Metformin and statins reduce hepatocellular carcinoma risk in chronic hepatitis C patients with failed antiviral therapy.

Backgrounds and Aim: Chronic hepatitis C (CHC) patients who fail antiviral therapy have a high risk of developing hepatocellular carcinoma (HCC). We investigated the effects of metformin and statins, commonly used to treat diabetes mellitus (DM) and hyperlipidemia (HLP), on HCC risk in CHC patients who failed antiviral therapy. Methods: CHC patients with failed interferon-based therapy were enrolled in a large-scale multicenter cohort study in Taiwan (T-COACH). HCC occurrence 1.5 years after the end of antiviral therapy was identified by linking to the cancer registry databases from 2003 to 2019. After considering death and liver transplantation as competing risks, Gray's cumulative incidence and Cox sub-distribution hazards for HCC development were used. Results: Among the 2,779 CHC patients, 480 (17.3%) developed new-onset HCC and 238 (8.6%) died after antiviral therapy. Metformin non-users with DM had a 51% higher risk of liver cancer than patients without DM, while statin users with HLP had a 50% lower risk of liver cancer than patients without HLP. The 5-year cumulative incidence of HCC was 16.5% in metformin non-users, significantly higher in metformin non-users than in patients without DM (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Conversely, HLP statin users had a significantly lower HCC risk than patients without HLP (3.8% vs. 12.5%; aSHR=0.50; P<0.001). Notably, the unfavorable effect of non-metformin use on increased HCC risk was mainly observed among patients without cirrhosis but not in patients with cirrhosis. In contrast, a favorable effect of statins reduced the risk of HCC in both cirrhotic and non-cirrhotic patients. Conclusion: Metformin for DM and statins for HLP have chemopreventive effects on HCC risk in CHC patients who failed antiviral therapy. These findings emphasize the importance of personalized preventive strategies for managing patients with these clinical profiles.

Real-World Efficacy and Safety of Universal 8-Week Glecaprevir/Pibrentasvir for Treatment-Naïve Patients from a Nationwide HCV Registry in Taiwan.

INTRODUCTION: Eight-week glecaprevir/pibrentasvir (GLE/PIB) is indicated for treatment-naïve (TN) patients with chronic hepatitis C (CHC), with or without compensated cirrhosis. Given that the Taiwanese government is committed to eliminating hepatitis C virus (HCV) by 2025, this study aimed to measure real-world evidence for TN patients using 8-week GLE/PIB in the Taiwan HCV Registry (TACR). METHODS: The data of patients with CHC treated with 8-week GLE/PIB were retrieved from TACR, a nationwide registry program organized by the Taiwan Association for the Study of the Liver (TASL). Treatment efficacy, defined as a sustained virologic response at posttreatment week 12 (SVR12), was assessed in the modified intention-to-treat (mITT) population, which excluded patients who were lost to follow-up or lacked SVR12 data. The safety profile of the ITT population was assessed. RESULTS: A total of 7246 (6897 without cirrhosis; 349 with cirrhosis) patients received at least one dose of GLE/PIB (ITT), 7204 of whom had SVR12 data available (mITT). The overall SVR12 rate was 98.9% (7122/7204) among all patients, 98.9% (6780/6856) and 98.3% (342/348) among patients without and with cirrhosis, respectively. For the selected subgroups, which included patients with genotype 3 infection, diabetes, chronic kidney disease, people who injected drugs, and those with human immunodeficiency virus coinfection, the SVR12 rates were 95.1% (272/286), 98.9% (1084/1096), 99.0% (1171/1183), 97.4% (566/581), and 96.1% (248/258), respectively. Overall, 14.1% (1021/7246) of the patients experienced adverse events (AEs). Twenty-two patients (0.3%) experienced serious AEs, and 15 events (0.2%) resulted in permanent drug discontinuation. Only one event was considered treatment drug related. CONCLUSION: Eight-week GLE/PIB therapy was effective and well tolerated in all TN patients, regardless of cirrhosis status.

Expert consensus on the diagnosis and treatment of end-stage liver disease complicated by infections.

