scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.

The etiological diagnostic value of metagenomic next-generation sequencing in suspected community-acquired pneumonia.

BACKGROUND: The emergence of metagenomic next-generation sequencing (mNGS) may provide a promising tool for early and comprehensive identification of the causative pathogen in community-acquired pneumonia (CAP). In this study, we aim to further evaluate the etiological diagnostic value of mNGS in suspected CAP. METHODS: A total of 555 bronchoalveolar lavage fluid (BALF) samples were collected for pathogen detection by mNGS from 541 patients with suspected CAP. The clinical value was assessed based on infection diagnosis and treatment guidance. The diagnostic performance for pathogen identification by mNGS and sputum culture and for tuberculosis (TB) by mNGS and X-pert MTB/RIF were compared. To evaluate the potential for treatment guidance, we analyzed the treatment regimen of patients with suspected CAP, including imaging changes of lung after empirical antibacterial therapy, intensified regimen, antifungal treatment, and a 1-year follow up for patients with unconfirmed diagnosis and non-improvement imaging after anti-infective treatment and patients with high suspicion of TB or NTM infection who were transferred to the Wuhan Pulmonary Hospital for further diagnosis and even anti-mycobacterium therapy. RESULTS: Of the 516 BALF samples that were analyzed by both mNGS and sputum culture, the positivity rate of mNGS was significantly higher than that of sputum culture (79.1% vs. 11.4%, P = 0.001). A total of 48 samples from patients with confirmed TB were analyzed by both mNGS and X-pert MTB/RIF, and the sensitivity of mNGS for the diagnosis of active TB was significantly lower than that of X-pert MTB/RIF (64.6% vs. 85.4%, P = 0.031). Of the 106 pathogen-negative cases, 48 were ultimately considered non-infectious diseases, with a negative predictive value of 45.3%. Of the 381 pathogen-positive cases, 311 were eventually diagnosed as CAP, with a positive predictive value of 81.6%. A total of 487 patients were included in the evaluation of the therapeutic effect, and 67.1% improved with initial empirical antibiotic treatment. Of the 163 patients in which bacteria were detected, 77.9% improved with antibacterial therapy; of the 85 patients in which fungi were detected, 12.9% achieved remission after antifungal therapy. CONCLUSIONS: Overall, mNGS had unique advantages in the detection of suspected CAP pathogens. However, mNGS was not superior to X-pert MTB/RIF for the diagnosis of TB. In addition, mNGS was not necessary as a routine test for all patients admitted with suspected CAP. Furthermore, when fungi are detected by mNGS, antifungal therapy should be cautious.

Dual role of dissolved black carbon in sensitized ofloxacin photooxidation: Mechanism and influential factors.

Dissolved black carbon (DBC) is the more photoactive component of dissolved organic matter (DOM) pool, which plays a dual role in the photoconversion of aquatic contaminants, acting as both a photosensitizer and an inhibitor. However, little is known about the more systematic mechanism by which DBC exhibits a dual effect, which is closely related to the structure composition of DBC. In this study, the differences in characteristics of DBC obtained from 300 °C and 500 °C were compared via UV-vis absorption spectrum, Fluorescence excitation emission matrix spectra (3D-EEM), Fourier transform infrared (FT-IR), and X-ray photoelectron spectroscopy (XPS), and evaluated the promoting and inhibiting effects of DBC on ofloxacin (OFL) photodegradation. It was found that higher pyrolysis temperature reduced the UV absorbance, molecular weight, aromaticity, and phenolics of DBC while increasing the content of quinone/aromatic ketone and humic substances. Photochemical data showed that 3DBC*, 1O2 and ·OH were all participated in the DBC-mediated OFL photodegradation. Wherein, DBC300 (DBCT, where T = pyrolysis temperature) had strong light screening and dynamic quenching effect, but the formation ability of 3DBC*, 1O2 and ·OH was poor, which significantly retarded the photodegradation of OFL. While DBC500 exhibited a slight promotion effect due to its higher formation ability of reactive species and weak light screening effect. Moreover, DBC500 had higher steady-state concentration and (kOFL,3DBC⁎) than DBC300, which might be due to the higher contents of quinone/aromatic ketone and the lower contents of phenol in DBC500, thus enhancing the reactivity of 3DBC* and OFL. Our research systematically revealed the trade-off mechanism of DBC on the photodegradation of fluoroquinolones, and provided an important theoretical guidance for the photodegradation of fluoroquinolones under the evolution of DBC composition.

