Clinical and prognostic significance of FBXL6 expression in ovarian cancer.

Objective: Growing evidence indicates that F-box and leucine-rich repeat protein 6 (FBXL6) is associated with the progression of various cancers, including gastric cancer, hepatocellular carcinoma, and colorectal cancer. This study focuses on the prognostic significance of FBXL6 in OC.

Methods: Differential levels of FBXL6 in multiple cancers were evaluated using the TCGA and GSE26712 databases.

Participation in Multidisciplinary Teams Among Healthcare Professionals: A Discrete Choice Experiment in Tertiary Public Hospitals in China.

Purpose: Healthcare professionals' participation is crucial for the efficient implementation of multidisciplinary team (MDT) collaboration models. We identified the key factors influencing healthcare professionals' preference to participate in MDTs in tertiary hospitals.

Methods: To clarify the attributes and levels of the discrete choice experiment (DCE), we conducted a targeted literature review and conducted in-depth interviews with MDT service providers.

Integrated immuno-transcriptomic analysis of ovarian cancer identifies a four-chemokine-dominated subtype with antitumor immune-active phenotype and favorable prognosis.

Background: Ovarian cancer (OV) is a heterogeneous disease but has traditionally been treated as an immunologically cold malignancy. The relationship between the immune-active cancer phenotype typified by a T helper 1 (Th-1) immune response and clinical outcome in OV remains uncertain.

Methods: A cohort-scale compendium of transcriptomic data from 2850 OV samples from 19 individual datasets was compiled for integrative immuno-transcriptomic analysis.

Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective study.

Background: Ovarian cancer (OV) is a prevalent and deadly disease with high mortality rates. The development of accurate prognostic tools and personalized therapeutic strategies is crucial for improving patient outcomes.

Methods: A graph-based deep learning model, the Ovarian Cancer Digital Pathology Index (OCDPI), was introduced to predict prognosis and response to adjuvant therapy using hematoxylin and eosin (H&E)-stained whole-slide images (WSIs).

Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA.

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data.

miR-708 and miR-335-3p Inhibit the Apoptosis of Retinal Ganglion Cells Through Suppressing Autophagy.

This study aimed to clarify the regulation role of miR-708 and miR-335-3p in retinal ganglion cell (RGC) autophagy and apoptosis in glaucoma. Chronic glaucoma mice were established by laser photocoagulation. RGCs were isolated and transfected with a series of plasmids and the cultured in 60 mmHg pressure.

Large population-based study using the SEER database: is endoscopic resection appropriate for early gastric cancer patients in the United States?

Aim: To investigate whether endoscopic treatment is applicable to American patients and explores the predictors of lymph node metastasis (LNM) in early gastric cancer (EGC).

Methods: Patients with EGC confined to either mucosa (T1a,  = 1799) and submucosa (T1b,  = 1689) were identified from the Surveillance, Epidemiology, and End Result database. Multivariate logistic regression, Kaplan-Meier method, and univariate/multivariate Cox regression were used to assess the correlation between invasion depth and LNM or prognosis.

Abnormal serum carbohydrate antigen 19-9 levels in a patient with splenic retiform haemangioendothelioma concomitant with hepatic amyloidosis: A case report.

Background: Carbohydrate antigen 19-9 (CA 19-9) is a glycoprotein that is used as a reliable tool for monitoring pancreatic cancer. Serum CA 19-9 levels are increased in patients suffering from liver, lung, and other non-malignant diseases. Haemangioendothelioma is a vascular neoplasm with a borderline biological behaviour.

The antibiotic jinggangmycin increases brown planthopper (BPH) fecundity by enhancing rice plant sugar concentrations and BPH insulin-like signaling.

The brown planthopper (BPH), Nilaparvata lugens, is a resurgent pest with an unexpected response to jinggangmycin (JGM), a broadly applied antibiotic used to control rice sheath blight disease. JGM stimulates BPH fecundity, but the underlining molecular mechanisms remain unclear. Here we report that JGM sprays led to increased glucose concentrations, photosynthesis and gene expression, specifically Rubsico, sucrose phosphate synthase, invertase 2 (INV2) and INV3 in rice plants.

MicroRNA-145 regulates immune cytokines via targeting FSCN1 in Staphylococcus aureus-induced mastitis in dairy cows.

Dairy cow mastitis is a detrimental factor in milk quality and food safety. Mastitis generally refers to inflammation caused by infection by pathogenic microorganisms. Our studies in recent years have revealed the role of miRNA regulation in Staphylococcus aureus-induced mastitis.

Enhanced community structure detection in complex networks with partial background information.

Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable.