Polycomb repressive complex 1 modulates granulosa cell proliferation in early folliculogenesis to support female reproduction.

Rationale: Premature ovarian insufficiency (POI) is an accelerated reduction in ovarian function inducing infertility. Folliculogenesis defects have been reported to trigger POI as a consequence of ovulation failure. However, the underlying mechanisms remain unclear due to the genetic complexity and heterogeneity of POI. Methods: We used whole genome sequencing (WGS), conditional knockout mouse models combined with laser capture microdissection (LCM), and RNA/ChIP sequencing to analyze the crucial roles of polycomb repressive complex 1 (PRC1) in clinical POI and mammalian folliculogenesis. Results: A deletion mutation of MEL18, the key component of PRC1, was identified in a 17-year-old patient. However, deleting Mel18 in granulosa cells (GCs) did not induce infertility until its homolog, Bmi1, was deleted simultaneously. Double deficiency of BMI1/MEL18 eliminated PRC1 catalytic activity, upregulating cyclin-dependent kinase inhibitors (CDKIs) and thus blocking GC proliferation during primary-to-secondary follicle transition. This defect led to damaged intercellular crosstalk, eventually resulting in gonadotropin response failure and infertility. Conclusions: Our findings highlighted the pivotal role of PRC1 as an epigenetic regulator of gene transcription networks in GC proliferation during early folliculogenesis. In the future, a better understanding of molecular details of PRC1 structural and functional abnormalities may contribute to POI diagnosis and therapeutic options.

Mesenchymal stem cell-derived exosomes ameliorate TGF-ß1-induced endometrial fibrosis by altering their miRNA profile.

OBJECTIVE: Mesenchymal stem cell (MSC)-derived exosomes (MSC-exo) can treat reproductive disorders. However, the action of microRNAs (miRNAs) in this mechanism has yet to be systematically investigated. This study aimed to explore the effect of MSC-exo on TGF-ß1-induced endometrial fibrosis in intrauterine adhesions and elucidate the regulatory mechanisms involved in key genes by comparing miRNA expression profiles. METHODS: MSC-exo were isolated and identified based on particle size and protein marker detection. Cell counting kit-8, flow cytometry, and western blotting were used to determine the effects of MSC-exo on cell function and fibrosis in human endometrial epithelial cells (hEECs). Subsequently, we sequenced and annotated the small RNA in MSC-exo and TGF-ß1-induced MSC-exo to screen for differentially expressed (DE) miRNAs. After the prediction and functional enrichment of target genes of DE miRNAs, key genes were selected for functional experiments. RESULTS: TGF-ß1 inhibited the proliferation of hEECs and promoted apoptosis and fibrosis. However, these effects were significantly reversed by the addition of MSC and MSC-exo. Fifteen DE miRNAs were identified by comparing the miRNA profiles of MSC-exo and TGF-ß1-induced MSC-exo. Among these, miR-145-5p was found to be significantly upregulated in TGF-ß1-induced MSC-exo. Furthermore, the addition of miR-145-5p mimic was found to reverse fibrosis in hEECs while promoting the expression of key autophagy protein P62. CONCLUSION: MSC-exo ameliorated TGF-ß1-induced endometrial fibrosis. RNA sequencing, bioinformatic analysis, and functional experiments revealed that miR-145-5p may exert its action through the P62-dependent autophagy pathway.

Machine learning and radiomics for the prediction of multidrug resistance in cavitary pulmonary tuberculosis: a multicentre study.

OBJECTIVES: Multidrug-resistant tuberculosis (MDR-TB) is a major challenge to global health security. Early identification of MDR-TB patients increases the likelihood of treatment success and interrupts transmission. We aimed to develop a predictive model for MDR to cavitary pulmonary TB using CT radiomics features. METHODS: This retrospective study included 257 consecutive patients with proven active cavitary TB (training cohort: 187 patients from Beijing Chest Hospital; testing cohort: 70 patients from Infectious Disease Hospital of Heilongjiang Province). Radiomics features were extracted from the segmented cavitation. A radiomics model was constructed to predict MDR using a random forest classifier. Meaningful clinical characteristics and subjective CT findings comprised the clinical model. The radiomics and clinical models were combined to create a combined model. ROC curves were used to validate the capability of the models in the training and testing cohorts. RESULTS: Twenty-one radiomics features were selected as optimal predictors to build the model for predicting MDR-TB. The AUCs of the radiomics model were significantly higher than those of the clinical model in either the training cohort (0.844 versus 0.589, p < 0.05) or the testing cohort (0.829 versus 0.500, p < 0.05). The AUCs of the radiomics model were slightly lower than those of the combined model in the training cohort (0.844 versus 0.881, p > 0.05) and testing cohort (0.829 versus 0.834, p > 0.05), but there was no significant difference. CONCLUSIONS: The radiomics model has the potential to predict MDR in cavitary TB patients and thus has the potential to be a diagnostic tool. KEY POINTS: • This is the first study to build and validate models that distinguish MDR-TB from DS-TB with clinical and radiomics features based on cavitation. • The radiomics model demonstrated good performance and might potentially aid in prior TB characterisation treatment. • This noninvasive and convenient technique can be used as a diagnosis tool into routine clinical practice.

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer.

Background: Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis. Methods: The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using "MethylMix" and "corrplot" R packages. The connection between methylated genes and the expression of screened driven genes were explored using "survminer" and "beeswarm" packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using "CIBERSORT" package. Results: In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell-cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration. Conclusions: Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.

Negative influence of detector noise on ghost imaging based on the photon counting technique at low light levels.

The influence of detector noise on ghost imaging (GI) is investigated at low light levels. Based on the characteristics of the additive detector noise, we establish the analytical model and display the ghost images through numerical and experimental demonstrations. It is shown that the contrast-to-noise ratio and visibility of reconstructed images are sharply affected by the detector noise. Following the increase of the ratio of average signal intensity to the average noise, the quality of reconstructions is enhanced. To reduce the measurement numbers and, thus, shorten the consuming time without sacrificing the imaging quality, we propose a sorting technique in the traditional GI algorithm for a high quality image reconstruction. The results demonstrated here will be favorable to the applications of low-light-level imaging.