Research Approaches for the Discovery of Trypanocidal Molecular Prototypes from Plants

Natural Product Communications, Год журнала: 2025, Номер 20(1)

Опубликована: Янв. 1, 2025

In the absence of vaccines, chemotherapy remains a viable alternative for control African trypanosomiasis. To facilitate eradication trypanosomiasis, there is need to pay attention chemotherapeutic potential secondary metabolites obtained from natural sources. Despite increasing level paid towards pharmacological and medicinal properties trypanocidal molecular prototypes plants, integrative synthesis research methods relevant this progress lacking. Furthermore, applications machine learning in discovery plant-derived antitrypanosomal have not been well explored. The aim study thus provide critical appraisal plant context made, as highlight standard emerging investigative tools applicable drug process. It explores phytochemical, antitrypanosomals light traditional approaches. vitro, silico, vivo strategies investigations that may be employed are highlighted. Finally, promising particular benefit future also discussed. These insights ultimately optimization, target identification clinical development process against

Язык: Английский

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FT-NIR Spectra of Different Dimensions Combined with Machine Learning and Image Recognition for Origin Identification: An Example of Panax notoginseng

ACS Omega, Год журнала: 2025, Номер 10(7), С. 7242 - 7255

Опубликована: Фев. 11, 2025

Panax notoginseng (P. notoginseng) is a traditional medicinal plant with high and economic values. The authenticity of P. often determines its quality, the quality geographical indication (GI)-producing areas usually superior to that other producing areas, which are exploited by unscrupulous traders affect market order. aim this study was characterize identify geographic origin using Fourier transform near-infrared (FT-NIR) spectroscopy, rapid detection combined multivariate analysis. use principal component analysis correlation spectral enabled initial differential characterization identification from different production areas. Then, random forest (RF) support vector machine (SVM) models were established, results show showed second-order derivative preprocessing successive projection algorithm feature extraction achieved 100% classification correctness model training time shortest. Further constructing image recognition model, synchronous two-dimensional spectroscopy (2DCOS) residual convolutional neural network accurate (accuracy 100%) did not require complex artificial process, maximize avoidance errors caused human factors. externally validated set method has strong generalization ability potential for application in

Язык: Английский

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DOSY‐Assisted Multicomponent Characterization Enables Classification of Precious Chinese Materia Medica and Identification of Adulteration: A Case Study of Calculus Bovis

Phytochemical Analysis, Год журнала: 2025, Номер unknown

Опубликована: Март 4, 2025

Despite significant advances in analytical methods, the quality evaluation and adulteration detection of chemically complex traditional Chinese medicines continues to be a major challenge. We developed custom diffusion-ordered spectroscopy (DOSY) approach for qualitative analysis precious Calculus Bovis (CB) its adulterants. Using Artificial as an example, we focused on effects NMR spectrometers, solvent viscosity, matrices, matrix concentration, temperature separation efficiency DOSY. The optimal conditions also successfully applied identification Natural Bovis, Sativus, adulterated samples. accuracy DOSY was verified by 13C NMR. Via inspection solvents, temperature, experiments CB were found using methanol-d4 solvent, adding 4 mg polyvinyl pyrrolidone (PVP) matrix, 298 K. Under these conditions, five types compounds, cholic acid (CA), deoxycholic (DCA), hyodeoxycholic (HDCA), taurine-conjugated bile acids (tBAs), glycine-conjugated (gBAs), unequivocally identified three CB. validated results. As sugar-adulterated CB, signals sugar could clearly spectrum. This study feasibility provided reference point future quantitative other medicines, well characterization

Язык: Английский

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Diagnostic workout of glandular malignant lesions of the bladder according to the 5th WHO classification

Critical Reviews in Clinical Laboratory Sciences, Год журнала: 2025, Номер unknown, С. 1 - 12

Опубликована: Март 9, 2025

Glandular lesions involving the bladder are less common than conventional urothelial carcinoma, and they often diagnostically challenging diseases, carrying different clinical outcomes. As a group, encompass both primary secondary neoplasms, with sometimes overlapping morphological features. In this scenario, proper information is important, in that involvement of may occur by direct extension or lymphatic/hematogenous spread from carcinomas at other sites, comprising prostate, colon, cervix, breast, lung. According to 5th edition WHO Classification urological tumors, glandular morphology major hallmark following entities: carcinoma differentiation, adenocarcinoma, NOS, urachal tumors Mullerian type. The distinction among these entities, between heavily relies on their biological immunophenotypical This article will review neoplasms bladder, highlighting main markers. Furthermore, molecular data associated pathogenesis, prognosis, treatment be described. aim study provide practical comprehensive up-to-date overview complex topic.

