Influences of Pre-Existing Fissure Angles and Bridge Angles on Concrete Tensile Failure Characteristics: Insights from Meshless Numerical Simulations.

The existence of cracks is a key factor affecting the strength of concrete. However, traditional numerical methods still have some limitations in the simulation of crack growth in fissured concrete structures. Based on this background, the numerical treatment method of particle failure in smoothed particle hydrodynamics (SPH) is proposed, and the generation method for concrete meso-structures under the smoothed particle hydrodynamics (SPH) framework is developed. The concrete meso-models under different pre-existing micro-fissure inclinations and bridge angles (the inner tip line of the double pre-existing micro-fissure is defined as a bridge, and the angle between the bridge and the horizontal direction is defined as the bridge angle) were established, and numerical simulations of the crack propagation processes of concrete structures under tensile stress were carried out. The main findings were as follows: The concrete meso-structures and the pre-existing micro-fissures all have great impacts on the final failure modes of concrete. The stress-strain curve of the concrete model presents four typical stages. Finally, the crack initiation and propagation mechanisms of fissured concrete are discussed, and the application of smoothed particle hydrodynamics (SPH) in crack simulations of fissured concrete is prospected.

Misdiagnosed as a perianal abscess: case report of perianal endometriosis.

Perianal endometriosis represents a rare form of endometriosis occurring outside the pelvic cavity. Owing to its infrequency in clinical practice, this condition is highly susceptible to misdiagnosis and inappropriate treatment. This case report details a young female patient who was erroneously diagnosed with a perianal abscess. We conducted a para-anal mass resection under spinal anesthesia, and subsequent histopathological examination definitively confirmed the diagnosis of perianal endometriosis.

Metabolomics reveals changes in soil metabolic profiles during vegetation succession in karst area.

Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.

Experimental and Meshless Numerical Simulations on the Crack Propagation of Semi-Circular Bending Specimens Containing X-Shaped Fissures Under Three-Point Bending.

Cracks in rock and concrete have a great adverse effect on the stability of engineering structures; however, there are few studies on X-shaped fissures which widely exist in rock and concrete structures. Based on this background, three-point bending fracture tests of SCB specimens containing X-shaped fissures are carried out. The momentum equations in the SPH method are improved, and the crack propagations of SCB specimens under three-point bending are simulated. The results show that cracks grow simply along the vertical direction in the sample with no X-shaped fissures, and the existence of an X-shaped fissure changes the crack growth path and final failure modes of the SCB samples. The crack propagation simulation results are consistent with the experimental results, which verifies the rationality of the improved SPH method. The load-displacement curves mainly present three typical stages: the initial compaction stage, linear elastic deformation stage, and failure stage. The peak load decreases first then increases with an increase in eccentricity. With an increase in X-shaped fissure length and decrease in X-shaped fissure angle, the peak load decreases. The damage counts remain at 0 at the initial loading stage, corresponding to the initial compaction stage and the linear elastic deformation stage, and increase sharply at the later loading stage, corresponding to the failure stage, which is consistent with the experimental results. The influence mechanisms of X-shaped fissures on the crack propagation paths are discussed; the existence of different X-shaped fissure morphologies aggravate the tensile stress concentration at specific positions, leading to different crack propagation modes in the experiments. The research results can provide a certain reference for understanding the failure mechanisms of engineering structures containing X-shaped fissures and promote the applications of the SPH method into the simulations of cross-fissure crack propagations.

Whispers of the polycystic ovary syndrome theater: Directing role of long noncoding RNAs.

Polycystic Ovary Syndrome (PCOS) is a multifaceted endocrine disorder that implicates a spectrum of clinical manifestations, including hormonal imbalance, metabolic dysfunction, and even compromised ovarian granulosa cell (GC) activity. The underlying molecular mechanisms of PCOS remain elusive, presenting a significant barrier to effective diagnosis and treatment. This review delves into the emerging role of long non-coding RNAs (lncRNAs) in the pathophysiology of PCOS, articulating their intricate interactions with mRNAs, microRNAs, and other epigenetic regulators that collectively influence the hormonal and metabolic milieu of PCOS. We examine the dynamic regulatory networks orchestrated by lncRNAs that impact GC function, steroidogenesis, insulin resistance, and inflammatory pathways. By integrating findings from recent studies, we illuminate the potential of lncRNAs as biomarkers for PCOS and highlight their contribution to the disorder, offering a detailed perspective on the lncRNA-mediated modulation of gene expression and pathogenic pathways. Understanding targeted lncRNA interactions with PCOS proposes novel avenues for therapeutic intervention to ameliorate the reproductive and metabolic disturbances characteristic of the syndrome.

