The presence of the appendix in an inguinal hernial sac is described as Amyand's hernia. It is a rare entity which presents mostly at the exploration of the inguinal canal. The appendix may be apparently normal or have all the features of acute appendicitis with its possible complications. We report four cases of Amyand's hernia which were treated at Sultan Qaboos University Hospital, Oman. All patients underwent appendectomy. In three cases, the inguinal hernia were repaired with Vipro mesh while, in the remaining case, a darning repair was done with Prolene sutures.

Smooth muscle tumours of the round ligament of the uterus are rare and can mimic an inguinal hernia. Preoperative diagnosis can be established by a computed tomography scan of the abdomen or an exploration of the inguinal canal. Surgical excision is the curative treatment.

Perforation of small bowel due to ingested fish bone is rare, the most common site is ileum and occasionally, it can involve the appendix and/or Meckel diverticulum. We report six patients who, developed bowel perforation after fish bone ingestion, four of them found to have rent in the ileum and two through Meckel's diverticulum and presented with abdominal pain and localized peritonitis. All underwent surgical exploration and removal of the fish bone and closure of the small intestine/excision of the diverticulum. Foreign body ingestion should be kept in mind in suspicious cases, and laparoscopy is very important to diagnose such rare cases as they may commonly be missed by imaging.

Objectives: This study aimed to assess the anxiety and depression symptoms in healthcare professionals in Pakistan as a result of the coronavirus pandemic. Methods: This cross-sectional study was conducted from May to June 2020 and included six different hospitals in Pakistan. An English-language demographics questionnaire, a validated COVID-19 fear scale, depression scale PHQ-9 and anxiety scale GAD-7 were sent to doctors, nurses and paramedical staff via WhatsApp. Data were analysed using descriptive statistics, Chi-square test and Student’s t-test. Results: A total of 400 participants were included in this study (response rate: 80.0%); 263 (65.8%) were doctors and 137 (34.3%) were nurses and paramedical staff. Of the participants, 57.0% were less than 40 years old and 18.3% were aged above 50. Most of the participants (n = 262; 65.5 %) experienced moderate levels of fear and 16.5% (n = 66) experienced a high level of fear. Moreover, 19.5% feared death and 56.5% reported social media to be responsible for increasing their fears. On the depression PHQ-9 and anxiety GAD-7 scales, 21.8% (n = 87) reported moderate to severe depression and anxiety symptoms. A significant relationship was demonstrated between the depression level and age, education and profession (P <0.001 each). Similarly, anxiety and depression scores were strongly related to the availability of personal protective equipment (P <0.001). Conclusion: It was found that 21.8% of healthcare professionals are suffering from moderate to severe depression symptoms, 15.5% had moderate to severe anxiety, whereas 65.5% had moderate symptoms of fear. The predictors are age, education level and co-morbidities. These moderate to high levels of fear and anxiety and depression raise concerns about the psychological well-being of healthcare staff and should be addressed through different programmes.

Heterotopic gastric mucosa (HGM) is gastric mucosal tissue outside the stomach. It can be discovered anywhere throughout the gastrointestinal tract and is mostly asymptomatic. HGM, although rare beyond the ligament of Treitz, should be included in the differential diagnosis in a young patient with a polyp causing obstructive symptoms or bleeding. Very few cases are published in literature. We describe a case of young male who presented with an episode of large amount of melena, from a bleeding jejunal lesion, diagnosed by endoscopy. Laparotomy and wedge resection of the jejunal lesion was done, and histopathology showed gastric heterotopia in a small jejunal diverticulum.

Barret’s esophagus (BE) is a precursor of adenocarcinoma of the esophagus. The detection of high-grade dysplasia and adenocarcinoma at an early stage can improve survival but is very challenging. Artificial intelligence (AI)-based models have been claimed to improve diagnostic accuracy. The aim of the current study was to carry out a meta-analysis of papers reporting the results of artificial intelligence-based models used in real-time white light endoscopy of patients with BE to detect early esophageal adenocarcinoma (EEAC). This meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO; Reg No. CRD42021246148) and its conduction and reporting followed the Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy (PRISMA-DTA) statement guidelines. All peer-reviewed and preprint original articles that reported the sensitivity and specificity of AI-based models on white light endoscopic imaging as an index test against the standard criterion of histologically proven early oesophageal cancer on the background of Barret's esophagus reported as per-patient analysis were considered for inclusion. There was no restriction on type and year of publication, however, articles published in the English language were searched. The search engines used included Medline, PubMed, EMBASE, EMCARE, AMED, BNI, and HMIC. The search strategy included the following keywords for all search engines: ("Esophageal Cancer" OR "Esophageal Neoplasms" OR " Oesophageal Cancer" OR "Oesophageal Neoplasms” OR "Barrett's Esophagus" OR "Barrett's Oesophagus") And ("Artificial Intelligence" OR "Deep Learning" OR "Machine Learning" OR "Convolutional Network"). This search was conducted on November 30, 2020. Duplicate studies were excluded. Studies that reported more than one dataset per patient for the diagnostic accuracy of the AI-based model were included twice. Quantitative and qualitative data, including first author, year of publication, true positives (TP), false negatives (FN), false positives (FP), true negatives (TN), the threshold of the index test, and country where the study was conducted, were extracted using a data extraction sheet. The Quality Appraisal for Diverse Studies 2 (QUADS-2) tool was used to assess the quality of each study. Data were analyzed using MetaDTA, interactive online software for meta-analysis of diagnostic studies. The diagnostic performance of the meta-analysis was assessed by a summary receiver operating characteristics (sROC) plot. A meta-analysis tree was constructed using MetaDTA software to determine the effect of cumulative sensitivity and specificity on surveillance of patients with BE in terms of miss rate and overdiagnosis. The literature search revealed 171 relevant records. After removing duplicates, 117 records were screened. Full-text articles of 28 studies were assessed for eligibility. Only three studies reporting four datasets met the inclusion criteria. The summary sensitivity and specificity of AI-based models were 0.90 (95% CI, 0.83- 0.944) and 0.86 (95% CI, 0.781-0.91), respectively. The area under the curve for all the available evidence was 0.88. Collective evidence for the routine usage of AI-based models in the detection of EEAC is encouraging but is limited by the low number of studies. Further prospective studies reporting the patient-based diagnostic accuracy of such models are required.