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Na3V2(PO4)3(NVP) in aqueous sodium-ion batteries cathode material was prepared by hydrothermal method and doped with non-metallic N and metal ions Al and Mn to improve the electrochemical performance of NVP. The effects of doping amount on the performance modification of NVP were investigated. When the doping amount of Mn is 0.08 mol, the samples have an obvious layered structure and show the best discharge specific capacity of 439.8 F/g. The structure and morphology of NVP were characterized by XRD, SEM, BET and XPS, and the electrochemical performance of the sample was analyzed by cyclic voltammetry and charge and discharge test. The results show that all the samples are pure NVP, and ion doping does not change the crystal structure of NVP. Al doping improves the dispersion of particles, and N, Mn doping forms obvious layered structure. The discharge specific capacity of NVP is 342 F/g, and 380.8, 405, 439.8 F/g for NVP/N, NVP/Al, NVP/Mn, at a current rate of 1 A/g. Thus, it can be seen that doping with appropriate amounts of metal ions and non-metal elements can significantly improve the aqueous electrochemical performance of NVP.

ABSTRACT Background Methods Results Conclusion Several recent studies have found that the efficacy of computer‐aided polyp detection (CADe) on the adenoma detection rate (ADR) diminished in real‐world settings. The role of unmeasured factors in AI–human interaction, such as monitor approaches, remains unknown. This study aimed to validate the effectiveness of CADe in the real world and assess the impact of monitor approaches.A retrospective propensity score–matched cohort study was conducted using routine data from a tertiary endoscopy center in China before and after the implementation of CADe. Four propensity score–matched cohorts were established: Cohort 1: pre‐CADe matched with dual‐monitor CADe‐assisted group; Cohort 2: dual‐monitor CADe‐assisted with single‐monitor CADe‐assisted group; Cohort 3: pre‐CADe with single‐monitor CADe‐assisted group; and Cohort 4: pre‐CADe with CADe period. ADR was set as the primary outcome.There were 5390, 6083, and 6131 eligible patients in the pre‐CADe group, dual‐monitor group, and single‐monitor group, respectively. In the matched analysis, results indicated that regardless of the monitor setup, CADe‐assisted groups showed a trend of increased ADR compared with the pre‐CADe period (CADe period: OR 1.141, 95% CI 1.047–1.243; p = 0.003; dual‐monitor: OR 1.178, 95% CI 1.069–1.299, p = 0.001; single‐monitor: OR 1.094, 95% CI 0.998–1.200, p = 0.056). Moreover, no significant difference between different monitor approaches was observed, although dual‐monitor setup showed an increasing tendency on ADR compared with single‐monitor setup (OR 1.069, 95% CI 0.985–1.161, p = 0.109).CADe shows great potential to improve ADR during colonoscopy in the real world. Meanwhile, changes in monitor setup do not significantly impact the assistance capability of CADe. Further research dedicated to evaluating the unmeasured elements in the AI–clinician hybrid for better implementation of CADe would be beneficial. [ABSTRACT FROM AUTHOR]

Background and Aim: False positives (FPs) pose a significant challenge in the application of artificial intelligence (AI) for polyp detection during colonoscopy. The study aimed to quantitatively evaluate the impact of computer‐aided polyp detection (CADe) systems' FPs on endoscopists. Methods: The model's FPs were categorized into four gradients: 0–5, 5–10, 10–15, and 15–20 FPs per minute (FPPM). Fifty‐six colonoscopy videos were collected for a crossover study involving 10 endoscopists. Polyp missed rate (PMR) was set as primary outcome. Subsequently, to further verify the impact of FPPM on the assistance capability of AI in clinical environments, a secondary analysis was conducted on a prospective randomized controlled trial (RCT) from Renmin Hospital of Wuhan University in China from July 1 to October 15, 2020, with the adenoma detection rate (ADR) as primary outcome. Results: Compared with routine group, CADe reduced PMR when FPPM was less than 5. However, with the continuous increase of FPPM, the beneficial effect of CADe gradually weakens. For secondary analysis of RCT, a total of 956 patients were enrolled. In AI‐assisted group, ADR is higher when FPPM ≤ 5 compared with FPPM > 5 (CADe group: 27.78% vs 11.90%; P = 0.014; odds ratio [OR], 0.351; 95% confidence interval [CI], 0.152–0.812; COMBO group: 38.40% vs 23.46%, P = 0.029; OR, 0.427; 95% CI, 0.199–0.916). After AI intervention, ADR increased when FPPM ≤ 5 (27.78% vs 14.76%; P = 0.001; OR, 0.399; 95% CI, 0.231–0.690), but no statistically significant difference was found when FPPM > 5 (11.90% vs 14.76%, P = 0.788; OR, 1.111; 95% CI, 0.514–2.403). Conclusion: The level of FPs of CADe does affect its effectiveness as an aid to endoscopists, with its best effect when FPPM is less than 5. [ABSTRACT FROM AUTHOR]