Incretin-based drugs decrease the incidence of prostate cancer in type 2 diabetics: A pooling-up analysis.

Incretin-based drugs, a class of Antidiabetic medications (ADMs) used in the treatment of type 2 diabetes, may affect the incidence of prostate cancer (PCa). But real-world evidence for this possible effect is lacking. Therefore, the aim of this study is to assess the effect of incretin-based drugs on the incidence of PCa, including glucagon-like peptide-1 (GLP-1) receptor agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors. We searched PubMed, Embase, and Cochrane Library databases for eligible studies through September 2023. Two independent reviewers performed screening and data extraction. We used the Cochrane Handbook for Systematic Reviews and the Newcastle-Ottawa Scale (NOS) to assess the quality of included randomized controlled trials (RCTs) and cohort studies. We did a meta-analysis of available trial data to calculate overall risk ratios (RRs) for PCa. A total of 1238 articles were identified in our search. After screening for eligibility, 7 high-quality studies met the criteria for meta-analysis, including 2 RCTs and 5 cohort studies, with a total of 1165,738 patients. Compared with the control group, we found that incretin-based drugs reduced the relative risk of PCa by 35% (95% confidence interval (CI), 0.17-0.49; P = .0006). In subgroup analysis, the RR values for GLP-1 receptor agonists and DPP-4 inhibitors were 62% (95% CI, 0.45-0.85; P = .003) and 72% (95% CI, 0.46-1.12; P = .14), respectively. Incretin-based drugs are associated with lower incidence of prostate cancer and may have a preventive effect on prostate cancer in patients with type 2 diabetes.

CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues.

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

Activation of peroxymonosulfate with natural pyrite-biochar composite for sulfamethoxazole degradation in soil: Organic matter effects and free radical conversion.

Peroxymonosulfate (PMS)-based advanced oxidation processes (AOPs) represent an effective method for the remediation of antibiotic-contaminated soils. In this study, a natural pyrite-biochar composite material (FBCx) was developed, demonstrating superior activation performance and achieving a 76% removal rate of SMX from soil within 120 min. There existed different degradation mechanisms for SMX in aqueous and soil solutions, respectively. The production of 1O2 and inherent active species produced by soil slurry played an important role in the degradation process. The combination of electron paramagnetic resonance (EPR) and free radical probe experiments confirmed the presence of free radical transformation processes in soil. Wherein, the·OH and SO4·- generated in soil slurry did not directly involve in the degradation process, but rather preferentially reacted with soil organic matter (SOM) to form alkyl-like radicals (R·), thereby maintaining a high concentration of reactive species in the system. Furthermore, germination and growth promotion of mung bean seeds observed in the toxicity test indicated the environmental compatibility of this remediation method. This study revealed the influence mechanism of SOM in the remediation process of contaminated soil comprehensively, which possessed enormous potential for application in practical environments.

Dietary exposure to 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) induces oxidative damage promoting cell apoptosis primarily via mitochondrial pathway in the hepatopancreas of carp, Cyprinus carpio.

