Search Results (1,136)

Introduction: Risk factors, a 75-year-old concept, are instrumental in the management of the general population. Newer biomarkers can explain residual risk and protection from risk. The population needs a new platform to make more comprehensible the importance of managing risk. Biologic age, the number of years left to live, is the platform that will receive the attention of patients. Method: Risk factor odds ratios are used to approximate the years lost to the modifiable risk, calculating a biologic age. Newer biomarkers confirm the predication and can be used to explain the pleomorphic properties of medications and unrealized risk. The biomarkers represent the following biologic processes: repair, inflammation, immune function, hematologic, clotting factors, metabolic-nutritional, organ maintenance, anthropomorphic, environmental, endothelial function, sleep, co-morbidities, frailty, and electromagnetic. Risk factors and biomarkers are ranked in the order of significance in reducing biologic age. Results: A six-step method of patient management using biologic age and biomarkers is presented. Conclusions: Knowledge of risk factors and therapies to improve risk has increased over the last 75 years. Biologic age is more appropriate in explaining the significance of this knowledge and may improve patient compliance to lifestyle changes and medication compliance. Appropriate counseling with utilization of biomarkers of biologic processes, such as high sensitivity-CRP, circulating stem cells, number of co-morbidities, frailty, electrocardiogram, and pulse wave velocity will improve compliance and personalize care. The 6-minute walk should be incorporated into the vital signs due to prognostic significance. Full article

Background/Objectives: Preoperative muscle atrophy leads to persistent gait abnormalities in patients undergoing total hip arthroplasty (THA). Efficient motor learning of the gluteus medius is crucial for their recovery. In this study, a single-joint hybrid assistive limb (HAL) was developed to assist hip abduction. We aimed to evaluate the muscle activity and safety of this device during hip abduction in healthy adults. Methods: Ten healthy adults (five males and five females; mean age, 40.7 years) with no hip disorders performed one set of 30 repetitions of side-lying hip abduction under three conditions: without HAL (pre-HAL), with HAL, and without HAL (post-HAL). Muscle activities of the gluteus medius, gluteus maximus, tensor fasciae latae, rectus femoris, and biceps femoris (expressed as percentage of maximum voluntary contraction [%MVC]); vital signs; hip visual analog scale (VAS); and hip abduction and flexion angles were assessed. The mean values were compared among the conditions. Results: The %MVC of the gluteus medius significantly increased from 52% (pre-HAL) to 75.4% (HAL) and then decreased slightly to 61.6% (post-HAL). No other muscle groups showed significant changes. Vital signs and hip VAS scores showed no significant variation. Although no significant differences were found in the hip abduction and flexion angles, a reduction in the hip flexion angle was observed in the HAL and post-HAL conditions. Conclusions: The hip abduction HAL effectively and safely enhanced gluteus medius activity. Reduction in the hip flexion angle during HAL and post-HAL suggests the possibility of appropriate abduction movements and motor learning effects. Full article

Objective: This study aimed to evaluate the effects of an 8-week physiotherapy program on muscle strength, functional capacity, respiratory function, and quality of life in women recovering from COVID-19. Methods: A prospective cohort study was conducted with 42 women aged 18–65 who experienced muscle strength loss and functional impairments post-COVID-19. Participants underwent personalized physiotherapy interventions, including resistance training, respiratory therapy, and functional mobility exercises, for 8 weeks. Data were collected at baseline and post-intervention, including handgrip strength, 6-Minute Walk Test (6MWT), forced vital capacity (FVC), Fatigue Severity Scale (FSS), and SF-36 scores. Statistical analyses were performed using paired t-tests and Wilcoxon signed-rank tests. Results: Significant improvements were observed in muscle strength, with right-handgrip strength increasing from 18.5 ± 4.2 kg to 22.8 ± 4.6 kg (p < 0.001) and left-handgrip strength from 17.2 ± 4.1 kg to 21.1 ± 4.5 kg (p < 0.001). Functional capacity improved, as evidenced by a 6MWT distance increase from 382 ± 62 m to 438 ± 57 m (p < 0.001). Respiratory function parameters, including FVC and FEV1, also showed significant gains (p < 0.01). Quality of life scores improved significantly, particularly in physical functioning and vitality domains, while fatigue levels decreased markedly (p < 0.001). Conclusions: The results demonstrate the effectiveness of physiotherapy in addressing the physical and functional consequences of COVID-19 in women. These findings emphasize the importance of incorporating physiotherapy into post-COVID-19 rehabilitation protocols to enhance recovery and quality of life. Full article