In order to improve and replace the enantiomer method outlined in the olodaterol hydrochloride draft monograph (From the European Pharmacopoeia forum), one new, simple, and fast enantioselective normal phase high‐performance liquid chromatography chiral method was developed on polysaccharide‐based Chiral MX (2) (4.6 × 250 mm, 5 μm) column. n‐Hexane, ethanol, and diethylamine in the ratio of 40:60:0.1 (V/V/V) were selected as mobile phase at a flow rate of 0.8 mL/min, and the detection was performed on a photodiode array detector at 225 nm with 5 μL injection volume. The column temperature was set at 40°C for better peak shape and sensitivity. The analysis time can be shortened to 15 min, whereas the resolution between enantiomer and olodaterol was found to be even more than 10.0, which was far better than that obtained with the reported method in this draft monograph. The developed chiral method was validated in accordance with ICH Q2 (R1), including specificity, LOD&LOQ, precision, linearity, accuracy, and robustness. Thereby, the proposed method was demonstrated to be suitable for the determination of enantiomer in olodaterol hydrochloride bulk drug and drug product. Besides, the thermodynamic parameters were evaluated on the basis of Van't Hoff plots that was used to explain correlative chiral recognition mechanisms with the chiral stationary phase. [ABSTRACT FROM AUTHOR]

Functional cure for chronic hepatitis B (CHB) remains challenging due to the lack of direct intervention methods for hepatic inflammation. Multi‐omics research offers a promising approach to understand hepatic inflammation mechanisms in CHB. A Bayesian linear model linked gene expression with clinical parameters, and population‐specific expression analysis (PSEA) refined bulk gene expression into specific cell types across different clinical phases. These models were integrated into our analysis of key factors like inflammatory cells, immune activation, T cell exhaustion, chemokines, receptors, and interferon‐stimulated genes (ISGs). Validation through multi‐immune staining in liver specimens from CHB patients bolstered our findings. In CHB patients, increased gene expression related to immune cell activation and migration was noted. Marker genes of macrophages, T cells, immune‐negative regulators, chemokines, and ISGs showed a positive correlation with serum alanine aminotransferase (ALT) levels but not hepatitis B virus DNA levels. The PSEA model confirmed T cells as the source of exhausted regulators, while macrophages primarily contributed to chemokine expression. Upregulated ISGs (ISG20, IFI16, TAP2, GBP1, PSMB9) in the hepatitis phase were associated with T cell and macrophage infiltration and positively correlated with ALT levels. Conversely, another set of ISGs (IFI44, ISG15, IFI44L, IFI6, MX1) mainly expressed by hepatocytes and B cells showed no correlation with ALT levels. Our study presents a multi‐omics analysis integrating bulk transcriptomic, single‐cell sequencing data, and clinical data from CHB patients to decipher the cause of intrahepatic inflammation in CHB. The findings confirm that macrophages secrete chemokines like CCL20, recruiting exhausted T cells into liver tissue; concurrently, hepatocyte innate immunity is suppressed, hindering the antiviral effects of ISGs. Highlights: This study integrates liver bulk transcriptomic data, single‐cell sequencing data, and clinical data to analyze the factors inducing hepatic inflammation in chronic hepatitis B from a multi‐omics perspective by Bayesian regression.Macrophages secrete chemokines like CCL20 and CXCL8 to recruit immune‐exhausted T lymphocytes (CTLA4, TIGIT) into liver tissue.Innate immunity within hepatocytes is suppressed, impeding interferon‐stimulated genes from initiating antiviral effects.Activation of innate immune pathways in infiltrating T cells and macrophages further exacerbates inflammation formation. [ABSTRACT FROM AUTHOR]

Background and Aim: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction. Methods: We collected 4558 images from two institutions to train and test models. We first developed two sole DL models (1 and 2) using supervised and semi‐supervised algorithms. Then we selected diagnosis‐related features through literature research and developed feature‐extraction models to determine features including boundary, surface, roundness, depression, and location. Then predictions of the five feature‐extraction models and sole DL model were combined and inputted into seven machine‐learning (ML) based fitting‐diagnosis models. The optimal model was selected as ENDOANGEL‐WD (whitish‐diagnosis) and compared with endoscopists. Results: Sole DL 2 had higher sensitivity (83.12% vs 68.67%, Bonferroni adjusted P = 0.024) than sole DL 1. Adding domain knowledge, the decision tree performed best among the seven ML models, achieving higher specificity than DL 1 (84.38% vs 72.27%, Bonferroni adjusted P < 0.05) and higher accuracy than DL 2 (80.47%, Bonferroni adjusted P < 0.001) and was selected as ENDOANGEL‐WD. ENDOANGEL‐WD showed better accuracy compared with 10 endoscopists (75.70%, P < 0.001). Conclusions: We developed a novel system ENDOANGEL‐WD combining domain knowledge and traditional DL to detect gastric whitish neoplasms. Adding domain knowledge improved the performance of traditional DL, which provided a novel solution for establishing diagnostic models for other rare diseases potentially. [ABSTRACT FROM AUTHOR]

Background The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. Methods ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. Results The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P <0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P <0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P <0.001). Conclusions ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals. [ABSTRACT FROM AUTHOR]