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Marine corrosion and biofouling are challenges that affect marine industrial equipment, and protecting equipment with functional coatings is a simple and effective approach. However, it is extremely difficult to combine anti-corrosion and anti-fouling properties in a single coating. In this work, we combine reduced graphene oxide (rGO)/silver nanoparticles (AgNPs) with a hydrophilic polymer in a bio-based silicone–epoxy resin to create a coating with both anti-fouling and anti-corrosion properties. The excellent anti-fouling performance of the coating results from a ternary synergistic mechanism involving fouling release, contact inhibition, and a hydration effect, while the outstanding anti-corrosion performance is provided by a ternary synergistic anti-corrosion mechanism that includes a dense interpenetrating network (IPN) structure, a barrier effect, and passivation. The results show that the obtained coating possesses superior anti-fouling activity against protein, bacteria, algae, and other marine organisms, as well as excellent anti-corrosion and certain self-healing properties due to its dynamic cross-linked network of rGO/AgNPs and the hydrophilic polymer. This work provides an anti-corrosion and anti-fouling integrated coating for marine industrial equipment.

Introduction: Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors, making intra-operative diagnosis crucial for GBM treatment strategies. However, the current methods for evaluating the expression of PD-L1 are still time-consuming. Objective: To overcome the PD-L1 heterogeneity and enable rapid, accurate, and label-free imaging of PD-L1 expression level in GBM IME at the tissue level. Methods: We proposed a novel intra-operative diagnostic method, Machine Learning Cascade (MLC)-based Raman histopathology, which uses a coordinate localization system (CLS), hierarchical clustering analysis (HCA), support vector machine (SVM), and similarity analysis (SA). This method enables visualization of PD-L1 expression in glioma cells, CD8+ T cells, macrophages, and normal cells in addition to the tumor/normal boundary. The study quantified PD-L1 expression levels using the tumor proportion, combined positive, and cellular composition scores (TPS, CPS, and CCS, respectively) based on Raman data. Furthermore, the association between Raman spectral features and biomolecules was examined biochemically. Results: The entire process from signal collection to visualization could be completed within 30 min. In an orthotopic glioma mouse model, the MLC-based Raman histopathology demonstrated a high average accuracy (0.990) for identifying different cells and exhibited strong concordance with multiplex immunofluorescence (84.31 %) and traditional pathologists' scoring (R2 ≥ 0.9). Moreover, the peak intensities at 837 and 874 cm−1 showed a positive linear correlation with PD-L1 expression level. Conclusions: This study introduced a new and extendable diagnostic method to achieve rapid and accurate visualization of PD-L1 expression in GBM IMB at the tissular level, leading to great potential in GBM intraoperative diagnosis for guiding surgery and post-operative immunotherapy.

Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate noise in the training data, which may deteriorate machine learning model performance. Existing approaches addressing noisy training data typically rely on strict modeling assumptions, classification models and well-curated dataset. To address these, we propose a novel reliability-based training-data-cleaning method employing inductive conformal prediction (ICP). This method uses a small set of well-curated training data and leverages ICP-calculated reliability metrics to selectively correct mislabeled data and outliers within vast quantities of noisy training data. The efficacy is validated across three classification tasks with distinct modalities: filtering drug-induced-liver-injury (DILI) literature with free-text title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced via label permutation. Our training-data-cleaning method significantly enhanced the downstream classification performance (paired t-tests, p ≤ 0 . 05 among 30 random train/test partitions): significant accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4% increase from 0.812 to 0.905), significant AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% increase from 0.597 to 0.739 for AUROC, and 69.8% increase from 0.183 to 0.311 for AUPRC), and significant accuracy and macro-average F1-score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% increase from 0.351 to 0.613 for accuracy, and 89.0% increase from 0.267 to 0.505 for F1-score). The improvement can be both statistically and clinically significant for information retrieval, disease diagnosis and prognosis. The method offers the potential to substantially boost classification performance in biomedical machine learning tasks without necessitating an excessive volume of well-curated training data or strong data distribution and modeling assumptions in existing semi-supervised learning methods.

Inescapable stressful events often precipitate long-term alterations in emotion-related behaviors and poor sleep quality, with anxiety being a prevalent associated disorder. The defensive burying behavior of rodents is a response to imminent threats that becomes markedly pronounced in response to anxiety. However, the neural foundations of defensive burying behavior and etiology of anxiety remain largely unknown. In this study, we established a model employing object binding to elicit increased burying behavior in mice, thereby enhancing fear resolution and subsequently reducing anxious behaviors. Notably, the mice that associated shock with an object exhibited less object exploration and the zona incerta (ZI) neurons showed higher calcium activity during object exploration as compared to the Shock only mice. Although the calcium activity in ZI neurons of the Object mice was identical to the Shock only mice, the Object mice exhibited more burying behavior. Furthermore, the time spent in the center of the open-field test was directly proportional to the duration of burying behavior. Chemogenetic activation of ZI neurons extended the burying time and concomitantly ameliorated anxiety-like behavior. Importantly, chemogenetic enhancement of projection from ZI neurons to the ventral periaqueductal gray (vPAG), a brain region that plays a critical role in autonomic function, normalizes anxious behavior without influencing burying behavior. Collectively, these findings systematically reveal the functions and underlying mechanisms of the ZI-vPAG circuit in controlling behaviors akin to anxiety, offering significant insights into ZI's role in the pathophysiology of anxiety disorders.

