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.

Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

<b>Background:</b> Sleep deprivation is increasingly prevalent among young adults due to academic, occupational, and social demands, making them susceptible to circadian disruption. Executive functioning—encompassing working memory, inhibitory control, and cognitive flexibility is essential for academic and professional success. This meta-analysis quantifies the effect of sleep deprivation on executive functioning in healthy young adults. Practical Implications: These findings highlight the need for evidence-based interventions such as university-level sleep education programs, flexible academic scheduling, and workplace policies promoting adequate sleep to optimize cognitive performance and productivity among young adults. <b>Methods: </b>Following PRISMA 2020 guidelines, PubMed, Scopus, PsycINFO, and Web of Science were searched (January 2000–March 2024) for studies assessing acute (<6 hours sleep or ≥24 hours total deprivation) or chronic (<6 hours/night over multiple days) sleep deprivation in young adults. Outcomes included validated executive function tests (e.g., Stroop, N-Back). Random-effects meta-analysis (Hedges’ g) was conducted using R (version 4.3.2) with metafor/meta packages. Heterogeneity (I², Q-test), sensitivity (leave-one-out), and publication bias (funnel plot, Egger’s test) were evaluated. <b>Results: </b>Out of 2,478 screened studies, 39 met inclusion criteria (n=4,578). Sleep deprivation had a moderate-to-large detrimental effect on executive functioning (Hedges’ g = -0.62, 95% CI [-0.78, -0.45], p<0.001). Subdomain analysis revealed greatest impairment in working memory (g = -0.71), followed by inhibitory control (g = -0.59) and cognitive flexibility (g = -0.49) (all p<0.001). Moderate heterogeneity was present (I² = 58%), with results robust to sensitivity analysis. Egger’s test indicated no significant publication bias (p=0.22). <b>Interpretation</b><b>: </b>Sleep deprivation significantly impairs executive functioning in young adults, especially working memory. Interventions improve sleep may enhance cognitive performance and should be integrated into public health strategies and educational policies. Future research should assess chronic restriction and individual vulnerability factors.

<b>Background: </b>Tobacco control policies nationwide have contributed to a substantial decline in cigarette and tobacco use, with particularly sharp reductions observed in states such as California that have implemented restrictive bans, strong prevention measures, and high excise taxes. While these policies have led to overall decreases in tobacco use, progress has not necessarily been distributed equally across racial groups. Understanding long-term trends by race is critical for addressing equity gaps in tobacco prevention and control. Evidence suggests that some racialized groups may experience slower or delayed declines, raising concerns about equity in public health gains. <b>Methods: </b>We analyzed data from the California Health Interview Survey (CHIS) spanning 2003–2023. Trends in current smoking were examined separately for non-Latino Black and non-Latino White adults. We also assessed current use of flavored tobacco products, given California’s statewide ban enacted in 2021. Changes were evaluated in both absolute terms (percentage point declines) and relative terms (percent reduction from baseline). <b>Results: </b>Smoking prevalence declined from 17.2% in 2003 to 5.2% in 2023 among White adults and from 19.9% to 9.0% among Black adults. This represents a 12.0 percentage point (69.8%) decline for Whites compared with a 10.9 percentage point (54.8%) decline for Blacks. For flavored tobacco use, prevalence decreased from 8.0% to 4.7% among White adults but only from 11.9% to 10.8% among Black adults. This corresponds to a 3.3 percentage point (41.3%) decline for Whites compared with a 1.1 percentage point (9.2%) decline for Blacks. <b>Conclusions: </b>Although both Black and White adults in California experienced reductions in smoking over the past two decades, White adults showed larger declines in both absolute and relative terms. Disparities were even more pronounced for flavored tobacco use, where declines were minimal among Black adults despite the statewide ban. These findings suggest that Black populations in California may have been left behind by tobacco control progress, especially regarding flavored products. Given the history of targeted marketing by the tobacco industry, the role of flavors in increasing dependence, and reduced access to cessation resources in Black communities, targeted policies and culturally tailored interventions are needed to ensure equitable reductions in tobacco use. Greater attention to flavored tobacco in Black communities may help narrow these disparities and advance California’s tobacco endgame goals.

