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The recycling and reuse of solid wastes mark the construction industry's development towards sustainable and green technologies. 3D printing technology using recycled solid materials is in great demand for intelligent and low-carbon construction. This paper incorporates crumb rubber (0–1 mm) into the printing ink and analyses the fresh and hardened properties. The results showed that the flowability was positively correlated with rubber ratio but negatively with fiber content. R40F15 (40% rubber and 1.5% fiber) obtained the optimal flowability for 3D printing, which remained above 15 mm for 30min. The compressive strength peaked when incorporating 1.5% fiber, while the flexural strength reached the bottom. X direction generated the highest compressive strength in the whole anisotropic tests, followed by the Y and Z directions. The highest strengths of 1.5% and 2.0% fiber content were 69.45 MPa and 64.5 MPa, incorporating 20% rubber. Thus, surfaces anticipated to bear higher loads in compression should be aligned with the X-axis or, at a minimum, avoid alignment with the Z-axis for optimal structural performance. Y direction obtained the highest flexural strength in all anisotropic tests when incorporating 1.5% fiber. Results from digital image correlation test supported that fiber tended to agglomerate in the Y direction. Besides, the environmental analysis was conducted in this paper to quantitively calculate the impacts of all raw materials to the environment.

Abstract Background Blood biomarkers predictive of the progression of idiopathic pulmonary fibrosis (IPF) would be of value for research and clinical practice. We used data from the IPF-PRO Registry to investigate whether the addition of “omics” data to risk prediction models based on demographic and clinical characteristics improved prediction of the progression of IPF. Methods The IPF-PRO Registry enrolled patients with IPF at 46 sites across the US. Patients were followed prospectively. Median follow-up was 27.2 months. Prediction models for disease progression included omics data (proteins and microRNAs [miRNAs]), demographic factors and clinical factors, all assessed at enrollment. Data on proteins and miRNAs were included in the models either as raw values or based on clusters in various combinations. Least absolute shrinkage and selection operator (Lasso) Cox regression was applied for time-to-event composite outcomes and logistic regression with L1 penalty was applied for binary outcomes assessed at 1 year. Model performance was assessed using Harrell’s C-index (for time-to-event outcomes) or area under the curve (for binary outcomes). Results Data were analyzed from 231 patients. The models based on demographic and clinical factors, with or without omics data, were the top-performing models for prediction of all the time-to-event outcomes. Relative changes in average C-index after incorporating omics data into models based on demographic and clinical factors ranged from 1.7 to 3.2%. Of the blood biomarkers, surfactant protein-D, serine protease inhibitor A7 and matrix metalloproteinase-9 (MMP-9) were among the top predictors of the outcomes. For the binary outcomes, models based on demographics alone and models based on demographics plus omics data had similar performances. Of the blood biomarkers, CC motif chemokine 11, vascular cell adhesion protein-1, adiponectin, carcinoembryonic antigen and MMP-9 were the most important predictors of the binary outcomes. Conclusions We identified circulating protein and miRNA biomarkers associated with the progression of IPF. However, the integration of omics data into prediction models that included demographic and clinical factors did not materially improve the performance of the models. Trial registration ClinicalTrials.gov; No: NCT01915511; registered August 5, 2013; URL: www.clinicaltrials.gov .

Abstract Methylation quantitative trait loci (meQTLs) quantify the effects of genetic variants on DNA methylation levels. However, most published studies utilize bulk methylation datasets composed of different cell types and limit our understanding of cell-type-specific methylation regulation. We propose a hierarchical Bayesian interaction (HBI) model to infer cell-type-specific meQTLs, which integrates a large-scale bulk methylation data and a small-scale cell-type-specific methylation data. Through simulations, we show that HBI enhances the estimation of cell-type-specific meQTLs. In real data analyses, we demonstrate that HBI can further improve the functional annotation of genetic variants and identify biologically relevant cell types for complex traits.

Abstract Background Oenococcus oeni is a commercial wine-fermenting bacterial strain, owing to its high efficiency of malolactic fermentation and stress tolerance. The present study explored the function of key genes in O. oeni to enhance stress resistance by heterologous expression of these genes in another species. Results The orf00404 gene that encodes a two-component signal transduction response regulator in O. oeni was heterologously expressed in Lactiplantibacillus plantarum WCFS1. The expression of orf00404 significantly enhanced the growth rate of the recombinant strain under acid stress. At 60 h, 72 h, and 108 h of culture at pH 4.0, the recombinant strain had 1562, 641, and 748 differentially expressed genes compared to the control strain, respectively. At all three time points, 20 genes were upregulated in the recombinant strain, including the lamA-D operon-coding genes of the quorum-sensing two component signal transduction system and the spx5 RNA polymerase-binding protein coding gene, which may help adaptation to acid stress. In addition, 47 genes were downregulated in the recombinant strain at all three time points, including the hsp1 heat shock protein-coding gene, the trxA1 thioredoxin-coding gene, and the dinP, mutY, umuC, and uvrB DNA damage repair-related protein-coding genes, potentially indicating that the recombinant strain was less susceptible to stress and had less DNA damage than the control strain in acid stress conditions. The recombinant strain had higher membrane fluidity, permeability, and integrity at an early stage of logarithmic growth (72 h), suggesting that it had a more complete and active cell membrane state at this stage. The intracellular ATP content was significantly reduced in the recombinant strain at the beginning of logarithmic growth (60 h), implying that the recombinant strain consumed more energy at this stage to resist acid stress and growth. Conclusions These results indicated that the recombinant strain enhances acid stress tolerance by regulating a gene expression pattern, increasing ATP consumption, and enhancing cell membrane fluidity, membrane permeability, and membrane integrity at specific growth stages. Thus, the recombinant strain may have potential application in the microbial biotechnology industry.