To investigate the mechanisms of BDE-47 on hepatotoxicity in fish, this study examined the effects of dietary exposure to BDE-47 (40 and 4000 ng/g) on carp for 42 days. The results showed that BDE-47 significantly increased carp's condition factor and hepatosomatic index. Pathological results revealed unclear hepatic cord structure, hepatocytes swelling, cellular vacuolization, and inflammatory cell infiltration in the hepatopancreas of carp. Further investigation showed that ROS levels significantly increased on days 7, 14, and 42. Moreover, the activities of antioxidant enzymes SOD, GSH, CAT, and GST increased significantly from 1 to 7 days, and the transcription levels of antioxidant enzymes CAT, Cu-Zn SOD, Mn-SOD, GST, and GPX, and antioxidant pathway genes Keap1, Nrf2, and HO-1 changed significantly at multiple time-points during the 42 days. The results of apoptosis pathway genes showed that the mitochondrial pathway genes Bax, Casp3, and Casp9 were significantly upregulated and Bcl2 was significantly downregulated, while the transcription levels of FADD and PERK were significantly enhanced. These results indicate that BDE-47 induced oxidative damage in hepatopancreas, then it promoted cell apoptosis mainly through the mitochondrial pathway. This study provides a foundation for analyzing the mechanism of hepatotoxicity induced by BDE-47 on fish.

Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.

Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.

Salvia miltiorrhiza polysaccharide promotes the health of crayfish (Procambarus clarkii) by promoting hemocyte phagocytosis, protecting hepatopancreas and enhancing intestinal barrier function.

Plant polysaccharides as immunomodulators are considered one of the effective measures to reduce antibiotic therapy in aquaculture. The immunomodulatory function of Salvia miltiorrhiza polysaccharides (SMP) has been demonstrated and begun to be applied in vertebrates, but its potential effect on crustaceans is unclear. In this study, crayfish (Procambarus clarkii) was fed with 0 %, 0.3 %, 0.7 %, 1.1 %, and 1.5 % SMP for 4 weeks to investigate the effects of SMP on hemocytes phagocytosis, hepatopancreatic function, and intestinal barrier function. The results revealed that hemocyte phagocytic activity was increased in all SMP groups. During the process of hemocytes phagocytic recognition and formation of phagosomes and phagolysosomes, the mRNA expression levels of mas, hem, rab3, ctsb, and lamp-1 were up-regulated mainly in the 0.3 % SMP group. During the clearance phase of phagocytosis, respiratory burst activity, ROS level, T-SOD, CAT, GST, and LZM activities were mainly increased in the 1.5 % SMP group. Hepatopancreas AKP and GOT activity were no significant change in all SMP groups. ACP activity was significantly enhanced in the 1.1 % SMP group. The GPT activity of 0.3-0.7 % SMP group was significantly decreased. The 0.7 % SMP group had the highest intestinal fold height. The highest index values of OTUs, Ace, Chao, and Shannon were in the 0.3 % SMP group. The dietary addition of 0.3 % SMP led to a tendency of increased relative abundance of Firmicutes and Bacteroidota at the phylum level, while the relative abundance of Proteobacteria at the phylum level decreased. In conclusion, dietary SMP could promote crayfish health by enhancing phagocytosis, protecting hepatopancreas and enhancing intestinal barrier function. This study contributes to the theoretical foundation for exploring the potential application of plant polysaccharides in crustaceans.

Microbiome analysis reveals the intestinal microbiota characteristics and potential impact of Procambarus clarkii.

The intestinal microbiota interacts with the host and plays an important role in the immune response, digestive physiology, and regulation of body functions. In addition, it is also well documented that the intestinal microbiota of aquatic animals are closely related to their growth rate. However, whether it resulted in different sizes of crayfish in the rice-crayfish coculture model remained vague. Here, we analyzed the intestinal microbiota characteristics of crayfish of three sizes in the same typical rice-crayfish coculture field by high-throughput sequencing technology combined with quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme activity, investigating the relationship between intestinal microbiota in crayfish and water and sediments. The results showed that the dominant intestinal microbiota of crayfish was significantly different between the large size group (BS), normal size group (NS), and small size group (SS), where Bacteroides and Candidatus_Bacilloplasma contributed to the growth of crayfish by facilitating food digestion through cellulolysis, which might be one of the potential factors affecting the difference in sizes. Follow-up experiments confirmed that the activity of lipase (LPS) and protease was higher in BS, and the relative expression of development-related genes, including alpha-amylase (α-AMY), myocyte-specific enhancer factor 2a (MEF2a), glutathione reductase (GR), chitinase (CHI), and ecdysone receptor (EcR), in BS was significantly higher than that in SS. These findings revealed the intestinal microbiota characteristics of crayfish of different sizes and their potential impact on growth, which is valuable for managing and manipulating the intestinal microbiota in crayfish to achieve high productivity in practice. KEY POINTS: • Significant differences in the dominant microflora of BS, NS, and SS in crayfish. • Cellulolysis might be a potential factor affecting different sizes in crayfish. • Adding Bacteroides and Candidatus_Bacilloplasma helped the growth of crayfish.

