ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding.

BACKGROUND: Hepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability. RESULTS: This study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis- and anti-cancer-related genes, validating the reliability of the ViroISDC. CONCLUSIONS: ViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC.

A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance.

A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an F1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an F1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model's reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.

Tennis shot side-view and top-view data set for player analysis in Tennist.

Tennis is a popular sport, and the introduction of technology has allowed players to diversify their training. Tennis ball tracking is currently a focal point, serving not only to assist referees but also to enhance sports analysis. We introduce the Tennis Shot Side-View and Top-View Dataset, which serves as an invaluable resource for analyzing tennis movements and verifying landing positions after flight. This dataset combines side-view and top-view video clips, capturing various shot types and player movements from both outdoor and indoor fields. The dataset includes the actual ball positions of each clip for verification purposes. The Tennis Shot Side-View and Top-View Dataset represents a significant advancement in tennis research. Its multidimensional nature opens doors for in-depth player analysis, performance enhancement, and strategy development. We believe that this dataset will be a valuable asset to the tennis community, fostering innovation and excellence in the sport.

Influence of administration timing of San-Huang-Xie-Xin-Tang treatment on attenuating Salmonella enterica serovar Typhimurium infection.

Salmonella infections are a serious global health concern, particularly in developing countries, and are further exacerbated by the emergence of antibiotic resistance. San-Huang-Xie-Xin-Tang (SHXXT), a traditional herbal medicine with potent anti-inflammatory properties, has recently gained attention as an alternative treatment. Our study emphasizes on the importance of precise timing in accordance with traditional Chinese medicine principles. A mouse infection model was established while different administration times of SHXXT were recorded for the body weight, clinical scores, bacterial counts in blood, and organs. Additionally, cytokine levels, fatty acids, and amino acids in the serum were also monitored. We found that administering SHXXT 1 day after Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) infection (T1 group) leads to positive outcomes. This includes restoration of body weight, improved clinical scores, and reduced bacterial counts in blood and vital organs. Interferon-gamma levels remained consistently high across all treatment groups 6 days post-infection. However, the T1 group showed exclusive suppression of serum levels of tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1ß). The timing of administration significantly influenced serum fatty acid concentrations, countering Salmonella-induced disruptions, aligning with TNF-α and IL-1ß levels. SHXXT had also restored amino acid profiles disrupted by the infection, with notable effects when administered at the correct timing. Our research highlights SHXXT's potential in treating S. Typhimurium infection, emphasizing the importance of precise timing in line with traditional Chinese medicine principles for effective treatment at different disease stages.

Attention deficit hyperactivity disorder in children with epilepsy: a multicenter cross-sectional analysis in China.

BACKGROUND: The diagnosis and treatment of attention deficit hyperactivity disorder (ADHD) comorbid with epilepsy have been insufficiently addressed in China. We conducted a study in China to investigate the current status, diagnosis, and treatment of ADHD in children to further our understanding of ADHD comorbid with epilepsy, strengthen its management, and improve patients' quality of life. METHODS: We carried out a multicenter cross-sectional survey of children with epilepsy across China between March 2022 and August 2022. We screened all patients for ADHD and compared various demographic and clinical factors between children with and without ADHD, including gender, age, age at epilepsy onset, duration of epilepsy, seizure types, seizure frequency, presence of epileptiform discharges, and treatment status. Our objective was to explore any possible associations between these characteristics and the prevalence of ADHD. RESULTS: Overall, 395 epilepsy patients aged 6-18 years were enrolled. The age at seizure onset and duration of epilepsy ranged from 0.1-18 to 0.5-15 years, respectively. Focal onset seizures were observed in 212 (53.6%) patients, while 293 (76.3%) patients had epileptiform interictal electroencephalogram (EEG) abnormalities. Among the 370 patients treated with anti-seizure medications, 200 (54.1%) had monotherapy. Although 189 (47.8%) patients had ADHD, only 31 received treatment for it, with the inattentive subtype being the most common. ADHD was more common in children undergoing polytherapy compared to those on monotherapy. Additionally, poor seizure control and the presence of epileptiform interictal EEG abnormalities may be associated with a higher prevalence of ADHD. CONCLUSIONS: While the prevalence of ADHD was higher in children with epilepsy than in normal children, the treatment rate was notably low. This highlights the need to give more importance to the diagnosis and treatment of ADHD in children with epilepsy.

