Search Results (72)

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

Objective: Tumor progression is regulated by systemic immune status, nutritional metabolism, and the inflammatory microenvironment. This study aims to investigate inflammatory–nutritional biomarkers associated with metachronous liver metastasis (MLM) in colorectal cancer (CRC) and develop a machine learning model for accurate prediction. Methods: This study enrolled 680 patients with CRC who underwent curative resection, randomly allocated into a training set (n = 477) and a validation set (n = 203) in a 7:3 ratio. Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. Seven machine learning (ML) models were constructed using the training set, with the optimal predictive model selected based on comprehensive evaluation metrics. An interactive visualization tool was developed to interpret the dynamic impact of key features on individual predictions. The partial dependence plots (PDPs) revealed a potential dose–response relationship between inflammatory–nutritional markers and MLM risk. Results: Among 680 patients with CRC, the cumulative incidence of MLM at 6 months postoperatively was 39.1%. Multimodal feature selection identified nine key predictors, including the N stage, vascular invasion, carcinoembryonic antigen (CEA), systemic immune–inflammation index (SII), albumin–bilirubin index (ALBI), differentiation grade, prognostic nutritional index (PNI), fatty liver, and T stage. The gradient boosting machine (GBM) demonstrated the best overall performance (AUROC: 0.916, sensitivity: 0.772, specificity: 0.871). The generalized additive model (GAM)-fitted SHAP analysis established, for the first time, risk thresholds for four continuous variables (CEA > 8.14 μg/L, PNI < 44.46, SII > 856.36, ALBI > −2.67), confirming their significant association with MLM development. Conclusions: This study developed a GBM model incorporating inflammatory-nutritional biomarkers and clinical features to accurately predict MLM in colorectal cancer. Integrated with dynamic visualization tools, the model enables real-time risk stratification via a freely accessible web calculator, guiding individualized surveillance planning and optimizing clinical decision-making for precision postoperative care. Full article

Background. Several psychological processes can influence the adjustment of cardiac patients. Psychological flexibility has been linked to significant improvements in psychological well-being during cardiac rehabilitation (CR). It can be understood as the dynamic interaction of three key processes: openness to experience (OE), behavioral awareness (BA), and value-driven action (VA). This study aimed to (1) evaluate the distinct role of these processes in predicting anxiety, depression, and psychological well-being in cardiac patients, and (2) assess these associations over the course of CR. Methods. A total of 194 CR patients participated in this longitudinal study, with 156 completing follow-up assessments at T2. Anxiety and depression were measured using the Patient Health Questionnaire-4, psychological well-being with the Psychological Well-being Index-Short, and psychological flexibility using the Comprehensive Assessment of ACT Processes. Results. Cross-sectional regression analysis revealed that all three psychological flexibility dimensions were negatively associated with anxiety and depression and positively associated with psychological well-being at T1. However, longitudinal analyses showed that only VA was positively associated with a decrease in depressive symptoms following CR. A sensitivity analysis conducted on the subgroup of patients with mild to severe symptoms of anxiety and depression further confirmed the robustness of these findings. Conclusions. These results highlight the potential benefits of measuring specific psychological flexibility processes when examining the psychological status of cardiac patients and when planning psychological interventions during CR. Full article

Understanding how hospital length of stay (LoS) evolves with successive SARS-CoV-2 variants is central to the multiscale modeling and forecasting of COVID-19 and other respiratory virus dynamics. Using records from 1249 COVID-19 patients admitted to Chungbuk National University Hospital (2021–2023), we quantified LoS across three distinct variant phases (Pre-Delta, Delta, and Omicron) and three age groups (0–39, 40–64, and 65+ years). A gamma-distributed multi-state model—capturing transitions between semi-critical and critical wards—incorporated variant phase and age as log-linear covariates. Parameters were estimated via maximum likelihood with 95% confidence intervals derived from bootstrap resampling, and Monte Carlo iterations yielded detailed LoS distributions. Omicron-phase stays were 5–8 days, shorter than the 10–14 days observed in earlier phases, reflecting improved treatment protocols and reduced virulence. Younger adults typically stayed 3–5 days, whereas older cohorts required 8–12 days, with prolonged admissions (over 30 days) clustering in the oldest group. These time-dependent transition probabilities can be integrated with real-time bed-availability alert systems, highlighting the need for variant-specific ward/ICU resource planning and underscoring the importance of targeted management for elderly patients during current and future pandemics. Full article

