Risk factor analysis and nomogram for predicting gastroparesis in patients with type 2 diabetes mellitus.

Purpose: The incidence of gastroparesis is higher in individuals diagnosed with type 2 diabetes mellitus (T2DM) compared to the healthy individuals. Our study aimed to explore the risk factors for gastroparesis in T2DM and to establish a clinical prediction model (nomogram). Methods: Our study enlisted 694 patients with T2DM from two medical centers over a period of time. From January 2020 to December 2022, 347 and 149 patients were recruited from the Beilun branch of Zhejiang University's First Affiliated Hospital in the training and internal validation cohorts, respectively. The external validation cohort consisted of 198 patients who were enrolled at Nanchang University's First Affiliated Hospital from October 2020 to September 2021. We conducted univariate and multivariate logistic regression analyses to select the risk factors for gastroparesis in patients with T2DM; subsequently,we developed a nomogram model. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis(DCA). Results: Four clinical variables, including age, regular exercise, glycated hemoglobin level(HbA1c), and Helicobacter pylori (H. pylori) infection, were identified and included in the model. The model demonstrated excellent discrimination, with an AUC of 0.951 (95% CI = 0.925-0.978) in the training group, and 0.910 (95% CI = 0.859-0.961) and 0.875 (95% CI = 0.813-0.937) in the internal and external validation groups, respectively. The calibration curve showed good consistency between prediction of the model and observed gastroparesis. The DCA also demonstrated good clinical efficacy. Conclusion: The nomogram model developed in this study showed good performance in predicting gastroparesis in patients with T2DM.

Bioinspired Multifunctional Self-Sensing Actuated Gradient Hydrogel for Soft-Hard Robot Remote Interaction.

The development of bioinspired gradient hydrogels with self-sensing actuated capabilities for remote interaction with soft-hard robots remains a challenging endeavor. Here, we propose a novel multifunctional self-sensing actuated gradient hydrogel that combines ultrafast actuation and high sensitivity for remote interaction with robotic hand. The gradient network structure, achieved through a wettability difference method involving the rapid precipitation of MoO2 nanosheets, introduces hydrophilic disparities between two sides within hydrogel. This distinctive approach bestows the hydrogel with ultrafast thermo-responsive actuation (21° s-1) and enhanced photothermal efficiency (increase by 3.7 °C s-1 under 808 nm near-infrared). Moreover, the local cross-linking of sodium alginate with Ca2+ endows the hydrogel with programmable deformability and information display capabilities. Additionally, the hydrogel exhibits high sensitivity (gauge factor 3.94 within a wide strain range of 600%), fast response times (140 ms) and good cycling stability. Leveraging these exceptional properties, we incorporate the hydrogel into various soft actuators, including soft gripper, artificial iris, and bioinspired jellyfish, as well as wearable electronics capable of precise human motion and physiological signal detection. Furthermore, through the synergistic combination of remarkable actuation and sensitivity, we realize a self-sensing touch bioinspired tongue. Notably, by employing quantitative analysis of actuation-sensing, we realize remote interaction between soft-hard robot via the Internet of Things. The multifunctional self-sensing actuated gradient hydrogel presented in this study provides a new insight for advanced somatosensory materials, self-feedback intelligent soft robots and human-machine interactions.

Development and Validation of a Risk Nomogram Model for Predicting Constipation in Patients with Type 2 Diabetes Mellitus.

Purpose: Constipation is a common complication of diabetic patients, which has a negative impact on their own health. This study aims to establish and internally validate the risk nomogram of constipation in patients with type 2 diabetes mellitus (T2DM) and to test its predictive ability. Patients and Methods: This retrospective study included 746 patients with T2DM at two medical centers. Among the 746 patients with T2DM, 382 and 163 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University were enrolled in the training cohort and the validation cohort, respectively. A total of 201 patients in the First Affiliated Hospital of Nanchang University were enrolled in external validation cohorts. The nomogram was established by optimizing the predictive factors through univariate and multivariable logistic regression analysis. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA). Furthermore, its applicability was internally and independently validated. Results: Among the 16 clinicopathological features, five variables were selected to develop the prediction nomogram, including age, glycated hemoglobin (HbA1c), calcium, anxiety, and regular exercise. The nomogram revealed good discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.908 (95% CI = 0.865-0.950) in the training cohort, and 0.867 (95% CI = 0.790-0.944) and 0.816 (95% CI = 0.751-0.881) in the internal and external validation cohorts, respectively. The calibration curve presented a good agreement between the prediction by the nomogram and the actual observation. The DCA revealed that the nomogram had a high clinical application value. Conclusion: In this study, the nomogram for pretreatment risk management of constipation in patients with T2DM was developed which could help in making timely personalized clinical decisions for different risk populations.

