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Aqueous ammonium ion hybrid supercapacitors (A-HSCs) have sparked significant interest due to their safety, sustainability, and low cost. However, the quest for superior electrode materials for storing ammonium ions still encounters many challenges. Since layered vanadium-based materials usually have a high theoretical capacity and open crystal structure, and the introduction of sodium ions can enhance ionic bonds and cycle stability, this work reports a Na+-doped NH4V4O10 nano-array grown in situ on carbon cloth through a hydrothermal approach (NaNVO@CC), which serves as a novel cathode material for A-HSCs. The irreversible deammoniation reaction of NH4V4O10 was effectively inhibited due to the growth on the carbon cloth and the synergistic effect of Na+. Thanks to these advantages, the NaNVO@CC cathode demonstrates outstanding electrochemical performance, achieving an ultra-high specific capacity of 712 F g−1 at 0.50 A g−1 and exceptional cycle stability, retaining 71.1% of its capacitance at 5.00 A g−1 over 20 000 cycles. The built A-HSC exhibits significant power density (8556.6 W kg−1), energy density (77.7 W h kg−1) and superior cycle life. This work proves the enormous potential of doping in enhancing electrode performance, which will offer insights for the creation of high-performance A-HSCs. [ABSTRACT FROM AUTHOR]

Background and aim: The prevalence and adverse outcomes of metabolic dysfunction associated with steatotic liver disease (MAFLD) are increasing. The changes in the gut microbiota and metabolites associated with metabolic dysfunction-associated steatohepatitis (MASH) are regarded as an essential part of the progression of MAFLD. This study aimed to identify the gut microbiota and metabolites involved in the development of MAFLD in patients. Method: This study enrolled 90 patients (healthy controls, HC: n = 30; MASH: n = 30; MASH-related cirrhosis, MC: n = 30), and their fecal samples were collected for 16S rRNA sequencing and non-targeted LC–MS/MS metabolomics analysis. Data preprocessing and statistical analyses were performed using QIIME2 software, Pynast, QIIME2 package, Progenesis QI, and R program. Results: The abundance of Prevotellaceae at the family level and Prevotella at the genus level was lower in the MASH and NC samples than in the HC samples. Both Prevotellaceae and Prevotella showed the strongest correlation with MASH progression via random forest analysis. Untargeted metabolomics was used to quantitatively screen for discrepant metabolites in the stool samples from the three groups. Linolenic acid (LA)-related metabolite levels were significantly lower in MASH and NC samples. Associations between Prevotella- or LA-related metabolites and liver function were discovered. A high abundance of Prevotella was associated with LA-related metabolites and MASH. Conclusion: This study identified that gut microbiota and metabolites are associated with MASH-related metabolic dysfunction. LA and Prevotella are depleted during MASH progression, and additional supplementation with Prevotella may be a potential strategy for the future treatment of MAFLD. [ABSTRACT FROM AUTHOR]

Objective was to estimate the 5-year cumulative risk of HCC in patients with HBC by utilizing an artificial neural network (ANN). Methods: We conducted this study with 1589 patients hospitalized at Beijing Ditan Hospital of Capital Medical University and People's Liberation Army Fifth Medical Center. The training cohort consisted of 913 subjects from Beijing Ditan Hospital of Capital Medical University, while the validation cohort comprised 676 subjects from People's Liberation Army Fifth Medical Center. Through univariate analysis, we identified factors that independently influenced the occurrence of HCC, which were then used to develop the ANN model. To evaluate the ANN model, we assessed its predictive accuracy, discriminative ability, and clinical net benefit using metrics such as the area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curves. Results: In total, we included nine independent risk factors in the development of the ANN model. Remarkably, the AUC of the ANN model was 0.880, significantly outperforming the AUC values of other existing models including mPAGE-B (0.719) (95% CI 0.670– 0.768), PAGE-B (0. 710) (95% CI 0.660– 0.759), FIB-4 (0.693) (95% CI 0.640– 0.745), and Toronto hepatoma risk index (THRI) (0.705) (95% CI 0.654– 0.756) (p< 0.001 for all). The ANN model effectively stratified patients into low, medium, and high-risk groups based on their 5-year In the training cohort, the positive predictive value (PPV) for low-risk patients was 26.2% (95% CI 25.0– 27.4), and the negative predictive value (NPV) was 98.7% (95% CI 95.2– 99.7). For high-risk patients, the PPV was 54.7% (95% CI 48.6– 60.7), and the NPV was 91.6% (95% CI 89.4– 93.4). These findings were validated in the independent validation cohort. Conclusion: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of the 5-year risk of HCC in patients with HBC. [ABSTRACT FROM AUTHOR]

