Causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity.

The causative mechanisms underlying the genetic relationships of neurodegenerative diseases with epigenetic aging and human longevity remain obscure. We aimed to detect causal associations and shared genetic etiology of neurodegenerative diseases with epigenetic aging and human longevity. We obtained large-scale genome-wide association study summary statistics data for four measures of epigenetic age (GrimAge, PhenoAge, IEAA, and HannumAge) (N = 34,710), multivariate longevity (healthspan, lifespan, and exceptional longevity) (N = 1,349,462), and for multiple neurodegenerative diseases (N = 6618-482,730), including Lewy body dementia, Alzheimer's disease (AD), Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis. Main analyses were conducted using multiplicative random effects inverse-variance weighted Mendelian randomization (MR), and conditional/conjunctional false discovery rate (cond/conjFDR) approach. Shared genomic loci were functionally characterized to gain biological understanding. Evidence showed that AD patients had 0.309 year less in exceptional longevity (IVW beta = -0.309, 95% CI: -0.38 to -0.24, p = 1.51E-19). We also observed suggestively significant causal evidence between AD and GrimAge age acceleration (IVW beta = -0.10, 95% CI: -0.188 to -0.013, p = 0.02). Following the discovery of polygenic overlap, we identified rs78143120 as shared genomic locus between AD and GrimAge age acceleration, and rs12691088 between AD and exceptional longevity. Among these loci, rs78143120 was novel for AD. In conclusion, we observed that only AD had causal effects on epigenetic aging and human longevity, while other neurodegenerative diseases did not. The genetic overlap between them, with mixed effect directions, suggested complex shared genetic etiology and molecular mechanisms.

A versatile role for lung ultrasound in systemic autoimmune rheumatic diseases related pulmonary involvement: a narrative review.

Systemic autoimmune rheumatic diseases (SARDs) related pulmonary disease is highly prevalent, with variable clinical presentation and behavior, and thus is associated with poor outcomes and negatively impacts quality of life. Chest high resolution computed tomography (HRCT) is still considered a fundamental imaging tool in the screening, diagnosis, and follow-up of pulmonary disease in patients with SARDs. However, radiation exposure, economic burden, as well as lack of point-of-care CT equipment limits its application in some clinical situation. Ultrasound has found a place in numerous aspects of the rheumatic diseases, including the vasculature, skin, muscle, joints, kidneys and in screening for malignancies. Likewise it has found increasing use in the lungs. In the past two decades, lung ultrasound has started to be used for pulmonary parenchymal diseases such as pneumonia, pulmonary edema, lung fibrosis, pneumothorax, and pleural lesions, although the lung parenchymal was once considered off-limits to ultrasound. Lung ultrasound B-lines and irregularities of the pleural line are now regarded two important sonographic artefacts related to diffuse parenchymal lung disease and they could reflect the lesion extent and severity. However, its role in the management of SARDs related pulmonary involvement has not been fully investigated. This review article will focus on the potential applications of lung ultrasound in different pulmonary scenarios related with SARDs, such as interstitial lung disease, diffuse alveolar hemorrhage, diaphragmatic involvement, and pulmonary infection, in order to explore its value in clinical daily practice.

Mesenchymal stromal cells restrain the Th17 cell response via L-amino-acid oxidase within lymph nodes.

Mesenchymal stromal/stem cells (MSC) have emerged as a promising therapeutic avenue for treating autoimmune diseases, eliciting considerable interest and discussion regarding their underlying mechanisms. This study revealed the distinctive ability of human umbilical cord MSC to aggregate within the lymph nodes of mice afflicted with autoimmune diseases, but this phenomenon was not observed in healthy mice. The specific distribution is driven by the heightened expression of the CCL21-CCR7 axis in mice with autoimmune diseases, facilitating the targeted homing of MSC to the lymph nodes. Within the lymph nodes, MSC exhibit a remarkable capacity to modulate Th17 cell function, exerting a pronounced anti-inflammatory effect. Transplanted MSC stimulates the secretion of L-amino-acid oxidase (LAAO), a response triggered by elevated levels of tumor necrosis factor-α (TNF-α) in mice with autoimmune diseases through the NF-κB pathway. The presence of LAAO is indispensable for the efficacy of MSC, as it significantly contributes to the inhibition of Th17 cells. Furthermore, LAAO-derived indole-3-pyruvic acid (I3P) serves as a potent suppressor of Th17 cells by activating the aryl hydrocarbon receptor (AHR) pathway. These findings advance our understanding of the global immunomodulatory effects exerted by MSC, providing valuable information for optimizing therapeutic outcomes.