Recombinant human metallothionein-III alleviates oxidative damage induced by copper and cadmium in Caenorhabditis elegans.

Recombinant human metallothionein III (rh-MT-III) is a genetically engineered product produced by Escherichia coli fermentation technology. Its molecules contain abundant reducing sulfhydryl groups, which possess the ability to bind heavy metal ions. The present study was to evaluate the binding effects of rh-MT-III against copper and cadmium in vitro and to investigate the antioxidant activity of rh-MT-III using Caenorhabditis elegans in vivo. For in vitro experiments, the binding rates of copper and cadmium were 91.4% and 97.3% for rh-MT-III at a dosage of 200 µg/mL at 10 h, respectively. For in vivo assays, the oxidative stress induced by copper (CuSO4 , 10 µg/mL) and cadmium (CdCl2 , 10 µg/mL) was significantly reduced after 72 h of exposure to different doses of rh-MT-III (5-500 µg/mL), indicated by restoring locomotion behavior and growth, and reducing malondialdehyde and reactive oxygen species levels in C. elegans. Moreover, rh-MT-III decreased the deposition of lipofuscin and fat content, which could delay the progression of aging. In addition, rh-MT-III (500 µg/mL) promoted the up-regulation of Mtl-1 and Mtl-2 gene expression in C. elegans, which could enhance the resistance to oxidative stress by increasing the enzymatic activity of antioxidant defense system and scavenging free radicals. The results indicated that supplemental rh-MT-III could effectively protect C. elegans from heavy metal stress, providing an experimental basis for the future application and development of rh-MT-III.

A Nomogram for Predicting Risk of Thromboembolism in Gastric Cancer Patients Receiving Chemotherapy.

Purpose: The aims of this study were to develop and validate a novel nomogram to predict thromboembolism (TE) in gastric cancer (GC) patients receiving chemotherapy and to test its predictive ability. Methods: This retrospective study included 544 GC patients who received chemotherapy as the initial treatment at two medical centers. Among the 544 GC patients who received chemotherapy, 275 and 137 patients in the First Affiliated Hospital of Nanchang University from January 2014 to March 2019 were enrolled in the training cohort and the validation cohort, respectively. A total of 132 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University from January 2015 to August 2019 were enrolled in external validation cohorts. The nomogram was based on parameters determined by univariate and multivariate logistic analyses. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis (DCA). The applicability of the nomogram was internally and independently validated. Results: The predictors included the Eastern Cooperative Oncology Group Performance Status (ECOG), presence of an active cancer (AC), central venous catheter (CVC), and D-dimer levels. These risk factors are shown on the nomogram and verified. The nomogram demonstrated good discrimination and fine calibration with an AUROC of 0.875 (0.832 in internal validation and 0.807 in independent validation). The DCA revealed that the nomogram had a high clinical application value. Conclusions: We propose the nomogram for predicting TE in patients with GC receiving chemotherapy, which can help in making timely personalized clinical decisions for different risk populations.

Nomogram Model for Prediction of Portal Vein Thrombosis in Patients with Liver Cirrhosis After Splenectomy: A Retrospective Analysis of 2 Independent Cohorts.

BACKGROUND The aim of this study was to establish and validate an easy-to-use nomogram to predict portal vein thrombosis (PVT) in patients with cirrhosis after splenectomy and to test its predictive ability. MATERIAL AND METHODS This retrospective study included 315 patients with cirrhosis who underwent splenectomy at 2 high-volume medical centers. The least absolute shrinkage and selection operator (LASSO) regression method was used to select the predictors in the training cohort, and multivariable logistic regression analysis was performed to establish the predictive nomogram model. We determined the prediction value of the nomogram by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis. Finally, the applicability of the nomogram was internally and independently validated. RESULTS The predictors of PVT included portal vein diameter, splenic vein diameter, body mass index, and platelet count. Based on the clinical and radiomic models, the nomogram had good predictive efficiency for predicting PVT in patients with cirrhosis after splenectomy, with an AUROC of 0.887 (0.856 in internal validation and 0.796 in independent validation). The decision curve analysis revealed that the nomogram had good clinical application value. CONCLUSIONS We successfully developed an easy-to-use nomogram to predict the probability of PVT in patients with cirrhosis after splenectomy. The nomogram can help clinicians make timely, individualized clinical decisions for PVT in patients with cirrhosis after splenectomy.

A Simple-to-Use Nomogram for Predicting the Survival of Early Hepatocellular Carcinoma Patients.