Abstract Simultaneously activating metal and lattice oxygen sites to construct a compatible multi-mechanism catalysis is expected for the oxygen evolution reaction (OER) by providing highly available active sites and mediate catalytic activity/stability, but significant challenges remain. Herein, Fe and S dually modulated NiFe oxyhydroxide (R-NiFeOOH@SO4) is conceived by complete reconstruction of NiMoO4·xH2O@Fe,S during OER, and achieves compatible adsorbate evolution mechanism and lattice oxygen oxidation mechanism with simultaneously optimized metal/oxygen sites, as substantiated by in situ spectroscopy/mass spectrometry and chemical probe. Further theoretical analyses reveal that Fe promotes the OER kinetics under adsorbate evolution mechanism, while S excites the lattice oxygen activity under lattice oxygen oxidation mechanism, featuring upshifted O 2p band centers, enlarged d-d Coulomb interaction, weakened metal-oxygen bond and optimized intermediate adsorption free energy. Benefiting from the compatible multi-mechanism, R-NiFeOOH@SO4 only requires overpotentials of 251 ± 5/291 ± 1 mV to drive current densities of 100/500 mA cm−2 in alkaline media, with robust stability for over 300 h. This work provides insights in understanding the OER mechanism to better design high-performance OER catalysts.

Abstract Background Premature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers. Methods This study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning. Results The proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF. Conclusions Omics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis.

Abstract With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.

This study delves into the intricate relationship between iron (Fe) content in kaolinite and its impact on the adsorption behavior of sodium oleate. The effects of different iron concentrations on adsorption energy, hydrogen bond kinetics and adsorption efficiency were studied through simulation and experimental verification. The results show that the presence of iron in the kaolinite structure significantly improves the adsorption capacity of sodium oleate. Kaolinite samples with high iron content have better adsorption properties, lower adsorption energy levels and shorter and stronger hydrogen bonds than pure kaolinite. The optimal concentration of oleic acid ions for achieving maximum adsorption efficiency was identified as 1.2 mmol/L across different kaolinite samples. At this concentration, the adsorption rates and capacities reach their peak, with Fe-enriched kaolinite samples exhibiting notably higher flotation recovery rates. This optimal concentration represents a balance between sufficient oleic acid ion availability for surface interactions and the prevention of self-aggregation phenomena that could hinder adsorption. This study offers promising avenues for optimizing the flotation process in mineral processing applications.

Objective: To investigate the combined effect of transcranial magnetic stimulation (TMS) and transcranial direct current stimulation on improving lower limb function in stroke patients. Design: Randomized controlled trial. Subjects/Patients: Subacute stroke patients. Methods: 169 post-stroke hemiplegia patients were randomly divided into 4 groups (control, transcranial direct current stimulation, transcutaneous auricular vagus nerve stimulation, and transcutaneous auricular vagus nerve stimulation combined with transcranial direct current stimulation) and evaluated using the Fugl-Meyer Assessment-Lower Extremity (FMA-LL), Timed Up-and-Go (TUG) test, Modified Barthel Index (MBI), Berg Balance Scale (BBS), gait parameters, and surface electromyography (sEMG). Results: Significant improvements in FMA-LL, MBI, BBS, TUG, gait parameters, and sEMG were noted in the intervention groups compared with the control, with the transcutaneous auricular vagus nerve stimulation combined with transcranial direct current stimulation group showing the most pronounced improvements. Differences in some outcomes were also notable between the transcutaneous auricular vagus nerve stimulation and transcranial direct current stimulation groups. Conclusion: The combination of transcutaneous auricular vagus nerve stimulation and transcranial direct current stimulation effectively enhances gait, balance, and daily living activities in subacute stroke patients. These benefits are likely due to transcutaneous auricular vagus nerve stimulation activating the solitary and trigeminal nuclei and transcranial direct current stimulation stimulating the motor cortex. Wearable gait analysis systems and electromyography are valuable in clinical gait assessment for these patients.

Aims: This study investigated whether the relationship between neuroticism and sleep quality in nursing interns is mediated by traditional Chinese medicine (TCM) constitution. In addition, the study examined whether sedentary behaviour affects this mediating model. Background: The importance of sleep quality for nursing interns cannot be overstated. Previous studies have demonstrated that neuroticism negatively affects sleep quality. However, little is known about the path of the relationship between neuroticism and sleep quality among nursing interns. Design: A cross-sectional study was conducted in Central and South China. Methods: A total of 630 nursing interns participated in this study. Neuroticism, TCM constitution, sleep quality, and sedentary behaviour were measured using reliable and valid questionnaires. Data were analysed using the PROCESS macro in SPSS. Results: Neuroticism significantly predicted the Pittsburgh Sleep Quality Index (PSQI; β = 0.144, P

Rheumatoid arthritis (RA) is a common autoimmune disease characterized primarily by persistent synovial inflammation and joint destruction. In recent years, ferroptosis, as a novel form of cell death, has garnered widespread attention due to its critical role in various diseases. This review explores the potential mechanisms of ferroptosis in RA and its relationship with the pathogenesis of RA, systematically analyzing the regulatory role of ferroptosis in synovial cells, chondrocytes, and immune cells. We emphasize the evaluation of ferroptosis-related pathways and their potential as therapeutic targets, including the development and application of inhibitors and activators. Although ferroptosis shows some promise in RA treatment, its dual role and safety issues in clinical application still require in-depth study. Future research should focus on elucidating the specific mechanisms of ferroptosis in RA pathology and developing more effective and safer therapeutic strategies to provide new treatment options for RA patients.

Abstract Toll-like receptor 9 (TLR9) recognizes bacterial, viral and self DNA and play an important role in immunity and inflammation. However, the role of TLR9 in obesity is less well-studied. Here, we generate B-cell-specific Tlr9-deficient (Tlr9 fl/fl /Cd19Cre +/- , KO) B6 mice and model obesity using a high-fat diet. Compared with control mice, B-cell-specific-Tlr9-deficient mice exhibited increased fat tissue inflammation, weight gain, and impaired glucose and insulin tolerance. Furthermore, the frequencies of IL-10-producing-B cells and marginal zone B cells were reduced, and those of follicular and germinal center B cells were increased. This was associated with increased frequencies of IFNγ-producing-T cells and increased follicular helper cells. In addition, gut microbiota from the KO mice induced a pro-inflammatory state leading to immunological and metabolic dysregulation when transferred to germ-free mice. Using 16 S rRNA gene sequencing, we identify altered gut microbial communities including reduced Lachnospiraceae, which may play a role in altered metabolism in KO mice. We identify an important network involving Tlr9, Irf4 and Il-10 interconnecting metabolic homeostasis, with the function of B and T cells, and gut microbiota in obesity.

