Toxicity comparison and risk assessment of two chlorinated organophosphate flame retardants (TCEP and TCPP) on Polypedates megacephalus tadpoles.

Tris(2-chloroethyl) phosphate (TCEP) and tris(1­chloro-2-propyl) phosphate (TCPP) are widely used as chlorinated organophosphate flame retardants (OPFRs) due to their fire-resistance capabilities. However, their extensive use has led to their permeation and pollution in aquatic environments. Using amphibians, which are non-model organisms, to test the toxic effects of OPFRs is relatively uncommon. This study examined the acute and chronic toxicity differences between TCEP and TCPP on Polypedates megacephalus tadpoles and evaluated the potential ecological risks to tadpoles in different aquatic environments using the risk quotient (RQ). In acute toxicity assay, the tadpole survival rates decreased with increased exposure time and concentrations, with TCEP exhibiting higher LC50 values than TCPP, at 305.5 mg/L and 70 mg/L, respectively. In the chronic assay, prolonged exposure to 300 µg/L of both substances resulted in similar adverse effects on tadpole growth, metamorphosis, and hepatic antioxidant function. Based on RQ values, most aquatic environments did not pose an ecological risk to tadpoles. However, the analysis showed that wastewater presented higher risks than rivers and drinking water, and TCPP posed a higher potential risk than TCEP in all examined aquatic environments. These findings provide empirical evidence to comprehend the toxicological effects of OPFRs on aquatic organisms and to assess the safety of aquatic environments.

Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning.

Due to the presence of non-line-of-sight (NLOS) obstacles, the localization accuracy in ultra-wideband (UWB) wireless indoor localization systems is typically substantially lower. To minimize the influence of these environmental factors and improve the accuracy of indoor wireless positioning, this paper proposes a density clustering with noise combined with particle swarm optimization (DCNPSO) to improve UWB positioning. Which exploits the advantages of the density-based spatial clustering algorithm with noise (DBSCAN) and particle swarm optimization (PSO) algorithm. The experimental results show that the DCNPSO algorithm achieves 45.25% and 36.14% higher average positioning accuracy than the DBSCAN and PSO algorithms, respectively. The positioning error of this algorithm remains stable within 3 cm in static positioning and can achieve high accuracy in NLOS environments.

Determination of collision mechanisms at low energies using four-vector correlations.

In molecular dynamics, a fundamental question is how the outcome of a collision depends on the relative orientation of the collision partners before their interaction begins (the stereodynamics of the process). The preference for a particular orientation of the reactant complex is intimately related to the idea of a collision mechanism and the possibility of control, as revealed in recent experiments. Indeed, this preference holds not only for chemical reactions involving complex polyatomic molecules, but also for the simplest inelastic atom-diatom collisions at cold collision energies. In this work, we report how the outcome of rotationally inelastic collisions between two D2 molecules can be controlled by changing the alignment of their internuclear axes under the same or different polarization vectors. Our results demonstrate that a higher degree of control can be achieved when two internuclear axes are aligned, especially when both molecules are relaxed in the collision. The possibility of control extends to very low energies, even to the ultracold regime, when no control could be achieved just by the alignment of the internuclear axis of one of the colliding partners.

Molecule Crowding Strategy in Polymer Electrolytes Inducing Stable Interfaces for All-Solid-State Lithium Batteries.

All-solid-state lithium batteries with polymer electrolytes suffer from the electrolyte decomposition and lithium dendrites because of the unstable electrode/electrolyte interfaces. Herein, a molecule crowding strategy was proposed to modulate the Li+ coordinated structure, thus in-situ constructing the stable interfaces. Since 15-crown-5 possesses superior compatibility with polymer and electrostatic repulsion for anion of lithium salt, the anions are forced to crowd into Li+ coordinated structure to weaken the Li+ coordination with polymer and boost the Li+ transport. The coordinated anions prior decompose to form LiF-rich, thin and tough interfacial passivation layers for stabilizing the electrode/electrolyte interfaces. Thus, the symmetric Li-Li cell can stably operate over 4360 h, the LiFePO4||Li full battery presents 97.18% capacity retention in 700 cycles at 2 C, and the NCM811||Li full battery possesses the capacity retention of 83.17% after 300 cycles. The assembled pouch cell shows the excellent flexibility (stand for folding over 2000 times) and stability (89.42% capacity retention after 400 cycles). This work provides a promising strategy to regulate the interfacial chemistry by modulating ion environment to accommodate the interfacial issues, and would inspire more effective approaches to general interface issues for polymer electrolytes. This article is protected by copyright. All rights reserved.

