Dynamic changes of the immune microenvironment in the development of gastric cancer caused by inflammation.

Precancerous lesions typically precede gastric cancer (GC), but the molecular mechanisms underlying the transition from these lesions to GC remain unclear. Therefore, it is urgent to understand this transition from precancerous lesions to GC, which is crucial for the early diagnosis and treatment of GC. In this study, we merged multiple single-cell RNA sequencing datasets to investigate the molecular changes in distinct cell types associated with the progression of GC. First, we observed an increasing abundance of immune cells and a decrease in non-immune cells from non-atrophic gastritis to GC. Five immune cell types were significantly enriched in GC compared to precancerous lesions. Moreover, we found that the interleukin (IL)-17 signaling pathway and Th17 cell differentiation were significantly up-regulated in immune cell subsets during GC progression. Some genes in these processes were predominantly expressed at the GC stage, highlighting their potential as diagnostic markers. Furthermore, we validated our findings using bulk RNA sequencing data from The Cancer Genome Atlas and confirmed consistent immune cell changes during GC progression. Our study provides insights into the immune infiltration and signaling pathways involved in the development of GC, contributing to the development of early diagnosis and targeted treatment strategies for this malignancy.

Spectroscopic Study of a Novel Binaphthyl Amine Fluorescent Probe for Chiral Recognition of D/L-Lysine.

Lysine plays a crucial role in promoting development, enhancing immune function, and improving the function of central nervous system tissues. The two configurational isomers of amino acids have significantly different effects. Currently, methods for chiral recognition of lysine have been reported; however, previous detection methods have drawbacks such as expensive equipment and complicated detection processes. Fluorescence analysis, on the other hand, boasts high sensitivity, strong selectivity, and simple operation. In this study, we synthesized four novel Binaphthyl-Amine (BINAM)-based fluorescent probes capable of specifically identifying the L-configuration of lysine among the twenty amino acids that constitute human proteins. The enantiomeric fluorescence enhancement ratio (ef or ΔIL/ΔID) reached up to 15.29, demonstrating high enantioselectivity. In addition, we assessed the probe's recognition capabilities under varying pH levels, reaction times, and metal ion conditions, along with its limit of detection (LOD) and quantum yield. Our results suggest that this probe serves as a highly stable tool for the detection of chiral lysine.

Optical-Electrical Coordinately Modulated Memristor Based on 2D Ferroelectric RP Perovskite for Artificial Vision Applications.

Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception-storage-processing of light signals. Here, an optical-electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden-Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109-cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio-synaptic long/short-term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two-layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical-electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.

Infection dynamics of subtype H9N2 low pathogenic avian influenza a virus in turkeys.

While mammals can be infected by influenza A virus either sporadically or with well adapted lineages, aquatic birds are the natural reservoir of the pathogen. So far most of the knowledge on influenza virus dynamics was however gained on mammalian models. In this study, we infected turkeys using a low pathogenic avian influenza virus and determined the infection dynamics with a target-cell limited model. Results showed that turkeys had a different set of infection characteristics, compared with humans and ponies. The viral clearance rates were similar between turkeys and ponies but higher than that in humans. The cell death rates and cell to cell transmission rates were similar between turkeys and humans but higher than those in ponies. Overall, this study indicated the variations of within-host dynamics of influenza infection in avian, humans, and other mammalian systems.

How air pollution affects corporate total factor productivity?

To explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises.

A new mouse-fixation device for IOP measurement in awake mice.

Glaucoma is an irreversible blinding eye disease. The mechanisms underlying glaucoma are complex. Up to now, no successful remedy has been found to completely cure the condition. High intraocular pressure (IOP) is an established risk factor for glaucoma and the only known modifiable factor for glaucoma treatment. Mice have been widely used to study glaucoma pathogenesis. IOP measurement is an important tool for monitoring the potential development of glaucomatous phenotypes in glaucoma mouse models. Currently, there are two methods of IOP measurement in mice: invasive and non-invasive. As the invasive method can cause corneal damage and inflammation, and most of the noninvasive method involves the use of anesthetics. In the course of our research, we designed a mouse fixation device to facilitate non-invasive measurements of mouse IOPs. Using this device, mouse IOPs can be accurately measured in awake mice. This device will help researchers to accurately assess mouse IOP without the use of anesthetics.

