Clarifying varied Helicobacter pylori eradication therapies: A comprehensive review.

Current global variations exist in Helicobacter pylori (H. pylori) eradication regimens. Triple therapy (TT), bismuth quadruple therapy (BQT), and high-dose dual therapy (HDDT) currently represent the predominant regimens. These regimens diverge in terms of treatment duration, the utilization of susceptibility testing, acid-inhibiting drug administration, and patient education. We conducted a comprehensive systematic literature review on these H. pylori treatment regimens. Our review aims to provide standardized treatment recommendations for H. pylori, reducing the risk of amalgamating findings from diverse eradication regimens. Recent research suggests that the optimal treatment duration for TT and BQT may be 14 and 10 days, respectively. Selecting the appropriate treatment duration for HDDT should rely on regional research evidence, and 14 days may be the optimal duration. The incorporation of susceptibility testing in TT is of paramount importance. In the case of BQT, the absence of susceptibility testing may be considered as an option, contingent upon cost and availability, and should be determined based on local antibiotic resistance patterns and the efficacy of empirical regimens. The type and dosage of acid-inhibiting drug would affect the efficacy of these regimens. Acid-inhibiting drugs should be selected and applied reasonably according to the population and therapies. Adequate patient education plays a pivotal role in the eradication of H. pylori. In regions with accessible local research evidence, the 10-day empirical BQT regimen may be considered a preferred choice for H. pylori eradication.

Advancements and trends in exosome research in lung cancer from a bibliometric analysis (2004-2023).

Background: Lung cancer, characterized by its high morbidity and lethality, necessitates thorough research to enhance our understanding of its pathogenesis and discover novel therapeutic approaches. Recent studies increasingly demonstrate that lung cancer cells can modulate the tumor microenvironment, promoting tumor growth, and metastasis through the release of exosomes. Exosomes are small vesicles secreted by cells and contain a variety of bioactive molecules such as proteins, nucleic acids, and metabolites. This paper presents a comprehensive review of exosome research in lung cancer and its progress through bibliometric analysis. Methods: Publications related to exosomes in lung cancer patients were systematically searched on the Web of Science Core Collection (WoSCC) database. Bibliometric analysis was performed using VOSviwers, CiteSpace, and the R package "Bibliometrics". Publications were quantitatively analyzed using Microsoft Office Excel 2019. The language of publication was restricted to "English" and the search strategy employed TS=(exosomes or exosomes or exosomes) and TS=(lung cancer). The search period commenced on January 1, 2004, and concluded on November 12, 2023, at noon. The selected literature types included Articles and Reviews. Results: The study encompassed 1699 papers from 521 journals across 71 countries and 2105 institutions. Analysis revealed a consistent upward trend in lung cancer exosome research over the years, with a notable surge in recent times. This surge indicates a growing interest and depth of inquiry into lung cancer exosomes. Major research institutions in China and the United States, including Nanjing Medical University, Shanghai Jiao Tong University, Chinese Academy Of Sciences, and Utmd Anderson Cancer Center, emerged as crucial research hubs. The annual publication count in this field witnessed a continuous rise, particularly in recent years. Key terms such as lung cancer, non-small cell lung cancer (NSCLC), microvesicles, intercellular communication, exosomal miRNAs, and oncology dominated the research landscape. Fields like cell biology, biochemistry, biotechnology, and oncology exhibited close relation with this research. Clotilde Théry emerged as the most cited author in the field, underlining her significant contributions. These results demonstrate the broad impact of exosome research in lung cancer, with key terms covering not only disease-specific aspects such as lung cancer and NSCLC but also basic biological concepts like microvesicles and intercellular communication. Explorations into exosomal microRNAs and oncology have opened new avenues for lung cancer exosome research. In summary, lung cancer exosome research is poised to continue receiving attention, potentially leading to breakthroughs in treatment and prevention. Conclusion: Publications on lung cancer exosomes show a rising trend year by year, with China and the United States ranking first and second in terms of the number of publications. However, there is insufficient academic learning cooperation and exchanges between the two sides, and Chinese universities account for a large proportion of research institutions in this field. Jing Li is the most productive author, Clotilde Théry is the most co-cited author, and Cancers is the journal with the highest number of publications. The current focus in the field of lung cancer exosomes is on biomarkers, liquid biopsies, immunotherapy, and tumor microenvironment.