End-stage liver disease (ESLD) is a life-threatening clinical syndrome and when complicated with infection the mortality is markedly increased. In patients with ESLD, bacterial or fungal infection can induce or aggravate the occurrence or progression of liver decompensation. Consequently, infections are among the most common complications of disease deterioration. There is an overwhelming need for standardized protocols for early diagnosis and appropriate management for patients with ESLD complicated by infections. Asia Pacific region has the largest number of ESLD patients, due to hepatitis B and the growing population of alcohol and NAFLD. Concomitant infections not only add to organ failure and high mortality but also to financial and healthcare burdens. This consensus document assembled up-to-date knowledge and experience from colleagues across the Asia-Pacific region, providing data on the principles as well as evidence-based current working protocols and practices for the diagnosis and treatment of patients with ESLD complicated by infections.

A Water-Based Biocoating to Increase the Infection Resistance and Osteoconductivity of Titanium Surfaces.

As the population ages, the number of patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA) continues to increase. Infections after primary arthroplasty are rare but have high rates of morbidity and mortality, as well as enormous financial implications for healthcare systems. Numerous methods including the use of superhydrophobic coatings, the incorporation of antibacterial agents, and the application of topographical treatments have been developed to reduce bacterial attachment to medical devices. However, most of these methods require complex manufacturing processes. Thus, the main purpose of this study was to apply biocoatings to titanium (Ti) surfaces to increase their infection resistance and osteoconductivity via simple processes, without organic reagents. We modified titanium surfaces with a combination of aminomalononitrile (AMN) and an antibiotic-loaded mesoporous bioactive glass (MBG) and evaluated both the antibacterial effects of the coating layer and its effect on osteoblast proliferation and differentiation. The properties of the modified surface, such as the hydrophilicity, roughness, and surface morphology, were characterized via contact angle measurements, atomic force microscopy, and scanning electron microscopy. The cell proliferation reagent WST-1 assay and the alkaline phosphatase (ALP) assay were used to determine the degrees of adhesion and differentiation, respectively, of the MG-63 osteoblast-like cells on the surface. Antimicrobial activity was evaluated by examining the survival rate and inhibition zone of Escherichia coli (E. coli). The AMN coating layer reduced the water contact angle (WCA) of the titanium surface from 87° ± 2.5° to 53° ± 2.3° and this change was retained even after immersion in deionized water for five weeks, demonstrating the stability of the AMN coating. Compared with nontreated titanium and polydopamine (PDA) coating layers, the AMN surface coating increased MG-63 cell attachment, spreading, and early ALP expression; reduced E. coli adhesion; and increased the percentage of dead bacteria. In addition, the AMN coating served as an adhesion layer for the subsequent deposition of MBG-containing antibiotic nanoparticles. The synergistic effects of the AMN layer and antibiotics released from the MBG resulted in an obvious E. coli inhibition zone that was not observed in the nontreated titanium group.

Explainable Learning-Based Timeout Optimization for Accurate and Efficient Elephant Flow Prediction in SDNs.

Accurately and efficiently predicting elephant flows (elephants) is crucial for optimizing network performance and resource utilization. Current prediction approaches for software-defined networks (SDNs) typically rely on complete traffic and statistics moving from switches to controllers. This leads to an extra control channel bandwidth occupation and network delay. To address this issue, this paper proposes a prediction strategy based on incomplete traffic that is sampled by the timeouts for the installation or reactivation of flow entries. The strategy involves assigning a very short hard timeout (Tinitial) to flow entries and then increasing it at a rate of r until flows are identified as elephants or out of their lifespans. Predicted elephants are switched to an idle timeout of 5 s. Logistic regression is used to model elephants based on a complete dataset. Bayesian optimization is then used to tune the trained model Tinitial and r over the incomplete dataset. The process of feature selection, model learning, and optimization is explained. An extensive evaluation shows that the proposed approach can achieve over 90% generalization accuracy over 7 different datasets, including campus, backbone, and the Internet of Things (IoT). Elephants can be correctly predicted for about half of their lifetime. The proposed approach can significantly reduce the controller-switch interaction in campus and IoT networks, although packet completion approaches may need to be applied in networks with a short mean packet inter-arrival time.

Mac-2-Binding Protein Glycosylation Isomer to Albumin Ratio Predicts Bacterial Infections in Cirrhotic Patients.

