PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex signaling mechanisms within the microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. However, the background signaling networks are highly complex and interactive. It remains challenging to infer the core intra- and inter-multi-cell signaling communication networks using scRNA-seq data. In this study, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy. This model divides complex signaling networks into signaling paths, which are then scored and ranked using a novel graph transformer architecture to infer intra- and inter-cell signaling communications. We evaluated the performance of PathFinder using two scRNA-seq data cohorts. The first cohort is an APOE4 genotype-specific AD, and the second is a human cirrhosis cohort. The evaluation confirms the promising potential of using PathFinder as a general signaling network inference model.

Factors predicting resolution of left ventricular thrombus in different time windows after myocardial infarction.

BACKGROUND: Left ventricular thrombus (LVT) is a serious complication after myocardial infarction. However, due to its asymptomatic nature, early detection is challenging. We aimed to explore the differences in clinical correlates of LVT found in acute to subacute and chronic phases of myocardial infarction. METHODS: We collected data from 153 patients who were diagnosed with LVT after myocardial infarction at the Affiliated Hospital of Qingdao University from January 2013 to December 2022. Baseline information, inflammatory markers, transthoracic echocardiograph (TTE) data and other clinical correlates were collected. Patients were categorized into acute to subacute phase group (< 30 days) and chronic phase group (30 days and after) according to the time at which echocardiograph was performed. The resolution of thrombus within 90 days is regarded as the primary endpoint event. We fitted logistic regression models to relating clinical correlates with phase-specific thrombus resolution. RESULTS: For acute to subacute phase thrombus patients: C-reactive protein levels (OR: 0.95, 95% CI: 0.918-0.983, p = 0.003) were significantly associated with thrombus resolution. For chronic phase thrombus patients: anticoagulant treatment was associated with 5.717-fold odds of thrombus resolution (OR: 5.717, 95% CI: 1.543-21.18, p = 0.009). CONCLUSIONS: Higher levels of CRP were associated with lower likelihood of LVT resolution in acute phase myocardial infarction; Anticoagulant therapy is still needed for thrombus in the chronic stage of myocardial infarction.

mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development.

Multi-omic data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data. Graph neural network (GNN) AI models have been widely used to analyze graph-structure datasets and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data by node and edge ranking analysis for signaling flow/cascade inference. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert them into graph-structure data for individual samples, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), a novel computational tool that generates multi-omics signaling graphs of individual samples by mapping the multi-omics data onto a biologically meaningful multi-level background signaling network. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. We evaluated the mosGraphGen using both multi-omics datasets of cancer and Alzheimer's disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/Multi-OmicGraphBuilder/mosGraphGen.

Proteo-genomics of soluble TREM2 in cerebrospinal fluid provides novel insights and identifies novel modulators for Alzheimer's disease.

Triggering receptor expressed on myeloid cells 2 (TREM2) plays a critical role in microglial activation, survival, and apoptosis, as well as in Alzheimer's disease (AD) pathogenesis. We previously reported the MS4A locus as a key modulator for soluble TREM2 (sTREM2) in cerebrospinal fluid (CSF). To identify additional novel genetic modifiers of sTREM2, we performed the largest genome-wide association study (GWAS) and identified four loci for CSF sTREM2 in 3,350 individuals of European ancestry. Through multi-ethnic fine mapping, we identified two independent missense variants (p.M178V in MS4A4A and p.A112T in MS4A6A) that drive the association in MS4A locus and showed an epistatic effect for sTREM2 levels and AD risk. The novel TREM2 locus on chr 6 contains two rare missense variants (rs75932628 p.R47H, P=7.16×10-19; rs142232675 p.D87N, P=2.71×10-10) associated with sTREM2 and AD risk. The third novel locus in the TGFBR2 and RBMS3 gene region (rs73823326, P=3.86×10-9) included a regulatory variant with a microglia-specific chromatin loop for the promoter of TGFBR2. Using cell-based assays we demonstrate that overexpression and knock-down of TGFBR2, but not RBMS3, leads to significant changes of sTREM2. The last novel locus is located on the APOE region (rs11666329, P=2.52×10-8), but we demonstrated that this signal was independent of APOE genotype. This signal colocalized with cis-eQTL of NECTIN2 in the brain cortex and cis-pQTL of NECTIN2 in CSF. Overexpression of NECTIN2 led to an increase of sTREM2 supporting the genetic findings. To our knowledge, this is the largest study to date aimed at identifying genetic modifiers of CSF sTREM2. This study provided novel insights into the MS4A and TREM2 loci, two well-known AD risk genes, and identified TGFBR2 and NECTIN2 as additional modulators involved in TREM2 biology.