The aqueous micro batteries (AMBs) are expected to be one of the most promising micro energy storage devices for its safe operation and cost‐effectiveness. However, the performance of the AMBs is not satisfactory, which is attributed to strong interaction between metal ions and the electrode materials. Here, the first AMBs are developed with NH4+ as charge carrier. More importantly, to solve the low conductivity and the dissolution during the NH4+ intercalation/extraction problem of perylene material represented by perylene‐3,4,9,10‐tetracarboxylic dianhydride (PTCDA), the Ti3C2Tx MXene with high conductivity and polar surface terminals is introduced as a conductive skeleton (PTCDA/Ti3C2Tx MXene). Benefitting from this, the PTCDA/Ti3C2Tx MXene electrodes exhibit ultra‐high cycle life and rate capability (74.31% after 10 000 galvanostatic chargedischarge (GCD) cycles, and 91.67 mAh g−1 at 15.0 A g−1, i.e., capacity retention of 45.2% for a 30‐fold increase in current density). More significantly, the AMBs with NH4+ as charge carrier and PTCDA/Ti3C2Tx MXene anode provide excellent energy density and power density, cycle life, and flexibility. This work will provide strategy for the development of NH4+ storage materials and the design of AMBs. [ABSTRACT FROM AUTHOR]

Background: Liver cirrhosis patients are at risk for esophagogastric variceal bleeding (EGVB). Herein, we aimed to estimate the EGVB risk in patients with liver cirrhosis using an artificial neural network (ANN). Methods: We included 999 liver cirrhosis patients hospitalized at the Beijing Ditan Hospital, Capital Medical University in the training cohort and 101 patients from Shuguang Hospital in the validation cohort. The factors independently affecting EGVB occurrence were determined via univariate analysis and used to develop an ANN model. Results: The 1-year cumulative EGVB incidence rates were 11.9 and 11.9% in the training and validation groups, respectively. A total of 12 independent risk factors, including gender, drinking and smoking history, decompensation, ascites, location and size of varices, alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), hematocrit (HCT) and neutrophil-lymphocyte ratio (NLR) levels as well as red blood cell (RBC) count were evaluated and used to establish the ANN model, which estimated the 1-year EGVB risk. The ANN model had an area under the curve (AUC) of 0.959, which was significantly higher than the AUC for the North Italian Endoscopic Club (NIEC) (0.669) and revised North Italian Endoscopic Club (Rev-NIEC) indices (0.725) (all P < 0.001). Decision curve analyses revealed improved net benefits of the ANN compared to the NIEC and Rev-NIEC indices. Conclusions: The ANN model accurately predicted the 1-year risk for EGVB in liver cirrhosis patients and might be used as a basis for risk-based EGVB surveillance strategies. [ABSTRACT FROM AUTHOR]

Highlights: The excellent performance of MoS2/PANI cathode is due to better conductivity and desolvation shielding. The self-charging zinc battery was successfully assembled that achieve deep self-discharge and long cycle life. The quasi solid state battery and battery module have excellent performance and practicability. Self-charging power systems collecting energy harvesting technology and batteries are attracting extensive attention. To solve the disadvantages of the traditional integrated system, such as highly dependent on energy supply and complex structure, an air-rechargeable Zn battery based on MoS2/PANI cathode is reported. Benefited from the excellent conductivity desolvation shield of PANI, the MoS2/PANI cathode exhibits ultra-high capacity (304.98 mAh g−1 in N2 and 351.25 mAh g−1 in air). In particular, this battery has the ability to collect, convert and store energy simultaneously by an air-rechargeable process of the spontaneous redox reaction between the discharged cathode and O2 from air. The air-rechargeable Zn batteries display a high open-circuit voltage (1.15 V), an unforgettable discharge capacity (316.09 mAh g−1 and the air-rechargeable depth is 89.99%) and good air-recharging stability (291.22 mAh g−1 after 50 air recharging/galvanostatic current discharge cycle). Most importantly, both our quasi-solid zinc ion batteries and batteries modules have excellent performance and practicability. This work will provide a promising research direction for the material design and device assembly of the next-generation self-powered system. [ABSTRACT FROM AUTHOR]

The article titled "Multi-objective optimization scheduling of integrated energy system interval under multiple uncertainty environment" by L. Zhao et al. has been retracted by the publisher due to concerns about compromised editorial handling and peer review processes, inappropriate references, and being out of scope for the journal. The authors did not respond to correspondence regarding the retraction. The online version of the article includes the retracted full text as Supplementary Information. [Extracted from the article]

