Comparing Sonazoid contrast-enhanced ultrasound to contrast-enhanced CT and MRI for differentially diagnosing renal lesions: a prospective multicenter study.

PURPOSE: To evaluate the diagnostic performance of contrast-enhanced (CE) ultrasound using Sonazoid (SNZ-CEUS) by comparing with contrast-enhanced computed tomography (CE-CT) and contrast-enhanced magnetic resonance imaging (CE-MRI) for differentiating benign and malignant renal masses. MATERIALS AND METHODS: 306 consecutive patients (from 7 centers) with renal masses (40 benign tumors, 266 malignant tumors) diagnosed by both SNZ-CEUS, CE-CT or CE-MRI were enrolled between September 2020 and February 2021. The examinations were performed within 7 days, but the sequence was not fixed. Histologic results were available for 301 of 306 (98.37%) lesions and 5 lesions were considered benign after at least 2 year follow-up without change in size and image characteristics. The diagnostic performances were evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and compared by McNemar's test. RESULTS: In the head-to-head comparison, SNZ-CEUS and CE-MRI had comparable sensitivity (95.60 vs. 94.51%, P = 0.997), specificity (65.22 vs. 73.91%, P = 0.752), positive predictive value (91.58 vs. 93.48%) and negative predictive value (78.95 vs. 77.27%); SNZ-CEUS and CE-CT showed similar sensitivity (97.31 vs. 96.24%, P = 0.724); however, SNZ-CEUS had relatively lower than specificity than CE-CT (59.09 vs. 68.18%, P = 0.683). For nodules > 4 cm, CE-MRI demonstrated higher specificity than SNZ-CEUS (90.91 vs. 72.73%, P = 0.617) without compromise the sensitivity. CONCLUSIONS: SNZ-CEUS, CE-CT, and CE-MRI demonstrate desirable and comparable sensitivity for the differentiation of renal mass. However, the specificity of all three imaging modalities is not satisfactory. SNZ-CEUS may be a suitable alternative modality for patients with renal dysfunction and those allergic to gadolinium or iodine-based agents.

Efficient Template-Catalyzed In Situ Polymerization for Carbon Xerogels with Large Specific Surface Area and High Adsorption.

The limited specific surface area (SSA), long preparation period, and high cost are significant challenges for carbon xerogels (CXs). To overcome these limitations, we propose an approach to prepare tannin-resorcinol-formaldehyde-based CXs through template-catalyzed in situ polymerization. ZnCl2 acts as a catalyst and significantly accelerates the polymerization reaction through the coordination of Zn2+ to the carbonyl group in formaldehyde, while atmospheric drying instead of special drying and without solvent exchange reduces the preparation period to 24 h. In addition, ZnCl2 acts as an activator for the formation of many pores. Plant-derived tannins not only reduce the preparation cost but also regulate the pore structure. The resulted CXs with hierarchical porous structures show an optimal SSA of 1308 m2/g, high adsorption capabilities (for cationic, nitrosoaniline dyes, metal, and nonmetallic ions, especially for methylene blue with 454.93 mg/g), low shrinkage down to 10%, and reusability with 92.9% retention after 5 cycles. This work provides a promising and cost-effective method for the large-scale preparation of porous carbon materials with large SSA, offering potential applications in adsorption, energy storage, and catalysis.

Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC. SIGNIFICANCE: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

Regional analysis to delineate intrasample heterogeneity with RegionalST.

MOTIVATION: Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. RESULTS: To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.

Analysis of endophytic bacterial diversity in seeds of different genotypes of cotton and the suppression of Verticillium wilt pathogen infection by a synthetic microbial community.

