Metagenomic and flavoromic profiling reveals the correlation between the microorganisms and volatile flavor compounds in Monascus-fermented cheese.

The Monascus-fermented cheese (MC) is a unique cheese product that undergoes multi-strain fermentation, imparting it with distinct flavor qualities. To clarify the role of microorganisms in the formation of flavor in MC, this study employed SPME (arrow)-GC-MS, GC-O integrated with PLS-DA to investigate variations in cheese flavors represented by volatile flavor compounds across 90-day ripening periods. Metagenomic datasets were utilized to identify taxonomic and functional changes in the microorganisms. The results showed a total of 26 characteristic flavor compounds in MC at different ripening periods (VIP＞1, p < 0.05), including butanoic acid, hexanoic acid, butanoic acid ethyl ester, hexanoic acid butyl ester, 2-heptanone and 2-octanone. According to NR database annotation, the genera Monascus, Lactococcus, Aspergillus, Lactiplantibacillus, Staphylococcus, Flavobacterium, Bacillus, Clostridium, Meyerozyma, and Enterobacter were closely associated with flavor formation in MC. Ester compounds were linked to Monascus, Meyerozyma, Staphylococcus, Lactiplantibacillus, and Bacillus. Acid compounds were linked to Lactococcus, Lactobacillus, Staphylococcus, and Bacillus. The production of methyl ketones was closely related to the genera Monascus, Staphylococcus, Lactiplantibacillus, Lactococcus, Bacillus, and Flavobacterium. This study offers insights into the microorganisms of MC and its contribution to flavor development, thereby enriching our understanding of this fascinating dairy product.

A Microfluidic Chip-Based Automated System for Whole-Course Monitoring the Drug Responses of Organoids.

Tumor patients-derived organoids, as a promising preclinical prediction model, have been utilized to evaluate ex vivo drug responses for formulating optimal therapeutic strategies. Detecting adenosine triphosphate (ATP) has been widely used in existing organoid-based drug response tests. However, all commercial ATP detection kits containing the cell lysis procedure can only be applied for single time point ATP detection, resulting in the neglect of dynamic ATP variations in living cells. Meanwhile, due to the limited number of viable organoids from a single patient, it is impractical to exhaustively test all potential time points in search of optimal ones. In this work, a multifunctional microfluidic chip was developed to perform all procedures of organoid-based drug response tests, including establishment, culturing, drug treatment, and ATP monitoring of organoids. An ATP sensor was developed to facilitate the first successful attempt on whole-course monitoring the growth status of fragile organoids. To realize a clinically applicable automatic system for the drug testing of lung cancer, a microfluidic chip based automated system was developed to perform entire organoid-based drug response test, bridging the gap between laboratorial manipulation and clinical practices, as it outperformed previous methods by improving data repeatability, eliminating human error/sample loss, and more importantly, providing a more accurate and comprehensive evaluation of drug effects.

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology.

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively-figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Expression of Anoikis-Related Genes and Potential Biomarkers in Colon Cancer Based on RNA-seq and scRNA-seq.

