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Objective: To investigate the long-term outcomes of patients with combined primary sclerosing cholangitis/inflammatory bowel disease (PSC-IBD) undergoing both liver transplantation (LT) and total abdominal colectomy (TAC)., Summary Background Data: The fraction of patients with PSC-IBD that require both LT and TAC is small, thereby limiting significant conclusions regarding long-term outcomes., Methods: Adult and pediatric patients from nine centers from the US IBD Surgery Collaborative who underwent staged LT and TAC for PSC-IBD were included. Long-term outcomes, including survival, were assessed., Results: Among 127 patients, 66 underwent TAC-before-LT, with a median time from TAC to LT of 7.9 yrs, while 61 underwent LT-before-TAC, with a median time from LT to TAC of 4.4 years. Median patient survival post TAC was significantly worse in those undergoing LT-before-TAC (16.0 yrs vs. 42.6 yrs, P=0.007), while post LT survival was not impacted by the order of TAC and LT (21.6 yrs vs. 22.0 yrs, P=0.81). Patients undergoing TAC for medically refractory disease had a higher incidence of recurrent PSC (rPSC) (P=0.02) and biliary complications (0.09) compared to those undergoing TAC for oncologic indications. Definitive TAC reconstruction with either end ileostomy or ileal-pouch anal anastomosis (IPAA) did not impact post-LT or post-TAC outcomes., Conclusions: Long term survival in PSC-IBD was contingent upon progression to LT and was not impacted by the need for TAC. PSC-IBD patients undergoing TAC for medically refractory disease had a higher incidence of rPSC and biliary complications. The use of IPAA in PSC-IBD was a viable alternative to end ileostomy., Competing Interests: Conflict of Interest Statement: All authors report no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Almansouri et al.)

Human emotion recognition has been a major field of research in the last decades owing to its noteworthy academic and industrial applications. However, most of the state-of-the-art methods identified emotions after analyzing facial images. Emotion recognition using electroencephalogram (EEG) signals has got less attention. However, the advantage of using EEG signals is that it can capture real emotion. However, very few EEG signals databases are publicly available for affective computing. In this work, we present a database consisting of EEG signals of 44 volunteers. Twenty-three out of forty-four are females. A 32 channels CLARITY EEG traveler sensor is used to record four emotional states namely, happy, fear, sad, and neutral of subjects by showing 12 videos. So, 3 video files are devoted to each emotion. Participants are mapped with the emotion that they had felt after watching each video. The recorded EEG signals are considered further to classify four types of emotions based on discrete wavelet transform and extreme learning machine (ELM) for reporting the initial benchmark classification performance. The ELM algorithm is used for channel selection followed by subband selection. The proposed method performs the best when features are captured from the gamma subband of the FP1-F7 channel with 94.72% accuracy. The presented database would be available to the researchers for affective recognition applications., Competing Interests: The authors declare no conflict of interest., (Copyright © 2020 Ayan Seal et al.)