CYP24A1 affected macrophage polarization through degradation of vitamin D as a candidate biomarker for ovarian cancer prognosis.

Ovarian cancer (OC) is a fatal gynecological malignancy with a poor prognosis in which mitochondria-related genes are involved deeply. In this study, we aim to screen mitochondria-related genes that play a role in OC prognosis and investigate its effects. Through single-cell sequencing technology and bioinformatics analysis, including TCGA ovarian cancer data analysis, gene expression signature analysis (GES), immune infiltration analysis, Gene Ontology (GO) enrichment analysis, Gene Set Enrichment Analysis (GSEA), and Principal Component Analysis (PCA), our findings revealed that CYP24A1 regulated macrophage polarization through vitamin D (VD) degradation and served as a target gene for the second malignant subtype of OC through bioinformatics analyses.

Anti-Müllerian Hormone Accelerates Pathological Process of Insulin Resistance in Polycystic Ovary Syndrome Patients.

Insulin resistance (IR) is one of the most common features of polycystic ovary syndrome (PCOS), which is related to obesity. Whether increased anti-Müllerian hormone (AMH) levels in PCOS are involved in the pathogenesis of insulin resistance remains unclear. We investigated serum levels of leptin and AMH along with basic clinical and metabolic parameters in 114 PCOS patients and 181 non-PCOS women.

Proteomics of Uterosacral Ligament Connective Tissue from Women with and without Pelvic Organ Prolapse.

Purpose: Damage to the uterosacral ligaments is an important contributor to uterine and vaginal prolapse. The aim of this study is to identify differentially expressed proteins (DEPs) in the uterosacral ligaments of women with and without pelvic organ prolapse (POP) and analyze their relationships to cellular mechanisms involved in the pathogenesis of POP.

Experimental Design: Uterosacral ligament connective tissue from four patients with POP and four control women undergo iTRAQ analysis followed by ingenuity pathway analysis (IPA) of DEPs.

[A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation].

Traditional hyperspectral image classification algorithms focus on spectral information application, however, with the increase of spatial resolution of hyperspectral remote sensing images, hyperspectral imaging presents clustering properties on spatial domain for the same category. It is critical for hyperspectral image classification algorithms to use spatial information in order to improve the classification accuracy. However, the marginal differences of different categories display more obviously.

[A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection].

With high-resolution spatial information and continuous spectrum information, hyperspectral remote sensing image -has a unique advantage in the field of target detection. Traditional hyperspectral remote sensing image target detection methods emphasis on using spectral information to determine deterministic algorithm and statistical algorithms. Deterministic algorithms find the target by calculating the distance between the target spectrum and detected spectrum however, they are unable to detect sub-pixel target and are easily affected by noise.

Human leukocyte antigen-G (HLA-G) expression in cervical cancer lesions is associated with disease progression.

The immunotolerant human leukocyte antigen (HLA)-G has direct inhibitory effects on natural killer cells, dendritic cells, T cells and can indirectly induce tolerant regulatory cells. The significance of the aberrant HLA-G expression in malignant contexts has been intensively investigated. In the current study, HLA-G expression in 22 normal cervical tissues, 14 cervical intraepithelial neoplasia (CIN) patients and 129 patients with squamous cell cervical cancer were examined using immunohistochemistry.