BACKGROUND: In agricultural production, fungal diseases significantly impact the yield and quality of cotton (Gossypium spp.) with Verticillium wilt posing a particularly severe threat. RESULTS: This study is focused on investigating the effectiveness of endophytic microbial communities present in the seeds of disease-resistant cotton genotypes in the control of cotton Verticillium wilt. The technique of 16S ribosomal RNA (16S rRNA) amplicon sequencing identified a significant enrichment of the Bacillus genus in the resistant genotype Xinluzao 78, which differed from the endophytic bacterial community structure in the susceptible genotype Xinluzao 63. Specific enriched strains were isolated and screened from the seeds of Xinluzao 78 to further explore the biological functions of seed endophytes. A synthetic microbial community (SynCom) was constructed using the broken-rod model, and seeds of the susceptible genotype Xinluzao 63 in this community that had been soaked with the SynCom were found to significantly control the occurrence of Verticillium wilt and regulate the growth of cotton plants. Antibiotic screening techniques were used to preliminarily identify the colonization of strains in the community. These techniques revealed that the strains can colonize plant tissues and occupy ecological niches in cotton tissues through a priority effect, which prevents infection by pathogens. CONCLUSION: This study highlights the key role of seed endophytes in driving plant disease defense and provides a theoretical basis for the future application of SynComs in agriculture.

Oral bacteriome and mycobiome of patients with idiopathic membranous nephropathy with different tongue coatings treated with a Chinese herbal formula.

ETHNOPHARMACOLOGICAL RELEVANCE: Moshen Fuyuan Formula (MSFY) is one of the representative Chinese medicine compound for Idiopathic membranous nephropathy (IMN), that originate from Fang Ji Huang Qi decoction in the Han dynasty. IMN is usually accompanied by different tongue coatings in traditional Chinese medicine (TCM), and tongue microorganisms are important factors affecting the formation of the tongue coating. Recently, oral microbiomes, including bacteria and fungi, have been identified as pivotal factors that contribute to disease development. However, the regulation of oral microbiomes by MSFY has not been defined. AIM OF THE STUDY: In this work, we explore the characteristics of oral bacteria and fungi in IMN patients with different tongue coatings, and clarify the therapeutic effect of MSFY based on oral microbiome. MATERIALS AND METHODS: We enrolled 24 patients with IMN, including 11 with white tongue (IMN-W) and 13 with yellow tongue (IMN-Y), and recruited an additional 10 healthy individuals. Patients with IMN were treated with the MSFY. The oral bacteriome and fungi before and after treatment were detected using full-length 16S rRNA and internal transcribed spacer gene sequencing. RESULTS: The therapeutic effect of MSFY on patients with yellow tongue coating was more significant than that on patients with white tongue coating. In terms of oral bacteriome, Campylobacter bacteria were enriched in patients with yellow tongue and could be a promising biomarker for yellow coating. Enrichment of Veillonella parvula_A may partially account for the therapeutic effect of MSFY. As for oral fungi, Malassezia globosa was enhanced in patients with IMN-W and reduced in patients with IMN-Y. Notably, it was reduced by MSFY. We also found that mycobiome-bacteriome interactions were highly complex and dynamic in patients with IMN. CONCLUSION: The regulation of the dynamic balance between oral fungi and bacteria by MSFY contributes to the treatment of IMN. This study determined the oral bacteriome and mycobiome of patients with IMN with different tongue coatings before and after MSFY treatment, which aids in promoting personalized treatment in clinical TCM and provides direction for investigating the mechanism of Chinese herbal medicines.

Thermal and Magnetic Dual-Responsive Catheter-Assisted Shape Memory Microrobots for Multistage Vascular Embolization.

Catheters navigating through complex vessels, such as sharp turns or multiple U-turns, remain challenging for vascular embolization. Here, we propose a novel multistage vascular embolization strategy for hard-to-reach vessels that releases untethered swimming shape-memory magnetic microrobots (SMMs) from the prior catheter to the vessel bifurcation. SMMs, made of organo-gel with magnetic particles, ensure biocompatibility, radiopacity, thrombosis, and fast thermal and magnetic responses. An SMM is initially a linear shape with a 0.5-mm diameter at 20 °C inserted in a catheter. It transforms into a predetermined helix within 2 s at 38 °C blood temperature after being pushed out of the catheter into the blood. SMMs enable agile swimming in confined and tortuous vessels and can swim upstream using helical propulsion with rotating magnetic fields. Moreover, we validated this multistage vascular embolization in living rabbits, completing 100-cm travel and renal artery embolization in 2 min. After 4 weeks, the SMMs maintained the embolic position, and the kidney volume decreased by 36%.

Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses.