Colon cancer (CC) is a malignant tumor in the colon. Despite some progress in the early detection and treatment of CC in recent years, some patients still experience recurrence and metastasis. Therefore, it is urgent to better predict the prognosis of CC patients and identify new biomarkers. Recent studies have shown that anoikis-related genes (ARGs) play a significant role in the progression of many tumors. Hence, it is essential to confirm the role of ARGs in the development and treatment of CC by integrating scRNA-seq and transcriptome data. This study integrated transcriptome and single-cell sequencing (scRNA-seq) data from CC samples to evaluate patient stratification, prognosis, and ARG expression in different cell types. Specifically, differential expression of ARGs was identified through consensus clustering to classify CC subtypes. Subsequently, a CC risk model composed of CDKN2A, NOX4, INHBB, CRYAB, TWIST1, CD36, SERPINE1, and MMP3 was constructed using prognosis-related ARGs. Finally, using scRNA-seq data of CC, the expression landscape of prognostic genes in different cell types and the relationship between important immune cells and other cells were explored. Through the above analysis, two CC subtypes were identified, showing significant differences in prognosis and clinical factors. Subsequently, a risk model comprising aforementioned genes successfully categorized all CC samples into two risk groups, which also exhibited significant differences in prognosis, clinical factors, involved pathways, immune landscape, and drug sensitivity. Multiple pathways (cell adhesion molecules (CAMs), and extracellular matrix (ECM) receptor interaction) and immune cells/immune functions (B cell naive, dendritic cell activate, plasma cells, and T cells CD4 memory activated) related to CC were identified. Furthermore, it was found that prognostic genes were highly expressed in various immune cells, and B cells exhibited more and stronger interaction pathways with other cells. The results of this study may provide references for personalized treatment and potential biomarker identification in CC.

DIRMC: a database of immunotherapy-related molecular characteristics.

Cancer immunotherapy has brought about a revolutionary breakthrough in the field of cancer treatment. Immunotherapy has changed the treatment landscape for a variety of solid and hematologic malignancies. To assist researchers in efficiently uncovering valuable information related to cancer immunotherapy, we have presented a manually curated comprehensive database called DIRMC, which focuses on molecular features involved in cancer immunotherapy. All the content was collected manually from published literature, authoritative clinical trial data submitted by clinicians, some databases for drug target prediction such as DrugBank, and some experimentally confirmed high-throughput data sets for the characterization of immune-related molecular interactions in cancer, such as a curated database of T-cell receptor sequences with known antigen specificity (VDJdb), a pathology-associated TCR database (McPAS-TCR) et al. By constructing a fully connected functional network, ranging from cancer-related gene mutations to target genes to translated target proteins to protein regions or sites that may specifically affect protein function, we aim to comprehensively characterize molecular features related to cancer immunotherapy. We have developed the scoring criteria to assess the reliability of each MHC-peptide-T-cell receptor (TCR) interaction item to provide a reference for users. The database provides a user-friendly interface to browse and retrieve data by genes, target proteins, diseases and more. DIRMC also provides a download and submission page for researchers to access data of interest for further investigation or submit new interactions related to cancer immunotherapy targets. Furthermore, DIRMC provides a graphical interface to help users predict the binding affinity between their own peptide of interest and MHC or TCR. This database will provide researchers with a one-stop resource to understand cancer immunotherapy-related targets as well as data on MHC-peptide-TCR interactions. It aims to offer reliable molecular characteristics support for both the analysis of the current status of cancer immunotherapy and the development of new immunotherapy. DIRMC is available at http://www.dirmc.tech/. Database URL: http://www.dirmc.tech/.

Landscape of extrachromosomal circular DNAs, transcriptome, and proteome analysis reveals insights into alcoholic liver cirrhosis.

Alcoholic liver cirrhosis (ALC) is a result of excessive and chronic alcohol consumption. Because alchol can cause DNA damage, extrachromosomal circular DNA (eccDNA) was investigated in ALC liver due to it can be a result of DNA damage. Considering eccDNA has ability to lead to genomic instability as an enhancer of gene transcription, we utilized Circle-Seq to identify differences in eccDNA profiles and gene expression patterns in liver samples obtained from ALC patients (n = 3) and healthy controls (n = 3) to investigate the role of eccDNA in the development of ALC. The abundance of eccDNA in ALC (mean = 13,349) were higher than the healthy control (mean = 11,557) without significant difference (pvalue = 0.6530). We observed 1,032 eccDNA containing genes showed higher expression in ALC patients compared to healthy controls (p < 0.05, log2FC > 1). Notably, we discovered seven genes that exhibited a significant positive correlation between eccDNA abundance and gene expression levels. These genes include A disintegrin and metalloproteinase with thrombospondin motifs 2 (ADAMTS2), Voltage-dependent L-type calcium channel subunit alpha-1C (CACNA1C), Protein TANC1 (TANC1), Integrin alpha-2 (ITGA2), EH domain-containing protein 4 (EHD4), Phosphofurin acidic cluster sorting protein 1 (PACS1), and Neuron navigator 2 (NAV2). Through mass spectrometry proteomics, ITGA2 were found to have significantly higher abbudance in ALC. Integrins are a family of proteins plays key roles in the fibrosis development of liver. Thus, our study opens a new perspective for liver fibrosis development.

