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The endoplasmic reticulum (ER) engages mitochondria at specialized ER domains known as mitochondria-associated membranes (MAMs). Here, we used three-dimensional high-resolution imaging to investigate the formation of pleomorphic "megamitochondria" with altered MAMs in brown adipocytes lacking the Sel1L-Hrd1 protein complex of ER-associated protein degradation (ERAD). Mice with ERAD deficiency in brown adipocytes were cold sensitive and exhibited mitochondrial dysfunction. ERAD deficiency affected ER-mitochondria contacts and mitochondrial dynamics, at least in part, by regulating the turnover of the MAM protein, sigma receptor 1 (SigmaR1). Thus, our study provides molecular insights into ER-mitochondrial cross-talk and expands our understanding of the physiological importance of Sel1L-Hrd1 ERAD.

Background: Cardiovascular complications are major concerns for Chinese patients with type 2 diabetes. Accurately predicting these risks remains challenging due to limitations in traditional risk models. We aimed to develop a dynamic prediction model using machine learning and longitudinal trajectories of cardiovascular risk factors to improve prediction accuracy. Methods: We included 16,378 patients from the Kailuan cohort, splitting them into training and testing datasets. Using baseline characteristics and changes over a four-year observation period, we developed the ML-CVD-C (Machine Learning Cardiovascular Disease in Chinese) score to predict 10-year cardiovascular risk, including cardiovascular death, nonfatal myocardial infarction, and stroke. We compared the discrimination and calibration of ML-CVD-C with models using only baseline variables (ML-CVD-C [base]), China-PAR (Prediction for ASCVD Risk in China), and PREVENT (Predict Risk of cardiovascular disease EVENTs). Risk stratification improvements were assessed through net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Transition analysis examined the changes in risk stratification over time. Results: The ML-CVD-C score achieved a C-index of 0.80 (95% CI: 0.78–0.82) in the testing cohort, significantly outperforming the ML-CVD-C (base) score, China-PAR, and PREVENT, which had C-index values of 0.62–0.65. ML-CVD-C also provided more accurate cardiovascular risk estimates, though all models tended to overestimate the prevalence of high-risk cases. Stratification by the ML-CVD-C score showed substantial improvement, with NRI gains of 57.7%, 44.1%, and 47.3%, and IDI gains of 10.1%, 7.9%, and 8.4% compared to the other three scores. Both the trajectory and machine learning algorithm contributed significantly to the enhancement of model performance. Transition analysis revealed that participants who remained in the same risk category or were reclassified to a lower category exhibited 22% and 86% reductions in cardiovascular risk compared to those reclassified to a higher risk category during the observation period. Conclusions: The ML-CVD-C model, incorporating dynamic cardiovascular risk trajectories and a machine learning algorithm, significantly improves risk prediction accuracy for Chinese patients with diabetes. This model may serve as a valuable tool for more personalized cardiovascular risk management in type 2 diabetes. [ABSTRACT FROM AUTHOR]

AbstractBackgroundMethodsResultsConclusionLong non-coding RNAs (lncRNAs) are emerging regulators of pathophysiological processes in a variety of diseases including asthma. In this study, we reported the identification of lncRNA-AK007111 as an essential modulator of mast cell apoptosis and investigated its potential mechanism.RNA-seq profiling and transcriptome sequencing technology were adopted to screen for differentially expressed genes. Transfection was done by small interfering RNAs (siRNAs) to down-regulate lncRNA-AK007111 and Modulator of apoptosis 1 (MOAP1). Starvation was used to induce apoptosis. The apoptotic rate was measured by flow cytometry. Western Blot was conducted to detect the expression of apoptosis-related proteins.LncRNA-AK007111 was highly expressed in IgE/Ag-mediated activation of mast cells. Down-regulation of LncRNA-AK007111 promoted apoptosis of mast cells. Down-regulation of MOAP1 attenuated apoptosis in mast cells induced by the down-regulation of lncRNA-AK007111.LncRNA-AK007111 may be a potential regulator of mast cell apoptosis by interaction with MOAP1. [ABSTRACT FROM AUTHOR]

Deep language models (DLMs) have exhibited remarkable language understanding and generation capabilities, prompting researchers to explore the similarities between their internal mechanisms and human language cognitive processing. This study investigated the representational similarity (RS) between the abstractive summarization (ABS) models and the human brain and its correlation to the performance of ABS tasks. Specifically, representational similarity analysis (RSA) was used to measure the similarity between the representational patterns (RPs) of the BART, PEGASUS, and T5 models' hidden layers and the human brain's language RPs under different spatiotemporal conditions. Layer-wise ablation manipulation, including attention ablation and noise addition was employed to examine the hidden layers' effect on model performance. The results demonstrate that as the depth of hidden layers increases, the models' text encoding becomes increasingly similar to the human brain's language RPs. Manipulating deeper layers leads to more substantial decline in summarization performance compared to shallower layers, highlighting the crucial role of deeper layers in integrating essential information. Notably, the study confirms the hypothesis that the hidden layers exhibiting higher similarity to human brain activity play a more critical role in model performance, with their correlations reaching statistical significance even after controlling for perplexity. These findings deepen our understanding of the cognitive mechanisms underlying language representations in DLMs and their neural correlates, potentially providing insights for optimizing and improving language models by aligning them with the human brain's language-processing mechanisms. [ABSTRACT FROM AUTHOR]