Virtual Water Embodied in Interregional Energy Trade in China: A City-Level Analysis.

Large volumes of water are used in energy production for both primary (e.g., fuel extraction) and secondary energy (e.g., electricity). In countries such as China, with a large internal trade in fuels and long-distance transmission grids, this can result in considerable water inequalities. Previous research focused on the water impacts of energy production at the national and provincial levels, which is too coarse to identify the spatial differences and make specific case studies. Here, we take the next step toward a spatially explicit economically integrated water-use for energy assessment by combining a bottom-up assessment approach with a city-level multiregional input-output model. Specifically, we examine the water consumption of energy production in China, distinguishing between water for primary and secondary energy at the level of coal mines, oil and gas fields, and power plants for the first time. Of the total energy-related freshwater consumption of 4.9 Gm3 in 2017, primary energy accounted for 19% (940 Mm3) and secondary energy accounted for 81% (3955 Mm3). Coal was the largest water consumer for both primary and secondary energy (540 and 3880 Mm3, respectively), with both oil (361, and 0.5 Mm3, respectively) and gas (7 and 69 Mm3, respectively) also consuming large amounts. Intercity virtual water, that is, water embodied in energy trade across cities, reached 54% (2.6 Gm3) of energy-related freshwater consumption. Across China, 32% of cities see a bilateral trade in secondary- and primary-energy-related virtual water (e.g., Daqing city exports virtual water embodied in primary fuel to other cities that is then used to produce electricity in those cities, part of which is used back in Daqing via transmission). For these 32% of cities, 73% export more virtual water than import and 27% import more virtual water than export. This study reveals significant differences in city-level virtual water patterns (e.g., scale and direction) between primary and secondary energy to provide information for cities about their virtual water inflow and outflow and the potential collaboration partners for water management.

Pathological image profiling identifies onco-microbial, tumor immune microenvironment, and prognostic subtypes of colorectal cancer.

Histology slide, tissue microbes, and the host gene expression can be independent prognostic factors of colorectal cancer (CRC), but the underlying associations and biological significance of these multimodal omics remain unknown. Here, we comprehensively profiled the matched pathological images, intratumoral microbes, and host gene expression characteristics in 527 patients with CRC. By clustering these patients based on histology slide features, we classified the patients into two histology slide subtypes (HSS). Onco-microbial community and tumor immune microenvironment (TIME) were also significantly different between the two subtypes (HSS1 and HSS2) of patients. Furthermore, variation in intratumoral microbes-host interaction was associated with the prognostic heterogeneity between HSS1 and HSS2. This study proposes a new CRC classification based on pathological image features and elucidates the process by which tumor microbes-host interactions are reflected in pathological images through the TIME.

Predictable regulation of survival by intratumoral microbe-immune crosstalk in patients with lung adenocarcinoma.

Intratumoral microbiota can regulate the tumor immune microenvironment (TIME) and mediate tumor prognosis by promoting inflammatory response or inhibiting anti-tumor effects. Recent studies have elucidated the potential role of local tumor microbiota in the development and progression of lung adenocarcinoma (LUAD). However, whether intratumoral microbes are involved in the TIME that mediates the prognosis of LUAD remains unknown. Here, we obtained the matched tumor microbiome and host transcriptome and survival data of 478 patients with LUAD in The Cancer Genome Atlas (TCGA). Machine learning models based on immune cell marker genes can predict 1- to 5-year survival with relative accuracy. Patients were stratified into high- and low-survival-risk groups based on immune cell marker genes, with significant differences in intratumoral microbial communities. Specifically, patients in the high-risk group had significantly higher alpha diversity (p < 0.05) and were characterized by an enrichment of lung cancer-related genera such as Streptococcus. However, network analysis highlighted a more active pattern of dominant bacteria and immune cell crosstalk in TIME in the low-risk group compared to the high-risk group. Our study demonstrated that intratumoral microbiota-immune crosstalk was strongly associated with prognosis in LUAD patients, which would provide new targets for the development of precise therapeutic strategies.

Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer.

Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.

Drug repositioning based on weighted local information augmented graph neural network.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.

Adaptability of water resources development and utilization to social-economy system in Hunan province, China.

