We present here an overview on recent literature addressing the performance of Computer Assisted Detection (CADe) of colorectal polyps in colonoscopy.The number of publications in endoscopic journals that present deep learning applications has risen tremendously over the past years. Deep learning has shown great promise for automated detection, diagnosis and quality improvement in endoscopy. However, the interdisciplinary nature of these works has undoubtedly made it more difficult to estimate their value and applicability. In this review, the pitfalls and common misconducts when training and validating deep learning systems are discussed and some practical guidelines are proposed that should be taken into account when acquiring data and handling it to ensure an unbiased system that will generalize for application in routine clinical practice. Finally, some considerations are presented to ensure correct validation and comparison of AI systems.Gastric cancer is a common cause of death worldwide and its early detection is crucial to improve its prognosis. Its incidence varies throughout countries, and screening has been found to be cost-effective at least in high-incidence regions. Identification of individuals harbouring preneoplastic lesions and their surveillance or of those with early gastric cancer are extremely important processes and endoscopy play a key role for this purpose.  https://www.selleckchem.com/products/Glycyrrhizic-Acid.html  Unfortunately, also quality and accuracy for endoscopic detection varies among centres and endoscopists. Recent studies about Artificial Intelligence applied to endoscopic imaging show that these technologies perform very well and could be extremely useful for endoscopists to achieve the accuracy needed for gastric cancer screening. Nonetheless, as its introduction in this field is very recent, most studies are carried out offline and its results in clinical practice need to be further validated namely by incorporating all the components/dimensions of endoscopy from pre to post-assessment.Virtually every country in the world has been affected by coronavirus disease 2019 (COVID-19). Nepal is a landlocked country located in Southern Asia. Nepal's population has suffered greatly due to a shortage of critical care facilities, resources, and trained personnel. For appropriate care, patients need access to hospitals mostly in the centrally located capital city of Kathmandu. Unfortunately, Nepal's resources and personnel dedicated to transferring COVID-19 patients are scarce. Road and traffic infrastructure problems and mountainous terrain prevent ground ambulances from performing effectively. This, in addition to Nepal lacking national standards for prehospital care, create great challenges for transferring patients via ground emergency medical services. The concept of helicopter emergency medical services (HEMS) began in 2013 in Nepal. Presently, 3 hospitals, Nepal Mediciti Hospital, Hospital for Advanced Medicine and Surgery (HAMS), and Grande International Hospital, coordinate with private helicopter companies to run proper HEMS. One entity, Simrik Air, has dedicated 2 Airbus H125/AS350 helicopters for the sole purpose of transferring COVID-19 patients. HEMS effectiveness is expanding in Nepal, but much remains to be accomplished.Korea rarely has a system to transport patients from abroad. However, single-patient transfers are steadily being performed, and there was an experience of transferring a large number of personnel regardless of whether they were confirmed or not due to coronavirus disease 2019. Recently, a national soccer game was held abroad, and a total of 8 players and staff were infected. A total of 15 people were transported through a charter fully equipped with quarantine equipment by a medical response team with experience in air transport.A 29-year-old male paramedic on duty in a hospital-based emergency medical service system presented to the emergency room with complaints of chronic midback pain. In 2019, when the patient was on duty and complained of back pain for over 3 days, his supervisor instructed him to go to the emergency room. The patient collapsed and went into cardiac arrest; he received a total of 16 doses of 1 mg epinephrine (10 mL of a 110,000 solution), 2 doses of amiodarone, 1 dose of sodium bicarbonate, and an infusion of beta blocker agents, which were administered throughout the resuscitation that lasted for 63 minutes. The patient was discharged 27 days later with a patient cerebral performance category score of 1 and no neurologic deficit. Prolongation of resuscitation attempts can result in good outcomes for selected patients.
  To determine the ability for a simple pretransport mental health risk assessment tool for patients who are agitated or experiencing an acute psychiatric illness to predict in-transit disruptive behavior necessitating additional intervention(s) while being transported via air ambulance.
 
  We conducted this retrospective cohort study using existing data from the provincial air and land critical care transport system (Ornge) in Ontario, Canada, from April 2019 until March 2020. A total of 498 cases were included in this study. Transport medicine physicians fill in the modified mental health risk assessment tool as part of their pretransport assessment of each mental health patient undergoing transport. The transport medicine physician-derived risk score is categorized as low, moderate, and high. The primary outcomes were sensitivity, specificity, and predictive values of the modified tool for predicting pre- or in-transit disruptive behavior necessitating escalation in care.
 
  Of those patients meeting the study criteria, 207, 198, and 93 cases were assessed as low, moderate, and high risk, respectively, for potential agitation or disruptive behavior requiring escalation of care during transport. The sensitivity, specificity, positive predictive value, and negative predictive value were 70% (95% confidence interval [CI], 69.2%-70.8%), 87.1% (95% CI, 86.9%-87.2%), 37.6% (95% CI, 37.0%-38.2%), and 96.3% (95% CI, 96.2%-96.4%), respectively.
 
  A simple pretransport risk assessment tool can reliably rule out the need for escalation of care during air medical transport of the potentially agitated patient. This may help improve resource utilization and safety, without sacrificing quality of care.
 A simple pretransport risk assessment tool can reliably rule out the need for escalation of care during air medical transport of the potentially agitated patient. This may help improve resource utilization and safety, without sacrificing quality of care.