Search Results (1,351)

Leaders and managers frequently face the need to make highly complex decisions with incomplete or fragmented information. Traditional decision support systems largely emphasize the visualization of data but often fall short in producing context-sensitive insights that can directly inform decision-making. This paper examines how narrative techniques combined with machine learning can strengthen communication across organizational hierarchies, particularly by improving the transfer of tacit expertise and contextual knowledge. To explore this, a transdisciplinary literature review was conducted using articles published within the last five years from databases such as Scopus, Web of Science, and ScienceDirect. The review highlights that narrative-driven reporting has been most commonly applied in fields such as accounting and sustainability, where expert interpretation replaces purely numerical summaries with more meaningful analytical explanations. Such approaches can also embed sentiment and personalization, commonly referred to as Narrative Disclosure Tone. Building on this foundation, the study investigates how Artificial Intelligence-driven decision support can formally integrate narrative elements to enhance report clarity, usability, and strategic relevance. Findings suggest that combining machine learning with expert-driven narrative reporting supports more innovative decision support systems and facilitates the alignment of tacit knowledge with data-driven insights. Full article

Fibromyalgia (FM) is a complex chronic syndrome marked by widespread musculoskeletal pain, neurocognitive dysfunction (“fibro-fog”), and autonomic disturbances. Clinical management remains challenging due to subjective symptom reporting and the lack of definitive diagnostics. Emerging evidence points to a multifactorial origin involving central sensitization, neuroendocrine imbalance, and systemic immune-inflammatory alterations. A wide array of candidate biomarkers has been reported in FM, encompassing neurotransmitters (serotonin, norepinephrine), excitatory and inhibitory amino acids, metabolic and glycolytic enzymes, stress-related proteins, autoantibodies, oxidative stress markers and pro-inflammatory cytokines. This molecular heterogeneity reflects the systemic and multidimensional nature of FM. However, most of these biomarkers have been primarily investigated in serum or plasma, where analytical validation and reference ranges are more established. In contrast, the exploration of salivary biomarkers—although highly attractive due to its non-invasive, stress-free, and repeatable collection—remains comparatively limited. Saliva contains a reduced concentration range of many systemic markers and is strongly influenced by circadian rhythms, stress, flow rate, and oral health conditions. While promising candidates such as α-amylase, cortisol, calgranulins, and selected metabolic enzymes have shown potential in saliva, many proposed FM-related biomarkers lack full analytical validation, standardized protocols, and clinically defined reference intervals in this matrix. In this context, non-invasive electrochemical biosensors represent a transformative technological approach. Advanced electrode architectures incorporating nucleic acid probes, redox reporters, and nanostructured materials offer high sensitivity in low-volume and low-concentration biofluids such as saliva. The integration of multiplexed biomarker panels into portable platforms could enable real-time, longitudinal monitoring of FM pathophysiology, supporting phenotype stratification, personalized therapeutic adjustment, and objective disease activity tracking. Full article

With the growing interest in personalized medicine, alternative biological matrices to blood are increasingly explored as sources of diagnostic information. Saliva has emerged as a promising diagnostic matrix due to its non-invasive collection, suitability for home sampling, and minimal requirements for personnel training. Numerous studies have demonstrated the presence of metabolites in saliva that enable disease diagnosis and monitoring. However, the influence of pre-analytical and analytical factors on salivary metabolomics outcomes remains insufficiently characterized. In this study, we investigated factors potentially affecting the number and abundance of detected metabolites in untargeted salivary metabolomics using liquid chromatography coupled with mass spectrometry (LC–MS). The impact of chromatographic column type, extraction protocol, and saliva type (stimulated versus resting) was evaluated. Additionally, the effect of swab type on analyte recovery was assessed. The use of a synthetic swab for saliva collection yielded results most comparable to those obtained without swabs, for both resting and stimulated saliva samples, indicating minimal pre-analytical interference. The greatest metabolite coverage was obtained using ACN:MeOH (1:1, v/v), with a ZIC-HILIC column for polar metabolites and a C18 column for non-polar metabolite separation. These findings demonstrate that swab type, chromatographic column, extraction solvent, and saliva type critically shape metabolite coverage in untargeted salivary metabolomics. Importantly, the distinct metabolic profiles of resting and stimulated saliva suggest that these matrices may provide complementary clinical insights, underscoring the need for saliva type selection tailored to specific diagnostic and biomarker discovery objectives. Full article