PathFinder: a novel graph transformer model to infer multi-cell intra- and inter-cellular signaling pathways and communications.

Recently, large-scale scRNA-seq datasets have been generated to understand the complex and poorly understood signaling mechanisms within microenvironment of Alzheimer's Disease (AD), which are critical for identifying novel therapeutic targets and precision medicine. Though a set of targets have been identified, however, it remains a challenging to infer the core intra- and inter-multi-cell signaling communication networks using the scRNA-seq data, considering the complex and highly interactive background signaling network. Herein, we introduced a novel graph transformer model, PathFinder, to infer multi-cell intra- and inter-cellular signaling pathways and signaling communications among multi-cell types. Compared with existing models, the novel and unique design of PathFinder is based on the divide-and-conquer strategy, which divides the complex signaling networks into signaling paths, and then score and rank them using a novel graph transformer architecture to infer the intra- and inter-cell signaling communications. We evaluated PathFinder using scRNA-seq data of APOE4-genotype specific AD mice models and identified novel APOE4 altered intra- and inter-cell interaction networks among neurons, astrocytes, and microglia. PathFinder is a general signaling network inference model and can be applied to other omics data-driven signaling network inference.

sc2MeNetDrug: A computational tool to uncover inter-cell signaling targets and identify relevant drugs based on single cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) is a powerful technology to investigate the transcriptional programs in stromal, immune, and disease cells, like tumor cells or neurons within the Alzheimer's Disease (AD) brain or tumor microenvironment (ME) or niche. Cell-cell communications within ME play important roles in disease progression and immunotherapy response and are novel and critical therapeutic targets. Though many tools of scRNA-seq analysis have been developed to investigate the heterogeneity and sub-populations of cells, few were designed for uncovering cell-cell communications of ME and predicting the potentially effective drugs to inhibit the communications. Moreover, the data analysis processes of discovering signaling communication networks and effective drugs using scRNA-seq data are complex and involve a set of critical analysis processes and external supportive data resources, which are difficult for researchers who have no strong computational background and training in scRNA-seq data analysis. To address these challenges, in this study, we developed a novel open-source computational tool, sc2MeNetDrug (https://fuhaililab.github.io/sc2MeNetDrug/). It was specifically designed using scRNA-seq data to identify cell types within disease MEs, uncover the dysfunctional signaling pathways within individual cell types and interactions among different cell types, and predict effective drugs that can potentially disrupt cell-cell signaling communications. sc2MeNetDrug provided a user-friendly graphical user interface to encapsulate the data analysis modules, which can facilitate the scRNA-seq data-based discovery of novel inter-cell signaling communications and novel therapeutic regimens.

Ablation for atrial fibrillation improves the outcomes in patients with heart failure with preserved ejection fraction.

AIMS: Patients with heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) have worse clinical outcomes than those with sinus rhythm (SR). We aim to investigate whether maintaining SR in patients with HFpEF through a strategy such as AF ablation would improve outcomes. METHODS AND RESULTS: This is a cohort study that analysed 1034 patients (median age 69 [63-76] years, 46.2% [478/1034] female) with HFpEF and AF. Of these, 392 patients who underwent first-time AF ablation were assigned to the ablation group, and the remaining 642 patients, who received only medical therapy, were assigned to the no ablation group. The primary endpoint was a composite of all-cause death or rehospitalization for worsening heart failure. After a median follow-up of 39 months, the cumulative incidence of the primary endpoint was significantly lower in the ablation group compared to the no ablation group (adjusted hazard ratio [HR], 0.55 [95% CI, 0.37-0.82], P = 0.003) in the propensity score-matched model. Secondary endpoint analysis showed that the benefit of AF ablation was mainly driven by a reduction in rehospitalization for worsening heart failure (adjusted HR, 0.52 [95% CI, 0.34-0.80], P = 0.003). Patients in the ablation group showed a 33% relative decrease in atrial tachycardia/AF recurrence compared to the no ablation group (adjusted HR, 0.67 [95% CI, 0.54-0.84], P < 0.001). CONCLUSION: Among patients with HFpEF and AF, the strategy of AF ablation to maintain SR was associated with a lower risk of the composite outcome of all-cause death or rehospitalization for worsening heart failure.

