Search Results (6,347)

Despite the increasing adoption of a syndemic approach in HIV research, few health communication campaigns have used a syndemic approach in messaging to improve health outcomes for persons living with HIV (PWH). This paper introduces a framework for practitioners and researchers developing health communication messages in support of a syndemic approach to HIV care for PWH in the United States. Grounded in insights from a review of counseling and psychosocial interventions that demonstrated significant positive effects on HIV clinical outcomes, the C4H Framework emphasizes four components: compassion, comprehensive messaging, capacity-building, and coordination. Compassion ensures that messages resonate with individuals experiencing the intertwined challenges of HIV, substance abuse, and mental health issues. Comprehensive messaging integrates a holistic view of the barriers faced by PWH. Capacity-building empowers individuals to effectively engage with and act upon health information. Coordination promotes alignment between stakeholders and resources to ensure consistent and supportive messaging. The C4H Framework bridges the gap between research and practice, offering a foundation for crafting effective communication messages that resonate with individuals facing the complex challenges inherent in HIV syndemics. Future research should explicitly test the effectiveness and acceptability of messages developed using the C4H Framework with people living with HIV. Full article

Inflammatory Bowel Diseases (IBDs) are complex, multifactorial disorders with no known cure, necessitating lifelong care and often leading to surgical interventions. This ongoing healthcare requirement, coupled with the increased use of biological drugs and rising disease prevalence, significantly increases the financial burden on the healthcare systems. Thus, a number of novel technological approaches have emerged in order to face some of the pivotal questions still associated with IBD. In navigating the intricate landscape of IBD, biosensors act as indispensable allies, bridging the gap between traditional diagnostic methods and the evolving demands of precision medicine. Continuous progress in biosensor technology holds the key to transformative breakthroughs in IBD management, offering more effective and patient-centric healthcare solutions considering the One Health Approach. Here, we will delve into the landscape of biomarkers utilized in the diagnosis, monitoring, and management of IBD. From well-established serological and fecal markers to emerging genetic and epigenetic markers, we will explore the role of these biomarkers in aiding clinical decision-making and predicting treatment response. Additionally, we will discuss the potential of novel biomarkers currently under investigation to further refine disease stratification and personalized therapeutic approaches in IBD. By elucidating the utility of biosensors across the spectrum of IBD care, we aim to highlight their importance as valuable tools in optimizing patient outcomes and reducing healthcare costs. Full article

Total knee replacement (TKR) is a common procedure for pain relief and restoration of the mobility of the knee joint in patients with severe knee joint problems. Despite this, some patients still suffer from stiffness, instability, or pain caused by soft tissue imbalance, malalignment, or implant-related issues. Previously, surgeons have had to use their experience and visual judgment to balance the knee, which has resulted in variability of outcomes. Smart knee implants are addressing these issues by using sensor technology to provide real-time feedback on joint motion, pressure distribution, and loading forces. This enables more accurate intra-operative adjustment, enhancing implant positioning and soft tissue balance and eliminating post-operative adjustment. These implants also enable post-operative monitoring, simplifying the ability to have more effective individualized rehabilitation programs directed at optimizing patient mobility and minimizing complications. While the patient pool for smart knee implantation remains not commonly documented, it was found in a study that 83.6% of the patients would opt to have the monitoring device implemented, and nearly 90% find reassurance in monitoring their healing indicators. As the number of knee replacements is likely to rise due to aging populations and the rising prevalence of joint disease, smart implants are a welcome development in orthopedics, optimizing long-term success and patient satisfaction. Smart knee implants are built with embedded sensors such as force, motion, temperature, and pressure detectors placed within the implant structure. These sensors provide real-time data during surgery and recovery, allowing earlier detection of complications and supporting tailored rehabilitation. The design aims to improve outcomes through better monitoring and personalized care. Full article

