[Exploration of mechanism of total triterpenoids from fruits of Chaenomeles speciosa against senescent GES-1 cells induced by D-galactose based on Gln/GLS1/α-KG metabolic axis and mitochondrial apoptosis signaling pathway].

Total triterpenoids from the fruits of Chaenomeles speciosa(TCS) are active components in the prevention and treatment of gastric mucosal damage, which have potential anti-aging effects. However, it is still unclear whether TCS can improve gastric aging, especially its molecular mechanism against gastric aging. On this basis, this study explored the effect and mechanism of TCS on senescent GES-1 cells induced by D-galactose(D-gal) to provide scientific data for the clinical use of TCS to prevent gastric aging. GES-1 cells cultured in vitro and those transfected with overexpression GLS1(GLS1-OE) plasmid of glutaminase 1(GLS1) were induced to aging by D-gal, and then TCS and or GLS1 inhibitor bis-2-(5-phenylacetamido-1,3,4-thiadiazol-2-yl) ethyl sulfide(BPTES) were given. Cell survival rate, positive rate of ß-galactosidase(SA-ß-gal) staining, mitochondrial membrane potential(MMP), and apoptosis were investigated. GLS1 activity, levels of glutamine(Gln), glutamate(Glu), α-ketoglutarate(α-KG), urea, and ammonia in supernatant and cells were detected by enzyme-linked immunosorbent assay(ELISA) and colorimetric methods. The mRNA and protein expressions of GLS1 and the related genes of the mitochondrial apoptosis signaling pathway were measured by real-time fluorescence quantitative PCR and Western blot. The results manifested that compared with the D-gal model group and GLS1-OE D-gal model group, TCS significantly decreased the SA-ß-gal staining positive cell rate and MMP of D-gal-induced senescent GES-1 cells and GLS1-OE senescent GES-1 cells, inhibited the survival of senescent cells, and promoted their apoptosis(P<0.01). It decreased the activity of GLS1 and the content of Gln, Glu, α-KG, urea, and ammonia in supernatant and cell(P<0.01), reduced the concentration of cytochrome C(Cyto C) in mitochondria and the mRNA and protein expressions of GLS1 and proliferating nuclear antigen in cells(P<0.01). The mRNA expression of Bcl-2 and Bcl-xl, the protein expression of pro-caspase-9 and pro-caspase-3, and the ratio of Bcl-2/Bax and Bcl-xl/Bad in cells were decreased(P<0.01). Cyto C concentration in the cytoplasm, the mRNA expressions of Bax, Bad, apoptosis protease activating factor 1(Apaf-1), and protein expressions of cleaved-caspase-9, cleaved-caspase-3, cleaved-PARP-1 were increased(P<0.01). The aforementioned results indicate that TCS can counteract the senescent GES-1 cells induced by D-gal, and its mechanism may be closely related to suppressing the Gln/GLS1/α-KG metabolic axis, activating the mitochondrial apoptosis pathway, and thereby accelerating the apoptosis of the senescent cells and eliminating senescent cells.

Analysis of the coupling degree between regional logistics efficiency and economic development coordination.

This paper aims to study the relationship between regional logistics efficiency and economic development in 31 provinces of China and analyze their coupling coordination. To comprehensively evaluate the coordination between logistics and the economy, we introduced external indicators, such as carbon emissions, based on traditional evaluation indicators. We constructed an evaluation index system to coordinate regional logistics efficiency and economic development. The research approach used in this paper is the cross-DEA method, and data from 2010 to 2019 were selected for empirical calculation. The research findings indicate that Eastern and Northern regions of China show higher logistics efficiency, while Northwestern and Southwestern regions exhibit lower logistics efficiency. Coastal areas have relatively higher economic development levels compared to inland areas. Regarding the coupling coordination between logistics efficiency and economic development, different regions show temporal fluctuations and spatial disparities. Some regions demonstrate higher coordination between logistics efficiency and economic development, while others show lower coordination. Additionally, as the economy experiences rapid growth, logistics efficiency also improves, but the level of coordination varies among different provinces.

Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics.

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

Protective effect of quercetin on lung epithelial cell bystander-effect in the conditioned medium model of lung cancer metastasis induced by radiation.

To investigate the protective effect of Quercetin (Que) on lung epithelial cells (BEAS-2B) induced bystander effect (RIBE) after heavy ion irradiation of A549 cells. A549 cells were irradiated with 2 Gy X heavy ion rays to obtain a conditioned medium. BEAS-2B was incubated with a conditioned medium or Que. CCK-8 assay was used to screen the optimal effective concentration of Que and detect cell proliferation. Cell number was measured by cell counter and apoptosis rate was measured by flow cytometry. HMGB1 and ROS levels were measured by ELISA. Western blot was used to detect the protein expression of HMGB1, TLR4, p65, Bcl-2, Bax, Caspase3 and Cleaved Caspase3. The growth and proliferation rate of BEAS-2B decreased while the apoptosis rate increased after conditioned medium stimulation, and Que intervention inhibited this effect. The expression of HMGB1 and ROS increased after conditioned medium stimulation, and this effect was inhibited by Que intervention. In addition, the conditioned medium increased the levels of proteins of HMGB1, TLR4, p65, Bax, Caspase3 and Cleaved Caspase 3, and decreased levels of Bcl-2 protein, but Que intervention decreased the levels of HMGB1, TLR4, p65, Bax, Caspase3 and Cleaved Caspase 3proteins, and increased levels of Bcl-2 protein. The RIBE of BEAS-2B induced by irradiation of A549 is associated with HMGB1TLR4/NF-κB signaling pathway in conditioned medium inducing apoptosis by activating ROS, and Que may block RIBE-induced apoptosis by regulating HMGB1/TLR4/NF-κB pathway.

