Browse Articles
Discover research articles across all indexed journals
Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach
Abstract Diabetic foot Ulceration (DFU) is a severe complication of diabetic foot syndrome, often leading to amputation. In patients with neuropathy, ulcer formation is facilitated by elevated plantar tissue stress under insensate feet. This study presents a plantar pressure distribution analysis method to predict diabetic peripheral neuropathy. The Win-Track platform was used to gather clinical and plantar pressure data from 86 diabetic patients with different degrees of neuropathy. An automated image processing algorithm segmented plantar pressure images into forefoot and hindfoot regions for precise pressure distribution measurement. Comparative analysis of static and dynamic assessment showed that static analysis consistently outperformed dynamic methods. Gradient Boosting achieved the highest accuracy (88% dynamic, 100% static), with Random Forest and Decision Tree also performing well. Explainable AI techniques (SHAP, Eli5, Anchor Explanations) provided insights into feature importance, enhancing model interpretability. Additionally, a foot classification system based on the forefoot-hindfoot pressure ratio categorized feet as flat, regular, or arched. These findings support the development of improved diagnostic tools for early neuropathy detection, aiding risk stratification and prevention strategies. Enhanced screening can help reduce DFU incidence, lower amputation rates, and ultimately decrease diabetes-related mortality.
Correction of syndesmotic malreduction following fixation flexibilization
Factors influencing AI adoption by Chinese mathematics teachers in STEM education
Toughening and strengthening by off-stoichiometric TiC in equilibrium with Mo solid solution
Translation and validation of the Swahili pediatric quality of life family impact module for caregivers of children with congenital heart disease
Loss of adhesion impairs invasiveness and cell survival, contributing to the antimetastatic effect of cysteine proteases from Vasconcella cundinamarcensis in melanoma
Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification
A new drug target for NRAS(Q61) mutant-expressing cancers
Retraction: Nitric Oxide Induces Hypoxia-inducible Factor 1 Activation That Is Dependent on MAPK and Phosphatidylinositol 3-Kinase Signaling
Contrast-enhanced ultrasound with VEGFR2-targeted microbubbles for monitoring combined anti-PD-L1/anti-CTLA-4 immunotherapy effects in a murine melanoma model with immunohistochemical validation
Background Immune checkpoint inhibitors (ICIs) have emerged as a highly effective treatment option for patients with metastatic melanoma. As not all patients respond to ICI immunotherapy, imaging biomarkers are required to accurately monitor early response to therapy. Therefore, the aim of this study was to evaluate contrast-enhanced ultrasound (CEUS) with VEGFR2-targeted microbubbles for monitoring the effects of combined anti-PD-L1/anti-CTLA-4 immunotherapy in a murine melanoma model. Methods Murine melanoma allografts (B16-F10) were implanted subcutaneously in n = 10 therapy and n = 10 control female C57BL/6 mice. CEUS with VEGFR2-targeted microbubbles was performed on day 7 and 12. The therapy group received 3 intraperitoneal injections on days 7, 9, 11 of combined anti-PD-L1/anti-CTLA-4 immunotherapy, the control group received a placebo. CEUS assessed tumour perfusion during an early vascular phase (wash-in area under the curve = WiAUC) and VEGFR2-specific binding during a late molecular phase (signal intensity at 8 minutes (SI8min) and 10 minutes (SI10min)). For pathophysiological validation immunohistochemistry was performed. Results At follow-up, the CEUS perfusion parameter WiAUC demonstrated a significantly higher decrease in the therapy than in the control group (p = 0.021). At follow-up, the signal enhancement in the late phase was significantly lower in the therapy than in the control group (SI8min p = 0.003; SI10min p = 0.002). Immunohistochemistry revealed significantly more apoptotic tumour cells (p = 0.001), more tumour infiltrating lymphocytes (p = 0.049), lower tumour cell proliferation (p = 0.001), lower microvascular density (p = 0.003) and lower VEGFR2 expression (p = 0.003) in the therapy than in the control group. Conclusions CEUS with VEGFR2-targeted microbubbles allowed for monitoring early treatment effects of a combined anti-PD-L1/anti-CTLA-4 immunotherapy on melanoma allografts with significantly lower tumour perfusion and significantly lower binding of VEGFR2-targeted microbubbles in the therapy than in the control group.