Background/Objectives: Patients with breast cancer who do not achieve a complete response to neoadjuvant chemotherapy (NAC) may benefit from intensified adjuvant systemic therapy. However, such treatment escalation is typically delayed until after tumour resection, which occurs several months into the treatment course. Quantitative ultrasound (QUS) can detect early microstructural changes in tumours and may enable timely identification of non-responders during NAC, allowing for earlier treatment intensification. In our previous prospective observational study, 100 breast cancer patients underwent QUS imaging before and four times during NAC. Machine learning algorithms based on QUS texture features acquired in the first week of treatment were developed and achieved 78% accuracy in predicting treatment response. In the current study, we aimed to validate these algorithms in an independent prospective cohort to assess reproducibility and confirm their clinical utility. Methods: We included breast cancer patients eligible for NAC per standard of care, with tumours larger than 1.5 cm. QUS imaging was acquired at baseline and during the first week of treatment. Tumour response was defined as a ≥30% reduction in target lesion size on the resection specimen compared to baseline imaging. Results: A total of 51 patients treated between 2018 and 2021 were included (median age 49 years; median tumour size 3.6 cm). Most were estrogen receptor–positive (65%) or HER2-positive (33%), and the majority received dose-dense AC-T (n = 34, 67%) or FEC-D (n = 15, 29%) chemotherapy, with or without trastuzumab. The support vector machine algorithm achieved an area under the curve of 0.71, with 86% accuracy, 91% specificity, 50% sensitivity, 93% negative predictive value, and 43% positive predictive value for predicting treatment response. Misclassifications were primarily associated with poorly defined tumours and difficulties in accurately identifying the region of interest. Conclusions: Our findings validate QUS-based machine learning models for early prediction of chemotherapy response and support their potential as non-invasive tools for treatment personalization and clinical trial development focused on early treatment intensification. 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

Cardiovascular diseases (CVDs) are the leading global cause of death, with long-term hospitalization becoming increasingly frequent in advanced or chronic cases. In this context, the interplay between systemic factors such as lipid metabolism, circulating metabolites, gut microbiota, and oral health is gaining attention for its potential role in influencing inflammation, cardiometabolic risk, and long-term outcomes. Despite their apparent independence, these domains are increasingly recognized as interconnected and influential in cardiovascular pathophysiology. Methods: This narrative review was conducted by analyzing studies published between 2015 and 2024 from databases including PubMed, Scopus, and Web of Science. Keywords such as “lipid profile,” “metabolomics,” “gut microbiota,” “oral health,” and “cardiovascular disease” were used. Original research, meta-analyses, and reviews relevant to hospitalized cardiac patients were included. A critical integrative approach was applied to highlight cross-domain connections. Results and Discussion: Evidence reveals significant interrelations between altered lipid profiles, gut dysbiosis (including increased TMAO levels), metabolic imbalances, and oral inflammation. Each component contributes to a systemic pro-inflammatory state that worsens cardiovascular prognosis, particularly in long-term hospitalized patients. Despite isolated research in each domain, there is a paucity of studies integrating all four. The need for interdisciplinary diagnostic models and preventive strategies is emphasized, especially in populations with frailty or immobilization. Conclusions: Monitoring lipid metabolism, metabolomic shifts, gut microbial balance, and oral status should be considered part of comprehensive cardiovascular care. Gut microbiota exerts a dual role in cardiac health: when balanced, it supports anti-inflammatory and metabolic homeostasis; when dysbiotic, it contributes to systemic inflammation and worsened cardiac outcomes. Future research should aim to develop integrative screening tools and personalized interventions that address the multifactorial burden of disease. A systemic approach may improve both short- and long-term outcomes in this complex and vulnerable patient population. Full article

Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents, which is also considered an aggressive disease due to its rapid growth rate, ability to metastasize early, and complex and heterogeneous tumor microenvironment (TME). Although we are developing improved surgical and chemotherapeutic approaches, the presence of metastatic or recurrent disease is still detrimental to the patient’s outcome. Major advances in understanding the molecular mechanisms of OS are needed to substantially improve outcomes for patients being treated for OS. This review integrates new data on the molecular biology, pathophysiology, and immune landscape of OS, as well as introducing salient areas of tumorigenesis underpinning these findings, such as chromothripsis; kataegis; cancer stem cell dynamics; and updated genetic, epigenetic, and glycosylation modifiers. In addition, we review promising biomarkers, diagnostic platforms, and treatments, including immunotherapy, targeted small molecule inhibitors, and nanomedicine. Using genomic techniques, we have defined OS for its significant genomic instability due to TP53 and RB1 mutations, chromosomal rearrangements, and aberrant glycosylation. The TME is also characterized as immunosuppressive and populated by tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, ultimately inhibiting immune checkpoint inhibitors. Emerging fields such as glycomics and epigenetics, as well as stem cell biology, have defined promising biomarkers and targets. Preclinical studies have identified that glycan-directed CAR therapies could be possible, as well as metabolic inhibitors and 3D tumor models, which presented some preclinical success and could allow for tumoral specificity and enhanced efficacy. OS is a biologically and clinically complex disease; however, advances in exploring the molecular and immunologic landscape of OS present new opportunities in biomarkers and the development of new treatment options with adjunctive care. Successful treatments in the future will require personalized, multi-targeted approaches to account for tumor heterogeneity and immune evasion. This will help us turn the corner in providing improved outcomes for patients with this resilient malignancy. Full article

