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Inflammatory depression is a treatment-resistant subtype of depression. A causal role of the gut microbiota as a source of low-grade inflammation remains unclear. Here, as part of an observational trial, we first analyze the gut microbiota composition in the stool, inflammatory factors and short-chain fatty acids (SCFAs) in plasma, and inflammatory and permeability markers in the intestinal mucosa of patients with inflammatory depression (ChiCTR1900025175). Gut microbiota of patients with inflammatory depression exhibits higher Bacteroides and lower Clostridium , with an increase in SCFA-producing species with abnormal butanoate metabolism. We then perform fecal microbiota transplantation (FMT) and probiotic supplementation in animal experiments to determine the causal role of the gut microbiota in inflammatory depression. After FMT, the gut microbiota of the inflammatory depression group shows increased peripheral and central inflammatory factors and intestinal mucosal permeability in recipient mice with depressive and anxiety-like behaviors. Clostridium butyricum administration normalizes the gut microbiota, decreases inflammatory factors, and displays antidepressant-like effects in a mouse model of inflammatory depression. These findings suggest that inflammatory processes derived from the gut microbiota can be involved in neuroinflammation of inflammatory depression. Inflammatory depression is a treatment-resistant subtype of depression. Here, the authors show that patients with inflammatory depression exhibit a disrupted microbiota, which upon FMT in mice leads to increased peripheral and central inflammatory factors, intestinal mucosal permeability, and depressive and anxiety-like behaviors. Probiotic administration ameliorates the disease phenotype.

Limiting the rise in global mean temperatures relies on reducing carbon dioxide (CO 2 ) emissions and on the removal of CO 2 by land carbon sinks. China is currently the single largest emitter of CO 2 , responsible for approximately 27 per cent (2.67 petagrams of carbon per year) of global fossil fuel emissions in 2017 1 . Understanding of Chinese land biosphere fluxes has been hampered by sparse data coverage 2 – 4 , which has resulted in a wide range of a posteriori estimates of flux. Here we present recently available data on the atmospheric mole fraction of CO 2 , measured from six sites across China during 2009 to 2016. Using these data, we estimate a mean Chinese land biosphere sink of −1.11 ± 0.38 petagrams of carbon per year during 2010 to 2016, equivalent to about 45 per cent of our estimate of annual Chinese anthropogenic emissions over that period. Our estimate reflects a previously underestimated land carbon sink over southwest China (Yunnan, Guizhou and Guangxi provinces) throughout the year, and over northeast China (especially Heilongjiang and Jilin provinces) during summer months. These provinces have established a pattern of rapid afforestation of progressively larger regions 5 , 6 , with provincial forest areas increasing by between 0.04 million and 0.44 million hectares per year over the past 10 to 15 years. These large-scale changes reflect the expansion of fast-growing plantation forests that contribute to timber exports and the domestic production of paper 7 . Space-borne observations of vegetation greenness show a large increase with time over this study period, supporting the timing and increase in the land carbon sink over these afforestation regions. Newly available atmospheric carbon dioxide measurements from six sites across China during 2009 to 2016 indicate a larger land carbon sink than previously thought, reflecting increased afforestation.

In recent years, online lending has created many risks while providing lending convenience to Chinese individuals and small and medium-sized enterprises. The timely assessment and prediction of the status of industry indicators is an important prerequisite for effectively preventing the spread of risks in China’s new financial formats. The role of investor sentiment should not be underestimated. We first use the BERT model to divide investor sentiment in the review information of China’s online lending third-party information website into three categories and analyze the relationship between investor sentiment and quantitative indicators of online lending product transactions. The results show that the percentage of positive comments has a positive relationship to the borrowing interest rate of P2P platforms that investors are willing to participate in for bidding projects. The percentage of negative comments has an inverse relationship to the borrowing period. Second, after introducing investor sentiment into the long short-term memory (LSTM) model, the average RMSE of the three forecast periods for borrowing interest rates is 0.373, and that of the borrowing period is 0.262, which are better than the values of other control models. Corresponding suggestions for the risk prevention of China’s new financial formats are made.