While spirituality and contemplative practices are increasingly invoked in response to environmental crisis, the specific mechanisms by which they may mediate professional ethical action remain underdeveloped. This is particularly evident regarding nuclear harm, an existential planetary threat often siloed from health scholarship. This paper investigates the mediating mechanism of contemplative formation as the analytical link between spiritual ethics and planetary health. By centering this link, we demonstrate how professional nursing identity can be restructured to address existential threats like nuclear harm, which are currently under-integrated in health scholarship. We employed a convergent, integrative design combining a scoping review of the literature published in 2015–2025 with a contemplative autoethnography. The scoping review (n = 39) maps the scholarly evidence of spiritual–ecological constructs, while the autoethnography provides a situated, analytical account of the first author’s professional and spiritual formation. Integration was achieved through a four-step thematic synthesis that explicitly identifies where first-person lived experience and third-person scholarly evidence converge to illuminate the process of ethical integration. Four convergent themes describe the pathways linking contemplative practice to planetary health: (1) embodied practice (somatic resilience); (2) narrative meaning-making (transforming grief into purpose); (3) interconnected ethics (reframing remote harms as proximate responsibilities); and (4) reflective integration (the reflexive weaving of clinical and spiritual identities). The findings reveal that while contemplative traditions offer robust resources for systems thinking and equity, nuclear harm and nursing perspectives remain significantly under-integrated in the current planetary health literature. Contemplative formation functions as the mediating mechanism that turns planetary threats into sustained professional advocacy. The Interbeing Planetary Health Framework provides a pragmatic guide for nursing ethics under existential risks. Full article

On the basis of data from ten semi-structured interviews and selected secondary data from surveys conducted by the Office for National Statistics in Great Britain, this paper explores how young men and women articulate attitudes and experiences related to sustainable transport, using gender as an analytical lens. The study is exploratory and qualitative. Both traffic safety and personal safety appear to have a much more limiting influence on women’s travel mode choices than on men’s. Perceptions of safety, comfort, distance, convenience and accessibility are defined and shaped by the surrounding urban environment and transport infrastructure, and emerge as important considerations in the narratives of the study participants. The use of the car by men and women is somewhat linked to barriers to sustainable transport, such as infrequent and unreliable public transport, and, in the case of women, safety concerns. Concern for the environment is largely similar across male and female participants. Full article

Student attrition remains a persistent challenge in higher education and is shaped by interacting socioeconomic, academic, institutional, and wellbeing-related mechanisms. Although learning analytics and educational data mining increasingly support early-warning and intervention workflows, dataset reuse is often limited by incomplete documentation and inconsistent variable definitions. This Data Descriptor presents a structured cross-sectional survey dataset on factors influencing student persistence at a Colombian public university campus (La Paz). Data were collected between August and December 2025 through an online questionnaire and subsequently cleaned to remove duplicate entries and personally identifiable information. The released dataset contains 333 student records and 33 variables covering demographics (e.g., age, gender, first-generation status), socioeconomic conditions (e.g., residential stratum, housing, financial aid), academic experience and satisfaction (multiple 1–5 Likert items), perceived dropout intention across personal/socioeconomic/academic domains, thematically coded open-ended items describing challenges and motives, and a self-allocation of 0–100 weights across three dropout-factor domains. We provide a machine-readable codebook, a transparent preprocessing description, and technical validation checks (value ranges, category consistency, and composite-score integrity). The dataset is intended to support reproducible retention research, equity-oriented analyses, and benchmarking of predictive models, while encouraging responsible reuse through privacy-preserving release practices and FAIR-aligned metadata, repository deposition, and versioning. Full article

