Search Results (3,649)

Collagen implants in neurosurgery are widely used due to their biocompatibility, biodegradability, and ability to support tissue regeneration, but their mechanical properties, such as low tensile strength and susceptibility to enzymatic degradation, remain challenging. Current technologies are improving these implants through cross-linking, synthetic reinforcements, and advanced manufacturing techniques such as 3D bioprinting to improve durability and predictability. Industry 4.0 is contributing to this by automating production, using data analytics and machine learning to optimize implant properties and ensure quality control. In Industry 5.0, the focus is shifting to personalization, enabling the creation of patient-specific implants through human–machine collaboration and advanced biofabrication. eHealth integrates digital monitoring systems, enabling real-time tracking of implant healing and performance to inform personalized care. Despite progress, challenges such as cost, material property variability, and scalability for mass production remain. The future lies in smart biomaterials, AI-driven design, and precision biofabrication, which could mean the possibility of creating more effective, accessible, and patient-specific collagen implants. The aim of this article is to examine the current state and determine the prospects for the development of mechanical properties of collagen implant used in neurosurgery towards Industry 4.0/5.0, including ML model. Full article

Adverse drug events (ADEs) significantly impact patient safety and health outcomes. Manual ADE detection from clinical narratives is time-consuming, labor-intensive, and costly. Recent advancements in large language models (LLMs), including transformer-based architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pretrained Transformer (GPT) series, offer promising methods for automating ADE extraction from clinical data. These models have been applied to various aspects of pharmacovigilance and clinical decision support, demonstrating potential in extracting ADE-related information from real-world clinical data. Additionally, chatbot-assisted systems have been explored as tools in clinical management, aiding in medication adherence, patient engagement, and symptom monitoring. This narrative review synthesizes the current state of LLMs in ADE detection from a clinical perspective, organizing studies into categories such as human-facing decision support tools, immune-related ADE detection, cancer-related and non-cancer-related ADE surveillance, and personalized decision support systems. In total, 39 articles were included in this review. Across domains, LLM-driven methods have demonstrated promising performances, often outperforming traditional approaches. However, critical limitations persist, such as domain-specific variability in model performance, interpretability challenges, data quality and privacy concerns, and infrastructure requirements. By addressing these challenges, LLM-based ADE detection could enhance pharmacovigilance practices, improve patient safety outcomes, and optimize clinical workflows. Full article

Objectives: Surface-Guided Radiation Therapy (SGRT) has been widely adopted in breast cancer radiotherapy, particularly for improving setup accuracy and motion management. Recently, its application in lung cancer has attracted growing interest due to similar needs for precision. This study investigates the feasibility and clinical utility of SGRT in lung cancer treatment, focusing on its effectiveness in patient setup and real-time motion monitoring under frameless immobilization conditions. Materials and Methods: A total of 204 treatment records from 17 patients with primary lung cancer who underwent radiotherapy at Korea University Guro Hospital between October 2024 and April 2025 were retrospectively analyzed. Patients were initially positioned using the Identify system (Varian) in the CT suite, with surface data transferred to the treatment room system. Alignment was performed to within ±1 cm and ±2° across six degrees of freedom. Cone-beam CT (CBCT) was acquired prior to treatment for verification, and treatment commenced when the Distance to Correspondence Surface (DCS) was ≤0.90. Setup deviations from the Identify system were recorded and compared with CBCT in three translational axes to evaluate positioning accuracy and PTV displacement. Results and Conclusions: The Identify system was shown to provide high setup accuracy and reliable real-time motion monitoring in lung cancer radiotherapy. Its ability to detect patient movement and automatically interrupt beam delivery contributes to enhanced treatment safety and precision. In addition, even though the maximum longitudinal (Lng) shift reached up to −1.83 cm with surface-guided setup, and up to 1.78 cm (Lat) 5.26 cm (Lng), 9.16 cm (Vrt) with CBCT-based verification, the use of Identify’s auto-interruption mode (±1 cm in translational axes, ±2° in rotational axes) allowed treatment delivery with PTV motion constrained within ±0.02 cm. These results suggest that, due to significant motion in the longitudinal direction, appropriate PTV margins should be considered during treatment planning. The Identify system enhances setup accuracy in lung cancer patients using a surface-guided approach and enables real-time tracking of intra-fractional errors. SGRT, when implemented with systems such as Identify, shows promise as a feasible alternative or complement to conventional IGRT in selected lung cancer cases. Further studies with larger patient cohorts and diverse clinical settings are warranted to validate these findings. Full article

