Itaconate facilitates viral infection via alkylating GDI2 and retaining Rab GTPase on the membrane.

Metabolic reprogramming of host cells plays critical roles during viral infection. Itaconate, a metabolite produced from cis-aconitate in the tricarboxylic acid cycle (TCA) by immune responsive gene 1 (IRG1), is involved in regulating innate immune response and pathogen infection. However, its involvement in viral infection and underlying mechanisms remain incompletely understood.

Siglec-G Suppresses CD8 T Cells Responses through Metabolic Rewiring and Can be Targeted to Enhance Tumor Immunotherapy.

CD8 T cells play a critical role in cancer immune-surveillance and pathogen elimination. However, their effector function can be severely impaired by inhibitory receptors such as programmed death-1 (PD-1) and T cell immunoglobulin domain and mucin domain-3 (Tim-3). Here Siglec-G is identified as a coinhibitory receptor that limits CD8 T cell function.

Understanding learners' perceptions of ChatGPT: A thematic analysis of peer interviews among undergraduates and postgraduates in China.

ChatGPT, an artificial intelligence (AI)-driven language model engineered by OpenAI, has experienced a substantial upsurge in adoption within higher education due to its versatile applications and sophisticated capabilities. Although prevailing research on ChatGPT has predominantly concentrated on its technological aspects and pedagogical ramifications, a comprehensive understanding of students' perceptions and experiences regarding ChatGPT remains elusive. To address this gap, this study employed a peer interview methodology, conducting a thematic analysis of 106 first-year undergraduates and 81 first-year postgraduate students' perceptions from diverse disciplines at a comprehensive university in East China.

Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer.

Background: Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion.

Materials And Methods: The patients from No.

Establishment and validation of a ferroptosis-related prognostic signature for hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer with high heterogeneity. The prognosis of HCC is quite poor and the prognostic prediction also has challenges. Ferroptosis is recently recognized as a kind of iron-dependent cell death, which is involved in tumor progression.

ncRNA-mediated upregulation of is associated with poor prognosis and immune infiltration in pancreatic cancer.

Introduction: Malignant pancreatic cancer has poor long-term survival. Increasing evidence shows that (family with sequence similarity 83 member A) plays a vital role in tumorigenesis and malignant progression in some human cancer types. The present study explored the potential mechanism of in improving the prognosis of pancreatic cancer patients.

Development of a metabolism-related signature for predicting prognosis, immune infiltration and immunotherapy response in breast cancer.

Breast cancer (BRCA) is the most commonly diagnosed cancer and among the top causes of cancer deaths globally. The abnormality of the metabolic process is an important characteristic that distinguishes cancer cells from normal cells. Currently, there are few metabolic molecular models to evaluate the prognosis and treatment response of BRCA patients.

Jiu-Wei-Yong-An Formula suppresses JAK1/STAT3 and MAPK signaling alleviates atopic dermatitis-like skin lesions.

Ethnopharmacological Relevance: Jiu-Wei-Yong-An (JWYA) formula is a traditional Chinese medicine (TCM) prescription used to treat atopic dermatitis (AD) in the clinic. JWYA is considered to have anti-inflammatory and antipruritic properties. However, the mechanism of JWYA remains unclear.

Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department.

Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation.