Oligogenic basis of premature ovarian insufficiency: an observational study.

Background: The etiology of premature ovarian insufficiency, that is, the loss of ovarian activity before 40 years of age, is complex. Studies suggest that genetic factors are involved in 20-25% of cases. The aim of this study was to explore the oligogenic basis of premature ovarian insufficiency.

Single-cell RNA-seq identified novel genes involved in primordial follicle formation.

Introduction: The number of primordial follicles (PFs) in mammals determines the ovarian reserve, and impairment of primordial follicle formation (PFF) will cause premature ovarian insufficiency (POI).

Methods: By analyzing public single-cell RNA sequencing performed during PFF on mice and human ovaries, we identified novel functional genes and novel ligand-receptor interaction during PFF. Based on immunofluorescence and in vitro ovarian culture, we confirmed mechanisms of genes and ligand-receptor interaction in PFF.

CXADR promote epithelial-mesenchymal transition in endometriosis by modulating AKT/GSK-3β signaling.

Endometriosis is a benign high prevalent disease exhibiting malignant features. However, the underlying pathogenesis and key molecules of endometriosis remain unclear. By integrating and analysis of existing expression profile datasets, we identified coxsackie and adenovirus receptor (CXADR), as a novel key gene in endometriosis.

TT U-Net: Temporal Transformer U-Net for Motion Artifact Reduction Using PAD (Pseudo All-Phase Clinical-Dataset) in Cardiac CT.

Involuntary motion of the heart remains a challenge for cardiac computed tomography (CT) imaging. Although the electrocardiogram (ECG) gating strategy is widely adopted to perform CT scans at the quasi-quiescent cardiac phase, motion-induced artifacts are still unavoidable for patients with high heart rates or irregular rhythms. Dynamic cardiac CT, which provides functional information of the heart, suffers even more severe motion artifacts.

Global Research Trends of Artificial Intelligence on Histopathological Images: A 20-Year Bibliometric Analysis.

Cancer has become a major threat to global health care. With the development of computer science, artificial intelligence (AI) has been widely applied in histopathological images (HI) analysis. This study analyzed the publications of AI in HI from 2001 to 2021 by bibliometrics, exploring the research status and the potential popular directions in the future.

Bibliometric Analysis of the Knowledge Base and Future Trends on Sarcopenia from 1999-2021.

Sarcopenia is characterized by progressive loss of muscle mass and function, and it is becoming a serious public health problem with the aging population. However, a comprehensive overview of the knowledge base and future trends is still lacking. The articles and reviews with "sarcopenia" in their title published from 1999 to 2021 in the SCIE database were retrieved.

Customized Total Variation Algorithm for Metal Artifact Reduction in Computed Tomography.

Metal artifact reduction (MAR) is a challenge for commercial CT systems. The metal objects of high density adversely affect the measurement process and bring difficulties to image reconstruction. Compressed sensing (CS) reconstruction algorithms have been successfully applied in MAR.

A Self-supervised Learning Based Framework for Automatic Heart Failure Classification on Cine Cardiac Magnetic Resonance Image.

Heart failure (HF) is a serious syndrome, with high rates of mortality. Accurate classification of HF according to the left ventricular ejection faction (EF) plays an important role in the clinical treatment. Compared to echocardiography, cine cardiac magnetic resonance images (Cine-CMR) can estimate more accurate EF, whereas rare studies focus on the application of Cine-CMR.