Introduction and Objectives: Risk-sharing agreements (RSAs) have emerged as a key strategy for financing high-cost medical technologies while ensuring financial sustainability. These payment mechanisms mitigate clinical and financial uncertainties, optimizing pricing and reimbursement decisions. Despite their widespread adoption globally, Latin America has reported limited implementation, particularly for high-cost medical devices. This study aims to share insights from designing RSAs in the form of Outcome Protection Programs (OPPs) for medical devices in Latin America from the perspective of a medical devices company. Methods: The report follows a structured approach, defining key OPP dimensions: payment base, access criteria, pricing schemes, risk assessment, and performance incentives. Risks were categorized as financial, clinical, and operational. The framework applied principles from prior models, emphasizing negotiation, program design, implementation, and evaluation. A multidisciplinary task force analyzed patient needs, provider motivations, and payer constraints to ensure alignment with health system priorities. Results: Over two semesters, a panel of seven experts from the manufacturer designed n = 105 innovative payment programs implemented in Argentina (n = 7), Brazil (n = 7), Colombia (n = 75), Mexico (n = 9), Panama (n = 4), and Puerto Rico (n = 3). The programs targeted eight high-burden conditions, including Coronary Artery Disease, atrial fibrillation, Heart Failure, and post-implantation arrhythmias, among others. Private providers accounted for 80% of experiences. Challenges include clinical inertia and operational complexities, necessitating structured training and monitoring mechanisms. Conclusions: Outcome Protection Programs offer a viable and practical risk-sharing approach to financing high-cost medical devices in Latin America. Their implementation requires careful stakeholder alignment, clear eligibility criteria and endpoints, and robust monitoring frameworks. These findings contribute to the ongoing dialogue on sustainable healthcare financing, emphasizing the need for tailored approaches in resource-constrained settings. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

Introduction/Objective: Post-bariatric surgery patients require long-term, coordinated care to address complex nutritional, physiological, and behavioral challenges. Personalized smart nutrition, combining individualized dietary strategies with targeted monitoring, has emerged as a valuable direction for optimizing recovery and long-term outcomes. This article examines how traditional medical protocols can be enhanced by digital solutions in a multidisciplinary framework. Methods: The study analyzes current clinical practices, including personalized meal planning, physical rehabilitation, biochemical marker monitoring, and psychological counseling, as applied in post-bariatric care. These established approaches are then analyzed in relation to the NutriMonitCare system, a digital health system developed and tested in a laboratory environment. Used here as an illustrative example, the NutriMonitCare system demonstrates the potential of digital tools to support clinicians through real-time monitoring of dietary intake, activity levels, and physiological parameters. Results: Findings emphasize that medical protocols remain the cornerstone of post-surgical management, while digital tools may provide added value by enhancing data availability, supporting individualized decision making, and reinforcing patient adherence. Systems like the NutriMonitCare system could be integrated into interdisciplinary care models to refine nutrition-focused interventions and improve communication across care teams. However, their clinical utility remains theoretical at this stage and requires further validation. Conclusions: In conclusion, the integration of digital health tools with conventional post-operative care has the potential to advance personalized smart nutrition. Future research should focus on clinical evaluation, real-world testing, and ethical implementation of such technologies into established medical workflows to ensure both efficacy and patient safety. Full article

Eosinophilic Granulomatosis with Polyangiitis (EGPA) is a rare systemic vasculitis with eosinophilic inflammation and variable clinical presentations. Although skin manifestations are frequent, current classification criteria do not include them, which may underestimate their diagnostic value. This prospective observational study aimed to assess systemic and skin involvement as well as eosinophilia, anti-neutrophil cytoplasmic antibody (ANCA), and Anti-nuclear antibodies (ANA) serum levels in 20 EGPA patients followed for one year at the University Hospital of Messina, Italy, before starting Mepolizumab, 300 mg. Eosinophilia, ANCA status, systemic and skin involvement were also evaluated at 6 and 12 months; a literature review on these data supplements our findings. Skin involvement was present in 55% of patients, including purpura, urticarial vasculitis, angioedema, maculopapular rash, and nodules, mostly in ANCA-negative patients, though purpura was more frequent in ANCA-positive cases but without any statistically significant correlation. ANAs were present in 50% of patients, together with ANCA in two subjects and without in eight. Mepolizumab significantly reduced eosinophil levels, BVASs, and corticosteroid dependence, with notable improvement in skin symptoms. In conclusion, skin manifestations are common in EGPA and may represent useful indicators of disease activity. Their integration with ANCA status, eosinophil counts, and positivity to other autoantibodies could enhance diagnostic and monitoring strategies identifying different clusters of EGPA patients even if the small sample size limits the generalizability of the findings. Full article

