Search Results (10,997)

Anxiety and depression are common comorbidities in patients with chronic obstructive pulmonary disease (COPD), which can contribute to increased morbidity, reduced quality of life, and worse clinical outcomes. Nevertheless, these psychological conditions remain largely overlooked. This narrative review includes studies published between 1983 and 2025 to synthesise the current evidence on the risk factors, clinical impacts, and therapeutic strategies for these comorbidities. While the exact mechanisms leading to their increased prevalence are not fully understood, growing evidence implicates a combination of biological (e.g., systemic inflammation), social (e.g., isolation and stigma), and behavioural (e.g., smoking and inactivity) factors. Despite current guidelines recommending the identification and management of these comorbidities in COPD, they are not currently included in COPD assessments. Undetected and unmanaged anxiety and depression have serious consequences, including poor self-management, non-adherence to medications, increased risk of exacerbation and hospitalisations, and even mortality; thus, there is a need to incorporate screening as part of COPD assessments. There is robust evidence showing that pulmonary rehabilitation, a core non-pharmacological intervention, can improve mood symptoms, enhance functional capacity, and foster psychosocial resilience. Psychological therapies such as cognitive behavioural therapy (CBT), mindfulness-based approaches, and supportive counselling have also demonstrated value in reducing emotional distress and improving coping mechanisms. Pharmacological therapies, particularly selective serotonin reuptake inhibitors (SSRIs) and serotonin–norepinephrine reuptake inhibitors (SNRIs), are commonly prescribed in moderate to severe cases or when non-pharmacological approaches prove inadequate. However, the evidence for their efficacy in COPD populations is mixed, with concerns about adverse respiratory outcomes and high discontinuation rates due to side effects. There are also barriers to optimal care, including underdiagnosis, a lack of screening protocols, limited provider training, stigma, and fragmented multidisciplinary coordination. A multidisciplinary, biopsychosocial approach is essential to ensure early identification, integrated care, and improved outcomes for patients with COPD. Full article

Obstructive sleep apnea syndrome (OSAS) is a prevalent disorder with significant systemic and oral health consequences. This narrative review synthesizes the current knowledge on the interplay between dental health and sleep apnea, highlighting the expanding role of dentists in the screening, early detection, and management of OSAS. Validated questionnaires, anatomical assessments, and anthropometric measurements have enhanced dentists’ capacity for early screening. However, knowledge and training gaps remain, particularly in low- and middle-income countries. Dentists are uniquely positioned to identify anatomical and oral risk factors, facilitate referrals for diagnosis, and provide therapeutic interventions such as oral appliance therapy. Interdisciplinary collaboration between dental and medical professionals is essential to improve early detection, treatment outcomes, and patient quality of life. Enhancing education, standardizing protocols, and integrating dentists into multidisciplinary care pathways are critical steps for advancing the management of sleep apnea. Full article

Zirconia is used widely for high-precision custom abutments; however, stress concentration can compromise osseointegration. Although glass–ceramic spray deposition (GCSD) can enhance the surface properties of zirconia, its biological effects remain unclear. In this study, the biological responses of human osteoblast-like (MG-63) cells to GCSD-modified zirconia surfaces were evaluated to assess the potential application in zirconia abutments. Disk-shaped zirconia and titanium alloy samples were prepared; titanium served as the control (Ti). Zirconia was subjected to polishing (NT), airborne-particle abrasion (AB), or GCSD with (GE) or without (GC) hydrofluoric acid (HF) etching. Surface characteristics, including wettability, surface energy (SE), and surface potential (SP), were analyzed. Cytotoxicity and MG-63 cell adhesion were assessed using the PrestoBlue assay, scanning electron microscopy (SEM), viability staining, and confocal laser scanning microscopy (CLSM). Statistical analysis was performed with a significance level of 0.05. GCSD produced a dense glass–ceramic coating on the zirconia surface, which significantly enhanced hydrophilicity as indicated by reduced water contact angles and increased SE in the GC and GE groups (p < 0.05). HF etching increased SP (p < 0.05). No cytotoxicity was observed in any group. SEM, viability staining, and CLSM revealed enhanced MG-63 cell attachment on Ti and GE surfaces and the highest viability ratio in the GE group. The NT group exhibited the lowest cell attachment and viability at all time points. GCSD effectively improved zirconia abutment surface properties by enhancing hydrophilicity and promoting MG-63 cell adhesion, with biocompatibility comparable to or better than that of titanium. Full article

