Identification of SARS-CoV-2 inhibitors using lung and colonic organoids.

There is an urgent need to create novel models using human disease-relevant cells to study severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) biology and to facilitate drug screening. Here, as SARS-CoV-2 primarily infects the respiratory tract, we developed a lung organoid model using human pluripotent stem cells (hPSC-LOs). The hPSC-LOs (particularly alveolar type-II-like cells) are permissive to SARS-CoV-2 infection, and showed robust induction of chemokines following SARS-CoV-2 infection, similar to what is seen in patients with COVID-19. Nearly 25% of these patients also have gastrointestinal manifestations, which are associated with worse COVID-19 outcomes1. We therefore also generated complementary hPSC-derived colonic organoids (hPSC-COs) to explore the response of colonic cells to SARS-CoV-2 infection. We found that multiple colonic cell types, especially enterocytes, express ACE2 and are permissive to SARS-CoV-2 infection. Using hPSC-LOs, we performed a high-throughput screen of drugs approved by the FDA (US Food and Drug Administration) and identified entry inhibitors of SARS-CoV-2, including imatinib, mycophenolic acid and quinacrine dihydrochloride. Treatment at physiologically relevant levels of these drugs significantly inhibited SARS-CoV-2 infection of both hPSC-LOs and hPSC-COs. Together, these data demonstrate that hPSC-LOs and hPSC-COs infected by SARS-CoV-2 can serve as disease models to study SARS-CoV-2 infection and provide a valuable resource for drug screening to identify candidate COVID-19 therapeutics.

Blood-based molecular and cellular biomarkers of early response to neoadjuvant PD-1 blockade in patients with non-small cell lung cancer.

BACKGROUND: Despite the improved survival observed in PD-1/PD-L1 blockade therapy, a substantial proportion of cancer patients, including those with non-small cell lung cancer (NSCLC), still lack a response. METHODS: Transcriptomic profiling was conducted on a discovery cohort comprising 100 whole blood samples, as collected multiple times from 48 healthy controls (including 43 published data) and 31 NSCLC patients that under treatment with a combination of anti-PD-1 Tislelizumab and chemotherapy. Differentially expressed genes (DEGs), simulated immune cell subsets, and germline DNA mutational markers were identified from patients achieved a pathological complete response during the early treatment cycles. The predictive values of mutational markers were further validated in an independent immunotherapy cohort of 1661 subjects, and then confirmed in genetically matched lung cancer cell lines by a co-culturing model. RESULTS: The gene expression of hundreds of DEGs (FDR p < 0.05, fold change < -2 or > 2) distinguished responders from healthy controls, indicating the potential to stratify patients utilizing early on-treatment features from blood. PD-1-mediated cell abundance changes in memory CD4 + and regulatory T cell subset were more significant or exclusively observed in responders. A panel of top-ranked genetic alterations showed significant associations with improved survival (p < 0.05) and heightened responsiveness to anti-PD-1 treatment in patient cohort and co-cultured cell lines. CONCLUSION: This study discovered and validated peripheral blood-based biomarkers with evident predictive efficacy for early therapy response and patient stratification before treatment for neoadjuvant PD-1 blockade in NSCLC patients.

Aging Increases Global Annual Food Greenhouse Gas Emissions up to 300 Million Tonnes by 2100.

The dietary preferences of the elderly population exhibit distinct variations from the overall averages in most countries, gaining increasing significance due to aging demographics worldwide. These dietary preferences play a crucial role in shaping global food systems, which will result in changed environmental impacts in the future such as greenhouse gas (GHG) emissions. We present a quantitative evaluation of the influence of population aging on the changes in GHG emissions from global food systems. To achieve this, we developed regional dietary coefficients (DCs) of the elderly based on the Global Dietary Database (GDD). We then reconciled the GDD with the dataset from the Food and Agriculture Organization of the United Nations (FAO) to calculate the food GHG emissions of the average population in each of the countries. By applying the DCs, we estimated the national food GHG emissions and obtained the variations between the emissions from aged and average populations. We employed a modified version of the regional integrated model of climate and the economy model (RICE) to forecast the emission trends in different countries based on FAO and GDD data. This integrated approach allowed us to evaluate the dynamic relationships among aging demographics, food consumption patterns, and economic developments within regions. Our results indicate that the annual aging-embodied global food GHG emissions will reach 288 million tonnes of CO2 equivalent (Mt CO2e) by 2100. This estimation is crucial for policymakers, entrepreneurs, and researchers as it provides insights into a potential future environmental challenge and emphasizes the importance of sustainable food production and consumption strategies to GHG emission mitigations associated with aging dietary patterns.

