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.

Melatonin serves as a novel treatment in bladder fibrosis through TGF-β1/Smad and EMT.

Background: Melatonin (MEL) is an indole amine molecule primarily produced in the pineal gland. Melatonin has been shown in numerous studies to have antifibrotic effects on the kidney, liver, and other organs. However, it is still unclear how melatonin works in bladder fibrosis.

Artificial intelligence in liver imaging: methods and applications.

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).

CT-based radiomics: predicting early outcomes after percutaneous transluminal renal angioplasty in patients with severe atherosclerotic renal artery stenosis.

This study aimed to comprehensively evaluate non-contrast computed tomography (CT)-based radiomics for predicting early outcomes in patients with severe atherosclerotic renal artery stenosis (ARAS) after percutaneous transluminal renal angioplasty (PTRA). A total of 52 patients were retrospectively recruited, and their clinical characteristics and pretreatment CT images were collected. During a median follow-up period of 3.

Zexieyin formula alleviates atherosclerosis with cognitive impairment: A novel role in the treatment of comorbidities and its underlying mechanisms.

Ethnopharmacological Relevancy: Zexieyin formula (ZXYF) has been identified to have therapeutic actions of atherosclerosis (AS). It's unknown that whether ZXYF has therapeutic potential of atherosclerosis (AS) with cognitive impairment (CI) and its underlying mechanisms.

Aim Of The Study: To elucidate therapeutic effect of ZXYF for AS with CI as well as its underlying mechanisms in AS with CI mice model.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma.

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

Background: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

Background: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet.

Purpose: This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value.

A multi-view co-training network for semi-supervised medical image-based prognostic prediction.

Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma.

Effect of non-pharmaceutical interventions in the early phase of the COVID-19 epidemic in Saudi Arabia.

Non-pharmaceutical interventions have been widely employed to control the COVID-19 pandemic. Their associated effect on SARS-CoV-2 transmission have however been unequally studied across regions. Few studies have focused on the Gulf states despite their potential role for global pandemic spread, in particular in the Kingdom of Saudi Arabia through religious pilgrimages.

Genome-wide identification, expression analysis, and transcriptome analysis of the IAA gene family in Zoysia japonica.

Background: AUX/IAA is an essential signaling molecule and has great physiological importance in various plants, but its function in Zoysia japonica remains unknown.

Methods And Results: Genome-wide identification and analysis of AUX/IAA genes used bioinformatics methods to investigate the ZjIAA genes' expression of exogenous IAA hydroponics treatment for 2 h by qRT-PCR, control and exogenous IAA treated zoysia were subjected to transcriptome sequencing. ZjIAAs were distributed across the 13 subfamilies by phylogenetic analysis with Oryza sativa and Arabidopsis thaliana.