Impact of the prognostic nutritional index on renal replacement therapy-free survival and mortality in patients on continuous renal replacement therapy.

BACKGROUND: The survival of critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT) is highly dependent on their nutritional status. OBJECTIVES: The prognostic nutritional index (PNI) is an indicator used to assess nutritional status and is calculated as: PNI = (serum albumin in g/dL) × 10 + (total lymphocyte count in/mm3) × 0.005. In this retrospective study, we investigated the correlation between this index and clinical outcomes in critically ill patients with AKI receiving CRRT. METHODS: We analyzed data from 2076 critically ill patients admitted to the intensive care unit at Changhua Christian Hospital, a tertiary hospital in central Taiwan, between January 1, 2010, and April 30, 2021. All these patients met the inclusion criteria of the study. The relationship between PNI and renal replacement therapy-free survival (RRTFS) and mortality was examined using logistic regression models, Cox proportional hazard models, and propensity score matching. High utilization rate of parenteral nutrition (PN) was observed in our study. Subgroup analysis was performed to explore the interaction effect between PNI and PN on mortality. RESULTS: Patients with higher PNI levels exhibited a greater likelihood of achieving RRTFS, with an adjusted odds ratio of 2.43 (95% confidence interval [CI]: 1.98-2.97, p-value < 0.001). Additionally, these patients demonstrated higher survival rates, with an adjusted hazard ratio of 0.84 (95% CI: 0.72-0.98) for 28-day mortality and 0.80 (95% CI: 0.69-0.92) for 90-day mortality (all p-values < 0.05), compared to those in the low PNI group. While a high utilization rate of parenteral nutrition (PN) was observed, with 78.86% of CRRT patients receiving PN, subgroup analysis showed that high PNI had an independent protective effect on mortality outcomes in AKI patients receiving CRRT, regardless of their PN status. CONCLUSIONS: PNI can serve as an easy, simple, and efficient measure of lymphocytes and albumin levels to predict RRTFS and mortality in AKI patients with require CRRT.

Walking Dead Macrophage-Based Positive Enhancement MRI for Ultrahighly Efficient Diagnosis of Nephritis.

Nephritis is an inflammatory condition of the glomerulus, and the clinical gold standard for its diagnosis is a kidney biopsy. However, obtaining biopsy results can take several days, which does not meet the requirement of rapid diagnosis, especially for rapidly progressive types. To achieve an effective and noninvasive diagnosis, we propose a nephritis-specific, positive magnetic resonance imaging (MRI) contrast agent based on Gd3+ anchored walking dead macrophage Gd-RAW. Gd-RAW exhibits high selectivity for inflammatory renal parenchyma and provides comparable results to histopathology methods. The Gd-RAW-based MRI contrast agent reduces the diagnostic time of nephritis from 14 days of biopsy to 1 h. Furthermore, in a unilateral nephritis model constructed by increasing the glycerol concentration, the T1WI of renal parenchyma exhibits an increased signal-to-noise ratio, which is crucial for evaluating nephritic severity. This work promotes rapid diagnosis of nephritis and potentially provides sufficient evidence for clinicians to offer timely treatment to patients. The methodology of paramagnetic ion-anchored macrophage corpse also opens up new prospects for designing more specific and biosafe MRI contrast agents.

Intelligent Identification of Cerebrospinal Fluid for the Diagnosis of Parkinson's Disease.

Cerebrospinal fluid (CSF) biomarkers are more sensitive than the Movement Disorder Society (MDS) criteria for detecting prodromal Parkinson's disease (PD). Early detection of PD provides the best chance for successful implementation of disease-modifying treatments, making it crucial to effectively identify CSF extracted from PD patients or normal individuals. In this study, an intelligent sensor array was built by using three metal-organic frameworks (MOFs) that exhibited varying catalytic kinetics after reacting with potential protein markers. Machine learning algorithms were used to process fingerprint response patterns, allowing for qualitative and quantitative assessment of the proteins. The results were robust and capable of discriminating between PD and non-PD patients via CSF detection. The k-nearest neighbor regression algorithm was used to predict MDS scores with a minimum mean square error of 38.88. The intelligent MOF sensor array is expected to promote the detection of CSF biomarkers due to its ability to identify multiple targets and could be used in conjunction with MDS criteria and other techniques to diagnose PD more sensitively and selectively.