Genome-wide characterization and expression analysis of MYB transcription factors in Chrysanthemum nankingense.

BACKGROUND: Chrysanthemum is a popular ornamental plant worldwide. MYB (v-myb avian myeloblastosis viral oncogene homolog) transcription factors play an important role in everything from stress resistance to plant growth and development. However, the MYB family of chrysanthemums has not been the subject of a detailed bioinformatics and expression investigation. RESULTS: In this study, we examined 324 CnMYB transcription factors from Chrysanthemum nankingense genome data, which contained 122 Cn1R-MYB, 183 CnR2R3-MYB, 12 Cn3R-MYB, 2 Cn4R-MYB, and 5 atypical CnMYB. The protein motifs and classification of CnMYB transcription factors were analyzed. Among them, motifs 1, 2, 3, and 4 were found to encode the MYB DNA-binding domain in R2R3-MYB proteins, while in other-MYB proteins, the motifs 1, 2, 3, 4, 5, 6, 7, and 8 encode the MYB DNA-binding domain. Among all CnMYBs, 44 genes were selected due to the presence of CpG islands, while methylation is detected in three genes, including CnMYB9, CnMYB152, and CnMYB219. We analyzed the expression levels of each CnMYB gene in ray floret, disc floret, flower bud, leaf, stem, and root tissues. Based on phylogenetic analysis and gene expression analysis, three genes appeared likely to control cellulose and lignin synthesis in stem tissue, and 16 genes appeared likely to regulate flowering time, anther, pollen development, and flower color. Fifty-one candidate genes that may be involved in stress response were identified through phylogenetic, stress-responseve motif of promoter, and qRT-PCR analyses. According to genes expression levels under stress conditions, six CnMYB genes (CnMYB9, CnMYB172, CnMYB186, CnMYB199, CnMYB219, and CnMYB152) were identified as key stress-responsive genes. CONCLUSIONS: This research provides useful information for further functional analysis of the CnMYB gene family in chrysanthemums, as well as offers candidate genes for further study of cellulose and lignin synthesis, flowering traits, salt and drought stress mechanism.

Effect of viewing angle difference on spaceborne optical estimation of floating Ulva prolifera biomass in the Yellow Sea.

Optical remote sensing provides optimal technical support for the detection and quantification of floating macroalgae. Although the spatial scale effect on optical estimation of floating macroalgae coverage or biomass from different images has been clarified, the directional effect on them has not been investigated until now. In this study, synchronous multi-angle imaging spectroradiometer (MISR) and MODIS images were collected to investigate the multi-angle remote sensing of green tides. A dual thresholding method, based on the difference vegetation index (DVI) and scaled algae index, was employed to determine algae pixels. In addition, piecewise empirical models were developed for MISR and MODIS images to estimate the total biomass of green tides based on laboratory measurements and DVI values. Comparative analysis of DVI histograms and total biomass shows that the sensor zenith angle has a significant impact on the quantification of green tides. Under the same solar conditions, as the sensor zenith angle increases, the optical signals received from algae pixels weaken, resulting in a decrease in the quantification of green tides. In future research, the observation geometry (including the solar/sensor zenith angle and the solar/sensor azimuth angle) needs to be considered to improve the accuracy of optical remote detection and quantification of floating macroalgae.

The impact of the COVID-19 pandemic on global trade-embodied carbon emissions.

We evaluate the response of global supply chains to carbon emissions through compiling multi-regional input-output (MRIO) models for import and export shocks in 14 countries/territories dominated by the COVID-19 crisis. Instead of traditional production-based inventories, we achieve CO2 emissions inventories based on intermediate inputs and final consumption to analyze the connected environmental impacts. In addition, we adopt the available data up to date to construct inventories of carbon emissions involved in imports and exports from different sectors. The results show that global carbon emissions could be decreased by 6.01% during the COVID-19, while export carbon emissions remained basically unchanged. As a result, imported carbon emissions fell by 5.2%, with the energy products sector most affected by the pandemic. Transport sector witnessed 18.42% carbon emission reduction. The impact of developing countries with a large proportion of resource-based industries is comparatively higher than that of developed countries with the technological advantage. International trade plays a crucial role in the choice of supply chain partners to control carbon emissions. Building a sustainable supply chain and reducing the "trade carbon deficit" between countries/regions requires the coordination of all departments of each country/region to promote the trade of energy-saving products, environmental protection services and environmental services.

Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction.

BACKGROUND: Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of methods based on deep learning have emerged. However, these methods do not take into account the long-distance dependency information between each two amino acids in sequence. In addition, most existing models based on graph neural networks only aggregate the first-order neighbors in protein-protein interaction (PPI) network. Although multi-order neighbor information can be aggregated by increasing the number of layers of neural network, it is easy to cause over-fitting. So, it is necessary to design a network that can capture long distance dependency information between amino acids in the sequence and can directly capture multi-order neighbor information in protein-protein interaction network. RESULTS: In this study, we propose a multi-hop neural network (LDMGNN) model combining long distance dependency information to predict the multi-label protein-protein interactions. In the LDMGNN model, we design the protein amino acid sequence encoding (PAASE) module with the multi-head self-attention Transformer block to extract the features of amino acid sequences by calculating the interdependence between every two amino acids. And expand the receptive field in space by constructing a two-hop protein-protein interaction (THPPI) network. We combine PPI network and THPPI network with amino acid sequence features respectively, then input them into two identical GIN blocks at the same time to obtain two embeddings. Next, the two embeddings are fused and input to the classifier for predict multi-label protein-protein interactions. Compared with other state-of-the-art methods, LDMGNN shows the best performance on both the SHS27K and SHS148k datasets. Ablation experiments show that the PAASE module and the construction of THPPI network are feasible and effective. CONCLUSIONS: In general terms, our proposed LDMGNN model has achieved satisfactory results in the prediction of multi-label protein-protein interactions.

Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.

BACKGROUND: Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability. RESULTS: In this paper, we propose a multi-type feature fusion based on graph neural network model (MFFGNN) for DDI prediction, which can effectively fuse the topological information in molecular graphs, the interaction information between drugs and the local chemical context in SMILES sequences. In MFFGNN, to fully learn the topological information of drugs, we propose a novel feature extraction module to capture the global features for the molecular graph and the local features for each atom of the molecular graph. In addition, in the multi-type feature fusion module, we use the gating mechanism in each graph convolution layer to solve the over-smoothing problem during information delivery. We perform extensive experiments on multiple real datasets. The results show that MFFGNN outperforms some state-of-the-art models for DDI prediction. Moreover, the cross-dataset experiment results further show that MFFGNN has good generalization performance. CONCLUSIONS: Our proposed model can efficiently integrate the information from SMILES sequences, molecular graphs and drug-drug interaction networks. We find that a multi-type feature fusion model can accurately predict DDIs. It may contribute to discovering novel DDIs.

Diffusion Behaviors of Integrins in Single Cells Altered by Epithelial to Mesenchymal Transition.

Cell morphology and migration depend critically on the adhesions on the extracellular matrix (ECM), determined by the transmembrane protein integrins. The epithelial to mesenchymal transition (EMT) is a prominent transformation process in which adherent cells acquire a mesenchymal phenotype and a promoted migration. EMT plays important roles in embryonic development and cancer metastasis, and its hallmarks include the acquisition of front-back cell polarity and loss of cell-cell contact. However, how integrins dynamically regulate cell-ECM adhesions and cellular behaviors during EMT is still unclear. Using single-particle tracking of ß1-integrins labeled with quantum dots, the temporal-spatial on-membrane dynamics of integrins in the EMT of MCF10A cells is revealed. ß1-integrins exhibit significantly enhanced dynamics, which temporally behave more diffusive and less immobilized, and spatially become distributed asymmetrically with front regions being more dynamic. These dynamic alterations are shown to arise from microtubule remodeling in EMT. The results shed new light on the EMT mechanism from the cell-ECM adhesion perspective, and suggest that the enhanced integrin diffusion may represent as a new hallmark of EMT.

Thrombospondin 1-induced exosomal proteins attenuate hypoxia-induced paraptosis in corneal epithelial cells and promote wound healing.