Synergistic effect of GN-Ag NPs enhancing the efficient catalytic degradation of MB and CR by PDA@MMT composite hydrogel.

A novel composite hydrogel prepared from polyacrylamide (PAM), polydopamine-modified montmorillonite (PDA@MMT), graphene and hydroxypropyl cellulose (HPC), loaded with Ag NPs, was prepared for the catalytic degradation of methylene blue (MB) and Congo red (CR) using in situ reduction. HPC significantly enhanced the dispersion of PDA@MMT within the hydrogel, endowing the hydrogel with excellent mechanical properties, with stress and strain of 1773 kPa and 4005 %, and elastic modulus and toughness of 43.4 kPa and 29.54 MJ/m3, respectively. The introduction of graphene (GN) increased the rate of electron transfer during the catalytic process and significantly improved the catalytic efficiency, with catalytic rate constants of 1.360 and 0.803 min-1 for MB and CR at 20 °C, respectively. The hydrogels were endowed with excellent antimicrobial properties due to the introduction of Ag NPs. In the future, this hydrogel is expected to play an important role in environmental pollution control.

Deep learning-assisted distinguishing breast phyllodes tumors from fibroadenomas based on ultrasound images: a diagnostic study.

OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumors (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences. METHODS: We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting six radiologists with different levels of experience. RESULTS: Upon testing, Xception model demonstrated the best diagnostic performance (AUC: 0.87, 95%CI: 0.81-0.92), outperforming all radiologists (all p < 0.05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively. CONCLUSIONS: The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work). ADVANCES IN KNOWLEDGE: We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate two types of breast tumors which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.

Integrative Transcriptomic Analysis of Peripheral Blood Monocytes in Systemic Sclerosis and Shared Pathogenic Pathways in Autoimmune Diseases.

BACKGROUND: Systemic sclerosis (SSc) is an autoimmune disease (AD), that receives less attention compared to rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and primary Sjögren's syndrome (pSS). This study aims to analyze transcriptional profiles and immune cell composition in peripheral blood mononuclear cells (PBMC) from SSc patients compared to other ADs. METHODS: RNA-seq data from 119 untreated patients (eight with SSc, 42 with RA, 41 with pSS, 28 with SLE) and 20 healthy controls were analyzed. Bioinformatics tools were employed to identify differentially expressed genes (DEGs), biological functions and immune cell profiles unique to SSc and shared with other ADs. RESULTS: 1,148 DEGs were found in SSc, with upregulated genes associated with megakaryocyte processes and downregulated genes associated with neutrophil function and immune response. DEGs, including ALDH1A1 and MEGF9, were associated with neutropenia. Upregulated transcription factors (TFs) were linked to embryonic hematopoiesis and downregulated TFs were involved in leukocyte differentiation and immune regulation. Comparative analysis with other ADs revealed common pathogenic pathways, emphasizing megakaryocyte proliferation. Neutrophils count was significantly decreased in ADs (p < 0.001) compared to healthy controls. Comparative analysis highlighted common pathways, particularly in megakaryocyte proliferation, and unique genes (MEGF9, MMP8, and KRT family members) in SSc, suggesting roles in neutrophil function, skin integrity, and fibrosis. CONCLUSIONS: This study identifies dysregulated gene expression (KRT and MMP8) associated with neutrophil function and increased megakaryocytes in SSc, highlighting common patterns across autoimmune diseases. These findings offer new insights into the potential pathogenesis of SSc, and help to explore new targets for the treatment.