Background/Objectives: Pulmonary vein isolation (PVI) via cryoballoon ablation (CBA) is a recommended therapeutic strategy for patients with symptomatic paroxysmal and persistent atrial fibrillation (AF) who are refractory to antiarrhythmic drugs. Although PVI has demonstrated efficacy in reducing AF recurrence and improving patients’ quality of life, its impact on respiratory function is not well understood, particularly in patients with comorbid conditions. The aim of the study was to search for functional predictors of the respiratory system in the process of evaluating the efficiency of clinical assessment of CBA in patients with AF. Methods: We conducted a prospective study on 42 patients with symptomatic AF who underwent CBA, assessing their respiratory function through spirometry before and 30 days after the procedure. Exclusion criteria included pre-existing lung disease and cardiac insufficiency. The impact of variables such as body mass index (BMI), coronary artery disease (CAD) and heart failure (HF) on spirometry parameters was analyzed using statistical tests. Results: No significant changes were observed in overall post-PVI spirometry parameters for the full cohort. However, post hoc analyses revealed a significant decline in ΔMEF₇₅ in patients with CAD and BMI ≥ 30 kg/m², whereas ΔFEV₁/FVCex was significantly increased in patients with HF, as well as in patients with ejection fraction (EF) < 50%. Conclusions: CBA for AF does not universally affect respiratory function in the short term, but specific subgroups, including patients with CAD and a higher BMI, may require post-procedure respiratory monitoring. In addition, PVI may improve lung function in patients with HF and reduced EF. Full article

Rheumatoid arthritis (RA) is a chronic systemic inflammatory disorder primarily targeting joints, leading to pain, swelling, and stiffness. RA results from the body’s own immune system attacking its own tissues. Currently, there are various treatments available for RA including disease-modifying antirheumatic drugs (DMARDs) and NSAIDs. Leflunomide (LEF) is a USFDA-approved synthetic DMARD which is being widely prescribed for the management of RA; however, it faces several challenges such as prolonged drug elimination, hepatotoxicity, and others. LEF exerts its therapeutic effects by inhibiting dihydroorotate dehydrogenase (DHODH), thereby suppressing pyrimidine synthesis and modulating immune responses. Emerging nanotechnology-based therapies help in encountering the current challenges faced in LEF delivery to RA patients. This review enlists the LEF’s pharmacokinetics, mechanism of action, and clinical efficacy in RA management. A comparative analysis with methotrexate, biologics, and other targeted therapies, highlighting its role in monotherapy and combination regimens and the safety concerns, including hepatotoxicity, gastrointestinal effects, and teratogenicity, is discussed alongside recommended monitoring strategies. Additionally, emerging trends in novel formulations and drug delivery approaches are explored to enhance efficacy and minimize adverse effects. Overall, LEF remains a perfect remedy for RA patients, specifically individuals contraindicated with drugs like methotrexate. The therapeutic applicability of LEF could be enhanced by developing more customized treatments and advanced drug delivery approaches. Full article

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

Background/Objectives: Compared to in the general pregnant population, pregnancy in women with type 1 diabetes (T1D) is still associated with an increased number of perinatal complications affecting both the fetus and the mother. The Great Orchestra of Christmas Charity Foundation (GOCCF) program enables the use of continuous subcutaneous insulin infusion (CSII) enhanced by a hypo-stop function and real-time continuous glucose monitoring (rtCGM) during the preconception or early pregnancy period in patients with T1D. This observational study aimed to analyze the association between pregnancy planning and pregnancy outcomes in patients who qualified for the GOCCF program. Methods: Ninety-eight women with T1D, aged 21–41 years, who began using the CSII + rtCGM system at the planning/early pregnancy stage or at a later stage in the case of an unplanned pregnancy, were eligible for this study. We analyzed glucose control, the insulin requirements, the pregestational BMI, the maternal weight gain, the occurrence of preterm births, congenital malformations and the birthweight of newborns. Results: Women who planned their pregnancies had significantly better glycemic control before and throughout the entire pregnancy, and a significantly higher proportion of them achieved a TIR (time in range) > 70% (58.7% vs. 28.9%, p = 0.014) and TAR (time above range) < 25% (65.2% vs. 24.4%, p < 0.001). Their glucose variability at the end of the pregnancy was significantly lower (29.4 ± 5.5 vs. 31.9 ± 5.1, p = 0.030). They also gave birth later, at a mean of 37.8 ± 0.9 weeks compared to 36.9 ± 1.8 weeks in the non-planned group (p = 0.039). Preterm birth occurred in five women (10.4%) who planned their pregnancies and in fifteen women (30%) who did not, with p = 0.031. Conclusions: Pregnancy planning in women with type 1 diabetes (T1D) is associated with better glucose control before conception and throughout the entire pregnancy, resulting in better pregnancy outcomes. Full article

