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Background and study aims Competent endoscopic ultrasound (EUS) performance requires a combination of technical, cognitive, and non-technical skills. Direct observation assessment tools can be employed to enhance learning and ascertain clinical competence; however, there is a need to systematically evaluate validity evidence supporting their use. We aimed to evaluate the validity evidence of competency assessment tools for EUS and examine their educational utility. Methods We systematically searched five databases and gray literature for studies investigating EUS competency assessment tools from inception to May 2023. Data on validity evidence across five domains (content, response process, internal structure, relations to other variables, and consequences) were extracted and graded (maximum score 15). We evaluated educational utility using the Accreditation Council for Graduate Medical Education framework and methodological quality using the Medical Education Research Quality Instrument (MERSQI). Results From 2081 records, we identified five EUS assessment tools from 10 studies. All tools are formative assessments intended to guide learning, with four employed in clinical settings. Validity evidence scores ranged from 3 to 12. The EUS and ERCP Skills Assessment Tool (TEESAT), Global Assessment of Performance and Skills in EUS (GAPS-EUS), and the EUS Assessment Tool (EUSAT) had the strongest validity evidence with scores of 12, 10, and 10, respectively. Overall educational utility was high given ease of tool use. MERSQI scores ranged from 9.5 to 12 (maximum score 13.5). Conclusions The TEESAT, GAPS-EUS, and EUSAT demonstrate strong validity evidence for formative assessment of EUS and are easily implemented in educational settings to monitor progress and support learning., Competing Interests: Conflict of Interest N. Forbes does not have any current conflicts of interest but in the last 3 years was a consultant for AstraZeneca, a consultant and speaker for Boston Scientific, and a consultant and speaker for Pentax Medical. He received research funding from Pentax Medical. J. D. Mosko has been a consultant and speaker for Boston Scientific, ERBE, Fuji, Medtronic, Pendopharm, and Steris. He received research funding from Boston Scientific and ERBE. S. C. Grover has been a speaker for Abbvie, a stockholder and employee of Volo Healthcare, on the advisory board for Amgen, BioJAMP, Pfizer, and Sanofi, and has received education support from Fresenius Kabi, BioJAMP, Celltrion, Takeda, and Pfizer. A. Ceccacci, H. Hothi, R. Khan, N. Gimpaya, B. Chan, P. D. James, D. J. Low, E. Yeung, and C. M. Walsh do not have any conflicts of interest to indicate., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Introduction: Artificial intelligence has been rapidly deployed in gastroenterology and endoscopy. The acceleration of deep convolutional neural networks along with hardware development has allowed implementation of artificial intelligence algorithms into real-time endoscopy, particularly colonoscopy. However, artificial intelligence implementation in pancreaticobiliary endoscopy is nascent., Areas Covered: Initial studies have been conducted in endoscopic retrograde pancreatography (ERCP), endoscopic ultrasound (EUS), and digital single operator cholangioscopy (DSOC). Machine learning has been implemented in identifying significant landmarks, including the ampulla on ERCP, and the bile duct, pancreas, and portal confluence on EUS. Moreover, artificial intelligence algorithms have been deployed in differentiating pathology including pancreas cancer, autoimmune pancreatitis, pancreatic cystic lesions, and biliary strictures., Expert Opinion: There have been relatively few studies with limited sample sizes in developing these machine learning algorithms. Despite the early successful demonstration of artificial intelligence in pancreaticobiliary endoscopy, additional research needs to be conducted with larger data sets to improve generalizability and assessed in real-time endoscopy before clinical implementation. However, pancreaticobiliary endoscopy remains a promising avenue of artificial intelligence application with the potential to improve clinical practice and outcomes.

Introduction: Adequate bowel preparation is integral to effective colonoscopy. Inadequate bowel preparation has been associated with reduced adenoma detection rate and increased post-colonoscopy colorectal cancer (PCCRC). As a result, the USMSTF recommends early interval reevaluation for colonoscopies with inadequate bowel preparation. However, bowel preparation documentation is highly variable with subjective interpretation. In this study, we developed deep convolutional neural networks (DCNN) to objectively ascertain bowel preparation., Methods: Bowel preparation scores were assigned using the Boston Bowel Preparation Scale (BBPS). Bowel preparation adequacy and inadequacy were defined as BBPS ≥2 and BBPS <2, respectively. A total of 38523 images were extracted from 28 colonoscopy videos and split into 26966 images for training, 7704 for validation, and 3853 for testing. Two DCNNs were created using a Densenet-169 backbone in PyTorch library evaluating BBPS score and bowel preparation adequacy. We used Adam optimiser with an initial learning rate of 3 × 10 -4 and a scheduler to decay the learning rate of each parameter group by 0.1 every 7 epochs along with focal loss as our criterion for both classifiers., Results: The overall accuracy for BBPS subclassification and determination of adequacy was 91% and 98%, respectively. The accuracy for BBPS 0, BBPS 1, BBPS 2, and BBPS 3 was 84%, 91%, 85%, and 96%, respectively., Conclusion: We developed DCCNs capable of assessing bowel preparation adequacy and scoring with a high degree of accuracy. However, this algorithm will require further research to assess its efficacy in real-time colonoscopy., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Canadian Association of Gastroenterology.)