Early-onset neurodegenerative diseases (EO-NDs), such as early-onset Alzheimer’s disease (EOAD), Parkinson’s disease (EOPD), and familial amyotrophic lateral sclerosis (fALS), often stem from monogenic causes and manifest before typical age thresholds. These disorders frequently feature disrupted mitochondrial function and heightened oxidative stress, which together accelerate neuronal damage and degeneration. In this work, the author performs a comprehensive analysis of the literature and data related to mitochondrial dysfunction and redox imbalance in EO-NDs. Bibliometric trends were assessed using R-based tools on PubMed datasets, highlighting keyword networks and publication surges in recent years. Publicly available RNA-seq datasets from GEO and SRA were examined, with example DESeq2 analysis illustrating altered mitochondrial gene expression in EO-ND patient-derived samples. Network modeling of redox pathways using Python’s networkx demonstrates how oxidative stress can propagate through metabolic networks. Together, these computational approaches reinforce that mitochondrial DNA mutations, impaired electron transport chain (ETC) function, and reactive oxygen species (ROS) accumulation play central roles in EO-ND pathogenesis. The discussion further evaluates why antioxidant clinical trials have largely failed and how emerging therapies such as gene replacement, antisense oligonucleotides, and mitochondrial biogenesis modulators may provide more effective interventions.

The gut-brain axis (GBA) has emerged as a central focus in the study of neurodevelopmental disorders, particularly autism spectrum disorder (ASD). Research suggests that microbial composition and its metabolic byproducts influence neural development, synaptic plasticity, and behavior [1,2,3]. A structured bibliometric analysis of Scopus and Web of Science records was performed using Bibliometrix and VOSviewer to trace trends and thematic evolution of GBA–ASD literature [7,8]. In parallel, a data-driven pathway modeling approach maps microbial metabolites (e.g., short-chain fatty acids, tryptophan catabolites) to host signaling pathways including vagal stimulation, immune cytokine modulation, and blood–brain barrier (BBB) permeability [4,5]. Simulations implemented in Python’s NetworkX illustrate how perturbations in metabolite flux may influence CNS outcomes. The findings reveal growing emphasis on butyrate, serotonin, microglial priming, and maternal immune activation in ASD-related GBA studies, and highlight the need for rigorous empirical validation of computational predictions [9,10,11].

<b>Background:</b> Hepatocellular carcinoma (HCC) faces significant challenges in early diagnosis and prognostic assessment, necessitating novel molecular biomarkers. The role of GCN1 in tumorigenesis remains unclear, warranting systematic investigation of its clinical value. <b>Methods</b><b>:</b><b> </b>Utilizing multi-omics data from 164 HCC patients in the TCGA database, we comprehensively evaluated the diagnostic and prognostic value of GCN1 through differential expression analysis, Cox proportional hazards regression, and gene set enrichment analysis (GSEA). <b>Results</b><b>:</b><b> </b>GCN1 expression was significantly upregulated in tumor tissues (P<0.001), with ROC analysis demonstrating an AUC of 0.921 (95% CI: 0.893-0.950) for discriminating tumor from normal tissue. Clinical correlation analysis revealed that high GCN1 expression significantly associated with advanced T stage (OR=1.941, P=0.002) and AFP levels >400 ng/ml (OR=3.697, P<0.001). Multivariate survival analysis confirmed its independent prognostic value (HR=1.454, P=0.038). Functional analysis indicated GCN1 promotes tumor progression by regulating cell cycle (NES=2.385) and axon guidance (NES=2.307) pathways. <b>Conclusion</b><b>:</b><b> </b>This study first elucidates the dual clinical value of GCN1 in HCC, providing a theoretical foundation for developing novel diagnostic biomarkers and prognostic evaluation systems. Future research should validate its molecular mechanisms and explore potential targeted therapies.

