Cirrhosis of the Liver: A Case Report and Literature Review of a Rare Case Presentation of Autoimmune Hepatitis With Systemic Sclerosis.

Systemic sclerosis (SSc) is a chronic systemic disease that affects the skin, heart, lungs, kidneys, gastrointestinal tract, and musculoskeletal system. Although gastrointestinal involvement has been reported in approximately 90% of scleroderma patients, liver involvement is uncommon. A 51-year-old female was admitted to the hospital due to abdominal distension and pedal edema. She had a history of Raynaud's syndrome and multiple hypopigmented and hyperpigmented patches over her body for the last year. Her ascetic fluid analysis was transudative with a serum ascites albumin gradient >1.1, and the abdomen and pelvis ultrasonography reported liver cirrhosis with splenomegaly with perisplenic varices. Her antinuclear antibody and anti-centromere antibody were positive. Skin thickening was visible. Her alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum globulin were raised. Viral serology was negative. We managed her with diuretics, beta-blockers, prednisolone (30 mg/day administered orally), angiotensin-converting enzyme inhibitors, and calcium channel blockers. Edema and abdominal distension decreased with this management, and no Raynaud's phenomenon was observed during the hospital stay.

Methods for Facial Expression Recognition with Applications in Challenging Situations.

In the last few years, a great deal of interesting research has been achieved on automatic facial emotion recognition (FER). FER has been used in a number of ways to make human-machine interactions better, including human center computing and the new trends of emotional artificial intelligence (EAI). Researchers in the EAI field aim to make computers better at predicting and analyzing the facial expressions and behavior of human under different scenarios and cases. Deep learning has had the greatest influence on such a field since neural networks have evolved significantly in recent years, and accordingly, different architectures are being developed to solve more and more difficult problems. This article will address the latest advances in computational intelligence-related automated emotion recognition using recent deep learning models. We show that both deep learning-based FER and models that use architecture-related methods, such as databases, can collaborate well in delivering highly accurate results.

Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education.

Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control.

Prebiotic and probiotic fortified milk in prevention of morbidities among children: community-based, randomized, double-blind, controlled trial.

BACKGROUND: Recent reviews suggest common infectious diseases continue to be a major cause of death among preschool children in developing countries. Identification of feasible strategies to combat this disease burden is an important public health need. We evaluated the efficacy of adding prebiotic oligosaccharide and probiotic Bifidobacterium lactis HN019 to milk, in preventing diarrhea, respiratory infections and severe illnesses, in children aged 1-4 years as part of a four group study design, running two studies simultaneously. METHODS AND FINDINGS: In a community based double-masked, randomized controlled trial, children 1-3 years of age, willing to participate, were randomly allocated to receive either control milk (Co; n = 312) or the same milk fortified with 2.4 g/day of prebiotic oligosaccharide and 1.9x10(7) colony forming unit (c.f.u)/day of probiotic Bifidobacterium lactis HN019 (PP; n = 312). Children were followed up for 1 year providing data for 1-4 years. Biweekly household surveillance was conducted to gather information on compliance and morbidity. Both study groups were comparable at baseline; compliance to intervention was similar. Overall, there was no effect of prebiotic and probiotic on diarrhea (6% reduction, 95% Confidence Interval [CI]: -1 to 12%; p = 0.08). Incidence of dysentery episodes was reduced by 21% (95% CI: 0 to 38%; p = 0.05). Incidence of pneumonia was reduced by 24% (95% CI: 0 to 42%; p = 0.05) and severe acute lower respiratory infection (ALRI) by 35% (95% CI: 0 to 58%; p = 0.05). Compared to children in Co group, children in PP group had 16% (95% CI: 5 to 26%, p = 0.004) and 5% (95% CI: 0 to 10%; p = 0.05) reduction in days with severe illness and high fever respectively. CONCLUSIONS/SIGNIFICANCE: Milk can be a good medium for delivery of prebiotic and probiotic and resulted in significant reduction of dysentery, respiratory morbidity and febrile illness. Overall, impact of diarrhea was not significant. These findings need confirmation in other settings.

Development and use of behavior and social interaction software installed on Palm handheld for observation of a child's social interactions with the environment.

In settings in developing countries, children often socialize with multiple socializing agents (peers, siblings, neighbors) apart from their parents, and thus, a measurement of a child's social interactions should be expanded beyond parental interactions. Since the environment plays a role in shaping a child's development, the measurement of child-socializing agents' interactions is important. We developed and used a computerized observational software Behavior and Social Interaction Software (BASIS) with a preloaded coding scheme installed on a handheld Palm device to record complex observations of interactions between children and socializing agents. Using BASIS, social interaction assessments were conducted on 573 preschool children for 1 h in their natural settings. Multiple screens with a set of choices in each screen were designed that included the child's location, broad activity, state, and interactions with child-socializing agents. Data were downloaded onto a computer and systematically analyzed. BASIS, installed on Palm OS (M-125), enabled the recording of the complex interactions of child-socializing agents that could not be recorded with manual forms. Thus, this tool provides an innovative and relatively accurate method for the systematic recording of social interactions in an unrestricted environment.