Background and study aims  Colonoscopy completion reduces post-colonoscopy colorectal cancer. As a result, there have been attempts at implementing artificial intelligence to automate the detection of the appendiceal orifice (AO) for quality assurance. However, the utilization of these algorithms has not been demonstrated in suboptimal conditions, including variable bowel preparation. We present an automated computer-assisted method using a deep convolutional neural network to detect the AO irrespective of bowel preparation. Methods  A total of 13,222 images (6,663 AO and 1,322 non-AO) were extracted from 35 colonoscopy videos recorded between 2015 and 2018. The images were labelled with Boston Bowel Preparation Scale scores. A total of 11,900 images were used for training/validation and 1,322 for testing. We developed a convolutional neural network (CNN) with a DenseNet architecture pre-trained on ImageNet as a feature extractor on our data and trained a classifier uniquely tailored for identification of AO and non-AO images using binary cross entropy loss. Results  The deep convolutional neural network was able to correctly classify the AO and non-AO images with an accuracy of 94 %. The area under the receiver operating curve of this neural network was 0.98. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 0.96, 0.92, 0.92 and 0.96, respectively. AO detection was > 95 % regardless of BBPS scores, while non-AO detection improved from BBPS 1 score (83.95 %) to BBPS 3 score (98.28 %). Conclusions  A deep convolutional neural network was created demonstrating excellent discrimination between AO from non-AO images despite variable bowel preparation. This algorithm will require further testing to ascertain its effectiveness in real-time colonoscopy., Competing Interests: Competing interests Rishad Khan has received research grants from AbbVie and Ferring Pharmaceuticals and research funding from Pendopharm. Samir C. Grover has received research grants and personal fees from AbbVie and Ferring Pharmaceuticals, personal fees from Takeda, education grants from Janssen, and has equity in Volo Healthcare. All other authors have no relevant disclosures., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

This case report describes an atypical presentation of a relatively common opportunistic infection, without documented exposure to areas known at the time to be endemic, resulting in catastrophic complications. The patient presented with a two month progressive history of vomiting, diarrhea, productive cough, and shortness of breath, on the background of poorly-controlled HIV. There was a low index of suspicion for infection with histoplasmosis, particularly given the large differential diagnosis and the lack of travel or endemic precedence. The case was complicated by the development of disseminated intravascular coagulation (DIC) prior to identification of the pathogen. Ultimately, the patient's goals of care transitioned to palliation, and treatments were discontinued. It is our hope that in the future, such outcomes might be prevented by considering disseminated histoplasmosis in systemically unwell patients with HIV, regardless of endemic exposure or specific presentation., Competing Interests: The authors have no conflicts of interest to declare., (© 2021 The Authors. Published by Elsevier Ltd.)

We describe an outbreak of tuberculosis (TB) in the food preparation area of a hospital, which demonstrates that employees in healthcare settings may serve as potential risks for spread of TB even if they have no direct patient contact. Infect Control Hosp Epidemiol 2016;37:1111-1113.

