STN1 facilitates metastasis by promoting transcription of EMT-activator ZEB1 in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) poses a serious clinical challenge, demanding further exploration of its pathogenesis and therapeutic targets for metastasis, the main cause of mortality. Here, we identify STN1, a CST complex member crucial for maintaining telomere lengths and genome stability, as a key factor in promoting PDAC metastasis. Elevated STN1 levels correlate with poor patient survival, with oncogenic protein HOXB7 as an upstream transcription factor regulating STN1.

The impact of lymphocyte count on male erectile dysfunction with clinical validations and its potential metabolic mechanisms through mendelian randomization analysis.

Background: Erectile dysfunction (ED) is on the rise globally, affecting people's lives. This article uses Mendelian randomization (MR) to analyze the causal relationship between clinical indicators and ED, and explores the potential metabolic mechanisms involved.

Method: Two-sample MR analysis was performed to analyze the causal relationships between 1,400 metabolites and clinical data and ED for revealing its potential metabolic mechanisms using GWAS (Genome-wide association studies) data.

Large models in medical imaging: Advances and prospects.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks.

Bi-regional and bi-phasic automated machine learning radiomics for defining metastasis to lesser curvature lymph node stations in gastric cancer.

Background: Lymph node metastasis (LNM) is the primary metastatic mode in gastric cancer (GC), with frequent occurrences in lesser curvature. This study aims to establish a radiomic model to predict the metastatic status of lymph nodes in the lesser curvature for GC.

Methods: We retrospectively collected data from 939 gastric cancer patients who underwent gastrectomy and D2 lymphadenectomy across two centers.

Forecasting the Potential for Clean Mineral Recycling in the Electrification Transition of Public Buses in Extra-Large Cities: A Case Study of Beijing.

In the context of the low-carbon development of the transport industry, with the trend of low-carbon development of new energy vehicles and rapid growth in the number of demonstrations and promotions, urban public transport has also formally entered the transition period of electrification, and the rapid development of electrification of public transport is becoming an important part of the realization of low-carbon transport systems. To provide a detailed description of the clean minerals stock and its structure in the public transportation system of a megacity, this paper uses Beijing's urban public transport as a case study. By integrating material flow analysis methods and diverse heterogeneous data sets, this study investigates the stock of eight clean mineralslithium (Li), cobalt (Co), nickel (Ni), manganese (Mn), aluminum (Al), platinum (Pt), palladium (Pd), and rhodium (Rh)in the city's bus system.

Potential distribution projections of mangrove forests and invasive plants under climate change: case insights from mangrove management in Guangdong Province, China.

Mangrove ecosystems are vital for maintaining biodiversity, purifying water, sequestering carbon, and mitigating climate change in coastal regions. The geographical distribution of mangrove forests has been severely affected by global warming; therefore, it must be predicted under future climate scenarios to provide a scientific basis for conservation and restoration. In this study, we employed the MaxEnt model to predict the potential distribution of suitable mangrove areas in Guangdong Province under current conditions and two future climate scenarios (2030s and 2090s): SSP1-2.

The application of artificial intelligence in upper gastrointestinal cancers.

Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers.

Neuroprotective effects and mechanisms of the YiQiWenYangSanHan formula on Parkinson's disease mice.

Background: Parkinson's disease (PD) is a complex neurodegenerative disease, which is often treated with obvious side effects such as dopamine replacement therapy. Our team has validated the unique advantages of the traditional Chinese medicine formula, YiQiWenYangSanHan formula (YQWYSHF), through in vitro experiments, confirming its therapeutic potential for PD. Nevertheless, further research and validation are required to fully understand its protective effects and underlying mechanisms against PD.

HiCur-NPC: Hierarchical Feature Fusion Curriculum Learning for Multi-Modal Foundation Model in Nasopharyngeal Carcinoma.

Providing precise and comprehensive diagnostic information to clinicians is crucial for improving the treatment and prognosis of nasopharyngeal carcinoma. Multi-modal foundation models, which can integrate data from various sources, have the potential to significantly enhance clinical assistance. However, several challenges remain: (1) the lack of large-scale visual-language datasets for nasopharyngeal carcinoma; (2) the inability of existing pre-training and fine-tuning methods to capture the hierarchical features required for complex clinical tasks; (3) current foundation models having limited visual perception due to inadequate integration of multi-modal information.

PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation.

Federated learning (FL) has shown great potential in addressing data privacy issues in medical image analysis. However, varying data distributions across different sites can create challenges in aggregating client models and achieving good global model performance. In this study, we propose a novel personalized contrastive representation FL framework, named PCRFed, which leverages contrastive representation learning to address the non-independent and identically distributed (non-IID) challenge and dynamically adjusts the distance between local clients and the global model to improve each client's performance without incurring additional communication costs.

Enhancing Prognostic Prediction of Gastrointestinal Stromal Tumors Using Semi-Supervised Regression Based on CT Imaging Data.

