Factors influencing stress and resilience among Egyptian medical students: a multi-centric cross-sectional study.

BACKGROUND: Psychological stress is a common psychological comorbidity among medical students and worsens their quality of life. Psychological resilience is thought to have a protective role against stress. However, evidence regarding the prevalence of stress and resilience alongside their associated factors is scarce, especially in the Middle East. This is the first multicenter, cross-sectional study to investigate resilience and stress among Egyptian medical students. METHODS: The current cross-sectional study was conducted on 2465 university students in seven public universities in Egypt. The universities were selected using the simple randomization method. The data was collected using a self-administered questionnaire consisting of four parts: demographic data, socioeconomic tool represented in the Family Affluence Scale (FAS), the Kessler Psychological Distress Scale (K10), and the Brief Resilience Scale (BRS). Data was analyzed in SPSS version 26 software. RESULTS: The majority of the students were stressed (86.5%), most of whom had severe stress (48.9%). Most of the students had low resilience (49.9%), while only 3.2% had high resilience. In the logistic regression analysis, being a female, living alone, spending long hours on social media, and thinking of suicide or leaving medicine were associated with being stressed and having low resilience. Medical students with low resilience were significantly more liable to stress [Adjusted odds ratio (AOR) = 3.667, confidence interval (CI): 2.709-4.965, P = 0.000], and vice versa [AOR = 3.709, CI: 2.746-5.009, P = 0.000]. Interestingly, high socioeconomic status showed a significant association with high resilience (P = 0.004); nonetheless, it was not associated with stress (P = 0.993). Academic grades were not associated with both the level of stress and resilience. Aging, being in clinical or academic stages, smoking, having a chronic disease, and being financially-supported are neither associated with stress nor resilience. CONCLUSIONS: The present study revealed that Egyptian medical students had low resilience and high stress, with a significant relationship between both of them. Further investigations via longitudinal study design to understand the resilience-stress relationship are recommended. Developing and implementing resilience-improving strategies in medical schools is highly recommended to decrease the prevalence of stress and its subsequent burdens.

Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives.

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.

Subject-wise model generalization through pooling and patching for regression: Application on non-invasive systolic blood pressure estimation.

BACKGROUND: Subject-wise modeling using machine learning is useful in many applications requiring low error and complexity, such as wearable medical devices. However, regression accuracy depends highly on the data available to train the model and the model's generalization ability. Adversely, the prediction error may increase severely if unknown data patterns test the model; such a model is known to be overfitted. In medicine-related applications, such as Non-Invasive Blood Pressure (NIBP) estimation, the high error renders the estimation model useless and dangerous. METHODS: This paper presents a novel algorithm to handle overfitting by editing the training data to achieve generalization for subject-wise models. The pooling and patching (PaP) algorithms use a relatively short record segment of a subject as a Key-Segment (KS) to search through a larger dataset for similar subjects. Then samples taken from the matched subjects' pool records are used to patch the original subject's KS. Due to the significance of systolic blood pressure (SBP) and the complexity of its variability, non-invasive estimation of SBP from electrocardiography (ECG) and photoplethysmography (PPG) is introduced as an application to assess the algorithm. The study was performed on 2051 subjects with a wide range of age, height, weight, length, and health status. The subjects' records were taken from a large public dataset, VitalDB, which is acquired from subjects undergoing different surgeries. Finally, all the results are obtained without using other model generalization techniques. RESULTS: The generalization effect of the proposed algorithm, PaP, significantly outperformed cross-validation, which is widely used in regression model generalization. Moreover, the testing results show that a KS of 200 to 2000 samples is sufficient for providing high accuracy for much longer testing data of about 12000 to 24000 samples long, which is less than %10 of the record length on average. Furthermore, compared to other works based on the same dataset, PaP provides a significantly lower mean error of -0.75 ± 5.51 mmHg, with a small training data portion of 15% over 2051 subjects.

Robust Arrhythmia Classification Based on QRS Detection and a Compact 1D-CNN for Wearable ECG Devices.

