Prevalence and clinical characteristics of autoimmune liver disease in hospitalized patients with cirrhosis and acute decompensation in China

Exploring the Pathogenesis of Autoimmune Liver Diseases from the Heterogeneity of Target Cells

The incidence of autoimmune liver diseases (ALDs) and research on their pathogenesis are increasing annually. However, except for autoimmune hepatitis, which responds well to immunosuppression, primary biliary cholangitis and primary sclerosing cholangitis are insensitive to immunosuppressive therapy. Besides the known effects of the environment, genetics, and immunity on ALDs, the heterogeneity of target cells provides new insights into their pathogenesis. This review started by exploring the heterogeneity in the development, structures, and functions of hepatocytes and epithelial cells of the small and large bile ducts. For example, cytokeratin (CK) 8 and CK18 are primarily expressed in hepatocytes, while CK7 and CK19 are primarily expressed in intrahepatic cholangiocytes. Additionally, emerging technologies of single-cell RNA sequencing and spatial transcriptomic are being applied to study ALDs. This review offered a new perspective on understanding the pathogenic mechanisms and potential treatment strategies for ALDs.

Research advances in serum chitinase-3-like protein 1 in liver fibrosis

While liver fibrosis remains a serious, progressive, chronic liver disease, and factors causing damage persist, liver fibrosis may develop into cirrhosis and liver cancer. However, short-term liver fibrosis is reversible. Therefore, an early diagnosis of liver fibrosis in the reversible transition phase is important for effective treatment of liver diseases. Chitinase-3-like protein 1 (CHI3L1), an inflammatory response factor that participates in various biological processes and is abundant in liver tissue, holds promise as a potential biomarker for liver diseases. Here, we aimed to review research developments regarding serum CHI3L1 in relation to the pathophysiology and diagnosis of liver fibrosis of various etiologies, providing a reference for the diagnosis, treatment, and prognosis of liver diseases.

Evaluation of autoimmune liver disease natural history in patients referred to Middle East Liver Diseases (MELD) center

BACKGROUND

Autoimmune liver diseases (AILD) are increasing and common forms of chronic liver disease (CLD) with different clinical responses and characteristics which can result in cirrhosis. This study aimed to investigate the natural history and characteristics of AILD in an Iranian population.

METHODS

Patients with AILD [Autoimmune Hepatitis (AIH), Primary Biliary Cholangitis (PBC), Primary Sclerosing Cholangitis (PSC) and Overlap Syndrome (OS)] referred to Middle East Liver Diseases (MELD) center, Tehran, Iran, between January 2002 and December 2022 were included in this retrospective cohort study. The main features of natural history (the trends of liver functional tests (LFT), Auto-Antibodies, response to treatment and cirrhotic status) along with demographic data were studied.

RESULTS

Two hundred sixty-five patients (160 (60.4%) AIH, 37 (14.0%) PBC, 20 (7.5%) PSC, 48 (18.1%) overlap syndrome) with a median follow-up time of 5 years (IQR 4 to 8 years) were included. Baseline laboratory tests revealed that patients with AIH exhibit elevated transaminase levels. However, patients suffering from PBC and PSC displayed increased alkaline phosphatase levels. Conversely, in overlap syndrome patients, both transaminases and alkaline phosphatase were observed at high levels. Autoantibodies represented themselves as important diagnostic markers for the AIH and PBC but not for PSC. The complete response occurred in 112 (70%) of and 28 (58.4%) patients with AIH and overlap syndrome respectively and 21 patients 11 (6.9%) of AIH and 10 (20.8%) of overlap syndrome) were non-responders. Other patients in these two categories were considered as insufficient responders. On the other side, 32 (91.9%) and 8 (40%) of patients with PBC and PSC biochemically responded to Ursodeoxycholic Acid (UDCA). Unpredictably, cirrhosis regression was observed in some AIH and PBC patients.

CONCLUSION

Appropriate medication management for AILD patients may leads to regression from cirrhosis and improvement of manifestations; while discontinuation of medication may cause relapses. However, patient suffering from PSC showed limited response to treatment.

Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy

BACKGROUND

Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning. The global community has recently witnessed an increase in the mortality rate due to liver disease. This could be attributed to many factors, among which are human habits, awareness issues, poor healthcare, and late detection. To curb the growing threats from liver disease, early detection is critical to help reduce the risks and improve treatment outcome. Emerging technologies such as machine learning, as shown in this study, could be deployed to assist in enhancing its prediction and treatment.

AIM

To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection, diagnosis, and reduction of risks and mortality associated with the disease.

METHODS

The dataset used in this study consisted of 416 people with liver problems and 167 with no such history. The data were collected from the state of Andhra Pradesh, India, through https://www.kaggle.com/datasets/uciml/indian-liver-patient-records. The population was divided into two sets depending on the disease state of the patient. This binary information was recorded in the attribute "is_patient".

RESULTS

The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36% and 73.24%, respectively, which was much better than the conventional method. The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis (scarring) and to enhance the survival of patients. The study showed the potential of machine learning in health care, especially as it concerns disease prediction and monitoring.

CONCLUSION

This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease. However, relevant authorities have to invest more into machine learning research and other health technologies to maximize their potential.