Service-Learning (SL) is an innovative educational methodology that integrates academic learning with active community engagement, fostering both technical and transversal competencies. This pilot study explores the implementation of an SL-based experience within the Physiotherapy Degree at the University of Extremadura. The primary objective was to design and deliver therapeutic exercise programs targeting patients with cardiorespiratory conditions, utilizing local community resources. A total of 44 third-year physiotherapy students participated in the design and simulated the implementation of community-based interventions targeting muscular strength, postural control, balance, and endurance. A mixed-methods approach was used, combining descriptive statistics (SPSS v23) and thematic analysis of student reflections to assess the impact of SL on the development of specific professional competencies, including clinical reasoning, patient communication, therapeutic planning, and adaptation of interventions to diverse environments. The results show a significant improvement in students’ theoretical and practical understanding, with over 70% of participants rating their learning experience between 8 and 10 (on a 0–10 scale) in aspects such as pathology description, clinical assessment, and exercise planning. Additionally, 92% reported improved teamwork, 89% noted better adaptability, and 87% reported enhanced decision-making skills. The findings suggest that SL can enhance perceived learning in musculoskeletal rehabilitation and support the transition from academic training to clinical practice. However, the study is exploratory and based on perceived outcomes, and future research should include validated tools and real patients to assess its impact more rigorously. This pilot study highlights the value of integrating musculoskeletal-focused training—targeting strength, balance, and endurance—into physiotherapy education through Service-Learning methodology. The study highlights SL’s potential to enrich physiotherapy education while leveraging community spaces—such as those in Extremadura, a region with three UNESCO World Heritage Sites—as dynamic learning environments. Full article

Background and Aim: Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with poor prognosis despite maximal surgical resection, radiotherapy (RT), and temozolomide (TMZ) per the Stupp protocol. IDH-wild-type GBM, the predominant molecular subtype, frequently harbors EGFR amplification and is resistant to therapy, while MGMT promoter methylation predicts improved TMZ response. This study aimed to assess the prognostic impact of EGFR and MGMT status on survival and recurrence patterns in IDH-wild-type GBM. Materials and Methods: We retrospectively analyzed 218 patients with IDH-wild-type GBM treated at the Azienda Ospedaliero-Universitaria Senese (2016–2024). All patients underwent maximal safe surgical resection whenever feasible. The cohort includes patients who received gross total resection (GTR), subtotal resection (STR), or biopsy only, depending on tumor location and clinical condition, followed by intensity-modulated RT (59.4–60 Gy) with concurrent and adjuvant TMZ. EGFR amplification was assessed via FISH/NGS and immunohistochemistry; MGMT promoter methylation was determined using methylation-specific PCR. Progression-free survival (PFS), overall survival (OS), and recurrence patterns (in-field, marginal, out-field) were evaluated using Kaplan–Meier, Cox regression, and logistic regression analyses. Results: Among patients (64.7% male; mean age 61.8), 58.7% had EGFR amplification and 49.1% showed MGMT methylation. Median OS and PFS were 14 and 8 months, respectively. EGFR non-amplified/MGMT methylated tumors had the best outcomes (OS: 22.0 months, PFS: 10.5 months), while EGFR-amplified/MGMT unmethylated tumors fared worst (OS: 10.0 months, PFS: 5.0 months; p < 0.001). MGMT methylation was an independent positive prognostic factor (HR: 0.48, p < 0.001), while EGFR amplification predicted worse survival (HR: 1.57, p = 0.02) and higher marginal recurrence (OR: 2.42, p = 0.01). Conclusions: EGFR amplification and MGMT methylation significantly influence survival and recurrence dynamics in IDH-wild-type GBM. Incorporating these biomarkers into treatment planning may enable tailored therapeutic strategies, potentially improving outcomes in this challenging disease. Prospective studies are needed to validate biomolecularly guided management approaches. Full article

Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cyber-secure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets—including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers—and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning. Full article

Atrial fibrillation (AF) is the most common sustained arrhythmia and a major contributor to stroke and cardiovascular morbidity. However, current approaches to rhythm control and stroke prevention are often limited by variable treatment responses and population-based risk stratification tools that fail to capture individual disease mechanisms. Digital twin technology—computational models built using patient-specific anatomical and physiological data—has emerged as a promising approach to address these limitations. In the context of AF, left atrial (LA) digital twins integrate structural, electrophysiological, and hemodynamic information to simulate arrhythmia behavior, therapeutic response, and thromboembolic risk with high mechanistic fidelity. Recent applications include stroke risk prediction using computational fluid dynamics, in silico testing of antiarrhythmic drugs, and virtual planning of catheter ablation strategies. These models have shown potential to enhance the personalization of care, offering a more nuanced and predictive framework than conventional scoring systems or imaging alone. Despite promising progress, challenges related to model personalization, computational scalability, and clinical validation remain. Nevertheless, LA digital twins are poised to advance the precision management of AF by bridging in silico modeling with real-world decision-making. This review summarizes the current state and future directions of left atrial digital twin models in AF, focusing on their application in stroke risk prediction, pharmacologic decision-making, and ablation strategy optimization. Full article

Background/Objectives: Positron emission tomography (PET) imaging with radiolabeled amino acids is increasingly used in glioma patients for biopsy planning, tumor delineation, prognostication, and therapy response assessment. This study investigated whether baseline amino acid PET imaging could identify regions at risk of future tumor recurrence. Methods: Retrospective case series of 14 patients with high-grade glioma. Contrast-enhanced magnetic resonance imaging (MRI) data of tumor recurrence and baseline imaging (PET-MRI) were co-registered. Volumes of interest (VOIs) of the high-grade glioma were derived from contrast-enhanced MRI at baseline and follow-up and from amino acid PET at baseline. The Dice similarity coefficient (DSC) was used to assess the overlap between VOIs. Furthermore, dynamic and static PET parameters were compared between the VOIs derived from contrast-enhanced MRI at follow-up and from the region of increased amino acid transport at baseline. Results: Regions of tumor recurrence in high-grade glioma patients overlap significantly more with baseline regions of increased amino acid transport on PET compared to regions of contrast enhancement on baseline MRI (p < 0.001). However, the static and dynamic PET statistics did not differentiate between regions that would later develop tumor recurrence and other areas of increased amino acid transport at baseline. Conclusions: These findings reaffirm the ability of amino acid PET to visualize the infiltrative components of gliomas not detected by contrast-enhanced MRI. Also, this study supports the role of amino acid PET in visualizing glioma infiltration beyond the MRI-visible tumor, but also indicates that accurately predicting the specific regions of recurrence based on baseline PET remains limited. Full article

Background: Nasal reconstruction requires a balance between aesthetic and functional restoration. Recent advances in three-dimensional (3D) printing have introduced new approaches to this field, enabling precise, patient-specific interventions. This review explores the applications, benefits, and challenges of integrating 3D printing in nasal reconstruction. Methods: A literature search was conducted using PubMed, Scopus, and Web of Science to identify studies on 3D printing in nasal reconstruction. Peer-reviewed articles and clinical trials were analyzed to assess the impact of 3D-printed models, implants, and bioengineered scaffolds. Results: 3D printing facilitates the creation of anatomical models, surgical guides, and implants, enhancing surgical precision and patient outcomes. Techniques such as stereolithography (SLA) and selective laser sintering (SLS) enable high-resolution, biocompatible constructs using materials like polylactic acid, titanium, and hydroxyapatite. Computational fluid dynamics (CFD) tools improve surgical planning by optimizing nasal airflow. Studies show that 3D-printed guides reduce operative time and improve symmetry. Emerging bioprinting techniques integrating autologous cells offer promise for tissue regeneration. Challenges and Future Directions: Challenges include high costs, imaging limitations, regulatory hurdles, and limited vascularization in bioprinted constructs. Future research should focus on integrating bioactive materials, artificial intelligence-assisted design, and regulatory standardization. Conclusions: 3D printing offers specific advantages in nasal reconstruction, improving precision and outcomes in selected cases. Addressing current limitations through technological and regulatory advancements will further its clinical integration, potentially enhancing reconstructive surgery techniques. Full article