In Situ Ratiometric Determination of Cerebral Ascorbic Acid after Ischemia Reperfusion.

Ascorbic acid (AA) is significant in protecting the brain from further damage and maintaining brain homeostasis after ischemia stroke (IS); however, the dynamic change of cerebral AA content after different degrees of ischemic stroke is still unclear. Herein, carboxylated single-walled carbon nanotube (CNT-COOH)- and polyethylenedioxythiophene (PEDOT)-modified carbon fiber microelectrodes (CFEs) were proposed to detect in situ cerebral AA with sensitivity, selectivity, and stability. Under differential pulse voltammetry scanning, the CFE/CNT-COOH/PEDOT gave a ratiometric, electrochemically responsive signal. The internal standard peak at -310 mV was from the reversible peak of O2 reduction and the deprotonation and protonation of quinone groups, while AA was oxidized at -70 mV. In vivo experimental results indicated that the cerebral AA level gradually increased with the ischemic time increasing in different middle cerebral artery occlusion (MCAO) model mice. This work implies that the increasing cerebral AA level may be highly related to the glutamate excitotoxicity and ROS-led cell apoptosis and paves a new way for further understanding the release and metabolic mechanisms of AA during ischemia reperfusion and IS.

Hydrogen-Bonded Organic Frameworks Chelated Manganese for Precise Magnetic Resonance Imaging Diagnosis of Cancers.

Magnetic resonance imaging (MRI) is an important tool in the diagnosis of many cancers. However, clinical gadolinium (Gd)-based MRI contrast agents have limitations, such as large doses and potential side effects. To address these issues, we developed a hydrogen-bonded organic framework-based MRI contrast agent (PFC-73-Mn). Due to the hydrogen-bonded interaction of water molecules and the restricted rotation of manganese ions, PFC-73-Mn exhibits high longitudinal relaxation r1 (5.03 mM-1 s-1) under a 3.0 T clinical MRI scanner. A smaller intravenous dose (8 µmol of Mn/kg) of PFC-73-Mn can provide strong contrast and accurate diagnosis in multiple kinds of cancers, including breast tumor and ultrasmall orthotopic glioma. PFC-73-Mn represents a prospective new approach in tumor imaging, especially in early-stage cancer.

Biomimetic Epigallocatechin Gallate-Cerium Assemblies for the Treatment of Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease that is so far incurable with long-term health risks. The high doses and frequent administration for the available RA drug always lead to adverse side effects. Aiming at the obstacles to achieving effective RA treatment, we prepared macrophage cell membrane-camouflaged nanoparticles (M-EC), which were assembled from epigallocatechin gallate (EGCG) and cerium(IV) ions. Due to its geometrical similarity to the active metal sites of a natural antioxidant enzyme, the EC possessed a high scavenge efficiency to various types of reactive oxygen species (ROS) and reactive nitrogen species (RNS). The macrophage cell membrane assisted M-EC in escaping from the immune system, being uptaken by inflammatory cells, and specifically binding IL-1ß. After tail vein injection to the collagen-induced arthritis (CIA) mouse model, the M-EC accumulated at inflamed joints and effectively repaired the bone erosion and cartilage damage of rheumatoid arthritis by relieving synovial inflammation and cartilage erosion. It is expected that the M-EC can not only pave a new way for designing metal-phenolic networks with better biological activity but also provide a more biocompatible therapeutic strategy for effective treatment of RA.

Effects of biochar supported nano zero-valent iron with different carbon/iron ratios on two-phase anaerobic digestion of food waste.