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (TJA), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin (<3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (>65 years of age), and body mass index of <25 kg/m2), 5-item (5MFI), and 6-item modified frailty index (6MFI). METHODS: Adult patients undergoing revision TJA between 2013 and 2020 were selected from the ACS-NSQIP database and randomly split 80:20 to compose the training and validation cohorts. There were three ML models - extreme gradient boosting (XGB), random forest (RF), and elastic-net penalized logistic regression (NEPLR) - that were developed and evaluated using discrimination, calibration metrics, and accuracy. The discrimination of CARDE-B, 5MFI, and 6MFI scores was assessed individually and compared to that of ML models. RESULTS: All models were equally accurate (Brier score = 0.005) and demonstrated outstanding discrimination with similar areas (AUC) under the receiver operating characteristic curve (XGB = 0.94, RF = NEPLR = 0.93). The NEPLR was the best-calibrated model overall (slope = 0.54, intercept = -0.004). The CARDE-B had the highest discrimination among the scores (AUC = 0.89), followed by 6MFI (AUC = 0.80), and 5MFI (AUC = 0.68). Albumin < 3.5 mg/dL and BMI (< 30.15) were the most important predictors of 30-day mortality following revision TJA. CONCLUSIONS: The ML models outperform traditional risk-assessment indices in predicting postoperative 30-day mortality after revision TJA. Our findings highlight the utility of ML for risk stratification in a clinical setting. The identification of hypoalbuminemia and BMI as prognostic markers may allow patient-specific perioperative optimization strategies to improve outcomes following revision TJA.

Survival benefit of postoperative radioiodine therapy among patients with intermediate-risk differentiated thyroid carcinoma.

BACKGROUND: The 2015 American Thyroid Association (ATA) guidelines proposed the use of the ATA Risk Stratification System and American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC/TNM) Staging System for postoperative radioiodine decision-making. However, the management of patients with intermediate-risk differentiated thyroid carcinoma (DTC) is not well defined. In this study, we aimed to evaluate the therapeutic efficacy of radioactive iodine therapy (RAIT) among various subgroups of patients with intermediate-risk DTC after surgery. METHODS: This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database (2010-2015). The DTC patients with intermediate risk of recurrence were divided into two groups (treated or not treated with radioactive iodine (RAI)). As the treatment was not randomly assigned, stabilized inverse probability treatment weighting (sIPTW) was used to reduce selection bias. We used the Kaplan-Meier method and log-rank test to analyze overall survival (OS) and cancer-specific survival (CSS). RESULTS: Kaplan-Meier analysis after sIPTW found a significant difference in OS and CSS between no RAIT and RAIT (log-rank test, P < 0.0001; P = 0.0019, respectively). The Kaplan-Meier curves of CSS in age cutoff of 55 years showed a significant association between no RAIT and RAIT (log-rank test, P = 0.0045). Univariate and multivariate Cox regression showed RAIT was associated with a reduced risk of mortality compared with no RAIT (hazard ratio [HR] 0.59, 95% confidence interval [95% CI 0.44-0.80]). Age (≥ 55) years showed a worse CSS regardless of whether or not a patient was treated or not treated with RAI ([HR] 8.91, 95% confidence interval [95% CI 6.19-12.84]). CONCLUSIONS: RAIT improves OS and CSS in patients with intermediate-risk DTC after surgery. 55 years is a more appropriate prognostic age cutoff for the relevant classification systems and is a crucial consideration in RAI decision-making. Therefore, we need individualized treatment plans.

A Biomimetic Cement-Based Solid-State Electrolyte with Both High Strength and Ionic Conductivity for Self-Energy-Storage Buildings.