Background/Objectives: Nonpharmaceutical interventions such as social distancing and face mask use were central to controlling infectious disease transmission during the early phases of the COVID-19 pandemic, particularly when vaccines and treatments were limited or unevenly available. Although public health strategies emphasized individual compliance, adherence varied widely. Empirical evidence remains limited regarding how individuals integrate influences across individual, interpersonal, and community levels when assessing their ability and readiness to socially distance. This study examined how residents evaluated, prioritized, and experienced multi-level factors shaping perceived ability and readiness to practice social distancing during the early phase of the COVID-19 epidemic. Methods: We conducted a cross-sectional online survey of adults (≥18 years) residing in St. Louis City and St. Louis County, Missouri, between April and July 2020. Participants selected and ranked individual/interpersonal and community-level factors influencing social distancing and provided open-ended explanations of their choices. Quantitative data were analyzed descriptively to assess selection frequency and ranking priority. Qualitative responses were analyzed using iterative thematic coding to examine how participants interpreted and combined these factors. Results: The analytic sample included 1692 respondents. At the individual/interpersonal level, family and friends’ distancing behavior (58.9%), desire for in-person interaction (52.4%), and personal risk of COVID-19 (48.9%) were frequently selected, while personal risk, caring for others, and ability to work from home were most often ranked as the highest priority. At the community level, others’ distancing in public spaces (66.2%), availability of COVID-19 testing (58.9%), and businesses’ ability to ensure distancing and sanitation (57.2%) were most frequently selected, with epidemic severity, testing availability, and treatment availability ranked as most influential. Qualitative findings indicated that respondents experienced these influences as interconnected, integrating personal and relational risk, local epidemic conditions, healthcare access, visible community norms, and employer policies. Conclusions: Perceived ability and readiness to practice social distancing emerge from interdependent social and structural conditions rather than isolated individual motivations. Public health responses to emerging infectious diseases may be more effective when individual-level guidance is complemented by accessible testing and treatment, supportive workplace policies, and community environments that visibly reinforce protective behaviors. Full article

Despite advancements in clinical diagnostics, traditional biomarker detection methods (e.g., ELISA) remain limited due to their invasive nature, slow results, and inadequate use for continuous monitoring in low-resource settings. With the rise in chronic, infectious, and metabolic diseases, there is a pressing demand for real-time, minimally invasive diagnostic tools. Nanoengineered microneedle (MN) biosensors offer a promising solution. These painless devices can access interstitial fluid (ISF), a rich source of biomarkers, while utilizing advanced nanomaterials for high sensitivity and multiplexed detection. When combined with AI, IoT connectivity, and cloud-based analytics, MN biosensors enable personalized health data and continuous disease management. This review outlines recent advances in MN technology, including innovations in design and nanomaterial integration, as well as translational challenges like manufacturing scalability and regulatory approval. We explore how MN designs incorporating various sensing modalities can facilitate real-time monitoring of biomarkers such as glucose, lactate, and inflammatory proteins. Importantly, we discuss how these devices can improve healthcare access, reduce costs, and empower patients through everyday monitoring. This review integrates developments in MN engineering with biosensing and therapeutics, positioning biosensor-integrated MNs as pivotal in enabling continuous, minimally invasive disease monitoring and personalized therapy beyond traditional hospital environments. Full article

Background/Objectives: Pharmacogenomics (PGx) enables personalized therapy by identifying genetic variants that influence drug response. Despite the advantages of next-generation sequencing (NGS), few clinically validated, guideline-aligned panels comprehensively detect common, rare, and structurally complex pharmacogenetic variants. Methods: We developed and analytically validated Action PharmaKitDx, a targeted NGS panel covering 335 pharmacogenes, including all priority genes recommended by CPIC, DPWG, and CPNDS. Performance was assessed using Coriell HapMap and GeT-RM reference materials across multiple library preparation workflows and Illumina platforms. Clinical feasibility was evaluated in 41 patient samples from diverse specialties. Results were compared with established reference methods, including PCR-based assays, STR analysis, Sanger sequencing, and whole-exome sequencing. Results: Analytical validation: More than 99% of target bases achieved ≥30× coverage. Analytical accuracy, sensitivity, specificity, and positive predictive value exceeded 99.3%, with repeatability and reproducibility >99.7%. Concordance with GeT-RM haplotypes reached 98% after star-allele harmonization. The panel accurately detected complex variants, including CYP2D6 copy-number changes and hybrid alleles. Clinical validation: Full concordance with prior genotyping was observed in clinical samples. Beyond the initial testing indication, each sample harbored a mean of six actionable variants (range 2–10). Thirty-six rare (minor allele frequency <1%) potentially actionable variants were additionally identified. Conclusions: Action PharmaKitDx demonstrates high analytical performance and broad clinical applicability, supporting its implementation as a scalable solution for comprehensive pharmacogenetic testing and precision prescribing. Full article