The promotion effects of biochar supported nano zero-valent iron (BC/nZVI) with different carbon/iron ratios on two-phase anaerobic digestion (AD) of food waste (FW) were studied. Results suggested that when the carbon/iron ratio was 3:1 AD system showed the best performance, with the concentration of volatile fatty acids (VFAs) in acidogenic phase (AP) and the cumulative methane production in methanogenic phase (MP) increased by 31.4% and 24.8%, respectively. Metagenomic analysis demonstrated that BC/nZVI increased the relative abundance of Defluviitoga in AP, and promoted the growth of Methanothrix in MP. Metabolic pathway analysis in AP indicated that BC/nZVI mainly promoted the abundances of acetate kinase and butyrate kinase to enhance acid production. Methane metabolism pathway analysis in MP revealed that BC/nZVI increased methane production by promoting the module of M00357 and activating related enzymes. The results of this sutdy showed that BC/nZVI promoted AD of FW mainly through acetoclastic methanogenic pathway.

Magnetic Resonance Diagnosis of Early Triple-Negative Breast Cancer Based on the Ionic Covalent Organic Framework with High Relaxivity and Long Retention Time.

Patients with triple-negative breast cancer (TNBC) have dismal prognoses due to the lack of therapeutic targets and susceptibility to lymph node (LN) metastasis. Therefore, it is essential to develop more effective approaches to identify early TNBC tissues and LNs. In this work, a magnetic resonance imaging (MRI) contrast agent (Mn-iCOF) was constructed based on the Mn(II)-chelated ionic covalent organic framework (iCOF). Because of the porous structure and hydrophilicity, the Mn-iCOF has a high longitudinal relaxivity (r1) of 8.02 mM-1 s-1 at 3.0 T. For the tumor-bearing mice, a lower dose (0.02 mmol [Mn]/kg) of Mn-iCOF demonstrated a higher signal-to-noise ratio (SNR) value (1.8) and longer retention time (2 h) compared to a 10-fold dose of commercial Gd-DOTA (0.2 mmol [Gd]/kg). Moreover, the Mn-iCOF can provide continuous and significant MR contrast for the popliteal LNs within 24 h, allowing for accurate evaluation and dissection of LNs. These excellent MRI properties of the Mn-iCOF may open new avenues for designing more biocompatible MRI contrast agents with higher resolutions, particularly in the diagnosis of TNBC.

The impact of acute kidney injury with or without recovery on long-term kidney outcome in patients undergoing living liver transplantation.

BACKGROUND: Acute kidney injury (AKI) is associated with an increased incidence of poor liver graft and renal outcomes in patients who have undergone liver transplantation (LT). To date, no comprehensive study has compared patients with and without post-LT AKI and analyzed patients who recovered from AKI versus those who did not. METHODS: Patients who received living LT between January 2003 and January 2019 were enrolled. We diagnosed and classified AKI patients based on AKI-KDIGO guidelines by increment of creatinine after surgery when compared with serum creatinine on the day of surgery. The recovered AKI subgroup included recipients whose estimated glomerular filtration rate (eGFR) recovered more than 90% of baseline eGFR within 90 days after surgery. The risk of chronic kidney disease (CKD; eGFR <60 mL/min/1.73 m2) was investigated. RESULTS: A total of 392 patients, 77.3% men and mean ± standard deviation age 54.1 ± 8.4 years, met the eligible criteria and were divided into two groups (AKI vs non-AKI) and 243 (62%) patients developed AKI within 7 days after surgery. Compared with the non-AKI group, the AKI group was associated with an adjusted hazard ratio of 1.55 (95% CI 1.12-2.14) for the risk of incident CKD. Among AKI patients, 160 (65.8%) patients recovered renal function and 83 (34.2%) patients did not. Compared with the non-AKI group, the AKI non-recovery group was associated with an adjusted hazard ratio of 2.87 (95% CI 1.95-4.21) for the risk of incident CKD, while the AKI recovery group had no significant difference in the adjusted risk of incident CKD. CONCLUSIONS: Post-LT AKI is associated with subsequent risk of CKD development. Taking into account recovery status, AKI was no longer associated with a higher risk of CKD if renal function recovered within 90 days after surgery. Identification and implementation of targeted and individualized therapies for patients at risk for AKI, particularly non-recovery AKI, is of paramount importance to reduce incident CKD during follow-up.