Hodgkin’s Lymphoma (HL) affects approximately 8500 individuals annually in the United States. The 5-year relative survival rate has improved to 88.5%, driven by transformative advances in immunotherapy and precision oncology. The integration of Brentuximab vedotin (BV) and immune checkpoint inhibitors (ICIs) has redefined treatment paradigms. The phase III SWOG S1826 trial established nivolumab plus doxorubicin, vinblastine, and dacarbazine (N + AVD) as an emerging new standard for advanced-stage HL, achieving a 2-year progression-free survival (PFS) of 92% compared to 83% for BV plus AVD (HR 0.48, 95% CI: 0.33–0.70), with superior safety, particularly in patients over 60. In relapsed/refractory HL, pembrolizumab outperforms BV, with a median PFS of 13.2 versus 8.3 months (HR 0.65, 95% CI: 0.48–0.88), as demonstrated in the KEYNOTE-204 trial. Emerging strategies, including novel ICI combinations, minimal residual disease (MRD) monitoring via circulating tumor DNA (ctDNA), and artificial intelligence (AI)-driven diagnostics, promise to further personalize therapy. This review synthesizes HL’s epidemiology, pathogenesis, diagnostic innovations, and therapeutic advances, highlighting the role of precision medicine in addressing unmet needs and disparities in HL care. 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

Background/Objectives: The COVID-19 pandemic caused pronounced disorder in healthcare delivery globally, including ophthalmology. Our study explores how ophthalmologists in Puerto Rico (PR) altered their practices during the pandemic, confronting obstacles such as resource shortages, evolving public health mandates, and unique socio-economic and geographic constraints. The study aims to enhance preparedness for future public health crises. Methods: We conducted descriptive analyses on four online surveys distributed at crucial time points of the pandemic (March 2020, May 2020, August 2020, August 2021) to all practicing ophthalmologists in PR (N ≈ 200), capturing data on closures, patient volume, personal protective equipment (PPE) access, telemedicine use, and financial relief. Results: Survey responses ranged from 41% (n = 81) to 56% (n = 111). By March 2020, 22% (24/111) of respondents closed their offices. By May 2020, 20% (19/93) of respondents maintained a closed office, while 89% (64/72) of open offices reported seeing less than 25% of their usual patient volume. Access to PPE was a challenge, with 59% (65/111) reporting difficulty obtaining N95 masks in March 2020. Telemedicine usage increased initially, peaking in May 2020 and declining in July 2020. By August 2021, all respondents were fully vaccinated and most practices returned to pre-pandemic levels. Overall, 86% (70/81) of respondents found the surveys to be useful for navigating practice changes during the pandemic. Conclusions: PR ophthalmologists showed adaptability during the COVID-19 pandemic to maintain care given limited resources. Guidelines from professional organizations and real time surveys play an important role in future crisis preparedness. Full article

