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Male faculty had higher mean Hirsch indices compared to women (11.4 vs. 5.5, P<0.001), and when adjusted for year of first gastroenterology certification, men had a larger Hirsch index by 2.8 (95% confidence interval 1.3-4.1, P<0.001). Women were also underrepresented in various subspecialties of gastroenterology, particularly advanced endoscopy.

Women in academic gastroenterology remain underrepresented in leadership positions and have lower Hirsch indices than men. Our findings may stem not only from differences in mentorship and career goals, but also from underlying structural factors that disadvantage women.

Women in academic gastroenterology remain underrepresented in leadership positions and have lower Hirsch indices than men. Our findings may stem not only from differences in mentorship and career goals, but also from underlying structural factors that disadvantage women.Diabetes mellitus is a risk factor for poor bowel preparation in patients who undergo colonoscopy, because of their decreased intestinal transit and slow gastric emptying. This might lead to neoplastic or preneoplastic lesions being missed, longer procedural time, a higher risk of procedure-related adverse events, significant cost burden, patient dissatisfaction, and the need for a repeat colonoscopy. Multiple strategies have been suggested to improve bowel preparation in these patients. Proposed pharmacologic strategies include adding magnesium citrate, bisacodyl, lubiprostone or pyridostigmine. Non-pharmacologic strategies include preferential procedure scheduling or using a diabetes-specific preparation protocol. In this article, we present a comprehensive review of the literature and provide specific recommendations to general practitioners and gastroenterologists for improving bowel preparation in patients with diabetes.The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature. Capsule endoscopy plays an important role in several areas of digestive pathology, namely in the investigation of obscure hemorrhagic lesions and the management of inflammatory bowel disease. Therefore, there is growing interest in the use of AI in capsule endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of capsule endoscopy. The exponential development of the usefulness of AI in capsule endoscopy requires consideration of its medium- and long-term impact on clinical practice. Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this review, we aim to illustrate the state of the art of AI in the field of capsule endoscopy.Benign esophageal strictures are one of the common clinical conditions managed by endoscopists. Nearly 90% of the benign esophageal strictures respond to endoscopic dilation. However, a small percentage of patients progress to recalcitrant strictures. The benign recalcitrant esophageal strictures are difficult to manage both medically and endoscopically as they do not respond to conventional treatment with proton pump inhibitors and esophageal dilations. Patients with benign recalcitrant esophageal strictures are at a high risk of developing debilitating malnutrition and morbidity due to severe dysphagia. This condition is associated with psychological trauma to patients as treatments are usually prolonged with poor outcomes. Also, this can be a financial burden on the healthcare industry due to several sessions of treatment. In this article, we discuss the classification of benign esophageal strictures, evidence-based treatment strategies, endoscopic procedural techniques, and complications of endoscopic interventions. We aim to guide providers in managing benign esophageal strictures with a focus on endoscopic management of benign recalcitrant esophageal strictures.The aim of this study was to evaluate the factors affecting the ability and willingness of dentists to work during the COVID-19 pandemic and the effect of this situation on occupational burnout. A 51-question survey, including demographic and pandemic questions and the Maslach Burnout Inventory (MBI), was used as a data collection method and administered to dentists in Turkey via the internet in two stages. A link to the survey (onlineanketler.com) was sent to the participants by e-mail or social media (WhatsApp©). A total of 442 dentists in the first stage and 264 dentists in the second stage answered the questionnaire. The second stage of the survey only applied to dentists who are assigned within the scope of COVID-19 measures in Turkey. Standard descriptive statistics, the chi-square test, independent samples t test and the Kruskal-Wallis test were used for statistical analysis. Most of the participants showed higher stress levels. Occupational burnout levels of participants according to filiation service (serve/FP, did not serve/FN) were 34.4% and 17.6%, respectively. The FP group showed significantly higher stress levels than the FN group. It is important to consider how these results, collected during an infectious disease epidemic, reflect the effects of psychological distress and burnout on dental staff. Trial Registration Number and Date of Registration NCT04605692-10/27/2020.

The online version contains supplementary material available at 10.1007/s12144-021-01764-x.

The online version contains supplementary material available at 10.1007/s12144-021-01764-x.To address the most pressing issues of our day, the United Nations must be redesigned to transform global social relations in ways that reduce corporate power and empower civil society and local authorities as global actors. People's movements have made deliberate efforts to advance what I have called human rights globalization, building foundations for an alternative global order from the ground up. These emerging transformative projects can end corporate impunity and foster global norms and identities that contest corporate governance and the monopoly authority of states.

Novel coronavirus (SARS-CoV-2) is responsible for the current global pandemic and understandably, Obstetrics is not spared. Private maternity hospitals have a unique challenge of reassuring unaffected patients of uneventful delivery with the lowest possible rate of coronavirus infection while consequently offering compassionate and state of art services to women who turn out to be positive for SARS-CoV-2. This has led to a routine SARS-CoV-2 testing of all patients before admission in many of the private hospitals in India. The current study was undertaken to determine the incidence of SARS-COV-2 among asymptomatic pregnant women and to ascertain the utility of universal screening in these women.

A retrospective observational multi-center study was conducted over a period of approximately 5months (1-May-2020 to 10-September-2020) in a chain of privately run maternity hospitals with presence in multiple cities across India. All asymptomatic pregnant women were tested for SARS-CoV-2 prior to elective/emergency hospital admission.

Among 4158 women tested, 54 (1.3%) were positive for SARS-CoV-2 and intra partum and postnatal period was uneventful for all of them.

Universal screening should be continued as preferred approach to ensure low anxiety levels of delivering women and safety of frontline workers. Further, universal screening helps avoid emergence of maternity centers as virus clusters by effective isolation of identified positive cases and minimizing points of contact.

Universal screening should be continued as preferred approach to ensure low anxiety levels of delivering women and safety of frontline workers. Further, universal screening helps avoid emergence of maternity centers as virus clusters by effective isolation of identified positive cases and minimizing points of contact.Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification.Imposter phenomenon is defined as a sense of intellectual fraudulence and an inability to internalize success and competency. Although imposter phenomenon has been noted in several populations, literature is sparse that focuses on mental health professionals. In addition, little is known about the relationships between imposter phenomenon, compassion fatigue, and compassion satisfaction for mental health workers. Using a survey design with a convenience sample of 158 mental health workers, this study found that imposter phenomenon was positively associated with compassion fatigue, as well as negatively associated with compassion satisfaction, when controlling for years of work and age. Sunitinib Further, the combination of lower levels of compassion satisfaction and higher levels of burnout predicted higher levels of imposter phenomenon. Implications and preventative measures are discussed.As the key precursors of O3, anthropogenic non-methane volatile organic compounds (NMVOCs) have been studied intensively. This paper performed a meta-analysis on the spatial and temporal variations of NMVOCs, their roles in photochemical reactions, and their sources in China, based on published research. The results showed that both non-methane hydrocarbons (NMHCs) and oxygenated VOCs (OVOCs) in China have higher mixing ratios in the eastern developed cities compared to those in the central and western areas. Alkanes are the most abundant NMHCs species in all reported sites while formaldehyde is the most abundant among the OVOCs. OVOCs have the highest mixing ratios in summer and the lowest in winter, which is opposite to NMHCs. Among all NMVOCs, the top eight species account for 50%-70% of the total ozone formation potential (OFP) with different compositions and contributions in different areas. In devolved regions, OFP-NMHCs are the highest in winter while OFP-OVOCs are the highest in summer. Based on positive matrix factorization (PMF) analysis, vehicle exhaust, industrial emissions, and solvent usage in China are the main sources for NMHCs.