Thrombospondin-1 (TSP1) is involved in corneal wound healing caused by chemical injury. Herein, we examined the effects of TSP1 on hypoxia-induced damages and wound-healing activity in human corneal epithelial (HCE) cells. Exosomal protein expression was determined using liquid chromatography-tandem mass spectrometry, and HCE cell migration and motility were examined through wound-healing assay and time-lapse microscopy. Reestablishment of cell junctions by TSP1 was assessed through confocal microscopy and 3D image reconstruction. Our results show that CoCl2 -induced hypoxia promoted HCE cell death by paraptosis. TSP1 protected these cells against paraptosis by attenuating mitochondrial membrane potential depletion, swelling and dilation of endoplasmic reticulum and mitochondria, and mitochondrial fission. Exosomes isolated from HCE cells treated with TSP1 contained wound healing-associated proteins that were taken up by HCE cells to promote tissue remodeling and repair. TSP1 protected HCE cells against hypoxia-induced damages and inhibited paraptosis progression by promoting cell migration, cell-cell adhesion, and extracellular matrix remodeling. These findings indicate that TSP1 ameliorates hypoxia-induced paraptosis in HCE cells and promotes wound healing and remodeling by regulating exosomal protein expression. TSP1 may, therefore, play important roles in the treatment of hypoxia-associated corneal diseases.

DNA polymerase Gp90 activities and regulations on strand displacement DNA synthesis revealed at single-molecule level.

Strand displacement DNA synthesis (SDDS) is an essential step in DNA replication. With magnetic tweezers, we investigated SDDS kinetics of wild-type gp90 and its exonuclease-deficient polymerase gp90 exo- at single-molecule level. A novel binding state of gp90 to the fork flap was confirmed prior to SDDS, suggesting an intermediate in the initiation of SDDS. The rate and processivity of SDDS by gp90 exo- or wt-gp90 are increased with force and dNTP concentration. The rate and processivity of exonuclease by wt-gp90 are decreased with force. High GC content decreases SDDS and exonuclease processivity but increases exonuclease rate for wt-gp90. The high force and dNTP concentration and low GC content facilitate the successive SDDS but retard the successive exonuclease for wt-gp90. Furthermore, increasing GC content accelerates the transition from SDDS or exonuclease to exonuclease. This work reveals the kinetics of SDDS in detail and offers a broader cognition on the regulation of various factors on SDDS at single-polymerase level.

Application of the IoT in the Food Supply Chain─From the Perspective of Carbon Mitigation.

With the rising demands on supply chain transparency and food security, the rapid outspread of the Internet of Things (IoT) to improve logistical efficiency, and the rising penetration of sensor technology into daily life, the extensive integration of the IoT in the food sector is well anticipated. A perspective on potential life cycle trade-offs in regard to the type of integration is necessary. We conduct life cycle assessment (LCA) integrated with shelf life-food loss (SL-FL) models, showing an overall 5-fold leverage on carbon reduction, which is diet dependent and a function of income. Meat presents the highest leverage, 35 ± 11-times, owing to its high carbon footprint. Two-thirds (65%) of global sensors (1 billion) engaged in monitoring fruits and vegetables can mitigate less than 7% of the total reduced carbon emissions. Despite the expected carbon emission reductions, widespread adoption of the IoT faces multiple challenges such as high costs, difficulties in social acceptance, and regional variability in technological development. Furthermore, changes in the distribution of transportation resources and dealer service models, requirements regarding the accuracy of sensor data analysis, efficient and persistent operation of devices, development of agricultural infrastructure, and farmer education and training have all increased uncertainty. Nonetheless, the research trend in smart sensors toward smaller chips and the potential integration of machine learning or blockchain as further steps make it possible to leverage these advantages to facilitate market penetration. These insights facilitate the future optimization of the application of IoT sensors for sustainability.

Intracellular transport is accelerated in early apoptotic cells.