Background Predictive machine learning models have made use of a variety of scoring systems to identify clinical deterioration in ICU patients. However, most of these scores include variables that are dependent on medical staff examining the patient. We present the development of a real-time prediction model using clinical variables that are digital and automatically generated for the early detection of patients at risk of deterioration. Methods Routine monitoring data were used in this analysis. ICU patients with at least 24 h of vital sign recordings were included. Deterioration was defined as qSOFA ≥ 2. Model development and validation were performed internally by splitting the cohort into training and test datasets and validating the results on the test dataset. Five different models were trained, tested, and compared against each other. The models were an artificial neural network (ANN), a random forest (RF), a support vector machine (SVM), a linear discriminant analysis (LDA), and a logistic regression (LR). Results In total, 7156 ICU patients were screened for inclusion in the study, which resulted in models trained from a total of 28,348 longitudinal measurements. The artificial neural network showed a superior predictive performance for deterioration, with an area under the curve of 0.81 over 0.78 (RF), 0.78 (SVM), 0.77 (LDA), and 0.76 (LR), by using only four vital parameters. The sensitivity was higher than the specificity for the artificial neural network. Conclusions The artificial neural network, only using four automatically recorded vital signs, was best able to predict deterioration, 10 h before documentation in clinical records. This real-time prediction model has the potential to flag at-risk patients to the healthcare providers treating them, for closer monitoring and further investigation. Full article

Invisible health monitoring is currently a topic of global interest within the scientific community. Sensorization of everyday objects can provide valuable health information without requiring any changes in people’s routines. In this work, a feasibility study of photoplethysmography (PPG) acquisition in the lower limbs for continuous and real-time monitoring of the vital signs, including heart rate (HR) and respiratory rate (RR), is presented. The proposed system uses two MAX30102 sensors to obtain PPG signals from the back of the thigh. As proof of concept, tests were conducted in 17 volunteers (age group between 22 and 40 years old, twelve females and five males), and the results were compared to those of reference sensors. A Pearson correlation coefficient of r = 0.92 and r = 0.77 and a mean difference of 1.2 bpm and 0.9 rpm for HR and RR, respectively, were obtained between the developed system and reference. System accuracies of 95.9% for HR and 91.3% for RR were achieved, showing the system viability for vital sign monitoring of the lower limbs. Full article

Continuous monitoring on the general ward leads to more and earlier interventions to prevent clinical deterioration. These clinical actions influence outcomes and may serve as an indicator of impending deterioration. This study aims to correlate clinical actions with clinical endpoints and deviating vital signs. Methods: This cohort study prospectively charted all patients undergoing continuous vital sign monitoring on a gastro-intestinal and oncological surgery, and an internal ward of an academic hospital in The Netherlands from 1 August 2018 till 31 July 2019 (METC 2018-4330, NCT04189653). Clinical actions recorded in electronic medical records were analyzed to assess correlations with patient outcomes, hospital length of stay, and alarming monitoring minutes. Results: A total of 1529 patients were included, of which 68 patients had a negative clinical endpoint. There were 2749 clinical actions recorded. Clinical actions correlated to negative clinical endpoints (ρ = 0.259; p < 0.001, OR: 3.4 to 79.5) and to the length of stay (ρ = 0.560; p < 0.001). Vital sign deviations correlated with clinical actions (ρ = 0.025–0.056; p < 0.001–p = 0.018). In the last 72 h before a clinical endpoint, for alarming minutes, this correlation with clinical actions was more pronounced (ρ = 0.340, p < 0.001). Conclusions: Predefined clinical actions performed on admitted general ward patients correlated with negative endpoints, an increased length of stay, and with deviating vital signs, especially in the period directly preceding severe deterioration. Clinical actions have potential as an intermediate measurement of deterioration. Full article