Background: Antimicrobial resistance is one of the 10 most pressing health problems worldwide. Methods: First steps toward harnessing the complex dynamics of antibiotic resistance are presented. To accomplish this, we first shift down a gear and try to understand the actual driving dynamics behind the development of resistance in a specific clinical department. Analyses are based on the clinical and microbiological data of a German hospital over an observation period of more than 7 years, which we evaluate descriptively and semi-quantitatively in order to obtain a basis for informed and intelligent action in terms of antibiotic stewardship. Results: The specific results include the observed increase in the resistance rate with increasing overall consumption, while increases over time independent of consumption are fairly moderate. Vancocymin and refoximin are an exception in the development of resistance, as resistance to these substances appears to decrease with increasing consumption. However, there have been substantial dose adjustments for these substances, which are likely to be decisive here. An intra-host increase in resistance due to treatment time on the one hand and repeated treatments on the other is observed. Within the sub-cohort of ineffectively treated patients, i.e., with resistance to the antibiotic, mortality increases on average, but with ampicillin/sulbactam as a striking exception. Patients with infections caused by ampicillin-resistant bacteria have a lower mortality rate. The observed resistance rates of the eight most frequently administered antibiotics show a temporal variability that includes random fluctuations as well as decidedly regular cycles. The time series associated with the various antibiotics show pairwise time lag correlations, which indicates the existence of retardedly mediated cross-resistance. Conclusions: We conclude with an outlook on upcoming further analyses and a draft action plan on how to control and harness the complex dynamics observed by means of successful, informed, and intelligent antibiotic stewardship. Full article

High-risk genetic multiple myeloma (HRMM) remains a major therapeutic challenge, as patients harboring adverse genetic abnormalities, such as del(17p), TP53 mutations, and biallelic del(1p32), continue to experience poor outcomes despite recent therapeutic advancements. This review explores the evolving definition and molecular features of HRMM, focusing on recent updates in risk stratification and treatment strategies. The new genetic classification proposed at the 2025 EMMA meeting offers improved prognostic accuracy and supports more effective, risk-adapted treatment planning. In transplant-eligible patients, intensified induction regimens, tandem autologous stem cell transplantation, and dual-agent maintenance have shown improved outcomes, particularly when sustained minimal residual disease negativity is achieved. Conversely, in the relapsed or refractory setting, novel agents have demonstrated encouraging activity, although their specific efficacy in HRMM is under investigation. Moreover, treatment paradigms are shifting toward earlier integration of immunotherapy, and therapeutic strategies are individualized based on refined molecular risk profiles and clone dynamics. Therefore, a correct definition of HRMM could help in significantly improving both clinical and therapeutic management of a subgroup of patients with an extremely aggressive disease. Full article

Background/Objectives: The accurate diagnosis and classification of brain tumors are critical for appropriate treatment planning and patient management. We evaluated the effectiveness of perfusion in differentiating glial tumors from metastases using dynamic susceptibility-weighted contrast enhanced perfusion MRI (DSC-MRI) Methods: A total of 95 consecutive patients with pathological diagnoses of brain tumors who underwent perfusion MRI between July 2021 and March 2023 were retrospectively recruited. Conventional and perfusion MRI were evaluated, and tumoral and peritumoral relative cerebral blood volume (rCBV) values were measured. Mann–Whitney U and Kruskal–Wallis tests were performed for non-parametric comparisons of continuous data. The optimal cut-off value of rCBV in differentiating tumor types was evaluated with the receiver operating characteristic (ROC) curve analysis. Results: Tumoral rCBV (p < 0.001) and peritumoral rCBV values (p = 0.001) were significantly higher in glial tumors than metastases. Further subgroup analyses showed that tumoral and peritumoral rCBV values of glial tumors were higher than those of non-small-cell lung cancers (p < 0.001 and p = 0.003, respectively) and those of breast cancer (p = 0.311 and p = 0.053, respectively) in discriminating high-grade glial tumors and metastases. ROC analyses showed that area under the curve values for tumoral and peritumoral rCBV were 0.816 and 0.725, respectively, for the optimal cut-off points 1.339 and 1.238 (87.5% and 58.33% sensitivity; 73.85% and 90.77% specificity, respectively). Multivariate analysis showed that increased tumoral rCBV and peritumoral rCBV values were independent risk factors for glial tumor occurrence. Conclusions: DSC-MRI is an effective method to differentiate glial tumors and metastases. Higher rCBV values may serve as a determinant for the diagnosis of glial tumors and metastatic brain tumors. Full article

