The aim of this study was to explore the effect of zinc on hydrogen peroxide-induced sperm damage in assisted reproduction techniques. First, sperms were selected from semen samples of 20 healthy men prepared by density gradient centrifugation. Selected sperm were treated with either 0.001% H(2)O(2), 12.5 nM ZnCL(2), 0.001% H(2)O(2) + 12.5 nM ZnCL(2) or 0.9% NaCl(2) (control). After this treatment, the motility, viability, membrane integrity and DNA fragmentation of sperms in each group were analysed by Goodline sperm detection system, optical microscopy and sperm DNA fragmentation assay. Poorer motility, vitality, membrane integrity and more DNA damage were found in sperms treated by H(2)O(2), compared with control. When sperms were treated with both H(2)O(2) and zinc, however, all indicators were improved compared with H(2)O(2) alone. There was a close association between oxidative stimulation and sperm injury; zinc could inhibit hydrogen peroxide-induced damage of sperm in assisted reproductive technology. However, the presence of zinc in culture medium can decrease the sperm quality without addition of peroxide.

ObjectiveUterine leiomyosarcoma (ULMS) is the most common subtype of uterine sarcoma and is difficult to discern from uterine leiomyoma (ULM) preoperatively. The aim of the study was to determine the potential and significance of immune-related diagnostic biomarkers in distinguishing ULMS from ULM.MethodsTwo public gene expression profiles (GSE36610 and GSE64763) from the GEO datasets containing ULMS and ULM samples were downloaded. Differentially expressed genes (DEGs) were selected and determined among 37 ULMS and 25 ULM control samples. The DEGs were used for Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Disease Ontology (DO) enrichment analyses as well as gene set enrichment analysis (GSEA). The candidate biomarkers were identified by least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analyses. The receiver operating characteristic curve (ROC) was applied to evaluate diagnostic ability. For further confirmation, the biomarker expression levels and diagnostic value in ULMS were verified in the GSE9511 and GSE68295 datasets (12 ULMS and 10 ULM), and validated by immunohistochemistry (IHC). The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in ULMS.ResultIn total, 55 DEGs were recognized via GO analysis, and KEGG analyses revealed that the DEGs were enriched in nuclear division, and cell cycle. The recognized DEGs were primarily implicated in non−small cell lung carcinoma and breast carcinoma. Gene sets related to the cell cycle and DNA replication were activated in ULMS. DPP6 and MFAP5 were distinguished as diagnostic biomarkers of ULMS (AUC = 0.957, AUC = 0.899, respectively), and they were verified in the GSE9511 and GSE68295 datasets (AUC = 0.983, AUC = 0.942, respectively). The low expression of DPP6 and MFAP5 were associated with ULMS. In addition, the analysis of the immune microenvironment indicated that resting mast cells were positively correlated with DPP6 and MFAP5 expression and that eosinophils and M0 macrophages were negatively correlated with DPP6 expression (P<0.05).ConclusionThese findings indicated that DPP6 and MFAP5 are diagnostic biomarkers of ULMS, thereby offering a novel perspective for future studies on the occurrence, function and molecular mechanisms of ULMS.

AIM: To investigate the protective effect of heme oxygenase-1 (HO-1) against H2O2-induced apoptosis in human ARPE-19 cells. METHODS: The lentiviral vector expressing HO-1 was prepared and transfected into apoptotic ARPE-19 cells induced by H2O2. Functional experiments including cell counting kit-8 (CCK-8) assay, flow cytometry (FCM) and mitochondrial membrane potential assay were conducted. RESULTS: The ultrastructure of ARPE-19 cells was observed using transmission electron microscope (TEM). It was found that exogenous HO-1 significantly ameliorated H2O2-induced loss of cell viability, apoptosis and intracellular levels of reactive oxygen species (ROS) in ARPE-19 cells. The overexpression of HO-1 facilitated the transfer of nuclear factor erythroid-2-related factor 2 (Nrf2) from cytoplasm to nucleus, which in turn upregualted expressions HO-1 and B-cell lymphoma-2 (Bcl-2). Furthermore, HO-1 upregulation further inhibited H2O2-induced release of cysteinyl aspartate specific proteinase-3 (caspase-3). CONCLUSION: Exogenous HO-1 protect ARPE-19 cells against H2O2-induced oxidative stress by regulating the expressions of Nrf2, HO-1, Bcl-2, and caspase-3.