Artificial intelligence (AI) is rapidly reshaping cardiovascular (CV) medicine, driving a paradigm shift toward truly personalized and data-driven care. This comprehensive review examines the conceptual foundations, clinical applications, and future implications of AI across the CV continuum, spanning prevention, diagnosis, risk stratification, and therapy. Core AI methodologies (including machine learning, deep learning, natural language processing, and computer vision) are discussed in the context of cardiology’s uniquely data-rich environment, encompassing imaging, electrocardiography, electronic health records, wearable devices, and multi-omics data. This systematic review highlights major clinical domains where AI has demonstrated a substantial impact, including CV imaging, ECG interpretation, hypertension and heart failure management, coronary artery disease, acute coronary syndromes, interventional cardiology, and cardiac surgery. AI-driven predictive analytics enable early detection of subclinical disease, improved prognostication, and individualized prevention strategies, while wearable technologies and remote monitoring platforms facilitate continuous, real-world patient surveillance. Emerging applications in pharmacotherapy, drug repurposing, and genomics further reinforce AI’s role in advancing precision cardiology. Equally emphasized are the ethical, legal, and social challenges accompanying AI adoption, such as algorithmic bias, data privacy, cybersecurity, interpretability, and regulatory oversight. Our review underscores the necessity of rigorous clinical validation, transparent model design, and seamless integration into clinical workflows to ensure safety, equity, and physician trust. Ultimately, AI is best positioned as an augmentative tool that complements (but does not replace!) clinical expertise. By fostering hybrid intelligence that integrates human judgment with computational power, AI has the potential to redefine CV care delivery, improve outcomes, and support a more proactive, patient-centered healthcare model. Full article

Background/Objectives: Helicobacter pylori (H. pylori) infection remains common globally, yet data on its prevalence and correlates among dental students in Eastern Europe are limited. Dental students may face potential occupational exposure through contact with saliva and aerosols during their clinical training. This study aimed to measure the seroprevalence of H. pylori among Lithuanian dental students and evaluate its associations with academic year, self-perceived oral health and hygiene factors, and gastrointestinal symptoms. Methods: An observational–analytical cross-sectional study was conducted in 2025 among 202 dental students from lower (I–II) and higher (IV–V) academic years at the Lithuanian University of Health Sciences. Participants underwent serological testing for H. pylori IgG antibodies using capillary blood and completed a structured questionnaire on sociodemographic factors, oral health behaviors, clinical exposure, and gastrointestinal symptoms assessed by the Gastrointestinal Symptoms Rating Scale (GSRS). Descriptive and bivariate statistical analyses were performed to assess associations. Results: Overall H. pylori seroprevalence was 12.4% and did not differ significantly in different academic years. Seropositivity was significantly associated with longer toothbrushing duration and a family history of stomach ulcer. No significant associations were found with the number of patients treated, the use of personal protective equipment, or most oral hygiene indicators. Higher-year students reported greater overall gastrointestinal symptom scores than lower-year students; however, GSRS scores did not differ between H. pylori-seropositive and -seronegative participants. Conclusions: H. pylori seroprevalence in this student population was relatively low, and no association was found with clinical exposure or gastrointestinal symptom severity. Household-related factors may be more relevant to transmission than occupational exposure in dental training. Further longitudinal studies are needed to clarify risk factors and transmission pathways. Full article

Background: Stable isotope-based analytical methods have brought about a significant transformation in the study of energy nutrient metabolism, enabling precise in vivo measurement of metabolic fluxes at systemic, tissue, and organ-specific levels in both healthy and diseased states. The regulation of these metabolic fluxes is governed by dynamic interactions between proteins, lipids, carbohydrates, and their precursors—such as glucose, fatty acids, and amino acids—as well as final metabolic products. Discussion: Advanced analytical technologies, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), which can offer enhanced precision, have been developed for investigating nutrient metabolism and fluxes in humans, providing precise information on metabolic pathways. These techniques have primarily utilized stable isotopes, such as ²H, ¹³C, ¹⁵N, and ¹⁸O, which have largely replaced radioactive isotopes and are now central to metabolic research. These isotopes have been used to label glucose, fatty acids, or amino acids—the main biomolecular precursors—enabling detailed investigation at systemic, tissue, and organ-specific levels of carbohydrate, lipid, and protein metabolism, and revealing pathway alterations associated with diseases conditions, such as diabetes, non-alcoholic fatty liver disease, cardiovascular disorders, and cancer. The use of deuterium oxide (D₂O) has allowed for long-term metabolic studies, providing a cost-effective and less invasive means to monitor metabolic changes over days to months. Total daily energy expenditure can be measured in free living conditions by the doubly stable isotopes ²H- and ¹⁸O-labeled water method. Stable isotope tracing, combined with advanced imaging and modeling, has also been instrumental in assessing body composition, energy expenditure, and nutrient bioavailability. Collectively, these methods have expanded our understanding of human physiology and disease, supporting the development of novel diagnostic tools, the identification of new biomarkers, and the tailoring of nutritional and therapeutic interventions. Conclusions: This review aimed to provide an overview of the applications of stable isotopes for the study of energy nutrient metabolic pathways. The ongoing integration of stable isotope approaches with artificial intelligence, omics technologies, and miniaturized detection techniques could promise to further refine our understanding of human metabolism and drive advances in personalized medicine. Full article

