Abstract Increasing energy expenditure and reducing energy intake are considered two classical methods to induce weight loss. Weight loss through physical methods instead of drugs has been a hot research topic nowadays, but how these methods function in adipose and cause weight loss in body remains unclear. In this study, we set up chronic cold exposure (CCE) and every other day fasting (EODF) as two distinct models in long-term treatment to induce weight loss. They showed their own characteristics in energy expenditure and metabolism. We demonstrated that CCE and EODF caused different types of thermogenic programs in white and brown adipose depots whether through Sympathetic Nervous System (SNS), Creatine-driven or FGF21-adiponectin axis. In this study, we further explained that thermogenic mechanisms function in adipose and metabolic benefits of the stable phenotype through physical treatments used for weight loss, providing more details for the study on weight-loss models.
We present a Human Artificial Intelligence Hybrid (HAIbrid) integrating framework that reweights Thyroid Imaging Reporting and Data System (TIRADS) features and the malignancy score predicted by a convolutional neural network (CNN) for nodule malignancy stratification and diagnosis. We defined extra ultrasonographical features from color Doppler images to explore malignancy-relevant features. We proposed Gated Attentional Factorization Machine (GAFM) to identify second-order interacting features trained via a 10 fold distribution-balanced stratified cross-validation scheme on ultrasound images of 3002 nodules all finally characterized by postoperative pathology (1270 malignant ones), retrospectively collected from 131 hospitals. Our GAFM-HAIbrid model demonstrated significant improvements in Area Under the Curve (AUC) value (p-value < 10−5), reaching about 0.92 over the standalone CNN (~0.87) and senior radiologists (~0.86), and identified a second-order vascularity localization and morphological pattern which was overlooked if only first-order features were considered. We validated the advantages of the integration framework on an already-trained commercial CNN system and our findings using an extra set of ultrasound images of 500 nodules. Our HAIbrid framework allows natural integration to clinical workflow for thyroid nodule malignancy risk stratification and diagnosis, and the proposed GAFM-HAIbrid model may help identify novel diagnosis-relevant second-order features beyond ultrasonography.
In recent years, the studies of the role of microRNAs in adipogenesis and adipocyte development and the corresponding molecular mechanisms have received great attention. In this work, we investigated the function of miR-140 in the process of adipogenesis and the molecular pathways involved, and we found that adipogenic treatment promoted the miR-140-5p RNA level in preadipocytes. Over-expression of miR-140-5p in preadipocytes accelerated lipogenesis along with adipogenic differentiation by transcriptional modulation of adipogenesis-linked genes. Meanwhile, silencing endogenous miR-140-5p dampened adipogenesis. Platelet-derived growth factor receptor alpha (PDGFRα) was shown to be a miR-140-5p target gene. miR-140-5p over-expression in preadipocyte 3T3-L1 diminished PDGFRα expression, but silencing of miR-140-5p augmented it. In addition, over-expression of PDGFRα suppressed adipogenic differentiation and lipogenesis, while its knockdown enhanced these biological processes of preadipocyte 3T3-L1. Altogether, our current findings reveal that miR-140-5p induces lipogenesis and adipogenic differentiation in 3T3-L1 cells by targeting PDGFRα, therefore regulating adipogenesis. Our research provides molecular targets and a theoretical basis for the treatment of obesity-related metabolic diseases.
The preoperative differentiation between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs) is of great significance for therapeutic decision-making. Deep learning (DL), an artificial intelligence algorithm based on neural networks, can help overcome inconsistencies in conventional ultrasonic (CUS) examination outcomes. Therefore, as an auxiliary diagnostic tool, DL can support accurate diagnosis using massive ultrasonic (US) images. This current study developed and validated a DL-based US diagnosis for the preoperative differentiation of BPGT from MPGT.A total of 266 patients, including 178 patients with BPGT and 88 patients with MPGT, were consecutively identified from a pathology database and enrolled in this study. Ultimately, considering the limitations of the DL model, 173 patients were selected from the 266 patients and divided into 2 groups: a training set, and a testing set. US images of the 173 patients were used to construct the training set (including 66 benign and 66 malignant PGTs) and testing set (consisting of 21 benign and 20 malignant PGTs). These were then preprocessed by normalizing the grayscale of each image and reducing noise. Processed images were imported into the DL model, which was then trained to predict the images from the testing set and evaluated for performance. Based on the training and validation datasets, the diagnostic performance of the 3 models was assessed and verified using receiver operating characteristic (ROC) curves. Ultimately, before and after combining the clinical data, we compared the area under the curve (AUC) and diagnostic accuracy of the DL model with the opinions of trained radiologists to evaluate the application value of the DL model in US diagnosis.The DL model showed a significantly higher AUC value compared to doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data (AUC =0.9583 vs. 0.6250, 0.7250, and 0.8025 respectively; all P<0.05). In addition, the sensitivity of the DL model was higher than the sensitivities of the doctors combined with clinical data (97.2% vs. 65%, 80%, and 90% for doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data, respectively; all P<0.05).The DL-based US imaging diagnostic model has excellent performance in differentiating BPGT from MPGT, supporting its value as a diagnostic tool for the clinical decision-making process.
Brief summary: This randomized, multicenter, controlled, phase II study compared the effects of high-dose (HD) once-weekly PEGylated-recombinant human growth hormone (PEG-rhGH) to low-dose (LD) and to an untreated control group of children with idiopathic short stature (ISS) over a period of 52 weeks. PEG-rhGH was effective in increasing height gain in a dose dependent manner with both doses being well tolerated during the observation period.
Abstract Background: As a newly discovered muscle factor secreted by skeletal muscle cells, irisin is a polypeptide fragment formed after hydrolysis by fibronectin type Ⅲ domain-containing protein 5 (FNDC5). Previous studies have shown that irisin has biological functions that promote beigeing of WAT, regulate glucose and lipid metabolism. However, the functions of irisin in muscle development and muscle fat metabolism remain unknown. Results: In order to study the expression of irisin in different growth stages of skeletal muscle, this study used SPF mice as experimental subjects to select skeletal muscle cells and muscle tissues of different developmental stages of mice. The expression of irisin precursor FNDC5 in different stages of cells and tissues was detected by western blotting and real-time fluorescent quantitative PCR, and the expression of FNDC5 in cells was detected by immunofluorescence. The results showed that FNDC5 was expressed in all stages of tissues and cells, but the expression was different at different stages. FNDC5 protein has the highest expression in muscle of sexually mature mice, followed by elderly mice and adolescent mice, and low expression in pups. Secondly, FNDC5 protein is mainly expressed in the cytoplasm and highest in muscle fibers. The myotubes were the second, and the lowest in C2C12 cells. Conclusions: This experiment can provide a theoretical basis for the subsequent study of irisin in skeletal muscle, and lay the foundation for targeted therapy of related diseases.
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