Aqueous zinc‐ion batteries (AZIBs) with low cost and high safety are promising energy storage equipment for large‐scale grids. However, the further development of AZIBs is obstructed by finite usability of cathode materials. Recently, layered materials represented by MoS2 have attracted attentions because they can provide facile two‐dimensional (2D) channels for the diffusion of Zn2+, but low capacity and poor rate capability limit their applications. Herein, two classical layered materials are combined to form a stable heterostructure (MoS2‐rGO) by in‐situ growing metallic 1T‐MoS2 nanoflowers on reduced graphene oxide (rGO) to provide more 2D channels for electrons transfer and Zn2+ diffusion. The MoS2‐rGO heterostructure exhibits high specific capacity (303.10 mAh g−1 at 0.20 A g−1) and ultrahigh rate capability (102.70 mAh g−1 at 20.00 A g−1, i. e., capacity retention of 33.88 % for a 100‐fold increase in current density) in aqueous electrolyte. Furthermore, the quasi‐solid‐state aqueous zinc‐ion battery based on MoS2‐rGO heterostructure also shows high electrochemical performance at bending states and extreme temperature. This work provides an inspiration for construction of AZIBs with high‐performance layered cathode. [ABSTRACT FROM AUTHOR]

Objective: T cell immunity plays an important role in anti-tumor effects and immunosuppression often leads to the development and relapse of cancer. This study aimed to investigate the effect of T cell numbers on the long-term prognosis of patients with hepatocellular carcinoma (HCC) and construct an artificial neural network (ANN) model to evaluate its prognostic value. Methods: We enrolled 3,427 patients with HCC at Beijing Ditan Hospital, Capital Medical University, and randomly divided them into two groups of 1,861 and 809 patients as the training and validation sets, respectively. Cox regression analysis was used to screen for independent risk factors of survival in patients with HCC. These factors were used to build an ANN model using Python. Concordance index, calibration curve, and decision curve analysis were used to evaluate the model performance. Results: The 1-year, 3-year, 5-year, and 10-year cumulative overall survival (OS) rates were 66.9%, 45.7%, 34.9%, and 22.6%, respectively. Cox multivariate regression analysis showed that age, white blood cell count, creatinine, total bilirubin, γ-GGT, LDH, tumor size ≥ 5 cm, tumor number ≥ 2, portal vein tumor thrombus, and AFP ≥ 400 ng/ml were independent risk factors for long-term survival in HCC. Antiviral therapy, albumin, T cell, and CD8 T cell counts were independent protective factors. An ANN model was developed for long-term survival. The areas under the receiver operating characteristic (ROC) curve of 1-year, 3-year, and 5-year OS rates by ANNs were 0.838, 0.833, and 0.843, respectively, which were higher than those of the Barcelona Clinic Liver Cancer (BCLC), tumor node metastasis (TNM), Okuda, Chinese University Prognostic Index (CUPI), Cancer of the Liver Italian Program (CLIP), Japan Integrated Staging (JIS), and albumin–bilirubin (ALBI) models (P < 0.0001). According to the ANN model scores, all patients were divided into high-, middle-, and low-risk groups. Compared with low-risk patients, the hazard ratios of 5-year OS of the high-risk group were 8.11 (95% CI: 7.0-9.4) and 6.13 (95% CI: 4.28-8.79) (P<0.0001) in the training and validation sets, respectively. Conclusion: High levels of circulating T cells and CD8 + T cells in peripheral blood may benefit the long-term survival of patients with HCC. The ANN model has a good individual prediction performance, which can be used to assess the prognosis of HCC and lay the foundation for the implementation of precision treatment in the future. [ABSTRACT FROM AUTHOR]

The occurrence and development of severe novel Coronavirus pneumonia (COVID-19) follows the pathogenesis characteristics and transmission law of the pestilential diseases, "pathogenic warm attacks the lungs first, and goes to the pericardium reversely", which belongs to the category of "wrongly-treated pattern" or "unfavorable pattern". The disease progressed rapidly. At first, the epidemic virus invaded the lungs, then blocked the lungs, leading to the critical stage of inner blocking causing collapse. Modern medicine believes that severe disease involves a series of pathophysiological changes, such as viral damage, immune inflammatory response, endothelial cell damage, thrombosis, and renin-angiotensin-aldosterone system (RAAS) disorders. Regarding the treatment of severe COVID-19, the first proposal is to prevent the occurrence of severe disease, When necessary, it is not limited to wee (defense), qi, ying (nutrient) and blood levels. and prior treatment should be given before development of patterns ; This article summarizes the role and value of the methods of detoxification and cooling blood, purging the lungs and opening the bowels, and strengthening spleen and eliminating dampness in the treatment of severe COVID-19. In combination with modern medical research, the possible mechanism of action of the above three treatments was also analyzed. [ABSTRACT FROM AUTHOR]

Background: Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten women's lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets. Methods: Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates. Results: Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression. Conclusion: These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC. [ABSTRACT FROM AUTHOR]

