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Many science, technology and innovation (STI) resources are attached with several different labels. To assign automatically the resulting labels to an interested instance, many approaches with good performance on the benchmark datasets have been proposed for multilabel classification task in the literature. Furthermore, several open-source tools implementing these approaches have also been developed. However, the characteristics of real-world multilabel patent and publication datasets are not completely in line with those of benchmark ones. Therefore, the main purpose of this paper is to evaluate comprehensively seven multi-label classification methods on real-world datasets.

Nanoliposomes loaded with curcumin (CUR) and reduced glutathione (GSH) were prepared using soybean lecithin as the wall material and modified by chitosan and sodium alginate on their surface through electrostatic self-assembly. The encapsulation efficiency, morphology, in vitro release, and stability of the fabricated nanoliposomes were characterized by laser particle size analyzer, transmission electron microscopy (TEM) and Fourier transform infrared spectroscopy (FTIR). The results showed that the encapsulation efficiency of CUR was 100%. Moreover, neither co-encapsulation with GSH nor polysaccharide modification affected the encapsulation efficiency of CUR. However, the encapsulation efficiency of GSH was increased from 7.90% to 27.03% when co-encapsulated with CUR, and further increased to 41.22% after polysaccharide modification. Co-encapsulation of CUR and GSH did not significantly influence the release of CUR, but reduced the release rate of GSH from 51.2% to 23.6%. After polysaccharide modification, the sustained release effects of nanoliposomes loaded with CUR and/or GSH were enhanced. Meanwhile, the co-encapsulation of CUR and GSH increased the average particle size of liposomes from (95.02 ± 1.93) to (132.47 ± 18.14) nm, and the zeta potential from (–22.47 ± 1.96) to (–14.70 ± 0.46) mV. After bilayer modification of chitosan and sodium alginate, the average particle size of liposomes further increased to (161.97 ± 5.58) nm, while the zeta potential decreased to (–40.87 ± 1.79) mV. In addition, the thermal stability was increased. The results of this study will provide a basis for the development of novel functional foods or drugs loaded with bioactive substances.

Photothermal therapy (PTT) of nanomaterials is an emerging novel therapeutic strategy for breast cancer. However, there exists an urgent need for appropriate strategies to enhance the antitumor efficacy of PTT and minimize damage to surrounding normal tissues. Piezo1 might be a promising novel photothermal therapeutic target for breast cancer. This study aims to explore the potential role of Piezo1 activation in the hyperthermia therapy of breast cancer cells and investigate the underlying mechanisms. Results showed that the specific agonist of Piezo1 ion channel (Yoda1) aggravated the cell death of breast cancer cells triggered by heat stress in vitro. Reactive oxygen species (ROS) production was significantly increased following heat stress, and Yoda1 exacerbated the rise in ROS release. GSK2795039, an inhibitor of NADPH oxidase 2 (NOX2), reversed the Yoda1-mediated aggravation of cellular injury and ROS generation after heat stress. The in vivo experiments demonstrate the well photothermal conversion efficiency of TiCN under the 1,064 nm laser irradiation, and Yoda1 increases the sensitivity of breast tumors to PTT in the presence of TiCN. Our study reveals that Piezo1 activation might serve as a photothermal sensitizer for PTT, which may develop as a promising therapeutic strategy for breast cancer.

BackgroundAdvanced non-small cell lung cancer (NSCLC) presents significant treatment challenges, with chemo-immunotherapy emerging as a promising approach. This study explores the potential of lipidomic biomarkers to predict responses to chemo-immunotherapy in advanced non-small cell lung cancer (NSCLC) patients.MethodsA prospective analysis was conducted on 68 NSCLC patients undergoing chemo-immunotherapy, divided into disease control (DC) and progressive disease (PD) groups based on treatment response. Pre-treatment serum samples were subjected to lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Key predictive lipids (biomarkers) were identified through projection to latent structures discriminant analysis. A biomarker combined model and a clinical combined model were developed to enhance the prediction accuracy. The predictive performances of the clinical combined model in different histological subtypes were also performed.ResultsSix lipids were identified as the key lipids. The expression levels of PC(16:0/18:2), PC(16:0/18:1), PC(16:0/18:0), CE(20:1), and PC(14:0/18:1) were significantly up-regulated. While the expression level of TAG56:7-FA18:2 was significantly down-regulated. The biomarker combined model demonstrated a receiver operating characteristic (ROC) curve of 0.85 (95% CI: 0.75–0.95) in differentiating the PD from the DC. The clinical combined model exhibited an AUC of 0.87 (95% CI: 0.79–0.96) in differentiating the PD from the DC. The clinical combined model demonstrated good discriminability in DC and PD patients in different histological subtypes with the AUC of 0.78 (95% CI: 0.62–0.96), 0.79 (95% CI: 0.64–0.94), and 0.86 (95% CI: 0.52–1.00) in squamous cell carcinoma, large cell carcinoma, and adenocarcinoma subtype, respectively. Pathway analysis revealed the metabolisms of linoleic acid, alpha-linolenic acid, glycerolipid, arachidonic acid, glycerophospholipid, and steroid were implicated in the chemo-immunotherapy response in advanced NSCLC.ConclusionLipidomic profiling presents a highly accurate method for predicting responses to chemo-immunotherapy in patients with advanced NSCLC, offering a potential avenue for personalized treatment strategies.