People with chronic illnesses rely on continuous monitoring systems to monitor their vital signs. Despite the advantages of these systems, patients experience discomfort from bulky wearable devices and the inconveniences associated with fully implantable continuous monitoring systems. A potential solution to these limitations is a wearable E-textile antenna. In this study, we conduct market research and introduce an antenna design made of conductive fabric on cotton substrate. Market research is performed to evaluate multiple conductive fabrics to select the best material for this application. The antenna design operates in the 2.4 GHz and 5.8 GHz ISM frequency band and is evaluated by simulation and in vitro testing with tissue-mimicking gels at various bend angles ranging between 0 and 45 degrees. Full article

Sepsis remains a major global health concern, causing high mortality rates, prolonged hospital stays, and substantial economic burdens. The accurate prediction of clinical outcomes, such as mortality and length of stay (LOS), is critical for optimizing hospital resource allocation and improving patient management. The present study investigates the potential of machine learning (ML) models to predict these outcomes using a dataset of 1492 sepsis patients with clinical, physiological, and demographic features. After rigorous preprocessing to address missing data and ensure consistency, multiple classifiers, including Random Forest, Extra Trees, and Gradient Boosting, were trained and validated. The results demonstrate that Random Forest and Extra Trees achieve high accuracy for LOS prediction, while Gradient Boosting and Bernoulli Naïve Bayes effectively predict mortality. Feature importance analysis identified ICU stay duration (ICU_DAYS_OBS) as the most influential predictor for both outcomes, alongside vital signs, white blood cell counts, and lactic acid levels. These findings highlight the potential of ML-driven clinical decision support systems (CDSSs) to enhance early risk assessment, optimize ICU resource planning, and support timely interventions. Future research should refine predictive features, integrate advanced biomarkers, and validate models across larger and more diverse datasets to improve scalability and clinical impact. Full article

Background and Aims: Telerehabilitation is essential for the recovery of post-COVID-19 patients, improving exercise tolerance, dyspnea, functional capacity, and daily activity performance. This study aimed to describe telerehabilitation protocols specifically designed for individuals with post-COVID-19 sequelae. Materials and Methods: A systematic review was conducted with registration number CRD42023423678, based on searches developed in the following databases: ScienceDirect, Scopus, Dimensions.ai and PubMed, using keywords such as “telerehabilitation” and “COVID-19”. The final search date was July 2024. The selection of studies involved an initial calibration process, followed by independent filtering by the researchers. The selection criteria were applied prior to critical appraisal, data extraction, and the risk of bias assessment. Results: After reviewing 405 full-text papers, 14 articles were included that focused on telerehabilitation interventions for post-COVID-19 patients. These interventions were designed for remote delivery and included exercise protocols, vital sign monitoring, and virtual supervision by physical therapists. The studies reported improvements in physical function, muscle performance, lung capacity, and psychological outcomes. Significant gains were observed in strength, mobility, and functional capacity, as well as reductions in dyspnea, fatigue, and improvements in quality of life, particularly in social domains. Intervention protocols included aerobic, strength, and respiratory exercises, monitored using tools such as heart rate monitors and smartphones. Conclusions: Telerehabilitation positively impacts lung volumes, pulmonary capacities, dyspnea reduction, functionality, muscle performance, and independence in post-COVID-19 patients. Full article

