Differential Impairment Mechanism of Sperm Production via Induction of miR-34c-Activated Apoptosis and Spermatogenesis Pathway in Diet-Induced Obesity and Resistant Mice and GC-1 Spg Cells.

Male reproductive dysfunction is a clinical disease, with a large number of cases being idiopathic. Reproductive disorders have been found in obese (diet-induced obesity and diet-induced obesity-resistant) mice, but the mechanism behind the male reproductive dysfunction between them may be different. The purpose of this study was to explore the possible role and mechanism of miR-34c on sperm production in high-fat-diet-induced obesity-resistant (DIO-R) mice and GC-1 spg cells, which may differ from those in high-fat-diet-induced obesity (DIO) mice. In vivo and in vitro experiments were performed. C57BL/6J mice were fed a high-fat diet for 10 weeks to establish the DIO and DIO-R mouse model. GC-1 spg cells were used to verify the mechanism of miR-34c on sperm production. During in vivo experiments, sperm production damage was found in both DIO and DIO-R male mice. Compared to the control mice, significantly decreased levels of testosterone, LH, activities of acrosome enzyme (ACE), HAse, and activating transcription factor 1 (ATF1) were found in both DIO and DIO-R male mice (p < 0.05). Compared with the control group, the ratio of B-cell lymphoma-2 (Bcl-2)/bcl-2-associated X protein (Bax) in the DIO group was significantly decreased, and the expression level of cleaved caspase-3 was significantly increased (p < 0.05). Compared with the control group, the Bcl-2 protein expression level in the testes of the DIO-R group significantly decreased (p < 0.05). However, the Bax expression level increased. Thus, the Bcl-2/Bax ratio significantly decreased (p < 0.01); however, the factor-related apoptosis (Fas), Fas ligand (FasLG), cleaved caspase-8, caspase-8, cleaved caspase-3, and caspase-3 protein expression levels significantly increased (p < 0.05). Compared with the DIO group, in DIO-R mice, the activities of ACE, ATF1, Bcl-2, and Bcl-2/Bax's spermatogenesis protein expression decreased, while the apoptosis-promoting protein expression significantly increased (p < 0.05). During the in vitro experiment, the late and early apoptotic ratio in the miR-34c over-expression group increased. MiR-34c over-expression enhanced the expression of apoptosis-related proteins Fas/FasLG and Bax/Bcl-2 while inhibiting the expression of ATF1 and the sperm-associated protein in GC-1 spg cells. DIO and DIO-R could harm sperm production. DIO-R could impair sperm production by inducing the miR-34c-activated apoptosis and spermatogenesis pathway, which may be different from that of DIO.

Oxidative Stress Mediated by N6-Methyladenosine Methylation Contributes to High-Fat Diet Induced Male Reproductive Dysfunction.

OBJECTIVE: To determine the mechanism of oxidative stress mediated by N6-methyladenosine (m6A) methylation contributing to high fat diet-induced reproductive dysfunction. RESULTS: In vivo, compared with those in the Control group, the sperm count and sperm motility decrease significantly; the testosterone, luteinizing hormone levels, hyaluronidase, acrosomal enzyme levels, and total antioxidant capacity decrease significantly; malondialdehyde increases significantly in the DIO and DIO-R groups. The expression of nuclear factor erythroid 2-related factor 2 (Nrf2), superoxide dismutase 1 (SOD1), and NAD(P)H quinone dehydrogenase 1 (NQO1) decreases significantly in the DIO and DIO-R groups; m6A levels in testis tissue in the DIO and DIO-R groups increase; the enrichment of m6A-modified Nrf2 mRNA in testis in the DIO group and DIO-R group increases significantly. Also the m6A regulatory proteins increase significantly in the DIO group and DIO-R group. In vitro, compared to palmitic acid treated cells, the reactive oxygen species (ROS) level significantly decreases in STM2457, S-Adenosylhomocysteine treated cells and YTHDC2, YTHDF2 gene silence cells; however, Nrf2 expression increases in all treated cells. In addition, m6A expression decreases. CONCLUSIONS: Oxidative stress mediates by methylation of m6A may contribute to high fat diet-induced male reproductive dysfunction.

Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.