Cement-based materials are the foundation of modern buildings but suffer from intensive energy consumption. Utilizing cement-based materials for efficient energy storage is one of the most promising strategies for realizing zero-energy buildings. However, cement-based materials encounter challenges in achieving excellent electrochemical performance without compromising mechanical properties. Here, we introduce a biomimetic cement-based solid-state electrolyte (labeled as l-CPSSE) with artificially organized layered microstructures by proposing an in situ ice-templating strategy upon the cement hydration, in which the layered micropores are further filled with fast-ion-conducting hydrogels and serve as ion diffusion highways. With these merits, the obtained l-CPSSE not only presents marked specific bending and compressive strength (2.2 and 1.2 times that of traditional cement, respectively) but also exhibits excellent ionic conductivity (27.8 mS·cm-1), overwhelming most previously reported cement-based and hydrogel-based electrolytes. As a proof-of-concept demonstration, we assemble the l-CPSSE electrolytes with cement-based electrodes to achieve all-cement-based solid-state energy storage devices, delivering an outstanding full-cell specific capacity of 72.2 mF·cm-2. More importantly, a 5 × 5 cm2 sized building model is successfully fabricated and operated by connecting 4 l-CPSSE-based full cells in series, showcasing its great potential in self-energy-storage buildings. This work provides a general methodology for preparing revolutionary cement-based electrolytes and may pave the way for achieving zero-carbon buildings.

Trends in prevalence of multimorbidity for chronic diseases in China: serial cross-sectional surveys from 2009 to 2018.

BACKGROUND: Multimorbidity, individuals suffering from two or more chronic diseases, has become a major health challenge worldwide, especially in populous and prosperous cities, where studies of this phenomenon in China are limited. We examined the prevalence, trends, patterns, and associated factors of multimorbidity from 2009 to 2018 among community-dwelling adults in Guangzhou, China. METHODS: We conducted serial cross-sectional surveys for chronic diseases in Guangzhou, China, in 2009, 2013, and 2018. General and stratified prevalence were standardized using demographic data. Multivariable logistic regression and hierarchical cluster analysis were applied to identify associated factors and to assess the correlations and patterns of multimorbidity, respectively. RESULTS: This study included 23,284 adults aged 18 and over in 2009, 18,551 in 2013, and 15,727 in 2018. The standardized prevalence of multimorbidity increased substantially, with 12.69% (95% CI: 10.45-15.33) in 2009, 25.44% (95% CI: 23.47-27.52) in 2013, and 35.13% (95% CI:32.64-37.70) in 2018 (P for trend <0.001). The highest bi- and triple-conditions of multimorbidity were dyslipidemia (DP) and overweight or obesity (OO) (12.54%, 95% CI: 11.68-13.46), and DP, OO, and Hypertension (HT) (3.99%, 95% CI: 3.47-4.58) in 2018. From 2009 to 2018, (1) The majority of multimorbidity patterns showed a high prevalence; (2) The percentage of participants with only one chronic condition was found lower, while the percentage with multiple conditions was higher. CONCLUSIONS: The prevalence of chronic disease multimorbidity in Guangzhou China, has increased substantially among adults. Effective policies targeting multimorbidity are urgently needed, especially for the health management of primary medical institutions.

Gene Co-Expression and miRNA Regulation: A Path to Early Intervention in Colorectal Cancer.

Early diagnosis and intervention are pivotal in reducing colorectal cancer (CRC) incidence and enhancing patient outcomes. In this study, we focused on three genes, AQP8, GUCA2B, and SPIB, which exhibit high co-expression and play crucial roles in suppressing early-stage CRC. Our objective was to identify key miRNAs that can mitigate CRC tumorigenesis and modulate the co-expression network involving these genes. We conducted a comprehensive analysis using large-scale tissue mRNA data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus to validate the co-expression of AQP8, GUCA2B, and SPIB, and to assess their diagnostic and prognostic significance in CRC. mRNA-miRNA interactions were examined using MiRNet and the Encyclopedia of RNA Interactomes. Furthermore, using various molecular techniques, we conducted miRNA inhibitor transfection experiments in HCT116 cells to evaluate their effects on cell growth, migration, and gene/protein expression. Our findings revealed that, compared to normal tissues, AQP8, GUCA2B, and SPIB exhibited high co-expression and were downregulated in CRC, particularly during tumorigenesis. OncoMirs, hsa-miR-182-5p, and hsa-miR-27a-3p, were predicted to regulate these genes. MiRNA inhibition experiments in HCT116 cells demonstrated the inhibitory effects of miR-27a-3p and miR-182-5p on GUCA2B mRNA and protein expression. These miRNAs promoted the proliferation of CRC cells, possibly through their involvement in the GUCA2B-GUCY2C axis, which is known to promote tumor growth. While the expressions of AQP8 and SPIB were barely detectable, their regulatory relationship with hsa-miR-182-5p remained inconclusive. Our study confirms that hsa-miR-27a-3p and hsa-miR-182-5p are oncomiRs in CRC. These miRNAs may contribute to GUCY2C dysregulation by downregulating GUCA2B, which encodes uroguanylin. Consequently, hsa-miR-182-5p and hsa-miR-27a-3p show promise as potential targets for early intervention and treatment in the early stages of CRC.