Cardiometabolic traits mediating the effect of education on the risk of DKD and CKD: a Mendelian randomization study.

Background: Both diabetic kidney disease (DKD) and chronic kidney disease (CKD) are more prevalent among individuals with lower levels of education in observational studies. To quantify the mediation effect of recognized cardiometabolic traits, we obtain causal estimates between education and DKD as well as CKD. Materials and methods: We assessed the causal effect of education on DKD and CKD, separately estimated the causal effect of 26 cardiometabolic traits on DKD and CKD, and finally calculated the mediating effects and mediating proportions of each using two-step, two-sample multivariable Mendelian randomization (MVMR). Furthermore, the genetic association between exposure, mediators, and outcomes was investigated using linkage disequilibrium score (LDSC) regression analysis. Expression quantitative trait loci (eQTL) were retrieved from the Genotype-Tissue Expression Project (GTEx) v8 to serve as genetic instrumental variables. Transcriptome-wide association studies (TWAS), Bayesian colocalization analysis, and Summary-data-based Mendelian Randomization (SMR) analysis were performed to explore underlying susceptibility genes between education, mediators, and kidney diseases. Results: Higher education with a genetically predicted 1-SD (4.2 years) was linked to a 48.64% decreased risk of DKD and a 29.08% decreased risk of CKD. After extensive evaluation of 26 cardiometabolic traits, 7 and 6 causal mediators were identified as mediating the effects of education on DKD and CKD, respectively. The largest mediating factor between education and DKD was BMI, which was followed by WHR, T2D, fasting insulin, SBP, fasting glucose, and DBP. In contrast, candidate mediators in the education-to-CKD pathway included BMI, followed by cigarettes smoked per day, WHR, SBP, T2D, and DBP. MR analysis revealed that TP53INP1 was found to be a shared susceptibility gene for cardiometabolic traits and DKD, while L3MBTL3 was found to be a shared susceptibility gene for cardiometabolic traits and CKD. Conclusion: Our findings provide solid evidence that education has a causally protective effect on the development of DKD and CKD. We additionally reveal significant directions for intervention on cardiometabolic traits that mitigate the negative effects of educational inequities on the onset of DKD and CKD. Our work demonstrates a shared genetic basis between education, cardiometabolic traits, and kidney diseases. Future research aiming at lowering kidney risk may benefit from these findings.

Multi-layered computational gene networks by engineered tristate logics.

So far, biocomputation strictly follows traditional design principles of digital electronics, which could reach their limits when assembling gene circuits of higher complexity. Here, by creating genetic variants of tristate buffers instead of using conventional logic gates as basic signal processing units, we introduce a tristate-based logic synthesis (TriLoS) framework for resource-efficient design of multi-layered gene networks capable of performing complex Boolean calculus within single-cell populations. This sets the stage for simple, modular, and low-interference mapping of various arithmetic logics of interest and an effectively enlarged engineering space within single cells. We not only construct computational gene networks running full adder and full subtractor operations at a cellular level but also describe a treatment paradigm building on programmable cell-based therapeutics, allowing for adjustable and disease-specific drug secretion logics in vivo. This work could foster the evolution of modern biocomputers to progress toward unexplored applications in precision medicine.

BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision.

Decoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems. However, current methods face limitations in decoding sentences from electroencephalography (EEG) signals. Improving decoding performance requires the development of a more effective encoder for the EEG modality. Nonetheless, learning generalizable EEG representations remains a challenge due to the relatively small scale of existing EEG datasets. In this paper, we propose enhancing the EEG encoder to improve subsequent decoding performance. Specifically, we introduce the discrete Conformer encoder (D-Conformer) to transform EEG signals into discrete representations and bootstrap the learning process by imposing EEG-language alignment from the early training stage. The D-Conformer captures both local and global patterns from EEG signals and discretizes the EEG representation, making the representation more resilient to variations, while early-stage EEG-language alignment mitigates the limitations of small EEG datasets and facilitates the learning of the semantic representations from EEG signals. These enhancements result in improved EEG representations and decoding performance. We conducted extensive experiments and ablation studies to thoroughly evaluate the proposed method. Utilizing the D-Conformer encoder and bootstrapping training strategy, our approach demonstrates superior decoding performance across various tasks, including word-level, sentence-level, and sentiment-level decoding from EEG signals. Specifically, in word-level classification, we show that our encoding method produces more distinctive representations and higher classification performance compared to the EEG encoders from existing methods. At the sentence level, our model outperformed the baseline by 5.45%, achieving a BLEU-1 score of 42.31%. Furthermore, in sentiment classification, our model exceeded the baseline by 14%, achieving a sentiment classification accuracy of 69.3%.

Association of serum 25-hydroxyvitamins D2 and D3 with hearing loss in US adults: analysis from National Health and Nutrition Examination Survey, 2015-2016.

Background: Hearing loss (HL) is increasingly recognized as a significant global public health issue, and research on its relationship with vitamin D levels has gained wider attention. However, the association between serum biomarkers 25-hydroxyvitamin D2 (25(OH)D2) and D3 (25(OH)D3) with different types of HL remains unclear. This study aimed to investigate the potential association of serum 25(OH)D2 and 25(OH)D3 with HL in US adults. Methods: A sample of 3,684 individuals aged 20-69 years from the 2015-2016 National Health and Nutrition Examination (NHANES) was analyzed in this study. HL was defined as a pure tone average > 25 dB in either ear at low frequencies (500, 1,000, 2000 Hz), speech frequencies (500, 1,000, 2000, 4,000 Hz), and high frequencies (3,000, 4,000, 6,000, 8,000 Hz). Logistic regression was employed to examine the association between serum 25(OH)D2 and 25(OH)D3 and HL. The study population was then stratified by age, gender, race, and education level to analyze potential differences between adults in different subgroups. Results: In the multivariate analysis, it was found that serum 25(OH)D2 was independently associated with low-frequency hearing loss (LFHL) (OR: 1.012 [95% CI, 1.005-1.020]) and speech-frequency hearing loss (SFHL) (OR: 1.011 [95% CI, 1.003-1.018]). Restrictive cubic spline analysis demonstrated a linear dose-response relationship between serum 25(OH)D2 levels and LFHL (p for linearity <0.001), as well as SFHL (p for linearity = 0.001). Conversely, an L-shaped association was observed between serum 25(OH)D3 levels and both LFHL (p for nonlinearity = 0.014) and SFHL (p for nonlinearity = 0.025), with threshold values identified at 35.3 and 36.5 nmol/L, respectively. Higher levels of serum 25(OH)D3 were associated with a lower probability of high-frequency hearing loss (HFHL) (OR: 0.994 [95% CI, 0.989-0.999]), with a threshold value identified at 53.9 nmol/L. Furthermore, a significant interaction between diabetes and serum 25(OH)D2 in LFHL was revealed through subgroup analysis (p = 0.041). In the non-diabetic population, serum 25(OH)D2 maintained its association with LFHL. Conclusion: Our findings suggested a positive association between serum 25(OH)D2 concentrations and both LFHL and SFHL in the studied cohort. Additionally, an L-shaped relationship was found between serum 25(OH)D3 and LFHL and SFHL, and higher levels of serum 25(OH)D3 were identified to be associated with a lower risk of HFHL.

Multi-domain interaction mediated strength-building in human α-actinin dimers unveiled by direct single-molecule quantification.

