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Gemcitabine (GEM) is widely used as an anticancer agent in several types of solid tumors. Silver nanoparticles (AgNPs) possess unique cytotoxic features and can induce apoptosis in a variety of cancer cells. In this study, we investigated whether the combination of GEM and AgNPs can exert synergistic cytotoxic effects in the human ovarian cancer cell line A2780. We synthesized AgNPs using resveratrol as a reducing and stabilizing agent. The synthesized nanomaterials were characterized using various analytical techniques. The anticancer effects of a combined treatment with GEM and AgNPs were evaluated using a series of cellular assays. The expression of pro- and antiapoptotic genes was measured using real-time reverse transcription polymerase chain reaction. Apoptosis was confirmed by TUNEL assay. In this study, combined treatment with GEM and AgNPs significantly inhibited viability and proliferation in A2780 cells. Moreover, the levels of apoptosis in cells treated with a combination of GEM and AgNPs were significantly higher compared with those in cells treated with GEM or AgNPs alone. Our data suggest that GEM and AgNPs exhibit potent apoptotic activity in human ovarian cancer cells. Combined treatment with GEM and AgNPs showed a significantly higher cytotoxic effect in ovarian cancer cells compared with that induced by either of these agents alone. Our study demonstrated that the interaction between GEM and AgNPs was cytotoxic in ovarian cancer cells. Combined treatment with GEM and AgNPs caused increased cytotoxicity and apoptosis in A2780 cells. This treatment may have therapeutic potential as targeted therapy for the treatment of ovarian cancer. To our knowledge, this study could provide evidence that AgNPs can enhance responsiveness to GEM in ovarian cancer cells and that AgNPs can potentially be used as chemosensitizing agents in ovarian cancer therapy.

Recently, silver nanoparticles (AgNPs) have been widely used in various applications as antimicrobial agents, anticancer, diagnostics, biomarkers, cell labels, and drug delivery systems for the treatment of various diseases. Microorganisms generally acquire resistance to antibiotics through the course of antibacterial therapy. Multi-drug resistance (MDR) has become a growing problem in the treatment of infectious diseases, and the widespread use of broad-spectrum antibiotics has resulted in the development of antibiotic resistance by numerous human and animal bacterial pathogens. As a result, an increasing number of microorganisms are resistant to multiple antibiotics causing continuing economic losses in dairy farming. Therefore, there is an urgent need for the development of alternative, cost-effective, and efficient antimicrobial agents that overcome antimicrobial resistance. Here, AgNPs synthesized using the bio-molecule quercetin were characterized using various analytical techniques. The synthesized AgNPs were highly spherical in shape and had an average size of 11 nm. We evaluated the efficacy of synthesized AgNPs against two MDR pathogenic bacteria, namely, Pseudomonas aeruginosa and Staphylococcus aureus, which were isolated from milk samples produced by mastitis-infected goats. The minimum inhibitory concentrations (MICs) of AgNPs against P. aeruginosa and S. aureus were found to be 1 and 2 μg/mL, respectively. Our findings suggest that AgNPs exert antibacterial effects in a dose- and time-dependent manner. Results from the present study demonstrate that the antibacterial activity of AgNPs is due to the generation of reactive oxygen species (ROS), malondialdehyde (MDA), and leakage of proteins and sugars in bacterial cells. Results of the present study showed that AgNP-treated bacteria had significantly lower lactate dehydrogenase activity (LDH) and lower adenosine triphosphate (ATP) levels compared to the control. Furthermore, AgNP-treated bacteria showed downregulated expression of glutathione (GSH), upregulation of glutathione S-transferase (GST), and downregulation of both superoxide dismutase (SOD) and catalase (CAT). These physiological and biochemical measurements were consistently observed in AgNP-treated bacteria, thereby suggesting that AgNPs can induce bacterial cell death. Thus, the above results represent conclusive findings on the mechanism of action of AgNPs against different types of bacteria. This study also demonstrates the promising use of nanoparticles as antibacterial agents for use in the biotechnology and biomedical industry. Furthermore, this study is the first to propose the mode of action of AgNPs against MDR pathogens isolated from goats infected with subclinical mastitis.

Numerous substrates have been identified for Type I and II arginine methyltransferases (PRMTs). However, the full substrate spectrum of the only type III PRMT, PRMT7, and its connection to type I and II PRMT substrates remains unknown. Here, we use mass spectrometry to reveal features of PRMT7-regulated methylation. We find that PRMT7 predominantly methylates a glycine and arginine motif; multiple PRMT7-regulated arginine methylation sites are close to phosphorylations sites; methylation sites and proximal sequences are vulnerable to cancer mutations; and methylation is enriched in proteins associated with spliceosome and RNA-related pathways. We show that PRMT4/5/7-mediated arginine methylation regulates hnRNPA1 binding to RNA and several alternative splicing events. In breast, colorectal and prostate cancer cells, PRMT4/5/7 are upregulated and associated with high levels of hnRNPA1 arginine methylation and aberrant alternative splicing. Pharmacological inhibition of PRMT4/5/7 suppresses cancer cell growth and their co-inhibition shows synergistic effects, suggesting them as targets for cancer therapy. Arginine methyltransferases (PRMTs) are involved in the regulation of various physiological and pathological conditions. Using proteomics, the authors here profile the methylation substrates of PRMTs 4, 5 and 7 and characterize the roles of these enzymes in cancer-associated splicing regulation.

