Search Results (235)

This research analyzes the relationships among environmental factors, organizational structure, strategic orientation, and organizational performance within the Romanian medical system, addressing a theoretical gap in this context. A quantitative approach was applied, analyzing data from 502 employees in the Romanian medical sector. The study used a dual framework, integrating gestalt theory and mediation to examine the environment–structure–strategy–performance relationship. Two-stage cluster analysis, one-way analysis of variance, and partial least squares structural equation modeling tested direct and mediated effects among the variables. From a gestalt perspective, five distinct clusters demonstrated the interplay between environment, structure, and strategy. Romanian healthcare organizations align their structural elements and strategic decisions coherently and distinctly, considering contextual constraints, with implications for several performance dimensions, including patient satisfaction, financial stability, innovation, and internal process improvement. From a mediation perspective, both direct and mediated relationships indicate that organizational structure and strategic orientation positively affect organizational performance and suppress the negative contextual effects. This study contributes theoretically by extending contingency and gestalt theories to the Romanian healthcare context, showing that contextual fit, rather than structural uniformity, determines performance variation. Practically, the findings guide healthcare managers and policymakers in attenuating contextual shocks and improving organizational performance through strategic alignment and flexible structural design. Full article

Background/Objectives: Qatar and other Gulf Cooperation Council (GCC) countries are experiencing a growing incidence of diet-related non-communicable diseases (NCDs). The lack of a culturally relevant food exchange list (FEL) for commonly consumed foods in Qatar and the GCC limits the application of cultural preferences in medical nutrition therapy (MNT) for managing diet-related NCDs, thereby reducing patient adherence and metabolic outcomes. Therefore, the primary objective of this study was to develop a culturally tailored FEL for 50 main course dishes widely consumed in the region. Methods: A four-phase approach was followed in this developmental study. First, common Qatari and GCC dishes were identified based on cultural practices and market availability. Second, nutrient composition was compiled from regional food composition tables and validated using dietary analysis software. Pearson correlation was conducted to compare macronutrient values, with significance set at p < 0.05. Third, standard serving sizes were determined using Wheeler et al.’s methodology and converted into household measures using a kitchen scale. Finally, we developed a macronutrient exchange list for the dishes based on the established Wheeler rounding-off criteria. Results: A culturally tailored FEL for 50 frequently consumed Qatari and GCC dishes was successfully developed. Significant correlations were observed between laboratory-derived and software-derived values for carbohydrates (r = 0.7) and protein (r = 0.9), with a weaker correlation for fat (r = 0.5). Macronutrient exchange analysis revealed substantial variation across dishes, with several carbohydrate-based dishes also contributing meaningful protein and fat exchanges. Findings indicated that visual assumptions about nutrient composition may not accurately reflect exchange values, highlighting the need for systematic analysis in diet planning. Conclusions: This study developed a novel culturally relevant FEL for commonly consumed composite dishes in Qatar and the GCC. The exchange list provides a practical tool for dietitians and healthcare professionals to support culturally tailored MNT and public health interventions in the region. It also serves as a valuable resource for researchers in nutritional epidemiology, enabling the analysis of dietary data by converting raw food intake information. Full article

Previous research has shown that medical practitioners’ conceptions of their profession play a significant role in their practice. This study extends that research by investigating ways in which different conceptions of ‘being a doctor’ may act to expand or limit the potential for future development as a doctor. Based on previous research that identified different conceptions of ‘being a doctor’ and ‘developing as a doctor’, a chi-square test of association between the two sets of conceptions was undertaken and a statistically significant association found. More and less complex conceptions of being a doctor were associated with more and less complex conceptions of developing as a doctor, respectively. This raises the likelihood that conceptions of being a doctor that develop early in one’s career may act to limit the potential for future development. Consequently, the paper recommends that different conceptions of medical practice be addressed as part of medical education. To help with this, the paper describes an innovative educational design based on the ‘variation theory of learning’ proposed within a phenomenographic epistemology. The educational design is specifically intended to help trainees become aware of elements of practice and development that they have not previously discerned. Full article

