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The liver is frequently subject to insult because of viral infection, alcohol abuse, or toxic chemical exposure. Extensive research has been conducted to identify blood markers that can better discern liver damage, but little progress has been achieved in clinical practice. Recently, circulating microRNAs (miRNAs) have been reported as potential biomarkers for the noninvasive diagnosis of cancer. In this study, we investigated whether plasma miRNAs have diagnostic utility in identifying liver disease. The study was divided into 2 phases: marker selection by real-time quantitative PCR analysis of a small set of plasma samples, and marker validation with a large set of plasma samples from 83 patients with chronic hepatitis B viral infections, 15 patients with skeletal muscle disease, and 40 healthy controls. Two mouse model systems, d-galactosamine- and alcohol-induced liver injury, were also developed to evaluate whether differences in miRNA concentration were associated with various liver diseases. Among the miRNA candidates identified, miR-122 presented a disease severity-dependent change in plasma concentration in the patients and animal models. Compared with an increase in aminotransferase activity in the blood, the change in miR-122 concentration appeared earlier. Furthermore, this change was more specific for liver injury than for other organ damage and was more reliable, because the change was correlated with liver histologic stage. Our findings suggest that circulating miR-122 has potential as a novel, predictive, and reliable blood marker for viral-, alcohol-, and chemical-induced liver injury.

Objectives The purpose of this study is to establish microvascular invasion (MVI) prediction models based on preoperative contrast-enhanced ultrasound (CEUS) and ethoxybenzyl-enhanced magnetic resonance imaging (EOB-MRI) in patients with a single hepatocellular carcinoma (HCC) ≤ 5 cm. Methods Patients with a single HCC ≤ 5 cm and accepting CEUS and EOB-MRI before surgery were enrolled in this study. Totally, 85 patients were randomly divided into the training and validation cohorts in a ratio of 7:3. Non-radiomics imaging features, the CEUS and EOB-MRI radiomics scores were extracted from the arterial phase, portal phase and delayed phase images of CEUS and the hepatobiliary phase images of EOB-MRI. Different MVI predicting models based on CEUS and EOB-MRI were constructed and their predictive values were evaluated. Results Since univariate analysis revealed that arterial peritumoral enhancement on the CEUS image, CEUS radiomics score, and EOB-MRI radiomics score were significantly associated with MVI, three prediction models, namely the CEUS model, the EOB-MRI model, and the CEUS-EOB model, were developed. In the validation cohort, the areas under the receiver operating characteristic curve of the CEUS model, the EOB-MRI model, and the CEUS-EOB model were 0.73, 0.79, and 0.86, respectively. Conclusions Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm. Clinical relevance statement Radiomics models based on CEUS and EOB-MRI are effective for MVI predicting and conducive to pretreatment decision-making in patients with a single HCC within 5 cm. Key Points • Radiomics scores based on CEUS and EOB-MRI, combined with arterial peritumoral enhancement on CEUS, show a satisfying performance of MVI predicting. • There was no significant difference in the efficacy of MVI risk evaluation between radiomics models based on CEUS and EOB-MRI in patients with a single HCC ≤ 5 cm.

PurposeTo assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodsPublications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models.ResultsTwenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82–0.89), 0.87 (95% CI 0.83–0.92), and 0.74 (95% CI 0.67–0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region.ConclusionRadiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.

Accurate prediction of renal outcomes in ANCA-associated vasculitis (AAV) is critical. This study evaluated the predictive value of urinary biomarkers for end-stage kidney disease (ESKD) progression. A prospective cohort of 88 AAV patients with renal involvement was followed for a median of 27 months. Urinary β-catenin, IL-18, trehalase, and NGAL were measured. Cox regression and ROC analyses assessed associations and predictive performance. 65 patients (73.9%) progressed to ESKD. Urinary trehalase and NGAL were significantly elevated in the ESKD group and were independent predictors of ESKD in multivariable analysis (both  < 0.05), with AUCs of 0.857 and 0.852, respectively. Their combination improved predictive accuracy (AUC = 0.869). β-catenin showed moderate predictive value (AUC = 0.705). Although urinary IL-18 showed statistical significance when analyzed as a continuous variable in an initial model, this association was not consistent across alternative modeling strategies and therefore was not considered a stable independent predictor. Stratified and landmark analyses confirmed the robustness of trehalase and NGAL's prognostic value. Urinary trehalase and NGAL are strong, noninvasive predictors of ESKD in AAV, and their combination enhances risk stratification, supporting potential clinical utility for personalized treatment decisions.

