Colistin-Conjugated Selenium Nanoparticles: A Dual-Action Strategy Against Drug-Resistant Infections and Cancer DOI Creative Commons
Mais E. Ahmed, Kholoud Alzahrani,

Nedal M. Fahmy

и другие.

Pharmaceutics, Год журнала: 2025, Номер 17(5), С. 556 - 556

Опубликована: Апрель 24, 2025

Background/Objective: Antimicrobial resistance (AMR) and therapy-resistant cancer cells represent major clinical challenges, necessitating the development of novel therapeutic strategies. This study explores use selenium nanoparticles (SeNPs) colistin-conjugated (Col-SeNPs) as a dual-function nanotherapeutic against multidrug-resistant Pseudomonas aeruginosa, antifungal-drug-resistant Candida spp., human breast carcinoma (MCF-7) cells. Methods: SeNPs were synthesized characterized using UV-Vis spectroscopy, atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDX), diffraction (XRD), field emission scanning electron (FESEM), transmission (TEM), Fourier-transform infrared (FTIR), confirming their nanoscale morphology, purity, stability. Results: The antimicrobial activity Col-SeNPs was assessed based on minimum inhibitory concentration (MIC) bacterial viability assays. exhibited enhanced antibacterial effects P. along with significant downregulation mexY efflux pump gene, which is associated colistin resistance. Additionally, demonstrated superior antifungal albicans, C. glabrata, krusei compared to alone. anticancer potential evaluated in MCF-7 MTT assay, revealing dose-dependent cytotoxicity through apoptosis oxidative stress pathways. Although not inherently drug-resistant, this model used explore overcoming mechanisms commonly encountered therapy. Conclusions: these findings support promise approach for addressing both treatment challenges. Further vivo studies, including pharmacokinetics combination therapies, are warranted advance translation.

Язык: Английский

Research on the prediction model of mastitis in dairy cows based on time series characteristics DOI Creative Commons
Rui Guo, Yongqiang Dai, Junjie Hu

и другие.

Frontiers in Veterinary Science, Год журнала: 2025, Номер 12

Опубликована: Апрель 24, 2025

Mastitis in dairy cows is a significant challenge faced by the global industry, significantly affecting quality and output of milk from enterprises causing them to suffer severe economic losses. With increasing public concern over food safety rational use antibiotics, how identify at risk disease early has become key issue that needs be urgently addressed. Especially subclinical mastitis, due lack obvious external symptoms, makes detection more difficult, so warning it particularly important. In this study, time series prediction method, combined with machine learning techniques, was used predict mastitis cows. The study data were obtained production records 4000 large farm Hexi region Gansu. By constructing time-series features, indicators such as yield, fat rate protein each cow two consecutive months, April May, utilized its health status June. To fully exploit value we designed multidimensional feature set included raw indicator values, monthly change rates, statistical features. After preprocessing sample balancing, 2821 selected for model training. Finally, applicability assessed comparing analyzing performance six models, namely eXtreme Gradient Boosting(XGBoost), Boosting Decision Tree (GBDT), Support Vector Machine (SVM), K Nearest Neighbors (KNN), Logistic Regression, Long Short-Term Memory Network (LSTM). XGBoost demonstrated optimal performance, achieving an area under ROC curve (AUC) 0.75 accuracy 71.36%. Feature importance analysis revealed three temporal influencing outcomes: May yield (22.29%), standard deviation percentage (20.27%), (19.87%). SHapley Additive exPlanations (SHAP) further validated predictive these providing managers clearly defined monitoring priorities. demonstrates strong potential accurate tool This presents effective early-warning approach through modeling offers practical prevention management.

Язык: Английский

Процитировано

0

Colistin-Conjugated Selenium Nanoparticles: A Dual-Action Strategy Against Drug-Resistant Infections and Cancer DOI Creative Commons
Mais E. Ahmed, Kholoud Alzahrani,

Nedal M. Fahmy

и другие.

Pharmaceutics, Год журнала: 2025, Номер 17(5), С. 556 - 556

Опубликована: Апрель 24, 2025

Background/Objective: Antimicrobial resistance (AMR) and therapy-resistant cancer cells represent major clinical challenges, necessitating the development of novel therapeutic strategies. This study explores use selenium nanoparticles (SeNPs) colistin-conjugated (Col-SeNPs) as a dual-function nanotherapeutic against multidrug-resistant Pseudomonas aeruginosa, antifungal-drug-resistant Candida spp., human breast carcinoma (MCF-7) cells. Methods: SeNPs were synthesized characterized using UV-Vis spectroscopy, atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDX), diffraction (XRD), field emission scanning electron (FESEM), transmission (TEM), Fourier-transform infrared (FTIR), confirming their nanoscale morphology, purity, stability. Results: The antimicrobial activity Col-SeNPs was assessed based on minimum inhibitory concentration (MIC) bacterial viability assays. exhibited enhanced antibacterial effects P. along with significant downregulation mexY efflux pump gene, which is associated colistin resistance. Additionally, demonstrated superior antifungal albicans, C. glabrata, krusei compared to alone. anticancer potential evaluated in MCF-7 MTT assay, revealing dose-dependent cytotoxicity through apoptosis oxidative stress pathways. Although not inherently drug-resistant, this model used explore overcoming mechanisms commonly encountered therapy. Conclusions: these findings support promise approach for addressing both treatment challenges. Further vivo studies, including pharmacokinetics combination therapies, are warranted advance translation.

Язык: Английский

Процитировано

0