Search Results (6,042)

Introduction: Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer entity in Germany, following basal cell carcinoma. Its incidence has increased fourfold over the past three decades. Early diagnosis and treatment are essential for achieving favorable outcomes. Our study aims to identify prognostic factors based on real-world data to improve follow-up protocols and raise clinical vigilance. Methods: We conducted a retrospective, monocenter analysis with a total of 124 patients with at least one cSCC thicker than 3 mm, treated at the Department of Dermatology, University Medical Center Mainz, between 2010 and 2020. Tumor-specific criteria were correlated with patient-specific data, such as gender, age, immunosuppression, UV exposure and mortality. Results: A higher incidence of cSCC was found on UV-exposed skin (91.1%); however, tumors on non-UV-exposed skin were on average thicker (6.55 mm vs. 9.25 mm, p = 0.011) and associated with higher metastasis rates (10.6% vs. 63.3%, p < 0.001). Immunosuppression was strongly associated with a younger age at diagnosis (74 years vs. 81 years), a higher metastasis rate (29% vs. 10.8%, p = 0.021) and a worse 5Y-OS-rate (36.1% vs. 97.8%, p = 0.04). SLNB was performed in eight patients, with one positive SLN identified (12.5%). Local recurrence was observed in 18.1% (n = 21) of patients who did not experience SLNB, whereas no local recurrences (0%) were reported in patients with SLNB (p = 0.349). Discussion: Tumors on non-UV-exposed areas were thicker and more often metastatic, suggesting delayed detection or more aggressive tumor subtypes. Immunosuppression was associated with worse outcomes, underscoring the need for intensified follow-up. SLNB was rarely performed, and larger studies are needed to assess its role. Full article

Metabolic syndrome comprises a constellation of comorbidities, including obesity, hypertension, and disorders in carbohydrate and lipid metabolism, associated with an elevated risk of cardiovascular mortality. Obesity is regarded as the principal cause of metabolic syndrome (both collectively and in relation to its components), frequently linked in previous scientific studies with a deficiency of magnesium, one of the most important cations found in the human body. Objectives: The objective of this study was to assess the prevalence of hypomagnesemia in patients with metabolic syndrome and to determine the most significant risk factor among its components for this nutritional deficiency. Methods: Retrospective medical data from 403 patients admitted to the hospital for conditions unrelated to magnesium levels from 2015 to 2019 were evaluated, encompassing serum magnesemia and specific data about components of metabolic syndrome. Data underwent statistical analysis, including linear and logistic regression, to assess the principal risk variables of hypomagnesemia. Results: Hypomagnesemia was observed in 14.89% of the patients with metabolic syndrome, exhibiting a 2.42-fold greater risk of this deficiency (95%CI: 1.40–3.40). Among the components of metabolic syndrome, hyperglycemia emerged as the most significant determinant affecting both the incidence and severity of hypomagnesemia, elevating the risk by a ratio of 2.72 (95%CI: 1.52–4.87). In the multivariate regression model, hyperglycemia was the sole factor independently influencing magnesium concentration (β = −0.145; p < 0.001). Conclusions: Patients presenting signs of metabolic syndrome are at heightened risk for hypomagnesemia. Hyperglycemia appears to be the most important variable affecting the risk of magnesium insufficiency; however, additional research is needed in this area. Full article

This study aimed at proposing a predictive model for real-time monitoring of epidemic dynamics at the municipal scale in Lombardy region, in northern Italy, leveraging Emergency Medical Services (EMS) dispatch data and Geographic Information Systems (GIS) methodologies. Unlike traditional epidemiological models that rely on official diagnoses and offer limited spatial granularity, our approach uses EMS call data (rapidly collected, geo-referenced, and unbiased by institutional delays) as an early proxy for outbreak detection. The model integrates spatial filtering and machine learning (random forest classifier) to categorize municipalities into five epidemic scenarios: from no diffusion to active spread with increasing trends. Developed in collaboration with the Lombardy EMS agency (AREU), the system is designed for operational applicability, emphasizing simplicity, speed, and interpretability. Despite the complexity of the phenomenon and the use of a five-class output, the model shows promising predictive capacity, particularly for identifying outbreak-free areas. Performance is affected by changing epidemic dynamics, such as those induced by widespread vaccination, yet remains informative for early warning. The framework supports health decision-makers with timely, localized insights, offering a scalable tool for epidemic preparedness and response. Full article