Mendelian randomization evidence based on European ancestry for the causal effects of leukocyte telomere length on prostate cancer.

BACKGROUND: Several lines of evidence suggest that leukocyte telomere length (LTL) can affect the development of prostate cancer (PC). METHODS: Here, we employed single nucleoside polymorphisms (SNPs) as instrumental variables (IVs) for LTL (n = 472,174) and conducted Mendelian randomization analysis to estimate their causal impact on PCs (79,148 patients/61,106 controls and 6311 patients/88,902 controls). RESULTS: Every 1-s.d extension of LTL increased the risk of PCs by 34%. Additionally, the analysis of candidate mediators between LTL and PCs via two-step Mendelian randomization revealed that among the 23 candidates, Alzheimer's disease, liver iron content, sex hormone binding global levels, naive CD4-CD8-T cell% T cell, and circulating leptin levels played substantial mediating roles. There is no robust evidence to support the reverse causal relationship between LTL and the selected mediators of PCs. Adjusting for the former four mediators, rather than adjusting for circulating leptin levels, decreased the impact of LTL on PCs. CONCLUSION: This study provides potential intervention measures for preventing LTL-induced PCs.

Urinary extracellular vesicle-derived miR-126-3p predicts lymph node invasion in patients with high-risk prostate cancer.

To investigate extracellular vesicles (EVs), biomarkers for predicting lymph node invasion (LNI) in patients with high-risk prostate cancer (HRPCa), plasma, and/or urine samples were prospectively collected from 45 patients with prostate cancer (PCa) and five with benign prostatic hyperplasia (BPH). Small RNA sequencing was performed to identify miRNAs in the EVs. All patients with PCa underwent radical prostatectomy and extended pelvic lymph node dissection. Differentially expressed miRNAs were identified in patients with and without pathologically-verified LNI. The candidate miRNAs were validated in low-risk prostate cancer (LRPCa) and BPH. Four miRNA species (e.g., miR-126-3p) and three miRNA species (e.g., miR-27a-3p) were more abundant in urinary and plasma EVs, respectively, of patients with PCa. None of these miRNA species were shared between urinary and plasma EVs. miR-126-3p was significantly more abundant in patients with HR PCa with LNI than in those without (P = 0.018). miR-126-3p was significantly more abundant in the urinary EVs of patients with HRPCa than in those with LRPCa (P = 0.017) and BPH (P = 0.011). In conclusion, urinary EVs-derived miR-126-3p may serve as a good biomarker for predicting LNI in patients with HRPCa.

Molecular Stratification and Treatment Monitoring of Lung Cancer Using a Small Extracellular Vesicle-Activated Nanocavity Architecture.

Development of molecular diagnostics for lung cancer stratification and monitoring is crucial for the rational planning and timely adjustment of treatments to improve clinical outcomes. In this regard, we propose a nanocavity architecture to sensitively profile the protein signature on small extracellular vesicles (sEVs) to enable accurate, noninvasive staging and treatment monitoring of lung cancer. The nanocavity architecture is formed by molecular recognition through the binding of sEVs with the nanobox-based core-shell surface-enhanced Raman scattering (SERS) barcodes and mirrorlike, asymmetric gold microelectrodes. By imposing an alternating current on the gold microelectrodes, a nanofluidic shear force was stimulated that supported the binding of sEVs and the efficient assembly of the nanoboxes. The binding of sEVs further induced a nanocavity between the nanobox and the gold microelectrode that significantly amplified the electromagnetic field to enable the simultaneous enhancement of Raman signals from four SERS barcodes and generate patient-specific molecular sEV signatures. Importantly, evaluated on a cohort of clinical samples (n = 76) on the nanocavity architecture, the acquired patient-specific sEV molecular signatures achieved accurate identification, stratification, and treatment monitoring of lung cancer patients, highlighting its potential for transition to clinical utility.

