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Compressive strength modelling of cenosphere and copper slag-based geopolymer concrete using deep learning model
Complete Structure of Delftibactin A and Its Function in Reductive Formation of Gold Nanoparticles
Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases
Abstract Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to construct machine learning models using the convenient blood routine and biochemical detection data for diagnosis of nervous system diseases. After the data preprocessing, 25,794 healthy people and 7518 nervous system disease patients with the blood routine and biochemical detection data were utilized for our study. We selected logistic regression, random forest, support vector machine, eXtreme Gradient Boosting (XGBoost), and deep neural network to construct models. Finally, the SHAP algorithm was used to interpret models. The nervous system disease prediction model constructed by XGBoost possessed the best performance (AUC: 0.9782). And the most models of distinguishing various nervous system diseases also had good performance, the model performance of distinguishing neuromyelitis optica from other nervous system diseases was the best (AUC: 0.9095). The model interpretation by SHAP algorithm indicated features from biochemical detection made major contributions to predicting nervous system disease. The present study constructed multiple models using 52 features from the blood routine and biochemical detection data for diagnosis of various nervous system diseases. Meanwhile, distinct hematologic features of various nervous system diseases also were explored. This cost-effective work will benefit more people and assist in diagnosis and prevention of nervous system diseases.
Iridium-Catalyzed Regio- and Enantioselective Reverse Prenylation of Tryptamines and Other 3-Substituted Indoles
Magnetically retrievable 2-(2-Pyridyl)benzimidazole-Cu(I) on SBA-15@Fe3O4 for sodium Azide-Induced amination of Aryl halides
Low-Electromotive Force-Driven Sodium Compensation for Optimizing Na Deposition in Rechargeable Sodium Batteries
Measured intrapatient radiomic variability as a predictor of treatment response in multi-metastatic soft tissue sarcoma patients
Abstract Radiomics offers a non-invasive approach to tumor characterization, yet its application in metastatic cancers is limited by intertumor heterogeneity—variability in radiomic phenotypes across lesions within the same patient. We introduce Measured Intrapatient Radiomic Variability (MIRV), a novel metric quantifying heterogeneity using standard-of-care imaging. Applied to 397 metastatic soft-tissue sarcoma patients from the SARC021 trial, MIRV was calculated from pretreatment CT scans using pairwise Euclidean distance and cosine dissimilarity between lesions. Euclidean distance captures absolute differences in radiomic features, while cosine dissimilarity assesses variation in feature patterns independent of magnitude. Higher MIRV correlated with greater variability in tumor-specific response classification and volumetric response, independent of baseline tumor volume. In a subset with liquid biopsy data, MIRV showed a moderate association with ctDNA positivity, suggesting links to molecular heterogeneity. While MIRV was not prognostic for overall survival in the full cohort, higher MIRV was significantly associated with worse survival in leiomyosarcoma patients ( n = 165, p = 0.007, FDR = 0.06). These findings establish MIRV as a biomarker for intertumor heterogeneity, with potential to predict mixed treatment responses and guide personalized therapy in metastatic STS. Future studies should assess its relevance across other tumor types and therapeutic settings.
The Discovery of Complex Heterocycles from Millipede Secretions
Manual restricted kinematic alignment technique restores postoperative limb alignment in severe knee deformities
Abstract Restricted kinematically aligned total knee arthroplasty (rKA-TKA) for severe deformity in the preoperative hip–knee–ankle angle (HKAA) has gained considerable interest. However, the widespread adoption of rKA-TKA has been limited by its requirement for expensive equipment such as navigation and robotic surgery systems. In this study, we developed manual rKA-TKA with modified soft tissue-respecting technique and investigated its surgical effects on postoperative HKAA. To achieve this, we examined factors affecting postoperative HKAA. Subsequently, the safe zone-related cut-off values of preoperative HKAA were calculated using the receiver operating characteristic curve, and postoperative HKAA was predicted using a linear regression model (LRM) and generalized additive model (GAM). Preoperative HKAA was identified as a factor influencing postoperative HKAA. The cut-off values of preoperative HKAA were −14 and −15° when the safe zones were defined as ± 1–3 and ± 4–5°, respectively. The GAM was more accurate in predicting the postoperative HKAA than the LRM. Additionally, the GAM showed a potential of falling within ± 5° of the postoperative HKAA, even in patients with preoperative HKAA ≤ − 19°. These findings suggest that manual rKA-TKA can be effective even for patients with severe deformities, providing an accessible alternative to conventional TKA for surgeons at resource-limited institutions.