INTRODUCTION: Mac-2-binding protein glycosylation isomer (M2BPGi) is a novel biomarker for liver fibrosis, but little is known about its role in cirrhosis-associated clinical outcomes. This study aimed to investigate the predictive role of M2BPGi in cirrhosis-associated complications. METHODS: One hundred and forty-nine cirrhotic patients were retrospectively enrolled. Patients were followed up for 1 year, and cirrhosis-associated clinical events were recorded. Receiver operating characteristic curve (ROC) analysis was used to establish the values of the predictive models for cirrhotic outcomes, and Cox proportional hazards regression models were used to identify predictors of clinical outcomes. RESULTS: Sixty (40.3%) patients experienced cirrhosis-associated clinical events and had higher M2BPGi levels compared to those without events (8.7 vs. 5.1 cutoff index, p < 0.001). The most common cirrhosis-associated complications were bacterial infections (24.2%). On ROC analysis, M2BPGi to albumin ratio (M2BPGi/albumin) had comparable discriminant abilities for all cirrhosis-associated events (area under the ROC curve [AUC] = 0.74) compared with M2BPGi, Child-Pugh, model for end-stage liver disease, albumin-bilirubin scores, and neutrophil-to-lymphocyte ratio and was superior to M2BPGi alone for all bacterial infectious events (AUC = 0.80). Cox regression analysis revealed that the M2BPGi/albumin, but not M2BPGi alone, independently predicted all cirrhosis-associated events (hazard ratio [HR] = 1.34, p = 0.038) and all bacterial infectious events (HR = 1.51, p = 0.011) within 1 year. However, M2BPGi/albumin did not predict other cirrhotic complications and transplant-free survival. DISCUSSION/CONCLUSION: M2BPGi/albumin might serve as a potential prognostic indicator for patients with cirrhosis, particularly for predicting bacterial infections.

Inhibition of visceral adipose tissue-derived pathogenic signals by activation of adenosine A2AR improves hepatic and cardiac dysfunction of NASH mice.

A2AR-disrupted mice is characterized by severe systemic and visceral adipose tissue (VAT) inflammation. Increasing adenosine cyclase (AC), cAMP, and protein kinase A (PKA) formation through A2AR activation suppress systemic/VAT inflammation in obese mice. This study explores the effects of 4 wk A2AR agonist PSB0777 treatment on the VAT-driven pathogenic signals in hepatic and cardiac dysfunction of nonalcoholic steatohepatitis (NASH) obese mice. Among NASH mice with cardiac dysfunction, simultaneous decrease in the A2AR, AC, cAMP, and PKA levels were observed in VAT, liver, and heart. PSB0777 treatment significantly restores AC, cAMP, PKA, and hormone-sensitive lipase (HSL) levels, decreased SREBP-1/FASN, MCP-1, and CD68 levels, reduces infiltrated CD11b+ F4/80+ cells and adipogenesis in VAT of NASH + PSB0777 mice. The changes in VAT were accompanied by the suppression of hepatic and cardiac lipogenic/inflammatory/injury/apoptotic/fibrotic markers, the normalization of cardiac contractile [sarco/endoplasmic reticulum Ca2+ ATPase (SERCA2)] marker, and cardiac dysfunction. The in vitro approach revealed that conditioned media (CM) of VAT of NASH mice (CMnash) trigger palmitic acid (PA)-like lipotoxic (lipogenic/inflammatory/apoptotic/fibrotic) effects in AML-12 and H9c2 cell systems. Significantly, A2AR agonist pretreatment-related normalization of A2AR-AC-cAMP-PKA levels was associated with the attenuation of CMnash-related upregulation of lipotoxic markers and the normalization of lipolytic (AML-12 cells) or contractile (H9C2 cells) marker/contraction. The in vivo and in vitro experiments revealed that A2AR agonists are potential agent to inhibit the effects of VAT inflammation-driven pathogenic signals on the hepatic and cardiac lipogenesis, inflammation, injury, apoptosis, fibrosis, hypocontractility, and subsequently improve hepatic and cardiac dysfunction in NASH mice.NEW & NOTEWORTHY Protective role of adenosine A2AR receptor (A2AR) and AC-cAMP-PKA signaling against nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH) possibly via its actions on adipocytes is well known in the past decade. Thus, this study evaluates pharmacological activities of A2AR agonist PSB0777, which has already demonstrated to treat NASH. In this study, the inhibition of visceral adipose tissue-derived pathogenic signals by activation of adenosine A2AR with A2AR agonist PSB0777 improves the hepatic and cardiac dysfunction of high-fat diet (HFD)-induced NASH mice.