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Taking advantage of the high-efficiency photocurrent response of bismuth oxychloride sensitized with gold nanoparticles (BiOClÀ Au) in combination with the antigen-antibody biological recognition reaction, a convenient photoelectrochemical (PEC) immunoassay (model target: prostate-specific antigen; PSA) has been fabricated. In particular, the enhanced photocurrent response could be attributed to the enhanced optical absorption of visible light from surface plasmon resonance (SPR) effect of gold nanoparticles in the hierarchical structure of BiOCl layered. To realize the biological detection process, an immunoreaction was implemented between target PSA and alkaline phosphatase (ALP)-labeled anti-PSA antibodies. With the formation of ternary sandwich immunocomplex, the carried and loaded ALP catalysed the substrate ascorbic acid 2-phosphate (AAP) to generate ascorbic acid for increasing the photocurrent intensity of BiOClÀ Au/FTO. Under optimum reaction time, the photocurrent peak intensity of BiOClÀ Au/FTO increased with the increasing of target PSA concentration in the range of 0.01 ng/mL to 50 ng/mL with a limit of detection (LOD) down to 2.3 pg/mL. In the photocurrent control experiment, the photocurrent of BiOClÀ Au/FTO was not only higher than that of BiOCl/FTO, but also showed greater changes in photocurrent under the same target PSA concentration. Impressively, the proposed split-type PEC immunoassay was applied to detect PSA concentration in human serum samples, giving acceptable and satisfactory accuracy compared with the gold standard PSA ELISA method.

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UNSTRUCTURED Background: The objective of this study was to address the prevalent issue of sleep disturbance among college students, which can lead to a range of mental and physical disorders. The identification of potential predictors and the development of an accurate prediction model are essential steps for the early detection of and appropriate intervention in sleep disturbances. However, previous studies have encountered notable limitations. Objective: This study aimed to provide a fresh perspective by developing and validating a model for the prediction of sleep quality among college students, which will improve the accuracy of predictions and facilitate timely interventions. Mehods: We analyzed data from 20,645 college students between 5 April and 16 April 2022 in Fujian Province, China.First, the Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and a sleep quality influencing factor questionnaire were conducted among the participants. Second, the collected data were used to select appropriate variables by comparing the outcomes of a multinomial logistic regression, LASSO regression, and Boruta feature selection. The data were then divided into a training–testing set (70%) and an independent validation set (30%) using stratified sampling. We developed and validated six machine learning techniques, which included an artificial neural network, a decision tree, a gradient-boosting tree, a k-nearest neighbor, a naïve Bayes, and a random forest. Finally, an online sleep evaluation website was established based on the best-fitting prediction model. Results: The mean global PSQI score was 6.02±3.112, and the sleep disturbance rate was 28.9% (defined as a global PSQI score of > 7 points). The LASSO regression model was preferred because it contained only the following eight predictors: age, specialty, respiratory history, coffee consumption, staying up late, long hours online, sudden changes, and impatient closed-loop management. Among the generated models, the artificial neural network (ANN) model was proven to have the best performance, with a cutoff, AUROC, accuracy, sensitivity, specificity, precision, F1-score, and KAPPA of 0.710, 0.713 (95%CI 0.696-0.730), 0.669 (95%CI 0.669-0.669), 0.682 (95%CI 0.699-0.665), 0.637 (95%CI 0.665-0.610), 0.822 (95%CI 0.837-0.807), 0.745 (95%CI 0.729-0.795), and 0.284 (95%CI 0.313-0.255), respectively. In addition, it had a Brier score of 0.182. The calibration curves showed good agreement between the predictions and the observations. A decision curve analysis demonstrated that the model could achieve a net benefit. A clinical impact curve confirmed the high clinical efficiency of the prediction model. Conclusions: The prediction model, which incorporated eight predictors, was built using a LASSO regression and an ANN to estimate the probability of sleep disturbance among college students. This model may be utilized as an intuitive and practical tool for sleep quality predictions to support better management and healthcare on college campuses.