In this study, polyamine metabolism related genes (PMRGs) were used to establish a breast cancer (BC) prognostic model. Using PMRGs, TCGA BC samples were divided into cluster1 and cluster2. A 13-gene BC prognostic model was constructed by screening differential genes. High-risk BC patients exhibited heightened immunoinfiltration levels, potentially impeding immunotherapy responses. Drug response predicted that BC patients in the low-risk group might benefit more from chemotherapy and targeted therapy. In conclusion, a novel 13-gene BC prognostic risk model based on PMRGs was established to effectively predict prognosis, immune microenvironment, and drug therapy response in patients with BC.

The early stage of the Epileptic Seizure Anticipation (ESA) model plays a significant part in supplying accurate medical care. In this research work, a novel Multi Serial Cascaded Network with Feature Specific model is developed. The scalogram images are given as input to a developed model. Here, the Target Feature Selection is performed optimally using the Improved Fitness Value Index-Archimedes Optimization (IFVI-AO) Algorithm. Finally, the selections of accurate features are subjected to 'Bi-directional Long Short-Term Memory (Bi-LSTM)'. The implemented model is validated and provides timely results to detect epileptic seizure disorder.

Skiing accidents may lead to severe head and neck injuries. This study uses the THUMS model to evaluate the initial conditions and related injury mechanisms of head impacts with the snow surface during skiing falls. Initial speed was found to be the main factor affecting head injury criterion (HIC). Impact position is the key factor affecting cervical curvature. Rear impacts cause excessive cervical spine extension, while frontal impacts cause excessive cervical spine flexion. Rear impacts are more likely to cause neck injuries. As the angle and speed increase, the degree of cervical spine injury also increases.

This study aimed to identify important clotting associated genes (CRGs) associated with septic shock in children and explore possible important mechanisms of the disease. Five hub genes with diagnostic performance were identified using GEO database and data from literature. These hub genes have strong correlation with immune cells. ceRNA network was constructed to explore potential pathogenic mechanisms. Ten candidate small molecule compounds were identified. In summary, the hub genes may play an important role in the immunity and disease development of septic shock, providing new ideas and strategies for future diagnosis and mechanism evaluation of children with septic shock.

Different forms of rugby may pose distinct risks to head injury. Video of rugby match footage was analyzed using head impact magnitude, frequency, and time interval for 15s and 7s athletes. Impacts were reconstructed in laboratory, and finite element modeling was used to estimate maximum principal strain. No difference was found in impact frequency or time interval between the two forms. Significantly more head impacts of higher severity levels were documented during 7s. These findings provide objective comparisons between 7s and 15s which may guide risk mitigation strategies in managing brain trauma for specific forms of rugby.

This study investigates NiTi-CoCr hybrid carotid artery stents to enhance mechanical properties over NiTi-only designs. Different configurations (24NiTi, 20NiTi-4CoCr, 16NiTi-8CoCr, and 12NiTi-12CoCr) were evaluated through radial compression and bending simulations. The 12NiTi-12CoCr stent showed the highest radial support (39.37 N) and increased bending strength by 77.96%. When modeled in a stenotic artery, this stent reduced stenosis from 81.52% to 29.33% and improved blood flow dynamics, alleviating high-pressure zones and balancing wall shear stress. These results suggest that CoCr wires improve stent performance, with the 12NiTi-12CoCr stent offering significant biomechanical and hemodynamic benefits.

Breast cancer concerns 1 in 8 women in the world and is followed in 40% of cases by a mastectomy. Only 14% of women receive reconstructive surgery because of unfavorable clinical issues. The need of innovative tissue engineering devices leads Lattice Medical company to bring a new 3D-printed device, allowing the regeneration of soft tissue in order to replace the withdrawn breast. The implant, based on TEC (tissue engineering chamber) and fat-flat surgical technique, is constituted with bioresorbable thermosensitive materials to be fully absorbed by the body in several months, once the regeneration process is completed. In this industrial context, we need to assess some properties for predictive simulation: the TEC mechanical and biological properties over time, its sensitivity to implantation in the body temperature, its batch raw material variability and its structural 3D-printed behavior. This would lead to a more enlightened numerical design and topological optimization work. To do so, mechanical testing are conducted to gather necessaries information for fully border the behaviour of the material and eventually the impact of the process on the final prosthesis. Then, the G'sell Law is chosen to model the mechanical behaviour of the material taking into account all particularities of this medical case. Finally, the behaviour law is used in Finite Element Method (FEM) in a compression simulation to compare with experimental results which find good similarity in the mechanical response.