Intravenous medications are frequently administered through shared catheter lines in neonatal intensive care units (NICUs) due to the limited venous access in preterm infants, raising concerns about drug incompatibilities that may cause serious complications. Hydrocortisone sodium (HDC), ampicillin (ABPC), and cefotaxime (CTX) are commonly used in NICUs and are often co-administered with unfractionated heparin (UFH), which is routinely infused to prevent catheter occlusion. This study evaluated the physicochemical compatibility of HDC, ABPC, and CTX when mixed with UFH. Each drug was combined with UFH at equal volumes, and the mixtures were assessed immediately and after 3 h of storage by visual inspection, pH measurement, UV absorbance, and HPLC-UV analysis. No precipitation, turbidity, or color changes were observed in any mixture, and UV absorbance showed no relevant deviations compared with controls. Slight pH variations were detected but remained within acceptable limits. In semi-quantitative HPLC analysis, relative peak area changes were all below 10%, indicating no major degradation of the drugs. These findings suggest that HDC, ABPC, and CTX maintain acceptable physicochemical compatibility when co-administered with UFH, supporting their safe concomitant use in NICU practice. Full article

Hyperspectral imaging (HSI) is increasingly used in studies for medical applications as it provides both structural and functional information of biological tissue, enhancing diagnostic accuracy and clinical decision-making. Recently, HSI cameras (HSICs) have been integrated with medical endoscopes (HSIEs), capturing hypercube data beyond conventional white light imaging endoscopes. However, there are currently no cleared or approved HSIEs by the U.S. Food and Drug Administration (FDA). HSI accuracy depends on technologies and experimental parameters, which must be assessed for reliability. Importantly, the reflectance spectrum of a target can vary across different cameras and under different environmental or operational conditions. Thus, before reliable clinical translation can be achieved, a fundamental question must be addressed: can the same target yield consistent spectral measurements across different HSI systems and under varying acquisition conditions? This study investigates the impact of eight parameters—ambient light, exposure time, camera warm-up time, spatial and temporal averaging, camera focus, working distance, illumination angle, and target angle—on spectral measurements using two HSI techniques: interferometer-based spectral scanning and snapshot. Controlled experiments were conducted to evaluate how each parameter affects spectral accuracy and whether normalization can mitigate these effects. Our findings reveal that several parameters significantly influence spectral measurements, with some having a more pronounced impact. While normalization reduced variations for most parameters, it was less effective at mitigating errors caused by ambient light and camera warm-up time. Additionally, normalization did not eliminate spectral noise resulting from low exposure time, small region of interest, or a spectrally non-uniform light source. From these results, we propose practical considerations for optimizing HSI system performance. Implementing these measures can minimize variations in reflectance spectra of identical targets captured by different cameras and under diverse conditions, thereby supporting the reliable translation of HSI techniques to clinical applications. Full article

Object detection in challenging imaging domains like security screening, medical analysis, and satellite imaging is often hindered by signal degradation (e.g., noise, blur) and spatial ambiguity (e.g., occlusion, extreme scale variation). We argue that many standard architectures fail by fusing multi-scale features prematurely, which amplifies noise. This paper introduces the Enhance-Fuse-Align (E-F-A) principle: a new architectural blueprint positing that robust feature enhancement and explicit spatial alignment are necessary preconditions for effective feature fusion. We implement this blueprint in a model named SecureDet, which instantiates each stage: (1) an RFCBAMConv module for feature Enhancement; (2) a BiFPN for weighted Fusion; (3) ECFA and ASFA modules for contextual and spatial Alignment. To validate the E-F-A blueprint, we apply SecureDet to the highly challenging task of X-ray contraband detection. Extensive experiments and ablation studies demonstrate that the mandated E-F-A sequence is critical to performance, significantly outperforming both the baseline and incomplete or improperly ordered architectures. In practice, enhancement is applied prior to fusion to attenuate noise and blur that would otherwise be amplified by cross-scale aggregation, and final alignment corrects mis-registrations to avoid sampling extraneous signals from occluding materials. Full article