Recently, the incidence of diabetes has increased across all socioeconomic groups, with a notable increase in developing countries. Although advances in medical devices have enhanced healthcare accessibility, these benefits remain largely out of reach for individuals residing in remote areas. Concurrently, a variety of devices have been created to detect glucose biomarkers. Among these, microfluidic paper-based sensors have received substantial attention due to their affordability, disposability, and ease of production. Research on microfluidic paper-based glucose sensors has become particularly prominent owing to their considerable potential and wide applicability, especially in the integration of artificial intelligence and machine learning in glucose sensor processing. This review aims to examine recent advancements and progress in the development of microfluidic paper-based glucose sensors over the past five years, highlighting their advantages, limitations, and prospects. The sensors combined with artificial intelligence and machine learning have potential for future applications. Full article

Background: The emergence and spread of antimicrobial-resistant bacteria (ARB) has become an urgent global concern as a silent pandemic. When taking measures to reduce the impact of antimicrobial resistance (AMR) on the environment, it is important to consider appropriate treatment of wastewater from medical facilities. Methods: In this study, a continuous-flow wastewater treatment system using ozone and ultraviolet light, which has excellent inactivation effects, was implemented in a hospital in an urban area of Japan. Results: The results showed that 99% (2 log₁₀) of Gram-negative rods and more than 99.99% (>99.99%) of ARB comprising ESBL-producing Enterobacterales were reduced by ozone treatment from the first day after treatment, and ultraviolet light-emitting diode (UV-LED) irradiation after ozone treatment; UV-LED irradiation after ozonation further inactivated the bacteria to below the detection limit. Inactivation effects were maintained throughout the treatment period in this study. Metagenomic analysis showed that the removal of these microorganisms at the DNA level tended to be gradual in ozone treatment; however, the treated water after ozone/UV-LED treatment showed a 2 log₁₀ (>99%) removal rate at the end of the treatment. The residual antimicrobials in the effluent were benzylpenicillin, cefpodoxime, ciprofloxacin, levofloxacin, azithromycin, clarithromycin, doxycycline, minocycline, and vancomycin, which were removed by ozone treatment on day 1. In contrast, the removal of ampicillin and cefdinir ranged from 19% to 64% even when combined with UV-LED treatment. Conclusions: Our findings will help to reduce the discharge of ARB and antimicrobials into rivers and maintain the safety of aquatic environments. 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

In the original randomised Dietary Intervention to Stop Coronary Atherosclerosis (DISCO-CT) trial, a 12-month Dietary Approaches to Stop Hypertension (DASH) project led by dietitians improved cardiovascular and metabolic risk factors and reduced platelet chemokine levels in patients with coronary artery disease (CAD). It is unclear whether these benefits are sustained. Objective: To determine whether the metabolic, inflammatory, and clinical benefits achieved during the DISCO-CT trial are sustained six years after the structured intervention ended. Methods: Ninety-seven adults with non-obstructive CAD confirmed in coronary computed tomography angiography were randomly assigned to receive optimal medical therapy (control group, n = 41) or the same therapy combined with intensive DASH counselling (DASH group, n = 43). After 301 ± 22 weeks, 84 individuals (87%) who had given consent underwent reassessment of body composition, meal frequency assessment, and biochemical testing (lipids, hs-CRP, CXCL4, RANTES and homocysteine). Major adverse cardiovascular events (MACE) were assessed. Results: During the intervention, the DASH group lost an average of 3.6 ± 4.2 kg and reduced their total body fat by an average of 4.2 ± 4.8 kg, compared to an average loss of 1.1 ± 2.9 kg and a reduction in total body fat of 0.3 ± 4.1 kg in the control group (both p < 0.01). Six years later, most of the lost body weight and fat tissue had been regained, and there was a sharp increase in visceral fat area in both groups (p < 0.0001). CXCL4 decreased by 4.3 ± 3.0 ng/mL during the intervention and remained lower than baseline values; in contrast, in the control group, it initially increased and then decreased (p < 0.001 between groups). LDL cholesterol and hs-CRP levels returned to baseline in both groups but remained below baseline in the DASH group. There was one case of MACE in the DASH group, compared with four cases (including one fatal myocardial infarction) in the control group (p = 0.575). Overall adherence to the DASH project increased by 26 points during counselling and then decreased by only four points, remaining higher than in the control group. Conclusions: A one-year DASH project supported by a physician and dietitian resulted in long-term suppression of the proatherogenic chemokine CXCL4 and fewer MACE over six years, despite a decline in adherence and loss of most anthropometric and lipid benefits. It appears that sustained systemic reinforcement of behaviours is necessary to maintain the benefits of lifestyle intervention in CAD. Full article