BACKGROUND: Currently, the diagnosis of invasive pulmonary aspergillosis (IPA) mainly depends on the integration of clinical, radiological and microbiological data. Artificial intelligence (AI) has shown great advantages in dealing with data-rich biological and medical challenges, but the literature on IPA diagnosis is rare. OBJECTIVE: This study aimed to provide a non-invasive, objective and easy-to-use AI approach for the early diagnosis of IPA. METHODS: We generated a prototype diagnostic deep learning model (IPA-NET) comprising three interrelated computation modules for the automatic diagnosis of IPA. First, IPA-NET was subjected to transfer learning using 300,000 CT images of non-fungal pneumonia from an online database. Second, training and internal test sets, including clinical features and chest CT images of patients with IPA and non-fungal pneumonia in the early stage of the disease, were independently constructed for model training and internal verification. Third, the model was further validated using an external test set. RESULTS: IPA-NET showed a marked diagnostic performance for IPA as verified by the internal test set, with an accuracy of 96.8%, a sensitivity of 0.98, a specificity of 0.96 and an area under the curve (AUC) of 0.99. When further validated using the external test set, IPA-NET showed an accuracy of 89.7%, a sensitivity of 0.88, a specificity of 0.91 and an AUC of 0.95. CONCLUSION: This novel deep learning model provides a non-invasive, objective and reliable method for the early diagnosis of IPA.

Deep Learning Analysis of the Adipose Tissue and the Prediction of Prognosis in Colorectal Cancer.

Research has shown that the lipid microenvironment surrounding colorectal cancer (CRC) is closely associated with the occurrence, development, and metastasis of CRC. According to pathological images from the National Center for Tumor diseases (NCT), the University Medical Center Mannheim (UMM) database and the ImageNet data set, a model called VGG19 was pre-trained. A deep convolutional neural network (CNN), VGG19CRC, was trained by the migration learning method. According to the VGG19CRC model, adipose tissue scores were calculated for TCGA-CRC hematoxylin and eosin (H&E) images and images from patients at Zhujiang Hospital of Southern Medical University and First People's Hospital of Chenzhou. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) of patients. The XCell and MCP-Counter algorithms were used to evaluate the immune cell scores of the patients. Gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) were used to analyze upregulated and downregulated pathways. In TCGA-CRC, patients with high-adipocytes (high-ADI) CRC had significantly shorter OS times than those with low-ADI CRC. In a validation queue from Zhujiang Hospital of Southern Medical University (Local-CRC1), patients with high-ADI had worse OS than CRC patients with low-ADI. In another validation queue from First People's Hospital of Chenzhou (Local-CRC2), patients with low-ADI CRC had significantly longer OS than patients with high-ADI CRC. We developed a deep convolution network to segment various tissues from pathological H&E images of CRC and automatically quantify ADI. This allowed us to further analyze and predict the survival of CRC patients according to information from their segmented pathological tissue images, such as tissue components and the tumor microenvironment.

Applications of Machine Learning to Predict Cisplatin Resistance in Lung Cancer.

PURPOSE: Lung cancer, mainly lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer, has the highest incidence and cancer-related mortality worldwide. Platinum-based chemotherapy plays an important role in the treatment of various lung cancer subtypes, but not all patients benefit from this treatment regimen; thus, it is worth identifying lung cancer patients who are resistant or sensitive to platinum-based therapy. METHODS: The drug response and sequencing data of 170 lung cancer cell lines were downloaded from the Genomics of Drug Sensitivity in Cancer (GDSC) database, and support vector machines (SVMs) and beam search were used to select an optimal gene panel that can predict the sensitivity of cell lines to cisplatin. Then, we used available cell line data to explore the potential mechanisms. RESULTS: In this work, the drug response and sequencing data of 170 lung cancer cell lines were downloaded from the GDSC database, and SVMs and beam search were used to screen a panel of genes related to lung cancer cell line resistance to cisplatin. A final panel of nine genes (PLXNC1, KIAA0649, SPTBN4, SLC14A2, F13A1, COL5A1, SCN2A, PLEC, and ALMS1) was identified, and achieved an area under the curve (AUC) of 0.873 ± 0.004. The natural logarithm of the half maximal inhibitory concentration (lnIC50) values of the mutant-type (panel-MT) group was significantly higher than that of the wild-type (panel-WT) group, regardless of the lung cancer subtype. The differentially expressed pathways between the two groups may explain this difference. CONCLUSION: In this study, we found that a panel of nine genes can accurately predict sensitivity to cisplatin, which may provide individualized treatment recommendations to improve the prognosis of patients with lung cancer.