Hepatocellular carcinoma (HCC) is a lethal malignancy, and predicting patient prognosis remains a significant challenge in clinical treatment. T cells play a crucial role in the tumor microenvironment, influencing tumorigenesis and progression. In this study, we constructed a T cell-related prognostic model for HCC. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, we identified 6,281 T cells from 10 HCC patients and subsequently identified 855 T cell-related genes. Comprehensive analyses were conducted on T cells and their associated genes, including enrichment analysis, cell-cell communication, trajectory analysis, and transcription factor analysis. By integrating scRNA-seq and bulk RNA-seq data with prognostic information from The Cancer Genome Atlas (TCGA), we identified T cell-related prognostic genes and constructed a model using LASSO regression. The model, incorporating PTTG1, LMNB1, SLC38A1, and BATF, was externally validated using the International Cancer Genome Consortium (ICGC) database. It effectively stratified patients into high- and low-risk groups based on risk scores, revealing significant differences in immune cell infiltration between these groups. Differential expression levels of PTTG1 and BATF between HCC and adjacent non-tumor tissues were further validated by immunohistochemistry (IHC) in 25 patient tissue samples. Moreover, a Cox regression analysis was performed to integrate risk scores with clinical features, resulting in a nomogram capable of predicting patient survival probabilities. This study introduces a novel prognostic risk model for HCC patients, aimed at stratifying patients by risk, enhancing personalized treatment strategies, and offering new insights into the role of T cell-related genes in HCC progression.

Super-resolution optical microscopy, which gives access to finer details of objects, is highly desired for fields of nanomaterial, nanobiology, nanophotonics, etc. Many efforts, including tip optimization and illumination optimization etc., have been made in both near-field and far-field super-resolution microscopy to achieve a spatial resolution beyond the diffraction limit. The development of vector light fields opens up a new avenue for super-resolution optical microscopy via special illumination modes. Cylindrical vector beam (CVB) has been verified to enable resolution improvement in tip-scanning imaging, nonlinear imaging, stimulated emission depletion (STED) microscopy, subtraction imaging, superoscillation imaging, etc. This paper reviews recent advances in CVB-based super-resolution imaging. We start with an introduction of the fundamentals and properties of CVB. Next, strategies for CVB based super-resolution imaging are discussed, which are mainly implemented by tight focusing, depletion effect, plasmonic nanofocusing, and polarization matching. Then, the roadmap of super-resolution imaging with CVB illumination in the past two decades is summarized. The typical CVB-based imaging techniques in fields of both near-field and far-field microscopy are introduced, including tip-scanning imaging, nonlinear imaging, STED, subtraction imaging, and superoscillation imaging. Finally, challenges and future directions of CVB-illuminated super-resolution imaging techniques are discussed.

Cirrhosis is a common endpoint in various chronic liver diseases, and often causes hepatocellular carcinoma. Studies have revealed the significant role of disulfidptosis in the occurrence and development of hepatocellular carcinoma; however, our understanding of this role is limited. Therefore, we aimed to identify potential disulfidptosis-related biomarkers for cirrhosis. We obtained the gene expression data of patients with cirrhosis from the Gene Expression Omnibus (GEO) database. Subsequently, weighted gene co-expression network analysis was performed, and the “limma” package was used to screen for differentially expressed genes (DEGs) associated with disulfidptosis. Significantly altered biological pathways were identified using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA). We constructed protein–protein interaction (PPI) networks using GeneMANIA and generated receiver operating characteristic (ROC) curves to identify hub-shared genes. Additionally, we assessed the distribution of immune cell populations in cirrhotic and control specimens using single-sample GSEA (ssGSEA) and explored their relationship with hub genes. Six hub genes ( CXCL12, COL1A1, CXCR4, COL1A2, CCR7, and CXCL8 ) were closely associated with disulfidptosis-related DEGs. Further immunohistochemical experiments confirmed the potential of CCR7, CXCL12, CXCR4, and CXCL8 as novel diagnostic biomarkers and suggested their potential as new therapeutic targets. These genes mainly promote the development of liver cirrhosis through the oxidative metabolism and cytokine pathways. Furthermore, we observed positive correlations among 23 of the 28 types of immune cells. This study highlights the potential utility of immune cell infiltration and efficient disulfidptosis-related early diagnostic biomarkers in cirrhosis, and highlights its strong useful as a therapeutic target, offering potential clinical application value.

