Application of metagenomic next-generation sequencing in optimizing the diagnosis of ascitic infection in patients with liver cirrhosis.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) is an emerging technique for the clinical diagnosis of infectious disease that has rarely been used for the diagnosis of ascites infection in patients with cirrhosis. This study compared mNGS detection with conventional culture methods for the on etiological diagnosis of cirrhotic ascites and evaluated the clinical effect of mNGS. METHODS: A total of 109 patients with ascites due to cirrhosis were included in the study. We compared mNGS with conventional culture detection by analyzing the diagnostic results, pathogen species and clinical effects. The influence of mNGS on the diagnosis and management of ascites infection in patients with cirrhosis was also evaluated. RESULTS: Ascites cases were classified into three types: spontaneous bacterial peritonitis (SBP) (16/109, 14.7%), bacterascites (21/109, 19.3%) and sterile ascites (72/109, 66.1%). In addition, 109 patients were assigned to the ascites mNGS-positive group (80/109, 73.4%) or ascites mNGS-negative group (29/109, 26.6%). The percentage of positive mNGS results was significantly greater than that of traditional methods (73.4% vs. 28.4%, P < 0.001). mNGS detected 43 strains of bacteria, 9 strains of fungi and 8 strains of viruses. Fourteen bacterial strains and 3 fungal strains were detected via culture methods. Mycobacteria, viruses, and pneumocystis were detected only by the mNGS method. The mNGS assay produced a greater polymicrobial infection rate than the culture method (55% vs. 16%). Considering the polymorphonuclear neutrophil (PMN) counts, the overall percentage of pathogens detected by the two methods was comparable, with 87.5% (14/16) in the PMN ≥ 250/mm3 group and 72.0% (67/93) in the PMN < 250/mm3 group (P > 0.05). Based on the ascites PMN counts combined with the mNGS assay, 72 patients (66.1%) were diagnosed with ascitic fluid infection (AFI) (including SBP and bacterascites), whereas based on the ascites PMN counts combined with the culture assay, 37 patients (33.9%) were diagnosed with AFI (P < 0.05). In 60 (55.0%) patients, the mNGS assay produced positive clinical effects; 40 (85.7%) patients had their treatment regimen adjusted, and 48 patients were improved. The coincidence rate of the mNGS results and clinical findings was 75.0% (60/80). CONCLUSIONS: Compared with conventional culture methods, mNGS can improve the detection rate of ascites pathogens, including bacteria, viruses, and fungi, and has significant advantages in the diagnosis of rare pathogens and pathogens that are difficult to culture; moreover, mNGS may be an effective method for improving the diagnosis of ascites infection in patients with cirrhosis, guiding early antibiotic therapy, and for reducing complications related to abdominal infection. In addition, explaining mNGS results will be challenging, especially for guiding the treatment of infectious diseases.

Ultrasound-based Radiomics Predicts Short-term Outcomes in Hepatitis B Virus-related Acute-on-chronic Liver Failure.

BACKGROUND: The prognosis in hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF) is challenging due to heterogeneity. Radiomics may enable noninvasive outcome prediction. OBJECTIVE: This study aimed to evaluate ultrasound-based radiomics for predicting outcomes in HBV-ACLF. METHODS: We enrolled 264 HBV-ACLF patients, dividing them into a training cohort (n=184) and a validation cohort (n=80). From hepatic ultrasound images, 455 radiomic features were extracted. Radiomics-based phenotypes were identified through unsupervised hierarchical clustering. A radiomic signature was developed using a Cox-LASSO algorithm to predict 30-day mortality. Furthermore, we integrated the signature with independent clinical predictors via multivariate Cox regression to construct a combined clinical-radiomic nomogram (CCR-nomogram). Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) assessed performance improvements achieved by adding radiomic features to clinical data. RESULTS: Both clustering and radiomic signature identified two distinct subgroups with significant differences in clinical characteristics and 30-day prognosis. In the training cohort, the signature achieved a C-index of 0.746, replicated in validation with a C-index of 0.747. The CCR-nomogram achieved C-indices of 0.834 and 0.819 for the training and validation cohorts. Incorporating radiomic features significantly improved the CCRnomogram over the signature and clinical-only models, evidenced by IDI of 0.108-0.264 and NRI of 0.292-0.540 in both cohorts (all p0.05). CONCLUSION: Ultrasound-based radiomics offered prognostic information complementary to clinical data and demonstrated potential to enhance outcome prediction in HBV-ACLF.