Background: A broad-spectrum anti-SARS-CoV-2 monoclonal antibody (mAb), SA55, is highly effective against SARS-CoV-2 variants. This trial aimed at demonstrating the safety, tolerability, local drug retention and neutralizing activity, systemic exposure level, and immunogenicity of the SA55 nasal spray in healthy individuals. Methods: This phase I, dose-escalation clinical trial combined an open-label design with a randomized, controlled, double-blind design. Healthy participants aged 18–65 years were enrolled and received a single dose of the SA55 nasal spray (1 mg or 2 mg) or multiple doses of SA55 nasal spray/placebo for 7 days (1 or 2 mg/dose, 3 or 6 doses/day). Safety monitoring was conducted throughout the study. Nasal swabs and venous blood samples were collected to analyze local drug concentration/neutralization, systemic exposure, and immunogenicity. Results: From 2 June to 11 August 2023, 80 participants were enrolled and received study intervention. The severity of adverse reactions (ADRs) reported during the study was mild in all cases, and all ADRs were laboratory test abnormalities without corresponding symptoms or vital signs. A total of 9 ADRs were reported, of which all were mild in severity. Overall ADR incidence rate was 16.67% (8/48) in single-dose groups and 4.17% (1/24) in multiple-dose groups. The nasal local drug concentration and neutralizing activity were generally stable within 4–8 h, with favorable neutralization activity against Omicron BF.7 and XBB strains. Conclusions: This study demonstrated favorable safety and tolerability of the SA55 nasal spray in healthy volunteers, exhibited satisfactory neutralizing activity against Omicron variants intranasally, and indicated low systemic toxicity risk. Full article

Introduction: Traditional medical education often emphasises theory, but clinical competence relies greatly on practical, hands-on skills. This quasi-experimental study examines how a combined approach—flipped classroom learning and simulation-based internships—affects medical students’ self-assessed clinical abilities. This model seeks to build students’ understanding, practical skills, and confidence in clinical settings through online preparation and realistic simulation exercises. Methods: This study examined changes in medical students’ self-perceived clinical skills after a flipped classroom and simulation-based internship. A total of 391 third- and fourth-year students completed a nine-hour program with morning practice sessions and afternoon high-fidelity scenarios. Surveys before and after the program assessed self-perceived thoracic and abdominal/general skills. ANCOVA controlled for baseline scores, and paired Wilcoxon signed-rank tests examined overall and subgroup improvements. Results: After the program, significant improvements were observed in self-perceived clinical skills across all domains. Thoracic skills increased from a median of 2.19 to 7.36, and abdominal skills from 5.11 to 9.46. Medical history, vital signs, and blood pressure scores also improved significantly. Third-year students and those attending the Clinical Methodology course showed the greatest gains. All post-intervention improvements were statistically significant (p < 0.001). Conclusions: The combination of flipped classroom learning and intensive simulation training markedly improved students’ perceived clinical competence. These findings suggest that such methods boost students’ practical skills and confidence. Further research is recommended to explore the long-term impact of this approach on skill retention and professional practice. Full article

Skull base tumors such as meningiomas and schwannomas are often pathologically benign. However, surgery for these tumors poses significant challenges because of their proximity to critical structures such as the brainstem, cerebral arteries, veins, and cranial nerves. These structures are compressed or encased by the tumor as they grow, increasing the risk of unintended injury to these structures, which can potentially lead to severe neurological deficits. Preoperative imaging is crucial for assessing the tumor size, location, and its relationship with adjacent vital structures. This study reviews advanced imaging techniques that allow detailed visualization of vascular structures and cranial nerves. Contrast-enhanced computed tomography and digital subtraction angiography are optimal for evaluating vascular structures, whereas magnetic resonance imaging (MRI) with high-resolution T2-weighted images and diffusion tensor imaging are optimal for evaluating cranial nerves. These methods help surgeons plan tumor resection strategies, including surgical approaches, more precisely. An accurate preoperative assessment can contribute to safe tumor resection and preserve neurological function. Additionally, we report the MRI contrast defect sign in skull base meningiomas, which suggests cranial nerve penetration through the tumor. This is an essential finding for inferring the course of cranial nerves completely encased within the tumor. These preoperative imaging techniques have the potential to improve the outcomes of patients with skull base tumors. Furthermore, this study highlights the importance of multimodal imaging approaches and discusses future directions for imaging technology that could further develop preoperative surgical simulations and improve the quality of complex skull base tumor surgeries. Full article