Single-cell RNA sequencing analysis provides novel insights into the role of apoptosis-related genes in muscle aging.

OBJECTIVE: This study employed a comprehensive single-cell analysis approach to explore the role of cell apoptosis-related genes in muscle aging. METHODS: The single-cell RNA sequencing data from the GSE143704 dataset were used to identify distinct cell clusters and assess gene expression patterns related to apoptosis activation. The "limma" package was used to identify hub genes, after which we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to identify relevant pathways. Additionally, Gene Set Enrichment Analysis(GSEA) and Gene Set Variation Analysis (GSVA) were used to uncover relevant biological pathways. The Receiver Operating Characteristic Curve (ROC) was used to evaluate the diagnostic value of the hub genes. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used to analyze the immune cell infiltration levels. RESULTS: Single-cell sequencing data from muscle aging patients allowed the identification of various cell types, including epithelial cells, adipocytes, and tissue-resident macrophages. By conducting a differential expression analysis that intersected active and nonactive apoptosis, as well as comparing elderly and young samples, a total of 22 hub genes were identified (p < 0.05). The 22 hub genes have discriminative ability as potential biomarkers for diagnosing muscle aging. The enrichment analysis indicated that these genes were closely associated with diverse pathways, including "response to UV-B" and "extracellular matrix organization" (p < 0.05). Furthermore, GSEA and GSVA indicated that multiple pathways emerged-for example, the "complement and coagulation cascades", "proteasome", "insulin signaling pathway", and "MAPK signaling pathway". Additionally, the analysis of immune cell infiltration revealed positive correlations between most of the hub genes and immune cells. CONCLUSION: Our study identified 22 apoptosis-related genes involved in muscle aging and indicated their potential diagnostic value. These findings offer a novel perspective on the pathogenesis of muscle aging and present potential targets for therapeutic interventions.

Altered nucleocytoplasmic export of adenosine-rich circRNAs by PABPC1 contributes to neuronal function.

Circular RNAs (circRNAs) are upregulated during neurogenesis. Where and how circRNAs are localized and what roles they play during this process have remained elusive. Comparing the nuclear and cytoplasmic circRNAs between H9 cells and H9-derived forebrain (FB) neurons, we identify that a subset of adenosine (A)-rich circRNAs are restricted in H9 nuclei but exported to cytosols upon differentiation. Such a subcellular relocation of circRNAs is modulated by the poly(A)-binding protein PABPC1. In the H9 nucleus, newly produced (A)-rich circRNAs are bound by PABPC1 and trapped by the nuclear basket protein TPR to prevent their export. Modulating (A)-rich motifs in circRNAs alters their subcellular localization, and introducing (A)-rich circRNAs in H9 cytosols results in mRNA translation suppression. Moreover, decreased nuclear PABPC1 upon neuronal differentiation enables the export of (A)-rich circRNAs, including circRTN4(2,3), which is required for neurite outgrowth. These findings uncover subcellular localization features of circRNAs, linking their processing and function during neurogenesis.

Presynaptic and postsynaptic mesolimbic dopamine D3 receptors play distinct roles in cocaine versus opioid reward in mice.

BACKGROUND: Past research illuminated pivotal roles of dopamine D3 receptors (D3Rs) in the rewarding effects of cocaine and opioids. However, the cellular and neural circuit mechanisms underlying these actions remain unclear. METHODS: We employed Cre-LoxP techniques to selectively delete D3R from presynaptic dopamine neurons or postsynaptic dopamine D1R-expressing neurons in male and female mice. We utilized RNAscope in situ hybridization, immunohistochemistry, RT-PCR, voltammetry, optogenetics, microdialysis, and behavioral assays (n≥8) to functionally characterize the roles of presynaptic versus postsynaptic D3Rs in cocaine and opioid actions. RESULTS: Our results revealed D3R expression in ∼20% of midbrain dopamine neurons and ∼70% of D1R-expressing neurons in the nucleus accumbens. While D2R was expressed in ∼80% dopamine neurons, we found no D2R and D3R colocalization among these cells. Selective deletion of D3Rs from dopamine neurons increased exploratory behavior in novel environments and enhanced pulse-evoked NAc dopamine release. Conversely, D3R deletion from D1R-expressing neurons attenuated locomotor responses to D1-like and D2-like agonists. Strikingly, D3R deletion from either cell type reduced oxycodone self-administration and oxycodone-enhanced brain-stimulation reward. In contrast, neither of these D3R deletions impacted cocaine self-administration, cocaine-enhanced brain-stimulation reward, or cocaine-induced hyperlocomotion. Furthermore, D3R knockout in dopamine neurons reduced oxycodone-induced hyperactivity and analgesia, while deletion from D1R-expressing neurons potentiated opioid-induced hyperactivity without affecting analgesia. CONCLUSIONS: We dissected presynaptic versus postsynaptic D3R function in the mesolimbic dopamine system. D2R and D3R are expressed in different populations of midbrain dopamine neurons, regulating dopamine release. The mesolimbic D3Rs are critically involved in the actions of opioids but not cocaine.