Embedded arrhythmia classification is the first step towards heart diseases prevention in wearable applications. In this paper, a robust arrhythmia classification algorithm, NEO-CCNN, for wearables that can be implemented on a simple microcontroller is proposed. The NEO-CCNN algorithm not only detects QRS complex but also accurately locates R-peak with the help of the proposed adaptive time-dependent thresholding technique, improving the accuracy and sensitivity in arrhythmia classification. An optimized compact 1D-CNN network (CCNN) with 9,701 parameters is used for classification. A QRS complex augmentation method is introduced in the training process to cater for R-peak location error (RLE). A nested k1k2-fold cross-validation method is utilized to evaluate the robustness of the proposed algorithm. Simulation results show that the proposed algorithm has the ability to detect more than 99.79% of R peaks with an RLE of 7.94 ms for the MIT-BIH database. Implemented on the STM32F407 microcontroller, NEO-CNN attains a classification accuracy of 97.83% and sensitivity of 96.46% using only 8s window size.

BHI-Net: Brain-Heart Interaction-Based Deep Architectures for Epileptic Seizures and Firing Location Detection.

Automatic detection of epileptic seizures is still a challenging problem due to the intolerance of EEG. Introducing ECG can help with EEG for detecting seizures. However, the existing methods depended on fusing either the extracted features or the classification results of EEG-only and ECG-only with ignoring the interaction between them, so the detection rate did not improve much. Also, all EEG channels were considered in a complex manner. Moreover, the detection of epilepsy firing location, which is an important issue for diagnosing epilepsy, is not considered before. Therefore, we propose a new method based on the brain-heart interaction (BHI) for detecting the seizure onset and its firing location in the brain with lower complexity and better performance. BHI allows us to study the nonlinear coupling and variation of phase-synchronization between brain regions and heart activity, which are effective for distinguishing seizures. In our method, the EEG channels are mapped into two surrogate channels to reduce the computational complexity. Moreover, the firing location detector is triggered only once the seizure is detected to save the system's power. Evaluation using different proposed classification networks based on the TUSZ, the largest available EEG/ECG dataset with 315 subjects and 7 seizure types, showed that our BHI method improves the sensitivity by 48% with only 4 false alarms/24h compared to using only EEG. Moreover, it outperforms the performance of the average human detector based on the quantitative EEG tools by achieving a sensitivity of 68.2% with 11.9 false alarms/ 24h and a latency of 11.94 sec.

Noninvasive prediction of carotid artery atherosclerosis by multiple abdominal fat indices measured via ultrasonography

Purpose@#The purpose of this study was to evaluate the efficiency of multiple abdominal fat indices as measured via ultrasonography for predicting the presence and severity of carotid artery atherosclerosis and to compare the predictive capacity of ultrasonographic measurements to that of anthropometric measurements. @*Methods@#A total of 92 patients were included in this study. All participants underwent clinical and laboratory assessments, and anthropometric measurements were obtained. Ultrasound examinations were performed to measure the values of all abdominal fat indices and the intimamedia thickness, as well as to detect the presence of atherosclerotic plaques. Univariate and multivariate logistic regression analyses were performed. @*Results@#In the multivariate analysis, significant associations were detected between carotid artery atherosclerosis and posterior right perinephric fat thickness (PRPFT) (hazard ratio [HR], 15.23; P<0.001), preperitoneal fat thickness (PPFT) (HR, 4.31; P=0.003), visceral adipose tissue volume (VAT) (HR, 7.61; P<0.001), visceral fat thickness (VFT) (HR, 8.84; P<0.001), the ratio of VFT to subcutaneous fat thickness (VFT/SCFT) (HR, 9.39; P<0.001), and waist-to-height ratio (WHtR) (HR, 2.65; P=0.046). In the multivariate analysis, significant associations were also detected between carotid artery plaque and PRPFT (HR, 7.09; P<0.001), the abdominal wall fat index (AFI) (HR, 3.58; P=0.010), and VFT/SCFT (HR, 4.17; P=0.006). @*Conclusion@#Many abdominal fat indices as measured by ultrasound were found to be strong predictors of carotid artery atherosclerosis, including PRPFT, VFT/SCFT, VFT, VAT, PPFT, and WHtR. Moreover, PRPFT, VFT/SCFT, and AFI were identified as strong predictors of the presence of carotid artery plaque.

