Search Results (14,507)

Background: Diabetes mellitus (DM) is a significant factor affecting prognosis and functional capacity in patients after cardiovascular events. This study aimed to assess the impact of coexisting diabetes on exercise tolerance and hemodynamic parameters in patients qualified for cardiac rehabilitation. Methods: A total of 452 patients (86 women, 366 men; mean age 63.21 ± 7.16 years) who had experienced cardiovascular incidents, including 226 individuals with coexisting DM (DM group) and 226 age- (±1 year) and sex-matched individuals without DM (non-DM group), were included in the analysis. All participants underwent an exercise test using a bicycle ergometer. Clinical data, comorbidities, medication use, left ventricular ejection fraction, and exercise test parameters were evaluated. Results: Patients with DM displayed a higher number of comorbidities (4.29 ± 1.26 vs. 3.19 ± 1.30; p < 0.001), greater medication use (8.71 ± 2.16 vs. 7.83 ± 2.05; p < 0.001), higher body mass (86.93 ± 13.35 kg vs. 80.92 ± 15.25 kg; p < 0.001), and a lower left ventricular ejection fraction (48.78 ± 8.99% vs. 50.01 ± 8.40%; p = 0.002) compared to those in the non-DM group. Diabetic patients also exhibited lower exercise capacity, expressed as peak power per kilogram of body mass (1.05 ± 0.27 W/kg vs. 1.16 ± 0.31 W/kg; p < 0.001). No significant differences were observed regarding absolute peak power or maximum heart rate. Conclusions: In patients after cardiovascular incidents, the presence of diabetes is associated with reduced relative exercise capacity and lower ejection fraction. Full article

Background and Objectives: Surgical smoke generated by energy devices during surgery contains hazardous substances and poses health risks to staff in the operating room. Exposure to surgical smoke must be reduced to minimize the risk of health hazards. Many studies have evaluated surgical smoke qualitatively, but few have performed quantitative assessment. The aim of this study was to quantify the number of particles generated during various breast surgery procedures. Materials and Methods: In this prospective, randomized study, breast surgeries performed at Tokyo Medical and Dental University Hospital (the present Institute of Science Tokyo Hospital) between December 2022 and August 2023 were randomly assigned to two groups: the electrosurgical device group and the electrosurgical device with smoke evacuator group. The number of particles generated by energy devices during surgery was measured using a particle counter. Results: Surgical smoke was generated in all procedures. The number of measured particles was significantly less in the electrosurgical device with smoke evacuator group than in the electrosurgical device group during all procedures (all p < 0.001). Conclusions: All breast surgery procedures produced a significant amount of surgical smoke, which was effectively reduced by using an electrosurgical device with a smoke evacuator. These findings support the routine use of smoke evacuators in breast surgery to reduce occupational exposure to hazardous particles. Implementation of such devices could improve operating room safety and may inform future guidelines and institutional policies regarding surgical smoke management. Full article

This paper presents a procedural method aimed at protecting maritime critical infrastructure, which is essential for the functioning of developed nations. A novel approach, developed by the authors, is introduced—focusing on the behavioral analysis of vessels to enable early identification of suspicious maritime activity and to prevent damage or destruction to key infrastructure elements. An integrated system is proposed, combining real-time electronic surveillance with continuous access to and analysis of data from both national and international databases. Drawing inspiration from medical sciences, a screening-based methodology has been developed. Data on vessels collected from various sources are processed according to the criteria adopted by the authors, using a multi-criteria decision analysis (MCDA) approach. MCDA is a decision-support method that considers multiple criteria simultaneously. It allows for the comparison and evaluation of different options, even when they are difficult to compare directly. This characteristic is used to select high-risk vessels for further monitoring. An initial classification of a vessel as suspicious does not constitute proof of criminal activity but rather serves as a trigger for further coordinated actions. Data on vessels is collected from the AIS (automatic identification system) and platforms that store vessel history. The AIS is a powerful tool that processes parameters such as a ship’s speed and course. This article presents sample results from surveillance and pre-selection analyses using the AIS, followed by a multi-criteria assessment of the behavior of vessels identified through this process. The results are presented both graphically and numerically. The authors conducted several scenarios, analyzing different groups of vessels. Based on this analysis, recommendations were developed for the interpretation of the findings. Full article

