Articles

Hypertension remains a leading cause of cardiovascular morbidity and mortality. Although effective antihypertensive therapies are available, sustained blood pressure control remains suboptimal due to inconsistent medication use. Most adherence research is quantitative and offers limited understanding of how individuals interpret lifelong treatment within daily life, particularly in culturally grounded contexts. To explore the lived experiences of Filipino adults taking antihypertensive medication. A qualitative study grounded in Heideggerian interpretive phenomenology was conducted. Ten Filipino adults diagnosed with hypertension were purposively recruited from outpatient clinics in Manila, Philippines. In-depth semi-structured interviews were transcribed verbatim and analyzed using the six-step IPA framework. Analysis revealed six interconnected themes describing how participants interpreted and sustained medication use: (1) Diagnosis as Disruption; (2) Medication as Protection and Responsibility; (3) The Paradox of the Silent Illness; (4) Everyday Barriers to Sustained Treatment; (5) Constructing Routine and Adaptive Self-Management; and (6) Family as Anchor within Cultural Contexts. These themes reflected emotional adjustment, symptom-driven adherence, financial and work-related barriers, adaptive coping strategies, and strong family-centered motivation. Medication-taking was experienced as an ongoing negotiation shaped by bodily cues, daily demands, and relational obligations. Conclusion: Antihypertensive medication use is shaped by relational, cultural, and socioeconomic contexts, underscoring the need for family-inclusive and culturally responsive hypertension care.

<b>Background:</b> The sacroiliac joint (SIJ) plays a crucial role in transmitting axial loads and maintaining pelvic stability. Sacroiliac joint arthropathy (SIJA) accounts for 10%–30% of low back pain cases but remains underrecognized due to overlapping pain referral patterns and nonspecific imaging findings. Diagnosis relies primarily on characteristic pain distribution and provocative maneuvers, with image-guided intra-articular block serving as the diagnostic gold standard. This study aimed to characterize the clinical profile of block-confirmed SIJA, emphasizing referral pain distribution, triggering position, and provocative test responses<b>. Methods:</b> A cross-sectional study was conducted on 98 patients with diagnostic block–confirmed SIJA at Siloam Hospital Lippo Village, Indonesia. Demographic data, referral pain sites, sitting duration, and results of FABER, compression, and distraction tests were analyzed descriptively. <b>Results:</b> The mean age was 52.07 ± 14.17 years, with 72.4% females. Referral pain most frequently involved the lower back (28.6%) and thigh (28.6%), with occasional extension to the groin (8.2%) or calf (4.1%). Over half of patients (55.1%) reported sitting more than six hours daily. Pain was predominantly triggered during sit-to-stand transitions (85.7%) and while sitting (74.5%). SIJ tenderness (98.0%) and FABER positivity (75.5%) were most consistent. <b>Conclusion:</b> The dominant referral pain in SIJA involves the lower back and posterior thigh. Sit-to-stand transition is the most frequent triggering position, while FABER testing demonstrates the highest diagnostic yield among provocative maneuvers. These consistent patterns may serve as practical clinical indicators to improve diagnostic accuracy in suspected SIJ-related pain.

Rabies is a virus present in more than 150 nations and territories worldwide, but it can be prevented through vaccination. Each year, tens of thousands of people die from rabies, primarily in Asia and Africa, with children under the age of 15 representing 40% of these fatalities. Up to 99% of rabies transmissions to humans occur through dog bites, making dogs the leading cause of rabies-related deaths in humans. We present a fatal case of rabies after a 5-month exposure incident. The patient, a 55-year-old Nigerian missionary from Enugu state, Nigeria presented with restlessness, hydrophobia, occasional barking gestures, abdominal discomfort and agitation. His condition was said to have progressively worsened as patients was unable to eat and drink fluids for days. His wife reported that the patient had experienced a bite from a stray dog on his right arm 5 months before presentation, for which no antirabies prophylaxis was given. The patient died on the second day of admission to the hospital (within 7 days of symptom onset).

Myeloproliferative neoplasms (MPNs) are hematological disorders affecting myeloid stem cells. They are classified as Philadelphia (Ph) chromosome positive-chronic myeloid leukemia, and Ph-negative polycythemia vera, essential thrombocythemia, primary myelofibrosis, chronic neutrophilic leukemia, chronic eosinophilic leukemia, juvenile myelomonocytic leukemia, and MPN unclassifiable. This review is mainly focused on the Ph-negative MPNs namely, PV, ET, and PMF. These affect both males and females with a slight male predominance, with patients mainly presenting in the seventh decade. Patients often present with thrombotic events resulting in complications that lower survival rates. The major driver mutations that have been identified in MPNs are <i>JAK2 </i>Exon 14, <i>JAK2 </i>Exon 12, <i>MPL </i>Exon 10, and<i> CALR </i>Exon 9. The importance of these driver mutations gives due recognition to their inclusion into the 2022 diagnostic criteria of the MPN WHO Classification. However, other non-driver mutations have also been reported, especially in triple-negative cases. These mutations lead to downstream constitutive activation of the JAK/STAT signaling pathway, as well as the MAPK, and PI3K/Akt pathways. Insights into the molecular pathogenesis of MPN and its association with <i>JAK2</i>, <i>CALR</i>, and <i>MPL</i> mutations have identified JAK2 as a rational therapeutic target. Thus, as an approach to MPN therapy, JAK2 inhibitors, such as ruxolitinib, have been shown to effectively inhibit JAK2, and are currently in clinical trials in combination with other drug classes. This review comprehensively examines the molecular markers of the main Ph-negative MPNs, as well as diagnosis and treatment options.