Intracellular transport of cellular proteins and organelles is critical for establishing and maintaining intracellular organization and cell physiology. Apoptosis is a process of programmed cell death with dramatic changes in cell morphology and organization, during which signaling molecules are transported between different organelles within the cells. However, how the intracellular transport changes in cells undergoing apoptosis remains unknown. Here, we study the dynamics of intracellular transport by using the single-particle tracking method and find that both directed and diffusive motions of endocytic vesicles are accelerated in early apoptotic cells. With careful elimination of other factors involved in the intracellular transport, the reason for the acceleration is attributed to the elevation of adenosine triphosphate (ATP) concentration. More importantly, we show that the accelerated intracellular transport is critical for apoptosis, and apoptosis is delayed when the dynamics of intracellular transport is regulated back to the normal level. Our results demonstrate the important role of transport dynamics in apoptosis and shed light on the apoptosis mechanism from a physical perspective.

miR-29a-3p/THBS2 Axis Regulates PAH-Induced Cardiac Fibrosis.

Pulmonary artery hypertension (PAH) pathology involves extracellular matrix (ECM) remodeling in cardiac tissues, thus promoting cardiac fibrosis progression. miR-29a-3p reportedly inhibits lung progression and liver fibrosis by regulating ECM protein expression; however, its role in PAH-induced fibrosis remains unclear. In this study, we aimed to investigate the role of miR-29a-3p in cardiac fibrosis progression in PAH and its influence on ECM protein thrombospondin-2 (THBS2) expression. The diagnostic and prognostic values of miR-29a-3p and THBS2 in PAH were evaluated. The expressions and effects of miR-29a-3p and THBS2 were assessed in cell culture, monocrotaline-induced PAH mouse model, and patients with PAH. The levels of circulating miR-29a-3p and THBS2 in patients and mice with PAH decreased and increased, respectively. miR-29a-3p directly targets THBS2 and regulates THBS2 expression via a direct anti-fibrotic effect on PAH-induced cardiac fibrosis. The circulating levels of miR-29a-3p and THBS2 were correlated with PAH diagnostic parameters, suggesting their independent prognostic value. miR-29a-3p targeted THBS2 expression via a direct anti-fibrotic effect on PAH-induced cardiac fibrosis, indicating miR-29a-3p acts as a messenger with promising therapeutic effects.

High Mobility Group Box 1 Promotes Lung Cancer Cell Migration and Motility via Regulation of Dynamin-Related Protein 1.

High mobility group box 1 (HMGB1) has been demonstrated to promote the migration and invasion of non-small cell lung cancer (NSCLC). However, the mechanism of action of HMGB1 in regulating tumor mobility remains unclear. Therefore, we aimed to investigate whether HMGB1 affects mitochondria distribution and regulates dynamin-related protein 1 (DRP1)-mediated lamellipodia/filopodia formation to promote NSCLC migration. The regulation of mitochondrial membrane tension, dynamics, polarization, fission process, and cytoskeletal rearrangements in lung cancer cells by HMGB1 was analyzed using confocal microscopy. The HMGB1-mediated regulation of DRP1 phosphorylation and colocalization was determined using immunostaining and co-immunoprecipitation assays. The tumorigenic potential of HMGB1 was assessed in vivo and further confirmed using NSCLC patient samples. Our results showed that HMGB1 increased the polarity and mobility of cells (mainly by regulating the cytoskeletal system actin and microtubule dynamics and distribution), promoted the formation of lamellipodia/filopodia, and enhanced the expression and phosphorylation of DRP1 in both the nucleus and cytoplasm. In addition, HMGB1 and DRP1 expressions were positively correlated and exhibited poor prognosis and survival in patients with lung cancer. Collectively, HMGB1 plays a key role in the formation of lamellipodia and filopodia by regulating cytoskeleton dynamics and DRP1 expression to promote lung cancer migration.

Heterogeneous groups of alveolar type II cells in lung homeostasis and repair.

Alveoli are the gas-exchanging units of the lung, and the alveolar barrier is often a key battleground where pathogens, allergens, and other insults from the environment are encountered. This is seen in the current coronavirus disease 2019 (COVID-19) pandemic, as alveolar epithelium is one of the major targets of SARS-COV-2, the virus that causes COVID-19. Thus, it is essential to understand the mechanisms in order to maintain the integrity of alveoli epithelium. Alveolar type II (AT2) cells behave as tissue stem cells that repair alveoli epithelium during steady-state replacement and after injury. However, not all AT2 cells are equal in their ability for self-renewal or differentiation. Through marker gene identification, lineage tracing, and single-cell RNA-sequencing (scRNA-seq), distinct subpopulations of AT2 cells have been identified that play the progenitor role in a different context. The revelation of AT2 heterogeneity has brought new insights into the role of AT2 cells in various lung disease settings and potentiates the finding of more therapeutics targets. In this mini review, we discuss the recently identified subpopulations of AT2 cells and their functions under steady-state, postinjury, and pathological conditions.

