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Low-density neutrophils (LDNs) have been described in tumors and various autoimmune diseases, where they exhibit immune dysfunction and alter disease progression. Nevertheless, LDNs have been rarely reported in sepsis. We studied sepsis patients admitted to the intensive care unit. Wright-Giemsa stain assay and Transmission electron microscopy were performed to detect the morphology of neutrophils. Flow cytometry was used to analyze the number and function of LDNs. Concentration of cytokines was measured using ELISA. Neutrophil chemotaxis was examined using an under-agarose chemotaxis model. We found that LDNs were significantly elevated in patients with sepsis. Phenotypes and morphological characteristics suggest that LDNs may be formed by mixtures of neutrophils at various maturation stages. In vitro experiments showed that LDN formation was closely associated with neutrophil degranulation. We preliminarily discussed changes in immune function in LDNs. Compared with high-density neutrophils, expression levels of CXC chemokine receptor 4 on LDN surfaces were increased, phagocytotic capacity was decreased, and life span was prolonged. The chemotactic ability of LDNs was significantly reduced, possibly related to the increased expression of P2X1. These data suggest that LDNs are essential components of neutrophils in sepsis. To clarify the source and dysfunction mechanism of LDN in sepsis may be helpful for the diagnosis and treatment of sepsis in the future.

Background The early diagnosis of sepsis is hampered by the lack of reliable laboratory measures. There is growing evidence that presepsin and Mid-regional pro-adrenomedullin (MR-proADM) are promising biomarkers in the diagnosis of sepsis. This study was conducted to evaluate and compare the diagnostic value of MR-proADM and presepsin in sepsis patients. Methods We searched Web of Science, PubMed, Embase, China national knowledge infrastructure, and Wanfang up to 22th July, 2022, for studies evaluating the diagnosis performance of presepsin and MR-proADM in adult sepsis patients. Risk of bias was assessed using quadas-2. Pooled sensitivity and specificity were calculated using bivariate meta-analysis. Meta-regression and subgroup analysis were used to find source of heterogeneity. Results A total of 40 studies were eventually selected for inclusion in this meta-analysis, including 33 for presepsin and seven for MR-proADM. Presepsin had a sensitivity of 0.86 (0.82–0.90), a specificity of 0.79 (0.71–0.85), and an AUC of 0.90 (0.87–0.92). The sensitivity of MR-proADM was 0.84 (0.78–0.88), specificity was 0.86 (0.79–0.91), and AUC was 0.91 (0.88–0.93). The profile of control group, population, and standard reference may be potential sources of heterogeneity. Conclusions This meta-analysis demonstrated that presepsin and MR-proADM exhibited high accuracy (AUC ≥ 0.90) in the diagnosis of sepsis in adults, with MR-proADM showing significantly higher accuracy than presepsin.

Abstract Oncolytic virotherapy is a promising therapeutic approach treating tumors, where oncolytic viruses (OVs) can selectively infect and lyse tumor cells through replication, while also triggering long-lasting anti-tumor immune responses. Vaccinia virus (VV) has emerged as a leading candidate for use as an OV due to its broad cytophilicity and robust capacity to express exogenous genes. Consequently, oncolytic vaccinia virus (OVV) has entered clinical trials. This review provides an overview of the key strategies used in the development of OVV, summarizes the findings from clinical trials, and addresses the challenges that must be overcome in the advancement of OVV-based therapies. Furthermore, it explores potential future strategies for enhancing the development and clinical application of OVV, intending to improve tumor treatment outcomes. The review aims to facilitate the further development and clinical adoption of OVV, thereby advancing tumor therapies.