Intelligent Clinical Lab for the Diagnosis of Post-Neurosurgical Meningitis Based on Machine-Learning-Aided Cerebrospinal Fluid Analysis.

Post-neurosurgical meningitis (PNM) often leads to serious consequences; unfortunately, the commonly used clinical diagnostic methods of PNM are time-consuming or have low specificity. To realize the accurate and convenient diagnosis of PNM, herein, we propose a comprehensive strategy for cerebrospinal fluid (CSF) analysis based on a machine-learning-aided cross-reactive sensing array. The sensing array involves three Eu3+-doped metal-organic frameworks (MOFs), which can generate specific fluorescence responding patterns after reacting with potential targets in CSF. Then, the responding pattern is used as learning data to train the machine learning algorithms. The discrimination confidence for artificial CSF containing different components of molecules, proteins, and cells is from 81.3 to 100%. Furthermore, the machine-learning-aided sensing array was applied in the analysis of CSF samples from post-neurosurgical patients. Only 25 µL of CSF samples was needed, and the samples could be robustly classified into "normal," "mild," or "severe" groups within 40 min. It is believed that the combination of machine learning algorithms with robust data processing capability and a lanthanide luminescent sensor array will provide a reliable alternative for more comprehensive, convenient, and rapid diagnosis of PNM.

Infection in Living Donor Liver Transplantation Leads to Increased Risk of Adverse Renal Outcomes.

(1) Background: Little is known about the subsequent renal function change following incident infectious diseases in living-donor liver transplant (LT) recipients. (2) Methods: We studied patients who underwent living-donor LT from January 2003 to January 2019 to evaluate the association of incident hospitalization with major infections or pneumonia with adverse renal outcomes, including a sustained 40% reduction in estimated glomerular filtration rate (eGFR) and renal composite outcome (a 40% decline in eGFR, end-stage renal disease, or death.). Multivariable-adjusted time-dependent Cox models with infection as a time-varying exposure were used to estimate hazard ratio (HR) with 95% confidence interval (CI) for study outcomes. (3) Results: We identified 435 patients (mean age 54.6 ± 8.4 years and 76.3% men), of whom 102 had hospitalization with major infections during follow-up; the most common cause of infection was pneumonia (38.2%). In multiple Cox models, hospitalization with a major infection was associated with an increased risk of eGFR decline > 40% (HR, 3.32; 95% CI 2.13−5.16) and renal composite outcome (HR, 3.41; 95% CI 2.40−5.24). Likewise, pneumonia was also associated with an increased risk of eGFR decline > 40% (HR, 2.47; 95% CI 1.10−5.56) and renal composite outcome (HR, 4.37; 95% CI 2.39−8.02). (4) Conclusions: Our results illustrated the impact of a single infection episode on the future risk of adverse renal events in LT recipients. Whether preventive and prophylactic care bundles against infection and judicious modification of the immunosuppressive regimen benefit renal outcomes may deserve further study.

Association of Albumin and Globulin with Mortality Risk in Incident Peritoneal Dialysis Patients.