Meta-analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be carefully considered. A critical problem in meta-analyses and systematic reviews is that outlying studies are frequently included, which can lead to invalid conclusions and affect the robustness of decision-making. Outliers may be caused by several factors such as study selection criteria, low study quality, small-study effects, and so on. Although outlier detection is well-studied in the statistical community, limited attention has been paid to meta-analysis. The conventional outlier detection method in meta-analysis is based on a leave-one-study-out procedure. However, when calculating a potentially outlying study's deviation, other outliers could substantially impact its result. This article proposes an iterative method to detect potential outliers, which reduces such an impact that could confound the detection. Furthermore, we adopt bagging to provide valid inference for sensitivity analyses of excluding outliers. Based on simulation studies, the proposed iterative method yields smaller bias and heterogeneity after performing a sensitivity analysis to remove the identified outliers. It also provides higher accuracy on outlier detection. Two case studies are used to illustrate the proposed method's real-world performance.

Prognostic value of right atrial strains in arrhythmogenic right ventricular cardiomyopathy.

OBJECTIVES: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy characterized by progressive fibrofatty infiltration of atrial and ventricular myocardium resulting in adverse cardiac events. Atrial function has been increasingly recognized as prognostically important for cardiovascular disease. As the right atrial (RA) strain is a sensitive parameter to describe RA function, we aimed to analyze the prognostic value of the RA strain in ARVC. METHODS: RA strain parameters were derived from cardiac magnetic resonance (CMR) images of 105 participants with definite ARVC. The endpoint was defined as a combination of sudden cardiac death, survival cardiac arrest, and appropriate implantable cardioverter-defibrillator intervention. Cox regression and Kaplan-Meier survival analyses were performed to evaluate the association between RA strain parameters and endpoint. Concordance index (C index), net reclassification index (NRI), and integrated discrimination improvement (IDI) were calculated to assess the incremental value of RA strain in predicting the endpoint. RESULTS: After a median follow-up of 5 years, 36 (34.3%) reaching the endpoint displayed significantly reduced RA strain parameters. At Kaplan-Meier analysis, impaired RA reservoir (RARS) and booster strains (RABS) were associated with an increased risk of the endpoint. After adjusting for conventional risk factors, RARS (hazard ratio [HR], 0.956; p = 0.005) and RABS (HR, 0.906; p = 0.002) resulted as independent predictors for endpoint at Cox regression analyses. In addition, RARS and RABS improved prognostic value to clinical risk factors and CMR morphological and functional predictors (all p < 0.05). CONCLUSION: RARS and RABS were independent predictors for adverse cardiac events, which could provide incremental prognostic value for conventional predictors in ARVC. CRITICAL RELEVANCE STATEMENT: We evaluated the prognostic value of right atrial strain in ARVC patients and suggested cardiologists consider RA strain as a predictive parameter when evaluating the long-term outcome of ARVC patients in order to formulate better clinical therapy. KEY POINTS: • Patients with ARVC had significantly reduced RA strain and strain rates compared with healthy participants. • Participants with lower RA reservoir and booster stains were associated with a significantly higher risk of adverse cardiac events. • RA booster and reservoir strain provide incremental value to conventional parameters.

Nilotinib in combination with sunitinib renders MCL-1 for degradation and activates autophagy that overcomes sunitinib resistance in renal cell carcinoma.

PURPOSE: Sunitinib is a recommended drug for metastatic renal cell carcinoma (RCC). However, the therapeutic potential of sunitinib is impaired by toxicity and resistance. Therefore, we seek to explore a combinatorial strategy to improve sunitinib efficacy of low-toxicity dose for better clinical application. METHODS: We screen synergistic reagents of sunitinib from a compound library containing 1374 FDA-approved drugs by in vitro cell viability evaluation. The synergistically antiproliferative and proapoptotic effects were demonstrated on in vitro and in vivo models. The molecular mechanism was investigated by phosphoproteomics, co-immunoprecipitation, immunofluorescence and western-blot assays, etc. RESULTS: From the four-step screening, nilotinib stood out as a potential synergistic killer combined with sunitinib. Subsequent functional evaluation demonstrated that nilotinib and sunitinib synergistically inhibit RCC cell proliferation and promote apoptosis in vitro and in vivo. Mechanistically, nilotinib activates E3-ligase HUWE1 and in combination with sunitinib renders MCL-1 for degradation via proteasome pathway, resulting in the release of Beclin-1 from MCL-1/Beclin-1 complex. Subsequently, Beclin-1 induces complete autophagy flux to promote antitumor effect. CONCLUSION: Our findings revealed that a novel mechanism that nilotinib in combination with sunitinib overcomes sunitinib resistance in RCC. Therefore, this novel rational combination regimen provides a promising therapeutic avenue for metastatic RCC and rationale for evaluating this combination clinically.