The two-directional prospective association between inflammatory bowel disease and neurodegenerative disorders: a systematic review and meta-analysis based on longitudinal studies.

Objective: Previous studies reported possible connections between inflammatory bowel disease (IBD) and several neurodegenerative disorders. However, the comprehensive relationships between IBD and various neurodegenerative disorders were not summarized. We executed a meta-analysis of longitudinal studies to provide an estimate of the strength of the two-directional prospective association between IBD and neurodegenerative disorders. Methods: We accomplished a thorough bibliographic search of PubMed, Web of Science, Embase, PsycINFO, and Cochrane Library databases until June 2023 to locate relevant longitudinal studies. The extracted data were then analyzed via meta-analysis using either a fixed or random effects model. Results: The final analysis encompassed 27 studies. Individuals with IBD faced an increased risk of developing four neurodegenerative disorders than the general public, namely, Alzheimer's disease (hazard ratio[HR] = 1.35, 95% confidence interval [CI]: 1.03-1.77, P=0.031), dementia (HR =1.24, 95% CI: 1.13-1.36, P<0.001), multiple sclerosis (HR =2.07, 95% CI:1.42-3.02, P<0.001) and Parkinson's disease (HR =1.23, 95% CI:1.10-1.38, P<0.001). Two articles reported an increased incidence of amyotrophic lateral sclerosis or multiple system atrophy in IBD patients. Three studies investigated the prospective association between multiple sclerosis and IBD, revealing an elevated risk of the latter in patients with the former. (HR=1.87, 95% CI:1.66-2.10, P<0.001). Interpretation: These findings verified the two-directional relationship between the brain-gut axis, specifically demonstrating a heightened risk of various neurodegenerative diseases among IBD patients. It may be profitable to prepare screening strategies for IBD patients to find neurodegenerative diseases during the long-term course of treatment for IBD with a view to potential earlier diagnosis and treatment of neurodegenerative diseases, reducing public health and social burden. Systematic Review Registration: PROSPERO (CRD42023437553).

Pyramidal and parvalbumin neurons modulate the process of electroacupuncture stimulation for stroke rehabilitation.

Electroacupuncture (EA) stimulation has been shown to be beneficial in stroke rehabilitation; however, little is known about the neurological mechanism by which this peripheral stimulation approach treats for stroke. This study showed that both pyramidal and parvalbumin (PV) neuronal activity increased in the contralesional primary motor cortex forelimb motor area (M1FL) after ischemic stroke induced by focal unilateral occlusion in the M1FL. EA stimulation reduced pyramidal neuronal activity and increased PV neuronal activity. These results were obtained by a combination of fiber photometry recordings, in vivo and in vitro electrophysiological recordings, and immunofluorescence. Moreover, EA was found to regulate the expression/function of N-methyl-D-aspartate receptors (NMDARs) altered by stroke pathology. In summary, our findings suggest that EA could restore disturbed neuronal activity through the regulation of the activity of pyramidal and PV neurons. Furthermore, NMDARs we shown to play an important role in EA-mediated improvements in sensorimotor ability during stroke rehabilitation.

Evaluation of Patient-Centric Sample Collection Technologies for Pharmacokinetic Assessment of Large and Small Molecules.