Research on spatial carving method of glutenite reservoir based on opacity voxel imaging.

The glutenite reservoir in an exploration area in eastern China is well-developed and holds significant exploration potential as an important oil and gas alternative layer. However, due to the influence of sedimentary characteristics, the glutenite reservoir exhibits strong lateral heterogeneity, significant vertical thickness variations, and low accuracy in reservoir space characterization, which affects the reasonable and effective deployment of development wells. Seismic data contains the three-dimensional spatial characteristics of geological bodies, but how to design a suitable transfer function to extract the nonlinear relationship between seismic data and reservoirs is crucial. At present, the transfer functions are concentrated in low-dimensional or high-dimensional fixed mathematical models, which cannot accurately describe the nonlinear relationship between seismic data and complex reservoirs, resulting in low spatial description accuracy of complex reservoirs. In this regard, this paper first utilizes a fusion method based on probability kernel to fuse seismic attributes such as wave impedance, effective bandwidth, and composite envelope difference. This provide a more intuitive reflection of the distribution characteristics of glutenite reservoirs. Moreover, a hybrid nonlinear transfer function is established to transform the fused attribute cube into an opaque attribute cube. Finally, the illumination model and ray casting method are used to perform voxel imaging of the glutenite reservoirs, brighten the detailed characteristics of reservoir space, and then form a set of methods for ' brightening reservoirs and darkening non-reservoirs ', which improves the spatial engraving accuracy of glutenite reservoirs.

Deep learning-based automated scan plane positioning for brain magnetic resonance imaging.

Background: Manual planning of scans in clinical magnetic resonance imaging (MRI) exhibits poor accuracy, lacks consistency, and is time-consuming. Meanwhile, classical automated scan plane positioning methods that rely on certain assumptions are not accurate or stable enough, and are computationally inefficient for practical application scenarios. This study aims to develop and evaluate an effective, reliable, and accurate deep learning-based framework that incorporates prior physical knowledge for automatic head scan plane positioning in MRI. Methods: A deep learning-based end-to-end automated scan plane positioning framework has been developed for MRI head scans. Our model takes a three-dimensional (3D) pre-scan image input, utilizing a cascaded 3D convolutional neural network to detect anatomical landmarks from coarse to fine. And then, with the determined landmarks, accurate scan plane localization can be achieved. A multi-scale spatial information fusion module was employed to aggregate high- and low-resolution features, combined with physically meaningful point regression loss (PRL) function and direction regression loss (DRL) function. Meanwhile, we simulate complex clinical scenarios to design data augmentation strategies. Results: Our proposed approach shows good performance on a clinically wide range of 229 MRI head scans, with a point-to-point absolute error (PAE) of 0.872 mm, a point-to-point relative error (PRE) of 0.10%, and an average angular error (AAE) of 0.502°, 0.381°, and 0.675° for the sagittal, transverse, and coronal planes, respectively. Conclusions: The proposed deep learning-based automated scan plane positioning shows high efficiency, accuracy and robustness when evaluated on varied clinical head MRI scans with differences in positioning, contrast, noise levels and pathologies.

Single-cell RNA sequencing reveals the MIF/ACKR3 receptor-ligand interaction between neutrophils and nucleus pulposus cells in intervertebral disc degeneration.