Cyclin-dependent kinase (CDK)4/6 inhibitors have become a key component of adjuvant treatment for patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2−) early breast cancer who are at high risk of recurrence. The addition of abemaciclib and ribociclib to standard endocrine therapy has demonstrated clinically meaningful improvements in invasive disease-free survival, supported by the monarchE and NATALEE trials, respectively. With expansion of patient eligibility for CDK4/6 inhibitors, multidisciplinary coordination among medical oncologists, surgeons, nurses, pharmacists, and other health care providers is critical to optimizing patient identification, monitoring, and management of adverse events. This expert guidance document provides practical recommendations for implementing adjuvant CDK4/6 inhibitor therapy in routine clinical practice, incorporating insights from multiple specialties and with patient advocacy representation. Key considerations include patient selection based on clinical trial data, treatment duration, dosing schedules, adverse event profiles, monitoring requirements, drug–drug interactions, and patient-specific factors such as tolerability, cost, and quality of life. This guidance aims to support Canadian clinicians in effectively integrating CDK4/6 inhibitors into clinical practice, ensuring optimal patient outcomes through a multidisciplinary and patient-centric approach. Full article

Autism Spectrum Disorder (ASD) remains significantly underdiagnosed in women, resulting in a persistent gender gap with important clinical, functional, and psychosocial implications. This narrative review explores the multifactorial barriers contributing to diagnostic disparities, including the male-oriented structure of current diagnostic criteria, the prevalence of co-occurring psychiatric conditions, and the phenomenon of social camouflaging shaped by culturally reinforced gender norms. These factors frequently lead to delayed identification, clinical misinterpretation, and suboptimal care. The review synthesizes evidence from clinical, psychological, and sociocultural research to demonstrate how the under-recognition of ASD in women impacts mental health outcomes, access to education, occupational stability, and overall quality of life. Special emphasis is placed on the consequences of missed or late diagnoses for healthcare delivery and the educational needs of clinicians involved in ASD assessment and care. This article concludes with actionable, evidence-based recommendations for enhancing diagnostic sensitivity, developing gender-responsive screening strategies, and integrating training on female autism presentation into medical and allied health education. Addressing these challenges is essential to reducing diagnostic inequities and ensuring timely, accurate, and person-centered care for autistic women throughout their lifespan. Full article

The increase in the elderly population globally reflects a change in the population’s mindset regarding preventive health measures and necessitates a rethinking of healthcare strategies. The integration of machine learning (ML)-type algorithms in geriatrics represents a direction for optimizing prevention, diagnosis, prediction, monitoring, and treatment. This paper presents a systematic review of the scientific literature published between 1 January 2020 and 31 May 2025. The paper is based on the applicability of ML techniques in the field of geriatrics. The study is conducted using the Web of Science database for a detailed discussion. The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. The performance metrics reported in the analyzed papers include the accuracy, sensitivity, F1-score, and Area under the Receiver Operating Characteristic Curve. Nine search categories are investigated through four databases: WOS, PubMed, Scopus, and IEEE. A comparative analysis shows that the field of geriatrics, through an ML approach in the context of elderly nutrition, is insufficiently explored, as evidenced by the 61 articles analyzed from the four databases. The analysis highlights gaps regarding the explainability of the models used, the transparency of cross-sectional datasets, and the validity of the data in real clinical contexts. The paper highlights the potential of ML models in transforming geriatrics within the context of personalized predictive care and outlines a series of future research directions, recommending the development of standardized databases, the integration of algorithmic explanations, the promotion of interdisciplinary collaborations, and the implementation of ethical norms of artificial intelligence in geriatric medical practice. Full article

