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Students' career decision is a relevant topic in career development with scholars and practitioners aiming at understanding which factors can contribute to students decision-making to support them in their career path. In this study, we argue that facilitating conditions can positively influence the career decisions of students. This relationship is mediated by career self-efficacy and moderated by optimism. To test our hypotheses, we conducted a longitudinal study. In total, 789 high school students from different educational streams took part in our three-way study. Results showed that career decision-making self-efficacy mediates the relationship between facilitating conditions for school motivations and career decision-making. While the direct effect of facilitating conditions for school motivations on career decidedness is negative, the relationship linking facilitating conditions for school motivations to career decision-making self-efficacy, as well as that linking career decision-making self-efficacy and career decidedness, is positive. These results provide insights for better-targeting counselling programmes.

Abstract Abstract Dapagliflozin (DAPA), a sodium-glucose cotransporter 2 (SGLT2) inhibitor, is well-recognized for its therapeutic benefits in type 2 diabetes (T2D) and cardiovascular diseases. In this comprehensive in vitro study, we investigated DAPA’s effects on cardiomyocytes, aortic endothelial cells (AECs), and stem cell-derived beta cells (SC-β), focusing on its impact on hypertrophy, inflammation, and cellular stress. Our results demonstrate that DAPA effectively attenuates isoproterenol (ISO)-induced hypertrophy in cardiomyocytes, reducing cell size and improving cellular structure. Mechanistically, DAPA mitigates reactive oxygen species (ROS) production and inflammation by activating the AKT pathway, which influences downstream markers of fibrosis, hypertrophy, and inflammation. Additionally, DAPA’s modulation of SGLT2, the Na+/H + exchanger 1 (NHE1), and glucose transporter (GLUT 1) type 1 highlights its critical role in maintaining cellular ion balance and glucose metabolism, providing insights into its cardioprotective mechanisms. In aortic endothelial cells (AECs), DAPA exhibited notable anti-inflammatory properties by restoring AKT and phosphoinositide 3-kinase (PI3K) expression, enhancing mitogen-activated protein kinase (MAPK) activation, and downregulating inflammatory cytokines at both the gene and protein levels. Furthermore, DAPA alleviated tumor necrosis factor (TNFα)-induced inflammation and stress responses while enhancing endothelial nitric oxide synthase (eNOS) expression, suggesting its potential to preserve vascular function and improve endothelial health. Investigating SC-β cells, we found that DAPA enhances insulin functionality without altering cell identity, indicating potential benefits for diabetes management. DAPA also upregulated MAFA, PI3K, and NRF2 expression, positively influencing β-cell function and stress response. Additionally, it attenuated NLRP3 activation in inflammation and reduced NHE1 and glucose-regulated protein GRP78 expression, offering novel insights into its anti-inflammatory and stress-modulating effects. Overall, our findings elucidate the multifaceted therapeutic potential of DAPA across various cellular models, emphasizing its role in mitigating hypertrophy, inflammation, and cellular stress through the activation of the AKT pathway and other signaling cascades. These mechanisms may not only contribute to enhanced cardiac and endothelial function but also underscore DAPA’s potential to address metabolic dysregulation in T2D. Graphical abstract

Abstract The incorporation of Artificial Intelligence (AI) into the fields of Neurosurgery and Neurology has transformed the landscape of the healthcare industry. The present study describes seven dimensions of AI that have transformed the way of providing care, diagnosing, and treating patients. It has exhibited unparalleled accuracy in analyzing complex medical imaging data and expediting precise diagnoses of neurological conditions. It has also enabled personalized treatment plans by harnessing patient-specific data and genetic information, promising more effective therapies. For instance, AI-powered surgical robots have brought precision and remote capabilities to neurosurgical procedures, reducing human error. In AI, machine learning models predict disease progression, optimizing resource allocation and patient care, whereas wearable devices with AI provide continuous neurological monitoring, and enable early intervention for chronic conditions. It has also accelerated drug discovery by analyzing vast datasets, potentially leading to breakthrough therapies. Chatbots and virtual assistants powered by AI, enhance patient engagement and adherence to treatment plans. It holds promise in further personalization of care, augmented decision-making, earlier intervention, and the development of groundbreaking treatments. The present study mainly focuses on the incorporation of blockchain technology and provides a reasonable understanding of the associated issues and challenges along with its solutions. It will allow AI and healthcare professionals to advance the field and contribute towards the improvement of an individual's well-being when facing neurological challenges.

Abstract Background In today’s digital age, various diseases drastically reduce people’s quality of life. Arthritis is one amongst the most common and debilitating maladies. Osteoarthritis affects several joints, including the hands, knees, spine, and hips. This study focuses on the medical disorder underlying Knee Osteoarthritis (KOA) which severely impairs people’s quality of life. KOA is characterised by restricted mobility, stiffness, and terrible pain and can be caused by a range of factors such as ageing, obesity, and traumas. This degenerative disorder leads to progressive wear and tear of the knee joint. Methods To combat arthritis in the kneecap, this study employs a 12-layer Convolutional Neural Network (CNN) to reach deep learning capabilities. A collection of data from the Osteoarthritis Initiative (OAI) is used to classify KOA. Through the use of medical image processing; the study ascertains whether an individual has this ailment. A sophisticated CNN architecture created especially for binary classification and KOA severity utilising deep learning algorithms is the main component of this work. Results The cross-entropy loss function is an important component of the model’s laborious design that classifies data into two groups. The remaining section uses the Kellgren-Lawrence (KL) grade to classify the disease’s severity. In the binary classification, the proposed algorithm outperforms previous methods with an accuracy rate of 92.3%, and in the multiclassification, its accuracy rate is 78.4% which is superior to the previous findings. Conclusion Looking ahead, the research broadens the scope of this work by gathering information from various sources and using these methods on a wider range of datasets and situations. The potential for major advancements in the field of osteoarthritis detection and classification is highlighted by this forward-looking approach. Furthermore, this method reduces the intervention of medical practitioners and ultimately results in accurate diagnosis. Clinical trial number Not applicable.

Abstract Free space optical (FSO) systems offer an attractive and cost-effective solution for providing communication services in remote regions, as they allow secure transmission without the need for licensing and with lower deployment costs. However, the performance of FSO systems can be significantly impacted by atmospheric turbulences, creating considerable challenges to their deployment. To meet the expanding bandwidth requirements in optical networks, dense wavelength division multiplexing (DWDM) has emerged as a viable option. The development of a 400-Gbps DWDM-FSO system with advanced modulation formats is the subject of this paper. To ensure efficient energy conservation in such a system, power consumption needs to be minimized while maintaining performance level; this calls for optimization of different components within the system. The system is made up of 10 channels and each channel can transmit data at 40 Gbps. Various modulation schemes like carrier-suppressed return-to-zero, modified duo binary return-to-zero, differential phase shift keying, and duo binary return-to-zero are studied for their impact on system performance parameters Q-factor and bit error rate (BER) in C-band around 1550 nm wavelengths. The assessment is also extended to the effects that changing FSO length, input power, and data rate have on these two parameters as well as an evaluation regarding how differing atmospheric conditions influence the FSO system’s effectiveness.