The TFPI2-PPARγ axis induces M2 polarization and inhibits fibroblast activation to promote recovery from post-myocardial infarction in diabetic mice.

BACKGROUND: Diabetes mellitus is one of the causes of poor ventricular remodelling and poor cardiac recovery after myocardial infarction (MI). We previously reported that tissue factor pathway inhibitor-2 (TFPI2) was downregulated in response to hyperglycaemia and that it played a pivotal role in extracellular matrix (ECM) degradation and cell migration. Nonetheless, the function and mechanism of TFPI2 in post-MI remodelling under diabetic conditions remain unclear. Therefore, in the present study, we investigated the role of TFPI2 in post-MI effects in a diabetic mouse model. RESULTS: TFPI2 expression was markedly decreased in the infarcted myocardium of diabetic MI mice compared with that in non-diabetic mice. TFPI2 knockdown in the MI mouse model promoted fibroblast activation and migration as well as matrix metalloproteinase (MMP) expression, leading to disproportionate fibrosis remodelling and poor cardiac recovery. TFPI2 silencing promoted pro-inflammatory M1 macrophage polarization, which is consistent with the results of TFPI2 downregulation and M1 polarization under diabetic conditions. In contrast, TFPI2 overexpression in diabetic MI mice protected against adverse cardiac remodelling and functional deterioration. TFPI2 overexpression also inhibited MMP2 and MMP9 expression and attenuated fibroblast activation and migration, as well as excessive collagen production, in the infarcted myocardium of diabetic mice. TFPI2 promoted an earlier phenotype transition of pro-inflammatory M1 macrophages to reparative M2 macrophages via activation of peroxisome proliferator-activated receptor gamma. CONCLUSIONS: This study highlights TFPI2 as a promising therapeutic target for early resolution of post-MI inflammation and disproportionate ECM remodelling under diabetic conditions.

Highly accurate disease diagnosis and highly reproducible biomarker identification with PathFormer.

Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data analysis, i.e., limited-prediction/diagnosis accuracy and limited-reproducible biomarker identification capacity across multiple datasets. The root of the challenges is the unique graph structure of biological signaling pathways, which consists of a large number of targets and intensive and complex signaling interactions among these targets. To resolve these two challenges, in this study, we presented a novel GNN model architecture, named PathFormer, which systematically integrate signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis. In the comparison results, PathFormer outperformed existing GNN models significantly in terms of highly accurate prediction capability (~30% accuracy improvement in disease diagnosis compared with existing GNN models) and high reproducibility of biomarker ranking across different datasets. The improvement was confirmed using two independent Alzheimer's Disease (AD) and cancer transcriptomic datasets. The PathFormer model can be directly applied to other omics data analysis studies.

Association of body fat distribution with all-cause and cardiovascular mortality in US adults: a secondary analysis using NHANES.