OBJECTIVES: To unravel the heterogeneity and function of microenvironmental neutrophils during intervertebral disc degeneration (IDD). METHODS: Single-cell RNA sequencing (scRNA-seq) was utilized to dissect the cellular landscape of neutrophils in intervertebral disc (IVD) tissues and their crosstalk with nucleus pulposus cells (NPCs). The expression levels of macrophage migration inhibitory factor (MIF) and ACKR3 in IVD tissues were detected. The MIF/ACKR3 axis was identified and its effects on IDD were investigated in vitro and in vivo. RESULTS: We sequenced here 71520 single cells from 5 control and 9 degenerated IVD samples using scRNA-seq. We identified a unique cluster of neutrophils abundant in degenerated IVD tissues that highly expressed MIF and was functionally enriched in extracellular matrix organization (ECMO). Cell-to-cell communication analyses showed that this ECMO-neutrophil subpopulation was closely interacted with an effector NPCs subtype, which displayed high expression of ACKR3. Further analyses revealed that MIF was positively correlated with ACKR3 and functioned via directly binding to ACKR3 on effector NPCs. MIF inhibition attenuated degenerative changes of NPCs and extracellular matrix, which could be partially reversed by ACKR3 overexpression. Clinically, a significant correlation of high MIF/ACKR3 expression with advanced IDD grade was observed. Furthermore, we also found a positive association between MIF+ ECMO-neutrophil counts and ACKR3+ effector NPCs density as well as higher expression of the MIF/ACKR3 signaling in areas where these two cell types were neighbors. CONCLUSIONS: These data suggest that ECMO-neutrophil promotes IDD progression by their communication with NPCs via the MIF/ACKR3 axis, which may shed light on therapeutic strategies.

Construction of Prediction Model of Foodborne Disease Outbreaks and Its Trend Prediction - Guizhou Province, China, 2023-2025.

Objective: Foodborne diseases pose a significant public health concern globally. This study aims to analyze the correlation between disease prevalence and climatic conditions, forecast the pattern of foodborne disease outbreaks, and offer insights for effective prevention and control strategies and optimizing health resource allocation policies in Guizhou Province. Methods: This study utilized the χ2 test and four comprehensive prediction models to analyze foodborne disease outbreaks recorded in the Guizhou Foodborne Disease Outbreak system between 2012 and 2022. The best-performing model was chosen to forecast the trend of foodborne disease outbreaks in Guizhou Province, 2023-2025. Results: Significant variations were observed in the incidence of foodborne disease outbreaks in Guizhou Province concerning various meteorological factors (all P≤0.05). Among all models, the SARIMA-ARIMAX combined model demonstrated the most accurate predictive performance (RMSE: Prophet model=67.645, SARIMA model=3.953, ARIMAX model=26.544, SARIMA-ARIMAX model=26.196; MAPE: Prophet model=42.357%, SARIMA model=37.740%, ARIMAX model=15.289%, SARIMA-ARIMAX model=13.961%). Conclusion: The analysis indicates that foodborne disease outbreaks in Guizhou Province demonstrate distinct seasonal patterns. It is recommended to concentrate prevention efforts during peak periods. The SARIMA-ARIMAX hybrid model enhances the precision of monthly forecasts for foodborne disease outbreaks, offering valuable insights for future prevention and control strategies.

Impact of Gender-Role Attitudes and Mental Health on Hostile Sexism and Acceptance of Cosmetic Surgery.

BACKGROUND: Each year, tens of thousands of people worldwide choose to undergo cosmetic surgery in order to alter their appearance. In recent years, young people have gradually emerged to comprise the main driving force behind the increasing demand for cosmetic surgery. Previous studies have found that sexism may motivate young people to undergo such surgeries. However, few studies have been conducted to determine if this psychological mechanism influences the acceptance of cosmetic surgery among Chinese university students. METHODS: A total of 579 Chinese university students (280 girls and 299 boys, 17-20 years) volunteered to participate in the online survey. They completed a questionnaire containing the Ambivalent Sexism Inventory, the 12-item General Health Questionnaire, the Gender-Role Attitudes Questionnaire and the Acceptance of Cosmetic Surgery Scale. We firstly evaluated the underlying factor structure of the Acceptance of Cosmetic Surgery Scale using exploratory and confirmatory factor analyses, and exploring pattern of associations between the constructs was analyzed via path analysis. RESULTS: According to the findings, hostile sexism was associated with greater levels of acceptance toward cosmetic surgery. Moreover, gender-role attitudes mediated the link between hostile sexism and the acceptance of cosmetic surgery, and this mediation was positively influenced by general mental health. CONCLUSION: Our study contributes to a deeper understanding of Chinese university students' attitudes toward cosmetic surgery, hostile sexism may contribute to normalizing traditional gender stereotypes and encourage cosmetic surgery acceptability among Chinese university students. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma.