Low-volume sampling devices offer the promise of lower discomfort and greater convenience for patients, potentially reducing patient burden and enabling decentralized clinical trials. In this study, we determined whether low-volume sampling devices produce pharmacokinetic (PK) data comparable to conventional venipuncture for a diverse set of monoclonal antibodies (mAbs) and small molecules. We adopted an open-label, non-randomized, parallel-group, single-site study design, with four cohorts of 10 healthy subjects per arm. The study drugs, doses, and routes of administration included: crenezumab (15 mg/kg, intravenous infusion), etrolizumab (210 mg, subcutaneous), GDC-X (oral), and hydroxychloroquine (HCQ, 200 mg, oral). Samples were collected after administration of a single dose of each drug using conventional venipuncture and three low-volume capillary devices: TassoOne Plus for liquid blood, Tasso-M20 for dry blood, both applied to the arm, and Neoteryx Mitra® for dry blood obtained from fingertips. Serum/plasma concentrations from venipuncture and TassoOne Plus samples overlapped and PK parameters were comparable for all drugs, except HCQ. After applying a baseline hematocrit value, the dry blood concentrations and PK parameters for the two monoclonal antibodies were comparable to those obtained from venipuncture. For the two small molecules, two bridging strategies were evaluated for converting dry blood concentrations to equivalent plasma concentrations. A baseline hematocrit correction and/or linear regression-based correction was effective for GDC-X, but not for HCQ. Additionally, the study evaluated the bioanalytical data quality and comparability from the various collection methods, as well as patient preference for the devices.

A Phase 1a Study to Evaluate Safety, Tolerability, Pharmacokinetics, and Pharmacodynamics of RO7303509, an Anti-TGFß3 Antibody, in Healthy Volunteers.

INTRODUCTION: Transforming growth factor beta (TGFß) cytokines (TGFß1, TGFß2, and TGFß3) play critical roles in tissue fibrosis. However, treatment with systemic pan-TGFß inhibitors have demonstrated unacceptable toxicities. In this study, we evaluated the safety, tolerability, pharmacokinetics, and pharmacodynamics of RO7303509, a high-affinity, TGFß3-specific, humanized immunoglobulin G1 monoclonal antibody, in healthy adult volunteers (HVs). METHODS: This phase 1a, randomized, double-blind trial included six cohorts for evaluation, with each cohort receiving single doses of placebo or RO7303509, administered intravenously (IV; 50 mg, 150 mg, 240 mg) or subcutaneously (SC; 240 mg, 675 mg, 1200 mg). The frequency and severity of adverse events (AEs) and RO7303509 serum concentrations were monitored throughout the study. We also measured serum periostin and cartilage oligomeric matrix protein (COMP) by immunoassay and developed a population pharmacokinetics model to characterize RO7303509 serum concentrations. RESULTS: The study enrolled 49 HVs, with a median age of 39 (range 18-73) years. Ten (27.8%) RO7303509-treated subjects reported 24 AEs, and six (30.8%) placebo-treated subjects reported six AEs. The most frequent AEs related to the study drug were injection site reactions and infusion-related reactions. Maximum serum concentrations (Cmax) and area under the concentration-time curve from time 0 to infinity (AUC0-inf) values for RO7303509 appeared to increase dose-proportionally across all doses tested. Serum concentrations across cohorts were best characterized by a two-compartment model plus a depot compartment with first-order SC absorption kinetics. No subjects tested positive for anti-drug antibodies (ADAs) at baseline; one subject (2.8%; 50 mg IV) tested positive for ADAs at a single time point (day 15). No clear pharmacodynamic effects were observed for periostin or COMP upon TGFß3 inhibition. CONCLUSION: RO7303509 was well tolerated at single SC doses up to 1200 mg in HVs with favorable pharmacokinetic data that appeared to increase dose-proportionally. TGFß3-specific inhibition may be suitable for development as a chronic antifibrotic therapy. TRIAL REGISTRATION: ISRCTN13175485.

Artificial-intelligence-based risk prediction and mechanism discovery for atrial fibrillation using heart beat-to-beat intervals.