Objectives: Diabetes Mellitus involves demanding challenges that interfere with family functioning and routines. In turn, family and social context impacts individual glycemic control. This study aims to identify this recursive interplay, the mutual influences of family systems and diabetes management. Design: Data was collected through a cross-sectional design comparing patients, aged 22–55, with and without metabolic control. Methods: Participants filled out a set of self-report measures of sociodemographic, clinical and family systems assessment. Patients (91) were also invited to describe their perception about disease management interference regarding family functioning. We first examined the extent to which family variables grouped dataset to determine if there were similarities and dissimilarities that fit with our initial diabetic groups’ classification. Results: Cluster analysis results identify a two-cluster solution validating initial classification of two groups of patients: 49 with metabolic control (MC) and 42 without metabolic control (NoMC). Independent sample tests suggested statistically significant differences between groups in family subscales- family difficulties and family communication (p < 0.05). Binary logistic regression shed light on predictors of explained variance to no metabolic control, in four models: Sociodemographic, Clinical data, SCORE-15/Congruence Scale and Eating Behavior. Furthermore, groups differ on family support, level and sources of family conflict caused by diabetes management issues. Considering only patients who co-habit with a partner for more than one year (N = 44), NoMC patients score lower on marital functioning in all categories (p < 0.05). Discussion: Family-Chronic illness interaction plays a significant role in a patient’s adherence to treatment. This study highlights the Standards of Medical Care for Diabetes, considering caregivers and family members on diabetes care. Full article

Background: Electrocardiograms (EKGs) are essential tools in emergency medicine, often used to evaluate chest pain, dyspnea, and other symptoms suggestive of cardiac dysfunction. Yet, EKGs are not universally administered to all emergency department (ED) patients. Understanding and predicting which patients receive an EKG may offer insights into clinical decision making, resource allocation, and potential disparities in care. This study examines whether integrating structured clinical data with free-text patient narratives can improve prediction of EKG utilization in the ED. Methods: We conducted a retrospective observational study to predict electrocardiogram (EKG) utilization using data from 13,115 adult emergency department (ED) visits in the nationally representative 2021 National Hospital Ambulatory Medical Care Survey–Emergency Department (NHAMCS-ED), leveraging both structured features—demographics, vital signs, comorbidities, arrival mode, and triage acuity, with the most influential selected via Lasso regression—and unstructured patient narratives transformed into numerical embeddings using Clinical-BERT. Four supervised learning models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGB)—were trained on three inputs (structured data only, text embeddings only, and a late-fusion combined model); hyperparameters were optimized by grid search with 5-fold cross-validation; performance was evaluated via AUROC, accuracy, sensitivity, specificity and precision; and interpretability was assessed using SHAP values and Permutation Feature Importance. Results: EKGs were administered in 30.6% of adult ED visits. Patients who received EKGs were more likely to be older, White, Medicare-insured, and to present with abnormal vital signs or higher triage severity. Across all models, the combined data approach yielded superior predictive performance. The SVM and LR achieved the highest area under the ROC curve (AUC = 0.860 and 0.861) when using both structured and unstructured data, compared to 0.772 with structured data alone and 0.823 and 0.822 with unstructured data alone. Similar improvements were observed in accuracy, sensitivity, and specificity. Conclusions: Integrating structured clinical data with patient narratives significantly enhances the ability to predict EKG utilization in the emergency department. These findings support a personalized medicine framework by demonstrating how multimodal data integration can enable individualized, real-time decision support in the ED. Full article