<b>Background: </b>Dysphagia is a common functional impairment in elderly populations, often leading to severe complications such as malnutrition and aspiration pneumonia, significantly increasing healthcare burdens. Currently, effective prognostic assessment tools are lacking. The absolute lymphocyte count (ALC), a biomarker reflecting immune-nutritional status, has potential predictive value in this context, though its role in dysphagia prognosis remains unclear. <b>Methods: </b>This retrospective cohort study included 253 dysphagic patients who received percutaneous endoscopic gastrostomy (PEG) or total parenteral nutrition (TPN) between 2014 and 2017. Five patients with missing ALC were excluded. Cox regression models assessed the association between ALC and mortality. ALC was analyzed as both continuous variable (using restriocted cubic splines) and categorical tertiles, with additional threshold analyses to assess non-linearity. Kaplan–Meier survival curves and subgroup analyses were also performed. <b>Results: </b>Lower ALC was associated with poorer nutritional status, higher inflammatory markers, and greater comorbidity burden. Higher ALC was independently associated with reduced mortality (adjusted HR: 0.60; 95% CI: 0.44–0.83; <i>p</i> = 0.002). Patients in the highest tertile had significantly better survival than those in the lowest (HR: 0.37; 95% CI: 0.23–0.59; <i>P</i> < 0.001). A non-linear threshold effect was identified at ALC = 1.899×10<SUP>9</SUP>/L (<i>p</i> for non-linearity = 0.009). Kaplan–Meier analysis confirmed improved survival with higher ALC (<i>p</i> < 0.0001). Subgroup analyses showed the protective effect of higher ALC was consistent across age, sex, BMI, PEG use, and comorbidity strata, with no significant interactions. <b>Conclusions: </b>ALC is an independent, non-linear predictor of mortality in older dysphagic patients and may aid clinical risk stratification across diverse patient subgroups.

The national museum of a country, as a cultural symbol of the nation, plays an important role in cultural communication at home and abroad. This study explores the online brand identity construction of two national museums—the British Museum and the National Museum of China—to inform cultural brands of the discursive strategies to distinguish themselves from others and communicate with their audiences effectively. Informed by multimodal critical discourse analysis, this paper analyzes the websites of the two museums and their social media posts, depicts their brand identity prisms, and evaluates the effectiveness of their online communication. The results show that both museums use multimodal and hypertextual resources to create unique and congruent brand images in website design and social media interaction with their target audiences, fulfilling the institutional functions of museums as the symbol of national culture or world civilization. They express differential personalities and cultural values to reinforce their brand identities in different sociocultural and political contexts. The findings may provide insight into the use of multimodality in online communication for cultural institutions to enhance their brand images and promote cultural exchanges.