GCNT3 Promotes Hepatocellular Carcinoma Progression and EMT by Activating the PI3K/AKT Pathway.

Primary liver cancer, specifically hepatocellular carcinoma (HCC), is a major global health concern. GCNT3 has been identified as an oncogene in various human malignancies. This investigation aimed to discover the GCNT3 function in HCC. The present study employed integrated bioinformatics analyses to assess the expression pattern, prognostic implications, and putative function of GCNT3 in HCC. Transwell flow cytometry, CCK-8, and wound healing assays were performed to examine HCC cell growth, cell cycle, apoptosis, invasion, and migration. In addition, the epithelial-mesenchymal transition (EMT) markers and PI3K/AKT mechanism markers were examined via western blot analysis to elucidate the underlying mechanisms. In HCC, GCNT3 was significantly overexpressed, which was connected with enhanced tumor aggressiveness and an unfavorable prognosis of individuals. In vitro experiments demonstrated that elevated levels of GCNT3 promoted cell growth, migration, cell cycle development, and invasion, in addition to EMT, while suppressing apoptosis. Conversely, knockdown of GCNT3 exerted the opposite effects. GCNT3 overexpression increased PI3K/AKT phosphorylation in HCC cells, and LY294002 counteracted the impacts of upregulated GCNT3 on cell cycle, migration, invasion, proliferation, and EMT in HCC. The investigation showed that GCNT3 may enhance HCC progression and EMT by stimulating PI3K/AKT mechanism.

[Mechanism of n-butanol extract of Pulsatilla Decoction in treating ulcerative colitis by activating BMP signaling pathway].

The study aimed to investigate the therapeutic effects of the n-butanol extract of Pulsatilla Decoction(BEPD) on ulcerative colitis(UC) via the bone morphogenetic protein(BMP) signaling pathway. C57BL/6 mice were divided into six groups: control, model, mesalazine, and BEPD low-, medium-, and high-dose groups. Except for the control group, the rest groups were treated with 3% dextran sulfate sodium(DSS) freely for seven consecutive days to establish the UC mouse model, followed by treatment with different concentrations of BEPD and mesalazine by gavage. The murine body weight and disease activity index(DAI) were recorded. After the mice were sacrificed, their colon tissues were collected for histological analysis. Alcian blue/periodic acid-Schiff(AB/PAS) staining was used to detect the number and mucus secretion status of goblet cells; immunohistochemistry was performed to measure the expression of ki67, cleaved caspase-3, mucin 2(Muc2), and matrix metalloproteinase-9(MMP9) in colon tissues; and immunofluorescence was used to analyze the expression of tight junction proteins in colon tissues, and enzyme linked immunosorbent assay(ELISA) was employed to quantify the levels of tumor necrosis factor-α(TNF-α), interleukin(IL)-1ß, and IL-6. Western blot was conducted to evaluate the expression of BMP pathway-related proteins in mouse colon tissues. Quantitative real-time PCR(qRT-PCR) was performed to measure the expression of genes related to goblet cell differentiation in mouse colon tissues. In addition, this study also examined the protective effect and underlying mechanism of BEPD-containing serum on lipopolysaccharide(LPS)-induced barrier damages in LS174T goblet cells in vitro. The results showed that BEPD significantly alleviated UC symptoms in mice, restored goblet cell diffe-rentiation function, promoted Muc2 secretion and tight junction protein expression, and suppressed inflammatory factor secretion while activating the BMP signaling pathway. Therefore, BEPD may exert its therapeutic effects on UC by activating the BMP signaling pathway, providing a new strategy for drug intervention in UC.