If we do not urgently educate current and future medical professionals to critically evaluate and distinguish credible AI-assisted diagnostic tools from those whose performance is artificially inflated by data leakage or improper validation, we risk undermining clinician trust in all AI diagnostics and jeopardizing future advances in patient care. For instance, machine learning models have shown high accuracy in diagnosing Parkinson’s Disease when trained on clinical features that are themselves diagnostic, such as tremor and rigidity. This study systematically investigates the impact of data leakage and feature selection on the true clinical utility of machine learning models for early Parkinson’s Disease detection. We constructed two experimental pipelines: one excluding all overt motor symptoms to simulate a subclinical scenario and a control including these features. Nine machine learning algorithms were evaluated using a robust three-way data split and comprehensive metric analysis. Results reveal that, without overt features, all models exhibited superficially acceptable F1 scores but failed catastrophically in specificity, misclassifying most healthy controls as Parkinson’s Disease. The inclusion of overt features dramatically improved performance, confirming that high accuracy was due to data leakage rather than genuine predictive power. These findings underscore the necessity of rigorous experimental design, transparent reporting, and critical evaluation of machine learning models in clinically realistic settings. Our work highlights the risks of overestimating model utility due to data leakage and provides guidance for developing robust, clinically meaningful machine learning tools for early disease detection. Full article

Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach. Full article

Heart failure (HF) is a major global health challenge, characterized by high morbidity, mortality, and frequent hospital readmissions. Despite the advent of guideline-directed medical therapies (GDMTs), the burden of HF continues to grow, necessitating a shift toward comprehensive, multidisciplinary care models. Heart Failure Disease Management Programs (HF-DMPs) have emerged as structured frameworks that integrate evidence-based medical therapy, patient education, telemonitoring, and support for social determinants of health to optimize outcomes and reduce healthcare costs. This review outlines the key components of HF-DMPs, including patient identification and risk stratification, pharmacologic optimization, team-based care, transitional follow-up, remote monitoring, performance metrics, and social support systems. Incorporating tools such as artificial intelligence, pharmacist-led titration, and community health worker support, HF-DMPs represent a scalable approach to improving care delivery. The success of these programs depends on tailored interventions, interdisciplinary collaboration, and health equity-driven strategies. Full article

Reactions of the angular 4,4′-oxybis[N-(pyridin-3-ylmethyl)benzamide] (L) with dicarboxylic acids and transition metal salts afforded non-entangled {[Cd(L)(1,3-BDC)(H₂O)]∙2H₂O}_n (1,3-BDC = 1,3-benzenedicarboxylic acid), 1; {[Cd(L)(1,4-HBDC)(1,4-BDC)_0.5]∙2H₂O}_n (1,4-BDC = 1,4-benzenedicarboxylic acid), 2; {[Cu₂(L)₂(1,3-BDC)₂]∙1.5H₂O}_n, 3; {[Ni(L)(1,3-BDC)(H₂O)]∙2H₂O}_n, 4; {[Zn(L)(1,3-BDC)]∙4H₂O}_n, 5; {[Zn(L)(1,4-BDC)]∙2H₂O}_n, 6; and [Cd₃(L)₂(1,4-BDC)₃]_n, 7, which have been structurally characterized by using single-crystal X-ray diffraction. Complexes 1–5 and 7 are 2D layers, giving (6⁴·8·10)(6)-2,4L3, (4²·8²·10²)(4²·8⁴)₂(4)₂, (4·5·6)(4·5⁵·6³·7)-3,5L66, (6⁴·8·10)(6)-2,4L3, interdigitated (8⁴·12²)(8)₂-2,4L2 and (3⁶·4⁶·5³)-hxl topologies, respectively, and 6 is a 1D chain with the (4³·6²·8)(4)-2,4C3 topology. The factors that govern the structures of 1–7 are discussed and the thermal properties of 1–7 and the luminescent properties of complexes 1, 2, 5 and 6 are investigated. The stabilities of complexes 1 and 5 toward the detection of Fe³⁺ ions are also evaluated. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a worldwide challenge. There are an estimated 17–24 million patients worldwide, with an estimated 60 percent or more who have not been diagnosed. Without a known cure, no specific curative medication, disability lasting years to being life-long, and disagreement among healthcare providers as to how to most appropriately treat these patients, ME/CFS patients are in need of assistance. Appropriate healthcare provider education would increase the percentage of patients diagnosed and treated; however, in-school healthcare provider education is limited. To address the latter issue, the New Jersey Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Association (NJME/CFSA) has developed an independent, incentive-driven, learning program for students of the health professions. NJME/CFSA offers a yearly scholarship program in which applicants write a scholarly paper on an ME/CFS-related topic. The efficacy of the program is demonstrated by the 2024–2025 first place scholarship winner’s essay, which addresses the biological basis of ME/CFS and how the healthcare provider can improve the quality of life of ME/CFS patients. For the reader, the essay provides an update on what is known regarding the biological underpinnings of ME/CFS, as well as a medical student’s perspective as to how the clinician can provide care and support for ME/CFS patients. The original essay has been slightly modified to demonstrate that ME/CFS is a worldwide problem and for publication. Full article