The interplay of water resources with social-economy spheres involves a reciprocal feedback mechanism. With the acceleration of the construction process of modernized water networks in Hunan Province, investigating the adaptation status of the "Water-Social-Economy " composite system (WSE) is crucial for promoting sustainability. This study clarifies the connotation of the adaptability of WSE, and the quantitative analyses were conducted through coupling coordinative degree, harmonious development capacity, and the evolution of development lag types among the 14 cities of Hunan Province from 2005 to 2020. The results show that: (1) The development index of the water resources subsystem (WRS) showed a "downward-fluctuation-upward" trend, while the development index of the social-economy subsystem (SES) showed signs of great improvement, the former didn't catch up with the latter. (2) The coupling coordination degree of WSE developed well, and reached the coordinative development stage by 2020, but the unbalanced spatial pattern between north to south and east to west still exists and is further intensified. (3) The development ability of WSE improved while the harmony ability reduced, and the development rate of WRS and SES hasn't achieved dynamic synchronization. Finally, the policies and suggestions to improve the adaptability are put forward, which is of instructive significance for the sustainable development of water suitability.

Integration of transcriptomes of senescent cell models with multi-tissue patient samples reveals reduced COL6A3 as an inducer of senescence.

Senescent cells are a major contributor to age-dependent cardiovascular tissue dysfunction, but knowledge of their in vivo cell markers and tissue context is lacking. To reveal tissue-relevant senescence biology, we integrate the transcriptomes of 10 experimental senescence cell models with a 224 multi-tissue gene co-expression network based on RNA-seq data of seven tissues biopsies from ∼600 coronary artery disease (CAD) patients. We identify 56 senescence-associated modules, many enriched in CAD GWAS genes and correlated with cardiometabolic traits-which supports universality of senescence gene programs across tissues and in CAD. Cross-tissue network analyses reveal 86 candidate senescence-associated secretory phenotype (SASP) factors, including COL6A3. Experimental knockdown of COL6A3 induces transcriptional changes that overlap the majority of the experimental senescence models, with cell-cycle arrest linked to modulation of DREAM complex-targeted genes. We provide a transcriptomic resource for cellular senescence and identify candidate biomarkers, SASP factors, and potential drivers of senescence in human tissues.

Enhancing Drug Repositioning through Local Interactive Learning with Bilinear Attention Networks.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear attention network to infer potential drugs for specific diseases. DRGBCN involves constructing a comprehensive drug-disease network by incorporating multiple similarity networks for drugs and diseases. Firstly, we introduce a layer attention mechanism to effectively learn the embeddings of graph convolutional layers from these networks. Subsequently, a bilinear attention network is constructed to capture pairwise local interactions between drugs and diseases. This combined approach enhances the accuracy and reliability of predictions. Finally, a multi-layer perceptron module is employed to evaluate potential drugs. Through extensive experiments on three publicly available datasets, DRGBCN demonstrates better performance over baseline methods in 10-fold cross-validation, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9399. Furthermore, case studies on bladder cancer and acute lymphoblastic leukemia confirm the practical application of DRGBCN in real-world drug repositioning scenarios. Importantly, our experimental results from the drug-disease network analysis reveal the successful clustering of similar drugs within the same community, providing valuable insights into drug-disease interactions. In conclusion, DRGBCN holds significant promise for uncovering new therapeutic applications of existing drugs, thereby contributing to the advancement of precision medicine.

Pan-cancer analyses reveal the stratification of patient prognosis by viral composition in tumor tissues.

The associations between cancer and bacteria/fungi have been extensively studied, but the implications of cancer-associated viruses have not been thoroughly examined. In this study, we comprehensively characterized the cancer virome of tissue samples across 31 cancer types, as well as blood samples from 23 cancer types. Our findings demonstrated the presence of viral DNA at low abundances in both tissue and blood across major human cancers, with significant differences in viral community composition observed among various cancer types. Furthermore, Cox regression analyses conducted on four cancers, including Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Stomach adenocarcinoma (STAD), and Uterine Corpus Endometrial Carcinoma (UCEC), revealed strong correlation between viral composition/abundance in tissues and patient survival. Additionally, we identified virus-associated prognostic signatures (VAPS) for these four cancers, and discerned differences in the interplay between VAPS and dominant bacteria in tissues among patients with varying survival risks. Notably, clinically relevant analyses revealed prognostic capacities of the VAPS in these four cancers. Taken together, our study provides novel insights into the role of viruses in tissue in the prognosis of multiple cancers and offers guidance on the use of tissue viruses to stratify prognosis for patients with cancer.