The Role of Extracellular Vesicles in the Treatment of Prostate Cancer.

Prostate cancer (PCa) has become a public health concern in elderly men due to an ever-increasing number of estimated cases. Unfortunately, the available treatments are unsatisfactory because of a lack of a durable response, especially in advanced disease states. Extracellular vesicles (EVs) are lipid-bilayer encircled nanoscale vesicles that carry numerous biomolecules (e.g., nucleic acids, proteins, and lipids), mediating the transfer of information. The past decade has witnessed a wide range of EV applications in both diagnostics and therapeutics. First, EV-based non-invasive liquid biopsies provide biomarkers in various clinical scenarios to guide treatment; EVs can facilitate the grading and staging of patients for appropriate treatment selection. Second, EVs play a pivotal role in pathophysiological processes via intercellular communication. Targeting key molecules involved in EV-mediated tumor progression (e.g., proliferation, angiogenesis, metastasis, immune escape, and drug resistance) is a potential approach for curbing PCa. Third, EVs are promising drug carriers. Naïve EVs from various sources and engineered EV-based drug delivery systems have paved the way for the development of new treatment modalities. This review discusses the recent advancements in the application of EV therapies and highlights EV-based functional materials as novel interventions for PCa.

Urinary extracellular vesicle-derived miR-126-3p predicts lymph node invasion in patients with high-risk prostate cancer.

To investigate extracellular vesicles (EVs) biomarkers for predicting lymph node invasion (LNI) in patients with high-risk prostate cancer (HRPCa), plasma and/or urine samples were prospectively collected from 45 patients with prostate cancer (PCa) and five with benign prostatic hyperplasia (BPH). Small RNA sequencing was performed to identify miRNAs in the EVs. All patients with PCa underwent radical prostatectomy and extended pelvic lymph node dissection. Differentially-expressed miRNAs were identified in patients with and without pathologically-verified LNI. The candidate miRNAs were validated in low-risk prostate cancer (LRPCa) and BPH. Four miRNA species (e.g. miR-126-3p) and three miRNA species (e.g. miR-27a-3p) were more abundant in urinary and plasma EVs, respectively, of patients with PCa. None of these miRNA species were shared between urinary and plasma EVs. miR-126-3p was significantly more abundant in patients with HR PCa with LNI than in those without (P = 0.018). miR-126-3p was significantly more abundant in the urinary EVs of patients with HRPCa than in those with LRPCa (P = 0.017) and BPH (P = 0.011). In conclusion, urinary EVs-derived miR-126-3p may serve as a good biomarker for predicting LNI in patients with HRPCa.

IL-6 Enhances the Activation of PI3K-AKT/mTOR-GSK-3ß by Upregulating GRPR in Hippocampal Neurons of Autistic Mice.

Autism spectrum disorder (ASD) is a neurological disorder associated with brain inflammation. The underlying mechanisms could be attributed to the activation of PI3K signaling in the inflamed brain of ASD. Multiple studies highlight the role of GRPR in regulating ASD like abnormal behavior and enhancing the PI3K signaling. However, the molecular mechanism by which GRPR regulates PI3K signaling in neurons of individuals with ASD is still unclear. In this study, we utilized a maternal immune activation model to investigate the effects of GRPR on PI3K signaling in the inflamed brain of ASD mice. We used HT22 cells with and without GRPR to examine the impact of GRP-GRPR on the PI3K-AKT pathway with IL-6 treatment. We analyzed a dataset of hippocampus samples from ASD mice to identify hub genes. Our results demonstrated increased expression of IL-6, GRPR, and PI3K-AKT signaling in the hippocampus of ASD mice. Additionally, we observed increased GRPR expression and PI3K-AKT/mTOR activation in HT22 cells after IL-6 treatment, but decreased expression in HT22 cells with GRPR knockdown. NetworkAnalyst identified GSK-3ß as the most crucial gene in the PI3K-AKT/mTOR pathway in the hippocampus of ASD. Furthermore, we found that IL-6 upregulated the expression of GSK-3ß in HT22 cells by upregulating GRP-GRPR. Our findings suggest that IL-6 can enhance the activation of PI3K-AKT/mTOR-GSK-3ß in hippocampal neurons of ASD mice by upregulating GRPR.