In the medical field, magnesium (Mg) alloys have been widely used due to their excellent antibacterial properties and biodegradability. However, in the marine environment, the antibacterial effect may be greatly attenuated, and consequently, microorganisms in the ocean are likely to adhere to the surface of Mg alloys, resulting in biocorrosion damage, which is really troublesome in the maritime industry and can even be disastrous to the navy. Currently, there is a lack of research on the biocorrosion of Mg alloys that may find important applications in marine engineering. In this paper, the biocorrosion mechanism of the Mg alloy Mg-3Nd-2Gd-Zn-Zr caused by Chlorella vulgaris (C. vulgaris), a typical marine microalga, was studied. The results showed that the biomineralization process in the artificial seawater containing a low concentration of C. vulgaris cells was accelerated compared with that in the abiotic artificial seawater, leading to the deposition of CaCO₃ on the surface to inhibit the localized corrosion of the Mg alloy, whereas a high concentration of C. vulgaris cells produced a high content of organic acids at some sites through photosynthesis to significantly accelerate the surface film rupture at some sites and severe localized corrosion there, but meanwhile, it resulted in the formation of a more protective biomineralized film in the other areas to greatly alleviate the corrosion. The contradictory biocorrosion behaviors on the Mg-3Nd-2Gd-Zn-Zr alloy induced by C. vulgaris were finally explained by a mechanism proposed in the paper. Full article

Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute illnesses. Additionally, the study explored the practical challenges associated with validating AI tools used for homeopathy and sought to generate insights on the potential value and limitations of these tools in the management of acute health complaints. Method: Randomly selected cases at a homeopathy teaching clinic (n = 100) were entered into a commercially available homeopathic remedy finder to investigate the consistency between automated and live recommendations. Client symptoms, medical disclaimers, remedies, and posology were compared. The findings of this study show that the purpose-built homeopathic remedy finder is not a one-to-one replacement for a live practitioner. Result: In the 100 cases compared, the automated online remedy finder provided between 1 and 20 prioritized remedy recommendations for each complaint, leaving the user to make the final remedy decision based on how well their characteristic symptoms were covered by each potential remedy. The live practitioner-recommended remedy was included somewhere among the auto-mated results in 59% of the cases, appeared in the top three results in 37% of the cases, and was a top remedy match in 17% of the cases. There was no guidance for managing remedy responses found in live clinical settings. Conclusion: This study also highlights the challenge and importance of validating AI remedy recommendations against real cases. The automated remedy finder used covered 74 acute complaints. The live cases from the teaching clinic included 22 of the 74 complaints. 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

In the field of deep learning, the design of optimization algorithms and neural network structures is crucial for improving model performance. Recent advances in medical image analysis have revealed that many pathological features exhibit fractal-like characteristics in their spatial distribution and morphological patterns. This observation has opened new possibilities for developing fractal-inspired deep learning approaches. In this study, we propose the following: (1) a novel Region-Module Adam (RMA) optimizer that incorporates fractal-inspired region-weighting to prioritize areas with higher fractal dimensionality, and (2) an ECA-Enhanced Shuffle MobileNet (ESM) architecture designed to capture multi-scale fractal patterns through its enhanced feature extraction modules. Our experiments demonstrate that this fractal-informed approach significantly improves classification accuracy compared to conventional methods. On gastrointestinal image datasets, the RMA algorithm achieved accuracies of 83.60%, 81.60%, and 87.30% with MobileNetV2, ShuffleNetV2, and ESM networks, respectively. For glaucoma fundus images, the corresponding accuracies reached 84.90%, 83.60%, and 92.73%. These results suggest that explicitly considering fractal properties in medical image analysis can lead to more effective diagnostic tools. Full article

Background and Objectives: There is a global rise of public interest in integrative medicine. The principles of integrative medicine combining conventional medicine with evidence-based complementary therapies have been implemented in many medical areas, including plastic surgery, to improve patient’s outcome. The aim of the present study was to systematically analyze the application and use of additional non-pharmacological interventions (NPIs) of patients of a German department of plastic surgery. Materials and Methods: The present real-world data study utilized data from the Network Oncology registry between 2016 and 2021. Patients included in this study were at the age of 18 or above, stayed at the department of plastic surgery and received at least one plastic surgical procedure. Adjusted multivariable logistic regression analyses were performed to detect associations between the acceptance of NPIs and predicting factors such as age, gender, year of admission, or length of hospital stay. Results: In total, 265 patients were enrolled in the study between January 2016 and December 2021 with a median age of 65 years (IQR: 52–80) and a male/female ratio of 0.77. Most of the patients received reconstructive surgery (90.19%), followed by hand surgery (5.68%) and aesthetic surgery (2.64%). In total, 42.5% of the enrolled patients accepted and applied NPIs. Physiotherapy, rhythmical embrocations, and compresses were the most often administered NPIs. Conclusions: This exploratory analysis provides a descriptive overview of the application and acceptance of NPIs in plastic surgery patients within a German integrative care setting. While NPIs appear to be well accepted by a subset of patients, further prospective studies are needed to evaluate their impact on clinical outcomes such as postoperative recovery, pain management, patient-reported quality of life, and overall satisfaction with care. Full article