3D Fractal Dimension Analysis: Prognostic Value of Right Ventricular Trabecular Complexity in Participants with Arrhythmogenic Cardiomyopathy.

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive myocardial fibro-fatty infiltration accompanied by trabecular disarray. Traditionally, two-dimensional (2D) instead of 3D fractal dimension (FD) analysis has been used to evaluate trabecular disarray. However, the prognostic value of trabecular disorder assessed by 3D FD measurement remains unclear. PURPOSE: To investigate the prognostic value of right ventricular trabecular complexity in ACM patients using 3D FD analysis based on cardiac MR cine images. STUDY TYPE: Retrospective. POPULATION: 85 ACM patients (mean age: 45 ± 17 years, 52 male). FIELD STRENGTH/SEQUENCE: 3.0T/cine imaging, T2-short tau inversion recovery (T2-STIR), and late gadolinium enhancement (LGE). ASSESSMENT: Using cine images, RV (right ventricular) volumetric and functional parameters were obtained. RV trabecular complexity was measured with 3D fractal analysis by box-counting method to calculate 3D-FD. Cox and logistic regression models were established to evaluate the prognostic value of 3D-FD for major adverse cardiac events (MACE). STATISTICAL TESTS: Cox regression and logistic regression to explore the prognostic value of 3D-FD. C-index, time-dependent receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) to evaluate the incremental value of 3D-FD. Intraclass correlation coefficient for interobserver variability. P < 0.05 indicated statistical significance. RESULTS: 26 MACE were recorded during the 60 month follow-up (interquartile range: 48-67 months). RV 3D-FD significantly differed between ACM patients with MACE (2.67, interquartile range: 2.51 ~ 2.81) and without (2.52, interquartile range: 2.40 ~ 2.67) and was a significant independent risk factor for MACE (hazard ratio, 1.02; 95% confidence interval: 1.01, 1.04). In addition, prognostic model fitness was significantly improved after adding 3D-FD to RV global longitudinal strain, LV involvement, and 5-year risk score separately. DATA CONCLUSION: The myocardial trabecular complexity assessed through 3D FD analysis was found associated with MACE and provided incremental prognostic value beyond conventional ACM risk factors. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 1.

The tRNA thiolation-mediated translational control is essential for plant immunity.

Plants have evolved sophisticated mechanisms to regulate gene expression to activate immune responses against pathogen infections. However, how the translation system contributes to plant immunity is largely unknown. The evolutionarily conserved thiolation modification of transfer RNA (tRNA) ensures efficient decoding during translation. Here, we show that tRNA thiolation is required for plant immunity in Arabidopsis. We identify a cgb mutant that is hyper-susceptible to the pathogen Pseudomonas syringae. CGB encodes ROL5, a homolog of yeast NCS6 required for tRNA thiolation. ROL5 physically interacts with CTU2, a homolog of yeast NCS2. Mutations in either ROL5 or CTU2 result in loss of tRNA thiolation. Further analyses reveal that both transcriptome and proteome reprogramming during immune responses are compromised in cgb. Notably, the translation of salicylic acid receptor NPR1 is reduced in cgb, resulting in compromised salicylic acid signaling. Our study not only reveals a regulatory mechanism for plant immunity but also uncovers an additional biological function of tRNA thiolation.

Identification of a hub gene VCL for atherosclerotic plaques and discovery of potential therapeutic targets by molecular docking.