Oncolytic mineralized bacteria as potent locally administered immunotherapeutics.

Oncolytic bacteria can trigger innate immune activity. However, the antitumour efficacy of inactivated bacteria is poor, and attenuated live bacteria pose substantial safety risks. Here we show that intratumourally injected paraformaldehyde-fixed bacteria coated with manganese dioxide potently activate innate immune activity, modulate the immunosuppressive tumour microenvironment and trigger tumour-specific immune responses and abscopal antitumour responses. A single intratumoural administration of mineralized Salmonella typhimurium suppressed the growth of multiple types of subcutaneous and orthotopic tumours in mice, rabbits and tree shrews and protected the cured animals against tumour rechallenge. We also show that mineralized bacteria can be administered via arterial embolization to treat orthotopic liver cancer in rabbits. Our findings support the further translational testing of oncolytic mineralized bacteria as potent and safe antitumour immunotherapeutics.

SPD Manifold Deep Metric Learning for Image Set Classification.

By characterizing each image set as a nonsingular covariance matrix on the symmetric positive definite (SPD) manifold, the approaches of visual content classification with image sets have made impressive progress. However, the key challenge of unhelpfully large intraclass variability and interclass similarity of representations remains open to date. Although, several recent studies have mitigated the two problems by jointly learning the embedding mapping and the similarity metric on the original SPD manifold, their inherent shallow and linear feature transformation mechanism are not powerful enough to capture useful geometric features, especially in complex scenarios. To this end, this article explores a novel approach, termed SPD manifold deep metric learning (SMDML), for image set classification. Specifically, SMDML first selects a prevailing SPD manifold neural network (SPDNet) as the backbone (encoder) to derive an SPD matrix nonlinear representation. To counteract the degradation of structural information during multistage feature embedding, we construct a Riemannian decoder at the end of the encoder, trained by a reconstruction error term (RT), to induce the generated low-dimensional feature manifold of the hidden layer to capture the pivotal information about the visual data describing the imaged scene. We demonstrate through theory and experiments that it is feasible to replace the Riemannian metric with Euclidean distance in RT. Then, the ReCov layer is introduced into the established Riemannian network to regularize the local statistical information within each input feature matrix, which enhances the effectiveness of the learning process. The theoretical analysis of the activation function used in the ReCov layer in terms of continuity and conditions for generating positive definite matrices is beneficial for network design. Inspired by the fact that the single cross-entropy loss used for training is unable to effectively parse the geometric distribution of the deep representations, we finally endow the suggested model with a novel metric learning regularization term. By explicitly incorporating the encoding and processing of the data variations into the network learning process, this term can not only derive a powerful Riemannian representation but also train an effective classifier. The experimental results show the superiority of the proposed approach on three typical visual classification tasks.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Comparative genomics analysis reveals genetic characteristics and nitrogen fixation profile of Bradyrhizobium.

Bradyrhizobium is a genus of nitrogen-fixing bacteria, with some species producing nodules in leguminous plants. Investigations into Bradyrhizobium have recently revealed its substantial genetic resources and agricultural benefits, but a comprehensive survey of its genetic diversity and functional properties is lacking. Using a panel of various strains (N = 278), this study performed a comparative genomics analysis to anticipate genes linked with symbiotic nitrogen fixation. Bradyrhizobium's pan-genome consisted of 84,078 gene families, containing 824 core genes and 42,409 accessory genes. Core genes were mainly involved in crucial cell processes, while accessory genes served diverse functions, including nitrogen fixation and nodulation. Three distinct genetic profiles were identified based on the presence/absence of gene clusters related to nodulation, nitrogen fixation, and secretion systems. Most Bradyrhizobium strains from soil and non-leguminous plants lacked major nif/nod genes and were evolutionarily more closely related. These findings shed light on Bradyrhizobium's genetic features for symbiotic nitrogen fixation.