The construction of prognostic prediction models based on follow-up data is crucial for devising individualized treatment plans for patients. However, the performance of current supervised survival analysis methods is constrained due to the prevalence of weakly supervised censored samples during follow-up. To address this limitation, this study introduces the Prognostic Co-Training Regression (PCTR) algorithm, a semi-supervised prognostic prediction model developed through the co-training of two KNN regressors.

Development and validation of a prognostic model based on disulfidptosis-related ferroptosis genes: and as biomarkers for predicting prognosis in colon cancer.

Background: Disulfidptosis and ferroptosis are emerging cell death modalities crucial to cancer progression, yet their prognostic potential in colon cancer (CC) remains underexplored. This study develops and validates a prognostic model based on and , two genes involved in key biological processes in CC. regulates cell proliferation, migration, and apoptosis, while enhances glucose uptake via the Warburg effect, promoting tumor growth.

Salt stress-related function of the oil body-associated protein gene LcOBAP2B in Leymus chinensis.

Oil bodies are dynamic organelles in plant seeds. Oil body-associated protein (OBAP) is involved in regulating the size and vitality of oil bodies in overwintering seeds, and affects lipid mobilization and stress resistance during seed germination. In this study, we cloned an oil body associated protein gene LcOBAP2B (Leymus chinensis oil body associated protein 2B).

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types.

Background: Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited.

Methods: We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns.

Temporal Mortality Trends Attributable to Stroke in South Asia: An Age-Period-Cohort Analysis.

South Asia contributes the most to stroke mortality worldwide. This study aimed to determine the long-term trends in stroke mortality across four South Asian countries and its associations with age, period, and birth cohort. In 2019, nearly one million stroke deaths occurred across South Asia, and the associated age-standardized mortality rate (ASMR) was 80.

Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma: a multicenter study.

Objective: To develop a deep learning model to predict lymph node (LN) status in clinical stage IA lung adenocarcinoma patients.

Methods: This diagnostic study included 1,009 patients with pathologically confirmed clinical stage T1N0M0 lung adenocarcinoma from two independent datasets (699 from Cancer Hospital of Chinese Academy of Medical Sciences and 310 from PLA General Hospital) between January 2005 and December 2019. The Cancer Hospital dataset was randomly split into a training cohort (559 patients) and a validation cohort (140 patients) to train and tune a deep learning model based on a deep residual network (ResNet).

Mechanism of Yishen Chuchan decoction intervention of Parkinson's disease based on network pharmacology and experimental verification.

Unlabelled: The incidence of Parkinson's disease (PD) rises rapidly with the increase of age. With the advent of global aging, the number of patients with PD is rising along with the elderly population, especially in China. Previously, we found that Yishen chuchan decoction (YCD), prescribed based on clinical experience, has the potential of alleviating symptoms, delaying the progression, and controlling the development of PD.

Assessing the impact of COVID-19 management on the workload of human resources working in India's National Tuberculosis Elimination Program.

Background: In 1993, WHO declared tuberculosis (TB) as a global health emergency considering 10 million people are battling TB, of which 30% are undiagnosed annually. In 2020 the COVID-19 pandemic took an unprecedented toll on health systems in every country. Public health staff already engaged in TB control and numerous other departments were additionally tasked with managing COVID-19, stretching human resource (HR) capacity beyond its limits.

A radiogenomic clinical decision support system to inform individualized treatment in advanced nasopharyngeal carcinoma.

Both concurrent chemoradiotherapy (CCRT) and induction chemotherapy (ICT) followed by CCRT are standard care of advanced nasopharyngeal carcinoma (NPC). However, tailoring personalized treatment is lacking. Herein, we established a radiogenomic clinical decision support system to classify patients into three subgroups according to their predicted disease-free survival (DFS) with CCRT and ICT response.

Policy gaps in addressing market failures and intervention misalignments in tuberculosis control: prospects for improvement in China, India, and Indonesia.

India, Indonesia, and China are the top three countries with the highest tuberculosis (TB) burden. To achieve the end TB target, we analyzed policy gaps in addressing market failures as well as misalignments between National TB Programs (NTP) and health insurance policies in TB control in three countries. In India and Indonesia, we found insufficient incentives to engage private practitioners or to motivate them to improve service quality.

Government responses to the COVID-19 pandemic of the Gulf Cooperation Council countries: good practices and lessons for future preparedness.

The COVID-19 pandemic has dramatically threatened the Gulf Cooperation Council (GCC) countries which have a large proportion of foreign workers. The governments of GCC countries have proactively implemented a comprehensive set of policy measures, and up to our knowledge, a systematic analysis of qualitative and quantitative evidence on the government response is still lacking. We summarized the GCC countries' government response and quantitatively measured that response using four indexes-the Government Response Index, the Stringency Index, the Vaccine Index, and the Initial Response Index, to analyse their response for future pandemic preparedness.