α-Actinins play crucial roles in cytoskeletal mechanobiology by acting as force-bearing structural modules that orchestrate and sustain the cytoskeletal framework, serving as pivotal hubs for diverse mechanosensing proteins. The mechanical stability of α-actinin dimer, a determinant of its functional state, remains largely unexplored. Here, we directly quantify the force-dependent lifetimes of homo- and hetero-dimers of human α-actinins, revealing an ultra-high mechanical stability of the dimers associated with > 100 seconds lifetime within 40 pN forces under shear-stretching geometry. Intriguingly, we uncover that the strong dimer stability is arisen from much weaker sub-domain pair interactions, suggesting the existence of distinct dimerized functional states of the dimer, spanning a spectrum of mechanical stability, with the spectrin repeats (SRs) in folded or unfolded conformation. In essence, our study supports a potent mechanism for building strength in biomolecular dimers through weak, multiple sub-domain interactions, and illuminates multifaceted roles of α-actinin dimers in cytoskeletal mechanics and mechanotransduction.

Mesenteric lymph nodes: a critical site for the up-regulatory effect of hUC-MSCs on Treg cells by producing TGF-ß1 in colitis treatment.

BACKGROUND: Mesenchymal stem cells (MSCs) demonstrate a wide range of therapeutic capabilities in the treatment of inflammatory bowel disease (IBD). The intraperitoneal injection of MSCs has exhibited superior therapeutic efficacy on IBD than intravenous injection. Nevertheless, the precise in vivo distribution of MSCs and their biological consequences following intraperitoneal injection remain inadequately understood. Additional studies are required to explore the correlation between MSCs distribution and their biological effects. METHODS: First, the distribution of human umbilical cord MSCs (hUC-MSCs) and the numbers of Treg and Th17 cells in mesenteric lymph nodes (MLNs) were analyzed after intraperitoneal injection of hUC-MSCs. Subsequently, the investigation focused on the levels of transforming growth factor beta1 (TGF-ß1), a key cytokine to the biology of both Treg and Th17 cells, in tissues of mice with colitis, particularly in MLNs. The study also delved into the impact of hUC-MSCs therapy on Treg cell counts in MLNs, as well as the consequence of TGFB1 knockdown hUC-MSCs on the differentiation of Treg cells and the treatment of IBD. RESULTS: The therapeutic effectiveness of intraperitoneally administered hUC-MSCs in the treatment of colitis was found to be significant, which was closely related to their quick migration to MLNs and secretion of TGF-ß1. The abundance of hUC-MSCs in MLNs of colitis mice is much higher than that in other organs even the inflamed sites of colon. Intraperitoneal injection of hUC-MSCs led to a significant increase in the number of Treg cells and a decrease in Th17 cells especially in MLNs. Furthermore, the concentration of TGF-ß1, the key cytokine for Treg differentiation, were also found to be significantly elevated in MLNs after hUC-MSCs treatment. Knockdown of TGFB1 in hUC-MSCs resulted in a noticeable reduction of Treg cells in MLNs and the eventually failure of hUC-MSCs therapy in colitis. CONCLUSIONS: MLNs may be a critical site for the regulatory effect of hUC-MSCs on Treg/Th17 cells and the therapeutic effect on colitis. TGF-ß1 derived from hUC-MSCs promotes local Treg differentiation in MLNs. This study will provide new ideas for the development of MSC-based therapeutic strategies in IBD patients.

BreathXplorer: Processing Online Breathomics Data Generated from Direct Analysis Using High-Resolution Mass Spectrometry.

Nontargeted breath analysis in real time using high-resolution mass spectrometry (HRMS) is a promising approach for high coverage profiling of metabolites in human exhaled breath. However, the information-rich and unique non-Gaussian metabolic signal shapes of real-time HRMS-based data pose a significant challenge for efficient data processing. This work takes a typical real-time HRMS technique as an example, i.e. secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), and presents BreathXplorer, an open-source Python package designed for the processing of real-time exhaled breath data comprising multiple exhalations. BreathXplorer is composed of four main modules. The first module applies either a topological algorithm or a Gaussian mixture model (GMM) to determine the start and end points of each exhalation. Next, density-based spatial clustering of applications with noise (DBSCAN) is employed to cluster m/z values belonging to the same metabolic feature, followed by applying an intensity relative standard deviation (RSD) filter to extract real breath metabolic features. BreathXplorer also offers functions of (1) feature alignment across the samples and (2) associating MS/MS spectra with their corresponding metabolic features for downstream compound annotation. Manual inspection of the metabolic features extracted from SESI-HRMS breath data suggests that BreathXplorer can achieve 100% accuracy in identifying the start and end points of each exhalation and acquire accurate quantitative measurements of each breath feature. In a proof-of-concept study on exercise breathomics using SESI-HRMS, BreathXplorer successfully reveals the significantly changed metabolites that are pertinent to exercise. BreathXplorer is publicly available on GitHub (https://github.com/HuanLab/breathXplorer). It provides a powerful and convenient-to-use tool for the researchers to process breathomics data obtained by directly analysis using HRMS.

