds were 0.670-0.854, suggesting their relevance to hemostasis. Dual machine learning of ANN/SVR were more appropriate than partial least squares regression for modeling spectrum-effect relationships of NR extract with smaller mean absolute errors and root mean square errors but larger coefficients of determination (R = 0.939/0.981). Combination of intersection for ANN/SVR top-10-PI compounds, containing gallocatechin, catechin, nuciferine, hyperoside, isoquercitrin and quercetin-3-O-glucuronide, exerted similar hemostatic indicators on zebrafish with whole NR extract.

[CONCLUSION] Using DML-SER, a potential HCC containing six compounds were discovered and initially verified, which could provide a material basis to facilitate mechanism studies, quality control and clinical application of NR.

MeSH Terms

Animals; Zebrafish; Hemostatics; Machine Learning; Plant Extracts; Tandem Mass Spectrometry; Chromatography, High Pressure Liquid; Drugs, Chinese Herbal; Disease Models, Animal; Gastrointestinal Hemorrhage

같은 제1저자의 인용 많은 논문 (5)