Artificial intelligence predicts direct-acting antivirals failure among hepatitis C virus patients: A nationwide hepatitis C virus registry program.

BACKGROUND/AIMS: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1-3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. METHODS: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. RESULTS: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. CONCLUSION: Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

An algorithm for simplified hepatitis C virus treatment with non-specialist care based on nation-wide data from Taiwan.

BACKGROUND: Both European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases and the Infectious Diseases Society of America (AASLD-IDSA) guidelines recommend simplified hepatitis C virus (HCV) treatment with pan-genotypic sofosbuvir/velpatasvir or glecaprevir/pibrentasvir for eligible patients. This observational study used real-world data to assess these regimens' safety in eligible patients and develop an algorithm to identify patients suitable for simplified treatment by non-specialists. METHODS: 7,677 HCV-infected patients from Taiwan Hepatitis C Registry (TACR) who received at least one dose of sofosbuvir/velpatasvir or glecaprevir/pibrentasvir, and fulfilled the EASL/AASLD-IDSA criteria for simplified treatment were analyzed. Multivariate analysis was conducted on patient characteristics and safety data. RESULTS: Overall, 92.8% (7,128/7,677) of patients achieved sustained virological response and only 1.9% (146/7,677) experienced Grades 2-4 laboratory abnormalities in key liver function parameters (alanine aminotransferase, aspartate aminotransferase, and total bilirubin), with only 18 patients (0.23%) experiencing Grades 3-4 abnormalities. Age > 70 years old, presence of hepatocellular carcinoma, total bilirubin > 1.2 mg/dL, estimated glomerular filtration rate < 60 mL/min/1.73 m2, and Fibrosis-4 > 3.25 were associated with higher risks of Grades 2-4 abnormalities. Patients with any of these had an odds of 4.53 times than that of those without in developing Grades 2-4 abnormalities (p < 0.01). CONCLUSIONS: Real-world data from Taiwan confirmed that simplified HCV treatment for eligible patients with pan-genotypic regimens is effective and well tolerated. The TACR algorithm, developed based on this study's results, can further identify patients who can be safely managed by non-specialist care.

Submicron Memtransistors Made from Monocrystalline Molybdenum Disulfide.

Multiterminal memtransistors made from two-dimensional (2D) materials have garnered increasing attention in the pursuit of low-power heterosynaptic neuromorphic circuits. However, existing 2D memtransistors tend to necessitate high set voltages (>1 V) or feature defective channels, posing concerns regarding material integrity and intrinsic properties. Herein, we present a monocrystalline monolayer MoS2 memtransistor designed for operation within submicron regimes. Under reverse drain bias sweeps, our experiments reveal memristive behavior within the device, further controllable through modulation of the gate terminal. This controllability facilitates the consistent manifestation of multistate memory effects. Notably, the memtransistor behavior becomes more significant as the channel length diminishes, particularly with channel lengths below 1.6 µm, showcasing an increase in the switching ratio alongside a decrease in the set voltage with the decreasing channel length. Our optimized memtransistor demonstrates the ability to exhibit individual resistance states spanning 5 orders of magnitude, with switching drain voltages of approximately 0.05 V. To elucidate these findings, we investigate hot carrier effects and their interplay with oxide traps within the HfO2 dielectric. This work highlights the importance of memtransisor behavior in highly scaled 2D transistors, particularly those featuring low contact resistances. This understanding holds the potential to tailor memory characteristics essential for the development of energy-efficient neuromorphic devices.