Object: Disease progression is an important factor affecting the long-term survival in hepatocellular carcinoma (HCC). The progression-free survival (PFS) has been used as a surrogate endpoint for overall survival (OS) in many solid tumors. However, there were few models to predict the PFS in HCC patients. This study aimed to explore the prognostic factors that affect the PFS in HCC and establish an individualized prediction model. Methods: We included 2890 patients with hepatitis B-related HCC hospitalized at Beijing Ditan Hospital, Capital Medical University and randomly divided into training and validation cohort. Cox multivariate regression was used to analyze independent risk factors affecting the 1-year PFS of HCC, and an artificial neural networks (ANNs) model was constructed. C-index, calibration curve, and decision curve analysis were used to evaluate the performance of the model. Results: The median survival time was 26.2 m (95% CI: 24.08–28.32) and the 1-year PFS rate was 52.3% in whole study population. Cox multivariate regression showed smoking history, tumor number ≥ 2, tumor size ≥ 5 cm, portal vein tumor thrombus, WBC, NLR, γ-GGT, ALP, and AFP ≥ 400 ng/mL were risk factors for 1-year progression-free survival, while albumin and CD4 T cell counts were protective factors in HCC patients. A prediction model for 1-year PFS was constructed (https://lixuan.me/annmodel/myg-v3/). The ANNs model's ability to predict 1-year PFS had an area under the receiver operating characteristic curve (AUROC) of 0.866 (95% CI 0.848–0.884) in HCC patients, which was higher than predicted by TNM, BCLC, Okuda, CLIP, CUPI, JIS, and ALBI scores (p < 0.0001). In addition, the ANNs model could also estimate the probability of 1-year OS and presented a higher AUROC value, 0.877 (95% CI 0.858–0.895), than those other models. All patients were divided into high-, medium-, and low-risk groups, according to the ANNs model scores. Compared with the hazard ratios (HRs) of PFS and OS in low-risk group, those in the high-risk group were 26.42 (95% CI 18.74–37.25; p < 0.0001) and 11.26 (95% CI 9.11–13.93; p < 0.0001), respectively. Conclusion: The ANNs model has good individualized prediction performance and may be helpful to evaluate the probability of progression-free survival in HCC during clinical practice. [ABSTRACT FROM AUTHOR]

Background: This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on-chronic liver failure (HBV-ACLF) through artificial neural network (ANN) systems.Methods: Six hundred and eight-four cases of consecutive HBV-ACLF patients were retrospectively reviewed. Four hundred and twenty-three cases were used for training and constructing ANN models, and the remaining 261 cases were for validating the established models. Predictors associated with mortality were determined by univariate analysis and were then included in ANN models for predicting prognosis of mortality. The receiver operating characteristic curve analysis was used to evaluate the predictive performance of the ANN models in comparison with various current prognostic models.Results: Variables with statistically significant difference or important clinical characteristics were input in the ANN training process, and eight independent risk factors, including age, hepatic encephalopathy, serum sodium, prothrombin activity, γ-glutamyltransferase, hepatitis B e antigen, alkaline phosphatase and total bilirubin, were eventually used to establish ANN models. For 28-day mortality in the training cohort, the model's predictive accuracy (AUR 0.948, 95% CI 0.925-0.970) was significantly higher than that of the Model for End-stage Liver Disease (MELD), MELD-sodium (MELD-Na), Chronic Liver Failure-ACLF (CLIF-ACLF), and Child-Turcotte-Pugh (CTP) (all p < 0.001). In the validation cohorts the predictive accuracy of ANN model (AUR 0.748, 95% CI: 0.673-0.822) was significantly higher than that of MELD (p = 0.0099) and insignificantly higher than that of MELD-Na, CTP and CLIF-ACLF (p > 0.05). For 90-day mortality in the training cohort, the model's predictive accuracy (AUR 0.913, 95% CI 0.887-0.938) was significantly higher than that of MELD, MELD-Na, CTP and CLIF-ACLF (all p < 0.001). In the validation cohorts, the prediction accuracy of the ANN model (AUR 0.754, 95% CI: 0.697-0.812 was significantly higher than that of MELD (p = 0.019) and insignificantly higher than MELD-Na, CTP and CLIF-ACLF (p > 0.05).Conclusions: The established ANN models can more accurately predict short-term mortality risk in patients with HBV- ACLF. The main content has been postered as an abstract at the AASLD Hepatology Conference (https://doi.org/10.1002/hep.30257). [ABSTRACT FROM AUTHOR]

Kinetics of acetone iodide synthesis was investigated in a microchannel reaction system mainly composed of a micromixer and a tubular reactor. This system can be used as a plug flow reactor, as proved by the low variance (0.006) of residence time distribution detected by a step stimuli–response tracer. An online ultraviolet monitoring system was built to fast and reliably detect the transmittance of the flowing solution. Reaction kinetics was tested at different reactant concentrations and temperatures (35‐50 °C). Second‐order kinetics concerning acetone and HCl was identified with activation energy of 67.86 kJ/mol. [ABSTRACT FROM AUTHOR]