Triangular lattice of rare-earth ions with interacting effective spin-1/2local moments is an ideal platform to explore the physics of quantum spin liquids (QSLs) in the presence of strong spin-orbit coupling, crystal electric fields, and geometrical frustration. The Yb delafossites,NaYbCh2(Ch=O, S, Se) with Yb ions forming a perfect triangular lattice, have been suggested to be candidates for QSLs. Previous thermodynamics, nuclear magnetic resonance, and powder-sample neutron scattering measurements onNaYbCh2have supported the suggestion of the QSL ground states. The key signature of a QSL, the spin excitation continuum, arising from the spin quantum number fractionalization, has not been observed. Here we perform both elastic and inelastic neutron scattering measurements as well as detailed thermodynamic measurements on high-quality single-crystalNaYbSe2samples to confirm the absence of long-range magnetic order down to 40 mK, and further reveal a clear signature of magnetic excitation continuum extending from 0.1 to 2.5 meV. The comparison between the structure of the magnetic excitation spectra and the theoretical expectation from the spinon continuum suggests that the ground state ofNaYbSe2is a QSL with a spinon Fermi surface.

The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.

Necroptosis is a necrotic programmed cell death with potent immunogenicity. Due to the dual effects of necroptosis on tumor growth, metastasis and immunosuppression, we evaluated the prognostic value of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC). We first analyzed RNA sequencing and clinical HCC patient data obtained to develop an NRG prognostic signature based on the TCGA dataset. Differentially expressed NRGs were further evaluated by GO and KEGG pathway analyses. Next, we conducted univariate and multivariate Cox regression analyses to build a prognostic model. We also used the dataset obtained from the International Cancer Genome Consortium (ICGC) database to verify the signature. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to investigate the immunotherapy response. Furthermore, we investigated the relationship between the prediction signature and chemotherapy treatment response in HCC. We first identified 36 differentially expressed genes out of 159 NRGs in hepatocellular carcinoma. Enrichment analysis showed that they were mainly enriched in the necroptosis pathway. Four NRGs were screened by Cox regression analysis to establish a prognostic model. The survival analysis revealed that the overall survival of patients with high-risk scores was significantly shorter than that of patients with low-risk scores. The nomogram demonstrated satisfactory discrimination and calibration. The calibration curves validated a fine concordance between the nomogram prediction and actual observation. The efficacy of the necroptosis-related signature was also validated by an independent dataset and immunohistochemistry experiments. TIDE analysis revealed that patients in the high-risk group were possibly more susceptible to immunotherapy. Furthermore, high-risk patients were found to be more sensitive to conventional chemotherapeutic medicines such as bleomycin, bortezomib, and imatinib. We identified 4 necroptosis-related genes and established a prognostic risk model that could potentially predict prognosis and response to chemotherapy and immunotherapy in HCC patients in the future.

Postoperative delirium (POD) is a common complication after surgery. The association between sleep disorders and the risk of POD has been widely studied. Sleep disorders have emerged as a potential risk factor for POD, but recent studies provide conflicting evidence regarding the existence and the extent of the association. The aim of this systematic review and meta-analysis will be to estimate the association between sleep disorders and the risk of POD in adult patients. A systematic review will be conducted to estimate the association between sleep disorders and the risk of POD in adult patients. This systematic review protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement. Literature searches will be carried out in the PubMed, Embase, CINAHL, and PsycINFO databases from inception until August 2025 without language restrictions. Only observational studies that investigated the association between sleep disorders and POD will be included. Two independent reviewers will be responsible for the selection of studies, extraction of information and risk of bias assessment of the articles. A random effects model will be used to calculate the pooled risk estimates for the outcome. Subgroup analysis will be conducted to explore potential sources of heterogeneity. Publication bias will be estimated by funnel plots and Egger's test. Sensitivity analysis will also be performed to explore the stability of the overall effect size. Also, evidence quality will be assessed. All data will be analysed using Review Manager (V.5.3) and Stata (V.14.0) statistical software. The proposed systematic review and meta-analysis will highlight the association of sleep disorders and the risk of POD in adult patients. The findings of this review and meta-analysis will help clinicians consider the sleep disorders to make better health decisions. Trial registration: Prospero registration number: CRD42024604118.

This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

Excitation-dependent multicolor emission from a single-component system, independent of aggregation, remains a fundamental challenge due to inherent difficulties in innovative principles. Herein, we propose a molecular symmetry-breaking strategy to enrich electronic processes, enabling the molecule to exhibit excitation-dependent multicolor emissions from one chemical entity. A star-shaped molecule, 1,3,5-(4- tert -butylphenyl- o -carboranyl-4-phenyl)benzene (Ph-3CP) is designed, where spatial restriction induces inequivalence among three bulky, non-planar branches. This asymmetry gives rise to a broad excitation-dependent emission range of nearly 175 nm across solution, amorphous, and crystalline states. Crystallization from different solvents successfully traps distinct asymmetric conformers of Ph-3CP, providing direct experimental evidence for the predicted symmetry-breaking structures from theoretical calculations. Structure-property relationship studies further reveal two distinct relaxation pathways that dominate the emission behavior of this molecular system. Leveraging these properties, we develop a single-component fluorescence sensor array that enables rapid and selective identification of chlorinated hydrocarbon vapors. This work provides a general strategy for designing multifunctional luminescent materials through symmetry-controlled excited-state engineering. Excitation-dependent multicolour emission is desirable, but hard to obtain. Here, the authors report the development of a molecular symmetry breaking strategy to all excitation-dependent multicolour emissions from one chemical entity.