OBJECTIVES: To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC). METHODS: This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems. RESULTS: Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001). CONCLUSION: A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC. CRITICAL RELEVANCE STATEMENT: The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC. KEY POINTS: A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.

Blood miRNAs as Potential Diagnostic Biomarkers for Chronic Obstructive Pulmonary Disease: A Meta-Analysis.

Purpose: Investigate the efficacy of blood microRNAs (miRNAs) as diagnostic biomarkers for Chronic Obstructive Pulmonary Disease (COPD). Patients and Methods: We conducted a comprehensive search in English and Chinese databases, selecting studies based on predetermined criteria. Diagnostic parameters like summarized sensitivity (SSEN), summarized specificity (SSPE), summarized positive likelihood ratio (SPLR), summarized negative likelihood ratio (SNLR), and diagnostic odds ratio (DOR), and area under the curve (AUC) of the summary receiver operating characteristic (SROC) curves were analyzed using a bivariate model. Each parameter was accompanied by a 95% confidence interval (CI). Results: Eighteen high-quality studies were included. For diagnosing COPD with blood miRNAs, the SSEN was 0.83 (95% CI 0.76-0.89), SSPE 0.76 (95% CI 0.70-0.82), SPLR 3.50 (95% CI 2.66-4.60), SNLR 0.22 (95% CI 0.15-0.33), DOR 15.72 (95% CI 8.58-28.77), and AUC 0.86 (95% CI 0.82-0.88). In acute exacerbations, SSEN was 0.85 (95% CI 0.76-0.91), SSPE 0.80 (95% CI 0.73-0.86), SPLR 4.26 (95% CI 3.05-5.95), SNLR 0.19 (95% CI 0.12-0.30), DOR 22.29 (95% CI 11.47-43.33), and AUC 0.89 (95% CI 0.86-0.91). Conclusion: Blood miRNAs demonstrate significant accuracy in diagnosing COPD, both in general and during acute exacerbations, suggesting their potential as reliable biomarkers.

Precise Fabrication and Manipulation of Individual Polymer Nanofibers.

Polymer nanofibers hold promise in a wide range of applications owing to their diverse properties, flexibility, and cost effectiveness. In this study, we introduce a polymer nanofiber drawing process in a scanning electron microscope and focused ion beam (SEM/FIB) instrument with in situ observation. We employed a nanometer-sharp tungsten needle and prepolymer microcapsules to enable nanofiber drawing in a vacuum environment. This method produces individual polymer nanofibers with diameters as small as ∼500 nm and lengths extending to millimeters, yielding nanofibers with an aspect ratio of 2000:1. The attachment to the tungsten manipulator ensures accurate transfer of the polymer nanofiber to diverse substrate types as well as fabrication of assembled structures. Our findings provide valuable insights into ultrafine polymer fiber drawing, paving the way for high-precision manipulation and assembly of polymer nanofibers.

Energy- and Angle-Resolved Scattering of Ne from Dodecane Liquid Surfaces: Theory Corroborating Experiment.

Motivated by recent experimental work by the Neumark group, we present here an all-atom molecular dynamics study of Ne scattering from a dodecane liquid surface with the objective of elucidating the fundamental aspects of gas-liquid dynamics. Using a fine-tuned force field, the GPU-accelerated simulations reproduced semiquantitatively the energy- and angle-resolved experimental results. The branching ratio between the impulsive scattering (IS) and thermal desorption (TD) channels exhibits a clear correlation with the incidence energy (Ei) and angle. Ne atoms with lower Ei values are more likely to be trapped, yielding an increased TD ratio. For a given Ei, a large incidence angle led to a higher IS ratio. The energy transfer between Ne atoms and liquid dodecane was found to be more sensitive to the deflection angle than to the incidence or reflection angle. With an increasing deflection angle, the fractional energy loss increases, suggesting that more kinetic energy is transferred to the liquid.

Endothelial HIF2α suppresses retinal angiogenesis in neonatal mice by upregulating NOTCH signaling.