Abstract Determining acute ischemic stroke (AIS) etiology is fundamental to secondary stroke prevention efforts but can be diagnostically challenging. We trained and validated an automated classification tool, StrokeClassifier, using electronic health record (EHR) text from 2039 non-cryptogenic AIS patients at 2 academic hospitals to predict the 4-level outcome of stroke etiology adjudicated by agreement of at least 2 board-certified vascular neurologists’ review of the EHR. StrokeClassifier is an ensemble consensus meta-model of 9 machine learning classifiers applied to features extracted from discharge summary texts by natural language processing. StrokeClassifier was externally validated in 406 discharge summaries from the MIMIC-III dataset reviewed by a vascular neurologist to ascertain stroke etiology. Compared with vascular neurologists’ diagnoses, StrokeClassifier achieved the mean cross-validated accuracy of 0.74 and weighted F1 of 0.74 for multi-class classification. In MIMIC-III, its accuracy and weighted F1 were 0.70 and 0.71, respectively. In binary classification, the two metrics ranged from 0.77 to 0.96. The top 5 features contributing to stroke etiology prediction were atrial fibrillation, age, middle cerebral artery occlusion, internal carotid artery occlusion, and frontal stroke location. We designed a certainty heuristic to grade the confidence of StrokeClassifier’s diagnosis as non-cryptogenic by the degree of consensus among the 9 classifiers and applied it to 788 cryptogenic patients, reducing cryptogenic diagnoses from 25.2% to 7.2%. StrokeClassifier is a validated artificial intelligence tool that rivals the performance of vascular neurologists in classifying ischemic stroke etiology. With further training, StrokeClassifier may have downstream applications including its use as a clinical decision support system.

Abstract Improving activity and stability of Ruthenium (Ru)-based catalysts in acidic environments is eager to replace more expensive Iridium (Ir)-based materials as practical anode catalyst for proton-exchange membrane water electrolyzers (PEMWEs). Here, a bicontinuous nanoreactor composed of multiscale defective RuO2 nanomonomers (MD-RuO2-BN) is conceived and confirmed by three-dimensional tomograph reconstruction technology. The unique bicontinuous nanoreactor structure provides abundant active sites and rapid mass transfer capability through a cavity confinement effect. Besides, existing vacancies and grain boundaries endow MD-RuO2-BN with generous low-coordination Ru atoms and weakened Ru-O interaction, inhibiting the oxidation of lattice oxygen and dissolution of high-valence Ru. Consequently, in acidic media, the electron- and micro-structure synchronously optimized MD-RuO2-BN achieves hyper water oxidation activity (196 mV @ 10 mA cm−2) and an ultralow degradation rate of 1.2 mV h−1. A homemade PEMWE using MD-RuO2-BN as anode also conveys high water splitting performance (1.64 V @ 1 A cm−2). Theoretical calculations and in-situ Raman spectra further unveil the electronic structure of MD-RuO2-BN and the mechanism of water oxidation processes, rationalizing the enhanced performance by the synergistic effect of multiscale defects and protected active Ru sites.

Abstract Mouse models of autosomal dominant polycystic kidney disease (ADPKD) show that intact primary cilia are required for cyst growth following the inactivation of polycystin-1. The signaling pathways underlying this process, termed cilia-dependent cyst activation (CDCA), remain unknown. Using translating ribosome affinity purification RNASeq on mouse kidneys with polycystin-1 and cilia inactivation before cyst formation, we identify the differential ‘CDCA pattern’ translatome specifically dysregulated in kidney tubule cells destined to form cysts. From this, Glis2 emerges as a candidate functional effector of polycystin signaling and CDCA. In vitro changes in Glis2 expression mirror the polycystin- and cilia-dependent changes observed in kidney tissue, validating Glis2 as a cell culture-based indicator of polycystin function related to cyst formation. Inactivation of Glis2 suppresses polycystic kidney disease in mouse models of ADPKD, and pharmacological targeting of Glis2 with antisense oligonucleotides slows disease progression. Glis2 transcript and protein is a functional target of CDCA and a potential therapeutic target for treating ADPKD.

Objective: The effects of Oviductus Ranae (OR) on cyclophosphamide-induced follicular developmental disorders were analyzed from the perspective of transcriptomics, which provided sufficient theoretical and experimental basis for the study of estrogen-like mechanism of OR. Methods: Sixty screened female Wistar rats were randomly divided into control group (C), model group (M), positive group (P), OR low-dose (ORL) and high-dose group (ORH). Except for the control group, all rats were injected with cyclophosphamide intraperitoneally to establish the rat model of follicular development dysfunction. Except for the model group, the other rats were given corresponding drugs by gavage, and the serum sex hormone contents were determined after 14~16 days of continuous gavage. The ovaries and uterus were extracted and weighed for recording and calculating the organ index. One ovary and uterus were stained with HE staining to calculate the number of follicles at all levels and the thickness of the endometrium, while the other ovary was analyzed by transcriptome, and the differentially expressed genes were screened and analyzed by GO and KEGG enrichment analyses. Results: The results showed that the rat follicular development dysfunction model was successfully established. Compared with the rats in the M, the wet weight and index of the uterus of rats in the ORL and ORH significantly increased (P

Abstract Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Abstract Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan–Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.

Abstract Objective Postoperative fasting following thoracoscopic surgery can cause intense thirst and oral discomfort. However, there is currently no research on ultraearly oral hydration (UEOH) in middle-aged or elderly patients after thoracoscopic surgery. The aim of this study was to investigate the effectiveness and safety of UEOH for improving oral discomfort after thoracoscopic surgery. Methods This single-center prospective double-blind randomized controlled trial was conducted from April 2022 to November 2023. A total of 64 middle-aged and elderly patients who underwent the first thoracoscopic surgery on the day were enrolled at our institution. Postoperatively, in the Postanesthesia Care Unit (PACU), patients were randomly assigned at a 1:1 ratio to either the UEOH group or the standard care (SC) group. The primary outcome was the patient’s thirst score at 6 h after surgery. Secondary outcomes included the incidence of postoperative oral discomfort; pain scores; the occurrence of adverse reactions such as nausea, vomiting, regurgitation and aspiration; anxiety scores on the first postoperative day; the time to first flatus; and recovery satisfaction scores. Results The demographic and surgical characteristics were similar between the two groups. Patients in the UEOH group had lower thirst scores 6 h after surgery than did those in the SC group(16.1 ± 6.70 vs. 78.4 ± 8.42, P 0.05). Conclusion For middle-aged and elderly patients undergoing thoracoscopic surgery, the use of a modified UEOH protocol postoperatively can improve thirst and promote gastrointestinal recovery without increasing complications. Trial registration This single-center, prospective, RCT has completed the registration of the Chinese Clinical Trial Center at 07/12/2023 with the registration number ChiCTR2300078425.