In today's society, with the rapid development of artificial intelligence, fostering students' critical thinking is especially important for nurturing their innovative capabilities. The "National English Curriculum Standards for General High School (2017 edition, revised in 2020)" outlines that the core competencies in English education encompass four dimensions: linguistic competence, cultural awareness, quality of thinking, and learning ability. Among these, the quality of thinking is primarily reflected in the logic, criticality, and creativity of thought. Critical thinking not only enhances students' language skills but also plays a crucial role in mental growth and the cultivation of independent thinking. It encourages students to go beyond the surface meaning of texts, delving into the analysis of the structure, argumentation, author's intent, and implicit meanings of articles. Whether for further education, employment, or personal development, critical thinking is an indispensable skill. This paper emphasizes the importance of cultivating critical thinking within the context of adolescent life themes, providing a new perspective for high school English teaching practices. It holds significant importance for promoting the comprehensive development of students' competencies in a globalized context. [ABSTRACT FROM AUTHOR]

Objective: Gain‐of‐function (GOF) variants of KCNJ11 cause neonate diabetes and maturity‐onset diabetes of the young (KCNJ11‐MODY), while loss‐of‐function (LOF) variants lead to hyperinsulinemia hypoglycemia and subsequent diabetes. Given the limited research of KCNJ11‐MODY, we aimed to analyse its phenotypic features and prevalence in Chinese patients with early‐onset type 2 diabetes (EOD). Design, Patients and Measurements: We performed next‐generation sequencing on 679 Chinese EOD patients to screen for KCNJ11 exons variants. Bioinformatics prediction and the American College of Medical Genetics and Genomics guidelines was used to determine the pathogenicity and diagnosed KCNJ11‐MODY. A literature review was conducted to investigate the phenotypic features of KCNJ11‐MODY. Results: We identified six predicted deleterious rare variants in six EOD patients (0.88%). They were classified as uncertain significance (variant of uncertain significance [VUS]), but more common in this EOD cohort than a general Chinese population database, however, without significant difference (53/10,588, 0.50%) (p =.268). Among 80 previously reported patients with KCNJ11‐MODY, 23.8% (19/80) carried 9 (32.1%) LOF variants, who had significantly older age at diagnosis, higher birthweight and higher fasting C‐peptide compared to patients with GOF variants. Many patients carrying VUS were not correctly diagnosed. Conclusions: Some rare variants of KCNJ11 might contribute to the development of Chinese EOD, although available evidence has not enough power to support them as cause of KCNJ11‐MODY. The clinical features of LOF variants were different from GOF variants in KCNJ11‐MODY patients. It is necessary to evaluate the pathogenicity of VUS through function experiments. [ABSTRACT FROM AUTHOR]

Context We propose an artificial-pancreas-like algorithm (AP-A) that could automatically determine the preprandial insulin dose based on intermittently scanned continuous glucose monitoring (isCGM) data trajectories in multiple dose injection (MDI) therapy. Objective We aim to determine whether preprandial insulin dose adjustments guided by the AP-A are as effective and safe as physician decisions. Methods We performed a randomized, single-blind, clinical trial at a tertiary, referral hospital in Beijing, China. Type 2 diabetes participants were eligible if they were aged 18 years or older, with a glycated hemoglobin A1c of 8.0% or higher. Eligible participants were randomly assigned (1:1) to the AP-A arm supervised by physician and the conventional physician treatment arm. The primary objective was to compare percentage time spent with sensor glucose level in 3.9 to 10.0 mmol/L (TIR) between the 2 study arms. Safety was assessed by the percentage time spent with sensor glucose level below 3.0 mmol/L (TBR). Results A total of 140 participants were screened, of whom 119 were randomly assigned to the AP-A arm (n = 59) or physician arm (n = 60). The TIR achieved by the AP-A arm was statistically noninferior compared with the control arm (72.4% [63.3%-82.1%] vs 71.2% [54.9%-81.4%]), with a median difference of 1.33% (95% CI, −6.00 to 10.94, noninferiority margin −7.5%). TBR was also statistically noninferior between the AP-A and control arms (0.0% [0.0%-0.0%] vs 0.0% [0.0%-0.0%]), respectively; median difference (95% CI, 0.00% [0.00%-0.00%], noninferiority margin 2.0%). Conclusion The AP-A–supported physician titration of preprandial insulin dosage offers noninferior glycemic control compared with optimal physician care in type 2 diabetes. [ABSTRACT FROM AUTHOR]

Pandemic-related sleep disorders affect human physical and mental health. The artificial intelligence (AI) based sleep staging with multimodal electrophysiological signals help people diagnose and treat sleep disorders. However, the existing AI-based methods could not capture more discriminative modalities and adaptively correlate these multimodal features. This paper introduces a multimodal attention network (MMASleepNet) to efficiently extract, perceive and fuse multimodal features of electrophysiological signals. The MMASleepNet has a multi-branch feature extraction (MBFE) module followed by an attention-based feature fusing (AFF) module. In the MBFE module, branches are designed to extract multimodal signals' temporal and spectral features. Each branch has two-stream convolutional networks with a unique kernel to perceive features of different time scales. The AFF module contains a modal-wise squeeze and excitation (SE) block to adjust the weights of modalities with more discriminative features and a Transformer encoder (TE) to generate attention matrices and extract the inter-dependencies among multimodal features. Our MMASleepNet outperforms state-of-the-art models in terms of different evaluation matrices on the datasets of Sleep-EDF and ISRUC-Sleep. The implementation code is available at: https://github.com/buptantEEG/MMASleepNet/.