Blue Rubber Bleb Nevus Syndrome because of 12 Years of Iron Deficiency Anemia in a Patient by Double Balloon Enteroscopy; A Case Report and Review of Literature.

Blue rubber bleb nevus syndrome (BRBNS) is a rare disorder comprised of venous malformation mostly involving the skin and gastrointestinal (GI) tract but can also involve other visceral organs. The most predominant site of GI tract involvement is the small bowel. In patients with GI lesions, treatment depends on the severity of bleeding, and extent of involvement. Conservative therapy with iron supplementation and blood transfusion is appropriate in cases with mild bleeding but in severe cases endoscopic and surgical interventions would be beneficial. Also, medical therapy with sirolimus significantly reduces bleeding. A 20-year-old woman was referred to our hospital after transfusion of six units of packed cell because of several episodes of lower GI bleeding within the past three months in the form of melena and a single episode of hematochezia. Her last hemoglobin level before admission was 10mg/dl. She underwent various unsuccessful investigations since she was eight years old to find the origin of refractory iron deficiency anemia. In upper endoscopy, five bleeding polypoid lesions were discovered in the jejunum. Lesions were excised by snare polypectomy. Over a six-month follow-up period, no signs of lower GI bleeding were noted and the patient had a normal hemoglobin level.

Sniffer dogs as a screening/diagnostic tool for COVID-19: a proof of concept study.

BACKGROUND: Sniffer dogs are able to detect certain chemical particles and are suggest to be capable of helping diagnose some medical conditions and complications, such as colorectal cancer, melanoma, bladder cancer, and even critical states such as hypoglycemia in diabetic patients. With the global spread of COVID-19 throughout the world and the need to have a real-time screening of the population, especially in crowded places, this study aimed to investigate the applicability of sniffer dogs to carry out such a task. METHODS: Firstly, three male and female dogs from German shepherd (Saray), German black (Kuzhi) and Labrador (Marco) breeds had been intensively trained throughout the classical conditioning method for 7 weeks. They were introduced to human specimens obtained from the throat and pharyngeal secretions of participants who were already reported positive or negative for SARS-COV-2 infection be RT-PCR. Each dog underwent the conditioning process for almost 1000 times. In the meantime another similar condition process was conducted on clothes and masks of COVID-19 patient using another three male and female dogs from Labrador (Lexi), Border gypsy (Sami), and Golden retriever (Zhico) breeds. In verification test for the first three dogs, 80 pharyngeal secretion samples consisting of 26 positive and 54 negative samples from different medical centers who underwent RT-PCR test were in a single-blind method. In the second verification test for the other three dogs, masks and clothes of 50 RT-PCR positive and 70 RT-PCR negative cases from different medical center were used. RESULTS: In verification test using pharyngeal secretion, the sniffer dogs' detection capability was associated with a 65% of sensitivity and 89% of specificity and they amanged to identify 17 out of the 26 positive and 48 out of the 54 true negative samples. In the next verification test using patients' face masks and clothes, 43 out of the 50 positive samples were correctly identified by the dogs. Moreover, out of the 70 negative samples, 65 samples were correctly found to be negative. The sensitivity of this test was as high as 86% and its specificity was 92.9%. In addition, the positive and negative predictive values were 89.6 and 90.3%, respectively. CONCLUSION: Dogs are capable of being trained to identify COVID-19 cases by sniffing their odour, so they can be used as a reliable tool in limited screening.

Acute transverse myelitis after SARS-CoV-2 infection: a rare complicated case of rapid onset paraplegia.

A 63-year-old Caucasian male, known case of controlled type 2 diabetes, chronic renal failure, and ischemic heart disease, was presented with weakness and loss of movement in lower limbs, an absent sensation from the chest below, constipation, and urinary retention. About 4 days before these symptoms, he experienced a flu-like syndrome. Suspicious for COVID-19, his nasopharyngeal specimen's reverse transcription-polymerase chain reaction (RT-PCR) resulted positive. Chest X-ray and HRCT demonstrated severe pulmonary involvement. Immediately, he was admitted to the emergency ward, and the treatment was started according to the national COVID-19 treatment protocol. Subsequently, diagnostic measures were taken to investigate the patient's non-heterogeneous peripheral (spinal) neuromuscular manifestations. Brain CT scan and MRI were normal, but spinal MRI with gadolinium contrast showed extensive increased T2 signal involving central gray matter and dorsal columns, extended from C7 to T12 with linear enhancement in the sagittal plane, posteriorly within the mid and lower thoracic cord. The CSF specimen demonstrated pleocytosis, positive RT-PCR for SARS-CoV-2, and elevated IgG index. Clinical presentation, MRI, CSF, and laboratory findings prioritized the acute transverse myelitis (ATM) as a probable complication of COVID-19 infection over other differential diagnoses. Intravenous methylprednisolone and, subsequently, IV human immunoglobulin were added to the treatment regimen. In the end, the complete resolution of dysesthesia, urinary retention, and constipation were achieved. After continuous and extended respiratory and motor rehabilitation programs, he was discharged asymptomatic.

The role of artificial intelligence in colon polyps detection.

Over the past few decades, artificial intelligence (AI) has evolved dramatically and is believed to have a significant impact on all aspects of technology and daily life. The use of AI in the healthcare system has been rapidly growing, owing to the large amount of data. Various methods of AI including machine learning, deep learning and convolutional neural network (CNN) have been used in diagnostic imaging, which have helped physicians in the accurate diagnosis of diseases and determination of appropriate treatment for them. Using and collecting a huge number of digital images and medical records has led to the creation of big data over a time period. Currently, considerations regarding the diagnosis of various presentations in all endoscopic procedures and imaging findings are solely handled by endoscopists. Moreover, AI has shown to be highly effective in the field of gastroenterology in terms of diagnosis, prognosis, and image processing. Herein, this review aimed to discuss different aspects of AI use for early detection and treatment of gastroenterology diseases.