Abstract With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Abstract Background Although whole-genome sequencing (WGS) is the preferred genotyping method for most genomic analyses, limitations are often experienced when studying genomes characterized by a high percentage of repetitive elements, high linkage, and recombination deserts. The Asian tiger mosquito (Aedes albopictus), for example, has a genome comprising up to 72% repetitive elements, and therefore we set out to develop a single-nucleotide polymorphism (SNP) chip to be more cost-effective. Aedes albopictus is an invasive species originating from Southeast Asia that has recently spread around the world and is a vector for many human diseases. Developing an accessible genotyping platform is essential in advancing biological control methods and understanding the population dynamics of this pest species, with significant implications for public health. Methods We designed a SNP chip for Ae. albopictus (Aealbo chip) based on approximately 2.7 million SNPs identified using WGS data from 819 worldwide samples. We validated the chip using laboratory single-pair crosses, comparing technical replicates, and comparing genotypes of samples genotyped by WGS and the SNP chip. We then used the chip for a population genomic analysis of 237 samples from 28 sites in the native range to evaluate its usefulness in describing patterns of genomic variation and tracing the origins of invasions. Results Probes on the Aealbo chip targeted 175,396 SNPs in coding and non-coding regions across all three chromosomes, with a density of 102 SNPs per 1 Mb window, and at least one SNP in each of the 17,461 protein-coding genes. Overall, 70% of the probes captured the genetic variation. Segregation analysis found that 98% of the SNPs followed expectations of single-copy Mendelian genes. Comparisons with WGS indicated that sites with genotype disagreements were mostly heterozygotes at loci with WGS read depth 20, indicating that the chip more accurately detects heterozygotes than low-coverage WGS. Sample sizes did not affect the accuracy of the SNP chip genotype calls. Ancestry analyses identified four to five genetic clusters in the native range with various levels of admixture. Conclusions The Aealbo chip is highly accurate, is concordant with genotypes from WGS with high sequence coverage, and may be more accurate than low-coverage WGS. Graphical Abstract

ABSTRACTThe epidemiological and clinical aspects of Human Papillomavirus (HPV) infection in women have been extensively studied. However, there is a lack of information regarding HPV characteristics in males. In this study, we conducted a retrospective and observational study of 3737 consecutive male individuals attending outpatient clinics of Guangdong Women and Children Hospital from 2012 to 2023 in Guangzhou, South China, to determine the age- and genotype-specific prevalence of HPV in men. The results showed the overall prevalence of HPV among men was 42.15% (1575/3737), with variations ranging from 29.55% to 81.31% across distinct diagnostic populations. Low-risk HPV6 (15.47%), HPV11 (8.94%), and high-risk HPV52 (5.51%) were the most common types. The annual HPV prevalence decreased significantly (Z = −3.882, p

ABSTRACTBackground Enteroviruses (EV) are common and can cause severe diseases, particularly in young children. However, the information of EV infection in infants in China is limited due to the vast population size and extensive geographical area of the country. Here, we conducted a retrospective multicenter analysis of available EV data to assess the current epidemiological situation in the infant population in southern China.Methods The study enrolled infants with suspected EV infection from 34 hospitals across 12 cities in southern China between 2019 to 2022, and the confirmation of EV was done using RT-PCR and VP1 gene sequencing.Results Out of 1221 infants enrolled, 330 (27.03%) were confirmed as EV-infected. Of these, 260 (78.79%) were newborns aged 0–28 days. The EV belonged to three species: EV-B (80.61%), EV-A (11.82%), and human rhinovirus (7.58%). Newborns were more susceptible to EV-B than older infants (p

Heritability and genetic covariance/correlation quantify the marginal and shared genetic effects across traits. They offer insights on the genetic architecture of complex traits and diseases. To explore how genetic variations contribute to brain function variations, we estimated heritability and genetic correlation across cortical thickness, surface area, and volume of 33 anatomically predefined regions in left and right hemispheres, using summary statistics of genome-wide association analyses of 31,968 participants in the UK Biobank. To characterize the relationships between these regions of interest, we constructed a genetic network for these regions using recursive two-way cut-offs in similarity matrices defined by genetic correlations. The inferred genetic network matches the brain lobe mapping more closely than the network inferred from phenotypic similarities. We further studied the associations between the genetic network for brain regions and 30 complex traits through a novel composite-linkage disequilibrium score regression method. We identified seven significant pairs, which offer insights on the genetic basis for regions of interest mediated by cortical measures.

Eco-friendly concrete becomes research hotspots since concrete production emits 8–10% of the total anthropogenic CO2 emissions worldwide. Industry solid waste modification exhibits great potentials on mitigating aggregates depletion and carbon emissions. Coal gangue aggregate (CGA) coated by silica fume manually (CGACM) and in a desiccator (CGACD) are utilised to optimize the 3D printing concrete. Specimens were printed by a 3D printing robotic arm with a 25 mm-diameter nozzle. The compressive strength of CGACM and CGACD increases by 49% and 44% than non-activated series. For splitting tensile strength, the figures are 43% and 36%. The density of activation series decreases over 16% compared with standard sand. However, both surface pretreated methods make negative effects on structure compactness. From the SEM, SiO2 particles filles defects of aggregates, resulting in a denser internal structure and promoting secondary hydration reaction. However, excessive SiO2 particles agglomerated on CGA leads to the lubricant film among particles. These results expand recycle methods for CG and promotes 3D printing technology application on mine.