Background/Objectives: Myocardial revascularization surgery (MR) is routinely performed in hospitals. However, there is a lack of an algorithm in the scientific literature aimed at predicting intraoperative parameters, such as total surgery time (TST) and cardiopulmonary bypass time (CBT), based on preoperative MR parameters. Therefore, the objective of the present study is to apply a complex network model to predict parameters associated with TST and CBT. Methods: Retrospective data from 124 patients who underwent MR, including medical history, vital signs, and laboratory/biochemical tests, were used, with 30 patients contributing to the construction of the network. Three complex networks were created to study the targets (TST and CBT). The Eigenvector metric was employed to investigate the parameters most relevant to these targets. Results: Regardless of the target, parameters derived from the blood gas analysis followed by erythrogram displayed greater relevance according to the eigenvector metric. However, for TST, the most prominent parameter was Red Blood Cells, while, for CBT, Diastolic Blood Pressure emerged as the most important variable. Conclusion: The targeted complex network model revealed that pulmonary, hemodynamic, and perfusion factors are relevant to the intraoperative parameters of MR. The networks also demonstrated that, although the targets show significant correlation with each other (TST and CBT-r = 0.76; p = 0.000), the importance of the parameters in the networks does not follow the same order. This reiterates the strength of the network in revealing specific information when a particular target is selected. Full article

The coal industry is a high risk, high difficulty industry, and the annual global mine accident rate is high, so the safety of coal mine underground operations has been a concern. With the development of technology, the application of intelligent security technology in coal mine safety has broad prospects. In this paper, the research progress of vital signs monitoring and support equipment for underground personnel in coal mines is reviewed. The two main methods to ensure the safety of miners are discussed. They consist of directly monitoring human vital signs through portable devices such as smart helmets and smartwatches and indirectly monitoring underground environmental parameters. In addition, the application of information technology, sensor technology and artificial intelligence in vital signs monitoring is briefly discussed, and some future research directions are proposed. For example, through big data and artificial intelligence technology, vital signs data can be compared with historical data, individual health trends and potential risks can be analyzed, and we can provide personalized health management programs for miners. These technologies not only improve the safety of underground coal mine operation, but also provide an important guarantee for the realization of intelligent and safe coal mine production. Full article

Background/Objectives: For effective exercise prescription for patients with cardiovascular disease, it is important to determine the target heart rate at the level of the anaerobic threshold (AT-HR). The AT-HR is mainly determined by cardiopulmonary exercise testing (CPET). The aim of this study is to develop a machine learning (ML) model to predict the AT-HR solely from non-exercise clinical features. Methods: From consecutive 21,482 cases of CPET between 2 February 2008 and 1 December 2021, an appropriate subset was selected to train our ML model. Data consisted of 78 features, including age, sex, anthropometry, clinical diagnosis, cardiovascular risk factors, vital signs, blood tests, and echocardiography. We predicted the AT-HR using a ML method called gradient boosting, along with a rank of each feature in terms of its contribution to AT-HR prediction. The accuracy was evaluated by comparing the predicted AT-HR with the target HRs from guideline-recommended equations in terms of the mean absolute error (MAE). Results: A total of 8228 participants included healthy individuals and patients with cardiovascular disease and were 62 ± 15 years in mean age (69% male). The MAE of the AT-HR by the ML-based model was 7.7 ± 0.2 bpm, which was significantly smaller than those of the guideline-recommended equations; the results using Karvonen formulas with the coefficients 0.7 and 0.4 were 34.5 ± 0.3 bpm and 11.9 ± 0.2 bpm, respectively, and the results using simpler formulas, rest HR + 10 and +20 bpm, were 15.9 ± 0.3 and 9.7 ± 0.2 bpm, respectively. The feature ranking method revealed that the features that make a significant contribution to AT-HR prediction include the resting heart rate, age, N-terminal pro-brain natriuretic peptide (NT-proBNP), resting systolic blood pressure, highly sensitive C-reactive protein (hsCRP), cardiovascular disease diagnosis, and β-blockers, in that order. Prediction accuracy with the top 10 to 20 features was comparable to that with all features. Conclusions: An accurate prediction model of the AT-HR from non-exercise clinical features was proposed. We expect that it will facilitate performing cardiac rehabilitation. The feature selection technique newly unveiled some major determinants of AT-HR, such as NT-proBNP and hsCRP. Full article