Establishing a fractional creep model with few parameters and explicit physical interpretation is of significant meaning for predicting rheological deformation of rock. In this study, based on the Caputo variable-order fractional derivative, a Caputo variable-order fractional creep model is proposed, whose physical interpretation is clearly stated by setting a varying-order function related to relaxation time. The significance of relaxation time is firstly highlighted to reveal the evolution mechanism of viscoelasticity of creep and relaxation response by constructing equivalence between rheological responses of constant-order fractional Maxwell model and that of time-varying viscosity Maxwell model. Meanwhile, considering the importance of relaxation time in rheology, a modified damage factor is also presented and introduced in proposed model. Next, for verifying the applicability of proposed damage creep model, a series of uniaxial creep experiments were conducted on sandstone under step by step loading, the creep data predicted by proposed damage creep model are well agreement with experimental creep data. And then, a comparative study with constant-order fractional damage creep model was performed to present the advantages of proposed Caputo variable-order fractional damage creep model, which gives further references for application of Caputo variable-order fractional derivative in rheological model. Finally, the variations and influence of elastic modulus and relaxation time on creep response based on proposed Caputo variable-order fractional damage creep model are discussed and expounded deeply.

Proton pump inhibitors (PPIs) are widely used for the treatment of acid-related disorders, but long-term use has been increasingly associated with an elevated risk of osteoporosis. However, the underlying molecular mechanisms and specific targets of PPIs-induced bone loss remain poorly understood. This study aimed to explore the molecular mechanisms and key genes of PPIs-induced osteoporosis using network toxicology, molecular docking, and molecular dynamics simulations. We identified common targets of four widely used PPIs (omeprazole, lansoprazole, pantoprazole, and rabeprazole) and osteoporosis by screening large-scale biological databases. A protein-protein interaction network was constructed, and key hub genes were determined based on topological parameters such as degree, betweenness centrality, and closeness centrality. Enrichment analysis was performed to explore the biological processes, cellular components, molecular functions, and KEGG pathways associated with the overlapping targets. Molecular docking was conducted to evaluate the binding affinities between PPIs and their potential targets, and molecular dynamics simulations were employed to assess the stability of these interactions over time. We identified 35 potential targets for omeprazole-induced osteoporosis, 39 for lansoprazole, 29 for pantoprazole, and 29 for rabeprazole. Topological analysis of the protein-protein interaction networks revealed the hub genes for each PPI: epidermal growth factor receptor (EGFR) for omeprazole, estrogen receptor 1 (ESR1) for lansoprazole, EGFR for pantoprazole, and Proto-oncogene tyrosine-protein kinase SRC for rabeprazole. Molecular docking demonstrated strong and stable binding affinities between PPIs and their respective targets, with binding energies all below -5 kcal/mol. Molecular dynamics simulations confirmed the structural stability of these complexes, characterized by low root mean square deviation and root mean square fluctuation values and consistent hydrogen bond formation. This study identified EGFR, ESR1, and SRC as key regulatory genes in PPIs-induced osteoporosis, highlighting their roles in bone metabolism. The stable interactions between PPIs and these targets suggest their involvement in bone loss, providing a foundation for future experimental validation.

At present, no study has established a survival prediction model for non-metastatic primary malignant bone tumors of the spine (PMBS) patients. The clinical features and prognostic limitations of PMBS patients still require further exploration. Data on patients with non-metastatic PBMS from 2004 to 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariate regression analysis using Cox, Best-subset and Lasso regression methods was performed to identify the best combination of independent predictors. Then two nomograms were structured based on these factors for overall survival (OS) and cancer-specific survival (CSS). The accuracy and applicability of the nomograms were assessed by area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Results: The C-index indicated that the nomograms of OS (C‐index 0.753) and CSS (C‐index 0.812) had good discriminative power. The calibration curve displays a great match between the model’s predictions and actual observations. DCA curves show our models for OS (range: 0.09–0.741) and CSS (range: 0.075–0.580) have clinical value within a specific threshold probability range compared with the two extreme cases. Two nomograms and web-based survival calculators based on established clinical characteristics was developed for OS and CSS. These can provide a reference for clinicians to formulate treatment plans for patients.