Pseudomonas aeruginosa stimulates nuclear sphingosine-1-phosphate generation and epigenetic regulation of lung inflammatory injury.

INTRODUCTION: Dysregulated sphingolipid metabolism has been implicated in the pathogenesis of various pulmonary disorders. Nuclear sphingosine-1-phosphate (S1P) has been shown to regulate histone acetylation, and therefore could mediate pro-inflammatory genes expression. METHODS: Profile of sphingolipid species in bronchoalveolar lavage fluids and lung tissue of mice challenged with Pseudomonas aeruginosa (PA) was investigated. The role of nuclear sphingosine kinase (SPHK)2 and S1P in lung inflammatory injury by PA using genetically engineered mice was determined. RESULTS: Genetic deletion of Sphk2, but not Sphk1, in mice conferred protection from PA-mediated lung inflammation. PA infection stimulated phosphorylation of SPHK2 and its localisation in epithelial cell nucleus, which was mediated by protein kinase C (PKC) δ. Inhibition of PKC δ or SPHK2 activity reduced PA-mediated acetylation of histone H3 and H4, which was necessary for the secretion of pro-inflammatory cytokines, interleukin-6 and tumour necrosis factor-α. The clinical significance of the findings is supported by enhanced nuclear localisation of p-SPHK2 in the epithelium of lung specimens from patients with cystic fibrosis (CF). CONCLUSIONS: Our studies define a critical role for nuclear SPHK2/S1P signalling in epigenetic regulation of bacterial-mediated inflammatory lung injury. Targeting SPHK2 may represent a potential strategy to reduce lung inflammatory pulmonary disorders such as pneumonia and CF.

Investigation of Listeriosis-Related Knowledge, Attitudes, and Practices Among Medical Staff in Fangshan, Beijing, China.

The aim of this study was to gain insight into the knowledge of, attitude toward, and practical experience with listeriosis among medical staff. In two hospitals in Fangshan, Beijing, 410 medical staff members were randomly selected using a random sampling method. Each selected staff member was invited to participate in a standardized questionnaire interview. In total, 397 valid questionnaires were collected. With regard to the staff members' general knowledge of listeriosis, they answered 65.96% of the items correctly. The knowledge scores among obstetricians and gynecologists were higher than those of other clinical doctors (p < 0.05); however, obstetricians and gynecologists were less knowledgeable about which drugs are effective against listeriosis than the other doctors (p = 0.007). The percentage of participants with a positive attitude about preventing listeriosis was 96.47%, the percentage with practice formation was 52.39%. The medical staff's mean score for knowledge of listeriosis was 4.61 ± 1.83. The mean score for attitude toward listeriosis was 9.71 ± 1.31. There was a significant association between attitude and knowledge of listeriosis (r = 0.221, p < 0.001). Medical staff obtained a mean score of 2.10 ± 1.07 for the practice formation. There was a significant association between practice formation and knowledge of listeriosis (r = 0.502, p < 0.001). The mean knowledge-attitude-practice (KAP) score for listeriosis among medical staff was 16.41 ± 3.19. The KAP scores were significantly correlated with age (r = 0.129, p = 0.011), occupation (r = -0.103, p = 0.041), department (r = -0.168, p = 0.001), and professional title (r = 0.166, p = 0.001). To improve medical outcomes and foodborne disease surveillance, medical staff should receive more training on listeriosis and the content of the training should be adjusted.

Extracellular Vesicles in Bladder Cancer: Biomarkers and Beyond.

Tumor-derived extracellular vesicles (TEVs) are membrane-bound, nanosized vesicles released by cancer cells and taken up by cells in the tumor microenvironment to modulate the molecular makeup and behavior of recipient cells. In this report, we summarize the pivotal roles of TEVs involved in bladder cancer (BC) development, progression and treatment resistance through transferring their bioactive cargos, including proteins and nucleic acids. We also report on the molecular profiling of TEV cargos derived from urine and blood of BC patients as non-invasive disease biomarkers. The current hurdles in EV research and plausible solutions are discussed.