BACKGROUND: Early diagnosis of atrial fibrillation (AF) is important for preventing stroke and other complications. Predicting AF risk in advance can improve early diagnostic efficiency. Deep learning has been used for disease risk prediction; however, it lacks adherence to evidence-based medicine standards. Identifying the underlying mechanisms behind disease risk prediction is important and required. METHODS: We developed an explainable deep learning model called HBBI-AI to predict AF risk using only heart beat-to-beat intervals (HBBIs) during sinus rhythm. We proposed a possible AF mechanism based on the model's explainability and verified this conjecture using confirmed AF risk factors while also examining new AF risk factors. Finally, we investigated the changes in clinicians' ability to predict AF risk using only HBBIs before and after learning the model's explainability. FINDINGS: HBBI-AI consistently performed well across large in-house and external public datasets. HBBIs with large changes or extreme stability were critical predictors for increased AF risk, and the underlying cause was autonomic imbalance. We verified various AF risk factors and discovered that autonomic imbalance was associated with all these factors. Finally, cardiologists effectively understood and learned from these findings to improve their abilities in AF risk prediction. CONCLUSIONS: HBBI-AI effectively predicted AF risk using only HBBI information through evaluating autonomic imbalance. Autonomic imbalance may play an important role in many risk factors of AF rather than in a limited number of risk factors. FUNDING: This study was supported in part by the National Key R&D Program and the National Natural Science Foundation of China.

Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.

We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.

Incorporating longitudinal history of risk factors into atherosclerotic cardiovascular disease risk prediction using deep learning.

It is increasingly clear that longitudinal risk factor levels and trajectories are related to risk for atherosclerotic cardiovascular disease (ASCVD) above and beyond single measures. Currently used in clinical care, the Pooled Cohort Equations (PCE) are based on regression methods that predict ASCVD risk based on cross-sectional risk factor levels. Deep learning (DL) models have been developed to incorporate longitudinal data for risk prediction but its benefit for ASCVD risk prediction relative to the traditional Pooled Cohort Equations (PCE) remain unknown. Our study included 15,565 participants from four cardiovascular disease cohorts free of baseline ASCVD who were followed for adjudicated ASCVD. Ten-year ASCVD risk was calculated in the training set using our benchmark, the PCE, and a longitudinal DL model, Dynamic-DeepHit. Predictors included those incorporated in the PCE: sex, race, age, total cholesterol, high density lipid cholesterol, systolic and diastolic blood pressure, diabetes, hypertension treatment and smoking. The discrimination and calibration performance of the two models were evaluated in an overall hold-out testing dataset. Of the 15,565 participants in our dataset, 2170 (13.9%) developed ASCVD. The performance of the longitudinal DL model that incorporated 8 years of longitudinal risk factor data improved upon that of the PCE [AUROC: 0.815 (CI 0.782-0.844) vs 0.792 (CI 0.760-0.825)] and the net reclassification index was 0.385. The brier score for the DL model was 0.0514 compared with 0.0542 in the PCE. Incorporating longitudinal risk factors in ASCVD risk prediction using DL can improve model discrimination and calibration.

Immune escape of BA.2.86 is comparable to XBB subvariants from the plasma of BA.5- and BA.5-XBB-convalescent subpopulations.