Prolyl hydroxylase domain (PHD) proteins are oxygen sensors that use intracellular oxygen as a substrate to hydroxylate hypoxia-inducible factor (HIF) α proteins, routing them for polyubiquitylation and proteasomal degradation. Typically, HIFα accumulation in hypoxic or PHD-deficient tissues leads to upregulated angiogenesis. Here, we report unexpected retinal phenotypes associated with endothelial cell (EC)-specific gene targeting of Phd2 (Egln1) and Hif2alpha (Epas1). EC-specific Phd2 disruption suppressed retinal angiogenesis, despite HIFα accumulation and VEGFA upregulation. Suppressed retinal angiogenesis was observed both in development and in the oxygen-induced retinopathy (OIR) model. On the other hand, EC-specific deletion of Hif1alpha (Hif1a), Hif2alpha, or both did not affect retinal vascular morphogenesis. Strikingly, retinal angiogenesis appeared normal in mice double-deficient for endothelial PHD2 and HIF2α. In PHD2-deficient retinal vasculature, delta-like 4 (DLL4, a NOTCH ligand) and HEY2 (a NOTCH target) were upregulated by HIF2α-dependent mechanisms. Inhibition of NOTCH signaling by a chemical inhibitor or DLL4 antibody partially rescued retinal angiogenesis. Taken together, our data demonstrate that HIF2α accumulation in retinal ECs inhibits rather than stimulates retinal angiogenesis, in part by upregulating DLL4 expression and NOTCH signaling.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN: Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS: Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS: Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Leakage Proof, Flame-Retardant, and Electromagnetic Shield Wood Morphology Genetic Composite Phase Change Materials for Solar Thermal Energy Harvesting.

Phase change materials (PCMs) offer a promising solution to address the challenges posed by intermittency and fluctuations in solar thermal utilization. However, for organic solid-liquid PCMs, issues such as leakage, low thermal conductivity, lack of efficient solar-thermal media, and flammability have constrained their broad applications. Herein, we present an innovative class of versatile composite phase change materials (CPCMs) developed through a facile and environmentally friendly synthesis approach, leveraging the inherent anisotropy and unidirectional porosity of wood aerogel (nanowood) to support polyethylene glycol (PEG). The wood modification process involves the incorporation of phytic acid (PA) and MXene hybrid structure through an evaporation-induced assembly method, which could impart non-leaking PEG filling while concurrently facilitating thermal conduction, light absorption, and flame-retardant. Consequently, the as-prepared wood-based CPCMs showcase enhanced thermal conductivity (0.82 W m-1 K-1, about 4.6 times than PEG) as well as high latent heat of 135.5 kJ kg-1 (91.5% encapsulation) with thermal durability and stability throughout at least 200 heating and cooling cycles, featuring dramatic solar-thermal conversion efficiency up to 98.58%. In addition, with the synergistic effect of phytic acid and MXene, the flame-retardant performance of the CPCMs has been significantly enhanced, showing a self-extinguishing behavior. Moreover, the excellent electromagnetic shielding of 44.45 dB was endowed to the CPCMs, relieving contemporary health hazards associated with electromagnetic waves. Overall, we capitalize on the exquisite wood cell structure with unidirectional transport inherent in the development of multifunctional CPCMs, showcasing the operational principle through a proof-of-concept prototype system.

Zinc-associated phospholipid metabolic alterations and their impacts on ALT levels in workers.

Zinc (Zn) is an essential trace element that is required for various biological functions, but excessive exposure to Zn is associated with many disorders and even diseases. However, the health effects and underlying mechanisms of long-term and high concentration exposure of Zn remain to be unclear. In the present study, we investigated the association between occupational exposure to Zn and liver function indicators (like alanine aminotransferase (ALT)) in workers. We found a positive association between Zn exposure and ALT level in workers. Workers having higher blood Zn (7735.65 (1159.15) µg/L) shows a 30.4 % increase in ALT level compared to those with lower blood Zn (5969.30 (989.26) µg/L). Furthermore, we explored the effects of phospholipids (PLs) and their metabolism on ALT level and discovered that Zn exposure in workers was associated with changes in PL levels and metabolism, which had further effects on increased ALT levels in workers. The study provides insights into the relationship between occupational Zn exposure and liver function, highlights the risk of long-term exposure to high concentrations of Zn, and paves the way for understanding the underlying mechanisms of Zn exposure on human health.