Liver transplantation in patients with hepatocellular carcinoma: a single-center experience.

OBJECTIVES: Liver transplantation has become one of the best treatment options for early hepatocellular carcinoma in cirrhosis. We sought to study the results of liver transplantation in patients with hepatocellular carcinoma and to evaluate the outcome of the patients. MATERIALS AND METHODS: The medical records of 256 recipients who underwent a liver transplantation from April 2001 to January 2010 were reviewed. One hundred seventy-six patients received their livers from deceased donors, and 80 received their livers from living donors. Fifty-two patients underwent liver transplantation for hepatocellular carcinoma. RESULTS: From April 2001 until now, 52 patients (20.3%) underwent liver transplantation for hepatocellular carcinoma. Eighteen patients (34.6%) were performed from living-related donors, and 34 (65.4%) were from deceased donors. The patients were 37 males and 15 females (median, 55 years old; age range, 5 through 68 years). Model for end-stage liver disease score ranged from 6 to 40 with a median of 14. All patients were within the Milan criteria by the preoperative evaluation. Hospital stay ranged from 6 to 338 days with a median 14 days. Operating time ranged from 4 to 15 hours with a median 7.5 hours. Blood transfusion ranged from 0 to 19 units median 5 units. Thirty-four complications occurred in 23 patients (44.2%). Recurrence of hepatocellular carcinoma in 7 patients (13.5%), of which recurrent cholangiocarcinoma was diagnosed in 3 (5.7%), accidentally discovered in the explant. One deceased donor had hepatitis B core antibody positive. One explant showed macrovascular invasion. Sixteen patients died, 7 of 52 (13.5%) from hepatocellular carcinoma recurrence, including the 3 cases of accidental discovery of cholangiocarcinoma (5.7%). CONCLUSIONS: Apart from the common complications that can occur with any transplantation, liver transplantation remains the most-promising solution for patients with hepatocellular carcinoma among the available ones, and represents a cornerstone in managing hepatocellular carcinoma. It is the only acceptable option for complete eradication of both the disease and the predisposing factor.

Biochemical prognostic markers of outcome in non-paracetamol-induced fulminant hepatic failure.

BACKGROUND: Fulminant hepatic failure (FHF) is associated with major metabolic disturbances, the onset and severity of which can predict clinical outcome. This study uses admission blood samples to identify early biochemical markers of clinical outcome in patients with non-paracetamol-induced FHF. PATIENTS AND METHODS: Fifty-nine patients admitted to the Scottish Liver Transplant Unit with non-paracetamol-induced FHF were studied. Plasma samples were collected at a median of 5.4 hr after admission to our unit and analyzed using conventional laboratory tests and nuclear magnetic resonance spectroscopy. RESULTS: A total of 19 patients underwent transplantation, 15 patients died without undergoing transplantation, and 25 patients survived with medical management alone. There were significantly lower levels of lactate, alanine, valine, and bilirubin and significantly higher levels of pyruvate and albumin in patients who survived spontaneously compared with the other two groups. By use of multiple logistic regression analysis, an equation was devised that best predicted clinical outcome: 0.5x(albumin [g/L])-2x(lactate [mmol/L])-36x(valine [mmol/L])-38x(pyruvate [mmol/L]). Values of less than 2 were associated with poor clinical outcome and had a positive predictive value of 91%, a negative predictive value of 86%, a sensitivity of 94%, and a specificity of 86% for death or transplantation. This algorithm can be applied on admission, thus expediting decision-making. CONCLUSION: We identified biochemical markers that may be useful in predicting outcome in patients with non-paracetamol-induced FHF and should be evaluated further in a different patient population.