Cell-free DNA (cfDNA) released from damaged or dead cells can activate DNA sensors that exacerbate the pathogenesis of rheumatoid arthritis (RA). Here we show that ~40 nm cationic nanoparticles (cNP) can scavenge cfDNA derived from RA patients and inhibit the activation of primary synovial fluid monocytes and fibroblast-like synoviocytes. Using clinical scoring, micro-CT images, MRI, and histology, we show that intravenous injection of cNP into a CpG-induced mouse model or collagen-induced arthritis rat model can relieve RA symptoms including ankle and tissue swelling, and bone and cartilage damage. This culminates in the manifestation of partial mobility recovery of the treated rats in a rotational cage test. Mechanistic studies on intracellular trafficking and biodistribution of cNP, as well as measurement of cytokine expression in the joints and cfDNA levels in systemic circulation and inflamed joints also correlate with therapeutic outcomes. This work suggests a new direction of nanomedicine in treating inflammatory diseases. Cell-free DNA (cfDNA) released from damaged or dead cells can activate DNA sensors that exacerbate the pathogenesis of rheumatoid arthritis (RA). Here the authors use ~40 nm cationic nanoparticles to scavenge cfDNA, and demonstrate the potential for nanomedicine to relieve debilitating RA symptoms.

Improving underwater image quality is crucial for marine detection applications. However, in the marine environment, captured images are often affected by various degradation factors due to the complexity of underwater conditions. In addition to common color distortions, marine snow noise in underwater images is also a significant issue. The backscatter of artificial light on marine snow generates specks in images, thereby affecting image quality, scene perception, and subsequently impacting downstream tasks such as target detection and segmentation. Addressing the issues caused by marine snow noise, we have designed a new network structure. In this work, a novel skip-connection structure called a dual channel multi-scale feature transmitter (DCMFT) is implemented to reduce information loss during downsampling in the feature encoding and decoding section. Additionally, in the feature transfer process for each stage, iterative attentional feature fusion (iAFF) modules are inserted to fully utilize marine snow features extracted at different stages. Finally, to further optimize the network’s performance, we incorporate the multi-scale structural similarity index (MS-SSIM) into the loss function to ensure more effective convergence during training. Through experiments conducted on the Marine Snow Removal Benchmark (MSRB) dataset with an augmented sample size, our method has achieved significant results. The experimental results demonstrate that our approach excels in removing marine snow noise, with a peak signal-to-noise ratio reaching 38.9251 dB, significantly outperforming existing methods.

Gas sensors based on titanium dioxide (TiO2) have attracted much public attention during the past decades due to their excellent potential for applications in environmental pollution remediation, transportation industries, personal safety, biology, and medicine. Numerous efforts have therefore been devoted to improving the sensing performance of TiO2. In those effects, the construct of nanoheterostructures is a promising tactic in gas sensing modification, which shows superior sensing performance to that of the single component-based sensors. In this review, we briefly summarize and highlight the development of TiO2-based heterostructure gas sensing materials with diverse models, including semiconductor/semiconductor nanoheterostructures, noble metal/semiconductor nanoheterostructures, carbon-group-materials/semiconductor nano- heterostructures, and organic/inorganic nanoheterostructures, which have been investigated for effective enhancement of gas sensing properties through the increase of sensitivity, selectivity, and stability, decrease of optimal work temperature and response/recovery time, and minimization of detectable levels.

Background Centromere Protein F (CENPF) associates with the centromere–kinetochore complex and influences cell proliferation and metastasis in several cancers. The role of CENPF in breast cancer (BC) bone metastasis remains unclear. Methods Using the ONCOMINE database, we compared the expression of CENPF in breast cancer and normal tissues. Findings were confirmed in 60 BC patients through immunohistochemical (IHC) staining. Microarray data from GEO and Kaplan–Meier plots were used analyze the overall survival (OS) and relapse free survival (RFS). Using the GEO databases, we compared the expression of CENPF in primary lesions, lung metastasis lesions and bone metastasis lesions, and validated our findings in BALB/C mouse 4T1 BC models. Based on gene set enrichment analysis (GSEA) and western blot, we predicted the mechanisms by which CENPF regulates BC bone metastasis. Results The ONCOMINE database and immunohistochemical (IHC) showed higher CENPF expression in BC tissue compared to normal tissue. Kaplan–Meier plots also revealed that high CENPF mRNA expression correlated to poor survival and shorter progression-free survival (RFS). From BALB/C mice 4T1 BC models and the GEO database, CENPF was overexpressed in primary lesions, other target organs, and in bone metastasis. Based on gene set enrichment analysis (GSEA) and western blot, we predicted that CENPF regulates the secretion of parathyroid hormone-related peptide (PTHrP) through its ability to activate PI3K–AKT–mTORC1. Conclusion CENPF promotes BC bone metastasis by activating PI3K–AKT–mTORC1 signaling and represents a novel therapeutic target for BC treatment.