Background/Objectives: Neuromodulation is under investigation as a possibly effective add-on therapy in Alzheimer’s disease (AD). While transcranial pulse stimulation (TPS) has shown positive short-term effects, long-term effects have not yet been fully explored. This study aims to evaluate the long-term feasibility, safety, and potential cognitive benefits of TPS over one year in patients with Alzheimer’s disease, focusing on domains such as memory, speech, orientation, visuo-construction, and depressive symptoms. Methods: We analyzed preliminary data from the first ten out of thirty-five patients enrolled in a prospective TPS study who completed one year of follow-up and were included in a dedicated long-term database. The protocol consisted of six initial TPS sessions over two weeks, followed by monthly booster sessions delivering 6000 pulses each for twelve months. Patients underwent regular neuropsychological assessments using the Alzheimer Disease Assessment Scale (ADAS), Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Beck Depression Inventory (BDI-II). All adverse events (AEs) were documented and monitored throughout the study. Results: Adverse events occurred in less than 1% of stimulation sessions and mainly included mild focal pain or transient unpleasant sensations, as well as some systemic behavioral or vigilance changes, particularly in patients with underlying medical conditions, with some potentially related to the device’s stimulation as adverse device reactions (ADRs). Cognitive test results showed significant improvement after the initial stimulation cycle (ADAS total improved significantly after the first stimulation cycle (M_pre = 28.44, M_post = 18.56; p = 0.001, d = 0.80, 95% CI (0.36, 1.25)), with stable scores across all domains over one year. Improvements were most notable in memory, speech, and mood. Conclusions: TPS appears to be a generally safe and feasible add-on treatment for AD, although careful patient selection and monitoring are advised. While a considerable number of participants were lost to follow-up for various reasons, adverse events and lack of treatment effect were unlikely primary causes. A multifocal stimulation approach (F-TOP2) is proposed to enhance effects across more cognitive domains. Full article

Background: Malnutrition and unintended weight loss are frequent in cancer patients and linked to poorer outcomes. However, data on long-term weight trajectories, particularly comparing different cancer entities, remain limited. Methods: In this retrospective, multicenter study, we analyzed 145 patients diagnosed with either head and neck cancer (HNC; n = 48) or high-grade B-cell lymphoma (HGBCL; n = 97). Body weight, C-reactive protein (CrP), albumin, and modified Glasgow Prognostic Score (mGPS) were assessed at diagnosis and at 3, 6, 9, and 12 months. Clinically relevant weight loss was defined as >5% from baseline. Survival analyses were performed for HGBCL patients. Results: Weight loss was common in both cohorts, affecting 32.2% at 3 months and persisting in 42.3% at 12 months. Nearly half of HNC patients had sustained >5% weight loss at one year, whereas HGBCL patients were more likely to regain weight, with significantly higher rates of weight gain at 6 and 12 months (p = 0.04 and p = 0.02). At baseline, HGBCL patients showed elevated CrP and lower albumin compared to HNC (both p < 0.001). Weight loss at 6 months was significantly associated with reduced overall survival in HGBCL (p < 0.01). Both Δweight at 6 months and mGPS emerged as useful prognostic indicators. Conclusions: This study reveals distinct patterns of weight change and systemic inflammation between HNC and HGBCL patients during the first year after diagnosis. These findings highlight the need for entity-specific nutritional monitoring and tailored supportive care strategies extending into survivorship. Prospective studies integrating body composition analyses are warranted to better guide long-term management. Full article

This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R²_CV = 0.980; FSS: R²_CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article