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Background and Aim: Risk assessment is of paramount importance for the detection and treatment of colorectal cancer. We developed and validated a feature interpretability screening framework to identify high‐risk populations and recommend colonoscopy for them. Methods: We utilized a training cohort consisting of 1 252 605 participants who underwent colonoscopies in Shanghai from 2013 to 2015 to develop the screening framework. We incorporated Shapley additive explanation values into feature selection to provide interpretability for the framework. Two sampling methods were separately employed to mitigate potential model bias caused by class imbalance. Furthermore, we employed various machine learning algorithms to construct risk assessment models and compared their performance. We tested the screening models on an external validation cohort of 359 462 samples and conducted comprehensive evaluation and statistical analysis of the validation results. Results: The external validation results demonstrated that the models in the proposed framework achieved sensitivity over 0.734, specificity over 0.790, and area under the receiver operating characteristic curve ranging from 0.808 to 0.859. In the predictions of the best‐performing model, the prevalence rates of colorectal cancer were 0.059% and 1.056% in the low‐ and high‐risk groups, respectively. If colonoscopies were performed only on the high‐risk group predicted by the model, only 14.36% of total colonoscopies would be needed to detect 74.86% of colorectal cancer cases. Conclusions: We developed and validated a novel framework to identify populations at high risk for colorectal cancer. Those classified as high risk should undergo colonoscopy for further diagnosis. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, characterized by enduring hyperglycemia, precipitates oxidative stress, engendering a spectrum of complications, notably increased bone vulnerability. The genesis of reactive oxygen species (ROS), a byproduct of oxygen metabolism, instigates oxidative detriment and impairs bone metabolism in diabetic conditions. This review delves into the mechanisms of ROS generation and its impact on bone homeostasis within the context of diabetes. Furthermore, the review summarizes the cutting-edge progress in the development of ROS-neutralizing biomaterials tailored for the amelioration of diabetic osteopathy. These biomaterials are engineered to modulate ROS dynamics, thereby mitigating inflammatory responses and facilitating bone repair. Additionally, the challenges and therapeutic prospects of ROS-targeted biomaterials in clinical application of diabetic bone disease treatment is addressed. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to establish a radiomics model based on 18F-FDG PET/CT images to predict visceral pleural invasion (VPI) of solid lung adenocarcinoma preoperatively. Methods: We retrospectively enrolled 165 solid lung adenocarcinoma patients confirmed by histopathology with 18F-FDG PET/CT images. Patients were divided into training and validation at a ratio of 0.7. To find significant VPI predictors, we collected clinicopathological information and metabolic parameters measured from PET/CT images. Three-dimensional (3D) radiomics features were extracted from each PET and CT volume of interest (VOI). Receiver operating characteristic (ROC) curve was performed to determine the performance of the model. Accuracy, sensitivity, specificity and area under curve (AUC) were calculated. Finally, their performance was evaluated by concordance index (C-index) and decision curve analysis (DCA) in training and validation cohorts. Results: 165 patients were divided into training cohort (n = 116) and validation cohort (n = 49). Multivariate analysis showed that histology grade, maximum standardized uptake value (SUVmax), distance from the lesion to the pleura (DLP) and the radiomics features had statistically significant differences between patients with and without VPI (P < 0.05). A nomogram was developed based on the logistic regression method. The accuracy of ROC curve analysis of this model was 75.86% in the training cohort (AUC: 0.867; C-index: 0.867; sensitivity: 0.694; specificity: 0.889) and the accuracy rate in validation cohort was 71.55% (AUC: 0.889; C-index: 0.819; sensitivity: 0.654; specificity: 0.739). Conclusions: A PET/CT-based radiomics model was developed with SUVmax, histology grade, DLP, and radiomics features. It can be easily used for individualized VPI prediction. [ABSTRACT FROM AUTHOR]

Nd:GdVO4 has the advantages of a small thermal expansion coefficient, a large absorption bandwidth, and a large absorption and emission cross section, making it a good gain material for LED-pumping without active temperature control. In this paper, the LED-pumped Nd:GdVO4 laser was first generated in the quasi-continuous-wave and the passively Q-switched regime. With the incident pump energy of 8.0 mJ, the Nd:GdVO4 laser demonstrated energy of 195 μJ at 1063 nm in the quasi-continuous-wave regime, the slope efficiency was 4.7%, and the optical conversion efficiency was 2.4%. In the passively Q-switched regime, Cr:YAG was used as a saturable absorber and the energy obtained was 6.53 μJ with a pulse duration of 806.8 ns. These experimental results indicated the application prospect of LED-pumped lasers. [ABSTRACT FROM AUTHOR]

Objective: In this study, based on PET/CT radiomics features, we developed and validated a nomogram to predict progression-free survival (PFS) for cases with diffuse large B cell lymphoma (DLBCL) treated with immunochemotherapy. Methods: This study retrospectively recruited 129 cases with DLBCL. Among them, PET/CT scans were conducted and baseline images were collected for radiomics features along with their clinicopathological features. Radiomics features related to recurrence were screened for survival analysis using univariate Cox regression analysis with p < 0.05. Next, a weighted Radiomics-score (Rad-score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Furthermore, the nomogram was tested for their ability to predict PFS using time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Blood platelet, Rad-score, and gender were included in the nomogram as independent DLBCL risk factors for PFS. We found that the training cohort areas under the curve (AUCs) were 0.79, 0.84, and 0.88, and validation cohort AUCs were 0.67, 0.83, and 0.72, respectively. Further, the DCA and calibration curves confirmed the predictive nomogram's clinical relevance. Conclusion: Using Rad-score, blood platelet, and gender of the DLBCL patients, a PET/CT radiomics-based nomogram was developed to guide cases' recurrence risk assessment prior to treatment. The developed nomogram can help provide more appropriate treatment plans to the cases. Key Points: • DLBCL cases can be classified into low- and high-risk groups using PET/CT radiomics based Rad-score. • When combined with other clinical characteristics (gender and blood platelet count), Rad-score can be used to predict the outcome of the pretreatment of DLBCL cases with a certain degree of accuracy. • A prognostic nomogram was established in this study in order to aid in assessing prognostic risk and providing more accurate treatment plans for DLBCL cases. [ABSTRACT FROM AUTHOR]