Prevalence and risk factors for impaired renal function among Asian patients with nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is associated with impaired renal function, and both diseases often occur alongside other metabolic disorders. However, the prevalence and risk factors for impaired renal function in patients with NAFLD remain unclear. The objective of this study was to identify the prevalence and risk factors for renal impairment in NAFLD patients. METHODS: All adults aged 18-70 years with ultrasound-diagnosed NAFLD and transient elastography examination from eight Asian centers were enrolled in this prospective study. Liver fibrosis and cirrhosis were assessed by FibroScan-aspartate aminotransferase (FAST), Agile 3+ and Agile 4 scores. Impaired renal function and chronic kidney disease (CKD) were defined by an estimated glomerular filtration rate (eGFR) with value of < 90 mL/min/1.73 m2 and < 60 mL/min/1.73 m2, respectively, as estimated by the CKD-Epidemiology Collaboration (CKD-EPI) equation. RESULTS: Among 529 included NAFLD patients, the prevalence rates of impaired renal function and CKD were 37.4% and 4.9%, respectively. In multivariate analysis, a moderate-high risk of advanced liver fibrosis and cirrhosis according to Agile 3+ and Agile 4 scores were independent risk factors for CKD (P< 0.05). Furthermore, increased fasting plasma glucose (FPG) and blood pressure were significantly associated with impaired renal function after controlling for the other components of metabolic syndrome (P< 0.05). Compared with patients with normoglycemia, those with prediabetes [FPG ≥ 5.6 mmol/L or hemoglobin A1c (HbA1c) ≥ 5.7%] were more likely to have impaired renal function (P< 0.05). CONCLUSIONS: Agile 3+ and Agile 4 are reliable for identifying NAFLD patients with high risk of CKD. Early glycemic control in the prediabetic stage might have a potential renoprotective role in these patients.

Taiwan Association for the Study of the Liver-Taiwan Society of Cardiology Taiwan position statement for the management of metabolic dysfunction- associated fatty liver disease and cardiovascular diseases.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

An Interpretable Classification Model Using Gluten-Specific TCR Sequences Shows Diagnostic Potential in Coeliac Disease.

Coeliac disease (CeD) is a T-cell mediated enteropathy triggered by dietary gluten which remains substantially under-diagnosed around the world. The diagnostic gold-standard requires histological assessment of intestinal biopsies taken at endoscopy while consuming a gluten-containing diet. However, there is a lack of concordance between pathologists in histological assessment, and both endoscopy and gluten challenge are burdensome and unpleasant for patients. Identification of gluten-specific T-cell receptors (TCRs) in the TCR repertoire could provide a less subjective diagnostic test, and potentially remove the need to consume gluten. We review published gluten-specific TCR sequences, and develop an interpretable machine learning model to investigate their diagnostic potential. To investigate this, we sequenced the TCR repertoires of mucosal CD4+ T cells from 20 patients with and without CeD. These data were used as a training dataset to develop the model, then an independently published dataset of 20 patients was used as the testing dataset. We determined that this model has a training accuracy of 100% and testing accuracy of 80% for the diagnosis of CeD, including in patients on a gluten-free diet (GFD). We identified 20 CD4+ TCR sequences with the highest diagnostic potential for CeD. The sequences identified here have the potential to provide an objective diagnostic test for CeD, which does not require the consumption of gluten.

Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics.

The proliferation of IoT devices has led to an unprecedented integration of machine learning techniques, raising concerns about data privacy. To address these concerns, federated learning has been introduced. However, practical implementations face challenges, including communication costs, data and device heterogeneity, and privacy security. This paper proposes an innovative approach within the context of federated learning, introducing a personalized joint learning algorithm for Non-IID IoT data. This algorithm incorporates multi-task learning principles and leverages neural network model characteristics. To overcome data heterogeneity, we present a novel clustering algorithm designed specifically for federated learning. Unlike conventional methods that require a predetermined number of clusters, our approach utilizes automatic clustering, eliminating the need for fixed cluster specifications. Extensive experimentation demonstrates the exceptional performance of the proposed algorithm, particularly in scenarios with specific client distributions. By significantly improving the accuracy of trained models, our approach not only addresses data heterogeneity but also strengthens privacy preservation in federated learning. In conclusion, we offer a robust solution to the practical challenges of federated learning in IoT environments. By combining personalized joint learning, automatic clustering, and neural network model characteristics, we facilitate more effective and privacy-conscious machine learning in Non-IID IoT data settings.

Cost-effective complete genome sequencing using the MinION platform for identification of recombinant enteroviruses.