A comprehensive review on agricultural waste utilization through sustainable conversion techniques, with a focus on the additives effect on the fate of phosphorus and toxic elements during composting process.

The increasing trend of using agricultural wastes follows the concept of "waste to wealth" and is closely related to the themes of sustainable development goals (SDGs). Carbon-neutral technologies for waste management have not been critically reviewed yet. This paper reviews the technological trend of agricultural waste utilization, including composting, thermal conversion, and anaerobic digestion. Specifically, the effects of exogenous additives on the contents, fractionation, and fate of phosphorus (P) and potentially toxic elements (PTEs) during the composting process have been comprehensively reviewed in this article. The composting process can transform biomass-P and additive-born P into plant available forms. PTEs can be passivated during the composting process. Biochar can accelerate the passivation of PTEs in the composting process through different physiochemical interactions such as surface adsorption, precipitation, and cation exchange reactions. The addition of exogenous calcium, magnesium and phosphate in the compost can reduce the mobility of PTEs such as copper, cadmium, and zinc. Based on critical analysis, this paper recommends an eco-innovative perspective for the improvement and practical application of composting technology for the utilization of agricultural biowastes to meet the circular economy approach and achieve the SDGs.

Dynamic changes of peripheral inflammatory markers link with disease severity and predict short-term poor outcome of myasthenia gravis.

The relationship between peripheral inflammatory markers, their dynamic changes, and the disease severity of myasthenia gravis (MG) is still not fully understood. Besides, the possibility of using it to predict the short-term poor outcome of MG patients have not been demonstrated. This study aims to investigate the relationship between peripheral inflammatory markers and their dynamic changes with Myasthenia Gravis Foundation of America (MGFA) classification (primary outcome) and predict the short-term poor outcome (secondary outcome) in MG patients. The study retrospectively enrolled 154 MG patients from June 2016 to December 2021. The logistic regression was used to investigate the relationship of inflammatory markers with MGFA classification and determine the factors for model construction presented in a nomogram. Finally, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were utilized to evaluate the incremental capacity. Logistic regression revealed significant associations between neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), aggregate index of systemic inflammation (AISI) and MGFA classification (p = 0.013, p = 0.032, p = 0.017, respectively). Incorporating dynamic changes of inflammatory markers into multivariable models improved their discriminatory capacity of disease severity, with significant improvements observed for NLR, systemic immune-inflammation index (SII) and AISI in NRI and IDI. Additionally, AISI was statistically associated with short-term poor outcome and a prediction model incorporating dynamic changes of inflammatory markers was constructed with the area under curve (AUC) of 0.953, presented in a nomograph. The inflammatory markers demonstrate significant associations with disease severity and AISI could be regarded as a possible and easily available predictive biomarker for short-term poor outcome in MG patients.

Unveiling the therapeutic promise: exploring Lysophosphatidic Acid (LPA) signaling in malignant bone tumors for novel cancer treatments.

Malignant bone tumors, including primary bone cancer and metastatic bone tumors, are a significant clinical challenge due to their high frequency of presentation, poor prognosis and lack of effective treatments and therapies. Bone tumors are often accompanied by skeletal complications such as bone destruction and cancer-induced bone pain. However, the mechanisms involved in bone cancer progression, bone metastasis and skeletal complications remain unclear. Lysophosphatidic acid (LPA), an intercellular lipid signaling molecule that exerts a wide range of biological effects mainly through specifically binding to LPA receptors (LPARs), has been found to be present at high levels in the ascites of bone tumor patients. Numerous studies have suggested that LPA plays a role in primary malignant bone tumors, bone metastasis, and skeletal complications. In this review, we summarize the role of LPA signaling in primary bone cancer, bone metastasis and skeletal complications. Modulating LPA signaling may represent a novel avenue for future therapeutic treatments for bone cancer, potentially improving patient prognosis and quality of life.