A CXCR3-Activating Peptide Increases Tear Break Up Time and Corrects Corneal haze in a Rabbit Model of Environmental Dry Eye.

Purpose: Environmentally-triggered dry eye disease (DED) or keratoconjunctivitis sicca (KCS), which constitutes the majority of DED cases, currently is palliatively treated with aqueous replacement solutions that do not target the dysfunction of the mucin and lipid components of tears. We tested whether a peptide that increased goblet cell numbers in a model of scleral chemical injury would also improve tear quality in environmental DED. Methods: Environmental DED was established by exposing New Zealand white rabbits (8 per group, female) to 20% humidity with rapid air replacement and b.i.d. atropine sulfate eyedrops for 3 weeks prior to test article administration; this continued for the subsequent 3 weeks of testing. Animals were dosed by (A) saline, (B) b.i.d. eyedrop of peptide in saline, (C) b.i.d. eyedrop of peptide in coacervate, or (D) weekly subconjunctival injection of peptide. In vitro, human conjunctival epithelial cells (HCjE) were exposed to TNFα in the presence or absence of peptide to determine inflammatory responsiveness. Results: The environmental DED was established with both Schirmer and TBUT being reduced at the start of test article; these levels were maintained as low through the testing period. All three treatment regimens increased TBUT approximately 3x to levels greater than prior to desiccation (P < 0.01), with little effect on Schirmer. Corneal haze was present in all eyes after induction, and completely reversed in 36 of 48 eyes across the treatments (P < 0.05). Co-treatment of HCjE with peptide reduced the production of TNFα in response to an inflammatory stimulus. Conclusions: The treatment of environmental DED/KCS with a peptide that activates CXCR3 improved tear quality and reversed corneal pathology by promoting tear stability and likely dampening the corneal inflammation, while not affecting aqueous volume of the tears.

Transformation of metallo-elastomer grafts in a carotid artery interposition model over a year.

Current vascular grafts, primarily Gore-Tex® and Dacron®, don't integrate with the host and have low patency in small-diameter vessels (<6 mm). Biomaterials that possess appropriate viscoelasticity, compliance, and high biocompatibility are essential for their application in small blood vessels. We have developed metal ion crosslinked poly(propanediol-co-(hydroxyphenyl methylene)amino-propanediol sebacate) (M-PAS), a biodegradable elastomer with a wide range of mechanical properties. We call these materials metallo-elastomers. An initial test on Zn-, Fe-, and Cu-PAS grafts reveals that Cu-PAS is the most suitable because of its excellent elastic recoil and well-balanced polymer degradation/tissue regeneration rate. Here we report host remodeling of Cu-PAS vascular grafts in rats over one year. 76 % of the grafts remain patent and >90 % of the synthetic polymer is degraded by 12 months. Extensive cell infiltration leads to a positive host remodeling. The remodeled grafts feature a fully endothelialized lumen. Circumferentially organized smooth muscle cells, elastin fibers, and widespread mature collagen give the neoarteries mechanical properties similar to native arteries. Proteomic analysis further reveals the presence of important vascular proteins in the neoarteries. Evidence suggests that Cu-PAS is a promising material for engineering small blood vessels.

[Application and Research Progress of Lung Cancer Organoid in Precision Medicine 
for Lung Cancer].

