Effects of Exercise Intervention on Type 2 Diabetes Patients With Abdominal Obesity and Low Thigh Circumference (EXTEND): Study Protocol for a Randomized Controlled Trial.

Introduction: Type 2 diabetes patients have abdominal obesity and low thigh circumference. Previous studies have mainly focused on the role of exercise in reducing body weight and fat mass, improving glucose and lipid metabolism, with a lack of evaluation on the loss of muscle mass, diabetes complications, energy metabolism, and brain health. Moreover, whether the potential physiological benefit of exercise for diabetes mellitus is related to the modulation of the microbiota-gut-brain axis remains unclear. Multi-omics approaches and multidimensional evaluations may help systematically and comprehensively correlate physical exercise and the metabolic benefits. Methods and Analysis: This study is a randomized controlled clinical trial. A total of 100 sedentary patients with type 2 diabetes will be allocated to either an exercise or a control group in a 1:1 ratio. Participants in the exercise group will receive a 16-week combined aerobic and resistance exercise training, while those in the control group will maintain their sedentary lifestyle unchanged. Additionally, all participants will receive a diet administration to control the confounding effects of diet. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include body fat mass change rate (%), and changes in anthropometric indicators (body weight, waist, hip, and thigh circumference), clinical biochemical indicators (glycated hemoglobin, blood glucose, insulin sensitivity, blood lipid, liver enzyme, and renal function), brain health (appetite, mood, and cognitive function), immunologic function, metagenomics, metabolomics, energy expenditure, cardiopulmonary fitness, exercise-related indicators, fatty liver, cytokines (fibroblast growth factor 21, fibroblast growth factor 19, adiponectin, fatty acid-binding protein 4, and lipocalin 2), vascular endothelial function, autonomic nervous function, and glucose fluctuation. Discussion: This study will evaluate the effect of a 16-week combined aerobic and resistance exercise regimen on patients with diabetes. The results will provide a comprehensive evaluation of the physiological effects of exercise, and reveal the role of the microbiota-gut-brain axis in exercise-induced metabolic benefits to diabetes. Clinical Trial Registration: http://www.chictr.org.cn/searchproj.aspx, identifier ChiCTR2100046148.

Meta multi-task nuclei segmentation with fewer training samples.

Cells/nuclei deliver massive information of microenvironment. An automatic nuclei segmentation approach can reduce pathologists' workload and allow precise of the microenvironment for biological and clinical researches. Existing deep learning models have achieved outstanding performance under the supervision of a large amount of labeled data. However, when data from the unseen domain comes, we still have to prepare a certain degree of manual annotations for training for each domain. Unfortunately, obtaining histopathological annotations is extremely difficult. It is high expertise-dependent and time-consuming. In this paper, we attempt to build a generalized nuclei segmentation model with less data dependency and more generalizability. To this end, we propose a meta multi-task learning (Meta-MTL) model for nuclei segmentation which requires fewer training samples. A model-agnostic meta-learning is applied as the outer optimization algorithm for the segmentation model. We introduce a contour-aware multi-task learning model as the inner model. A feature fusion and interaction block (FFIB) is proposed to allow feature communication across both tasks. Extensive experiments prove that our proposed Meta-MTL model can improve the model generalization and obtain a comparable performance with state-of-the-art models with fewer training samples. Our model can also perform fast adaptation on the unseen domain with only a few manual annotations. Code is available at https://github.com/ChuHan89/Meta-MTL4NucleiSegmentation.

Development and validation of a CT-based radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma.

OBJECTIVES: To develop and validate a radiomics nomogram for preoperative prediction of tumor histologic grade in gastric adenocarcinoma (GA). METHODS: This retrospective study enrolled 592 patients with clinicopathologically confirmed GA (low-grade: n=154; high-grade: n=438) from January 2008 to March 2018 who were divided into training (n=450) and validation (n=142) sets according to the time of computed tomography (CT) examination. Radiomic features were extracted from the portal venous phase CT images. The Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression model were used for feature selection, data dimension reduction and radiomics signature construction. Multivariable logistic regression analysis was applied to develop the prediction model. The radiomics signature and independent clinicopathologic risk factors were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration and discrimination. RESULTS: A radiomics signature containing 12 selected features was significantly associated with the histologic grade of GA (P<0.001 for both training and validation sets). A nomogram including the radiomics signature and tumor location as predictors was developed. The model showed both good calibration and good discrimination, in which C-index in the training set, 0.752 [95% confidence interval (95% CI): 0.701-0.803]; C-index in the validation set, 0.793 (95% CI: 0.711-0.874). CONCLUSIONS: This study developed a radiomics nomogram that incorporates tumor location and radiomics signatures, which can be useful in facilitating preoperative individualized prediction of histologic grade of GA.

A preoperative radiomics model for the identification of lymph node metastasis in patients with early-stage cervical squamous cell carcinoma.

OBJECTIVES: To develop and validate a radiomics model for preoperative identification of lymph node metastasis (LNM) in patients with early-stage cervical squamous cell carcinoma (CSCC). METHODS: Total of 190 eligible patients were randomly divided into training (n = 100) and validation (n = 90) cohorts. Handcrafted features and deep-learning features were extracted from T2W fat suppression images. The minimum redundancy maximum relevance algorithm and LASSO regression with 10-fold cross-validation were used for key features selection. A radiomics model that incorporated the handcrafted-signature, deep-signature, and squamous cell carcinoma antigen (SCC-Ag) levels was developed by logistic regression. The model performance was assessed and validated with respect to its calibration, discrimination, and clinical usefulness. RESULTS: Three handcrafted features and three deep-learning features were selected and used to build handcrafted- and deep-signature. The model, which incorporated the handcrafted-signature, deep-signature, and SCC-Ag, showed satisfactory calibration and discrimination in the training cohort (AUC: 0.852, 95% CI: 0.761-0.943) and the validation cohort (AUC: 0.815, 95% CI: 0.711-0.919). Decision curve analysis indicated the clinical usefulness of the radiomics model. The radiomics model yielded greater AUCs than either the radiomics signature (AUC = 0.806 and 0.779, respectively) or the SCC-Ag (AUC = 0.735 and 0.688, respectively) alone in both the training and validation cohorts. CONCLUSION: The presented radiomics model can be used for preoperative identification of LNM in patients with early-stage CSCC. Its performance outperforms that of SCC-Ag level analysis alone. ADVANCES IN KNOWLEDGE: A radiomics model incorporated radiomics signature and SCC-Ag levels demonstrated good performance in identifying LNM in patients with early-stage CSCC.