OBJECTIVE: To investigate the association of fat and lean mass in specific regions with all-cause and cardiovascular-related mortality. DESIGN: Population based cohort study. SETTING: US National Health and Nutrition Examination Survey (2003-2006 and 2011-2018). PARTICIPANTS: 22 652 US adults aged 20 years or older. EXPOSURES: Fat and lean mass in specific regions obtained from the whole-body dual-energy X-ray absorptiometry. MAIN OUTCOME MEASURES: All-cause and cardiovascular-related mortality. RESULTS: During a median of 83 months of follow-up, 1432 deaths were identified. Associations between body composition metrics and mortality risks were evident above specific thresholds. For all-cause mortality, Android fat mass showed elevated HRs above 2.46 kg (HR: 1.17, 95% CI 1.02 to 1.34), while Android lean mass (ALM) had similar trends above 2.75 kg (HR: 1.17, 95% CI 1.03 to 1.33), and Android total mass above 5.75 kg (HR: 1.08, 95% CI 1.01 to 1.16). Conversely, lower HRs were observed below certain thresholds: Gynoid fat mass (GFM) below 3.71 kg (HR: 0.72, 95% CI 0.56 to 0.93), Gynoid lean mass below 6.44 kg (HR: 0.77, 95% CI 0.64 to 0.92), and Gynoid total mass below 11.78 kg (HR: 0.76, 95% CI 0.70 to 0.84). Notably, below 0.722 kg, the HR of visceral adipose tissue mass (VATM) was 1.25 (95% CI 1.04 to 1.48) for all-cause mortality, and above 3.18 kg, the HR of total abdominal fat mass was 2.41 (95% CI 1.15 to 5.05). Cardiovascular-related mortality exhibited associations as well, particularly for Android fat mass (AFM) above 1.78 kg (HR: 1.22, 95% CI 1.01 to 1.47) and below 7.16 kg (HR: 0.50, 95% CI 0.36 to 0.69). HRs varied for Gynoid total mass below and above 10.98 kg (HRs: 0.70, 95% CI 0.54 to 0.93, and 1.12, 95% CI 1.02 to 1.23). Android per cent fat, subcutaneous fat mass (SFM), AFM/GFM, and VATM/SFM were not statistically associated with all-cause mortality. Android per cent fat, Gynoid per cent fat, AFM/GFM, and VATM/SFM were not statistically associated with cardiovascular-related mortality. Conicity index showed that the ALM/GLM had the highest performance for all-cause and cardiovascular-related mortality with AUCs of 0.785, and 0.746, respectively. CONCLUSIONS: The relationship between fat or lean mass and all-cause mortality varies by region. Fat mass was positively correlated with cardiovascular mortality, regardless of the region in which they located. ALM/GLM might be a better predictor of all-cause and cardiovascular-related mortality than other body components or body mass index.

HNEAP Regulates Necroptosis of Cardiomyocytes by Suppressing the m5 C Methylation of Atf7 mRNA.

PIWI-interacting RNAs (piRNAs) are highly expressed in various cardiovascular diseases. However, their role in cardiomyocyte death caused by ischemia/reperfusion (I/R) injury, especially necroptosis, remains elusive. In this study, a heart necroptosis-associated piRNA (HNEAP) is found that regulates cardiomyocyte necroptosis by targeting DNA methyltransferase 1 (DNMT1)-mediated 5-methylcytosine (m5 C) methylation of the activating transcription factor 7 (Atf7) mRNA transcript. HNEAP expression level is significantly elevated in hypoxia/reoxygenation (H/R)-exposed cardiomyocytes and I/R-injured mouse hearts. Loss of HNEAP inhibited cardiomyocyte necroptosis and ameliorated cardiac function in mice. Mechanistically, HNEAP directly interacts with DNMT1 and attenuates m5 C methylation of the Atf7 mRNA transcript, which increases Atf7 expression level. ATF7 can further downregulate the transcription of Chmp2a, an inhibitor of necroptosis, resulting in the reduction of Chmp2a level and the progression of cardiomyocyte necroptosis. The findings reveal that piRNA-mediated m5 C methylation is involved in the regulation of cardiomyocyte necroptosis. Thus, the HNEAP-DNMT1-ATF7-CHMP2A axis may be a potential target for attenuating cardiac injury caused by necroptosis in ischemic heart disease.

Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling.

Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations.

Recovery of platinum group metal resources from high-level radioactive liquid wastes by non-contact photoreduction.