Background: Previous research showed that the majority of the variation in providers’ practice patterns is unexplained by patient, physician, and primary care practice characteristics. This study assessed physicians’ personal behavioral characteristics as explanatory components of medical practice variation (MPV). Methods: In this cross-sectional study, primary care physicians from Clalit Health Services in southern Israel were interviewed using validated surveys assessing risk-taking, tolerance for ambiguity, stress due to uncertainty, fear of malpractice, and empathy. We analyzed how much these traits explained MPV compared to patient, physician demographic, occupational, and practice characteristics using generalized linear mixed models and Nakagawa’s R². Results: Of the 160 physicians approached, 146 (91.3%) participated. The median practicing time was 22 years; 48% were male, with a median age of 49. The median number of patients per practice was 1135. Overall, 40.4% of MPV was explained, mostly by patient characteristics (18.9%), practice characteristics (10.2%), and physician demographics (8.3%). Physician behavioral traits explained only 2.3%. Conclusions: Personal behavior characteristics explain a minority of MPV, leaving 60% of the MPV unexplained. This suggests either limitations in survey assessments or that these traits are not key drivers of MPV. Full article

Randomized controlled trials (RCTs) have been regarded as the gold standard for evaluating the efficacy of treatments for chronic pain and are the foundation for regulatory approval and guideline development. However, their restrictive design and dependence on idealized populations can limit their applicability to the diverse patients seen in routine chronic pain management. Real-world data (RWD), collected from electronic medical records, registries, claims databases, and digital health platforms, can offer a more comprehensive view of treatment adherence and safety that RCTs often overlook. A key issue in pain medicine is the efficacy–effectiveness gap, where discrepancies exist between the outcomes of therapies and interventions in RCTs versus in real-world practice due to variations in patient populations and adherence. Bridging this gap ensures that observed improvements align with patients’ preferred outcomes and functional goals. Integrating the strengths of RCTs and RWD provides a more comprehensive evidence base to guide clinical decision-making, influence reimbursement policies, and develop equitable guidelines. The primary aim of this paper is to identify factors used in FDA-regulated RCTs and RWD that could be implemented or enhanced in everyday practice to deliver more holistic and patient-centered care in the management of chronic pain. Full article

Over recent years, machine learning and artificial intelligence have become critical components of many cancer detection tests, in particular multi-omic tests such as spectroscopic liquid biopsies. The complexity and multi-variate nature of spectral datasets makes machine learning invaluable in uncovering patterns that enable robust differentiation of cancer signals. However, introducing any AI-enabled medical device into clinical practice is challenging due to the regulatory requirements needed to progress from fundamental research to clinical and patient use. This review explores some of the fundamental concerns in bringing spectroscopic liquid biopsies to the clinic, including the need for explainable artificial intelligence and diverse validation sets. Addressing these issues is essential to accelerate clinical uptake with the ultimate goal of improving patient survival and quality of life. Full article

Background/Objectives: Whether thyroidectomy confers a long-term survival advantage over non-surgical management in real-world practice remains uncertain. We primarily evaluated the association between surgery and all-cause mortality in thyroid cancer; specialty-stratified outcomes were prespecified as secondary, exploratory analyses. Methods: Using the TriNetX US Collaborative Network (2008–2024), we identified adults with thyroid cancer and created 1:1 propensity score-matched cohorts of patients who did or did not undergo thyroidectomy, balancing demographics, comorbidities, medications, and laboratory variables. Overall survival was assessed with Kaplan–Meier curves and Cox proportional hazard models. Among the surgical patients, we performed exploratory analyses stratified by operating specialty (otolaryngology–head and neck surgery (reference) vs. general/endocrine surgery and other/unknown, reported descriptively). Results: After matching, 49,219 patients were included per cohort. Thyroidectomy was associated with lower long-term mortality versus non-surgical care (adjusted HR 0.685, 95% CI 0.652–0.721). Among the surgical patients, secondary, exploratory specialty-stratified analyses suggested differences: compared with otolaryngology–head and neck surgery (ENT–HNS; reference), general/endocrine surgery (GS/ES) had a lower adjusted hazard of death (aHR 0.561, 95% CI 0.481–0.654), whereas other/unknown specialties had a higher adjusted hazard (aHR 1.583, 95% CI 1.302–1.924). These patterns are hypothesis-generating and may reflect residual confounding, including the tumor stage and histology, referral pathways, and surgeon or center experience. Conclusions: In a large, propensity-matched real-world cohort, surgery was linked to improved long-term survival regarding thyroid cancer. Observed specialty-related variation should be interpreted cautiously, and prospective studies incorporating tumor-level variables and provider/center characteristics are needed. Emphasis should remain on timely surgery within multidisciplinary care pathways. Full article