Photo-Reactivity of dissolved black carbon unveiled by combination of optical spectroscopy and FT-ICR MS analysis: Effects of pyrolysis temperature.

Dissolved black carbon (DBC) has high photoactivity, which plays an important role in contaminants photodegradation. However, it is unclear how pyrolysis temperatures would affect the composition and photo-reactivity of DBC at the molecular level. Herein, we combined complementary techniques to study the characteristics of DBC pyrolyzed at 200 - 500 ℃, as well as the photoproduction of reactive species and the photodegradation of tetracycline (TC). Bulk composition characterization found that condensed aromatic carbonyl compounds (ConAC) with narrow molecular weights in DBC experienced an increase from 200 to 500 °C, which enhanced the photoproduction of 3DBC*,1O2, and ·OH. Molecular-level data suggested that 3DBC* and 1O2 were both related to the same DBC compounds. Comparatively, the patterns for ·OH were less pronounced, implying its precursor was not 3DBC* and had more complexity. Plentiful CHOx species of ConAC in DBC400 and DBC500 (DBCT, where T = pyrolysis temperature) accelerated the generation of 3DBC* and 1O2, enhancing the photodegradation of TC, and mainly triplet states of quinones reacted with TC. In contrast, DBC200 and DBC300 exhibited inhibition since massive CHOx species in lignin-like reduced 3TC* to TC. Our data revealed the diverse photochemical behavior mechanisms of DBC pyrolyzed at 200 - 500 ℃ at the molecular level and the implications for aquatic contaminants photochemistry.

GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data.

With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.

The changing pattern of common respiratory viruses among children from 2018 to 2021 in Wuhan, China.

BACKGROUND: Acute respiratory infections in children are a global public health challenge. Owing to the coronavirus disease (COVID-19) pandemic, non-pharmaceutical interventions, including patient isolation, social distancing, hand washing, and mask wearing, have been widely implemented, impacting the transmission of common respiratory viruses. The aim of this study was to clarify the epidemiological features of respiratory viruses in children less than 14 years of age in Wuhan before and after COVID-19. METHODS: Respiratory specimens were collected from patients aged < 14 years at two hospitals in Wuhan, China, from January 2018 to December 2021. Seven respiratory viruses were identified using an immunofluorescence assay. Pathogen profiles and seasonality were analysed. RESULTS: The number of visits and virus detection rate decreased dramatically after February 2020. The respiratory virus detection rate peaked in January and December and decreased dramatically in February and August. The detection rate was lower in 2021 than in 2018 and 2019. Respiratory syncytial virus (RSV) was identified as the leading pathogen in children aged < 1 year and 1-4 years before and after the COVID-19 pandemic. In children aged 5-14 years, influenza virus was detected at the highest rate before, and RSV after, the COVID-19 pandemic. RSV was the most common virus in coinfections. CONCLUSIONS: This study revealed the epidemiological patterns of common respiratory viruses from 2018 to 2021. The spectrum of pathogens involved in paediatric respiratory infections had partly changed. Non-pharmaceutical interventions resulted in fewer opportunities for the spread of common viruses but also in an "immunity debt" that could have negative consequences when the pandemic is under control in Wuhan.

