A comparison of career plans of gastroenterology fellows compared to fellows in other internal medicine subspecialties.

Background: The career trajectory of medical professionals, particularly in specialized fields like gastroenterology, can significantly impact healthcare and research. This study aimed to analyze career choices among gastroenterology fellows in the US and investigate the factors influencing these choices. Methods: We utilized data from the American Medical Association on internal medicine subspecialty fellows. The study examined career plans of gastroenterology fellows and compared them with those of other subspecialties. A chi-square test was performed to assess differences in career choices and practice settings. Results: Among gastroenterology fellows, 46% opted for private practice, 28% pursued further training, and 26% chose academia. Notably, gastroenterology fellows were more inclined toward private practice than their counterparts in other subspecialties (46.3% vs 38.4%) and were less likely to pursue academic careers (25.6% vs 30.7%). Conclusion: This study highlights a concerning trend among recent gastroenterology fellowship graduates favoring private practice over academic careers or additional training. To sustain and strengthen academic medicine in gastroenterology, interventions such as scholarships, mentorship programs, and loan repayment initiatives tailored to academic pursuits could play a crucial role.

A Study of Psychometric Instruments and Constructs of Work-Related Stress among Seafarers: A Qualitative Approach.

Due to unpredictable and demanding working circumstances and the significant potential for dangers and accidents, seafaring has been characterised as one of the world's riskiest and stressful vocations that lead to physical and mental health problems. However, very few instruments measure work-related stress, particularly in a seafaring context. None of the instruments are psychometrically sound. Therefore, a valid and reliable instrument to measure seafaring work-related stress is indispensable. This study aims to review work-related stress instruments and to explore the work-related stress construct among seafarers in Malaysia. This study uses a systematic review and semi-structured interviews across two phases. In Phase 1, we conducted a systematic review of several databases: Academic Search Ultimate, Emerald Journal Premier, Journal Storage (JSTOR), ScienceDirect, Springer Link, Taylor and Francis Online, and Wiley Online Library based on Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). In 8975 articles, only 4 (four) studies used psychological instruments and 5 (five) studies used survey questionnaires to measure work-related stress. In Phase 2, we conducted a semi-structured interview with 25 (twenty-five) seafarers, online due to COVID-19 restrictions. The semi-structured interview indicated 6 (six) themes, namely, physical stress, personal issues, social living onboard, technostress, work factors, and the effect of the COVID-19 pandemic. In conclusion, the present study has identified three psychometric instruments for measuring work-related stress among seafarers: The Psychological General Well-Being Index, Perceived Stress Scale, and Job Content Questionnaire. We also found psychometric elements in some of the instruments are questionable, such as theoretical basis, construct development, and inadequate internal consistency value. In addition, this study also found that work-related stress is a multidimensional construct that needs to be studied based on work contexts. The findings of this study can contribute to the body of knowledge of a work-related stress construct in a seafaring context and could help to inform policy makers in the maritime industry. This study suggests a psychological instrument to measure work-related stress among seafarers in future studies.

The Impact of COVID-19 on Psychological and Social Stigma for Indonesian Nurses: A Cross-Sectional Survey.

Introduction: Nurses are on the front line and are at high risk of experiencing a mental health crisis during the pandemic due to the psychological impact and stigma. The aim of this study was to identify the role of psychological status and social stigma in anxiety, fear, depression, and mental health crises during the pandemic. Materials and Methods: A cross-sectional design during December 2020-August 2021. A total of 2,156 nurses who work in health facilities, either hospitals, or communities based on the criteria of nurses who interact directly with COVID-19 patients, work at least 3 months, age 20-54 years, are literate, have internet access, and have the ability to access the electronic form. The eligible participants filled in online questionnaires that were sent to them via WhatsApp. Data were analyzed using Spearman rho correlation test with statistically significant p value < 0.05. Results: A total of 2,156 respondents responded to the questionnaire, and the response rate was 100%. The psychological status of nurses was 78.4% moderate, 18.5% experienced social stigma, 44.0% showed an anxiety response, 53.5% fear, 64.5% depression in the very severe category, and 63.5% fell into a mental health crisis. The results of the inferential analysis showed that all P < 0.05 which indicated that psychological status and social stigma had a significant relationship with anxiety, fear, depression, and mental health crisis in nurses. Conclusion: The psychological status and social stigma experienced by nurses during the COVID-19 pandemic indicate a bad situation and lead to a mental health emergency crisis.

Knowledge Gap in Understanding the Steroidogenic Acute Regulatory Protein Regulation in Steroidogenesis Following Exposure to Bisphenol A and Its Analogues.

The use of bisphenols has become extremely common in our daily lives. Due to the extensive toxic effects of Bisphenol A (BPA), the industry has replaced this endocrine-disrupting chemical (EDC) with its analogues, which have been proven to decrease testosterone levels via several mechanisms, including targeting the steroidogenic acute regulatory (StAR) protein. However, when exposed to BPA and its analogues, the specific mechanism that emerges to target StAR protein regulations remains uncertain. Hence, this review discusses the effects of BPA and its analogues in StAR protein regulation by targeting cAMP-PKA, PLC-PKC, EGFR-MAPK/ERK and Ca2+-Nur77. BPA and its analogues mainly lead to decreased LH in blood and increased ERK expression and Ca2+ influx, with no relationship with the StAR protein regulation in testicular steroidogenesis. Furthermore, the involvement of the cAMP-PKA, PLC-PKC, and Nur77 molecules in StAR regulation in Leydig cells exposed to BPA and its analogues remains questionable. In conclusion, although BPA and its analogues have been found to disrupt the StAR protein, the evidence in connecting the signaling pathways with the StAR regulations in testicular steroidogenesis is still lacking, and more research is needed to draw a solid conclusion.

Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals.

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals.

Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible diagnostic tool to diagnose CAD that captures abnormal activity of the heart. However, it lacks diagnostic sensitivity. One reason is that, it is very challenging to visually interpret the ECG signal due to its very low amplitude. Hence, identification of abnormal ECG morphology by clinicians may be prone to error. Thus, it is essential to develop a software which can provide an automated and objective interpretation of the ECG signal. This paper proposes the implementation of long short-term memory (LSTM) network with convolutional neural network (CNN) to automatically diagnose CAD ECG signals accurately. Our proposed deep learning model is able to detect CAD ECG signals with a diagnostic accuracy of 99.85% with blindfold strategy. The developed prototype model is ready to be tested with an appropriate huge database before the clinical usage.

Computer aided diagnosis of diabetic foot using infrared thermography: A review.

Diabetes mellitus (DM) is a chronic metabolic disorder that requires regular medical care to prevent severe complications. The elevated blood glucose level affects the eyes, blood vessels, nerves, heart, and kidneys after the onset. The affected blood vessels (usually due to atherosclerosis) may lead to insufficient blood circulation particularly in the lower extremities and nerve damage (neuropathy), which can result in serious foot complications. Hence, an early detection and treatment can prevent foot complications such as ulcerations and amputations. Clinicians often assess the diabetic foot for sensory deficits with clinical tools, and the resulting foot severity is often manually evaluated. The infrared thermography is a fast, nonintrusive and non-contact method which allows the visualization of foot plantar temperature distribution. Several studies have proposed infrared thermography-based computer aided diagnosis (CAD) methods for diabetic foot. Among them, the asymmetric temperature analysis method is more superior, as it is easy to implement, and yielded satisfactory results in most of the studies. In this paper, the diabetic foot, its pathophysiology, conventional assessments methods, infrared thermography and the different infrared thermography-based CAD analysis methods are reviewed.

A deep convolutional neural network model to classify heartbeats.

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.

Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals.

Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.

Transformational leadership, empowerment, and job satisfaction: the mediating role of employee empowerment.

BACKGROUND: Recent studies have revealed that nursing staff turnover remains a major problem in emerging economies. In particular, nursing staff turnover in Malaysia remains high due to a lack of job satisfaction. Despite a shortage of healthcare staff, the Malaysian government plans to create 181 000 new healthcare jobs by 2020 through the Economic Transformation Programme (ETP). This study investigated the causal relationships among perceived transformational leadership, empowerment, and job satisfaction among nurses and medical assistants in two selected large private and public hospitals in Malaysia. This study also explored the mediating effect of empowerment between transformational leadership and job satisfaction. METHODS: This study used a survey to collect data from 200 nursing staff, i.e., nurses and medical assistants, employed by a large private hospital and a public hospital in Malaysia. Respondents were asked to answer 5-point Likert scale questions regarding transformational leadership, employee empowerment, and job satisfaction. Partial least squares-structural equation modeling (PLS-SEM) was used to analyze the measurement models and to estimate parameters in a path model. Statistical analysis was performed to examine whether empowerment mediated the relationship between transformational leadership and job satisfaction. RESULTS: This analysis showed that empowerment mediated the effect of transformational leadership on the job satisfaction in nursing staff. Employee empowerment not only is indispensable for enhancing job satisfaction but also mediates the relationship between transformational leadership and job satisfaction among nursing staff. CONCLUSIONS: The results of this research contribute to the literature on job satisfaction in healthcare industries by enhancing the understanding of the influences of empowerment and transformational leadership on job satisfaction among nursing staff. This study offers important policy insight for healthcare managers who seek to increase job satisfaction among their nursing staff.

Estimation of adult antiretroviral treatment coverage in South Africa.

OBJECTIVES: To estimate the annual numbers of individuals receiving antiretroviral treatment in South Africa up to mid-2008, and the coverage of antiretroviral treatment in adults according to various definitions of need. METHODS: Antiretroviral coverage is defined as the number of patients receiving antiretroviral treatment at a point in time, divided by the number needing treatment. Numbers of patients receiving antiretroviral treatment are estimated from public sector data, and data provided by disease management programmes and NGO programmes. The unmet need for treatment in adults is estimated using a Markov model of HIV progression in adults, combined with estimates of annual new HIV infections from a national AIDS and demographic model. RESULTS: By the middle of 2008, 568 000 adults and children were receiving antiretroviral treatment in South Africa, with the public health sector accounting for 79% of this total. Using the current Department of Health criteria for defining antiretroviral eligibility (CD4+ count <200/microl or World Health Organization (WHO) stage 4), antiretroviral coverage in adults was 40.2% in 2008--up from 4.9% in 2004. Coverage increases to 54.2% if eligibility is based on WHO stage 4 only, but falls to 22.2% if the Southern African HIV Clinicians Society guidelines are used to define eligibility. Coverage in 2008 varied between provinces, from 25.8% in the Free State to 71.7% in the Western Cape. CONCLUSIONS: Significant progress has been made in expanding access to antiretroviral treatment in South Africa since 2004, but a substantial unmet need for treatment in adults remains.