Background/Objectives: Neuropathic pain, resulting from damage or pathology affecting the somatosensory nervous system, is a prevalent form of chronic pain that significantly impacts quality of life. Combined therapies are often utilised to manage this condition. Three-dimensional printing (3DP) offers a promising approach for personalising medication doses and dosage forms to meet individual patient needs. Methods: In this study, a formulation suitable for 3D printing was developed using magnesium citrate, uridine monophosphate, vitamins B₃ (niacin), B₆ (pyridoxine), B₁₂ (cobalamin), B₉ (folic acid), and spermidine to create a novel gel-based oral tablet for the targeted treatment of neurological pain. The antioxidant potential of the active pharmaceutical ingredients (APIs) was assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) methods. The physical properties of the tablets were evaluated using a texture analyser, while the in vitro release profiles were determined by high-performance liquid chromatography (HPLC). Results: Results demonstrated that pectin–gelatin tablets hardened over time, with higher citric acid concentrations further enhancing this effect. Formulation AVII exhibited good hardness and low stickiness. Formulation AV, however, showed poor performance across all physical parameters and lacked sufficient structural integrity for practical application. While uridine monophosphate, B₁₂, and B₉ showed no significant differences in the release profiles of the tablets, spermidine, B₆, and B₃ displayed statistically significant variations. Specifically, AVII outperformed AV in terms of spermidine and B₆ release, and AV showed a higher release of B₃ compared to AV. Conclusions: The AVII tablet demonstrates potential for use in combined therapy targeting neurological pain disorders. Full article

The traditional use of medicinal plants around the world has a long history, predominantly in low- and middle-income countries. Previous ethnobotanical research pertaining to urban environments demonstrated that the legacy of the use of medicinal plant species persists worldwide; however, information about the main city in the occidental part of Mexico is scarce regarding this traditional knowledge and its variation during the last few decades. A database was created from interviews with local people who had inhabited the oldest neighborhoods of Guadalajara for at least 30 years and by using different electronic databases. In addition, the correct taxonomic identification of species was supported via corroboration through local and other digital herbariums. Furthermore, a Principal Coordinate Analysis (PCoA) was performed on the database information to search for relationships among the medicinal plant species used. An inventory of 137 medicinal plants was created, where the plant species most commonly used in the five old neighborhoods of Guadalajara City were muicle (Justicia spicigera Schltdl.), pirul (Schinus molle L.), manzanilla (Matricaria chamomilla L.), valeriana (Valeriana sp.), calabaza (Cucurbita pepo L.), cola de caballo (Equisetum arvense L.), tepezcohuite (Mimosa tenuiflora Poir.), salvia (Salvia officinalis L.), canela (Cinnamomum verum J. Presl.), tila estrella (Tilia americana var. mexicana (Schltdl.) Hardin), cedrón (Aloysia citrodora Paláu), uva (Vitis vinifera L.), jengibre (Zingiber officinale Roscoe) and gobernadora (Larrea tridentata (DC.) Coville). Illnesses of the cardiovascular, digestive, urinary, respiratory, nervous, muscular and reproductive systems, as well as culture-bound syndromes, were mostly treated with these plant species. Moreover, J. spicigera, M. chamomilla and L. tridentata were used for eight medical purposes, followed by Z. officinale with five medicinal practices. In contrast, only two medicinal uses were recorded for C. pepo, M. tenuiflora and S. officinale. The PCoA explained 65.88% of the variation accumulated at the first three ordination axes and formed four groups of species, which were related to their geographical origin. Eight of the fourteen species that are commonly used as medicinal plants are from America, and the rest come from Europe and Asia. This study confirms the persistence of traditional knowledge related to medicinal plants, and the diseases empirically addressed among the inhabitants of Guadalajara City are common in other parts of the world and in different regions of Mexico. These findings are supported by electronic databases that comprise multiple studies related to the phytochemical compounds and medical validation regarding their biological activity, supporting the empirical use and efficacy of these medicinal plants. Full article