The EG.5.1 variant of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been prevalent since mid-July 2023 in the United States and China. The variant BA.2.86 has become a major concern because it is 34 mutations away from the parental variant BA.2 and >30 mutations from XBB.1.5. There is an urgent need to evaluate whether the immunity of the population and current vaccines are protective against EG.5.1 and BA.2.86. Based on a cohort of two breakthrough-infected groups, the levels of neutralizing antibodies (NAbs) against different subvariants were measured using pseudovirus-based neutralization assays. XBB.1.5, EG.5.1, and BA.2.86 are comparably immune-evasive from neutralization by the plasma of individuals recovered from BA.5 infection (BA.5-convalescent) or XBB.1.9.2/XBB.1.5 infection following BA.5 infection (BA.5-XBB-convalescent). NAb levels against EG.5.1 and BA.2.86 subvariants remained >120 geometric mean titers (GMTs) in BA.5-XBB-convalescent individuals 2 months postinfection but were <40 GMTs in BA.5-convalescent individuals. Furthermore, the XBB-targeting messenger RNA (mRNA) vaccine RQ3033 induced higher levels of NAbs against XBB.1.5, EG.5.1, and BA.2.86 than against BA.5-XBB infection. The results suggest that BA.2.86 and EG.5.1 are unlikely to cause more severe concerns than the currently circulating XBB subvariants and that the XBB.1.5-targeting mRNA vaccine tested has promising protection against EG.5.1 and BA.2.86.

Influence mechanism of green finance on regional emission reduction.

The implementation of green finance is crucial in achieving a reduction in regional emissions. As such, understanding how green finance affects regional emission reduction is essential. Using provincial panel data from 2008 to 2019, we employed the fixed effects model to examine the impact of green finance on regional emission reduction. The empirical results reveal the following: (1) Green finance has a negative effect on sulfur dioxide intensity, and the development of green finance can significantly reduce the emission of regional pollutants. (2) Among the different instruments of green finance, green credit and green investment exhibit more substantial emission reduction effects than green securities and green insurance. (3) The mechanism by which green finance affects regional emission reduction is mainly through the advanced industrial structure and green technology innovation. (4) The development of green finance shows geographical discrepancies: The eastern region of China is more effective in reducing emissions than the central and western regions. To fully maximize the role of green finance in emission reduction, this paper offers pertinent suggestions for strengthening the green financial system, improving the advanced industrial process, increasing investment in green energy technology, and formulating specific development tactics that consider the prominent characteristics of distinct regions.

Deep learning-based diagnosis of aortic dissection using an electrocardiogram: Development, validation, and clinical implications of the AADE score.

BACKGROUND: Aortic dissection (AD) is frequently associated with abnormalities in electrocardiographic findings. Advancements in medical technology present an opportunity to leverage these observations to improve patient diagnosis and care. OBJECTIVES: This study aimed to develop a deep learning artificial intelligence (AI) model for AD detection using electrocardiograms (ECGs) and introduce the AI-Aortic-Dissection-ECG (AADE) score to provide clinicians with a measure to determine AD severity. METHODS: From a cohort of 1878 patients, including 313 with AD, and 313 with chest pain (control group), we created training and validation subsets (7:3 ratio). A convolutional neural networks (CNN) model was trained for AD detection, with performance metrics like accuracy and F1 score (the harmonic mean of precision and recall) monitored. The AI-derived AADE score (0-1) was investigated against clinical parameters and ECG features over a median follow-up of 21.2 months. RESULTS: The CNN model demonstrated robust performance with an accuracy of 0.93 and an F1 score of 0.93 for the AD group, and an accuracy of 0.871 with an F1 score of 0.867 for the chest pain group. The AADE score showed correlations with specific ECG patterns and demonstrated that higher scores aligned with increased mortality risk. CONCLUSIONS: Our CNN-based AI model offers a promising approach for AD detection using ECG. The AADE score, based on AI, can serve as a pivotal tool in refining clinical assessments and management strategies.

A novel method for determination of peroxide value and acid value of extra-virgin olive oil based on fluorescence internal filtering effect correction.

Peroxide value (PV) and acid value (AV) are widely used indicators for evaluating oxidation degree of olive oils. Fluorescence spectroscopy has been extensively studied on the detection of oil oxidation, however, the detection accuracy is limited due to internal filtering effect (IFE). Due to the primary and secondary IFE, at least two wavelengths of absorption information are required. Least squares support vector regression (LSSVR) models for PV and AV were established based on two absorption coefficients (µa) at 375 nm and emission wavelength and one fluorescence intensity at corresponding wavelength. The regression results proved that the model based on 375 and 475 nm could reach the best performance, with the highest correlation coefficient for prediction (rp) of 0.889 and 0.960 for PV and AV respectively. Finally, the explicit formulations for PV and AV were determined by nonlinear least squares fitting, and the rp could reach above 0.94 for two indicators.