Shear wave elastography-based deep learning model for prognosis of patients with acutely decompensated cirrhosis.

PURPOSE: This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis. METHODS: We prospectively enrolled 288 acutely decompensated cirrhosis patients with a minimum 1-year follow-up, divided into a training cohort (202 patients, 1010 2D SWE images) and a test cohort (86 patients, 430 2D SWE images). Using transfer learning by Resnet-50 to analyze 2D SWE images, a SWE-based deep learning signature (DLswe) was developed for 1-year mortality prediction. A combined nomogram was established by incorporating deep learning SWE information and laboratory data through a multivariate Cox regression analysis. The performance of the nomogram was evaluated with respect to predictive discrimination, calibration, and clinical usefulness in the training and test cohorts. RESULTS: The C-index for DLswe was 0.748 (95% CI 0.666-0.829) and 0.744 (95% CI 0.623-0.864) in the training and test cohorts, respectively. The combined nomogram significantly improved the C-index, accuracy, sensitivity, and specificity of DLswe to 0.823 (95% CI 0.763-0.883), 86%, 75%, and 89% in the training cohort, and 0.808 (95% CI 0.707-0.909), 83%, 74%, and 85% in the test cohort (both p < 0.05). Calibration curves demonstrated good calibration of the combined nomogram. Decision curve analysis indicated that the nomogram was clinically valuable. CONCLUSIONS: The 2D SWE-based deep learning model holds promise as a noninvasive tool to capture valuable prognostic information, thereby improving outcome prediction in patients with acutely decompensated cirrhosis.

Avicularin alleviates acute liver failure by regulation of the TLR4/MyD88/NF-κB and Nrf2/HO-1/GPX4 pathways to reduce inflammation and ferroptosis.

Acute liver failure (ALF) is an inflammation-mediated hepatocyte death process associated with ferroptosis. Avicularin (AL), a Chinese herbal medicine, exerts anti-inflammatory and antioxidative effects. However, the protective effect of AL and the mechanism on ALF have not been reported. Our in vivo results suggest that AL significantly alleviated lipopolysaccharide (LPS)/D-galactosamine (D-GalN)-induced hepatic pathological injury, liver enzymes, inflammatory cytokines, reactive oxygen species and iron levels and increased the antioxidant enzyme activities (malondialdehyde and glutathione). Our further in vitro experiments demonstrated that AL suppressed inflammatory response in LPS-stimulated RAW 264.7 cells via blocking the toll-like receptor 4 (TLR4)/myeloid differentiation protein-88 (MyD88)/nuclear factor kappa B (NF-κB) pathway. Moreover, AL attenuated ferroptosis in D-GalN-induced HepG2 cells by activating the nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase 1 (HO-1)/glutathione peroxidase 4 (GPX4) pathway. Therefore, AL can alleviate inflammatory response and ferroptosis in LPS/D-GalN-induced ALF, and its protective effects are associated with blocking TLR4/MyD88/NF-κB pathway and activating Nrf2/HO-1/GPX4 pathway. Moreover, AL is a promising therapeutic option for ALF and should be clinically explored.

Development and application of a novel model to predict the risk of non-alcoholic fatty liver disease among lean pre-diabetics with normal blood lipid levels.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) has been associated with type 2 diabetes, but its relationship with pre-diabetes is still unknown. This study aims to determine whether pre-diabetes is associated with NAFLD, followed by establishing a NAFLD predictive nomogram for lean Chinese pre-diabetics with normal blood lipids. METHODS: Datasets from 3 previous studies, 1 (2774 pre-diabetics with normal blood lipids for training, 925 for validation), 2 (546 for longitudinal internal validation, post-5-year follow-up), and 3 (501 from another institution for external validation), were used. Kaplan-Meier determined cumulative NAFLD hazard, and least absolute shrinkage and selection operator regression analysis uncovered its risk factors. Multivariate logistic regression analysis constructed the nomogram, followed by validation with receiver operating characteristic curve, calibration plot, and decision curve analyses. RESULTS: NAFLD incidence increased with diabetes progression, and pre-diabetics had higher cumulative risk versus non-diabetics, even for lean individuals with normal blood lipids. Six risk factors were identified: body mass index, total cholesterol, alanine aminotransferase:aspartate aminotransferase, triglyceride:high density lipoprotein cholesterol, fasting blood glucose and γ-glutamyl-transferase. The nomogram yielded areas under the curve of 0.808, 0.785, 0.796 and 0.832, for respectively, training, validation, longitudinal internal validation, and external validation, which, along with calibration curve values of p = 0.794, 0.875, 0.854 and 0.810 for those 4 datasets and decision curve analyses, validated its clinical utility. CONCLUSIONS: Lean pre-diabetic Chinese with normal blood lipids have higher NAFLD risk versus non-diabetics. The nomogram is able to predict NAFLD among such individuals, with high discrimination, enabling its use for early detection and intervention.

