Reversible Oxygen Redox Chemistry in High-Entropy P2-Type Manganese-Based Cathodes via Self-Regulating Mechanism.

The irreversible oxygen-redox reactions in the high-voltage region of sodium-layered cathode materials lead to poor capacity retention and structural instability during cycling, presenting a significant challenge in the development of high-energy-density sodium-ion batteries. This work introduces a high-entropy design for layered Na0.67Li0.1Co0.1Cu0.1Ni0.1Ti0.1Mn0.5O2 (Mn-HEO) cathode with a self-regulating mechanism to extend specific capacity and energy density. The oxygen redox reaction was activated during the initial charging process, accompanied by the self-regulation of active elements, enhancing the ionic bonds to form a vacancy wall near the TM vacancies and thus preventing the migration of transition metal elements. Systematic in situ/ex situ characterizations and theoretical calculations comprehensively support the understanding of the self-regulation mechanism of Mn-HEO. As a result, the Mn-HEO cathode exhibits a stable structure during cycling. It demonstrates almost zero strain within a wide voltage range of 2.0-4.5 V with a remarkable specific capacity (177 mAh g-1 at 0.05 C) and excellent long-term cycling stability (87.6% capacity retention after 200 cycles at 2 C). This work opens a new pathway for enhancing the stability of oxygen-redox chemistry and revealing a mechanism of crystal structure evolution for high-energy-density layered oxides.

Technical development and application of supercritical CO2 foaming technology in PCL foam production.

Polycaprolactone (PCL) has the advantages of good biocompatibility, appropriate biodegradability, non-toxicity, flexibility, and processability. As a result, PCL-based foams can successfully work in bone tissue engineering, medical patches, drug delivery, reinforcing materials, and other applications. A promising technology for producing PCL foam products is supercritical CO2 (ScCO2) foaming technology, which avoids using organic solvents, is green, and has low foaming agent costs. However, due to the limitations of ScCO2 foaming technology, it is no longer possible to use this technology alone to meet current production requirements. Therefore, ScCO2 foaming technology must combine with other technologies to develop PCL foam products with better performance and matching requirements. This paper systematically reviews the technological development of ScCO2 foaming in producing PCL foams. The molding process of ScCO2 foaming and the conventional preparation process of PCL foam products are discussed comprehensively, including the preparation process, advantages, and disadvantages, challenges faced, etc. Six combined technologies for ScCO2 foaming in the production of PCL foams and the applications of PCL foams are presented. Finally, the future remaining research for producing PCL foams by ScCO2 foaming is analyzed.

TIRPnet: Risk prediction of traditional Chinese medicine ingredients based on a deep neural network.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine (TCM) has a history of over 3000 years of medical practice. Due to the complex ingredients and unclear pharmacological mechanism of TCM, it is very difficult to predict its risks. With the increase in the number and severity of spontaneous reports of adverse drug reactions (ADRs) of TCM, its safety has received widespread attention. AIM OF THE STUDY: In this study, we proposed a framework based on deep learning to predict the probability of adverse reactions caused by TCM ingredients and validated the model using real-world data. MATERIALS AND METHODS: The spontaneous reporting data from Jiangsu Province of China was selected as the research data, which included 72,561 ADR reports of TCMs. All the ingredients of these TCMs were collected from the medical website and correlated with the corresponding ADRs. Then, a risk prediction model was constructed based on a deep neural network (DNN), named TIRPnet. Based on one-hot encoded data, our model achieved the optimal performance by fine-tuning some hyperparameters. The ten most commonly used TCM ingredients and their ADRs were collected as the test set to evaluate their performance as objective criteria. RESULTS: TIRPnet was constructed as a 7-layer DNN. The experimental results showed that TIRPnet performs excellently in all indicators, with a sensitivity of 0.950, specificity of 0.995, accuracy of 0.994, precision of 0.708, and F1 of 0.811. CONCLUSIONS: The proposed TIRPnet owns the ability to predict the ADRs of a single TCM ingredient by learning a large number of TCM-related spontaneous reports, which can help doctors design safe prescriptions and provide technical support for the pharmacovigilance of TCM.

Prediction of Medical Disputes Between Health Care Workers and Patients in Terms of Hospital Legal Construction Using Machine Learning Techniques: Externally Validated Cross-Sectional Study.