Recovery of platinum group metals (PGMs) including palladium (Pd), rhodium (Rh), and ruthenium (Ru) from high-level radioactive liquid waste (HLLW) possesses enormous environmental and economic benefits. A non-contact photoreduction method was herein developed to selectively recover each PGM from HLLW. Soluble Pd(II), Rh(III), and Ru(III) ions were reduced to insoluble zero-valent metals and separated from simulated HLLW containing neodymium (Nd) as a representative for lanthanides, another main component in HLLW. Detailed investigation on the photoreduction of different PGMs revealed that Pd(II) could be reduced under 254- or 300-nm UV exposure using either ethanol or isopropanol as reductants. Only 300-nm UV light enabled the reduction of Rh(III) in the presence of ethanol or isopropanol. Ru(III) was the most difficult to reduce, which was only realized by 300-nm UV illumination in isopropanol solution. The effects of pH was also studied, suggesting that lower pH favored the separation of Rh(III) but hindered the reduction of Pd(II) and Ru(III). A delicate three-step process was accordingly designed to achieve the selective recovery of each PGM from simulated HLLW. Pd(II) was reduced by 254-nm UV light with the help of ethanol in the first step. Then Rh(III) was reduced by 300-UV light in the second step after the pH was adjusted to 0.5 to suppress the Ru(III) reduction. In the third step, Ru(III) was reduced by 300-nm UV light after isopropanol was added and the pH was adjusted to 3.2. The separation ratios of Pd, Rh, and Ru exceeded 99.8%, 99.9%, and 90.0%, respectively. Meanwhile, all Nd(III) still remained in the simulated HLLW. The separation coefficients between Pd/Rh and Rh/Ru exceeded 56,000 and 75,000, respectively. This work may provide an alternative method to recover PGMs from HLLW, which minimize the secondary radioactive wastes compared with other approaches.

Motor Assessment With the STEGA iPad App to Measure Handwriting in Children.

IMPORTANCE: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control. OBJECTIVE: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting. DESIGN: Cross-sectional, single-arm observational study. SETTING: Academic research institution. PARTICIPANTS: Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive. OUTCOMES AND MEASURES: Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children's Handwriting-Cursive [ETCH-C]) and predicted legibility (calculated from STEGA's 120 Hz, nine-variable data). RESULTS: STEGA successfully predicted handwriting (r2 = .437, p < .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH-C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2). CONCLUSIONS AND RELEVANCE: Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds: The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice.

Circular RNA FEACR inhibits ferroptosis and alleviates myocardial ischemia/reperfusion injury by interacting with NAMPT.

BACKGROUND: Emerging research has reported that circular RNAs (circRNAs) play important roles in cardiac cell death after myocardial ischemia and reperfusion (I/R). Ferroptosis, a new form of cell death discovered in recent years, has been proven to participate in the regulation of myocardial I/R. This study used circRNA sequencing to explore the key circRNA in the regulation of cardiac ferroptosis after I/R and study the mechanisms of potential circRNA function. METHODS: We performed circRNA sequencing to explore circRNAs differentially expressed after myocardial I/R. We used quantitative polymerase chain reactions to determine the circRNA expression in different tissues and detect the circRNA subcellular localization in the cardiomyocyte. Gain- and loss-of-function experiments were aimed to examine the function of circRNAs in cardiomyocyte ferroptosis and cardiac tissue damage after myocardial I/R. RNA pull-down was applied to explore proteins interacting with circRNA. RESULTS: Here, we identified a ferroptosis-associated circRNA (FEACR) that has an underlying regulatory role in cardiomyocyte ferroptosis. FEACR overexpression suppressed I/R-induced myocardial infarction and ameliorated cardiac function. FEACR inhibition induces ferroptosis in cardiomyocytes and FEACR overexpression inhibits hypoxia and reoxygenation-induced ferroptosis. Mechanistically, FEACR directly bound to nicotinamide phosphoribosyltransferase (NAMPT) and enhanced the protein stability of NAMPT, which increased NAMPT-dependent Sirtuin1 (Sirt1) expression, which promoted the transcriptional activity of forkhead box protein O1 (FOXO1) by reducing FOXO1 acetylation levels. FOXO1 further upregulated the transcription of ferritin heavy chain 1 (Fth1), a ferroptosis suppressor, which resulted in the inhibition of cardiomyocyte ferroptosis. CONCLUSIONS: Our finding reveals that the circRNA FEACR-mediated NAMPT-Sirt1-FOXO1-FTH1 signaling axis participates in the regulation of cardiomyocyte ferroptosis and protects the heart function against I/R injury. Thus, FEACR and its downstream factors could be novel targets for alleviating ferroptosis-related myocardial injury in ischemic heart diseases.

Rheological behaviour, setting time, compressive strength and microstructure of mortar incorporating supplementary cementitious materials and nano-silica.