Abstract Various polygenic risk scores (PRS) methods have been proposed to combine the estimated effects of single nucleotide polymorphisms (SNPs) to predict genetic risks for common diseases, using data collected from genome-wide association studies (GWAS). Some methods require external individual-level GWAS dataset for parameter tuning, posing privacy and security-related concerns. Leaving out partial data for parameter tuning can also reduce model prediction accuracy. In this article, we propose PRStuning, a method that tunes parameters for different PRS methods using GWAS summary statistics from the training data. PRStuning predicts the PRS performance with different parameters, and then selects the best-performing parameters. Because directly using training data effects tends to overestimate the performance in the testing data, we adopt an empirical Bayes approach to shrinking the predicted performance in accordance with the genetic architecture of the disease. Extensive simulations and real data applications demonstrate PRStuning’s accuracy across PRS methods and parameters.

Abstract Many deep learning-based methods have been proposed to handle complex single-cell data. Deep learning approaches may also prove useful to jointly analyze single-cell RNA sequencing (scRNA-seq) and single-cell T cell receptor sequencing (scTCR-seq) data for novel discoveries. We developed scNAT, a deep learning method that integrates paired scRNA-seq and scTCR-seq data to represent data in a unified latent space for downstream analysis. We demonstrate that scNAT is capable of removing batch effects, and identifying cell clusters and a T cell migration trajectory from blood to cerebrospinal fluid in multiple sclerosis.

Obesity-induced insulin resistance (IR) can precipitate metabolic disorders such as diabetes. Zeaxanthin, a crucial member of the carotenoid family, has been found to mitigate the damage caused by obesity. However, reports on the effects of zeaxanthin on obesity-induced IR are lacking. Our objective was to examine the metabolic regulatory impacts of zeaxanthin on mice subjected to a high-fat diet (HFD) that triggered IR and to explore their influence on gut microbiota regulation. This study constructed a mouse model of metabolic dysfunction caused by lipid-rich nutritional patterns to investigate physiological and biochemical indices, liver pathway expression, and the intestinal microbiota. The mechanisms by which zeaxanthin improved both IR and glucose metabolic disorders were elucidated. The results demonstrate that zeaxanthin effectively suppressed obesity. The fasting blood glucose, area under curve of oral glucose tolerance test and insulin tolerance test, and homeostatic model assessment–insulin resistance (HOMA-IR) indices in the HFDZEA group decreased by 14.9%, 25.2%, 28.9%, and 29.8%. Additionally, zeaxanthin improved the lipid metabolism and alleviated damage to the liver and pancreas while also activating the PI3K/Akt pathway, regulating hepatic gluconeogenesis and the glycogen metabolism. The number of OTUs in the HFDZEA group increased by 29.04%. Zeaxanthin improved the structure and profile of the gastrointestinal microbiome and enhanced its diversity, increasing probiotics abundance, decreasing pathogen abundance, and thereby ameliorating the dysbiosis of enteric microbial communities in rodents with obesity resulting from excessive fat consumption. The outcomes of our analysis provide a rational basis for advancing zeaxanthin-based nutritional products.

Abstract In this study, we apply a three‐dimensional (3D) thermomechanically coupled higher‐order ice flow model to simulate the East Rongbuk Glacier (ERG), Mt. Everest. We first diagnostically investigate its present‐day ice dynamic features in 2009 and then prognostically simulate the glacier during the time period 2010–2100. The ice flow model is initialized based on a Robin‐type inversion method by conducting six sensitivity experiments relating to glacier thermal boundary conditions. We apply two different surface mass balance parameterizations in the model, and both of them can reproduce the observed ice volume loss (around 0.1 km3) during 2010–2020. We find that ERG is likely to experience maximum meltwater runoff at the year 2030 under the SSP‐126 scenario, while under SSP‐370 and ‐585 scenarios, the peak water will both likely occur at around 2060. The ice dynamics may contribute more to ice loss as climate warms in time.

Summary: Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.

Objective Medication non-adherence to immunosuppressants threatens allograft survival and function maintenance among solid organ transplant (SOT) recipients. This study aimed to investigate the prevalence of immunosuppressant medication non-adherence and associated factors during the COVID-19 reopening period among Chinese SOT recipients.Design Cross-sectional study.Setting South-central China.Population Adult patients who received SOT with functioning graft.Methods Sociodemographic questionnaire and scales to measure physical activity, depression and medication non-adherence were used to collect data. Logistic regression analysis was conducted to identify factors associated with medication non-adherence. Mediation and moderated mediation analyses were performed to examine the potential mechanisms influencing medication behaviour during the pandemic reopening period using SPSS PROCESS macro 4.3 software.Results A total of 1121 participants were recruited and the prevalence of medication non-adherence was 36.3% in this study. Recipients who were men, had a higher monthly income, lived alone, had received transplantation for a minimum of 3 years, had received COVID-19 vaccination and experienced depressive symptoms exhibited an increased risk of non-adherence. Contrarily, those who engaged in high-intensity physical activity exhibited a decreased risk. Physical activity was negatively associated with medication non-adherence (r=−0.124, p

Abstract This study was to develop a computer vision evaluation method to automatically measure the degree of scoliosis based on the machine learning algorithm. For the X-ray images of 204 patients with idiopathic scoliosis who underwent full-spine radiography, histogram equalization of original image was performed before a flipping method was used to magnify asymmetric elements, search for the global maximum pixel value in each line, and scan local maximal pixel value, with the intersection set of two point sets being regarded as candidate anchor points. All fine anchors were fitted with cubic spline algorithm to obtain the approximate curve of the spine, and the degree of scoliosis was measured by the standardized integral area. All measured data were analyzed. In manual measurement, the Cobb angle was 11.70–25.00 (20.15 ± 3.60), 25.20–44.70 (33.89 ± 5.41), and 45.10–49.40 (46.98 ± 1.25) in the mild, moderate and severe scoliosis group, respectively, whereas the value for the standardized integral area algorithm was 0.072–0.298 (0.185 ± 0.040), 0.100–0.399 (0.245 ± 0.050), and 0.246–0.901 (0.349 ± 0.181) in the mild, moderate and severe scoliosis group, respectively. Correlation analysis between the manual measurement of the Cobb angle and the evaluation of the standardized integral area algorithm demonstrated the Spearman correlation coefficient r = 0.643 (P

Abstract To elucidate the pathogenesis of vein of Galen malformations (VOGMs), the most common and most severe of congenital brain arteriovenous malformations, we performed an integrated analysis of 310 VOGM proband-family exomes and 336,326 human cerebrovasculature single-cell transcriptomes. We found the Ras suppressor p120 RasGAP (RASA1) harbored a genome-wide significant burden of loss-of-function de novo variants (2042.5-fold, p = 4.79 x 10−7). Rare, damaging transmitted variants were enriched in Ephrin receptor-B4 (EPHB4) (17.5-fold, p = 1.22 x 10−5), which cooperates with p120 RasGAP to regulate vascular development. Additional probands had damaging variants in ACVRL1, NOTCH1, ITGB1, and PTPN11. ACVRL1 variants were also identified in a multi-generational VOGM pedigree. Integrative genomic analysis defined developing endothelial cells as a likely spatio-temporal locus of VOGM pathophysiology. Mice expressing a VOGM-specific EPHB4 kinase-domain missense variant (Phe867Leu) exhibited disrupted developmental angiogenesis and impaired hierarchical development of arterial-capillary-venous networks, but only in the presence of a “second-hit” allele. These results illuminate human arterio-venous development and VOGM pathobiology and have implications for patients and their families.