Novel kinase 1 regulates CD8+T cells as a potential therapeutic mechanism for idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a rare, chronic and progressively worsening lung disease that poses a significant threat to patient prognosis, with a mortality rate exceeding that of some common malignancies. Effective methods for early diagnosis and treatment remain for this condition are elusive. In our study, we used the GEO database to access second-generation sequencing data and associated clinical information from IPF patients. By utilizing bioinformatics techniques, we identified crucial disease-related genes and their biological functions, and characterized their expression patterns. Furthermore, we mapped out the immune landscape of IPF, which revealed potential roles for novel kinase 1 and CD8+T cells in disease progression and outcome. These findings can aid the development of new strategies for the clinical diagnosis and treatment of IPF.

Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease.

Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.

Practice Algorithm of Rotational Thromboelastometry-Guided (ROTEM-Guided) Bleeding Management in Liver Transplantation.

Liver transplantation (LT) is frequently complicated by coagulopathy associated with end-stage liver disease, which is often multifactorial and associated with hemostatic disturbances affecting both the procoagulant and anticoagulant systems. This rebalanced coagulation system may lead to bleeding diathesis or increased clot formation. Conventional coagulation tests cannot reflect these complex changes because they can only illustrate deficiencies in the procoagulant system. Viscoelastic tests such as rotational thromboelastometry (ROTEM) have been used in LT and have shown useful for detecting coagulopathy and guiding transfusions. Implementation of ROTEM-guided bleeding management algorithms has proven effectiveness in reducing bleeding, transfusion needs, complication rates, and healthcare costs in LT. This document is intended to provide a practice algorithm for the management of major bleeding and coagulopathy during LT and to encourage adaptation of the guidelines to individual institutional circumstances and resources.

Histone deacetylase Hos2 regulates protein expression noise by potentially modulating the protein translation machinery.

Non-genetic variations derived from expression noise at transcript or protein levels can result in cell-to-cell heterogeneity within an isogenic population. Although cells have developed strategies to reduce noise in some cellular functions, this heterogeneity can also facilitate varying levels of regulation and provide evolutionary benefits in specific environments. Despite several general characteristics of cellular noise having been revealed, the detailed molecular pathways underlying noise regulation remain elusive. Here, we established a dual-fluorescent reporter system in Saccharomyces cerevisiae and performed experimental evolution to search for mutations that increase expression noise. By analyzing evolved cells using bulk segregant analysis coupled with whole-genome sequencing, we identified the histone deacetylase Hos2 as a negative noise regulator. A hos2 mutant down-regulated multiple ribosomal protein genes and exhibited partially compromised protein translation, indicating that Hos2 may regulate protein expression noise by modulating the translation machinery. Treating cells with translation inhibitors or introducing mutations into several Hos2-regulated ribosomal protein genes-RPS9A, RPS28B and RPL42A-enhanced protein expression noise. Our study provides an effective strategy for identifying noise regulators and also sheds light on how cells regulate non-genetic variation through protein translation.

The role of adjuvant transcatheter arterial chemoembolization following repeated curative resection/ablation for hepatocellular carcinoma with early recurrence: a propensity score matching analysis.