Background: Nutrition and inflammation have been implicated in predicting mortality in patients on peritoneal dialysis (PD). Serum albumin and globulin can be regarded for the nutritional and inflammatory status. However, there is lack of data to evaluate the synergistic effect of albumin and globulin on mortality prediction. Methods: In 554 patients initiating PD from January 2001 to July 2016, we divided them into four groups by the combination of two categories of low vs. high albumin and low vs. high globulin. The median values for albumin and globulin were chosen to classify them into low or high groups. Their associations with all-cause and cardiovascular (CV) mortality were examined in Cox regression models adjusted for confounding clinical and laboratory data. Results: Patients, 52.91 ± 15.2 years old and 47.8% men, had a median (interquartile range) value of 3.3 (2.9−3.8) g/dL for albumin and 2.8 (2.5−3.2) g/dL for globulin, respectively. Patients with low albumin and high globulin had the highest all-cause mortality and CV mortality, with adjusted hazard ratios of 3.87 (95% CI 1.83−8.20, p < 0.001) and 5.65 (95% CI 2.23−14.34, p < 0.001), respectively, compared with those with a high albumin and low globulin having the lowest mortality rate. Sensitivity analyses further confirmed this relationship. Conclusions: A patient profile of either low albumin or high globulin is linked to a higher risk for mortality, particularly for a profile of both low albumin and high globulin compared with one without either of them. Further studies are needed to explore the mechanisms underlying this phenomenon and how to improve clinical outcomes in those high-risk patients.

Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation.

In this study, we established an explainable and personalized risk prediction model for in-hospital mortality after continuous renal replacement therapy (CRRT) initiation. This retrospective cohort study was conducted at Changhua Christian Hospital (CCH). A total of 2932 consecutive intensive care unit patients receiving CRRT between 1 January 2010, and 30 April 2021, were identified from the CCH Clinical Research Database and were included in this study. The recursive feature elimination method with 10-fold cross-validation was used and repeated five times to select the optimal subset of features for the development of machine learning (ML) models to predict in-hospital mortality after CRRT initiation. An explainable approach based on ML and the SHapley Additive exPlanation (SHAP) and a local explanation method were used to evaluate the risk of in-hospital mortality and help clinicians understand the results of ML models. The extreme gradient boosting and gradient boosting machine models exhibited a higher discrimination ability (area under curve [AUC] = 0.806, 95% CI = 0.770-0.843 and AUC = 0.823, 95% CI = 0.788-0.858, respectively). The SHAP model revealed that the Acute Physiology and Chronic Health Evaluation II score, albumin level, and the timing of CRRT initiation were the most crucial features, followed by age, potassium and creatinine levels, SPO2, mean arterial pressure, international normalized ratio, and vasopressor support use. ML models combined with SHAP and local interpretation can provide the visual interpretation of individual risk predictions, which can help clinicians understand the effect of critical features and make informed decisions for preventing in-hospital deaths.

First 24-Hour Potassium Concentration and Variability and Association with Mortality in Patients Requiring Continuous Renal Replacement Therapy in Intensive Care Units: A Hospital-Based Retrospective Cohort Study.

Serum potassium (K+) levels between 3.5 and 5.0 mmol/L are considered safe for patients. The optimal serum K+ level for critically ill patients with acute kidney injury undergoing continuous renal replacement therapy (CRRT) remains unclear. This retrospective study investigated the association between ICU mortality and K+ levels and their variability. Patients aged >20 years with a minimum of two serum K+ levels recorded during CRRT who were admitted to the ICU in a tertiary hospital in central Taiwan between January 01, 2010, and April 30, 2021 were eligible for inclusion. Patients were categorized into different groups based on their mean K+ levels: <3.0, 3.0 to <3.5, 3.5 to <4.0, 4.0 to <4.5, 4.5 to <5.0, and ≥5.0 mmol/L; K+ variability was divided by the quartiles of the average real variation. We analyzed the association between the particular groups and in-hospital mortality by using Cox proportional hazard models. We studied 1991 CRRT patients with 9891 serum K+ values recorded within 24 h after the initiation of CRRT. A J-shaped association was observed between serum K+ levels and mortality, and the lowest mortality was observed in the patients with mean K+ levels between 3.0 and 4.0 mmol/L. The risk of in-hospital death was significantly increased in those with the highest variability (HR and 95% CI = 1.61 [1.13−2.29] for 72 h mortality; 1.39 [1.06−1.82] for 28-day mortality; 1.43 [1.11−1.83] for 90-day mortality, and 1.31 [1.03−1.65] for in-hospital mortality, respectively). Patients receiving CRRT may benefit from a lower serum K+ level and its tighter control. During CRRT, progressively increased mortality was noted in the patients with increasing K+ variability. Thus, the careful and timely correction of dyskalemia among these patients is crucial.

Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease.

BACKGROUND: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. METHODS: We conducted a retrospective case-control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model's decisions. RESULTS: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745-0.921] and AUC = 0.836 [95% CI 0.757-0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. CONCLUSIONS: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model's decision-making process.

Characteristics of antibiotic resistance genes in full-scale anaerobic digesters of food waste and the effects of application of biogas slurry on soil antibiotic resistance genes.

The fate of antibiotic resistance genes (ARGs) in full-scale anaerobic digestion (AD) of food waste (FW) and in the soil applied with biogas slurry has not been fully understood. In this study, 12 targeted ARGs and intI1 in FW, intermediate product, and biogas slurry from three full-scale AD were analyzed. The results showed that subcritical water pretreatment was an effective method for ARG attenuation, by which the absolute abundance of total targeted ARGs was removed by 99.69%. The predominant ARGs (ermB, tetM, and tetW) in FW were removed more than 99% after subcritical water pretreatment. The result of field experiments with biogas slurry as fertilizer showed that the absolute abundance of several ARGs (sul2, tetM, blaOXA-1, blaTEM) and intI1 accumulated significantly compared to the control group (CK) during three consecutive growth stages of the rice. The detected abundance of ARGs in paddy field soil increased from 190.50 (CK) to 8.87 × 104 copies/g (wet weight) (soil) during tillering stage, and increased from 4102.65 (CK) to 4.38 × 104 copies/g (wet weight) (soil) during heading time. Biogas slurry improved the soil nutrients (TN, AN, TP, and AP); meanwhile, the concentrations of total salt and Cl- increased. Network analysis indicated that 28 genera were the possible hosts of ARGs; variation partitioning analysis (VPA) indicated that microbial communities (contribution 59.30%) were the main factors that affected the fate of ARGs and intI1.

Association of Hematuria with Renal Progression and Survival in Patients Who Underwent Living Donor Liver Transplant.

BACKGROUND: This study aimed to determine the association between episodic or persistent hematuria after liver transplantation and long-term renal outcomes. METHODS: Patients who underwent living donor liver transplantation between July 2005 and June 2019 were recruited and divided into two groups based on the finding of microscopic or gross hematuria after transplantation. All patients were followed up from the index date until the end date in May 2020. The risks of chronic kidney disease, death, and 30% and 50% declines in estimated glomerular filtration rate (eGFR) were compared between groups. RESULTS: A total of 295 patients underwent urinalysis for various reasons after undergoing transplantation. Hematuria was detected in 100 patients (group A) but was not present in 195 patients (group B). Compared with group B, group A had a higher risk of renal progression, including eGFR decline >50% [aHR = 3.447 (95%CI: 2.24~5.30), p < 0.001] and worse survival. In addition, patients who took non-steroidal anti-inflammatory drugs (NSAIDs) continuously for over seven days within six months before transplant surgery had high risks of rapid renal progression, including a >30% decline in eGFR [aHR = 1.572 (95%CI: 1.12~2.21), p = 0.009)]. CONCLUSION: Development of hematuria after surgery in patients who underwent living donor liver transplant and were exposed to NSAIDs before surgery were associated with worse long-term renal dysfunction and survival.

Effects of graphite, graphene, and graphene oxide on the anaerobic co-digestion of sewage sludge and food waste: Attention to methane production and the fate of antibiotic resistance genes.

This study explored and compared the influence of graphite, graphene, and graphene oxide (GO) on the performance of anaerobic co-digestion fed with sewage sludge and food waste, the variations of antibiotic resistance genes (ARGs), and the evolution of microbial community. Graphene exhibited the best performance for improving methane production and organic degradation, which increased by 36.09% and 23.07% compared with control group. The experimental results showed that graphene had the greatest influence on the removal efficiency of blaOXA-1, macrolide resistance genes (ermF and ermB), and some tetracycline resistance genes (tetQ and tetX); however, the removal efficiency of sulfonamide resistance genes (sul1 and sul2), intI1, and some tetracycline resistance genes (tetM, tetO, and tetW) were highest when GO was added. Network analysis indicated that the host cells of mefA, ermB, and tetO were different from other ARG host cells; moreover, graphene controlled the horizontal transfer of ARGs between microbial communities.