BACKGROUND: Atherosclerosis (AS) is a pathology factor for cardiovascular diseases and instability of atherosclerotic plaques contributes to acute coronary events. This study identified a hub gene VCL for atherosclerotic plaques and discovered its potential therapeutic targets for atherosclerotic plaques. METHODS: Differential expressed genes (DEGs) were screened between unstable and stable plaques from GSE120521 dataset and then used for construction of a protein-protein interactions (PPI) network. Through topological analysis, hub genes were identified within this PPI network, followed by construction of a diagnostic model. GSE41571 dataset was utilized to validate the diagnostic model. A key hub gene was identified and its association with immune characteristics and pathways were further investigated. Molecular docking and molecular dynamics (MD) simulation were employed to discover potential therapeutic targets. RESULTS: According to the PPI network, 3 tightly connected protein clusters were found. Topological analysis identified the top 5 hub genes, Vinculin (VCL), Dystrophin (DMD), Actin alpha 2 (ACTA2), Filamin A (FLNA), and transgelin (TAGLN). Among these hub genes, VCL had the highest diagnostic value. VCL was selected for further analysis and we found that VCL was negatively correlated with immune score and AS-related inflammatory pathways. Next, we identified 408 genes that were highly correlated with VCL and determined potential drug candidates. The results from molecular docking and MD simulation showed compound DB07117 combined with VCL protein stably, the binding energy is -7.7 kcal/mol, indicating that compound DB07117 was a potential inhibitor of VCL protein. CONCLUSION: This study identified VCL as a key gene for atherosclerotic plaques and provides a potential therapeutic target of VCL for the treatment of atherosclerotic plaques.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models.

BACKGROUND: Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets. METHODS: We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent. RESULTS: We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment. CONCLUSIONS: Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

MR Uniformity Ratio Estimates to Evaluate Ventricular Mechanical Dyssynchrony and Prognosis After ST-Segment Elevation Myocardial Infarction.

BACKGROUND: The impact of left ventricular mechanical dyssynchrony (LVMD) on the long-term prognosis of ST-segment elevation myocardial infarction (STEMI) is unclear. HYPOTHESIS: MR uniformity ratio estimates (URE) can detect LVMD and assess STEMI prognosis. STUDY TYPE: Retrospective analysis of a prospective multicenter registry (EARLY-MYO trial, NCT03768453). POPULATION: Overall, 450 patients (50 females) with first-time STEMI were analyzed, as well as 40 participants without cardiovascular disease as controls. FIELD STRENGTH/SEQUENCE: 3.0-T, balanced steady-state free precession cine and late gadolinium enhancement imaging. ASSESSMENT: MRI data were acquired within 1 week of symptom onset. Major adverse cardiovascular events (MACEs), including cardiovascular death, nonfatal re-infarction, hospitalization for heart failure, and stroke, were the primary clinical outcomes. LVMD was represented by circumferential URE (CURE) and radial URE (RURE) calculated using strain measurements. The patients were grouped according to clinical outcomes or URE values. Patients' clinical characteristics and MR indicators were compared. STATISTICAL TESTS: The Student's t-test, Mann-Whitney U test, chi-square test, Fisher's exact test, receiver operating characteristic curve analysis with area under the curve, Kaplan-Meier analysis, Cox regression, logistic regression, intraclass correlation coefficient, c-index, and integrated discrimination improvement were used. P < 0.05 was considered statistically significant. RESULTS: CURE and RURE were significantly lower in patients with STEMI than in controls. The median follow-up was 60.5 months. Patients with both lower CURE and RURE values experienced a significantly higher incidence of MACEs by 3.525-fold. Both CURE and RURE were independent risk factors for MACEs. The addition of UREs improved diagnostic efficacy and risk stratification based on infarct size and left ventricular ejection fraction (LVEF). The indicators associated with LVMD included male sex, serum biomarkers (peak creatine phosphokinase and cardiac troponin I), infarct size, and LVEF. DATA CONCLUSION: CURE and RURE may be useful to evaluate long-term prognosis after STEMI. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55% of metabolites. Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.