In the construction industry, the professional evaluation of construction schemes represents a crucial link in ensuring the safety, quality and economic efficiency of the construction process. However, due to the large number and diversity of construction schemes, traditional expert review methods are limited in terms of timeliness and comprehensiveness. This leads to an increasingly urgent requirement for intelligent check of construction schemes. This paper proposes an intelligent compliance checking method for construction schemes that integrates knowledge graphs and large language model (LLM). Firstly, a method for constructing a multi-dimensional, multi-granular knowledge graph for construction standards is introduced, which serves as the foundation for domain-specific knowledge support to the LLM. Subsequently, a parsing module based on text classification and entity extraction models is proposed to automatically parse construction schemes and construct pathways for querying the knowledge graph of construction standards. Finally, an LLM is leveraged to achieve an intelligent compliance check. The experimental results demonstrate that the proposed method can effectively integrate domain knowledge to guide the LLM in checking construction schemes, with an accuracy rate of up to 72%. Concurrently, the well-designed prompt template and the comprehensiveness of the knowledge graph facilitate the stimulation of the LLM’s reasoning ability. This work contributes to exploring the application of LLMs and knowledge graphs in the vertical domain of text compliance checking. Future work will focus on optimizing the integration of LLMs and domain knowledge to further improve the accuracy and practicality of the intelligent checking system.

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good.

Recently, the scale and complexity of water distribution networks (WDNs) have been increasing with the acceleration of urbanization process. It has become a hot research focus to use district metering area (DMA) for more efficient management and control of WDNs. This article proposes a multistage DMA planning method based on improved weighted spectral clustering and genetic algorithm, aiming to address issues such as high investment cost and large differences in the water network demand distribution. First, the actual case pipe network is transformed into an undirected weighted graph based on graph theory, and a similarity matrix is formed by combining the physical properties and hydraulic characteristics of the water network. Then, based on the similarity matrix, the weighted spectral clustering algorithm is used to preliminarily divide the WDN, and the performance of the water supply pipe network formed with different division quantities and different weighting schemes is discussed. Finally, the genetic algorithm is used to optimize the arrangement of valves and flow meters on boundary pipes to generate the final configuration of DMA. The results show that the proposed method has a significant improvement in pipe network topology, hydraulic performance index, and economy compared with the traditional DMA method. HIGHLIGHTS The similarity matrix of multiple data fusion of the water supply network is established to enrich the hydraulic information of the undirected weighted graph.; The weighted spectral clustering algorithm based on k-medoids significantly improves the initial district metering area partition performance.; The equipment layout scheme obtained by using the improved genetic algorithm is the most economical.;

Abstract The advancement of single cell RNA-sequencing (scRNA-seq) technology has enabled the direct inference of co-expressions in specific cell types, facilitating our understanding of cell-type-specific biological functions. For this task, the high sequencing depth variations and measurement errors in scRNA-seq data present two significant challenges, and they have not been adequately addressed by existing methods. We propose a statistical approach, CS-CORE, for estimating and testing cell-type-specific co-expressions, that explicitly models sequencing depth variations and measurement errors in scRNA-seq data. Systematic evaluations show that most existing methods suffered from inflated false positives as well as biased co-expression estimates and clustering analysis, whereas CS-CORE gave accurate estimates in these experiments. When applied to scRNA-seq data from postmortem brain samples from Alzheimer’s disease patients/controls and blood samples from COVID-19 patients/controls, CS-CORE identified cell-type-specific co-expressions and differential co-expressions that were more reproducible and/or more enriched for relevant biological pathways than those inferred from existing methods.

Objective: To study the effect of oviductus ranae (OR) on follicle development and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway in the ovaries of follicular developmental dysfunction rats. Methods: Randomly divide the screened 40 female WISTAR rats into control group (CG), model group (MG), positive qroup (PG), OR high-dose (ORHG) and low-dose group (ORLG). Except the CG rats, the rats in other groups were treated with triptolide solution (40 mg/kg) once a day. The PG were treated with estradiol valerate solution (0.1 mg/kg) and megestrol acetate solution (0.8 mg/kg). The ORHG and ORLG were treated with OR solution (400、200 mg/kg). The rat serum was collected to determine the contents of estradiol (E2), progesterone (P), Luteinizing hormone (LH), Follicle-stimulating hormone (FSH) and testosterone (T) at 8th week . The ovaries and uterus of rats were harvested and weighed, and the organ index was calculated. HE staining and TUNEL staining were performed on one ovary to calculate the number of follicles at all levels. The relative expression of PI3K, AKT, phosphatase and tensin homolog deleted on chromosome ten (PTEN) mammalian target of rapamycin (mTOR) mRNA and the contents of PI3K, Akt, phosphorylated protein kinase B(p-Akt) protein in the other ovary was measured. Result: The results indicated that the follicular development dysfunction model was successfully established. Compared with the MG, the wet weight and index of uterus of the ORHG was increased significantly (P

Abstract Circadian rhythms are crucial for regulating physiological and behavioral processes. Pineal hormone melatonin is often used to measure circadian amplitude but its collection is costly and time-consuming. Wearable activity data are promising alternative, but the most commonly used measure, relative amplitude, is subject to behavioral masking. In this study, we firstly derive a feature named circadian activity rhythm energy (CARE) to better characterize circadian amplitude and validate CARE by correlating it with melatonin amplitude (Pearson’s r = 0.46, P = 0.007) among 33 healthy participants. Then we investigate its association with cognitive functions in an adolescent dataset (Chinese SCHEDULE-A, n = 1703) and an adult dataset (UK Biobank, n = 92,202), and find that CARE is significantly associated with Global Executive Composite (β = 30.86, P = 0.016) in adolescents, and reasoning ability, short-term memory, and prospective memory (OR = 0.01, 3.42, and 11.47 respectively, all P

Highlights Density functional theory calculations indicates that interstitial B atoms can tune the atomic spacing of host metal Os and achieve a reversal hydrogen adsorption-distance relationship. The structure–activity relationship between the spacing of active Os atoms and catalytic activity is established. Prepared OsB2 with increasing dOs-Os of 2.96 Å presents the optimal hydrogen evolution reaction activity (8 mV @ 10 mA cm−2) and robust stability in alkaline media.