In patient-specific biomechanical modeling, the process of image-to-mesh-material mapping is important, and various strategies have been explored for assigning the number of groups of unique material properties to the mesh. This study aims to cross-compare different grouping strategies to identify the minimum number of unique groups necessary for accurately calculating the fracture load of vertebral bones. We analyzed 12 vertebral specimens by experimentally determining the biomechanical fracture load and acquiring corresponding CT scans. After geometry extraction and meshing, we applied commonly used fixed-value strategies for reducing the number of unique groups, such as Modulus Gaping and Percentual Thresholding. Additionally, we introduced a patient-specific adaptive grouping method based on K-means clustering, which allowed us to maintain a consistent number of groups of unique material properties across the study. A total of 204 simulations were performed, achieving a potential 98% reduction in the number of individual material parameters while maintaining a strong correlation with experimental results when utilizing Percentual Thresholding or Adaptive Clustering, compared to Modulus Gaping. The findings demonstrate the feasibility of significantly reducing simulation complexity while maintaining the accuracy of patient-specific models that strongly correlate with experimental results. This reduction enables efficient processing of patient-specific biomechanical models derived from image data, offering potential benefits for clinicians, particularly in resource-constrained settings.

We examine an SIRS reaction-diffusion model with local dispersal and spatial heterogeneity to study COVID-19 dynamics. Using the operator semigroup approach, we establish the existence of disease-free equilibrium (DFE) and endemic equilibrium (EE), and derive the basic reproduction number, . Simulations show that without dispersal, reinfection and limited medical resources problems can cause a plateau in cases. Dispersal and spatial heterogeneity intensify localised outbreaks, while integrated control strategies (vaccination and treatment) effectively reduce infection numbers and epidemic duration. The possibility of reinfection demonstrates the need for adaptable health measures. These insights can guide optimised control strategies for enhanced public health preparedness.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest diseases. Here are investigated two synthesized and two hypothetical coumarin derivatives, and their capacity to be used in the PDAC targeted treatment. The inhibitory activity of these four molecules against PARP, ATM, and CHK1 proteins responsible for DNA molecule repair was examined by docking and molecular dynamic analysis. ADMET analysis was applied to determine the pharmacokinetic properties of the tested compounds. The applied theoretical approach showed that the biomedical activity of the investigated coumarins is comparable to the inhibitory activity and pharmacokinetic properties of Olaparib, already used in the PDAC treatment.

Prostate cancer is a prevalent malignant disease among middle-aged and elderly men. Its prevention and detection are significant public health issues. We aimed to construct an interpretable model for predicting death risk in prostate cancer patients. We performed model development using the Cancer Genome Atlas and the Genotype-Tissue Expression databases. In comparison among models, the SVM model has the highest prediction performance among the eight models. The SHAP method, sorted by importance, reveals the top eight predictors of prostate cancer disease. This effective computer-aided approach can facilitate frontline clinicians in the diagnosis and management of patients with prostate cancer.

A physical model of soft tissue that provides realistic and real-time haptic and visual feedback is crucial for neurosurgical procedures. This paper investigates the interaction between surgical instruments and soft brain tissue, proposing a soft tissue deformation simulation method based on the principle of energy minimization and constrained energy function. The model includes a permanent deformation energy function induced by friction and a volume preservation energy function to more accurately depict tissue response during procedures such as resection of convex meningiomas and evacuation of intracerebral hematomas. Experimental results show that the proposed method meets the requirements of neurosurgical simulation.

This study aimed to explore the roles of radiotherapy-sensitive and ferroptosis genes in breast cancer (BRCA). Genes differentially expressed pre- and post-radiotherapy from the GSE59733 dataset were intersected with ferroptosis-related genes. Through a protein-protein interaction network, 10 hub genes were identified. BRCA patients were categorized into three clusters, with cluster 1 and cluster 2 showing the most significant survival difference. Cluster 1 demonstrated higher immune infiltration levels but poorer response to immune therapy compared to cluster 2. Moreover, cluster 1 and cluster 2 exhibited sensitivity to different drugs. These 10 hub genes can effectively classify patients and suggest potential drugs.

Wearable sensors allow mobility assessment required for better locomotion, neurological and musculoskeletal disorders, current limitations include unknown reliability and accuracy in real-life settings. This work determined the concurrent validity and repeatability of the proposed foot-worn gait evaluation system using objective gait features and recurrence quantification analysis from 52 participants. Its agreement with the commercially available OpenGo system in the unrestricted outdoor environment is determined. Reported measures showed no significant differences ( > 0.05) between systems. Test-retest reliability showed that the mean of the second-third trial (T2-T3) is the most significant. Thus, an affordable system provides accurate measurement of gait ensuring its suitability even in small clinical-settings.

We developed an original computational model for cell deformation and migration capable of accounting for the cell sensitivity to the environment and its appropriate adaptation. This cell model is ultimately intended to be used to address tissue morphogenesis. Hence it has been designed to comply with four requirements: (1) the cell should be able to probe and sense its environment and respond accordingly; (2) the model should be easy to parametrize to adapt to different cell types; (3) the model should be able to extend to 3D cases; (4) simulations should be fast enough to integrate many interacting cells. The simulations carried out focused on two aspects: first, the general behaviour of the cell on a homogeneous substrate, as observed experimentally, for model validation. This enabled us to decipher the mechanisms by which the cell can migrate, highlighting respective influences of the adhesions lifetimes and their sensitivity to traction; second, it predicts the sensitivity of the cell to an anisotropic patterned substrate, in agreement with recently published experiments. The results show that mechanosensors simulated by the model make it possible to reproduce such experiments in terms of migration bias generated by the substrate anisotropy. Here again, the model provides a biomechanical explanation of this phenomenon, depending on cell-matrix interactions and adhesion maturation rate.