<b>Background:</b> Scapulohumeral periarthritis commonly afflicts individuals in their middle age. Its etiology is multifaceted, and treatment presents a challenge with a high risk of recurrence. The main symptoms include shoulder pain and limited joint mobility, seriously affect the quality of life of the patients. Recent research indicate that acupuncture combined with bloodletting cupping can significantly improve the function of activity of shoulder joint and the pain in individuals with scapulohumeral periarthritis. However, these studies have typically been limited in scope, therefore additional research to substantiate the efficacy and safety of these intervention. <b>Methods:</b> To evaluate the efficacy of acupuncture combined with bloodletting cupping for treating patients with scapulohumeral periarthritis. We conducted an online search of databases in both Chinese and English, including PubMed, the Cochrane Library, Embase, Web of Science, CNKI, Wangfang Data, China Science and Technology Journal Database (VIP) and Chinese BioMedical Literature Database (CBM), to collect randomized controlled trials (RCTs) concerning the use of acupuncture combined with bloodletting cupping in scapulohumeral periarthritis patients. We also examined the references within the identified literature. Search utilised subject headings and free-text terms in both languages, without racial restrictions, for records up to April 3, 2024. Two researchers independently screened the literature, extracted data, and evaluated their qualities. RevMan 5.3 software was used for meta-analysis of the included studies. The protocol of this review was recorded in the International Platform of Registered Systematic Review and Meta-analysis Protocols (PROSPERO). Its registration number is CRD42023454614. <b>Results:</b> This review incorporated 22 RCTs involving a total of 1,774 patients. The results of meta-analysis showed that the clinical effective rate (RR=1.25, 95%CI [1.20, 1.30], P＜0.00001) of treating scapulohumeral periarthritis with acupuncture combined with bloodletting cupping was higher in the experimental group than in the control group. The all of Visual Analogue Scale (VAS) score (MD=-1.70, 95% CI [-2.17, -1.22], P＜0.00001). Melle score (SMD=-2.45, 95% CI [-2.55, -2.34], P=0.007]) and recurrence rate (RR=0.23, 95% CI [0.07, 0.77], P=0.02) were lower in the experimental group than in the control group with statistical significance (P<0.05). <b>Conclusion:</b> The acupuncture combined with bloodletting cupping for the treatment of shoulder impingement syndrome demonstrates definite efficacy and safety, with superior clinical effectiveness, pain relief, improvement in shoulder joint mobility, and reduction in recurrence compared to acupuncture alone. Therefore, it is worthy of being promoted and applied clinically.

<b>Background:</b><b> </b>Menthol-flavored tobacco products are disproportionately used in low-income African American communities, a result of decades of targeted marketing and systemic inequities. Menthol use has been associated with lower quit rates, often compounded by factors such as lower trust in healthcare systems, reduced access to cessation programs, and other structural barriers. Despite this, few studies have systematically examined the explanatory mechanisms that might clarify why menthol-flavored tobacco is linked to poorer cessation outcomes among participants in tobacco cessation programs. <b>Aims:</b><b> </b>This study aimed to investigate the potential mechanisms by which menthol tobacco use is associated with lower quit rates across three types of smoking cessation interventions. <b>Methods:</b><b> </b>Participants were randomized into one of three smoking cessation interventions: in-person (CEASE), self-help, or online/hybrid programs. Smoking abstinence was assessed three months post-intervention as the primary outcome. Secondary analyses explored whether demographic, socioeconomic, or behavioral factors mediated the association between menthol use and quit rates across the intervention arms. <b>Results:</b><b> </b>Menthol tobacco use was significantly associated with lower quit rates (p < 0.01). This association was not explained by demographic, socioeconomic, health, or addiction-related factors. While menthol use was associated with lower education and employment levels, demographic characteristics, physical or mental health, or addiction did not explain the effect of menthol on tobacco cessation. These findings suggest that the lower quit rates observed among menthol users cannot be attributed to any third factors assessed in this study. <b>Conclusions:</b><b> </b>Menthol tobacco use independently predicts lower quit rates, and the mechanisms behind this disparity remain unclear. The consistent findings across different intervention types highlight the need for further research to uncover the underlying pathways and to design targeted strategies to improve cessation outcomes for menthol users.

<b>Background:</b> Although some neural mechanisms underlying socioeconomic status (SES) disparities are known, the role of brain-wide resting-state functional connectivity in these effects remains less understood. <b>Aim:</b> This study aims to identify brain-wide resting-state functional connectivity signatures that may mediate the effects of SES on body mass index (BMI) and blood pressure in children, using data from the Adolescent Brain Cognitive Development (ABCD) study. <b>Methods:</b> Data were drawn from the ABCD study, a large, diverse cohort of children aged 9-10. Pre-processed resting-state functional MRI data were used, and factor analysis was conducted to extract a whole-brain connectivity factor. The first factor, capturing the greatest variance in brain-wide resting-state connectivity, was selected for further analysis in a structural equation model (SEM). This connectivity factor was tested as a potential mediator of the relationship between SES (measured by parental education, family income, and neighborhood characteristics) and two indicators of cardiometabolic health: BMI and systolic blood pressure. <b>Results:</b> Factor analysis revealed a robust first factor that accounted for a significant proportion of variance in brain-wide resting-state functional connectivity. This factor was significantly associated with SES, indicating that children from lower SES backgrounds exhibited distinct connectivity patterns. Additionally, the factor was linked to both BMI and systolic blood pressure, suggesting its relevance to cardiometabolic health. Mediation analysis showed that this connectivity factor partially mediated the relationship between SES and both BMI and systolic blood pressure. <b>Conclusions:</b> Brain-wide functional connectivity may be a mediator of SES effects on BMI and blood pressure in children. The first connectivity factor provides a promising neural signature linking SES with cardiometabolic risk. Comprehensive brain-wide approaches to functional connectivity may offer valuable insights into how social determinants of health shape neural and physical development in childhood.

Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.

Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.

<b>Background</b><b>: </b>Smoking cessation is a crucial public health goal due to its substantial impact on reducing the morbidity and mortality associated with tobacco use. However, significant disparities in smoking cessation success persist across socioeconomic groups in the United States. <b>Objectives:</b> This study aimed to examine differences in smoking cessation rates among daily smokers based on race, ethnicity, and socioeconomic status (SES) using data from the Population Assessment of Tobacco and Health (PATH) study, spanning waves 1 to 6 (eight years). <b>Methods:</b> Longitudinal data from PATH were analyzed, focusing on baseline daily cigarette smokers followed over an eight-year period to assess cessation outcomes. SES was measured by education and poverty status. Successful smoking cessation was defined as sustained abstinence from cigarettes for 12 months or more at the final wave. Logistic regression models identified predictors of successful cessation, adjusting for potential confounders, including age, nicotine dependence, and access to cessation resources. <b>Results:</b> The analysis revealed significant disparities in cessation success across racial, ethnic, and SES groups. Smokers living in poverty and those with lower educational attainment were less likely to achieve cessation success than their counterparts. Race (Black) and ethnicity (Latino) were also significantly associated with lower cessation success. <b>Conclusions:</b> This study highlights the social determinants of smoking cessation success among U.S. adult smokers, with lower success rates observed among those in poverty and with less educational attainment. These findings emphasize the need for targeted interventions that address the unique barriers to cessation faced by low-SES groups. Public health strategies should prioritize equitable access to cessation resources and culturally tailored interventions to reduce these disparities and improve cessation outcomes among all smokers.

Intersectionality has significantly enhanced our understanding of how overlapping social identities—such as race, ethnicity, gender, sex, class, and sexual orientation—interact to shape individual experiences. However, despite its theoretical importance, much of the existing literature has relied on qualitative approaches to define and study intersectionality, limiting its application in predictive modeling, comparative analysis, and policy development. This paper introduces the concept of Quantitative Intersectionality Scoring System (QISS), a novel approach that assigns numerical scores to intersecting identities, thereby enabling a more systematic and data-driven analysis of intersectional effects. We argue that QISS can substantially enhance the utility and predictive validity of quantitative models by capturing the complexities of multiple, overlapping social determinants. By presenting concrete examples, such as the varying impacts of socioeconomic mobility on life expectancy among different intersectional groups, we demonstrate how QISS can yield more precise and reliable forecasts. Such a shift would allow policymakers and service providers to dynamically assess economic and health needs, as well as the uncertainties around them, as individuals move through different social and economic contexts. QISS-based models could be more responsive to the complexities of intersecting identities, allowing for a more quantified and nuanced evaluation of policy interventions. We conclude by discussing the challenges of implementing QISS and emphasizing the need for further research to validate these quantifications using robust quantitative methods. Ultimately, adopting QISS has the potential to improve the accuracy of predictive models and the effectiveness of policies aimed at promoting social justice and health equity.