The setting time of the paste and the rheological properties and microstructure of the mortar after replacing OPC cement with silica fume (SF), fly ash cenosphere (FAC) and nano-silica are studied as a reference for shotcrete applications. The suggested contents of SF, FAC and nano-silica are around 5-7.5%, higher than 20% and 1-3%, respectively, to meet the initial setting time specification. Viscosity and yield stress of mortar are highly dependent on water/cement ratio and paste/sand ratio. At the higher water/cement ratio, viscosity is more based on the paste itself. For SF of 2.5-10%, viscosity and yield stress increase, and the flowability of the mixture decreases. For FAC of 5-25%, viscosity and yield stress increase with a lower rate than SF, and flowability increases at 5% and then decreases as FAC content increases, which, however, is at the same level as the control. When SF and FAC are both added, a tortuous behavior of viscosity is shown. As nano-silica is further added, significant increases in viscosity and yield stress are shown. The compressive strengths of mortar with different supplementary cementitious materials (SCMs) at early ages are close. The difference in compressive strength after 28 days of standard curing is significant. The SF5-FAC15 group exhibits the largest increase in strength for 32.82%. At the age of 2.5 h, the macropore areas distribution of SF5-FAC25-NS1.5 test groups were 31.96%, indicating the lowest macropore area distribution. The secondary hydration reaction of supplementary cementitious materials (SCMs) continuously generates products that fill the pores, and the ultrafine filling effect of nanomaterials improves the compactness of the mortar microstructure and reduces the macropore area distribution. The mercury intrusion test results of the SF5-FAC25-NS1.5 group show that the pores are concentrated within the range of 0.01 to 0.05 µm, and the most probable pore size is significantly smaller than that of the CTR group. As the overall replacement level of SCMs increases, the diffraction peak of calcium hydroxide gradually weakens.

Semaphorin3A Exacerbates Cardiac Microvascular Rarefaction in Pressure Overload-Induced Heart Disease.

Microvascular endothelial cells (MiVECs) impair angiogenic potential, leading to microvascular rarefaction, which is a characteristic feature of chronic pressure overload-induced cardiac dysfunction. Semaphorin3A (Sema3A) is a secreted protein upregulated in MiVECs following angiotensin II (Ang II) activation and pressure overload stimuli. However, its role and mechanism in microvascular rarefaction remain elusive. The function and mechanism of action of Sema3A in pressure overload-induced microvascular rarefaction, is explored, through an Ang II-induced animal model of pressure overload. RNA sequencing, immunoblotting analysis, enzyme-linked immunosorbent assay, quantitative reverse transcription polymerase chain reaction (qRT-PCR), and immunofluorescence staining results indicate that Sema3A is predominantly expressed and significantly upregulated in MiVECs under pressure overload. Immunoelectron microscopy and nano-flow cytometry analyses indicate small extracellular vesicles (sEVs), with surface-attached Sema3A, to be a novel tool for efficient release and delivery of Sema3A from the MiVECs to extracellular microenvironment. To investigate pressure overload-mediated cardiac microvascular rarefaction and cardiac fibrosis in vivo, endothelial-specific Sema3A knockdown mice are established. Mechanistically, serum response factor (transcription factor) promotes the production of Sema3A; Sema3A-positive sEVs compete with vascular endothelial growth factor A to bind to neuropilin-1. Therefore, MiVECs lose their ability to respond to angiogenesis. In conclusion, Sema3A is a key pathogenic mediator that impairs the angiogenic potential of MiVECs, which leads to cardiac microvascular rarefaction in pressure overload-induced heart disease.

Direct In-Mold Impregnation of Glass Fiber Fabric by Polypropylene with Supercritical Nitrogen in Microcellular Injection Molding Process.

Combining microcellular injection molding and insert injection molding, an injection molding technique for glass fiber fabric (GFF) reinforced polypropylene (PP) composite foams was proposed. The GFF was directly set in the mold cavity, and then the PP with supercritical nitrogen (SCN) was injected into the cavity for in-mold impregnation. The impregnation effects of two types of GFFs (EWR300 and EWR600) by the PP/SCF solutions at different injection temperatures (230, 240, and 250 °C) were investigated. The results of the morphological and tensile properties of the samples showed that the interfacial bonding was not good, because of the heterogeneity between the GFF and PP. In comparison with solid PP, the unfoamed GFF/PP did not present a higher tensile strength and presented a lower specific tensile strength. However, the increased tensile strength of the GFF/PP composite foams indicated an improvement in the impregnation effect and interfacial bonding. The SCN decreased the viscosity, which benefited the direct in-mold impregnation of the GFF. Increasing the temperature can improve the interfacial bonding, but it also influenced the foaming and thus led to a decrease in the tensile strength. According to the temperature distribution, the samples from different positions in the mold cavity had different properties.