Язык: Английский

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Multi-Source Satellite Imagery and Machine Learning for Detecting Geological Formations in Cameroon’s Western Highlands

Signals, Год журнала: 2025, Номер 6(1), С. 12 - 12

Опубликована: Март 11, 2025

Accurate identification of geological formations is essential for understanding tectonic structures, planning mining activities, and sustainably managing natural resources. It goes beyond the scientific framework to play a key role in economic development, environmental preservation, population security. This article proposes study using machine learning analyze different parameters from various sources satellite imagery: multispectral optics (Landsat-8), radar (ALOS PALSAR), soil morphometric (soil, altitude, slope, curvature, shady). The data were preprocessed remove atmospheric biases harmonize spatial resolutions. Techniques such as principal component analysis, band ratios, image fusion have made it possible enrich imagery by highlighting spectral textural characteristics. Finally, classifiers Random Forest, Gradient Boosting, XGBoost (version 1.6.2) used evaluate impact each parameter on classification. results show that geographic combined with PCA provide best overall performance achieving an accuracy 55.29% MCC 45.12% while ensuring rapid training speed (3.6 s). associated OLI spectrometric good balance, slightly higher (40.3%) moderate time (7.9 On other hand, coupled display significantly lower performance, 45.05% 31.81% Forest. These observations highlight potential suitable models improve multi-source approach thus proves optimal more robust precise results.

Язык: Английский

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Data integrity of food and machine learning: Strategies, advances and prospective

Food Chemistry, Год журнала: 2025, Номер unknown, С. 143831 - 143831

Опубликована: Март 1, 2025

Язык: Английский

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Digital intelligence technology: new quality productivity for precision traditional Chinese medicine

Frontiers in Pharmacology, Год журнала: 2025, Номер 16

Опубликована: Апрель 8, 2025

Traditional Chinese medicine is a complex medical system characterized by multiple metabolites, targets, and pathways, known for its low drug resistance significant efficacy. However, challenges persist within medicine, including difficulties in assessing the quality of Botanical drugs, reliance on experiential knowledge disease diagnosis treatment, lack clarity regarding pharmacological mechanisms medicine. The advancement digital intelligence technology driving shift towards precision model. This transition propels into an era precision, intelligence, digitalization. paper introduces standard technologies explores application control evaluation studies research status assisting diagnosis, treatment prevention diseases, further promotes development field

Язык: Английский

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Sample preparation techniques for quality evaluation and safety control of medicinal and edible plants: Overview, advances, applications, and future perspectives

Journal of Pharmaceutical Analysis, Год журнала: 2025, Номер unknown, С. 101296 - 101296

Опубликована: Апрель 1, 2025

Язык: Английский

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ML-Driven Metabolomics Predicts Anti-Inflammatory and Antioxidant Activities in D. officinale Leaves

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 18, 2025

Background Dendrobium officinale (D. officinale) leaves, rich in bioactive compounds comparable to those stems, remain underutilized as agricultural byproducts. Purpose This study aims establish an ML (machine learning)-driven metabolomic framework evaluate seasonal variations within D. identify germplasm-specific pharmacological activities, and determine core components driving anti-inflammatory antioxidant effects. Methods An integrated approach combining dynamic profiling (UHPLC-QTOF-MS, RP-HPLC, UPLC-QqQ-MS), vitro bioassays (TNF-α/IL-6 suppression assays ABTS radical scavenging assay), modeling was employed. Results Phenolics, flavonoids, terpenes, B-vitamins peaked October–November, while amino acids accumulated until December. Despite this, July-harvested leaves exhibited maximum activity. Random Forest Regression (RFR) models identified vanillic acid 4-β-D-glucoside, schaftoside, rutin key contributors, validated experimentally. Conclusion ML-enhanced strategy advances the quality assessment germplasm optimization of by linking phytochemical profiles bioactivity. The identification July optimal harvest period critical underscores approach’s utility nutraceutical pharmaceutical applications, promoting sustainable utilization

Язык: Английский

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Integrated deep learning neural network and hyperspectral imaging for non-destructive prediction of biochemical properties of plant organs

Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 102001 - 102001

Опубликована: Май 1, 2025

Язык: Английский

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