Objective: This study aimed to develop and validate a simple-to-use nomogram for early hepatocellular carcinoma (HCC) patients undergoing a preoperative consultation and doctors conducting a postoperative evaluation. Methods: A total of 2,225 HCC patients confirmed with stage I or II were selected from the Surveillance, Epidemiology, and End Results database between January 2010 and December 2015. The patients were randomly divided into two groups: a training group (n = 1,557) and a validation group (n = 668). Univariate and multivariate hazards regression analyses were used to identify independent prognostic factors. The Akaike information criterion (AIC) was used to select variables for the nomogram. The performance of the nomogram was validated concerning its ability of discrimination and calibration and its clinical utility. Results: Age, alpha-fetoprotein (AFP), race, the degree of differentiation, and therapy method were significantly associated with the prognosis of early HCC patients. Based on the AIC results, five variables (age, race, AFP, degree of differentiation, and therapy method) were incorporated into the nomogram. The concordance indexes of the simple nomogram in the training and validation groups were 0.707 (95% CI: 0.683-0.731) and 0.733 (95% CI: 0.699-0.767), respectively. The areas under the receiver operating characteristic (ROC) curve of the nomogram in the training and validation groups were 0.744 and 0.764, respectively, for predicting 3-year survival, and 0.786 and 0.794, respectively, for predicting 5-year survival. Calibration plots showed good consistency between the predictions of the nomogram and the actual observations in both the training and validation groups. Decision curve analysis (DCA) showed that the simple nomogram was clinically useful, and the overall survival significantly differed between low- and high-risk groups divided by the median score of the nomogram in the training group (P < 0.001). Conclusion: A simple-to-use nomogram based on a large population-based study is developed and validated, which is a conventional tool for doctors to facilitate the individual consultation of preoperative patients and the postoperative personalized evaluation.

Pretreatment risk management of a novel nomogram model for prediction of thoracoabdominal extrahepatic metastasis in primary hepatic carcinoma.

BACKGROUND: Extrahepatic metastasis is the independent risk factor of poor survival of primary hepatic carcinoma (PHC), and most occurs in the chest and abdomen. Currently, there is still no available method to predict thoracoabdominal extrahepatic metastasis in PHC. In this study, a novel nomogram model was developed and validated for prediction of thoracoabdominal extrahepatic metastasis in PHC, thereby conducted individualized risk management for pretreatment different risk population. METHODS: The nomogram model was developed in a primary study that consisted of 330 consecutive pretreatment patients with PHC. Large-scale datasets were extracted from clinical practice. The nomogram was based on the predictors optimized by data dimension reduction through Lasso regression. The prediction performance was measured by the area under the receiver operating characteristic (AUROC), and calibrated to decrease the overfit bias. Individualized risk management was conducted by weighing the net benefit of different risk population via decision curve analysis. The prediction performance was internally and independently validated, respectively. An independent-validation study using a separate set of 107 consecutive patients. RESULTS: Four predictors from 55 high-dimensional clinical datasets, including size, portal vein tumor thrombus, infection, and carbohydrate antigen 125, were incorporated to develop a nomogram model. The nomogram demonstrated valuable prediction performance with AUROC of 0.830 (0.803 in internal-validation, and 0.773 in independent-validation, respectively), and fine calibration. Individual risk probability was visually scored. Weighing the net benefit, threshold probability was classified for three-independent risk population, which was < 19.9%, 19.9-71.8% and > 71.8%, respectively. According to this classification, pretreatment risk management was based on a treatment-flowchart for individualized clinical decision-making. CONCLUSIONS: The proposed nomogram is a useful tool for pretreatment risk management of thoracoabdominal extrahepatic metastasis in PHC for the first time, and may handily facilitate timely individualized clinical decision-making for different risk population.

MicroRNAs as potential biomarkers for diagnosis, therapy and prognosis of gastric cancer.

Despite the widespread use of endoscopy and conventional tumor biomarkers, gastric cancer (GC) remains one of the most frequent causes of cancer-related deaths worldwide due to its late diagnosis and poor response to treatment. Valuable and practical biomarkers are urgently needed to screen patients with a high risk of GC that can complement endoscopic diagnosis. Such biomarkers will enable the efficient prediction of therapeutic response and prognosis of GC patients and favor the establishment of an effective treatment strategy for each and every patient. MicroRNAs (miRNAs) are a class of small non-coding RNA sequences that play important roles in modulating key biological processes by regulating the expression of target genes. Expectedly, miRNAs are abnormally expressed within the tumor tissue and in associated biological fluids of GC patients including their blood, gastric juice, and urine. Accumulating evidence indicates that miRNAs are potential biomarkers with multiple diagnostic functions for GC. Here, we review recent advances and challenges in using miRNAs, particularly biofluid miRNAs, as GC biomarkers with potential clinical applications including diagnosing, clinically staging, and predicting malignant behaviors, therapy response, recurrence after surgery and survival time.