Fine-grained plant image classification (FPIC) aims to distinguish plant species with subtle visual differences, but existing datasets often suffer from limited category diversity, homogeneous backgrounds, and insufficient environmental variation, limiting their effectiveness in complex real-world scenarios. To address these challenges, a novel dataset, FewMedical-XJAU, is presented, focusing on rare medicinal plants native to Xinjiang, China. This dataset offers higher intra-class variability, more complex and diverse natural backgrounds, varied shooting angles and lighting conditions, and more rigorous expert annotations, providing a realistic testbed for FPIC tasks. Building on this, an improved method called BDCC (Bilinear Deep Cross-modal Composition) is proposed, which incorporates textual priors into a deep metric learning framework to enhance semantic discrimination. A Class-Aware Structured Text Prompt Construction strategy is introduced to improve the model’s semantic understanding, along with a dynamic fusion mechanism to address high inter-class similarity and intra-class variability. In few-shot classification experiments, the method demonstrates superior accuracy and robustness under complex environmental conditions, offering strong support for practical applications of fine-grained classification. Full article

This paper proposes a new accelerated fixed-point algorithm based on a double-inertial extrapolation technique for solving structured variational inclusion and convex bilevel optimization problems. The underlying framework leverages fixed-point theory and operator splitting methods to address inclusion problems of the form $0\in (A+B)(x)$, where A is a cocoercive operator and B is a maximally monotone operator defined on a real Hilbert space. The algorithm incorporates two inertial terms and a relaxation step via a contractive mapping, resulting in improved convergence properties and numerical stability. Under mild conditions of step sizes and inertial parameters, we establish strong convergence of the proposed algorithm to a point in the solution set that satisfies a variational inequality with respect to a contractive mapping. Beyond theoretical development, we demonstrate the practical effectiveness of the proposed algorithm by applying it to data classification tasks using Deep Extreme Learning Machines (DELMs). In particular, the training processes of Two-Hidden-Layer ELM (TELM) models is reformulated as convex regularized optimization problems, enabling robust learning without requiring direct matrix inversions. Experimental results on benchmark and real-world medical datasets, including breast cancer and hypertension prediction, confirm the superior performance of our approach in terms of evaluation metrics and convergence. This work unifies and extends existing inertial-type forward–backward schemes, offering a versatile and theoretically grounded optimization tool for both fundamental research and practical applications in machine learning and data science. Full article

Deep learning has significantly advanced medical image analysis by enabling accurate, automated diagnosis across diverse clinical tasks such as lesion classification and disease detection. However, the practical deployment of these systems is still hindered by two major challenges: the limited availability of expert-annotated data and substantial domain shifts caused by variations in imaging devices, acquisition protocols, and patient populations. Although recent semi-supervised domain generalization (SSDG) approaches attempt to address these challenges, they often suffer from two key limitations: (i) reliance on computationally expensive uncertainty modeling techniques such as Monte Carlo dropout, and (ii) inflexible shared-head classifiers that fail to capture domain-specific variability across heterogeneous imaging styles. To overcome these limitations, we propose MultiStyle-SSDG, a unified semi-supervised domain generalization framework designed to improve model generalization in low-label scenarios. Our method introduces a multi-style ensemble pseudo-labeling strategy guided by entropy-based filtering, incorporates prototype-based conformity and semantic alignment to regularize the feature space, and employs a domain-specific multi-head classifier fused through attention-weighted prediction. Additionally, we introduce a dual-level neural-style transfer pipeline that simulates realistic domain shifts while preserving diagnostic semantics. We validated our framework on the ISIC2019 skin lesion classification benchmark using 5% and 10% labeled data. MultiStyle-SSDG consistently outperformed recent state-of-the-art methods such as FixMatch, StyleMatch, and UPLM, achieving statistically significant improvements in classification accuracy under simulated domain shifts including style, background, and corruption. Specifically, our method achieved 78.6% accuracy with 5% labeled data and 80.3% with 10% labeled data on ISIC2019, surpassing FixMatch by 4.9–5.3 percentage points and UPLM by 2.1–2.4 points. Ablation studies further confirmed the individual contributions of each component, and t-SNE visualizations illustrate enhanced intra-class compactness and cross-domain feature consistency. These results demonstrate that our style-aware, modular framework offers a robust and scalable solution for generalizable computer-aided diagnosis in real-world medical imaging settings. Full article