Intra-Abdominal Hypertension and Abdominal Compartment Syndrome in Liver Diseases

Intra-abdominal hypertension (IAH) is defined as an intra-abdominal pressure (IAP) above 12 mmHg. Abdominal compartment syndrome (ACS) is defined as an IAP above 20 mmHg with evidence of organ failure. Moreover, IAH/ACS is a condition that can cause acute renal failure, respiratory failure, circulatory disease, gastrointestinal dysfunction, and liver failure due to elevated IAP. The incidence of IAH/ACS increases in the more critically ill patient and is associated with significantly increased morbidity and mortality. Ascites, blood, or tumors increase IAP. In liver cirrhosis, massive ascites is often encountered. Hence, preventing IAH/ACS conditions may improve outcomes of patients with liver disease

Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients

Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes

Cross-Cultural Comparison of Organizational Structure in a Multinational Organizational Environment

Due to globalization, cross-cultural specifics exploration is of crucial importance for the success of businesses in opening and maintaining new markets. Whilst cultural differences are more obvious and remarkable than similarities and homogeneities, they must be taken into account in management practices being of high importance for understanding organizational behavior. This study investigates the extent to which relationships between context and structure are stable across cultures, it also examines if cultural differences between nations account for variations in their suggested desired structure. The study first analyzes the structure of sample companies and then compares the findings with data on matched analogue companies. The cross-cultural comparison between the resulting structure revealed that a resultant personnel bureaucracy structure for fitted well their high concentration of authority and low structuring of activities, but it did not match the desired full bureaucracy. In response to the analysis of the results obtained, new research directions on the influence of cultural differences and their application in global businesses were identified

Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients

Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes

An analytical investigation of body parts more susceptible to aging and composition changes using statistical hypothesis testing

In recent years, age-related changes in body composition in the elderly are attracting attention. This is associated with a decline in physical functions and an increased risk of disease development. In general, age-related changes in body composition can be minimized with appropriate exercise. However, there are no studies that investigate body parts susceptibility to aging and changes in body composition of those parts. Therefore, devising exercise programs and advising daily life while taking these into account becomes difficult. This study aims to identify body parts that are more susceptible to aging and their body composition changes. The body composition was obtained with a Direct Segmental Multi-frequency Bioelectrical Impedance Analysis using InBody770 in 8 male elderly patients who had been shortly hospitalized. Statistical hypothesis testing was used to determine whether site-specific body composition changed significantly between hospital discharge and 1 year, 1 year and 2 years, and hospital discharge and 2 years. The results showed that Lean body mass, Total Body Water, Intracellular Water, Extracellular Water in the right arm; Lean body mass and Total Body Water in the left arm and trunk are more sensitive to aging

Renal Impairment in Chronic Hepatitis B: A Review

The liver plays a key role in the metabolism of proteins. Liver dysfunction affects many organs because it communicates with the spleen and all digestive organs through the portal vein. Additionally, the kidney is an organ that is closely related to the liver and is involved in liver diseases. Glomerulonephritis is an important extrahepatic manifestation of chronic hepatitis B virus (HBV) infection. Nucleos(t)ide analog (NA) therapy effectively suppresses HBV replication by inhibiting HBV polymerase, thus decreasing the levels of serum HBV-DNA and delaying the progression of cirrhosis. Although NA therapy is recommended for all patients with chronic HBV infection, regardless of the level of renal dysfunction, there is limited information on NA use in patients with chronic kidney disease. In addition, in patients with end-stage liver cirrhosis, hepatorenal syndrome can be fatal. Hence, we should take into account the stage of impaired renal function in patients with cirrhosis. The aims of this article are to review the epidemiology, clinical presentation, treatment, and prevention of HBV-associated nephropathy

Long-term efficacy and safety of nalfurafine hydrochloride on pruritus in chronic liver disease patients: Patient-reported outcome based analyses.

BACKGROUND AND AIM:Among various symptoms accompanied with chronic liver disease, pruritus affects the quality of life of patients, causing physical and mental stress, and worsens hepatic function. Recently, κ-opioid receptor agonist, nalfurafine hydrochloride was approved to treat central pruritus in patients with liver disease in Japan. This study aimed to assess the long-term efficacy and safety of nalfurafine hydrochloride on pruritus in chronic liver disease patients. METHODS:A patient-reported outcome using questionnaire-based methods was used for 41 liver disease patients with or without pruritus symptoms. Nalfurafine hydrochloride (2.5 μg/day) was orally administered to 18 patients suffering from pruritus symptoms and whose current treatment was not effective. The same questionnaires and visual analogue scales (VAS) were repeatedly followed up for the patients for the entire follow-up period, and biochemical analyses were performed to evaluate the safety of the treatment. RESULTS:Pruritus completely disappeared in seven of 18 cases, and VAS scores showed a decreasing trend over time from the start of nalfurafine hydrochloride administration in all patients who received the medication. Among 11 patients who were followed up for more than 12 weeks, nine patients showed continuous improvement of symptoms, and this progress was still apparent at ≥20 weeks after starting administration (p < 0.0001). The medication was discontinued in four patients because of progression of primary disease, high cost, oral dryness, and anemia. No significant toxicity was observed on the serum biochemical analyses. CONCLUSIONS:Nalfurafine hydrochloride contributed to long-term suppression of pruritus without significant safety problems