This study investigates the benefits of forgiveness among adolescents in Junior High Schools (JHS) in Bimbilla, Ghana, focusing on the influence of age on the effectiveness of forgiveness interventions. The study adopted a mixed-method experimental design, a purposive selection of eight JHSs within the Nanumba North Municipality, from which 60 adolescents were randomly chosen to participate. The study employed the Enright Forgiveness Inventory, Depression Mood Scale, and Anger Self-Report items to assess participants' emotional states before and after the intervention. The interventions were structured around the REACH model of forgiveness, which included sessions aimed at helping participants identify sources of hurt, understand the concept of forgiveness, and recognise the emotional costs of holding onto grievances. Qualitative data were analysed into themes using an interpretative lens. A two-way Analysis of Covariance (ANCOVA) was used to analyse the data. The findings revealed that exposure to forgiveness therapies significantly reshaped participants' negative emotions, leading to a marked decrease in feelings of anger and depression. Post-intervention assessments indicated that participants developed a more positive outlook towards their offenders, highlighting the transformative power of forgiveness in fostering emotional well-being. The study's results align with previous research, indicating that forgiveness interventions can effectively reduce negative emotional states and promote psychological resilience. The implications of these findings suggest that integrating forgiveness education into school curricula could be beneficial for enhancing the mental health of adolescents. By fostering an environment that encourages forgiveness, educators and mental health professionals can help mitigate the adverse effects of unresolved emotional conflicts, ultimately contributing to healthier interpersonal relationships and improved overall well-being among young individuals.

<b>Background</b>: In the landscape of education, the decision-making process that leads students to pursue or reject nursing as a career is a multifaceted phenomenon shaped by a plethora of influences ranging from personal experiences to societal norms. <b>Aim</b>: To explore non-nursing students' insights on nursing education, seeking to shed light on the considerations and challenges that influence their views on nursing education. <b>Materials &#x26; Methods</b>: A qualitative approach using thematic analysis were utilized. Lincoln and Guba's framework for rigor and trustworthiness directed the validation process. Semi-structured interviews based on vetted questionnaires yielded the data<b>. Results</b>: Analysis of interviews with ten (10) non-nursing college students revealed three key themes: 1) initial insights, 2) factors influencing their insights, and 3) difficulty of nursing education. Non-nursing students view nursing education as multifaceted and rigorous, recognizing the profession's complexity but have reservations about the heavy workload, intense clinical demands, and health risks, particularly highlighted by the pandemic, which contributes to their reluctance to choose nursing as a career path. <b>Implications</b>: Addressing perceptions, enhancing curricula, offering mentorship, and providing emotional support, nursing education can be improved, steering more students towards a career in nursing. <b>Conclusion</b>: Non-nursing students respect the complexity of the nursing profession but are deterred by its demands and risks, indicating a need for educational reforms to better convey the role, value, and opportunities within nursing to encourage more students into the field.

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.

PLCs, or programmable logic controllers, are essential parts of contemporary industrial automation systems and are responsible for managing and keeping an eye on a variety of operations. PLC reliability is critical to maintaining industrial systems' continuous and secure operation. A wide range of reliability strategies were used to improve the reliability of Programmable Logic Controllers, and this article methodically looks at them all. The evaluation classified PLC reliability techniques into Root Cause Analysis (RCA), Reliability Centered Maintenance (RCM), Hazard analysis (HA), Reliability block diagram (RBD), Fault tree analysis (FTA), Physics of failure (PoF) and FMEA/FMECA, after thoroughly reviewing the body of literature. The proportion of reviewed papers using either RCA, RCM, FMEA/FMECA, FTA, RBD, RCM, PoF, or Hazard analysis to increase the reliability of PLCs showed that RCA, which makes up 20% of the publications reviewed, has been used the most to increase the reliability of the PLC, followed by HA, RCM, RBD, FTA, and PoF, which account for 17%, 16%, 16%,13%, 10%, and 8% of the articles reviewed, respectively. The paper discusses new developments and trends in PLC reliability, such as the application of machine learning (ML) and artificial intelligence (AI) to fault detection and predictive maintenance.