Quantitative prediction of AFB1 in various types of edible oil based on absorption, scattering and fluorescence signals at dual wavelengths.

This study aims to address the challenge of matrix interference of various types of edible oils on intrinsic fluorescence of aflatoxin B1 (AFB1) by developing a novel solution. Considering the fluorescence internal filtering effect, the absorption (µa) and reduced scattering (µ's) coefficients at dual wavelengths (excitation: 375 nm, emission: 450 nm) were obtained by using integrating sphere technique, and were used to improve the quantitative prediction results for AFB1 contents in six different kinds of edible oils. A research process of "Monte Carlo (MC) simulation - phantom verification - actual sample validation" was conducted. The MC simulation was used to determine interference rule and correction parameters for fluorescence, the results indicated that the escaped fluorescence flux nonlinearly decreased with the µa, µ's at emission wavelength (µa,em, µ's,em) and µa at excitation wavelength (µa,ex), however increased with the µ's at excitation wavelength (µ's,ex). And the required optical parameters to eliminate the interference of matrix on fluorescence intensity are: effective attenuation coefficients at excitation and emission wavelengths (µeff,ex, µeff,em) and µ's,ex. Phantom verification was conducted to explore the feasibility of fluorescence correction based on the identified parameters by MC simulation, and determine the optimal machine learning method. The modelling results showed that least squares support vector regression (LSSVR) model could reach the best performance. Three kinds of edible oil (peanut, rapeseed, corn), each with two brands were used to prepare oil samples with different AFB1 contamination. The LSSVR model for AFB1 based on µeff,ex, µeff,em, µ's,ex and fluorescence intensity at 450 nm was calibrated, both correlation coefficients for calibration (Rc) and the validation (Rv) sets could reach 1.000, root mean square errors for calibration (RMSEC) and the validation (RMSEV) sets were as low as 0.038 and 0.099 respectively. This study proposed a novel method which is based solely on the absorption, scattering, and fluorescence characteristics at excitation and emission wavelengths to achieve accurate prediction of AFB1 content in different types of vegetable oils.

Global evolving patterns and cross-country inequalities of inflammatory bowel disease burden from 1990 to 2019: a worldwide report.

AIMS: Inflammatory bowel disease (IBD) is a global disease. We aim to summarize the latest epidemiological patterns of IBD at the national, regional and global levels to give well-deserved attention and outline facilitating measures to reduce the disease burden. METHODS: We collected the incidence, prevalence, mortality and disability-adjusted life years (DALYs) of IBD in 204 countries and territories from 1990 to 2019 using data from the Global Burden of Disease Study 2019. We further calculated the estimated annual percentage change (EAPC) to qualify the temporal trends of IBD burden by sex, age and region over the past 30 years. RESULTS: Globally, a total of 404.55 thousand incident cases, 4898.56 thousand prevalent cases, 41.00 thousand deaths and 1622.50 thousand DALYs of IBD were estimated in 2019. The age-standardized DALYs decreased from 27.2 in 1990 to 20.15 per 100,000 people in 2019, with an EAPC of -1.04. The high socio-demographic index regions presented pronounced age-standardized rates (ASRs) consistently over the last 30 years. The high-income North America had the highest ASRs in 2019, followed by Western Europe and Australasia. No gender difference was observed after being stratified by sex. CONCLUSIONS: The accumulated IBD patients are expected to increase in the future due to the increased rate of IBD in developing countries, and social aging in developed countries. Understanding the changes in epidemiological patterns helps to provide evidence to mitigate the rising burden of IBD.