Impact of recycling on polymer binder integrity in metal injection molding
Abstract Metal Injection Molding (MIM) is a manufacturing process that integrates polymer binders with metal powders to produce high-precision components, offering both material efficiency and design flexibility. This study explores the recyclability of polymer-based feedstocks used in Metal Injection Molding, specifically evaluating how repeated recycling affects the structural integrity and thermal stability of polymer binders. Given the high cost of raw materials in MIM, optimizing recyclability is essential for reducing production costs and minimizing material waste, contributing to more sustainable manufacturing practices. To assess the feasibility of repeated material reuse, the study systematically subjected molded specimens to grinding and reinjection molding over eight consecutive cycles. The effects of reprocessing were analyzed using melt flow index (MFI) measurements, differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA) to track changes in polymer viscosity, thermal behavior, and degradation. The results indicate that wax precipitation during processing alters polymer viscosity and thermal stability, leading to gradual material property changes over successive recycling cycles. However, polymer degradation-induced viscosity reduction counterbalances these effects up to the fourth cycle, ensuring processability within standard injection molding conditions. The findings underscore the significance of analytical techniques in evaluating polymer binder integrity during multi-cycle reuse. Melt flow index (MFI) initially increased, peaking at the fourth recycling cycle, and then declined, while linear shrinkage rose by approximately 3% within the first three cycles before stabilizing. SEM–EDS analyses indicated around a 20% wax loss after multiple recycling cycles, significantly influencing binder rheology. Polymer binders can thus be successfully recycled up to four times while maintaining acceptable thermal and rheological properties, supporting resource-efficient and sustainable manufacturing strategies in MIM production.
Generative AI for weakly supervised segmentation and downstream classification of brain tumors on MR images
Characterization of odor markers associated with aging in old ICR mice
Kuroshio Extension and Gulf Stream dominate the Eddy Kinetic Energy intensification observed in the global ocean
Abstract Ocean mesoscale variability, including meanders and eddies, is a crucial component of the global ocean circulation. The Eddy Kinetic Energy (EKE) of these features accounts for about 90% of the ocean’s total kinetic energy. This study investigates if the global ocean mesoscale variability is becoming more energetic by analyzing 30 years of satellite altimetric observations. We use two observational products: one constructed from a consistent pair of altimeters and another including all available missions. Our results reveal a significant global EKE strengthening of 1–3% per decade. The intensification is concentrated in energetic regions, particularly in the Kuroshio Extension and the Gulf Stream, which show EKE increases of ~ 50% and ~ 20%, respectively, over the last decade. These observations raise new questions about the impact of the Gulf Stream strengthening on the Atlantic meridional overturning circulation (AMOC) and challenge existing climate models, emphasizing the need for improved representation of small-scale ocean processes.
Inverted internal limiting membrane flap technique for macular hole retinal detachment in high myopia compared to internal limiting membrane peeling
The mechanism of Bletilla striata inhibiting glioma proliferation through the PI3K/AKT/mTOR signaling pathway based on network Pharmacology analysis
Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis
Urban change detection of remote sensing images via deep-feature extraction
Abstract Urban change detection based on remote sensing images holds significant importance in environmental monitoring and emergency management. However, it poses several challenges including large disparity errors, diverse types of changes, and a substantial difference between the number of changed and unchanged areas. In this study, we propose an efficient model called BiUnet-Dense for extracting deep features by integrating the advantages of Bi-Unet, Dense Block, and long short-term memory (LSTM) networks. Building upon the classical architecture of U-Net, Bi-Unet utilizes bi-temporal images to compare and extract features. The incorporation of modified dense connections reduces network parameters while mitigating gradient disappearance through maximizing feature reuse. Additionally, LSTM facilitates information transmission from earlier to later using cell states to provide more meaningful feature vectors. We implemented our model on two datasets: Onera Satellite Change Detection (OSCD) and Change Detection Data of Guangzhou (CD_Data_GZ). Quantitative and qualitative results demonstrate that our method significantly improves detection effectiveness with enhanced F1-score and Kappa while effectively reducing false-positive detections as well as identifying labeling errors.