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Introduction Transcutaneous laser-assisted drug delivery (LADD) is recognized as a developing therapy for skin disorders. Method Current literature was reviewed to summarize current applications for LADD. Discussion 12 clinical applications for this therapy are currently reported. Conclusion LADD has potential for wide application in skin disorder treatment. Lay Summary Laser assisted drug delivery improves drug bioavailability for treatment of skin disorders. This technique is being assessed clinically in disorders ranging from skin cancers to alopecia.

Abstract 2D nanomaterials play a critical role in realizing high‐performance flexible electrodes for wearable energy storge devices, owing to their merits of large surface area, high conductivity and high strength. The electrode is a complex system and the performance is determined by multiple and interrelated factors including the intrinsic properties of materials and the structures at different scales from macroscale to atomic scale. Multiscale design strategies have been developed to engineer the structures to exploit full potential and mitigate drawbacks of 2D materials. Analyzing the design strategies and understanding the working mechanisms are essential to facilitate the integration and harvest the synergistic effects. This review summarizes the multiscale design strategies from macroscale down to micro/nano‐scale structures and atomic‐scale structures for developing 2D nanomaterials‐based flexible electrodes. It starts with brief introduction of 2D nanomaterials, followed by analysis of structural design strategies at different scales focusing on the elucidation of structure‐property relationship, and ends with the presentation of challenges and future prospects. This review highlights the importance of integrating multiscale design strategies. Finding from this review may deepen the understanding of electrode performance and provide valuable guidelines for designing 2D nanomaterials‐based flexible electrodes.

BackgroundOur study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort.MethodsA total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model.ResultsThe enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662–0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534–0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model.ConclusionsThis study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.

Object: Sertraline is a first-line SSRI for the treatment of depression and has the same effectiveness along with a superior safety profile compared to other medications. There are few population pharmacokinetic (PPK) studies of sertraline and a lack of studies in the Chinese population. Therefore, we performed a PPK analysis of Chinese patients treated with sertraline to identify factors that can influence drug exposure. In addition, the dosing and discontinuation regimen of sertraline when applied to adolescents was explored. Methods: Sertraline serum drug concentration data were collected from 140 hospitalized patients to generate a sertraline PPK dataset, and data evaluation and examination of the effects of covariates on drug exposure in the final model were performed using nonlinear mixed-effects models (NONMEM) and first-order conditional estimation with interaction (FOCE-I). Examining rational medication administration and rational withdrawal of sertraline based on significant covariates and final modeling. Results: A one-compartment model with first-order absorption and elimination of sertraline was developed for Chinese patients with psychiatric disorders. Analysis of covariates revealed that age was a covariate that significantly affected sertraline CL/F (P

Abstract As a new type of secondary ion battery, aqueous zinc‐ion battery has a broad application prospect in the field of large‐scale energy storage due to its characteristics of low cost, high safety, environmental friendliness, and high‐power density. In recent years, manganese dioxide (MnO2)‐based materials have been extensively explored as cathodes for Zn‐ion batteries. Based on the research experiences of our group in the field of aqueous zinc ion batteries and combining with the latest literature of system, we systematically summarize the research progress of Zn−MnO2 batteries. This article first reviews the current research progress and reaction mechanism of Zn−MnO2 batteries, and then respectively expounds the optimization of MnO2 cathode, Zn anodes, and diverse electrolytes and their effects on battery performance. Additionally, primary challenges related to different components and their respective strategies for mitigating them are discussed, with the ultimate objective of offering comprehensive guidance for the design and fabrication of high‐performance Zn−MnO2 batteries. Finally, the future research and development direction of aqueous Zn−MnO2 batteries with high energy density, high safety and long life is envisioned.

BackgroundThis meta-analysis investigated the influence of exercise on cognitive function in people living with diabetes.MethodsStringent criteria for literature inclusion and exclusion were defined. Searches were conducted across four English databases to gather randomized controlled trials investigating exercise interventions for cognitive function in people living with diabetes. Outcome indicators from 1193 subjects across 12 articles were analyzed using RevMan 5.4 software.ResultsExercise intervention demonstrated the ability to mitigate cognitive decline in people living with diabetes, with a combined effect size (standardized mean difference) of 0.91, 95% CI: 0.28, 1.54, P < 0.00001. The intervention effect showed significant modulation by intervention content (I2 = 95%), intervention duration (I2 = 95%), intervention frequency (I2 = 95%), and intervention cycle (I2 = 96%). Among these factors, multi-component exercise, sessions >40 minutes, exercise frequency >4 times per week, and sustained exercise for >6 months were paramount, all with P < 0.05.ConclusionExercise intervention emerges as a viable strategy for delaying cognitive decline in people living with diabetes. Its efficacy is subject to modulation by various variables. Optimal intervention includes multi-component exercise, individual sessions lasting 40-60 minutes, exercising >4 times a week, and continuous exercise for over 6 months.

BackgroundAnxiety and depression are common in patients with chronic obstructive pulmonary disease (COPD), especially older adult patients. This can complicate the disease progression and lead to increased clinical and economic burden. We sought to investigate the clinical and economic burdens associated with the presence of anxious and/or depressive symptoms among older adult COPD patients.MethodsWe screened 579 patients aged over 60 years and diagnosed with COPD via a lung function test following the 2017 Global Initiative Chronic Obstructive Lung Disease (GOLD) guidelines. Anxiety and depression were measured using the Hospital Anxiety and Depression Scale (HADS) through face-to-face interviews at admission. Follow-up was conducted by telephone calls at 6, 12, 18, 24, and 36 months after discharge to assess clinical and economic burden. COPD-anxiety and/or depression patients were matched to patients without anxiety and depression (COPD-only) using propensity scores. Multivariate regression models were used to compare clinical and economic burden between COPD-anxiety and/or depression and COPD-only groups.ResultsCompared with COPD-only patients, COPD patients complicated with anxiety and/or depression had increased clinical burden, including higher COPD-related outpatient visits, COPD-related hospitalizations, and length of COPD-related hospitalizations (p