Liujunzi decoction (LJZD) is commonly used to treat hepatitis B virus (HBV), though its active ingredients and mechanisms are not fully known. This study identified core targets and active components of LJZD for treating hepatitis B (HB) through network pharmacology, molecular docking, and molecular dynamics simulation. Screening from databases yielded 533 active components, 2619 targets for LJZD, and 2910 for HB, with 891 intersecting targets. STRING and CytoHubba analyses identified AR and VDR as core targets, with key pathways including PI3K-Akt and MAPK. The findings clarify LJZD's multicomponent, multitarget mechanisms, supporting its clinical application for HB treatment.

The COVID-19 pandemic has profoundly impacted health, emphasizing the need for timely disease detection. Blood tests have become key diagnostic tools due to the virus's effects on blood composition. Accurate COVID-19 prediction through machine learning requires selecting relevant features, as irrelevant features can lower classification accuracy. This study proposes Modified Mutual Information (MMI) for feature selection, ranking features by relevance and using backtracking to find the optimal subset. Support Vector Machines (SVM) are then used for classification. Results show that MMI with SVM achieves 95% accuracy, outperforming other methods, and demonstrates strong generalizability on various benchmark datasets.

In this work, we investigate a new deep learning reconstruction method of blood flow velocity within deformed vessels from contrast enhanced X-ray projections and vessel geometry. The principle of the method is to perform linear or nonlinear dimension reductions on the Radon projections and on the mesh of the vessel. These low dimensional projections are then fused to obtain the velocity field in the vessel. The accuracy of the reconstruction method is proved using various neural network architectures with realistic unsteady blood flows. The approach leverages the vessel geometry information and outperforms the simple PCA-net.

To investigate the impact of patient-specific boundary conditions (BC) on blood flow and thrombosis modelling for patients with chronic thromboembolic pulmonary hypertension (CTEPH), three types of BCs were utilized to construct CTEPH models based on computed tomography pulmonary angiography images. First BC type is the patient-specific velocity profiles at the main pulmonary artery using phase contrast MRI (PC-MRI). The other two simplified types are the pulsatile BC and steady BC, which are obtained by spatially and temporally averaging the PC-MRI BC. Hemodynamic features including helical density, time-averaged wall shear stress (TAWSS) and oscillatory shear index (OSI), and thrombosis were compared for the three types BCs. The results indicated that, compared to the MRI BC, steady BC overestimated helical density and TAWSS in the pulmonary arteries by approximately 63.1% and 60%, respectively. The impact of simplified pulsatile BC on TAWSS and OSI in most regions of the pulmonary arteries was negligible with differences within 5%. Regarding thrombosis, the area predicted under pulsatile BC was approximately 80% smaller than that under PC-MRI BC. In conclusion, compared to PC-MRI BC, steady inlet BC tend to overestimate hemodynamic parameters, while pulsatile inlet BC yield similar wall shear stress based on parameters in most regions of the pulmonary artery. Patient-specific PC-MRI inlet BC should be used for accurate predictions of helical flow pattern and thrombus formation.

Wearable technologies such as inertial measurement units (IMUs) can be used to evaluate human gait and improve mobility, but sensor fixation is still a limitation that needs to be addressed. Therefore, aim of this study was to create a machine learning algorithm to predict gait events using a single IMU mimicking the carrying of a smartphone. Fifty-two healthy adults (35 males/17 females) walked on a treadmill at various speeds while carrying a surrogate smartphone in the right hand, front right trouser pocket, and right jacket pocket. Ground-truth gait events (e.g. heel strikes and toe-offs) were determined bilaterally using a gold standard optical motion capture system. The tri-dimensional accelerometer and gyroscope data were segmented in 20-ms windows, which were labelled as containing or not the gait events. A long-short term memory neural network (LSTM-NN) was used to classify the 20-ms windows as containing the heel strike or toe-off for the right or left legs, using 80% of the data for training and 20% of the data for testing. The results demonstrated an overall accuracy of 92% across all phone positions and walking speeds, with a slightly higher accuracy for the right-side predictions (∼94%) when compared to the left side (∼91%). Moreover, we found a median time error <3% of the gait cycle duration across all speeds and positions (∼77 ms). Our results represent a promising first step towards using smartphones for remote gait analysis without requiring IMU fixation, but further research is needed to enhance generalizability and explore real-world deployment.

Few studies consider the variability of the model parameters. This study aimed to perform a sensitivity analysis of a trapeziometacarpal joint model, by performing 675 finite element simulations built from the combination of different morphologies, joint passive stiffness, and grip strategies to estimate the joint pressure. Pressure variability was significantly more affected by morphology than grip strategy and joint passive stiffness. The effect of morphology and grip strategy on joint pressure was significant. A significant correlation between the trapezium dorso-volar curvature and the joint pressure was found. Morphology seems more important than the other parameters to estimate joint contact pressure correctly.