[Guiqi Yiyuan Ointment protects rat left lung from bystander effect of right lung injury induced by ~(12)C~(6+) beam].

This study observed the effects of Guiqi Yiyuan Ointment(GQYY) on the left lung subjecting to bystander effect of right lung injury induced by ~(12)C~(6+) beam in rats and decipher the underlying mechanism from NOD-like receptor protein 3(NLRP3)/apoptosis-associated speck-like protein containing a CARD(ASC)/cysteinyl aspartate specific proteinase-1(caspase-1) pathway. Wistar rats were randomized into 7 groups: blank, model, inhibitor [200 mg·kg~(-1), N-acetylcysteine(NAC)], western drug [140 mg·kg~(-1) amifostine(AMI)], and high-, medium-, and low-dose(4.8, 2.4, and 1.2 g·kg~(-1), respectively) GQYY groups. The model of bystander effect damage was established by 4 Gy ~(12)C~(6+) beam irradiation of the right lung(with the other part shielded by a lead plate). The pathological changes in the lung tissue, the level of reactive oxygen species(ROS) in the lung tissue, and the levels of superoxide dismutase(SOD) and malondialdehyde(MDA) in the serum were observed and measured in each group. Furthermore, the mRNA and protein levels of NLRP3, ASC, caspase-1, and phosphorylated nuclear factor-κB p65(p-NF-κB p65)/nuclear factor-κB p65(NF-κB p65) were determined. Compared with the blank group, the model group showed thickened alveolar wall, narrowed alveolar cavity, and presence of massive red blood cells and inflammatory infiltration in the alveolar wall and alveolar cavity. In addition, the model group showed elevated ROS levels in both left and right lungs, elevated MDA level, lowered SOD level, and up-regulated mRNA and protein levels of NLRP3, ASC, caspase-1, and p-NF-κB p65/NF-κB p65. Compared with the model group, the drug administration in all the groups reduced inflammatory cell infiltration in the lung tissue. The inhibitor group and the western drug group showed enlarged alveolar cavity, thinned interstitium, and reduced inflammation. There was a small amount of alveolar wall rupture in the high-and medium-dose GQYY groups and reduced inflammatory cell infiltration in the low dose GQYY group. Compared with the model group, drug administration lowered level of ROS in the left and right lungs, lowered the MDA level, elevated the SOD level, and down-regulated the mRNA and protein levels of NLRP3, ASC, caspase-1, and p-NF-κB p65/NF-κB p65. GQYY can effectively reduce the damage caused by radiation and bystander effect, which may be associated with the ROS-mediated NLRP3 inflammasome activation.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling.

Complex traits such as cardiovascular diseases (CVD) are the results of complicated processes jointly affected by genetic and environmental factors. Genome-wide association studies (GWAS) identified genetic variants associated with diseases but usually did not reveal the underlying mechanisms. There could be many intermediate steps at epigenetic, transcriptomic, and cellular scales inside the black box of genotype-phenotype associations. In this article, we present a machine-learning-based cross-scale framework GRPath to decipher putative causal paths (pcPaths) from genetic variants to disease phenotypes by integrating multiple omics data. Applying GRPath on CVD, we identified 646 and 549 pcPaths linking putative causal regions, variants, and gene expressions in specific cell types for two types of heart failure, respectively. The findings suggest new understandings of coronary heart disease. Our work promoted the modeling of tissue- and cell type-specific cross-scale regulation to uncover mechanisms behind disease-associated variants, and provided new findings on the molecular mechanisms of CVD.

Discovering single-cell eQTLs from scRNA-seq data only.

eQTL studies are essential for understanding genomic regulation. The effects of genetic variations on gene regulation are cell-type-specific and cellular-context-related, so studying eQTLs at a single-cell level is crucial. The ideal solution is to use both mutation and expression data from the same cells. However, the current technology of such paired data in single cells is still immature. We present a new method, eQTLsingle, to discover eQTLs only with single-cell RNA-seq (scRNA-seq) data, without genomic data. It detects mutations from scRNA-seq data and models gene expression of different genotypes with the zero-inflated negative binomial (ZINB) model to find associations between genotypes and phenotypes at the single-cell level. On a glioblastoma and gliomasphere scRNA-seq dataset, eQTLsingle discovered hundreds of cell-type-specific tumor-related eQTLs, most of which cannot be found in bulk eQTL studies. Detailed analyses on examples of the discovered eQTLs revealed important underlying regulatory mechanisms. eQTLsingle is a uniquely powerful tool for utilizing the vast scRNA-seq resources for single-cell eQTL studies, and it is available for free academic use at https://github.com/horsedayday/eQTLsingle.

DualGCN: a dual graph convolutional network model to predict cancer drug response.

BACKGROUND: Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future. RESULTS: We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein-protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data. CONCLUSIONS: The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Thermocapillary dewetting-based dynamic spatial light modulator.

Dynamic spatial light modulators (SLMs) are capable of precisely modulating a beam of light by tuning the phase or intensity of an array of pixels in parallel. They can be utilized in applications ranging from image projection to beam front aberration and microscopic particle manipulation with optical tweezers. However, conventional dynamic SLMs are typically incompatible with high-power sources, as they contain easily damaged optically absorbing components. To address this, we present an SLM that utilizes a viscous film with a local thickness controlled via thermocapillary dewetting. The film is reflowable and can cycle through different patterns, representing, to the best of our knowledge, the first steps towards a dynamic optical device based on the thermocapillary dewetting mechanism.