Objective: This study aimed to investigate whether the subjective assessments of strong and weak muscles in the Functional Myodiagnosis muscle test (FMD-MT) can be objectively and reproducibly verified using physically measurable parameters. Additionally, we sought to evaluate the reliability of the manual muscle test in order to reinforce the scientific evidence supporting this accepted, yet not widely adopted, complementary medicine method. Methods: In a crossover observational study, three experienced medical practitioners conducted the FMD-MT of the rectus femoris muscle on 24 healthy participants using a specially designed therapy bench, with all measurements recorded via an oscillogram. The study investigated the force–time integral, joint angle change, additional force load, mean force turning point 1, as well as the interrater reliability and validity of both examiner assessments and instrumental analyses for the two muscle reaction variants: strong and weak. Results: A significant difference between the response pattern of strong and weak muscles was identified for the force–time integral (p = 0.005), the change in joint angle (p < 0.001), and the additional force load (p = 0.001). No difference between strong and weak muscles could be detected regarding the force turning point 1 (p = 0.972). The examiners demonstrated 100% accuracy in identifying weak muscle reactions as weak, and 99.2% accuracy in identifying strong muscle reactions as strong (p = 0.316). The overall intra-class correlation coefficient was 0.984. The oscillogram correctly visualized weak muscle reactions in weak muscles with an accuracy of 81.7%, and strong muscle reactions in strong muscles with an accuracy of 86.7% (p = 0.289). Conclusions: The Functional Myodiagnosis muscle test (FMD-MT) enables a clear and objective differentiation between strong and weak muscles, with statistically significant differences observed in the force–time integral, additional force load, and joint angle changes. Under rigorously standardized testing conditions, the FMD-MT of the rectus femoris muscle demonstrates a validity rate of 99.6% and an excellent reliability (ICC 0.984). Consequently, the FMD muscle test proves to be a reliable, reproducible, and objective diagnostic method. Trial registration: German Register of Clinical Studies U1111-1212-6622. Full article

The advancement of artificial intelligence (AI), deep learning, and radiomics has introduced novel methodologies for the detection, classification, prognosis, and treatment evaluation of pancreatic ductal adenocarcinoma (PDAC). As the integration of AI into medical imaging continues to evolve, its potential to enhance early detection, refine diagnostic precision, and optimize treatment strategies becomes increasingly evident. However, despite significant progress, various challenges remain, particularly in terms of clinical applicability, generalizability, interpretability, and integration into routine practice. Understanding the current state of research is crucial for identifying gaps in the literature and exploring opportunities for future advancements. This literature review aims to provide a comprehensive overview of the existing studies on AI applications in PDAC, with a focus on disease detection, classification, survival prediction, treatment response assessment, and radiogenomics. By analyzing the methodologies, findings, and limitations of these studies, we aim to highlight the strengths of AI-driven approaches while addressing critical gaps that hinder their clinical translation. Furthermore, this review aims to discuss future directions in the field, emphasizing the need for multi-institutional collaborations, explainable AI models, and the integration of multi-modal data to advance the role of AI in personalized medicine for PDAC. Full article

Background: Tranexamic acid is an antifibrinolytic medication often used to prevent hemorrhage. The dosage and route of administration can vary depending on specialty and indication, although one of the most common routes includes intravenous application. Other possible administration modalities include intramuscular and topical applications or irrigation. Although not the most common method, more research is emerging on the topical application of the drug to prevent bleeding. Methods: Specific search terms regarding the topical administration of tranexamic acid were input into PubMed and were reviewed via Covidence. Selected studies were stratified based on specialty (ears, nose, and throat; cardiology; plastic surgery; and orthopedics), and hematologic outcomes regarding tranexamic acid use were reviewed. Results: An evaluation of the studies demonstrated the feasibility of tranexamic acid in the topical form; however, it can depend on the specialty-specific indications. Each field utilizes unique procedures or surgeries, which can play a role in the effectiveness of the medication. Conclusions: While the current literature demonstrates the feasibility of tranexamic acid, further research is needed to understand its viability in other fields, such as obstetrics. Full article

