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Objective: In this study, the effect of in vitro Fertilization-Embryo Transfer (IVF-ET) on the clinical outcome of patients with syphilis infertility during resuscitation cycle. Methods: A retrospective single-center method was adopted. This study included 4430 pairs of infertile patients who underwent syphilis detection. The influence of the syphilis freeze-thaw embryos transplantation outcome was studied in the patients with infertility by comparing the general clinical characteristics of patients (age, years of infertility, body mass index (BMI), basal follicle stimulating hormone (FSH), serum basal estradiol (Estradiol, E2), transplanted intimal thickness, the number of embryos transferred) and the clinical pregnancy (biochemical pregnancy rate, clinical pregnancy rate, implantation rate, live birth rate and abortion rate). Results: Firstly, in the clinical outcome of one frozen-thawed embryos transfer, the live birth rate of the woman's syphilis-infected group was lower than that of the uninfected group (71.3 % vs. 50.0 %), while the abortion rate was higher than that of the uninfected group (7.8 % vs. 26.7 %), and there was a statistical difference (P < 0.05), and there was no statistical difference in other indicators between other groups (P > 0.05). Secondly, in the clinical outcome of two frozen-thawed embryos transfers, the biochemical pregnancy rate (61.3 % vs. 28.6 %) and clinical pregnancy rate (42.9 % vs. 14.3 %) of the group which was infected with syphilis alone were lower than those of the uninfected group (P < 0.05), and other indicators among the other groups showed no statistical difference (P > 0.05). Thirdly, in the clinical outcomes of frozen-thawed embryos transfer three times or more, there was no significant difference in the clinical indicators between the syphilis infertility patients and the non-infected infertility patients (P > 0.05). Conclusion: When the syphilis infertility patients and the non-infected infertile patients underwent IVF-ET treatment for the first time, the live birth rate and abortion rate of the syphilis group were significantly different (P < 0.05). In the outcome of two transplants, the biochemical pregnancy rate and clinical Pregnancy rates were significantly reduced so patients with syphilis infertility who undergo IVF-ET should be informed about the risk of adverse clinical outcomes.
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BACKGROUND: Cholangiocarcinoma (CCA) is a highly aggressive and invasive malignant tumor of the bile duct, with a poor prognosis and a high mortality rate. Currently, there is a lack of effective targeted treatment methods and reliable biomarkers for prognosis. METHODS: We downloaded RNA-seq and clinical data of CCA from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases as training and test sets. The apoptosis-related genes were obtained from the Molecular Signatures Database (MsigDB) database. We used univariate/multivariate Cox regression and Lasso regression analyses to construct a riskscore prognostic model. Based on the median riskscore, we clustered the patients into high-risk (HR) and low-risk (LR) groups. We carried out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differentially expressed genes (DEGs) in HR and LR groups. The single sample gene set enrichment analysis (ssGSEA) was employed to analyze the immune infiltration of the HR and LR groups. The CellMiner database was utilized to predict drugs and perform molecular docking on drugs and target proteins. RESULTS: We identified 8 genes with prognostic significance to construct a prognostic model. Results of GO and KEGG demonstrated that DEGs were mainly enriched in biological functions such as fatty acid metabolic processes and pathways such as the cAMP signaling pathway. Results of ssGSEA uncovered that immune cells such as DCs and Macrophages in the HR group, as well as immune functions such as Check-point and Parainflammation, were considerably higher than those in the LR group. Drug sensitivity prediction and results of molecular docking revealed that Rigosertib targeted the prognostic genes MAP3K1. HYPOTHEMYCIN and AMG900 effectively targeted JUN. CONCLUSION: Our project suggested that the prognostic model with apoptotic features can effectively predict prognosis in CCA patients, proffering prognostic biomarkers and potential therapeutic targets for CCA patients.
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BACKGROUND: Diagnostic challenges exist for CMV pneumonia in post-hematopoietic stem cell transplantation (post-HSCT) patients, despite early-phase radiographic changes. OBJECTIVE: The study aims to employ a deep learning model distinguishing CMV pneumonia from COVID-19 pneumonia, community-acquired pneumonia, and normal lungs post-HSCT. METHODS: Initially, 6 neural network models were pre-trained with COVID-19 pneumonia, community-acquired pneumonia, and normal lung CT images from Kaggle's COVID multiclass dataset (Dataset A), then Dataset A was combined with the CMV pneumonia images from our center, forming Dataset B. We use a few-shot transfer learning strategy to fine-tune the pre-trained models and evaluate model performance in Dataset B. RESULTS: 34 cases of CMV pneumonia were found between January 2018 and December 2022 post-HSCT. Dataset A contained 1681 images of each subgroup from Kaggle. Combined with Dataset A, Dataset B was initially formed by 98 images of CMV pneumonia and normal lung. The optimal model (Xception) achieved an accuracy of 0.9034. Precision, recall, and F1-score all reached 0.9091, with an AUC of 0.9668 in the test set of Dataset B. CONCLUSIONS: This framework demonstrates the deep learning model's ability to distinguish rare pneumonia types utilizing a small volume of CT images, facilitating early detection of CMV pneumonia post-HSCT.