Identification and Analysis of ZIC-Related Genes in Cerebellum of Autism Spectrum Disorders.

Objective: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with significant genetic heterogeneity. The ZIC gene family can regulate neurodevelopment, especially in the cerebellum, and has been implicated in ASD-like behaviors in mice. We performed bioinformatic analysis to identify the ZIC gene family in the ASD cerebellum. Methods: We explored the roles of ZIC family genes in ASD by investigating (i) the association of ZIC genes with ASD risk genes from the Simons Foundation Autism Research Initiative (SFARI) database and ZIC genes in the brain regions of the Human Protein Atlas (HPA) database; (ii) co-expressed gene networks of genes positively and negatively correlated with ZIC1, ZIC2, and ZIC3, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and receiver operating characteristic (ROC) curve analysis of genes in these networks; and (iii) the relationship between ZIC1, ZIC2, ZIC3, and their related genes with cerebellar immune cells and stromal cells in ASD patients. Results: (i) ZIC1, ZIC2, and ZIC3 were associated with neurodevelopmental disorders and risk genes related to ASD in the human cerebellum and (ii) ZIC1, ZIC2, and ZIC3 were highly expressed in the cerebellum, which may play a pathogenic role by affecting neuronal development and the cerebellar internal environment in patients with ASD, including immune cells, astrocytes, and endothelial cells. (iii) OLFM3, SLC27A4, GRB2, TMED1, NR2F1, and STRBP are closely related to ZIC1, ZIC2, and ZIC3 in ASD cerebellum and have good diagnostic accuracy. (iv) ASD mice in the maternal immune activation model demonstrated that Zic3 and Nr2f1 levels were decreased in the immune-activated cerebellum. Conclusion: Our study supports the role of ZIC1, ZIC2, and ZIC3 in ASD pathogenesis and provides potential targets for early and accurate prediction of ASD.

Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition.

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

Extracellular vesicles related gene HSPH1 exerts anti-tumor effects in prostate cancer via promoting the stress response of CD8 + T cells.

BACKGROUND: T cell stress response state (TSTR), as a novel immune concept previous studies have proposed, has not yet been explored in prostate cancer (PC). As a type of cellular efflux, exosomes play important roles in the occurrence and development of PC. METHOD: Here, we conducted a combined analysis on extracellular vesicle related genes (EVRGs) in PC using data from single-cell RNA (scRNA), spatial transcriptome (ST), and bulk RNA sequencing. RESULT: Preliminary findings have revealed that heat shock protein family H (Hsp110) member 1 (HSPH1) possesses two identities, one being EVRGs and the other being a member of the heat shock protein family involved in TSTR, which may promote the differentiation of conventional T cells towards Th1 or Th2 cells through the pathway of IL2-MYC-IL2RA, thereby promoting the increase of CD8 + T cells in the tumor area, especially in the invasive zone, and inhibiting the invasion of PCs. We also notice the negative response of HSPH1 + CD8 + T cell related genes in immune checkpoint blockade (ICB). Western blot (WB) and droplet digital Polymerase Chain Reaction (ddPCR) demonstrated that the mRNA and protein levels of HSPH1 in EVs of PCs were significantly higher than those in adjacent tissues. CONCLUSION: Results above indicate the potential of HSPH1 as a critical therapeutic target in PC.

Intranuclear assembly of leucine-rich peptides for selective death of osteosarcoma cells.

Herein, we show a pair of leucine-rich L- and D-phosphopeptides which self-assemble into twisting nanofibers, whose secondary structures contain a strong ß-sheet component after being dephosphorylated by alkaline phosphatase (ALP). While being incubated with ALP overexpressing osteosarcoma cells, both of the peptides self-assemble in the nuclei and induce cell death. The cell death involves multiple cell death modalities and occurs along with the disruption of cell membranes. Enzyme-instructed self-assembly (EISA) inhibits osteosarcoma cells and shows no side effect to other cells. In addition, the cancer cells hardly gain drug resistance after repeated treatment. This work reports a pair of EISA-based nanofibers to target cell nuclei, and also provides a novel chemotherapeutic agent to inhibit osteosarcoma cells without side effects and drug resistance.