ARTC1-mediated VAPB ADP-ribosylation regulates calcium homeostasis.

Mono-ADP-ribosylation (MARylation) is a post-translational modification that regulates a variety of biological processes, including DNA damage repair, cell proliferation, metabolism, and stress and immune responses. In mammals, MARylation is mainly catalyzed by ADP-ribosyltransferases (ARTs), which consist of two groups: ART cholera toxin-like (ARTCs) and ART diphtheria toxin-like (ARTDs, also known as PARPs). The human ARTC (hARTC) family is composed of four members: two active mono-ADP-ARTs (hARTC1 and hARTC5) and two enzymatically inactive enzymes (hARTC3 and hARTC4). In this study, we systematically examined the homology, expression, and localization pattern of the hARTC family, with a particular focus on hARTC1. Our results showed that hARTC3 interacted with hARTC1 and promoted the enzymatic activity of hARTC1 by stabilizing hARTC1. We also identified vesicle-associated membrane protein-associated protein B (VAPB) as a new target of hARTC1 and pinpointed Arg50 of VAPB as the ADP-ribosylation site. Furthermore, we demonstrated that knockdown of hARTC1 impaired intracellular calcium homeostasis, highlighting the functional importance of hARTC1-mediated VAPB Arg50 ADP-ribosylation in regulating calcium homeostasis. In summary, our study identified a new target of hARTC1 in the endoplasmic reticulum and suggested that ARTC1 plays a role in regulating calcium signaling.

Cardioprotective polyphenols from Geum japonicum var. chinense.

Seven undescribed tannins, namely gejaponin A-G, and one dehydrodigallic acid derivative 3,4-dihydroxy-5-(3,4,5-trihydroxy-1-ethoxycarbonyl phenoxy)benzoic acid, together with eighteen known polyphenols were isolated from the 95% ethanol extract of the aerial part of Geum japonicum Thunb. var. chinense F. Bolle. Their structures were elucidated on the basis of comprehensive analysis of UV, IR, NMR, HRMS, and CD spectroscopy experiments. To evaluate their bioactivities, sixteen major compounds were selected to intervene in hydrogen peroxide (H2O2)-induced oxidative damage on H9c2 rat cardiomyoblasts. Some compounds demonstrated high activity in this assay, of which, the known compounds 16 and 21 exhibited strong protective effects against H2O2-induced injury in H9c2 rat cardiomyoblasts, with a comparable cardioprotective activity as that of the positive control trimetazidine, thereby revealing cardioprotective activities from G. japonicum var. chinense.

Novel miniature transendoscopic telerobotic system for endoscopic submucosal dissection (with videos).

BACKGROUND AND AIMS: The lack of tissue traction and instrument dexterity to allow for adequate visualization and effective dissection were the main issues in performing endoscopic submucosal dissection (ESD). Robot-assisted systems may provide advantages. In this study we developed a novel transendoscopic telerobotic system and evaluated its performance in ESD. METHODS: A miniature dual-arm robotic endoscopic assistant for minimally invasive surgery (DREAMS) was developed. The DREAMS system contained the current smallest robotic ESD instruments and was compatible with the commercially available dual-channel endoscope. After the system was established, a prospective randomized controlled study was conducted to validate the performance of the DREAMS-assisted ESD in terms of efficacy, safety, and workload by comparing it with the conventional technique. RESULTS: Two robotic instruments can achieve safe collaboration and provide sufficient visualization and efficient dissection during ESD. Forty ESDs in the stomach and esophagus of 8 pigs were completed by DREAMS-assisted ESD or conventional ESD. Submucosal dissection time was comparable between the 2 techniques, but DREAMS-assisted ESD demonstrated a significantly lower muscular injury rate (15% vs 50%, P = .018) and workload scores (22.30 vs 32.45, P < .001). In the subgroup analysis of esophageal ESD, DREAMS-assisted ESD showed significantly improved submucosal dissection time (6.45 vs 16.37 minutes, P = .002), muscular injury rate (25% vs 87.5%, P = .041), and workload (21.13 vs 40.63, P = .001). CONCLUSIONS: We developed a novel transendoscopic telerobotic system, named DREAMS. The safety profile and technical feasibility of ESD were significantly improved with the assistance of the DREAMS system, especially in the narrower esophageal lumen.