Fine particulate matter (PM2.5) concentration, a crucial indicator reflecting changes in air quality, has frequently been used in previous studies. However, the effects of large-scale ecological restoration (ER) projects on PM2.5 concentrations are often overlooked. Therefore, we used net primary productivity (NPP) as an indicator of ER engineering benefits, used ensemble empirical modal decomposition (EEMD) to reveal the trend of linear and nonlinear relationships of PM2.5 driven by different types and amounts of ER projects, and utilized the Extreme Gradient Boosting (XGBoost) and Shapley's Additive Interpretation (SHAP) values to quantify the impact of each factor on long-term PM2.5 concentrations. The results suggest that: (1) The nonlinear trend better describes the trend of PM2.5 change, with a shift from increasing to decreasing in areas covering 74.15% of China's area, especially in the four major ER zones; (2) PM2.5 concentration exhibits regional effects, L-L and H-H aggregation areas account for a larger proportion of the area, and are distributed in the areas with low and high PM2.5 concentrations, respectively. (3) The year 1990 marks a turning point for 36.8% of the regions. Across four ER regions emerge: a dramatic increase in PM2.5 concentration before ER implementation, followed by a slowing growth trend in the initial stages of the project, and ultimately a gradual decrease. (4) While the contribution of ER to the decrease of PM2.5 concentration is lower than the contribution of the growth caused by human activities and climate change, plant and newly plant areas are the main regions in which the ER project suppresses its growth. These findings highlight the influence of ER projects on PM2.5 concentration in addition to climate and human activities and provide a theoretical basis and scientific and technological support for quantifying the suppression of air pollution by ER projects.

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference.

Background: Polygenic risk score (PRS) has proved useful in predicting the risk of cardiovascular diseases (CVD) based on the genotypes of an individual, but most analyses have focused on disease onset in the general population. The usefulness of PRS to predict CVD risk among type 2 diabetes (T2D) patients remains unclear.Methods: We built a meta-PRSCVD upon the candidate PRSs developed from state-of-the-art PRS methods for three CVD subtypes of significant importance: coronary artery disease (CAD), ischemic stroke (IS), and heart failure (HF). To evaluate the prediction performance of the meta-PRSCVD, we restricted our analysis to 21,092 white British T2D patients in the UK Biobank, among which 4,015 had CVD events.Results: Results showed that the meta-PRSCVD was significantly associated with CVD risk with a hazard ratio per standard deviation increase of 1.28 (95% CI: 1.23–1.33). The meta-PRSCVD alone predicted the CVD incidence with an area under the receiver operating characteristic curve (AUC) of 0.57 (95% CI: 0.54–0.59). When restricted to the early-onset patients (onset age ≤ 55), the AUC was further increased to 0.61 (95% CI 0.56–0.67).Conclusion: Our results highlight the potential role of genomic screening for secondary preventions of CVD among T2D patients, especially among early-onset patients.

Objectives Combination of Breast Cancer 1 protein-associated protein 1 (BAP1) and methylthioadenosine phosphorylase (MTAP) in the peritoneal mesothelioma (PeM) has yet to be explored. We aim to assess the diagnostic value of combined BAP1 and MTAP to distinguish biphasic mesothelioma (BM) from epithelioid mesothelioma (EM) with reactive stroma in peritoneum, as well as its prognostic value in PeM. Methods This is a retrospective study from June 2014 to December 2021. This study included 18 cases of BM and 27 cases of EM with reactive stroma, excluded sarcomatoid, and EM without reactive stroma cases, and clinicopathological information was collected. The associations between MTAP and BAP1 levels and clinicopathological features or prognosis were analyzed. Clinical follow-up data were reviewed to correlate with pathological prognostic factors using Kaplan–Meier estimator and univariate/multivariate Cox proportional hazards regression models. Results Loss/decrease of BAP1/MTAP was observed in 6 (33.3%) BM cases and 12 (44.4%) EM cases. In 5 (27.8%) cases, loss of or decreased BAP1/MTAP expression was observed in both EC and SC of BM. BAP1/MTAP loss/decrease was observed in 12 (44.4%) cases of only EC of EM but not in reactive stroma. Compared with histology alone, a combination of BAP1 and MTAP immunohistochemistry (IHC) in spindled PeM provides a more objective mean to distinguish BM from EM with reactive stroma. Loss/decrease of BAP1/MTAP was associated with peritoneal cancer index (PCI) score ( P = 0.047) and completeness of cytoreduction (CC) score ( P = 0.038). BM patients have worse overall survival (OS) than EM with reactive stroma ( P = 0 .007). Conclusions Combination of BAP1/MTAP by IHC is helpful for differential diagnosis of peritoneal BM from EM with reactive stroma. Nevertheless, BAP1/MTAP may help to evaluate the biological behavior of PeM.