IMPORTANCE: By employing a cost-effective approach for complete genome sequencing, the study has enabled the identification of novel enterovirus strains and shed light on the genetic exchange events during outbreaks. The success rate of genome sequencing and the scalability of the protocol demonstrate its practical utility for routine enterovirus surveillance. Moreover, the study's findings of recombinant strains of EVA71 and CVA2 contributing to epidemics in Malaysia and Taiwan emphasize the need for accurate detection and characterization of enteroviruses. The investigation of the whole genome and upstream ORF sequences has provided insights into the evolution and spread of enterovirus subgenogroups. These findings have important implications for the prevention, control, and surveillance of enteroviruses, ultimately contributing to the understanding and management of enterovirus-related illnesses.

The burden of bacterial antimicrobial resistance in Croatia in 2019: a country-level systematic analysis.

AIM: To deliver the most wide-ranging set of antimicrobial resistance (AMR) burden estimates for Croatia to date. METHODS: A complex modeling approach with five broad modeling components was used to estimate the disease burden for 12 main infectious syndromes and one residual group, 23 pathogenic bacteria, and 88 bug-drug combinations. This was represented by two relevant counterfactual scenarios: deaths/disability-adjusted life years (DALYs) that are attributable to AMR considering a situation where drug-resistant infections are substituted with sensitive ones, and deaths/DALYs associated with AMR considering a scenario where people with drug-resistant infections would instead present without any infection. The 95% uncertainty intervals (UI) were based on 1000 posterior draws in each modeling step, reported at the 2.5% and 97.5% of the draws' distribution, while out-of-sample predictive validation was pursued for all the models. RESULTS: The total burden associated with AMR in Croatia was 2546 (95% UI 1558-3803) deaths and 46958 (28,033-71,628) DALYs, while the attributable burden was 614 (365-943) deaths and 11321 (6,544-17,809) DALYs. The highest number of deaths was established for bloodstream infections, followed by peritoneal and intra-abdominal infections and infections of the urinary tract. Five leading pathogenic bacterial agents were responsible for 1808 deaths associated with resistance: Escherichia coli, Staphylococcus aureus, Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa (ordered by the number of deaths). Trimethoprim/sulfamethoxazole-resistant E coli and methicillin-resistant S. aureus were dominant pathogen-drug combinations in regard to mortality associated with and attributable to AMR, respectively. CONCLUSION: We showed that AMR represented a substantial public health concern in Croatia, which reflects global trends; hence, our detailed country-level findings may fast-track the implementation of multipronged strategies tailored in accordance with leading pathogens and pathogen-drug combinations.

Prediction models combining zonulin, LPS, and LBP predict acute kidney injury and hepatorenal syndrome-acute kidney injury in cirrhotic patients.

The development of acute kidney injury (AKI) and hepatorenal syndrome-acute kidney injury (HRS-AKI) in cirrhosis has been associated with intestinal barrier dysfunction and gut-kidney crosstalk. We use the related markers such as zonulin, lipopolysaccharides (LPS), and lipopolysaccharide-binding protein (LBP) to predict AKI and HRS-AKI in cirrhotic patients and evaluate their in vitro effects on intestinal (Caco-2) cells and renal tubular (HK-2) cells. From 2013 to 2020, we enrolled 70 cirrhotic patients and developed prediction models for AKI and HRS-AKI over a six-month period. There were 13 (18.6%) and 8 (11.4%) cirrhotic patients developed AKI and HRS-AKI. The prediction models incorporated zonulin, LPS, LBP, C-reactive protein, age, and history of hepatitis B for AKI, and zonulin, LPS, LBP, total bilirubin, and Child-Pugh score for HRS-AKI. The area under curve (AUC) for the prediction of AKI and HRS-AKI was 0.94 and 0.95, respectively. Furthermore, the conditioned medium of LPS+hrLBP pre-treated Caco-2 cells induced apoptosis, necrosis, and zonulin release in HK-2 cells, demonstrating the communication between them. This study found that zonulin, LPS, and LBP are potential practical markers for predicting AKI and HRS-AKI in cirrhotic patients, which may serve as potential targets for renal outcomes in cirrhotic patients.

A Trusted Reputation Management Scheme for Cross-Chain Transactions.

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node's reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.