Objectives: This study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.Methods: A total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.Results: The MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).Conclusions: Machine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

Atherosclerosis is the leading cause of cardiovascular disease and myocardial infarction. Precise and effective plaque targeting is a major objective for therapeutic outcomes throughout various stages of atherosclerosis. Inspired by the natural recruitment of neutrophils in atherosclerotic plaques, we fabricated a simvastatin (ST)-loaded and neutrophil membrane-cloaked nanoplatform (NNP^ST) for enhancing localized payload delivery and atherosclerosis management. The resulting NNP^ST mimicked neutrophil function and significantly decreased macrophage‐mediated phagocytosis to prolong its own circulation time in the blood. Compared to pristine nanoparticles (NP^ST) without a membrane coating, NNP^ST achieved better plaque targeting in ApoE^−/− mice, as indicated by neutrophils actively recruited in atherosclerotic lesions. The higher plaque homing with NNP^ST was monitored by dynamic fluorescence/magnetic resonance (MR) dual-modality imaging. The results further showed that NNP^ST efficiently prevented atherosclerosis development mainly by suppressing local inflammatory macrophages, and the percentage of plaques in the entire aortic area was reduced to 4.75 ± 1.48 % following NNP^ST treatment. A biosafety assessment indicated that the biomimetic NNP^ST induced no noticeable toxicity in the body. This approach of neutrophil membrane-camouflaged nanoparticles offers new opportunities to various therapeutic agents for on-demand delivery in neutrophil-involved inflammatory diseases.

Myocardial infarction (MI) is characterized by rapid onset and high mortality. Due to the complexity of its pathological process, targeted therapy for MI has been a long-standing challenge. Macrophages, as immune cells, selectively accumulate in the infarct area after MI. This inherent targeting ability, coupled with excellent immune evasion capabilities, makes macrophages an ideal source for targeted delivery to damaged myocardium as a biomimetic delivery carrier. Therefore, in this work, we aimed to develop biomimetic nanoparticles for specific targeted drug delivery to efficiently treat MI. Using cell membrane-coated nanotechnology, we prepared resveratrol-loaded nanoparticles (RESNPs) using poly(lactic-co-glycolic acid) (PLGA) nanoparticles, and then coated the surface of RESNPs with macrophage membranes to prepare biomimetic nanoparticles MM/RESNPs. We then described the physical properties of MM/RESNPs, and their targeting, therapeutic effects, and safety were evaluated in a MI mouse model. The results show that MM/RESNPs exhibited good biocompatibility and could effectively target damaged myocardial sites. MM/RESNPs were found to considerably improve cardiac function and reduce the infarct size in MI mice. Furthermore, MM/RESNPs demonstrated good biosafety after long-term administration. Macrophage membrane-coated biomimetic nanoparticles (MM/RESNPs) exhibit excellent nanoperformance and biosafety, enabling targeted drug delivery and achieving safe and effective treatment for MI.

Background: Autism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person’s perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure. Methods: We selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter‐regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups. Results: The PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes. Limitations: There is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results. Conclusions: ASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.