(1) Virtual reality (VR) technologies have shown significant potential for diagnosing and treating vision-related impairments. This rapid review evaluates and characterizes the existing literature on VR technologies for diagnosing and treating vision-based diseases. (2) Methods: A systematic search was conducted across Ovid MEDLINE, Ovid Embase, the Cochrane Database of Systematic Reviews (Ovid), and the Cochrane Central Register of Controlled Trials (Ovid). Abstracts were screened using Rayyan QCRI, followed by full-text screening and data extraction. Eligible studies were published in peer-reviewed journals, written in English, focused on human participants, used immersive and portable VR devices as the primary intervention, and reported on the clinical effectiveness of VR for therapeutic, diagnostic, or screening purposes for vision or auditory–visual impairments. Various study characteristics, including design and participant details, were extracted, and the MMAT assessment tool was used to evaluate study quality. (3) Results: Seventy-six studies met the inclusion criteria. Among these, sixty-four (84.2%) were non-randomized studies exploring VR’s effectiveness, while twenty-two (15.8%) were randomized-controlled trials. Of the included studies, 38.2% focused on diagnosing, 21.0% on screening, and 38.2% on treating vision impairments. Glaucoma and amblyopia were the most commonly studied visual impairments. (4) Conclusions: The use of standalone, remotely controlled VR headsets for screening and diagnosing visual diseases represents a promising advancement in ophthalmology. With ongoing technological developments, VR has the potential to revolutionize eye care by improving accessibility, efficiency, and personalization. Continued research and innovation in VR applications for vision care are expected to further enhance patient outcomes. Full article

Recent studies have demonstrated that the development and progression of hypertensive kidney injury comprise not only elevated systemic blood pressure but also a complex interplay of cellular, molecular, and genetic mechanisms. In this report, we outline the key emerging pathways—ranging from dysregulated renin–angiotensin system signaling, oxidative stress, immune-mediated inflammation, and metabolic abnormalities to epigenetic alterations and genetic susceptibilities—that contribute to kidney damage in hypertensive conditions. In addition, we also discuss precision medicine approaches like biomarker-directed therapies, pharmacologically targeted therapies, and device-based innovations for modulating these pathways. This integrative review emphasizes the application of omics technologies and genetically guided interventions to better stratify patients and offer personalized care for hypertensive kidney disease. Full article

Background: Since the outbreak of war in Ukraine, countless forcibly displaced individuals facing not only material loss, but also deep psychological distress, have sought refuge across Europe. For those traumatized by war, the absence of a shared language in therapy can hinder healing and exacerbate suffering. While cultural diversity in psychotherapy has gained recognition, the role of native-language communication—especially from a person-centered perspective—remains underexplored. Methods: This narrative review with a thematic analysis examines whether and how psychotherapy in the mother tongue facilitates access to therapy and enhances therapeutic efficacy. Four inter-related clusters emerged: (1) the psychosocial context of trauma and displacement; (2) language as a structural gatekeeper to care (RQ1); (3) native-language therapy as a mechanism of change (RQ2); (4) potential risks such as over-identification or therapeutic mismatch (RQ2). Results: The findings suggest that native-language therapy can support the symbolic integration of trauma and foster the core conditions for healing. The implications for multilingual therapy formats, training in interpreter-mediated settings, and future research designs—including longitudinal, transnational studies—are discussed. Conclusions: In light of the current crises, language is not just a tool for access to therapy, but a pathway to psychological healing. 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

Background: Drug–drug interactions (DDIs) may occur when two or more drugs are taken together, leading to undesired side effects or potential synergistic effects. Most clinical effects of drug combinations have not been assessed in clinical trials. Therefore, predicting DDIs can provide better patient management, avoid drug combinations that can negatively affect patient care, and exploit potential synergistic combinations to improve current therapies in women’s healthcare. Methods: A DDI prediction model was built to describe relevant drug combinations affecting reproductive treatments. Approved drug features (chemical structure of drugs, side effects, targets, enzymes, carriers and transporters, pathways, protein–protein interactions, and interaction profile fingerprints) were obtained. A unified predictive score revealed unknown DDIs between reproductive and commonly used drugs and their associated clinical effects on reproductive health. The performance of the prediction model was validated using known DDIs. Results: This prediction model accurately predicted known interactions (AUROC = 0.9876) and identified 2991 new DDIs between 192 drugs used in different female reproductive conditions and other drugs used to treat unrelated conditions. These DDIs included 836 between drugs used for in vitro fertilization. Most new DDIs involved estradiol, acetaminophen, bupivacaine, risperidone, and follitropin. Follitropin, bupivacaine, and gonadorelin had the highest discovery rate (42%, 32%, and 25%, respectively). Some were expected to improve current therapies (n = 23), while others would cause harmful effects (n = 11). We also predicted twelve DDIs between oral contraceptives and HIV drugs that could compromise their efficacy. Conclusions: These results show the importance of DDI studies aimed at identifying those that might compromise or improve their efficacy, which could lead to personalizing female reproductive therapies. Full article