Starvation induces hepatopancreas atrophy in Chinese mitten crab (Eriocheir sinensis) by inhibiting angiogenesis.

BACKGROUND: The hepatopancreas of crustaceans serves as a significant organ for both the synthesis and secretion of digestive enzymes, as well as energy storage. In the event of food shortage, the hepatopancreas can provide energy for survival. To investigate the potential regulatory mechanisms of the hepatopancreas in response to starvation in Eriocheir Sinensis, transcriptome analysis, histological study and qRT-PCR were performed. RESULTS: The results showed that starvation caused a decrease in the hepatopancreas index of E. sinensis, which had certain effects on the tissue structure, metabolism and angiogenesis in the hepatopancreas. In addition, WGCNA and linear regression analysis showed that the genes significantly related to the hepatopancreas index were mainly enriched in the angiogenesis pathway, in which AKT signaling played an important role. Starvation may inhibit AKT signaling pathway by reducing the expression of TGFBI, HSP27, HHEX, and EsPVF1, thereby hindering angiogenesis, promoting apoptosis, and leading to hepatopancreas atrophy. CONCLUSION: These results indicate that AKT plays an important role in the angiogenesis pathway and apoptosis of the starvation induced hepatopancreas index reduction, which is beneficial to further understand the effect of starvation stress on hepatopancreas of Chinese mitten crab.

An Intelligent Water Monitoring IoT System for Ecological Environment and Smart Cities.

Global precipitation is becoming increasingly intense due to the extreme climate. Therefore, creating new technology to manage water resources is crucial. To create a sustainable urban and ecological environment, a water level and water quality control system implementing artificial intelligence is presented in this research. The proposed smart monitoring system consists of four sensors (two different liquid level sensors, a turbidity and pH sensor, and a water oxygen sensor), a control module (an MCU, a motor, a pump, and a drain), and a power and communication system (a solar panel, a battery, and a wireless communication module). The system focuses on low-cost Internet of Things (IoT) devices along with low power consumption and high precision. This proposal collects rainfall from the preceding 10 years in the application region as well as the region's meteorological bureau's weekly weather report and uses artificial intelligence to compute the appropriate water level. More importantly, the adoption of dynamic adjustment systems can reserve and modify water resources in the application region more efficiently. Compared to existing technologies, the measurement approach utilized in this study not only achieves cost savings exceeding 60% but also enhances water level measurement accuracy by over 15% through the successful implementation of water level calibration decisions utilizing multiple distinct sensors. Of greater significance, the dynamic adjustment systems proposed in this research offer the potential for conserving water resources by more than 15% in an effective manner. As a result, the adoption of this technology may efficiently reserve and distribute water resources for smart cities as well as reduce substantial losses caused by anomalous water resources, such as floods, droughts, and ecological concerns.

Eucommia ulmoides Oliver repairs the disorder of intestinal microflora caused by high starch in Micropterus salmoides and improves resistance to pathogens.

Eucommia ulmoides Oliver (EuO) is a natural medicine that can improve the composition of intestinal flora in fish, but more experiments and data are needed to support whether it can effectively improve the changes of intestinal flora and intestinal damage caused by high starch. This study examined the changes in intestinal structure as well as intestinal flora before and after the addition of EuO to high-starch diets and analyzed the effects of such changes on immune and digestive functions. The results showed that EuO reduces mortality during Nocardia seriolae attack and can reduce starch-induced intestinal inflammation. Eucommia ulmoides Oliver supplementation was able to alter the changes of intestinal flora in fatty acid degradation, bacterial chemotaxis, porphyrin metabolism and flagella assembly caused by high starch. By analyzing the abundance and correlation of bacterial communities, three bacterial communities that were significantly related to the intervention effect of EuO were screened. Further analysis revealed that EuO supplementation reduced the increase in abundance of Limnochordaceae, Nitrolancea, Lysinibacillus, and Hydrogenispora induced by high starch, which were negatively correlated with levels of the immunoreactive substance LZM in fish. This study reveals the regulatory effects of EuO on the intestinal flora of Micropterus salmoides fed on high starch diets, and provides a theoretical basis for reducing starch damage to fish in production.