Molecular mechanism of human ISG20L2 for the ITS1 cleavage in the processing of 18S precursor ribosomal RNA.

The exonuclease ISG20L2 has been initially characterized for its role in the mammalian 5.8S rRNA 3' end maturation, specifically in the cleavage of ITS2 of 12S precursor ribosomal RNA (pre-rRNA). Here, we show that human ISG20L2 is also involved in 18S pre-rRNA maturation through removing the ITS1 region, and contributes to ribosomal biogenesis and cell proliferation. Furthermore, we determined the crystal structure of the ISG20L2 nuclease domain at 2.9 Å resolution. It exhibits the typical αßα fold of the DEDD 3'-5' exonuclease with a catalytic pocket located in the hollow near the center. The catalytic residues Asp183, Glu185, Asp267, His322 and Asp327 constitute the DEDDh motif in ISG20L2. The active pocket represents conformational flexibility in the absence of an RNA substrate. Using structural superposition and mutagenesis assay, we mapped RNA substrate binding residues in ISG20L2. Finally, cellular assays revealed that ISG20L2 is aberrantly up-regulated in colon adenocarcinoma and promotes colon cancer cell proliferation through regulating ribosome biogenesis. Together, these results reveal that ISG20L2 is a new enzymatic member for 18S pre-rRNA maturation, provide insights into the mechanism of ISG20L2 underlying pre-rRNA processing, and suggest that ISG20L2 is a potential therapeutic target for colon adenocarcinoma.

Artificial intelligence-enabled 8-lead ECG detection of atrial septal defect among adults: a novel diagnostic tool.

Background: Patients with atrial septal defect (ASD) exhibit distinctive electrocardiogram (ECG) patterns. However, ASD cannot be diagnosed solely based on these differences. Artificial intelligence (AI) has been widely used for specifically diagnosing cardiovascular diseases other than arrhythmia. Our study aimed to develop an artificial intelligence-enabled 8-lead ECG to detect ASD among adults. Method: In this study, our AI model was trained and validated using 526 ECGs from patients with ASD and 2,124 ECGs from a control group with a normal cardiac structure in our hospital. External testing was conducted at Wuhan Central Hospital, involving 50 ECGs from the ASD group and 46 ECGs from the normal group. The model was based on a convolutional neural network (CNN) with a residual network to classify 8-lead ECG data into either the ASD or normal group. We employed a 10-fold cross-validation approach. Results: Statistically significant differences (p < 0.05) were observed in the cited ECG features between the ASD and normal groups. Our AI model performed well in identifying ECGs in both the ASD group [accuracy of 0.97, precision of 0.90, recall of 0.97, specificity of 0.97, F1 score of 0.93, and area under the curve (AUC) of 0.99] and the normal group within the training and validation datasets from our hospital. Furthermore, these corresponding indices performed impressively in the external test data set with the accuracy of 0.82, precision of 0.90, recall of 0.74, specificity of 0.91, F1 score of 0.81 and the AUC of 0.87. And the series of experiments of subgroups to discuss specific clinic situations associated to this issue was remarkable as well. Conclusion: An ECG-based detection of ASD using an artificial intelligence algorithm can be achieved with high diagnostic performance, and it shows great clinical promise. Our research on AI-enabled 8-lead ECG detection of ASD in adults is expected to provide robust references for early detection of ASD, healthy pregnancies, and related decision-making. A lower number of leads is also more favorable for the application of portable devices, which it is expected that this technology will bring significant economic and societal benefits.