BACKGROUND: Medical disputes are a global public health issue that is receiving increasing attention. However, studies investigating the relationship between hospital legal construction and medical disputes are scarce. The development of a multicenter model incorporating machine learning (ML) techniques for the individualized prediction of medical disputes would be beneficial for medical workers. OBJECTIVE: This study aimed to identify predictors related to medical disputes from the perspective of hospital legal construction and the use of ML techniques to build models for predicting the risk of medical disputes. METHODS: This study enrolled 38,053 medical workers from 130 tertiary hospitals in Hunan province, China. The participants were randomly divided into a training cohort (34,286/38,053, 90.1%) and an internal validation cohort (3767/38,053, 9.9%). Medical workers from 87 tertiary hospitals in Beijing were included in an external validation cohort (26,285/26,285, 100%). This study used logistic regression and 5 ML techniques: decision tree, random forest, support vector machine, gradient boosting decision tree (GBDT), and deep neural network. In total, 12 metrics, including discrimination and calibration, were used for performance evaluation. A scoring system was developed to select the optimal model. Shapley additive explanations was used to generate the importance coefficients for characteristics. To promote the clinical practice of our proposed optimal model, reclassification of patients was performed, and a web-based app for medical dispute prediction was created, which can be easily accessed by the public. RESULTS: Medical disputes occurred among 46.06% (17,527/38,053) of the medical workers in Hunan province, China. Among the 26 clinical characteristics, multivariate analysis demonstrated that 18 characteristics were significantly associated with medical disputes, and these characteristics were used for ML model development. Among the ML techniques, GBDT was identified as the optimal model, demonstrating the lowest Brier score (0.205), highest area under the receiver operating characteristic curve (0.738, 95% CI 0.722-0.754), and the largest discrimination slope (0.172) and Youden index (1.355). In addition, it achieved the highest metrics score (63 points), followed by deep neural network (46 points) and random forest (45 points), in the internal validation set. In the external validation set, GBDT still performed comparably, achieving the second highest metrics score (52 points). The high-risk group had more than twice the odds of experiencing medical disputes compared with the low-risk group. CONCLUSIONS: We established a prediction model to stratify medical workers into different risk groups for encountering medical disputes. Among the 5 ML models, GBDT demonstrated the optimal comprehensive performance and was used to construct the web-based app. Our proposed model can serve as a useful tool for identifying medical workers at high risk of medical disputes. We believe that preventive strategies should be implemented for the high-risk group.

Expression of AKRs superfamily and prognostic in human gastric cancer.

The human aldo-keto reductase (AKRs) superfamily is involved in the development of various tumors. However, the different expression patterns of AKRs and their prognostic value in gastric cancer (GC) have not been clarified. In this study, we analyzed the gene expression and gene methylation level of AKRs in GC patients and the survival data and immune infiltration based on AKRs expression, using data from different databases. We found that the expression levels of AKR1B10, AKR1C1, AKR1C2, and AKR7A3 in GC tissues were lower and the expression level of AKR6A5 was higher in GC tissues than in normal tissue. These differentially expressed genes (AKR1B10, AKR1C1, AKR1C2, AKR7A3, and AKR6A5) were significantly correlated with the infiltration level. The expression of SPI1 and AKR6A5 in GC was positively correlated. Survival analysis showed that GC levels of AKR6A5 reduced or increased mRNA levels of AKR7A3, and AKR1B10 was expected to have higher overall survival (OS), first progression (FP) survival, and postprogression survival (PPS) rates and a better prognosis. Moreover, the expression of AKR1B1 was found to be correlated with the staging of GC. The methylation of AKR6A5 (KCNAB2) at cg05307871 and cg01907457 was significantly associated with the classification of GC. Meta-analysis and ROC curve analysis show that the expression level of AKR1B1 and the methylation of cg16156182 (KCNAB1), cg11194299 (KCNAB2), cg16132520 (AKR1B1), and cg13801416 (AKR1B1) had a high hazard ratio and a good prognostic value. These data suggest that the expression and methylation of AKR1B1 and AKR6A5 are significantly related to the prognosis.

Applications and Challenges of Supercritical Foaming Technology.

With economic development, environmental problems are becoming more and more prominent, and achieving green chemistry is an urgent task nowadays, which creates an opportunity for the development of supercritical foaming technology. The foaming agents used in supercritical foaming technology are usually supercritical carbon dioxide (ScCO2) and supercritical nitrogen (ScN2), both of which are used without environmental burden. This technology can reduce the environmental impact of polymer foam production. Although supercritical foaming technology is already in production in some fields, it has not been applied on a large scale. Here, we present a detailed analysis of the types of foaming agents currently used in supercritical foaming technology and their applications in various fields, summarizing the technological improvements that have been made to the technology. However, we have found that today's supercritical technologies still need to address some additional challenges to achieve large-scale production.

A Rising 2D Star: Novel MBenes with Excellent Performance in Energy Conversion and Storage.

As a flourishing member of the two-dimensional (2D) nanomaterial family, MXenes have shown great potential in various research areas. In recent years, the continued growth of interest in MXene derivatives, 2D transition metal borides (MBenes), has contributed to the emergence of this 2D material as a latecomer. Due to the excellent electrical conductivity, mechanical properties and electrical properties, thus MBenes attract more researchers' interest. Extensive experimental and theoretical studies have shown that they have exciting energy conversion and electrochemical storage potential. However, a comprehensive and systematic review of MBenes applications has not been available so far. For this reason, we present a comprehensive summary of recent advances in MBenes research. We started by summarizing the latest fabrication routes and excellent properties of MBenes. The focus will then turn to their exciting potential for energy storage and conversion. Finally, a brief summary of the challenges and opportunities for MBenes in future practical applications is presented.