Background:</b> The World Health Organization (WHO) has declared COVID-19 a public health emergency of international concern. In this context, effective and affordable diagnostic procedures are essential for identifying and managing cases. Complete blood counts (CBC) are among the most common and readily available diagnostic tests. The current study aimed to evaluate the efficacy of CBC in diagnosing COVID-19 and identifying cases. <b>Patients and Methods:</b> A case-control study was conducted on 173 patients at Ain Shams University Hospitals over a period of three months. Patients were allocated into two groups according to COVID-19 PCR results: Group 1 included patients with COVID-19 positive PCR, and Group 2 included patients with COVID-19 negative PCR. <b>Results:</b> The study found that differential CBC had significant value in diagnosing COVID-19 disease. Many COVID-19 patients had lymphopenia and leucopenia compared to non-COVID-19 suspected patients. The low values of leukocytes, neutrophils, lymphocytes, and eosinophils with a CBC test were found to be valuable in the initial diagnosis of COVID-19. <b>Conclusion:</b> The definitive diagnosis of COVID-19 requires RT-PCR analysis, which is time-consuming and less accessible. Thus, the initial diagnosis and treatment of patients may be delayed. This study suggests that CBC, which is easily available and affordable, can be valuable in the early identification of COVID-19 cases, allowing for prompt treatment and management.

The effects on the elderly are disproportionately Alzheimer’s disease (AD) is one of the most prevalent and chronic types of dementia. Alzheimer's disease (AD), a fatal illness that can harm brain structures and cells long before symptoms appear, is currently incurable and incurable. Using brain MRI pictures from a publicly accessible Kaggle dataset, this study suggests a prediction model based on Convolutional Neural Networks (CNNs) to help with the early detection of Alzheimer's disease. Four levels of dementia have been applied to the 6,400 photos in the collection: not demented, slightly demented, moderately demented, and considerably mildly demented. Pixel normalization, class balancing utilizing data augmentation techniques, and picture scaling to 128×128 pixels were all part of a thorough workflow for data preparation. To improve the gathering of spatial dependence in volumetric MRI data, a 3D convolutional neural network (CNN) architecture was used. We used important performance measures including F1-score, recall, accuracy, precision, and log loss to gauge the model's effectiveness. A review of the available data indicates that the total F1-score, accuracy, recall, and precision were 99.0%, 99.0%, and 99.38%, respectively. The findings demonstrate the model's potential for practical use in early AD diagnosis and establish its robustness with the help of confusion matrix analysis and performance curves.

Emerging smart medical instruments combined with advanced smart industrial equipment facilitate the collection of vast volumes of critical data. This data not only enables significantly more accurate and cost-effective diagnosis and maintenance but also enriches the datasets available for AI algorithms, leading to improved insights and outcomes. The integration of high-speed and ultra-reliable telecommunications infrastructure is crucial, as it supports the cloud model. This model allows for off-device aggregation in the cloud, which effectively offloads infrastructure demands and provides an extended runway for future technological improvements before the deployment of the next generation of devices. However, in certain scenarios, latency and bandwidth limitations present significant challenges. These limitations require that a substantial amount of AI and machine learning processing is conducted directly on the transmitted data, which places rigorous demands on both the processing subsystems and the communications links themselves. The current project directly addresses the accelerator side of this multifaceted issue. It will carry out comprehensive end-to-end demonstrations leveraging pilot 5G networks and telemedicine facilities, collaborating closely with major industry participants to showcase the capabilities and potential of this innovative technology. This collaborative effort is essential to pushing the boundaries of what is possible in smart medical instruments and industrial applications [1].

This study is a literature review with a qualitative approach. It is justified by the significant increase in diseases acquired through lifestyle habits that generate health risks, which impair and are responsible for decreasing longevity and decreasing quality of life, such as hypertension, depression, obesity and respiratory tract diseases. Physical activity is recognized as a protective factor for health, and its benefits are associated with the reduction of chronic diseases and a decrease in the risk of premature death from diseases related to a sedentary lifestyle. The objective of this research is to search and identify, within the scientific literature, if there are in fact contributions from the practice of physical activity in subjects diagnosed with depression. For the categorization of studies and selection of materials, the following keywords were determined: physical exercise and depression and physical activity and depression. As inclusion criteria for data analysis and interpretation, the following were considered: articles in Portuguese, full texts, published in health journals, between the years 2005 to 2015. As exclusion criteria, we considered articles found by descriptors that did not contain one or more of the inclusion criteria. In this study, articles were selected by searching the Scientific Electronic Library Online (SciELO) and Lilacs. The choice of these databases was prioritized due to the quality and reliability of the materials available, and their easy access. 77 articles were found, of which 4 were selected to be part of this research. It can be noted that physical activity showed positive aspects and possible contributions and can be considered as a bias in an adjunct to conventional pharmacological treatments. The need for further clarification about the disease in relation to psychological, social and physiological issues is also evident, thus opening the possibility for further studies and research on the subject, so that in this way they can guide possible interventions that help in the treatment of the depression.