Abstract Genetic generalized epilepsies (GGE) exhibit widespread morphometric alterations in the subcortical structures. Subcortical structures are essential for understanding GGE pathophysiology, but their fine-grained morphological diversity has yet to be comprehensively investigated. Furthermore, the relationships between macroscale morphological disturbances and microscale molecular chemoarchitectures are unclear. High-resolution structural images were acquired from patients with GGE (n = 97) and sex- and age-matched healthy controls (HCs, n = 184). Individual measurements of surface shape features (thickness and surface area) of seven bilateral subcortical structures were quantified. The patients and HCs were then compared vertex-wise, and shape anomalies were co-located with brain neurotransmitter profiles. We found widespread morphological alterations in GGE and prominent disruptions in the thalamus, putamen, and hippocampus. Shape area dilations were observed in the bilateral ventral, medial, and right dorsal thalamus, as well as the bilateral lateral putamen. We found that the shape area deviation pattern was spatially correlated with the norepinephrine transporter and nicotinic acetylcholine (Ach) receptor (α 4 β 2 ) profiles, but a distinct association was seen in the muscarinic Ach receptor (M 1 ). The findings provided a comprehensive picture of subcortical morphological disruptions in GGE, and further characterized the associated molecular mechanisms. This information may increase our understanding of the pathophysiology of GGE.

Atherosclerosis (AS) leads to atherosclerotic cardiovascular disease (ASCVD), the predominant cause of death worldwide. As traditional lipid-lowering therapies have encountered a bottleneck in dealing with diverse types of atherosclerotic plaques and ASCVD, alternative therapies that can target other mechanisms are urgently needed. Recent studies have revealed that AS is rooted in disrupted immune responses, such as chronic local inflammation and autoimmune responses, and immunotherapies have emerged as a nascent avenue to control plaque development owing to their satisfactory effects and high cost efficiency. The atherosclerotic immune microenvironment (AIME) is the microenvironment in which diverse immune responses occur dynamically. AIME is characterized by persistent inflammatory responses, shifted immunometabolism, and the formation of adventitial neuroimmune cardiovascular interfaces, all of which are regarded as prospective targets for AS immunotherapy. Recently, the advent of nanotechnology has advanced AS immunotherapy targeting the AIME. Manifold nanoplatforms have greatly enriched AS therapeutics owing to their multimodal imaging and multichannel intervention capabilities. Here, we offer an overview of AIME and discuss how nanomedicines can assist in AS diagnosis and intervention. We introduce nanoplatforms integrated with imaging techniques, such as magnetic resonance imaging, photoacoustic imaging, fluorescence imaging, positron emission tomography, and ultrasound imaging, which can target AIME to realize plaque diagnosis. Moreover, we elaborated on nanomaterials that regulate innate immune responses, adaptive immune responses, and aberrant immunometabolism to achieve AIME modulation. Furthermore, we highlight the possible future directions of AS therapeutics, with a focus on AIME-targeted nanomedicines.

Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants. Methods: We retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics. Results: The dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42–65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77–0.85 and AUPRC of 0.51–0.60 for variants. Conclusion: The tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts.

Abstract The ultrahigh flexibility and elasticity achieved in freestanding single-crystalline ferroelectric oxide membranes have attracted much attention recently. However, for antiferroelectric oxides, the flexibility limit and fundamental mechanism in their freestanding membranes are still not explored clearly. Here, we successfully fabricate freestanding single-crystalline PbZrO3 membranes by a water-soluble sacrificial layer technique. They exhibit good antiferroelectricity and have a commensurate/incommensurate modulated microstructure. Moreover, they also have good shape recoverability when bending with a small radius of curvature (about 2.4 μm for the thickness of 120 nm), corresponding to a bending strain of 2.5%. They could tolerate a maximum bending strain as large as 3.5%, far beyond their bulk counterpart. Our atomistic simulations reveal that this remarkable flexibility originates from the antiferroelectric-ferroelectric phase transition with the aid of polarization rotation. This study not only suggests the mechanism of antiferroelectric oxides to achieve high flexibility but also paves the way for potential applications in flexible electronics.

Background: To explore the value of arterial spin labeled (ASL) and blood oxygen level dependent (BOLD) imaging in evaluating allogeneic kidney function after renal transplantation. Methods: One hundred and thirty-five renal transplant patients were included. Demographic and imaging data were collected. Transplanted renal function, pathology, ASL and BOLD parameters were obtained. The patients were divided into normal, mild and severe injury group. The correlation between BOLD/ASL parameters and clinical data were evaluated. The prediction models were based on ASL and BOLD parameters using multivariate logistic analysis. Cox proportional hazards regression model was used to analyze the effects of gender, age, ASL and BOLD on the survival of renal transplant patients. Results: ASL and BOLD parameters were independently associated with renal function injury and renal allograft positive pathology. The AUC of prediction model for renal allograft function based on ASL and BOLD parameters was 0.85, while the AUC based on BOLD parameters was 0.70. Renal transplantation time showed a positive correlation with age, BOLD parameters and SCr，while a negative correlation with ASL parameters and eGFR. ASL parameter was positively correlated with eGFR and negatively correlated with Scr. BOLD parameter was negatively correlated with eGFR, ASL and positively correlated with Scr. Cox proportional hazards regression model showed that the increase of age could reduce the risk of renal function injury and positive pathology. Conclusions: ASL and BOLD were associated with renal function injury and renal allograft positive pathology. ASL and BOLD had some value in predicting renal allograft function.