BACKGROUND: The role of adjuvant transcatheter arterial chemoembolization (TACE) following repeated resection/ablation for recurrent hepatocellular carcinoma (HCC) remains uncertain. The aim of this study was to assess the effectiveness of adjuvant TACE following repeated resection or ablation in patients with early recurrent HCC. METHODS: Information for patients who underwent repeated surgery or radiofrequency ablation (RFA) for early recurrent HCCs (< 2 years) at our institution from January 2017 to December 2020 were collected. Patients were divided into adjuvant TACE and observation groups according to whether they received adjuvant TACE or not. The recurrence-free survival (RFS) and overall survival (OS) were compared between the two groups before and after propensity score matching (PSM). RESULTS: Of the 225 patients enrolled, the median time of HCC recurrence was 11 months (IQR, 6-16 months). After repeated surgery or radiofrequency ablation (RFA) for recurrent tumors, 45 patients (20%) received adjuvant TACE while the remaining 180 (80%) didn't. There were no significant differences in RFS (P = 0.325) and OS (P = 0.072) between adjuvant TACE and observation groups before PSM. There were also no significant differences in RFS (P = 0.897) and OS (P = 0.090) between the two groups after PSM. Multivariable analysis suggested that multiple tumors, liver cirrhosis, and RFA were independent risk factors for the re-recurrence of HCC. CONCLUSION: Adjuvant TACE after repeated resection or ablation for early recurrent HCCs was not associated with a long-term survival benefit in this single-center cohort.

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making.

Background: Vaginal birth after cesarean (VBAC) is generally regarded as a safe and viable birthing option for most women with prior cesarean delivery. Nonetheless, concerns about heightened risks of adverse maternal and perinatal outcomes have often dissuaded women from considering VBAC. This study aimed to assess the performance of an artificial intelligence (AI)-powered VBAC prediction system integrated into a decision-aid birth choice platform for shared decision-making (SDM). Materials and Methods: Employing a retrospective design, we collected medical records from a regional hospital in northern Taiwan from January 2019 to May 2023. To explore a suitable model for tabular data, we compared two prevailing modeling approaches: tree-based models and logistic regression models. We subjected the tree-based algorithm, CatBoost, to binary classification. Results: Forty pregnant women with 347 records were included. The CatBoost model demonstrated a robust performance, boasting an accuracy rate of 0.91 (95% confidence interval (CI): 0.86-0.94) and an area under the curve of 0.89 (95% CI: 0.86-0.93), surpassing both regression models and other boosting techniques. CatBoost captured the data characteristics on the significant impact of gravidity and the positive influence of previous vaginal birth, reinforcing established clinical guidelines, as substantiated by the SHapley Additive exPlanations analysis. Conclusion: Using AI techniques offers a more accurate assessment of VBAC risks, boosting women's confidence in selecting VBAC as a viable birthing option. The seamless integration of AI prediction systems with SDM platforms holds a promising potential for enhancing the effectiveness of clinical applications in the domain of women's healthcare.

The Prognostic Value of D-Dimer in Patients with Acute Myocardial Infarction: A Retrospective Longitudinal Cohort Study in Taiwan.

Background: Serum D-dimer level has been associated with worsening outcomes in patients with acute myocardial infarction. This study aimed to explore the association between serum D-dimer level and clinical outcomes in Taiwanese patients with acute myocardial infarction. Methods: We analyzed Tri-Service General Hospital-Coronary Heart Disease registry data related to patients with acute myocardial infarction who were admitted between January 2014 and December 2018. A total of 748 patients were enrolled and categorized into high (≥ 495 ng/ml) and low (< 495 ng/ml) D-dimer groups. The primary endpoint was in-hospital mortality, and secondary endpoints were post-discharge mortality and post-discharge major adverse cardiovascular events. Results: Overall, 139 patients died, with 77 from cardiovascular causes and 62 from non-cardiovascular causes. In-hospital mortality was higher in the high D-dimer group than in the low D-dimer group. Among the patients alive at discharge, those with a high D-dimer level had higher cardiovascular mortality and future major adverse cardiovascular events than those with a low D-dimer level. Multivariate Cox regression analysis revealed that higher serum D-dimer levels were significantly associated with higher risks of in-hospital mortality [hazard ratio (HR) = 1.11; 95% confidence interval (CI), 1.06-1.16, p < 0.001], subsequent cardiovascular mortality after discharge (HR = 1.15; 95% CI, 1.08-1.22, p < 0.001), and major adverse cardiovascular events (HR = 1.10; 95% CI, 1.04-1.16, p < 0.001). Conclusions: This is the first study in Taiwan to demonstrate that a higher baseline serum D-dimer level was independently associated with higher risks of in-hospital mortality, post-discharge mortality, and major adverse cardiovascular events in patients with acute myocardial infarction.