Background/Objective: Chemotherapy-induced peripheral neuropathy (CIPN) is a common dose-limiting adverse effect of various chemotherapeutic agents. Previous work demonstrated that cannabis alleviates symptoms of oxaliplatin-induced CIPN. To evaluate the effects of cannabis components, cannabidiol (CBD) and tetrahydrocannabinol (THC), on CIPN-related symptoms. Methods: We reviewed a patient-reported outcomes dataset from “Tikun Olam,” a major medical cannabis provider. Of 1493 patients, 802 reported at least one CIPN symptom at baseline, including a burning sensation, cold sensation, paresthesia (prickling) and numbness, and 751 of them met the study inclusion criteria. Patients were categorized into THC-high/CBD-low and CBD-high/THC-low groups. Symptom changes after six months of cannabis use were analyzed using K-means clustering and logistic regression, incorporating interactions between baseline symptoms and THC and CBD doses. Linear regression assessed changes in activities of daily living (ADL) and quality of life (QOL). Results: Both groups reported symptom improvement. The THC-high group showed significantly greater improvement in burning sensation and cold sensation (p = 0.024 and p = 0.008). Improvements in ADL and QOL were also significantly higher in the THC group (p = 0.029 and p = 0.006). A significant interaction between THC and CBD was observed for symptom improvement (p < 0.0001). Conclusions: Cannabis effectively reduces CIPN symptoms and improves QOL and ADL. Higher THC doses were more effective than lower doses, with combined CBD and THC doses yielding greater symptom relief. Full article

Teledermatology relies on digital transfer of dermatological images, but compression and resolution differences compromise diagnostic quality. Image enhancement techniques are crucial to compensate for these differences and improve quality for both clinical assessment and AI-based analysis. We developed a customized image degradation pipeline simulating common artifacts in dermatological images, including blur, noise, downsampling, and compression. This synthetic degradation approach enabled effective training of DermaSR-GAN, a super-resolution generative adversarial network tailored for dermoscopic images. The model was trained on 30,000 high-quality ISIC images and evaluated on three independent datasets (ISIC Test, Novara Dermoscopic, PH²) using structural similarity and no-reference quality metrics. DermaSR-GAN achieved statistically significant improvements in quality scores across all datasets, with up to 23% enhancement in perceptual quality metrics (MANIQA). The model preserved diagnostic details while doubling resolution and surpassed existing approaches, including traditional interpolation methods and state-of-the-art deep learning techniques. Integration with downstream classification systems demonstrated up to 14.6% improvement in class-specific accuracy for keratosis-like lesions compared to original images. Synthetic degradation represents a promising approach for training effective super-resolution models in medical imaging, with significant potential for enhancing teledermatology applications and computer-aided diagnosis systems. Full article

Background: Trichosporon spp. are opportunistic fungi, capable of causing infection, especially in critically ill individuals who often use broad-spectrum antibiotics, invasive devices, and have comorbidities. Objectives The aim of this study was to analyze individuals’ clinical characteristics, evaluate tolerance to biocides, as well as biofilm formation and efflux pump activity in isolates of Trichosporon asahii. Methods: Clinical isolates of T. asahii collected between 2020 and 2023 from both hospitalized and non-hospitalized individuals, of both sexes, regardless of age, were tested for tolerance to sodium hypochlorite, hydrogen peroxide, benzalkonium chloride, and ethyl alcohol. Efflux pump activity was also assessed using ethidium bromide, and biofilm formation was measured with the Safranin test. Clinical parameters such as outcomes, source, and length of hospitalization were analyzed through electronic medical records. Results: A total of 37 clinical isolates of T. asahii were identified. Thirty-three (83.8%) isolates were from hospitalized individuals, with 81.82% collected in ICUs, an average hospital stay of 35 days, and a mortality rate of 51.6%. The tested strains displayed the largest mean inhibition zone for 2% sodium hypochlorite, indicating lower tolerance. A high level of efflux pump expression was detected among clinical isolates. Biofilm formation was detected in 25/67.5% of the isolates. Conclusions: These findings highlight the clinical relevance of T. asahii, particularly in critically ill individuals, and underscore the pathogen’s ability to tolerate biocides, express efflux pumps, and form biofilms, all of which may contribute to its persistence and pathogenicity in hospital environments. Enhanced surveillance and effective microbial control measures are essential to mitigate the risks associated with T. asahii infections. Full article