Introduction: The aim of this study was to develop and validate a clinical prediction model for Clostridioides difficile associated diarrhea (CDAD) based on routine laboratory tests. Methodology: Data from 121 CDAD patients and 123 patients with non-CDAD who presented at the First Affiliated Hospital of Nanjing Medical University between May 2017 and January 2022 were used to create a nomogram based on logistic regression. In addition, 109 stool samples from diarrhea patients in Jurong People's Hospital were collected to detect Clostridioides difficile toxin genes. The performance of the prediction model was assessed by the area under the curve (AUC), Hosmer-Lemeshow goodness of fit, and decision curve analysis (DCA). Results: The following variables were included in the new multivariate regression model: white blood cell (WBC), lymphocyte (LY), hemoglobin (HGB), mean corpuscular volume (MCV), activated partial thromboplastin time (APTT), D-dimer, urea, creatinine (Cr), and uric acid (UA). The AUC of the prediction model was 0.793 (95% CI = 0.737–0.849) for the derivation sets and 0.708 (95% CI = 0.506–0.910) for the validation set. The calibrated values were 0.874 and 0.543, respectively. The nomogram showed better net benefit when prediction probability values were above 0.1 in the DCA curve. Conclusions: A new diagnostic prediction model for CDAD was established. Clinicians can use the nomogram to initially assess the likelihood of CDAD when the patient suffers diarrhea, to ensure timely specific laboratory tests, and appropriate diagnostic and treatment measures.

Nosocomial infection caused by carbapenem-resistant (CRKP) is a great threat to severely ill patients. Here we report an outbreak of ST15 isolates co-producing KPC-2, CTX-M-15, and SHV-28 in the cardiac surgery intensive care unit (CSICU) of a tertiary hospital. From November 2019 to August 2020, all non-duplicated CRKP isolates were collected from the CSICU. The VITEK-2 compact system was used for bacterial identification and antimicrobial susceptibility testing. Clinical data were retrieved from electronic case records. All strains were also subjected to antibiotic resistance genes detection. Clonal relationships were analyzed by multilocus sequence typing (MLST) and pulsed-field gel electrophoresis (PFGE). A total of 28 non-duplicated CRKP isolates were collected, including 23 strains belonging to ST15 and 5 strains belonging to ST11. All ST15 isolates were susceptible to amikacin, tigecycline, polymyxin B and ceftazidime/avibactam, but resistant to carbapenems, cephalosporins, quinolones, tobramycin and gentamicin. The detection of resistant determinants showed that 21 strains of ST15 CRKP co-harboured . All the 28 CRKP isolates were classified into five PFGE patterns (A, B, C, D and E), of which type A and B belonged to ST15 and type C, D and E belonged to ST11. PFGE type A was the predominant clonotype of this nosocomial infection and belonged to ST15. ST15 co-producing KPC-2, CTX-M-15, SHV-28, TEM-1, OXA-1 and aac(6')-Ib-cr is the predominant clone spread in the CSICU. Surveillance and comprehensive infection control measures should be strengthened in clinical practice.

Clinical characteristics of 33 asymptomatic COVID-19 infections were analyzed in this study. The data showed most of asymptomatic patients had small body mass index, good prognosis and low infectivity. This study suggests that screening from high-risk populations to find and isolate asymptomatic patients is an important disease prevention and control strategy for COVID-19.