Transcriptome-wide association studies (TWAS) integrate gene expression prediction models and genome-wide association studies (GWAS) to identify gene-trait associations. The power of TWAS is determined by the sample size of GWAS and the accuracy of the expression prediction model. Here, we present a new method, the Summary-level Unified Method for Modeling Integrated Transcriptome using Functional Annotations (SUMMIT-FA), which improves gene expression prediction accuracy by leveraging functional annotation resources and a large expression quantitative trait loci (eQTL) summary-level dataset. We build gene expression prediction models in whole blood using SUMMIT-FA with the comprehensive functional database MACIE and eQTL summary-level data from the eQTLGen consortium. We apply these models to GWAS for 24 complex traits and show that SUMMIT-FA identifies significantly more gene-trait associations and improves predictive power for identifying "silver standard" genes compared to several benchmark methods. We further conduct a simulation study to demonstrate the effectiveness of SUMMIT-FA.

3D Printed All-Natural Hydrogels: Flame-Retardant Materials Toward Attaining Green Sustainability.

Biomass-based hydrogel is a promising flame-retardant material and has a high potential for applications in transportation, aerospace, building and electrical engineering, and electronics. However, rapid vat photopolymerization (VP) 3D printing of biomass-based hydrogels, especially that of all-natural ones, is still rare. Herein, a new class of VP 3D-printed hydrogels with strong covalent networks, fabricating using fully biomass materials and a commercial liquid crystal display (LCD) printer assembled with low-intensity visible light is presented. Encouragingly, the highly ordered layer-by-layer packing structures provided by VP 3D printing technology endow these hydrogels with remarkable flame retardancy, exceptional temperature resistance, advantageous combustion behaviors, and favorable mechanical strength, in particular, giving them a better limit oxygen index (83.5%) than various biomass-based hydrogels. The proposed approach enables the green design as well as the precise and efficient preparation for flame-retardant materials, paving the way for the future flame-retardant materials toward attaining green sustainability.

Loss of LBP triggers lipid metabolic disorder through H3K27 acetylation-mediated C/EBPß- SCD activation in non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is associated with mutations in lipopolysaccharide-binding protein ( LBP), but the underlying epigenetic mechanisms remain understudied. Herein, LBP -/- rats with NAFLD were established and used to conduct integrative targeting-active enhancer histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation coupled with high-throughput and transcriptomic sequencing analysis to explore the potential epigenetic pathomechanisms of active enhancers of NAFLD exacerbation upon LBP deficiency. Notably, LBP -/- reduced the inflammatory response but markedly aggravated high-fat diet (HFD)-induced NAFLD in rats, with pronounced alterations in the histone acetylome and regulatory transcriptome. In total, 1 128 differential enhancer-target genes significantly enriched in cholesterol and fatty acid metabolism were identified between wild-type (WT) and LBP -/- NAFLD rats. Based on integrative analysis, CCAAT/enhancer-binding protein ß (C/EBPß) was identified as a pivotal transcription factor (TF) and contributor to dysregulated histone acetylome H3K27ac, and the lipid metabolism gene SCD was identified as a downstream effector exacerbating NAFLD. This study not only broadens our understanding of the essential role of LBP in the pathogenesis of NAFLD from an epigenetics perspective but also identifies key TF C/EBPß and functional gene SCD as potential regulators and therapeutic targets.

Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data.

Alzheimer disease (AD) is a common neurodegenerative disease with a late onset. It is critical to identify novel blood-based DNA methylation biomarkers to better understand the extent of the molecular pathways affected in AD. Two sets of blood DNA methylation genetic prediction models developed using different reference panels and modelling strategies were leveraged to evaluate associations of genetically predicted DNA methylation levels with AD risk in 111,326 (46,828 proxy) cases and 677,663 controls. A total of 1,168 cytosine-phosphate-guanine (CpG) sites showed a significant association with AD risk at a false discovery rate (FDR) < 0.05. Methylation levels of 196 CpG sites were correlated with expression levels of 130 adjacent genes in blood. Overall, 52 CpG sites of 32 genes showed consistent association directions for the methylation-gene expression-AD risk, including nine genes (CNIH4, THUMPD3, SERPINB9, MTUS1, CISD1, FRAT2, CCDC88B, FES, and SSH2) firstly reported as AD risk genes. Nine of 32 genes were enriched in dementia and AD disease categories (P values ranged from 1.85 × 10-4 to 7.46 × 10-6), and 19 genes in a neurological disease network (score = 54) were also observed. Our findings improve the understanding of genetics and etiology for AD.