Efficient Enhancement of Extracellular Electron Transfer in Shewanella oneidensis MR-1 via CRISPR-Mediated Transposase Technology.

Electroactive bacteria, exemplified by Shewanella oneidensis MR-1, have garnered significant attention due to their unique extracellular electron-transfer (EET) capabilities, which are crucial for energy recovery and pollutant conversion. However, the practical application of MR-1 is constrained by its EET efficiency, a key limiting factor, due to the complexity of research methodologies and the challenges associated with the practical use of gene editing tools. To address this challenge, a novel gene integration system, INTEGRATE, was developed, utilizing CRISPR-mediated transposase technologies for precise genomic insertion within the S. oneidensis MR-1 genome. This system facilitated the insertion of extensive gene segments at different sites of the Shewanella genome with an efficiency approaching 100%. The inserted cargo genes could be kept stable on the genome after continuous cultivation. The enhancement of the organism's EET efficiency was realized through two primary strategies: the integration of the phenazine-1-carboxylic acid synthesis gene cluster to augment EET efficiency and the targeted disruption of the SO3350 gene to promote anodic biofilm development. Collectively, our findings highlight the potential of utilizing the INTEGRATE system for strategic genomic alterations, presenting a synergistic approach to augment the functionality of electroactive bacteria within bioelectrochemical systems.

Symmetry-breaking of Dibenzo[b,d]thiophene Sulfone Enhancing Polaron Generation for Boosted Photocatalytic Hydrogen Evolution.

The current bottleneck in the development of efficient photocatalysts for hydrogen evolution is the limited availability of high-performance acceptor units. Over the past nine years, dibenzo[b,d]thiophene sulfone (DBS) has been the preferred choice for the acceptor unit. Despite extensive exploration of alternative structures as potential replacements for DBS, a superior substitute remains elusive. In this study, a symmetry-breaking strategy was employed on DBS to develop a novel acceptor unit, BBTT-1SO. The asymmetric structure of BBTT-1SO proved beneficial for increasing multiple moment and polarizability. BBTT-1SO-containing polymers showed higher efficiencies for hydrogen evolution than their DBS-containing counterparts by up to 166%. PBBTT-1SO exhibited an excellent hydrogen evolution rate (HER) of 222.03 mmol g-1 h-1 and an apparent quantum yield of 27.5% at 500 nm. Transient spectroscopic studies indicated that the BBTT-1SO-based polymers facilitated electron polaron formation, which explains their superior HERs. PBBTT-1SO also showed 14% higher HER in natural seawater splitting than that in deionized water splitting. Molecular dynamics simulations highlighted the enhanced water-PBBTT-1SO polymer interactions in salt-containing solutions. This study presents a pioneering example of a substitute acceptor unit for DBS in the construction of high-performance photocatalysts for hydrogen evolution.

Predicting early mortality and severe intraventricular hemorrhage in very-low birth weight preterm infants: a nationwide, multicenter study using machine learning.

Our aim was to develop a machine learning-based predictor for early mortality and severe intraventricular hemorrhage (IVH) in very-low birth weight (VLBW) preterm infants in Taiwan. We collected retrospective data from VLBW infants, dividing them into two cohorts: one for model development and internal validation (Cohort 1, 2016-2021), and another for external validation (Cohort 2, 2022). Primary outcomes included early mortality, severe IVH, and early poor outcomes (a combination of both). Data preprocessing involved 23 variables, with the top four predictors identified as gestational age, birth body weight, 5-min Apgar score, and endotracheal tube ventilation. Six machine learning algorithms were employed. Among 7471 infants analyzed, the selected predictors consistently performed well across all outcomes. Logistic regression and neural network models showed the highest predictive performance (AUC 0.81-0.90 in both internal and external validation) and were well-calibrated, confirmed by calibration plots and the lowest two mean Brier scores (0.0685 and 0.0691). We developed a robust machine learning-based outcome predictor using only four accessible variables, offering valuable prognostic information for parents and aiding healthcare providers in decision-making.