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Background Colonic abnormalities, ranging from benign haemorrhoids to malignancies, pose a significant global health burden. Despite extensive research in Western populations, regional data from Saudi Arabia remain limited. Objective To evaluate the prevalence, demographic trends and clinical relevance of colonic abnormalities in a Saudi Arabian population. Methods This retrospective cross-sectional study analysed 2523 colonoscopy reports from Al Noor Specialist Hospital, Makkah, Saudi Arabia, between 1 March 2010 and 30 December 2020. Reports were examined for key findings, including polyps, tumours, bleeding, diverticulosis, haemorrhoids and inflammation. Statistical analyses were used to assess age- and sex-specific trends. Results Haemorrhoids (38.1%) and polyps (12.2%) were the most frequent abnormalities. Polyps were most common in patients aged 50–59 years. Sex differences were significant; men showed a higher prevalence of haemorrhoids (p < 0.001) and active bleeding (p = 0.04), while women exhibited a higher prevalence of ulcers and erosions (p < 0.001). Older patients exhibited a higher prevalence of diverticular disease and bleeding; younger individuals (20–39 years) had higher rates of inflammatory changes. Conclusion Colonic abnormalities were prevalent in this Saudi population, with distinct age- and sex-specific patterns. These findings support the need for tailored screening strategies. Future multicentre studies should explore the effect of genetic, dietary and environmental factors on colonic health in the study region.

Background: There are limited data regarding the outcome of in-hospital cardiopulmonary resuscitation (CPR) in COVID-19 patients. In this study, we compared the outcomes of in-hospital cardiac arrests (IHCA) before and at the peak of the COVID-19 pandemic at Montefiore Medical Center in the Bronx, New York, United States. We also identified the most common comorbidities associated with poor outcomes in our community. Methods: This was a multi-site, single-center, retrospective, observational study. Inclusion criteria for COVID patients were all confirmed positive cases who had in-hospital cardiac arrest (IHCA) between 1 March 2020 and 30 June 2020. The non-COVID cohort included all cardiac arrest cases who had IHCA in 2019. We excluded all out-of-hospital cardiac arrest (OHCA). We compared actual survival to that predicted by the GO-FAR score, a validated prediction model for determining survival following IHCA. Results: There were 334 cases in 2019 compared to 450 cases during the specified period in 2020. Patients who initially survived cardiac arrest but then had their code statuses changed to do not resuscitate (DNR) were excluded. Groups were similar in terms of sex distribution, and both had an average age of about 66 years. Seventy percent of COVID patients were of Black or Hispanic ethnicity. A shockable rhythm was present in 7% of COVID patients and 17% of non-COVID patients (p < 0.05). COVID patients had higher BMI (30.7 vs. 28.4, p < 0.05), higher prevalence of diabetes mellitus (58% vs. 38%, p < 0.05), and lower incidence of coronary artery disease (22% vs. 35%, p < 0.05). Both groups had almost similar predicted average survival rates based on the GO-FAR score, but only 1.5% of COVID patients survived to discharge compared to 7% of non-COVID patients (p < 0.05). Conclusion: The rate of survival to hospital discharge in COVID-19 patients who suffer IHCA is worse than in non-COVID patients, and lower than that predicted by the GO-FAR score. This finding may help inform our patient population about risk factors associated with high mortality in COVID-19 infection, as well as educate hospitalized patients and healthcare proxies in the setting of code status designation.

In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical results show that the CNN–LSTM model outperforms these models.