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Jejunal pseudo-diverticulosis is a rare acquired herniation of the mucosa and submucosa through weakened areas of the muscularis mucosa of the mesenteric aspect of the bowel. They are asymptomatic in the majority of cases; however, they can present with a wide spectrum of non-specific symptoms such as chronic abdominal discomfort, postprandial flatulence, diarrhoea, malabsorption and steattorhoea. In up to 15% of cases, more serious acute complications may arise such as the development of intestinal obstruction, haemorrhage or as in our case, localized peritonitis secondary to perforation. Perforation carries an overall mortality rate of up to 40% and exploratory laparotomy followed by copious lavage with segmental resection and primary anastomosis remains the mainstay of managing such sequalae of jejunal pseudo-diverticulosis. Our case report highlights the importance of maintaining a high clinical suspicion of a perforated jejunal diverticulum in an elderly patient presenting with an acute abdomen.

Background: The novel coronavirus (COVID-19) pandemic has significantly increased the rate of mortality and morbidity worldwide due to its rapid transmission rate. The mental health status of individuals could have a negative impact attributed to this global situation. Therefore, this study was intended to explore the symptoms of depression and anxiety among healthcare workers (HCWs) of Pakistan during the ongoing COVID-19 pandemic.Methods: A cross-sectional survey was undertaken by administering a web-based questionnaire between May and June 2020. Two tools, including the Patient Health Questionnaire (PHQ9) and Generalized Anxiety Disorder-7 (GAD-7), were employed to measure anxiety and depression symptoms among HCWs. The data analyses were carried out using descriptive statistics, Man Whitney, and Kruskal Wallis tests.Results: Of 1094 HCWs who participated in this online survey, 742 (67.8%) were physicians, followed by nurses (n = 277, 25.3%) and pharmacists (n = 75, 6.9%). The survey respondents had a median depression and anxiety score of 5.00 (7.00–3.00) and 8.00 (11.00–5.00), respectively. A considerable number of HCWs (82.2%) utilized online psychological resources to deal with their psychological distress. Female HCWs, nurses, frontline HCWs, and HCWs aged 30–49 years were more likely to suffer from depression and anxiety (p < 0.05).Conclusion: During the recent ongoing pandemic of COVID-19, there is a mild level of symptoms of depression and anxiety among HCWs. Our findings call for urgent psychological interventions for vulnerable groups of Pakistani HCWs.

The prediction of human diseases precisely is still an uphill battle task for better and timely treatment. A multidisciplinary diabetic disease is a life-threatening disease all over the world. It attacks different vital parts of the human body, like Neuropathy, Retinopathy, Nephropathy, and ultimately Heart. A smart healthcare recommendation system predicts and recommends the diabetic disease accurately using optimal machine learning models with the data fusion technique on healthcare datasets. Various machine learning models and methods have been proposed in the recent past to predict diabetes disease. Still, these systems cannot handle the massive number of multifeatures datasets on diabetes disease properly. A smart healthcare recommendation system is proposed for diabetes disease based on deep machine learning and data fusion perspectives. Using data fusion, we can eliminate the irrelevant burden of system computational capabilities and increase the proposed system’s performance to predict and recommend this life-threatening disease more accurately. Finally, the ensemble machine learning model is trained for diabetes prediction. This intelligent recommendation system is evaluated based on a well-known diabetes dataset, and its performance is compared with the most recent developments from the literature. The proposed system achieved 99.6% accuracy, which is higher compared to the existing deep machine learning methods. Therefore, our proposed system is better for multidisciplinary diabetes disease prediction and recommendation. Our proposed system’s improved disease diagnosis performance advocates for its employment in the automated diagnostic and recommendation systems for diabetic patients.