Single-cell transcriptome analysis indicates fatty acid metabolism-mediated metastasis and immunosuppression in male breast cancer.

Male breast cancer (MBC) is a rare but aggressive malignancy with cellular and immunological characteristics that remain unclear. Here, we perform transcriptomic analysis for 111,038 single cells from tumor tissues of six MBC and thirteen female breast cancer (FBC) patients. We find that that MBC has significantly lower infiltration of T cells relative to FBC. Metastasis-related programs are more active in cancer cells from MBC. The activated fatty acid metabolism involved with FASN is related to cancer cell metastasis and low immune infiltration of MBC. T cells in MBC show activation of p38 MAPK and lipid oxidation pathways, indicating a dysfunctional state. In contrast, T cells in FBC exhibit higher expression of cytotoxic markers and immune activation pathways mediated by immune-modulatory cytokines. Moreover, we identify the inhibitory interactions between cancer cells and T cells in MBC. Our study provides important information for understanding the tumor immunology and metabolism of MBC.

High starch diets attenuate the immune function of Micropterus salmoides immune organs by modulating Keap1/Nrf2 and MAPK signaling pathways.

Based on their good physiological functions and physical properties, carbohydrates are widely used in fish feed. However, excessive use of carbohydrates such as starch in fish feed may reduce the immunity of the fish and cause a series of health problems. In order to more clearly clarify the effects of different starch levels in feed on the immune organs of Micropterus salmoides, this study took the immune organs as the entry point and explored it from several perspectives, including differences in enzyme activity in plasma, changes in gene expression in immune organs, and resistance to pathogenic bacteria. The results showed that (1) high starch feed activates inflammatory responses in the spleen and head kidney through the MAPK signaling pathway. This leads to a decrease in the number of lymphocytes and weakens the resistance to pathogens; (2) high starch diet affects the antioxidant capacity of the trunk kidney by regulating the Keap1/Nrf2 pathway; (3) There was a strong correlation between gene expression patterns in the head kidney and lysozyme content in plasma. This implies that the high starch diet may regulate lysozyme production by affecting gene expression in the head kidney and further affect immune function. This study helps to reveal the interaction between starch and the immune system and provide scientific basis for the development of reasonable dietary recommendations and disease prevention.

DeepICSH: a complex deep learning framework for identifying cell-specific silencers and their strength from the human genome.

Silencers are noncoding DNA sequence fragments located on the genome that suppress gene expression. The variation of silencers in specific cells is closely related to gene expression and cancer development. Computational approaches that exclusively rely on DNA sequence information for silencer identification fail to account for the cell specificity of silencers, resulting in diminished accuracy. Despite the discovery of several transcription factors and epigenetic modifications associated with silencers on the genome, there is still no definitive biological signal or combination thereof to fully characterize silencers, posing challenges in selecting suitable biological signals for their identification. Therefore, we propose a sophisticated deep learning framework called DeepICSH, which is based on multiple biological data sources. Specifically, DeepICSH leverages a deep convolutional neural network to automatically capture biologically relevant signal combinations strongly associated with silencers, originating from a diverse array of biological signals. Furthermore, the utilization of attention mechanisms facilitates the scoring and visualization of these signal combinations, whereas the employment of skip connections facilitates the fusion of multilevel sequence features and signal combinations, thereby empowering the accurate identification of silencers within specific cells. Extensive experiments on HepG2 and K562 cell line data sets demonstrate that DeepICSH outperforms state-of-the-art methods in silencer identification. Notably, we introduce for the first time a deep learning framework based on multi-omics data for classifying strong and weak silencers, achieving favorable performance. In conclusion, DeepICSH shows great promise for advancing the study and analysis of silencers in complex diseases. The source code is available at https://github.com/lyli1013/DeepICSH.