Background: Vestibular function implements head position regulation and body spatial navigation. It matures during childhood and adolescence and integrates with the completion of an individual’s motor development. Nevertheless, vestibular impairment is associated with profound paediatric hearing loss and has a negative impact on the child’s motor proficiency. Cochlear implantation (CI) is the treatment of choice for severe hearing loss, where conservative treatment plans are not appropriate or fail. The Teenager and Young Adults Cochlear Implant (TAYACI) study investigates the long-term outcomes of early implantation with respect to the hearing, speech, psychological, and balance development among CI users. Methods: This study focuses on the vestibular function and the appropriate methods for vestibular assessment. The results of two established vestibular test methods are explored: the video head impulse test (vHIT) and cervical/ocular vestibular-evoked myogenic potentials (cVEMP, oVEMP) with air and bone conduction vibration stimulation. The results of vHIT, cVEMP, and oVEMP, per implanted ear and the relation to the aetiology of hearing loss are reported. An additional dynamic visual acuity (DVA) test was included to assess clinical oscillopsia. Results: Overall abnormal lateral canal testing was detected in 35/76 (46.1%) of the implanted ears. Bone-conducted cVEMP (BC cVEMP) was pathological in 33/76 (43.3%) and BC oVEMP in 42/76 (55.3%). Lateral canal impairment was associated with the background diagnosis of the hearing loss. Oscillopsia was related to bilateral canal impairment (sensitivity 73% specificity 100%). Conclusions: Lateral canal testing together with BC VEMPs were the most reproducible modules for vestibular testing The above tests were related to each other and complemented the overall vestibular assessment. DVA is a helpful tool to screen dynamic oscillopsia in patients with bilateral vestibular impairment. Full article

Background/Objectives: Bipolar disorder (BD) is a complex and chronic mental health condition that poses significant challenges for both patients and healthcare providers. Traditional treatment methods, including medication and therapy, remain vital, but there is increasing interest in the application of artificial intelligence (AI) to enhance BD management. AI has the potential to improve mood episode prediction, personalize treatment plans, and provide real-time support, offering new opportunities for managing BD more effectively. Our primary objective was to explore the potential role of AI in transforming the management of BD, specifically in mood tracking, prediction, and personalized treatment regimens. Methods: To explore the potential role of AI in transforming BD management, we conducted a review of recent literature using key search terms. We included studies that discussed AI applications in mood tracking, prediction, and treatment personalization. The studies were selected based on their relevance to AI’s role in BD management, with attention to the PICO criteria: Population—individuals diagnosed with BD; Intervention—AI tools for mood prediction, treatment personalization, and real-time support; Comparison—traditional treatment methods (when available); Outcome—measures of mood episode prediction, treatment effectiveness, and improvements in patient care. Results: The findings from recent research reveal promising developments in the use of AI for BD management. Studies suggest that AI-powered tools can enable more proactive and personalized care, improving treatment outcomes and reducing the burden on healthcare professionals. AI’s ability to analyze data from wearable devices, smartphones, and even social media platforms provides valuable insights for early detection and more dynamic treatment adjustments. Conclusions: While AI’s application in BD management is still in its early stages, it presents transformative potential for improving patient care. However, further research and development are crucial to fully realize AI’s potential in supporting BD patients and optimizing treatment efficacy. Full article