Integrating BIM curriculum into traditional construction management (CM) programs is essential to meet the increasing industry demand for BIM-ready graduates. However, academia struggles with BIM curriculum integration due to unfamiliar emerging BIM technologies, and the increased workload associated with curriculum transformation. Disciplines including nursing, health science, and medical overcame the same challenges using the ability-desire-knowledge-ability-reinforcement (ADKAR) change management model, while CM programs have not explored this model for BIM curriculum development. Thus, this research introduces the ADKAR change management lens to BIM curriculum development by proposing a practically modified and replicable ADKAR model for CM programs. Focus group interviews with 14 academics from the UK, USA, Korea, and Australia, revealed establishing a sense of urgency by appointing a BIM champion is the most critical step before the BIM curriculum development. Instant advice demystifying uncertain BIM concepts is recognised the most effective motivation among academia. Well-balanced BIM concept integrations is ‘sine qua non’ since excessively saturating BIM aspects across the program can dilute students’ essential domain knowledge. Students’ evaluation over the BIM curriculum were collected through a six-year longitudinal focus group interviews, revealing that progressive BIM learnings scaffolded from foundational concepts to advanced applications throughout their coursework is the most valuable. 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

Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform—an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions. Our framework integrates (i) tick localization using a fine-tuned YOLOv7 object detection model, (ii) resolution enhancement of cropped images via super-resolution Generative Adversarial Networks (RealESRGAN and SwinIR), and (iii) image classification using deep convolutional (ResNet-50) and transformer-based (ViT) architectures across three datasets (12, 6, and 3 classes) of decreasing granularities in terms of taxonomic resolution, tick life stage, and specimen viewing angle. ViT consistently outperformed ResNet-50, especially in complex classification settings. The configuration yielding the best performance—relying on object detection without incorporating super-resolution—achieved a macro-averaged F1-score exceeding 86% in the 3-class model (Dermacentor sp., other species, bad images), with minimal critical misclassifications (0.7% of “other species” misclassified as Dermacentor). Given that Dermacentor ticks represent more than 60% of tick volume submitted on the eTick platform, the integration of a low granularity model in the processing workflow could save significant time while maintaining very high standards of identification accuracy. Our findings highlight the potential of combining modern AI methods to facilitate efficient and accurate tick image processing in community science platforms, while emphasizing the need to adapt model complexity and class resolution to task-specific constraints. Full article

The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling continuous patient monitoring, early diagnosis, and personalized treatments. However, the het-erogeneity of IoMT devices and the lack of standardized protocols introduce serious security vulnerabilities. To address these challenges, we propose a hybrid BiLSTM-DNN intrusion detection system, named HBiLD-IDS, that combines Bidirectional Long Short-Term Memory (BiLSTM) networks with Deep Neural Networks (DNNs), leveraging both temporal dependencies in network traffic and hierarchical feature extraction. The model is trained and evaluated on the CICIoMT2024 dataset, which accurately reflects the diversity of devices and attack vectors encountered in connected healthcare environments. The dataset undergoes rigorous preprocessing, including data cleaning, feature selection through correlation analysis and recursive elimination, and feature normalization. Compared to existing IDS models, our approach significantly enhances detection accuracy and generalization capacity in the face of complex and evolving attack patterns. Experimental results show that the proposed IDS model achieves a classification accuracy of 98.81% across 19 attack types confirming its robustness and scalability. This approach represents a promising solution for strengthening the security posture of IoMT networks against emerging cyber threats. Full article

Nanotechnology offers innovative methodologies for enhancing the diagnosis and treatment of ovarian cancer by utilizing specialized nanoparticles. The utilization of nanoparticles offers distinct advantages, specifically that these entities enhance the bioavailability of therapeutic agents and facilitate the targeted delivery of pharmacological agents to neoplastic cells. A diverse array of nanoparticles, including but not limited to liposomes, dendrimers, and gold nanoparticles, function as proficient carriers for drug delivery. Nevertheless, notwithstanding the auspicious potential of these applications, challenges pertaining to toxicity, biocompatibility, and the necessity for comprehensive clinical evaluations pose considerable barriers to the widespread implementation of these technologies. The incorporation of nanotechnology into clinical practice holds the promise of significantly transforming the management of ovarian cancer, offering novel diagnostic tools and therapeutic strategies that enhance patient outcomes and prognoses. In summary, the deployment of nanotechnology in the context of ovarian cancer epitomizes a revolutionary paradigm in medical science, amalgamating sophisticated materials and methodologies to enhance both diagnostic and therapeutic outcomes. Continued research and development endeavors are essential to fully realize the extensive potential of these innovative solutions and address the existing challenges associated with their application in clinical settings. Full article