Gender-based violence in public transportation is a global phenomenon that restricts women’s rights and autonomy. Most of the documentation relies on harassment and physical aggression, but the subtle internalized mechanisms that reproduce gender inequities remain insufficiently analyzed. This study involves the concept of symbolic violence as an analytical category to unveil how resignation and normalization of violence perpetuate gender power relations and limit women’s mobility. A cross-sectional survey of 263 women aged 15–60 was conducted in Querétaro, Mexico, a rapidly growing city with significant mobility challenges. The questionnaire included items on perceptions of safety, violent experiences, responses to acts of violence, and prevention strategies. An inductive–abductive analysis was implemented to construct empirical indicators derived from Bordieu’s concept of symbolic violence and habitus. Findings reveal that fear, avoidance, and self-regulation are normalized responses to violence in public transport. Women implement strategies such as changing routes, limiting night travel, or increasing their expenses to access safer options. Six empirical indicators were identified: perceived insecurity as normality, resignation to harassment, bodily and emotional self-regulation, preventive reorganization of mobility, personal costs of safety, and collective inaction. In conclusion, the study demonstrates how symbolic violence operates through behaviors, actions, perceptions, and thoughts that reproduce inequities. Operationalizing symbolic violence provides a methodological and conceptual tool to advance gender equality and inform gender-sensitive mobility policies. Full article

Minimal residual disease (MRD) refers to the persistence of low-level malignant cells or tumor-derived nucleic acids that remain after curative-intent therapy and are undetectable by conventional diagnostic methods. In oncology, MRD has emerged as a powerful biomarker with well-established prognostic value in hematologic malignancies and rapidly expanding relevance in solid tumors. Advances in sensitive detection technologies, including multiparameter flow cytometry, quantitative real-time polymerase chain reaction, next-generation sequencing, and digital polymerase chain reaction, have enabled the identification of residual disease at the molecular level, often preceding clinical or radiological relapse. Beyond its conventional role as a binary indicator of treatment response or cure, MRD is increasingly recognized as a dynamic longitudinal biomarker that supports personalized disease management. Within this evolving paradigm, patient-informed MRD strategies that incorporate tumor-specific molecular profiling and serial monitoring, particularly through circulating tumor DNA, offer the potential to guide treatment adaptation, including escalation, de-escalation, maintenance optimization, and surveillance strategies across both hematologic and solid malignancies. In this review, we summarize the biological basis of MRD, current and emerging detection methodologies, and clinical applications across cancer types, with a focus on patient-informed approaches. We also discuss key limitations, including assay standardization, biological variability in solid tumors, and the lack of clearly defined actionability thresholds. Finally, we highlight future directions for integrating MRD with multi-omics and AI-driven analytical frameworks to enable adaptive, risk-informed cancer management and advanced precision oncology. Full article

Personalized instruction remains a major bottleneck in higher education, especially in large classes where timely, individualized feedback is difficult to achieve. Existing automation typically relies on rigid rule-based pipelines or computationally heavy deep learning models, making it difficult to simultaneously achieve interpretability, instructional usability, and scalable deployment. In this study, we present CalcTutor, a generative-AI-based assessment and feedback system designed to support open-ended handwritten calculus problem solving. The system organizes instructional support through three coordinated components: (1) a multi-agent large language model (LLM) mechanism that evaluates solution processes and produces diagnostic feedback, (2) a retrieval-augmented generation (RAG) pipeline that links diagnosed difficulties to aligned instructional materials, and (3) real-time learner analytics for both students and instructors, forming an integrated instructional support workflow rather than an automated answer-checking tool. In offline evaluation and a pilot classroom deployment, the multi-agent grader achieved a weighted agreement accuracy of 0.931 and an F1-score of 0.934 on 1055 handwritten solutions. Participant feedback and workflow testing indicated that CalcTutor can be stably integrated into routine classroom use and enables students to interpret and act upon the provided feedback. These results indicate that automated assessment, diagnostic feedback, and targeted review can operate coherently within a single instructional process that supports instructor-led assessment practices. Using undergraduate calculus as an application domain for open-ended handwritten mathematical assessment, the study demonstrates the operational feasibility of a closed-loop assessment–feedback–revision workflow and provides a deployable instructional infrastructure for formative instructional support in real classroom contexts. Full article