Sleep Quality, and Fatigue As Predictive Factors For Mechanical Neck Pain

Aims: The purpose of our study was to investigate the relationship between mechanical neck pain and sleep disorders, mental and physical fatigue, and the rising expectations of people in modern societies regarding their work, families, and social lives. Mechanical neck pain is a common problem that can result in disability. Methods: We included 230 patients with mechanical neck discomfort, with a mean age of 25.62 ± 9.25years, in our study. The Neck Disability Index, Chalder Fatigue Scale, and Pitssburg Sleep Quality Index was applied to all participant.   Results: Multiple linear regression analysis revealed that the overall model explained 22% of the variance of NDI score. Both sleep quality scale and fatigue scale were significantly associated and can predict NDI score (p < 001). Conclusion: sleep quality and fatigue were found among the independent determinants of neck disability. Therefore, physiotherapists who treat patients with mechanical neck pain should advise them to get enough sleep and teach them relaxation techniques to help them feel less stressed and exhausted, which will lower their neck discomfor

Health Sciences Students’ Attitude, Perception, and Experience of Using Educational Simulation in Saudi Arabia: A Cross-Sectional Study

Background: Simulation-based education (SBE) provides a safe, effective, and stimulating environment for training medical and healthcare students. This is especially valuable for skills that cannot be practiced on real patients due to ethical and practical reasons. We aimed to assess medical students’ attitude, perception, and experience of simulation-based medical education in Saudi Arabia. Method: A validated cross-sectional survey, using the KidSIM scale, was conducted to measure the level of perception and experience of students from different health sciences specialties toward integrating simulation as an educational tool. Participants responded to questions investigated the importance of simulation, opportunities for Inter-Professional Education (IPE), communication, roles and responsibilities, and situation awareness. Only students with previous experience of SBE were considered for participation. Result: This survey was completed by 246 participants, of whom 165 (67%) were male students and 228 (93%) were aged between the range of 18–30 years old. Of the respondents, 104 (67%) were respiratory care students, 90 (37%) were anesthesia technology students, and 45 (18%) were nursing students. Most of the participants had previous experience in IPE simulation activities (84%), and more than half of the students (54%) had a grade point average (GPA) ranging between 5.00 and 4.50. Overall, students had positive attitudes toward and beliefs about SBE, with a mean score of 129.76 ± 14.27, on the KidSIM scale, out of 150. Students’ GPA was significantly associated with a better perception to the relevance of simulation (p = 0.005), communication (p = 0.003), roles and responsibilities (p = 0.04), and situation awareness (p = 0.009). GPA is merely the sole predictor for positive attitude toward simulation with coefficient Beta value of 4.285 (p = 0.001). There were no significant correlations between other students’ characteristic variables (gender, specialty, study year, experience in IPE, and prior critical care experience). Conclusion: We found that health sciences students’ perception of SBE in Saudi Arabia is generally positive, and students’ performance is a significant determinant of the positive perception

Multi-class Breast Cancer Classification Using CNN Features Hybridization

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective: This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods: The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results: The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion: This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods

Belle II Vertex Detector Performance

The Belle II experiment at the SuperKEKB accelerator (KEK, Tsukuba, Japan) collected its first e+e− collision data in the spring 2019. The aim of accumulating a 50 times larger data sample than Belle at KEKB, a first generation B-Factory, presents substantial challenges to both the collider and the detector, requiring not only state-of-the-art hardware, but also modern software algorithms for tracking and alignment. The broad physics program requires excellent performance of the vertex detector, which is composed of two layers of DEPFET pixels and four layers of double sided-strip sensors. In this contribution, an overview of the vertex detector of Belle II and our methods to ensure its optimal performance, are described, and the first results and experiences from the first physics run are presented

A systematic review of frameworks for the interrelationships of mental health evidence and policy in low- and middle-income countries