Paracrine effect is the major mechanism that underlies mesenchymal stem cells (MSC)-based therapy. This study aimed to examine how Rap1, telomeric repeat-binding factor 2-interacting protein 1 (Terf2IP), which is a novel modulator involved in the nuclear factor-kappaB (NF-κB) pathway, regulates the paracrine effects of MSC-mediated heart repair following infarction. NF-κB activity of stromal cells was increased by Rap1 as measured by pNF-κB-luciferase reporter activity, and this was abolished by IkB-dominant-negative protein. Knockdown of Rap1 with shRap1 resulted in diminished translocation of p65-NF-κB from the cytoplasm to nuclei in response to tumor necrosis factor-α (TNF-α) stimulation. Compared with BM-MSCs, Rap1(-/-)-BM-MSCs displayed a significantly reduced ratio of phosphorylated NF-κB to NF-κB-p65 and of Bax to Bcl-2, and increased resistance to hypoxia-induced apoptosis by the terminal deoxynucleotidal transferase-mediated dUTP nick end labeling (TUNEL) assay. In contrast, re-expression of Rap1 in Rap1(-/-)-BM-MSCs resulted in loss of resistance to apoptosis in the presence of hypoxia. Moreover, absence of Rap1 in BM-MSCs led to downregulation of NF-κB activity accompanied by reduced pro-inflammatory paracrine cytokines TNF-α, IL (interleukin)-6 and monocyte chemotactic protein-1 in Rap1(-/-)-BM-MSCs compared with BM-MSCs. The apoptosis of neonatal cardiomyocytes (NCMCs) induced by hypoxia was significantly reduced when cocultured with Rap1(-/-)-BM-MSC hypoxic-conditioned medium (CdM). The increased cardioprotective effects of Rap1(-/-)-BM-MSCs were reduced when Rap1(-/-)-BM-MSCs were reconstituted with Rap1 re-expression. Furthermore, in vivo study showed that transplantation of Rap1(-/-)-BM-MSCs significantly improved heart function, decreased infarct size, prevented cardiomyocyte apoptosis and inhibited inflammation compared with controls and BM-MSCs (P<0.01). This study reveals that Rap1 has a critical role in the regulation of MSC paracrine actions. Compared with BM-MSCs, Rap1(-/-)-BM-MSCs decreased NF-κB sensitivity to stress-induced pro-inflammatory cytokine production and reduced apoptosis. Selective inhibition of Rap1 in BM-MSCs may be a novel strategy to enhance MSC-based therapeutic efficacy in myocardial infarction.

Objective: We describe the efficacy of enhanced infection control measures, including those recommended in the Centers for Disease Control and Prevention's 2012 carbapenem-resistant Enterobacteriaceae (CRE) toolkit, to control concurrent outbreaks of carbapenemase-producing Enterobacteriaceae (CPE) and extensively drug-resistant Acinetobacter baumannii (XDR-AB)., Design: Before-after intervention study., Setting: Fifteen-bed surgical trauma intensive care unit (ICU)., Methods: We investigated the impact of enhanced infection control measures in response to clusters of CPE and XDR-AB infections in an ICU from April 2009 to March 2010. Polymerase chain reaction was used to detect the presence of blaKPC and resistance plasmids in CRE. Pulsed-field gel electrophoresis was performed to assess XDR-AB clonality. Enhanced infection-control measures were implemented in response to ongoing transmission of CPE and a new outbreak of XDR-AB. Efficacy was evaluated by comparing the incidence rate (IR) of CPE and XDR-AB before and after the implementation of these measures., Results: The IR of CPE for the 12 months before the implementation of enhanced measures was 7.77 cases per 1,000 patient-days, whereas the IR of XDR-AB for the 3 months before implementation was 6.79 cases per 1,000 patient-days. All examined CPE shared endemic blaKPC resistance plasmids, and 6 of the 7 XDR-AB isolates were clonal. Following institution of enhanced infection control measures, the CPE IR decreased to 1.22 cases per 1,000 patient-days (P = .001), and no more cases of XDR-AB were identified., Conclusions: Use of infection control measures described in the Centers for Disease Control and Prevention's 2012 CRE toolkit was associated with a reduction in the IR of CPE and an interruption in XDR-AB transmission.

Dehydration negatively impacts geriatric hospitalization outcomes. The purpose of this study was to investigate the hydration status of psychogeriatric inpatients and explore possible factors for dehydration. A descriptive, retrospective chart review of patients discharged from a psychogeriatric unit was completed. Clinical and biochemical information was collected at three time points (admission, mid-point, and discharge). Results were consistent with clinical observations that less hydrated patients seem to do worse clinically. It is important to closely monitor hydration status in psychogeriatric inpatients to reduce morbidity. Further studies are needed to develop strategies to prevent dehydration and its complications in this population.

The mathematical models used to describe the dynamical behavior of a group of road vehicles traveling in a single lane without overtaking are known as car-following models. These models are widely used in many commercially available microscopic traffic simulation software packages. They attempt to mimic the interactions between individual vehicles that are traveling sufficiently close together for the behavior of each vehicle to be dependent upon the motion of the vehicle immediately in front. In this paper we modify the traditional car-following model by adding a new nonlinear term to take account of the driver attempting to achieve a certain desired intervehicle separation distance as well as the traditional aim of matching the velocity of the vehicle ahead. Numerical solution of the resulting coupled system of nonlinear differential equations is used to analyze the stability of the equilibrium solution to a periodic perturbation. For certain parameter values chaotic oscillations are generated, consisting of a broad spectrum of frequency components. Such chaotic motion produces extremely complicated dynamical behavior that has an inherent lack of predictability associated with it. The results of simulating over a range of parameter values are presented and, where it is present, the degree of chaos is estimated. (c) 1998 American Institute of Physics.