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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.

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/.

Background: The association between adrenal size and metabolic profiles in patients with diabetes mellitus (DM) is unclear. This study was conducted to determine whether the adrenal thickness measured by computed tomography (CT) is correlated with the metabolic profiles of patients with DM. Methods: This was a cross‐sectional study including 588 Chinese hospitalized patients with DM without comorbidities or medications known to affect adrenal morphology or hormone secretion. Adrenal limb thickness was measured on unenhanced chest CT. Participants were stratified into tertiles according to their total adrenal limb thickness. Linear and logistic regression models were used to estimate the correlations. Results: After adjustment for sex and age, the adrenal thickness was positively associated with body mass index (BMI), waist circumference (WC), urinary albumin/creatinine ratio, and 24‐h urinary free cortisol (UFC) and negatively correlated with high‐density lipoprotein cholesterol. The sequential equation model (SEM) suggested UFC partially mediated the effect of adrenal limb thickness on WC by 12%. Adrenal thickness, but not UFC, was associated with a higher risk of existing hypertension (odds ratio [OR] = 3.78, 95% confidence interval [CI] 1.58, 9.02) and hyperlipidemia (OR = 2.76, 95% CI 1.03, 7.38), independent of age, gender, BMI, and WC. Conclusions: The adrenal thickness is independently associated with BMI, WC, cortisol levels, urinary albumin/creatinine ratio, hypertension, and dyslipidemia but not glycemic parameters in patients with diabetes. Our study encourages further studies to investigate the role of adrenal physiology in patients with diabetes. [ABSTRACT FROM AUTHOR]

Aim: To investigate whether stratifying participants with prediabetes according to their diabetes progression risks (PR) could affect their responses to interventions. Methods: We developed a machine learning‐based model to predict the 1‐year diabetes PR (ML‐PR) with the least predictors. The model was developed and internally validated in participants with prediabetes in the Pinggu Study (a prospective population‐based survey in suburban Beijing; n = 622). Patients from the Beijing Prediabetes Reversion Program cohort (a multicentre randomized control trial to evaluate the efficacy of lifestyle and/or pioglitazone on prediabetes reversion; n = 1936) were stratified to low‐, medium‐ and high‐risk groups using ML‐PR. Different effect of four interventions within subgroups on prediabetes reversal and diabetes progression was assessed. Results: Using least predictors including fasting plasma glucose, 2‐h postprandial glucose after 75 g glucose administration, glycated haemoglobin, high‐density lipoprotein cholesterol and triglycerides, and the ML algorithm XGBoost, ML‐PR successfully predicted the 1‐year progression of participants with prediabetes in the Pinggu study [internal area under the curve of the receiver operating characteristic curve 0.80 (0.72–0.89)] and Beijing Prediabetes Reversion Program [external area under the curve of the receiver operating characteristic curve 0.80 (0.74–0.86)]. In the high‐risk group pioglitazone plus intensive lifestyle therapy significantly reduced diabetes progression by about 50% at year l and the end of the trial in the high‐risk group compared with conventional lifestyle therapy with placebo. In the medium‐ or low‐risk group, intensified lifestyle therapy, pioglitazone or their combination did not show any benefit on diabetes progression and prediabetes reversion. Conclusions: This study suggests personalized treatment for prediabetes according to their PR is necessary. ML‐PR model with simple clinical variables may facilitate personal treatment strategies in participants with prediabetes. [ABSTRACT FROM AUTHOR]