<b>Background:</b> Cerebral palsy is not only a serious neurodevelopmental disease causing significant morbidity in children, but also a traumatic experience leading to psychosocial trauma to the parents/caregivers of the affected children. It is usually caused by prenatal or early post-natal insults to the newborn brain which may be associated with some congenital syndromes like congenital heart disease with transposition of the viscera but rarely a heterotaxy syndrome, a condition characterized with congenitally abnormal arrangement of the thoracic and abdominal viscera. <b>Method:</b> We present a case report of a 12-month-old boy with neurodevelopmental delay, recurrent episodes of non-mucoid and non-bloody diarhoea, occasional constipation, bilious vomiting, abdominal distension and fever with associated cough and difficulty in breathing. <b>Results: </b>We discuss an unusual presentation of cerebral palsy and heterotaxy syndrome diagnosed clinically with supporting evidence from both laboratory and radiological tests. Cerebral palsy was diagnosed from the history of birth asphyxia, delayed developmental milestone, limb spasticity and low values for all sub-scores of Bayley-III scale. Heterotaxy syndrome was diagnosed from the radiologic evidence of dextrocardia, left-sided stomach, centrally located liver and malrotation of gut with volvulus. We also provide a brief literature review of the incidence and prevalence, causes and risk factors, classification, clinical presentation and associated co-morbidities of heterotaxy syndrome. <b>Conclusion:</b> Diagnosis of heterotaxy syndrome in a child with background cerebral palsy is a great challenge to both physicians and radiologists. This is more so in developing countries due to poor availability of good diagnostic apparatus, therefore, a high index of suspicion is needed. A clear understanding of the clinical features, comprehensive history taking and thorough physical examination are important in making prompt diagnosis. Timely and appropriate imaging is necessary to prevent delays in diagnosis and treatment which lead to poor outcomes.

Predictive modeling, supported by machine learning technology, aims to analyze data in order to guide decision-making towards actions generating desired values in the future. It encompasses the set of techniques used to build models that estimate the value of a certain variable predicting a forthcoming event from the past or current values of relevant attributes. In predictive healthcare modeling, the built models represent the relationship among the data concerning customer, provider, production, and other aspects of the healthcare environment in order to assist the decision processes in the prevention of diseases and in the planning of preventive actions by detection of high-risk patients. Contrary to trend analysis, whose goal is to describe past events, predictive models aim to provide useful indications regarding future events and changes. Predictive healthcare modeling supports actions that try to prevent the manifestation of diseases in healthy individuals or try to diagnose as early as possible the incidence of a disease in patients at risk. A sound predictive analysis encompasses not only the model-training task, but also the aspects of data quality, preprocessing, and fusion during its entire implementation lifecycle to ensure appropriate input data preparation. The robustness of the predictive model and its results depends highly on data quality. Due to the variety of data sources in healthcare environments, it becomes essential to use preprocessing in order to remove noise and inconsistencies. The increasing number of endorsable data exchange standards makes each data exchange achievable, but it demands the implementation of a data-governance program. In addition, the influence of the hospital-database architect on the architecture of an early-diagnosis model is important to guarantee appropriate input-formatting modularity.

Neural networks are bringing a transformation in wearable healthcare technology analytics. These networks are able to analyze a vast amount of data to help in making decisions concerning patient care. Advancements in deep learning have brought neural networks to the forefront, making data analytics a straightforward process. This study will help in unveiling the use of ICT and AI in medical healthcare technology, crawling through some industry giants. Wearable Healthcare Technologies are becoming more popular every day. These technologies facilitate collecting, monitoring, and sharing every vital aspect of the human body necessary for diagnosing and treating an ailment. At the advent of global digitization, health data storage and systematic analysis are taking shape to ensure better diagnostics, preventive, and predictive healthcare. Healthcare analytics powered by neural networks can significantly improve health outcomes, maximizing individuals' potential and quality of life. The breadth and possibilities of connected devices are getting wider. From personal activity monitoring to quantifying every bit of health statistics, connected devices are making an impact in measurement, management, and manipulation. In healthcare, early diagnosis could be a lifesaver. Data analytics can help in a big way to make moves and predictions to save lives. We are in another phase of the digitization era, "Neural Network and Wearable Healthcare Technology Analytics." A neural network could be conceived as an adaptive system made up of a large number of neurons connected in multiple layers. A neural network processes data in a similar way as the human brain does. Using a collection of algorithms, for many neural networks, objects are composed of 'input' and 'output' layers along with the layers of the neural network.