ICAM-1 may promote the loss of dopaminergic neurons by regulating inflammation in MPTP-induced Parkinson's disease mouse models.

Parkinson's disease (PD) is a chronic neurodegenerative disease with unclear pathogenesis that involves neuroinflammation and intestinal microbial dysbiosis. Intercellular adhesion molecule-1 (ICAM-1), an inflammatory marker, participates in neuroinflammation during dopaminergic neuronal damage. However, the explicit mechanisms of action of ICAM-1 in PD have not been elucidated. We established a subacute PD mouse model by the intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and observed motor symptoms and gastrointestinal dysfunction in mice. Immunofluorescence was used to examine the survival of dopaminergic neurons, expression of microglial and astrocyte markers, and intestinal tight junction-associated proteins. Then, we use 16 S rRNA sequencing to identify alterations in the microbiota. Our findings revealed that ICAM-1-specific antibody (Ab) treatment relieved behavioural defects, gastrointestinal dysfunction, and dopaminergic neuronal death in MPTP-induced PD mice. Further mechanistic investigations indicated that ICAM-1Ab might suppress neuroinflammation by inhibiting the activation of astrocytes and microglia in the substantia nigra and relieving colon barrier impairment and intestinal inflammation. Furthermore, 16 S rRNA sequencing revealed that the relative abundances of bacterial Firmicutes, Clostridia, and Lachnospiraceae were elevated in the PD mice. However, ICAM-1Ab treatment ameliorated the MPTP-induced disorders in the intestinal microbiota. Collectively, we concluded that the suppressing ICAM-1 might lead to the a significant decrease of inflammation and restore the gut microbial community, thus ameliorating the damage of DA neurons.

Preparation and characterization of vitamin E microcapsules stabilized by Zein with different polysaccharides.

Vitamin E (VE) microencapsulation using a green surfactant emulsifier not only protects the active substance and is also environmentally friendly. In this study, we used alcohol ether glycoside as an emulsifier to prepare VE microcapsules using the biological macromolecule Zein and various polysaccharides. The resulting nano microcapsules exhibited a spherical structure, stable morphology, uniform size, and a >90% encapsulation efficiency. They also had good thermal stability and slow-release properties. Of these, xanthan gum/Zein-VE microcapsules were superior, with antioxidant properties up to 3.05-fold higher than untreated VE. We successfully developed VE nano microcapsules that meet eco-friendly and sustainable requirements, which may have applications in the food and pharmaceutical industries.

Experimental and numerical investigations on stress concentration factors of concrete filled steel tube X-joints.

An SHS-CFSHS X-joint is fabricated by welding two square hollow section (SHS) braces to a concrete-filled square hollow section (CFSHS) chord. In this paper, the stress concentration factors (SCFs) of SHS-CFSHS X-joints are investigated through experimental tests and finite element analysis (FEA), with the hot spot stress method serving as the analytical approach. Eight specimens are designed and manufactured, with FE models built in software ANSYS. These FE models are validated against the test results. The specimens are tested under brace axial tension to determine the SCFs of the X-joints. It shows that the concrete filled in the chord effectively reduces the SCFs of the X-joints. To further explore various load conditions and the influence of the parameters, FEA is carried out and a total of 64 FE models are built. Based on the FEA results, multiple regression analysis is used to obtain the SCF formulae of SHS-CFSHS X-joints under axial tension load and in-plane bending load in the brace, respectively. Comparison and analysis of the SCF results obtained from experimental tests, the proposed formulae, and FE simulations reveal that the formulae presented in this study are both conservative and suitable for predicting SCFs.