BackgroundTo explore the pathologic basis and prognostic value of tumor and liver stiffness measured pre-operatively by two-dimensional shear wave elastography (2D-SWE) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients who undergo hepatic resection.MethodsA total of 191 HBV-infected patients with solitary resectable HCC were prospectively enrolled. The stiffness of intratumoral tissue, peritumoral tissue, adjacent liver tissue, and distant liver tissue was evaluated by 2D-SWE. The correlations between stiffness and pathological characteristics were analyzed in 114 patients. The predictive value of stiffness for recurrence-free survival (RFS) was evaluated, and Cutoff Finder was used for determining optimal cut-off stiffness values. Cox proportional hazards analysis was used to identify independent predictors of RFS.ResultsPathologically, intratumoral stiffness was associated with stroma proportion and microvascular invasion (MVI) while peritumoral stiffness was associated with tumor size, capsule, and MVI. Adjacent liver stiffness was correlated with capsule and liver fibrosis stage while distant liver stiffness was correlated with liver fibrosis stage. Peritumoral stiffness, adjacent liver stiffness, and distant liver stiffness were all correlated to RFS (all p < 0.05). Higher peritumoral stiffness (> 49.4 kPa) (HR = 1.822, p = 0.023) and higher adjacent liver stiffness (> 24.1 kPa) (HR = 1.792, p = 0.048) were significant independent predictors of worse RFS, along with tumor size and MVI. The nomogram based on these variables showed a C-index of 0.77 for RFS prediction.ConclusionsStiffness measured by 2D-SWE could be a tumor microenvironment and tumor invasiveness biomarker. Peritumoral stiffness and adjacent liver stiffness showed important values in predicting tumor recurrence after curative resection in HBV-related HCC.Clinical relevance statementTumor and liver stiffness measured by two-dimensional shear wave elastography serve as imaging biomarkers for predicting hepatocellular carcinoma recurrence, reflecting biological behavior and tumor microenvironment.Key points• Stiffness measured by two-dimensional shear wave elastography is a useful biomarker of tumor microenvironment and invasiveness.• Higher stiffness indicated more aggressive behavior of hepatocellular carcinoma.• The study showed the prognostic value of peritumoral stiffness and adjacent liver stiffness for recurrence-free survival.• The nomogram integrating peritumoral stiffness, adjacent liver stiffness, tumor size, and microvascular invasion showed a C-index of 0.77.

Objective: To investigate the transmission pathway of a MRSA prevalence in a pancreatic surgery ward in a Chinese teaching hospital. Methods: Molecular epidemiology investigations were carried out combined PFGE, MLST, SCCmec typing and whole-genome sequencing for 20 successive MRSA isolates (2 isolates from the ward environment). Resistance and virulence genes were detected using specific PCR. Bacterial identification and AST were performed using the Vitek 2 Compact System. Clinical data of enrolled cases were retrieved from electronic case records. Results: From January 2020 to May 2020, successive isolated 20 MRSA strains were clarified to 2 PFGE patterns (A = 19, B = 1) in the ward. Both isolates from environment and patients belonged to sequence type ST5-SCCmec Il-spa type t311. MRSA- related resistance genes mecA, blaZ, ermA, ant(4')-Ia and norA were found in each clone. All 20 isolates carried tst, hlg, hla, eta, eap, fnbA and seo virulence genes, other virulence genes such as sea, sec, seb, seg, sei, sem, sen, ebpS and fnbB were also found in partial stains. All patients had fever symptom, 27.8% were accompanied by diarrhea, 88.9% had undergone surgery or invasive procedures within 30 days. Finally, 94.4% of these patients recovered. Conclusion: This study confirmed a prevalence of ST5-MRSA-II-t311 clone in a surgery ward, indicated MRSA is a risk factor for post-surgery nosocomial infection and hand hygiene and environmental surveillance should not be ignored. Keywords: methicillin-resistantStaphylococcus aureus, ST5, SCCmec II, spa type t311, diarrhea, virulence genes

The epidemic of coronavirus disease 2019 (COVID-19) pneumonia caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has expanded from China throughout the world. This study aims to estimate the risk of disease progression of patients who have been confirmed with COVID-19. Meta-analysis was performed in existing literatures to identify risk factors associated with COVID-19 pneumonia progression. Patients with COVID-19 pneumonia were admitted to hospitals in Wuhan or Hangzhou were retrospectively enrolled. The risk prediction model and nomogram were developed from Wuhan cohort through logistic regression algorithm, and then validated in Hangzhou and Yinchuan cohorts. A total of 270 patients admitted to hospital between Dec 30, 2019, and Mar 30, 2020, were retrospectively enrolled (Table 1). The development cohort (Wuhan cohort) included 87 (43%) men and 115 (57%) women, and the median age was 53 years old. Hangzhou validation cohort included 20 (48%) men and 22 (52%) women, and the median age was 59 years old. Yinchuan validation cohort included 12 (46%) men and 14 (54%) women, and the median age was 44 years old. The meta-analysis along with univariate logistic analysis in development cohort have shown that age, fever, diabetes, hypertension, CREA, BUN, CK, LDH, and neutrophil count were significantly associated with disease progression of COVID-19 pneumonia. The model and nomogram derived from development cohort show good performance in both development and validation cohorts. The severe COVID-19 pneumonia is associated with various types of risk factors including age, fever, comorbidities, and some laboratory examination indexes. The model integrated with these factors can help to evaluate the disease progression of COVID-19 pneumonia.