Weakly activated core neuroinflammation pathways were identified as a central signaling mechanism contributing to the chronic neurodegeneration in Alzheimer's disease.

Objectives: Neuroinflammation signaling has been identified as an important hallmark of Alzheimer's disease (AD) in addition to amyloid ß plaques (Aß) and neurofibrillary tangles (NFTs). However, the molecular mechanisms and biological processes of neuroinflammation remain unclear and have not well delineated using transcriptomics data available. Our objectives are to uncover the core neuroinflammation signaling pathways in AD using integrative network analysis on the transcriptomics data. Materials and methods: From a novel perspective, i.e., investigating weakly activated molecular signals (rather than the strongly activated molecular signals), we developed integrative and systems biology network analysis to uncover potential core neuroinflammation signaling targets and pathways in AD using the two large-scale transcriptomics datasets, i.e., Mayo Clinic (77 controls and 81 AD samples) and ROSMAP (97 controls and 260 AD samples). Results: Our analysis identified interesting core neuroinflammation signaling pathways, which are not systematically reported in the previous studies of AD. Specifically, we identified 7 categories of signaling pathways implicated on AD and related to virus infection: immune response, x-core signaling, apoptosis, lipid dysfunctional, biosynthesis and metabolism, and mineral absorption signaling pathways. More interestingly, most of the genes in the virus infection, immune response, and x-core signaling pathways are associated with inflammation molecular functions. The x-core signaling pathways were defined as a group of 9 signaling proteins: MAPK, Rap1, NF-kappa B, HIF-1, PI3K-Akt, Wnt, TGF-beta, Hippo, and TNF, which indicated the core neuroinflammation signaling pathways responding to the low-level and weakly activated inflammation and hypoxia and leading to the chronic neurodegeneration. It is interesting to investigate the detailed signaling cascades of these weakly activated neuroinflammation signaling pathways causing neurodegeneration in a chronic process, and consequently uncover novel therapeutic targets for effective AD treatment and prevention. Conclusions: The potential core neuroinflammation and associated signaling targets and pathways were identified using integrative network analysis on two large-scale transcriptomics datasets of AD.

Lung microbiome in children with hematological malignancies and lower respiratory tract infections.

Background: Respiratory infectious complications remain a major cause of morbidity and mortality in children with hematological malignancies. Knowledge regarding the lung microbiome in aforementioned children is limited. Methods: A prospective cohort was conducted, enrolling 16 children with hematological malignancies complicated with moderate-to-severe lower respiratory tract infections (LRTIs) versus 21 LRTI children with age, gender, weight, and infection severity matched, with no underlying malignancies, to evaluate the lung microbiome from bronchoalveolar lavage fluid samples in different groups. Results: The lung microbiome from children with hematological malignancies and LRTIs showed obviously decreased α and ß diversity; increased microbial function in infectious disease:bacteria/parasite; drug resistance:antimicrobial and human pathogenesis than the control group; a significantly reduced proportion of Firmicutes, Bacteroidota, Actinobacteriota; increased Proteobacteria at the phylum level; and distinctly elevated Parabacteroides, Klebsiella, Grimontia, Escherichia_Shigella, unclassified_Enterobacteriaceae at the genus level than the control group. Furthermore, it was revealed that α diversity (Shannon), ß diversity (Bray-Curtis dissimilarity), Proteobacteria at the phylum level, and unclassified_Enterobacteriaceae and Escherichia_Shigella at the genus level were significantly negatively associated with hospitalization course whereas Firmicutes at the phylum level was established positively correlated with the hospitalization course. Conclusions: Children with hematological malignancies and LRTIs showed obviously decreased α and ß diversity, significantly increased function in infectious disease pathogenesis, antimicrobial drug resistance, and unfavorable environment tolerance. Moreover, α diversity (Shannon), ß diversity (Bray-Curtis dissimilarity), and Proteobacteria may be used as negative correlated predictors for hospitalization course in these children whereas Firmicutes may be utilized as a positive correlated predictor.