Sleep disorders and stroke are intricately linked through a complex, bidirectional relationship. Sleep disturbances such as obstructive sleep apnea (OSA), insomnia, and restless legs syndrome (RLS) not only increase the risk of stroke but also frequently emerge as consequences of cerebrovascular events. OSA, in particular, is associated with a two- to three-fold increased risk of incident stroke, primarily through mechanisms involving intermittent hypoxia, systemic inflammation, endothelial dysfunction, and autonomic dysregulation. Conversely, stroke can disrupt sleep architecture and trigger or exacerbate sleep disorders, including insomnia, hypersomnia, circadian rhythm disturbances, and breathing-related sleep disorders. These post-stroke sleep disturbances are common and significantly impair rehabilitation, cognitive recovery, and quality of life, yet they remain underdiagnosed and undertreated. Early identification and management of sleep disorders in stroke patients are essential to optimize recovery and reduce the risk of recurrence. Therapeutic strategies include lifestyle modifications, pharmacological treatments, medical devices such as continuous positive airway pressure (CPAP), and emerging alternatives for CPAP-intolerant individuals. Despite growing awareness, significant knowledge gaps persist, particularly regarding non-OSA sleep disorders and their impact on stroke outcomes. Improved diagnostic tools, broader screening protocols, and greater integration of sleep assessments into stroke care are urgently needed. This narrative review synthesizes current evidence on the interplay between sleep and stroke, emphasizing the importance of personalized, multidisciplinary approaches to diagnosis and treatment. Advancing research in this field holds promise for reducing the global burden of stroke and improving long-term outcomes through targeted sleep interventions. Full article

Background: Quality care of individuals with sickle cell disease (SCD) is dependent upon education of the providers on their care team. Previous studies demonstrate lack of resident and provider comfort regarding care of patients with SCD, yet none have assessed these in medical students. Objective: This study aims to evaluate the adequacy of the research instrument for measuring medical students’ knowledge, confidence, and comfort regarding SCD and related complications prior to wider distribution. Methods: A self-assessment survey was distributed to medical students at two universities to evaluate their knowledge, confidence, and comfort in general SCD topics, in all clinical settings, and regarding common complications. Results: Of the 98 responses, knowledge (p < 0.001) and confidence (p = 0.02) were significantly different between topics, including epidemiology and genetics, pathophysiology, and treatment options. For “treatment options”, there were significant differences in knowledge (p = 0.02) and confidence (p = 0.02) between medical students at different levels of training. Students felt least knowledgeable and least comfortable with care of pregnant women and most knowledgeable and most comfortable with acute pain management. Caring for patients with specific SCD-related conditions increased knowledge and comfort across all domains. Conclusions: This instrument was adequate for measuring knowledge, confidence, and comfort in caring for those with SCD across all clinical settings. We identified a lack of knowledge, confidence, and comfort regarding treatment for those with SCD starting early in medical careers, which improves after caring for patients with various complications. Thus, educating and providing SCD patient experiences is crucial for medical student management confidence related to SCD. Full article