Background: The interrelationships between research evidence and policy-making are complex. Different theoretical frameworks exist to explain general evidence–policy interactions. One largely unexplored element of these interrelationships is how evidence interrelates with, and influences, policy/political agenda-setting. This review aims to identify the elements and processes of theories, frameworks and models on interrelationships of research evidence and health policy-making, with a focus on actionability and agenda-setting in the context of mental health in low- and middle-income countries (LMICs). Methods: A systematic review of theories was conducted based on the BeHeMOTh search method, using a tested and refined search strategy. Nine electronic databases and other relevant sources were searched for peer-reviewed and grey literature. Two reviewers screened the abstracts, reviewed full-text articles, extracted data and performed quality assessments. Analysis was based on a thematic analysis. The included papers had to present an actionable theoretical framework/model on evidence and policy interrelationships, such as knowledge translation or evidence-based policy, specifically target the agenda-setting process, focus on mental health, be from LMICs and published in English. Results: From 236 publications included in the full text analysis, no studies fully complied with our inclusion criteria. Widening the focus by leaving out ‘agenda-setting’, we included ten studies, four of which had unique conceptual frameworks focusing on mental health and LMICs but not agenda-setting. The four analysed frameworks confirmed research gaps from LMICs and mental health, and a lack of focus on agenda-setting. Frameworks and models from other health and policy areas provide interesting conceptual approaches and lessons with regards to agenda-setting. Conclusion: Our systematic review identified frameworks on evidence and policy interrelations that differ in their elements and processes. No framework fulfilled all inclusion criteria. Four actionable frameworks are applicable to mental health and LMICs, but none specifically target agenda-setting. We have identified agenda-setting as a research theory gap in the context of mental health knowledge translation in LMICs. Frameworks from other health/policy areas could offer lessons on agenda-setting and new approaches for creating policy impact for mental health and to tackle the translational gap in LMICs

Quantifying neutralising antibody responses against SARS-CoV-2 in dried blood spots (DBS) and paired sera

The ongoing SARS-CoV-2 pandemic was initially managed by non-pharmaceutical interventions such as diagnostic testing, isolation of positive cases, physical distancing and lockdowns. The advent of vaccines has provided crucial protection against SARS-CoV-2. Neutralising antibody (nAb) responses are a key correlate of protection, and therefore measuring nAb responses is essential for monitoring vaccine efficacy. Fingerstick dried blood spots (DBS) are ideal for use in large-scale sero-surveillance because they are inexpensive, offer the option of self-collection and can be transported and stored at ambient temperatures. Such advantages also make DBS appealing to use in resource-limited settings and in potential future pandemics. In this study, nAb responses in sera, venous blood and fingerstick blood stored on filter paper were measured. Samples were collected from SARS-CoV-2 acutely infected individuals, SARS-CoV-2 convalescent individuals and SARS-CoV-2 vaccinated individuals. Good agreement was observed between the nAb responses measured in eluted DBS and paired sera. Stability of nAb responses was also observed in sera stored on filter paper at room temperature for 28 days. Overall, this study provides support for the use of filter paper as a viable sample collection method to study nAb responses.</p

Thermal and phase change process of nanofluid in a wavy PCM installed triangular elastic walled ventilated enclosure under magnetic field

Coupled interactions between magnetic field, wall elasticity and corrugation of the packed bed container on the phase change and thermal process are analyzed during nanofluid convection in a triangular shaped vented cavity. The numerical analysis is performed considering Cauchy number (Ca between 10-7 and 5 x 10-5), Hartmann number (Ha between 0 and 50), number of waves (N between 1 and 8) and nanoparticle solid volume fraction (SV-F between 0 and 2%). Higher values of Ca and Ha contributes positively to the phase change and thermal process. The reduction of phase transition time (TP) reduces by 23% at the highest Ca while heat transfer improvements of 22.8% are obtained. The optimum value of wave number is found as N = 2. The optimum configuration is found for elastic wall case at the parameters (50, 2, 2%). The heat transfer enhancement factor is found as 13.8 while the TP reduction is 5% as compared to worst case which is found at (Ha, N, SV-F) = (0, 8, 0) with rigid wall