A meta-analysis of tissue microbial biomarkers for recurrence and metastasis in multiple cancer types.

Background. Local recurrence and distant metastasis are the main causes of death in patients with cancer. Only considering species abundance changes when identifying markers of recurrence and metastasis in patients hinders finding solutions.Hypothesis. Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.Aim. This study aims to simultaneously consider microbial abundance changes and microbial interactions to identify microbial markers of recurrence and metastasis in multiple cancer types.Method. One thousand one hundred and six non-RM (patients without recurrence and metastasis within 3 years after initial surgery) tissue samples and 912 RM (patients with recurrence or metastasis within 3 years after initial surgery) tissue samples representing 11 cancer types were collected from The Cancer Genome Atlas (TCGA).Results. Tumour tissue bacterial composition differed significantly among 11 cancers. Among them, the tissue microbiome of four cancers, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD) and uterine corpus endometrial carcinoma (UCEC), showed relatively good performance in predicting recurrence and metastasis in patients, with areas under the receiver operating characteristic curve (AUCs) of 0.78, 0.74, 0.91 and 0.93, respectively. Considering both species abundance changes and microbial interactions for the four cancers, a combination of nine genera (Niastella, Schlesneria, Thioalkalivibrio, Phaeobacter, Sphaerotilus, Thiomonas, Lawsonia, Actinobacillus and Spiroplasma) performed best in predicting patient survival.Conclusion. Taken together, our results imply that comprehensive consideration of microbial abundance changes and microbial interactions is helpful for mining bacterial markers that carry prognostic information.

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.

Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients.

Efficient capture of uranium by a hydroxyapatite-modified polyethyleneimine@carbon nanotube composite from radioactive nuclear waste.

The toxicity and radioactivity of uranium (U)-containing wastewater pose a serious threat to the environment of humans, animals, and plants. It is necessary to remove U from contaminated wastewater. With high adsorption capacity and fast adsorption rate, a composite CNT-P/HAP, which comprises carbon nanotubes (CNT) modified with polyethyleneimine (PEI), was functionalized further by hydroxyapatite (HAP) using the hydrothermal method. Adsorption experiments indicated that the optimal performance for CNT-P/HAP was 1330.64 mg g-1 of adsorption capacity and 40 min of adsorption equilibrium at a pH of 3. In addition, the adsorption capacity of CNT-P/HAP was over 2 times that of HAP at a pH of 7. The synergistic effect in both synthesis and adsorption gave CNT-P/HAP an excellent adsorption capacity for U. The XRD and FT-IR analysis indicated that the adsorption mechanism of CNT-P/HAP for U is decided by the pH of the solution. CNT-P/HAP could be used in multiple conditions to remediate U-containing wastewater.

Molecular differences in brain regional vulnerability to aging between males and females.

Background: Aging-related cognitive decline is associated with brain structural changes and synaptic loss. However, the molecular mechanisms of cognitive decline during normal aging remain elusive. Results: Using the GTEx transcriptomic data from 13 brain regions, we identified aging-associated molecular alterations and cell-type compositions in males and females. We further constructed gene co-expression networks and identified aging-associated modules and key regulators shared by both sexes or specific to males or females. A few brain regions such as the hippocampus and the hypothalamus show specific vulnerability in males, while the cerebellar hemisphere and the anterior cingulate cortex regions manifest greater vulnerability in females than in males. Immune response genes are positively correlated with age, whereas those involved in neurogenesis are negatively correlated with age. Aging-associated genes identified in the hippocampus and the frontal cortex are significantly enriched for gene signatures implicated in Alzheimer's disease (AD) pathogenesis. In the hippocampus, a male-specific co-expression module is driven by key synaptic signaling regulators including VSNL1, INA, CHN1 and KCNH1; while in the cortex, a female-specific module is associated with neuron projection morphogenesis, which is driven by key regulators including SRPK2, REPS2 and FXYD1. In the cerebellar hemisphere, a myelination-associated module shared by males and females is driven by key regulators such as MOG, ENPP2, MYRF, ANLN, MAG and PLP1, which have been implicated in the development of AD and other neurodegenerative diseases. Conclusions: This integrative network biology study systematically identifies molecular signatures and networks underlying brain regional vulnerability to aging in males and females. The findings pave the way for understanding the molecular mechanisms of gender differences in developing neurodegenerative diseases such as AD.