Physicians' very brief (30-sec) intervention for smoking cessation on 13 671 smokers in China: a pragmatic randomized controlled trial.

BACKGROUND AND AIMS: Three to 10 minutes of smoking cessation advice by physicians is effective to increase quit rates, but is not routinely practised. We examined the effectiveness of physicians' very brief (approximately 30 sec) smoking cessation intervention on quit rates among Chinese outpatient smokers. DESIGN: A pragmatic, open-label, individually randomized controlled trial. SETTING: Seventy-two medical outpatient departments of hospitals and/or community health centers in Guangdong, China. PARTICIPANTS: Chinese adults who were daily cigarette smokers (n = 13 671, 99% males) were invited by their physician to participate during outpatient consultation. Smokers who were receiving smoking cessation treatment or were judged to need specialist treatment for cessation were excluded. INTERVENTIONS: The intervention group (n = 7015) received a 30-sec intervention including physician's very brief advice, a leaflet with graphic warnings and a card with contact information of available cessation services. The control group (n = 6656) received a very brief intervention on consuming vegetables and fruit. A total of 3466 participants in the intervention group were further randomized to receive a brief booster advice from trained study personnel via telephone 1 month following their doctor visit. MEASUREMENTS: The primary outcome was self-reported 7-day point prevalence abstinence (PPA) in the intervention and control groups at the 12-month follow-up. Secondary outcomes included self-reported 30-day abstinence and biochemically validated abstinence at 12-month follow-up. FINDINGS: By intention-to-treat, the intervention (versus control) group had greater self-reported 7-day abstinence [9.1 versus 7.8%, odds ratio (OR) = 1.14, 95% confidence interval (CI) = 1.03-1.26, P = 0.008] and 30-day abstinence (8.0 versus 6.9%, OR = 1.14, 95% CI = 1.03-1.27, P = 0.01) at 12-month follow-up. The effect size increased when only participants who received the intervention from compliant physicians were included (7-day PPA, OR = 1.42, 95% CI = 1.11-1.74). The group difference in biochemically validated abstinence was small (0.8 versus 0.8%, OR = 1.00, 95% CI = 0.71-1.42, P = 0.99). CONCLUSION: A 30-sec smoking cessation intervention increased self-reported abstinence among mainly male smokers in China at 12-month follow-up (risk difference = 1.3%), and should be feasible to provide in most settings and delivered by all health-care professionals.

DFT, QTAIM, and NBO studies on the trimeric interactions in the protrusion domain of a piscine betanodavirus.

Crystal structure of the protrusion domain (P-domain) of the grouper nervous necrosis virus (GNNV) shows the presence of three-fold trimeric protrusions with two asymmetrical calcium cations along the non-crystallographic three-fold axis. The trimeric interaction natures of the interacting residues and the calcium cations with the neighboring residues within the trimeric interface have been studied by the quantum theory of atoms in molecules (QTAIM) and natural bond orbital (NBO) analyses in the framework of the density-functional theory (DFT) approach. The results revealed that residues Leu259, Val274, Trp280, and Gln322 of subunit A, Arg261, Asp275, Ala277, and Gln322 of subunit B, Leu259, Asp260, Arg261, Ala277, Val278, and Leu324 of subunit C are the main residues involved in the trimeric interactions. Charge-dipole, dipole-dipole, and hydrogen bonding interactions make the significant contributions to these trimeric interactions. Among different interacting residues within trimeric interface, residue pair Arg261 B-Leu259C forms the strongest hydrogen bond inside the interface between subunits B and C. It was also found that calcium cations interact with residues Asp273, Val274, and Asp275 of subunits A, B, and C through charge-charge and charge transfer interactions.