The combination of serum oligosaccharide chain (G-test), alpha-fetoprotein, and aspartate aminotransferase to alanine aminotransferase ratio provides the optimal diagnostic value for early detection of hepatocellular carcinoma.

BACKGROUND: The purpose of this study was to compare the diagnostic value of serum oligosaccharide chain (G-test), alpha-fetoprotein (AFP) and aspartic aminotransferase to alanine aminotransferase ratios (AAR), both alone and in combination, for predicting hepatocellular carcinoma (HCC) onset. METHODS: Between Januarys 2020-2022, 152 subjects admitted to the First Affiliated Hospital of Nanchang University was enrolled in this study, of which 77 had HCC, 18 chronic hepatitis (CH), 37 liver cirrhosis (LC) and 20 were healthy. Data for patient characteristics were collected, and differences between groups were analyzed by either Mann-Whitney U or χ2 tests. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of AFP, G-test, and AAR for HCC. RESULTS: G-test, AFP, and AAR were all found to have close correlations with HCC among the different patient groups, with G-test being the most predictive for HCC among healthy and CL patients, as represented by respective areas under the curve (AUC) of 0.953 and 0.792 (P < 0.001). By contrast, AAR had the greatest diagnostic ability for HCC among CH patients (AUC = 0.850; P < 0.001). However, the combination of all 3 biomarkers obtained the most optimal results for predicting HCC onset, in terms of predictive capability for all 3 non-HCC patient groups, yielding AUCs of 0.958, 0.898, and 0.808 (P < 0.001) for, respectively, healthy, CH, and LC patients. Additionally, AFP had higher specificity, but lower sensitivity, with increased threshold values, as the recommended threshold of AFP ≥ 400 ng/mL yielded a missed diagnosis rate of 72.7%. For AFP-negative HCC (AFP-NHCC) patients, G-test alone had the greatest diagnostic capability (AUC = 0.855; P < 0.001), sensitivity (83.8%), and specificity (87.5%). CONCLUSION: G-test has the greatest diagnostic capability for HCC and AFP-NHCC, with high sensitivity and specificity, among healthy and LC patients. However, AAR had the highest diagnostic capability and sensitivity for HCC in CH. Overall, though, the combination of G-test, AFP and AAR provided the most optimal outcomes for predicting HCC onset, no matter the patient pre-conditions.

A model based on two-dimensional shear wave elastography for acute-on-chronic liver failure development in patients with acutely decompensated hepatitis B cirrhosis.

Background: To evaluate the accuracy of two-dimensional (2D) shear wave elastography (SWE), develop and validate a novel prognostic model in predicting acute-on-chronic liver failure (ACLF) development in patients with acutely decompensated hepatitis B cirrhosis. Methods: This prospective cohort study enrolled 221 patients in the First Affiliated Hospital of Nanchang University from September 2019 to January 2021, and randomly assigned them to the derivation and validation cohorts (7:3 ratio). Ultrasound, 2D SWE, clinical and laboratory data were collected, and outcome (ACLF developed) was recorded during a 90-day follow-up period. We evaluated the ability of 2D SWE to predict the outcome, developed a model for predicting ACLF development in the derivation cohort, and assessed the model in the validation cohort. Results: 2D SWE values were significantly higher in patients with ACLF development (P<0.05). The accuracy of 2D SWE in predicting the outcome was better than that of serum parameters of liver fibrosis (all P<0.05). The SWE model for ACLF development had good calibration and discrimination [concordance index (C-index): 0.855 and 0.840 respectively] in derivation and validation cohorts, outperforming serum prognostic scores (all P<0.05). Conclusions: The SWE model, superior to serum prognostic scores in predicting ACLF development, could be a noninvasive tool to guide the individual management of patients with acutely decompensated hepatitis B cirrhosis.

Higher serum uric acid to HDL-cholesterol ratio is associated with onset of non-alcoholic fatty liver disease in a non-obese Chinese population with normal blood lipid levels.