To promote the sustainable development of the construction industry, this study examined the influence of the rubber particles addition on the fresh and hardened engineering properties of rubber particle (RP) modified 3D printable sustainable cementitious composites (3DPSCC) incorporating polypropylene (PP) fibers, in which RP derived from waste tires partially replaced sand between 0% and 100% volume replacement. A series of flowability tests, printing tests and mechanical tests in different loading directions were carried out to investigate the printability and anisotropic mechanical properties of the 3DPSCC. The results show that all test specimens can be successfully printed due to their good workability, extrudability, and buildability in the fresh state. The compressive, flexural strength, and impact strengths of 3DPSCC, in general, decreased with the increasing RP content and exhibited obvious anisotropy, which could be owing to the presence of both inter-layer and intra-layer interfaces in each group of specimens during the printing process. The impact resistant of the modified specimens improved with the addition of RP, as the average maximum displacement gradually increased from 0.41 mm to 0.92 mm, despite the weakening strength. The optimal mixture was regarded as RP-40 (40% RP replacement) exhibiting comparable anisotropic compressive strength (higher than 30 MPa), enough anisotropic flexural strength (higher than 4 MPa), and superior energy dissipation capacity (highest among all 3DPSCC), which can meet the strength requirement of concrete (i.e., 25 MPa for steel reinforced concrete structure) and is suitable for structural applications as a sustainable impact-resistant material.

Introduction To establish a prediction model to predict immunosuppressive medication (IM) nonadherence in kidney transplant recipients (KTRs) based on a combined theory framework.Methods This polycentric, cross-sectional study included 1191 KTRs from October 2020 to February 2021 in China, with 1011 KTRs enrolled in the derivation set and 180 in the external validation set. Variables selected based on the combined theory of planned behavior (TPB)/health belief model (HBM) theory were analyzed by the least absolute shrinkage and selection operator (LASSO). Internal 10 cross-validation was conducted to determine the optimal lambda value. The receiver operating characteristic (ROC) curve, specificity, and sensitivity were used to evaluate the prediction model, and further assessment was run by external validation.Results IM nonadherence rate was 38.48% in the derivation set and 37.22% in the validation set. The LASSO model was developed with eight predictors for IM nonadherence: age, preoperative drinking history, education, marital status, perceived barriers, social support, perceived behavioral control, and perceived susceptibility. The model demonstrated acceptable discrimination with the area under the ROC curve of 0.797 (95% CI: 0.745–0.850) in the internal validation set and 0.757 (95% CI: 0.684–0.829) in the external validation set. The specificity and sensitivity in the internal validation and external validation set were 0.741, 0.748, 0.673, and 0.716, respectively.Conclusions The LASSO model was developed to guide identifying high-risk nonadherent patients and timely and effective interventions to improve their prognosis and survival.

Abstract Background Idiopathic pulmonary fibrosis (IPF) is characterized by the accumulation of extracellular matrix in the pulmonary interstitium and progressive functional decline. We hypothesized that integration of multi-omics data would identify clinically meaningful molecular endotypes of IPF. Methods The IPF-PRO Registry is a prospective registry of patients with IPF. Proteomic and transcriptomic (including total RNA [toRNA] and microRNA [miRNA]) analyses were performed using blood collected at enrollment. Molecular data were integrated using Similarity Network Fusion, followed by unsupervised spectral clustering to identify molecular subtypes. Cox proportional hazards models tested the relationship between these subtypes and progression-free and transplant-free survival. The molecular subtypes were compared to risk groups based on a previously described 52-gene (toRNA expression) signature. Biological characteristics of the molecular subtypes were evaluated via linear regression differential expression and canonical pathways (Ingenuity Pathway Analysis [IPA]) over-representation analyses. Results Among 232 subjects, two molecular subtypes were identified. Subtype 1 (n = 105, 45.3%) and Subtype 2 (n = 127, 54.7%) had similar distributions of age (70.1 +/- 8.1 vs. 69.3 +/- 7.6 years; p = 0.31) and sex (79.1% vs. 70.1% males, p = 0.16). Subtype 1 had more severe disease based on composite physiologic index (CPI) (55.8 vs. 51.2; p = 0.002). After adjusting for CPI and antifibrotic treatment at enrollment, subtype 1 experienced shorter progression-free survival (HR 1.79, 95% CI 1.28,2.56; p = 0.0008) and similar transplant-free survival (HR 1.30, 95% CI 0.87,1.96; p = 0.20) as subtype 2. There was little agreement in the distribution of subjects to the molecular subtypes and the risk groups based on 52-gene signature (kappa = 0.04, 95% CI= -0.08, 0.17), and the 52-gene signature risk groups were associated with differences in transplant-free but not progression-free survival. Based on heatmaps and differential expression analyses, proteins and miRNAs (but not toRNA) contributed to classification of subjects to the molecular subtypes. The IPA showed enrichment in pulmonary fibrosis-relevant pathways, including mTOR, VEGF, PDGF, and B-cell receptor signaling. Conclusions Integration of transcriptomic and proteomic data from blood enabled identification of clinically meaningful molecular endotypes of IPF. If validated, these endotypes could facilitate identification of individuals likely to experience disease progression and enrichment of clinical trials. Trial registration NCT01915511

Abstract Smoking behaviors and alcohol use disorder (AUD), both moderately heritable traits, commonly co-occur in the general population. Single-trait genome-wide association studies (GWAS) have identified multiple loci for smoking and AUD. However, GWASs that have aimed to identify loci contributing to co-occurring smoking and AUD have used small samples and thus have not been highly informative. Applying multi-trait analysis of GWASs (MTAG), we conducted a joint GWAS of smoking and AUD with data from the Million Veteran Program (N = 318,694). By leveraging GWAS summary statistics for AUD, MTAG identified 21 genome-wide significant (GWS) loci associated with smoking initiation and 17 loci associated with smoking cessation compared to 16 and 8 loci, respectively, identified by single-trait GWAS. The novel loci for smoking behaviors identified by MTAG included those previously associated with psychiatric or substance use traits. Colocalization analysis identified 10 loci shared by AUD and smoking status traits, all of which achieved GWS in MTAG, including variants on SIX3, NCAM1, and near DRD2. Functional annotation of the MTAG variants highlighted biologically important regions on ZBTB20, DRD2, PPP6C, and GCKR that contribute to smoking behaviors. In contrast, MTAG of smoking behaviors and alcohol consumption (AC) did not enhance discovery compared with single-trait GWAS for smoking behaviors. We conclude that using MTAG to augment the power of GWAS enables the identification of novel genetic variants for commonly co-occuring phenotypes, providing new insights into their pleiotropic effects on smoking behavior and AUD.

Here, the authors present a TWAS framework OTTERS that adapts multiple polygenic risk score methods to estimate eQTL weights from summary-level eQTL data. Both simulation and real studies show OTTERS is powerful across a wide range of genetic architectures.