Association between hospital legal constructions and medical disputes: A multi-center analysis of 130 tertiary hospitals in Hunan Province, China.

Background: Medical disputes are common in hospitals and a major challenge for the operations of medical institutions. However, few studies have looked into the association between medical disputes and hospital legal constructions. The purpose of the study was to investigate the relationship between hospital legal constructions and medical disputes, and it also aimed to develop a nomogram to estimate the likelihood of medical disputes. Methods: Between July and September 2021, 2,716 administrators from 130 hospitals were enrolled for analysis. The study collected seventeen variables for examination. To establish a nomogram, administrators were randomly split into a training group (n = 1,358) and a validation group (n = 1,358) with a 50:50 ratio. The nomogram was developed using data from participants in the training group, and it was validated in the validation group. The nomogram contained significant variables that were linked to medical disputes and were identified by multivariate analysis. The nomogram's predictive performance was assessed utilizing discriminative and calibrating ability. A web calculator was developed to be conducive to model utility. Results: Medical disputes were observed in 41.53% (1,128/2,716) of participants. Five characteristics, including male gender, higher professional ranks, longer length of service, worse understanding of the hospital charters, and worse construction status of hospital rule of law, were significantly associated with more medical disputes based on the multivariate analysis. As a result, these variables were included in the nomogram development. The AUROC was 0.67 [95% confident interval (CI): 0.64-0.70] in the training group and 0.68 (95% CI: 0.66-0.71) in the validation group. The corresponding calibration slopes were 1.00 and 1.05, respectively, and intercepts were 0.00 and -0.06, respectively. Three risk groups were created among the participants: Those in the high-risk group experienced medical disputes 2.83 times more frequently than those in the low-risk group (P < 0.001). Conclusion: Medical dispute is prevailing among hospital administrators, and it can be reduced by the effective constructions of hospital rule of law. This study proposes a novel nomogram to estimate the likelihood of medical disputes specifically among administrators in tertiary hospitals, and a web calculator can be available at https://ymgarden.shinyapps.io/Predictionofmedicaldisputes/.

Simultaneous Determination of 18 Chemical Constituents in Traditional Chinese Medicine of Antitussive by UPLC-MS-MS.

In this study, a simple, rapid and sensitive liquid chromatography-tandem mass spectrometry (LC-MS-MS) method was developed for the simultaneous determination of 18 compounds, namely, morphine, codeine, tuberostemonine, thebaine, papaverine, scopoletin, liquiritin, narcotine, gynaroside, hyperoside, hesperidin, isoliquiritin, liquiritigenin, luteolin, isoliquiritigenin, apigenin, formononetin and glycyrrhizic acid in traditional Chinese medicine of antitussive. Analytes were chromatographically separated on a Waters C18 column under gradient elution with a mobile phase of water containing 0.1‰ HOAc (A) and methanol (MeOH) containing 0.1‰ HOAc (B). Detection was accomplished by multiple-reaction-monitoring scanning with an electrospray ionization source under positive and negative ion switching modes. Samples were prepared with dilution and ultrasonic extraction. All components were detected within the linear regime (r2 ≥ 0.9924) at the concentration ranges tested. The precision ranged from 1.3 to 4.6%, the accuracy was between 85.6 and 116.7% and the results of method validation tests were all within the required limits. The validated quantification method was successfully applied to analyze 40 batches of commercial traditional Chinese medicine of antitussive. These results will provide a basis for quality control of production process and the further pharmacokinetic study of traditional Chinese medicine of antitussive in vivo.

[Catastrophic Health Expenditure before and after Compensations from the New Rural Cooperative Medical Scheme in Rural Zigong of Sichuan Province].

OBJECTIVES: To estimate catastrophic health expenditure (CHE) of rural families in Zigong, and to determine the main influencing factors of CHE. METHODS: CHE was estimated using indicators such as occurrence and average deviations. The influencing factors of CHE were identified through binary logistic regression. RESULTS: We found 6.37% catastrophic health payment headcount, 1.13% mean catastrophic payment gap, and 17.80% mean positive gap after compensations. Compensations from the new rural cooperative medical scheme (NCMS) led to a reduction of 74.81% catastrophic health payment headcount for hospitalization costs and 48.00% catastrophic health payment headcount for outpatient costs, respectively. The numbers of hospitalizations in a family, presence of patients with chronic diseases, per capita household income, and numbers of family members with a job were found to be predictors of CHE. CONCLUSIONS: Rural families that have patients with chronic diseases are vulnerable to CHE.The government should develop policies to ease the financial burdens of the families with a high accumulated health expenditure over time.