BACKGROUND: Recent studies have demonstrated the presence of associations between metabolic syndrome and the onset of nonalcoholic fatty liver disease (NAFLD). Metabolic syndrome, in turn, has been found to be linked to high serum uric acid to HDL-cholesterol ratios (UHR). However, the relationship between UHR values and the occurrence of NAFLD in non-obese individuals remains unknown. The present study aimed to examine the possible correlation between UHR values and NAFLD onset among a non-obese Chinese population without dyslipidemia, as well as comparing the predictive value of UHR versus other NAFLD onset predictors. METHODS: A total of 9837 non-obese patients, with normal blood lipid levels, were included in a 5-year retrospective cohort study, and the onset of NAFLD in these patients was diagnosed by liver ultrasound. RESULTS: Out of the 9837 patients, 855 were diagnosed with NAFLD during the 5-year follow-up period, for an overall total prevalence of 8.7% at the end of the study period. Across quintiles 1, 2, 3, 4 and 5 of UHR (respectively, ratios of ≤ 120.88, 120.89-154.01, 154.02-189.91, 189.92-240.46, and ≥ 240.47), the prevalence of NAFLD among the patients increased from 2.4%, 5%, 7.9%, 10.3%, and 17.8%, respectively. After adjustments for age, gender, liver and kidney functional markers, as well as metabolic indicators, multivariate Cox proportional hazard regression analysis demonstrated that the hazard ratio (HR) was the highest in quintile 5, at 1.76 (1.12-2.75), and the lowest in quintile 1. The area under the curve (AUC) for UHR (0.690) was higher than that for serum uric acid (UA, 0.666) and HDL-C (0.636), suggesting the predictive ability of UHR for NAFLD onset was better than either alone. This finding was further supported by the presence of an independent association between UHR and NAFLD, even within the normal range of UA and HDL-C; the HR (95% confidence interval, CI) for NAFLD was 1.002 (1.000-1.004). Compared with other significant predictors, AUC for UHR (0.67) was similar to that of low-density lipoprotein cholesterol (LDL-C)/high-density lipoprotein cholesterol (HDL-C, 0.68), non-high-density lipoprotein cholesterol (NHDL-C)/HDL-C (0.68) and alanine aminotransferase (ALT)/aspartate aminotransferase (AST) ratios (0.7), and was higher than that of LDL-C (0.63), remnant cholesterol (RC,0.59), and albumin (ALB)/alkaline phosphatase (ALP) ratio (0.61). The sensitivity of UHR (71%) was the highest among all indicators. In the subgroup with ALT < 40U/L, the AUC for UHR was 0.70, which was the highest among all predictors; among ALT > 40U/L, UHR was able to predict the occurrence of NAFLD (AUC = 0.61, p = 0.007), which was not the case for RC (P = 0.441), ALB/ALP (P = 0.419), and ALT/AST (P = 0.159). CONCLUSIONS: UHR serve as an inexpensive and reliable predictor of NAFLD onset in non-obese Chinese people with normal blood lipid levels, allowing for identification of individuals at high risk for NAFLD.

The Serum Metabolomics Study of Liver Failure and Artificial Liver Therapy Intervention.

BACKGROUND This study was designed to study the serum metabolites of patients with liver failure. MATERIAL AND METHODS The study included 50 patients with liver failure, 30 patients with chronic hepatitis B treated with an artificial liver, 11 patients with an artificial liver, and 32 healthy controls. Clinical data were recorded, and blood samples were analyzed by gas chromatography-mass spectrometry (GC-MS). The random forest algorithm was used to construct a multidimensional scale map to preliminarily reflect the differences between samples. The data were then analyzed to obtain the correlation of different variables among samples, from which the differential metabolites were screened. RESULTS Thirty-five metabolites were identified by GC-MS. There were significant differences in serum metabolites levels before and after treatment in the liver failure group and in the chronic hepatitis group, healthy control group, and artificial liver group. Different metabolites were screened according to the importance of different variables among samples. Significant differences were found between the liver failure group, the chronic hepatitis group, and the healthy control group. In addition, there were significant differences in the liver group before and after treatment with an artificial liver, including differences in boric acid, 2-(methoxyamino)-propionic acid, glycine, l-methionine, aminopropionic acid, glyceryl monostearate, cholesterol, and other substances. CONCLUSIONS A variety of differences in metabolites were found in each group, some of which revealed possible metabolic pathways leading to differences between groups. Blood metabolomics analysis has great potential in real-time dynamic monitoring of liver failure and evaluation of artificial liver therapy.