Polymerization degree plays a vital role in material properties. Previous methodologies of molecular weight control generally cannot suppress or alleviate batch-to-batch variations in device performance, especially in polymer solar cells. Herein, we develop an in-situ photoluminescence system in tandem with a set of analysis and processing procedures to track and estimate the polymerization degree of organic photovoltaic materials. To support the development of this protocol, we introduce polymer acceptor PYT constructed by near-infrared Y-series small molecule acceptors via Stille polymerization, and shed light on the correlations between molecular weight, spectral parameters, and device efficiencies that enable the design of the optical setup and confirm its feasibility. The universality is verified in PYT derivatives with stereoregularity and fluoro-substitution as well as benzo[1,2-b:4,5-b’]dithiophene-based polymers. Overall, our result provides a tool to tailor suitable conjugated oligomers applied to polymer solar cells and other organic electronics for industrial scalability and desired cost reduction. Polymerization degree plays a vital role in controlling material properties and batch-to-batch variations in device performance of polymer solar cells. Here, authors develop in-situ photoluminescence system in tandem to track and estimate the polymerization degree of organic photovoltaic materials.

China’s reported cases of Human Immunodeficiency Virus (HIV) and AIDS increased from over 50000 in 2011 to more than 130000 in 2017, while AIDS related search indices on Baidu from 2.1 million to 3.7 million in the same time periods. In China, people seek AIDS related knowledge from Baidu which one of the world’s largest search engine. We study the relationship of national HIV surveillance data with the Baidu index (BDI) and use it to monitor AIDS epidemic and inform targeted intervention. After screening keywords and making index composition, we used seasonal autoregressive integrated moving average (ARIMA) modeling. The most correlated search engine query data was obtained by using ARIMA with external variables (ARIMAX) model for epidemic prediction. A significant correlation between monthly HIV/AIDS report cases and Baidu Composite Index ( r  = 0.845, P  < 0.001) was observed using time series plot. Compared with the ARIMA model based on AIDS surveillance data, the ARIMAX model with Baidu Composite Index had the minimal an Akaike information criterion (AIC, 839.42) and the most exact prediction (MAPE of 6.11%). We showed that there are close correlations of the same trends between BDI and HIV/AIDS reports cases for both increasing and decreasing AIDS epidemic. Therefore, the Baidu search query data may be a good useful indicator for reliably monitoring and predicting HIV/AIDS epidemic in China.

As frontline cells, the precise recruitment of neutrophils is crucial for resolving inflammation and maintaining the homeostasis of the organism. Increasing evidence suggests the pivotal role of neutrophil chemotaxis in cancer progression and metastasis. Here, we collected clinical data and peripheral blood samples from patients with tumours to examine the alterations in the neutrophil quantity and chemotactic function using the Cell Chemotaxis Analysis Platform (CCAP). Transcriptome sequencing data of pan-cancer were obtained from The Cancer Genome Atlas (TCGA). Using the least absolute shrinkage and selection operator (LASSO) Cox regression model, we selected a total of 29 genes from 155 neutrophil- and chemotaxis-related genes to construct the ChemoScore model. Meanwhile, nomogram-based comprehensive model was established for clinical application. Furthermore, immunofluorescence (IF) staining was employed to assess the relationship between the neutrophils infiltrating and the survival outcomes of tumours. In this observational study, the chemotactic function of neutrophils was notably diminished in patients. The establishment and validation of ChemoScore suggested neutrophil chemotaxis to be a risk factor in most tumours, whereby higher scores were associated with poorer survival outcomes and were correlated with various immune cells and malignant biological processes. Moreover, IF staining of tumour tissue substantiated the adverse correlation between neutrophil infiltration and the survival of patients with lung adenocarcinoma ( P  = 0.0002) and colon adenocarcinoma ( P  = 0.0472). Taken together, patients with tumours demonstrated a decrease in chemotactic function. ChemoScore potentially prognosticates the survival of patients with tumours. Neutrophil chemotaxis provides novel directions and theoretical foundations for anti-tumour treatment.

As a key AI technology, Machine learning (ML) has witnessed growing adoption in landscape architecture through advanced algorithms and computational techniques. Despite this progress, a critical gap persists in systematically analyzing ML’s transformative impacts and emerging opportunities through an application-driven lens. This study integrates bibliometric analysis with a systematic literature review to synthesize methodological advancements and domain-specific applications. After systematically reviewing the applications of machine learning in the field of landscape architecture, five categories were identified: simulation and prediction, layout generation, image post-processing, management and evaluation, and text analysis. Furthermore, this paper proposes strategic implementation frameworks for ML integration while establishing methodological benchmarks for intelligent design systems.