Background/Objectives. This manuscript presents an overview of advances in oncological radiotherapy as an effective treatment method for cancerous tumors, focusing on mechanisms of action within metabolite–antimetabolite systems. The urgency of this topic is underscored by the fact that cancer remains one of the leading causes of death worldwide: as of 2022, approximately 20 million new cases were diagnosed globally, accounting for about 0.25% of the total population. Given prognostic models predicting a steady increase in cancer incidence to 35 million cases by 2050, there is an urgent need for the latest developments in physics, chemistry, molecular biology, pharmacy, and strict adherence to oncological vigilance. The purpose of this work is to demonstrate the relationship between the nature and mechanisms of past diagnostic and therapeutic oncology approaches, their current improvements, and future prospects. Particular emphasis is placed on isotope technologies in the production of therapeutic nuclides, focusing on the mechanisms of formation of simple and complex theranostic compounds and their classification according to target specificity. Methods. The methodology involved searching, selecting, and analyzing information from PubMed, Scopus, and Web of Science databases, as well as from available official online sources over the past 20 years. The search was structured around the structure–mechanism–effect relationship of active pharmaceutical ingredients (APIs). The manuscript, including graphic materials, was prepared using a narrative synthesis method. Results. The results present a sequential analysis of materials related to isotope technology, particularly nucleus stability and instability. An explanation of theranostic principles enabled a detailed description of the action mechanisms of radiopharmaceuticals on various receptors within the metabolite–antimetabolite system using specific drug models. Attention is also given to radioactive nanotheranostics, exemplified by the mechanisms of action of radioactive nanoparticles such as Tc-99m, AuNPs, wwAgNPs, FeNPs, and others. Conclusions. Radiotheranostics, which combines the diagnostic properties of unstable nuclei with therapeutic effects, serves as an effective adjunctive and/or independent method for treating cancer patients. Despite the emergence of resistance to both chemotherapy and radiotherapy, existing nuclide resources provide protection against subsequent tumor metastasis. However, given the unfavorable cancer incidence prognosis over the next 25 years, the development of “preventive” drugs is recommended. Progress in this area will be facilitated by modern medical knowledge and a deeper understanding of ligand–receptor interactions to trigger apoptosis in rapidly proliferating cells. Full article

Introduction: Medication adherence is essential for treatment and recovery following hematopoietic stem cell transplantation (HSCT). However, limited data exist on the most effective methods to measure adherence and the factors influencing it in HSCT patients. Materials and Methods: A prospective longitudinal study assessed immunosuppressant medication adherence in 150 patients with hematologic malignancies undergoing allogeneic HSCT. Adherence was assessed using pill counts, immunosuppressant medication levels, patient-reported medication logs, and the Medication Adherence Response Scale-5 (MARS-5) at 30, 100, and 180 days post-HSCT. We evaluated adherence rates, agreement between methods, and sociodemographic and clinical predictors. From patient-reported logs, we calculated dose adherence (comparing reported doses to expected doses) and timing adherence (comparing medication intake within ±3 h of the prescribed time). Kappa analysis assessed agreement among methods. Results: Of 190 eligible patients, 150 (78.9%) enrolled. The mean age was 57.5 years (SD = 13.5); 41.3% (n = 62) were female, 85.3% (n = 128) were non-Hispanic White, and 73.3% (n = 110) were married or living with a partner. Medication adherence varied across the three timepoints and by measurement type: 52–64% (pill counts), 18–24% (medication levels), 96–98% (medication log dose adherence), 83–84% (medication log timing adherence), and 97–98% (MARS−5). There was minimal agreement between measures (Kappa range: 0.008–0.12). Conclusions: Despite the feasibility of leveraging objective and patient-reported measures to assess medication adherence in HSCT patients, there was little agreement between these measures. Patient-reported measures showed high adherence, while objective measures like pill counts and medication levels revealed more modest adherence. The complexity of medication regimens likely contributes to this discrepancy. A rigorous approach to understanding medication adherence in the HSCT population may entail both objective and subjective measures of medication adherence. Full article

The endoscopic capsule is a miniaturized device used for medical diagnosis, which is less invasive compared to traditional gastrointestinal endoscopy and can reduce patient discomfort. However, it faces challenges in communication transmission, such as high power consumption, serious signal interference, and low data transmission rate. To address these issues, this paper proposes an optimized modulation scheme that is low-cost, low-power, and robust in harsh environments, aiming to improve its transmission rate. The scheme is analyzed in terms of the in-body channel. The analysis and discussion for the scheme in wireless body area networks (WBANs) are divided into three aspects: bit error rate (BER) performance, energy efficiency (EE), and spectrum efficiency (SE), and complexity. These correspond to the following issues: transmission rate, communication quality, and low power consumption. The results demonstrate that the optimized scheme is more suitable for improving the communication performance of endoscopic capsules. Full article