The continuous advancement of molecular detection technology has greatly propelled the development of precision medicine for lung cancer. However, tumor heterogeneity is closely associated with tumor metastasis, recurrence, and drug resistance. Additionally, different lung cancer patients with the same genetic mutation may exhibit varying treatment responses to different therapeutic strategies. Therefore, the development of modern precision medicine urgently requires the precise formulation of personalized treatment strategies through personalized tumor models. Lung cancer organoid (LCO) can highly simulate the biological characteristics of tumor in vivo, facilitating the application of innovative drugs such as antibody-drug conjugate in precision medicine for lung cancer. With the development of co-culture model of LCO with tumor microenvironment and tissue engineering technology such as microfluidic chip, LCO can better preserve the biological characteristics and functions of tumor tissue, further improving high-throughput and automated drug sensitivity experiment. In this review, we combine the latest research progress to summarize the application progress and challenges of LCO in precision medicine for lung cancer.
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Side Reactions/Changes in Lithium-Ion Batteries: Mechanisms and Strategies for Creating Safer and Better Batteries.

Lithium-ion batteries (LIBs), in which lithium ions function as charge carriers, are considered the most competitive energy storage devices due to their high energy and power density. However, battery materials, especially with high capacity undergo side reactions and changes that result in capacity decay and safety issues. A deep understanding of the reactions that cause changes in the battery's internal components and the mechanisms of those reactions is needed to build safer and better batteries. This review focuses on the processes of battery failures, with voltage and temperature as the underlying factors. Voltage-induced failures result from anode interfacial reactions, current collector corrosion, cathode interfacial reactions, overcharge, and over-discharge, while temperature-induced failure mechanisms include SEI decomposition, separator damage, and interfacial reactions between electrodes and electrolytes. The review also presents protective strategies for controlling these reactions. As a result, the reader is offered a comprehensive overview of the safety features and failure mechanisms of various LIB components.

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses.

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

ZBTB16 inhibits DNA replication and induces cell cycle arrest by targeting WDHD1 transcription in lung adenocarcinoma.

Lung adenocarcinoma is a malignant tumor with high morbidity and mortality. ZBTB16 plays a double role in various tumors; however, the potential mechanism of ZBTB16 in the pathophysiology of lung adenocarcinoma has yet to be elucidated. We herein observed a decreased expression of ZBTB16 mRNA and protein in lung adenocarcinoma and a significantly increased DNA methylation level of ZBTB16 in patients with lung adenocarcinoma. Analysis of public databases and patients' clinical data indicated a close association between ZBTB16 and patient survival. Ectopic expression of ZBTB16 in lung adenocarcinoma cells significantly inhibited cell proliferation, invasion, and migration. It also induced cell cycle arrest in the S phase. Meanwhile, mitotic catastrophe was induced, and DNA damage and apoptosis occurred. In line with these findings, the overexpression of ZBTB16 in xenograft mice resulted in the inhibition of tumor growth. Comprehensive analysis showed that WDHD1 was a potential target for ZBTB16. The overexpression of both isoforms of WDHD1 significantly reversed the ZBTB16-mediated inhibition of lung adenocarcinoma proliferation and cell cycle. These studies suggest that ZBTB16 impedes the progression of lung adenocarcinoma by interfering with WDHD1 transcription, making it a potential novel therapeutic target in the management of lung adenocarcinoma.

Catalytic Synthesis of (S)-CHBE by Directional Coupling and Immobilization of Carbonyl Reductase and Glucose Dehydrogenase.

Ethyl (S)-4-chloro-3-hydroxybutyrate ((S)-CHBE) is an important chiral intermediate in the synthesis of the cholesterol-lowering drug atorvastatin. Studying the use of SpyTag/SpyCatcher and SnoopTag/SnoopCatcher systems for the asymmetric reduction reaction and directed coupling coenzyme regeneration is practical for efficiently synthesizing (S)-CHBE. In this study, Spy and Snoop systems were used to construct a double-enzyme directed fixation system of carbonyl reductase (BsCR) and glucose dehydrogenase (BsGDH) for converting 4-chloroacetoacetate (COBE) to (S)-CHBE and achieving coenzyme regeneration. We discussed the enzymatic properties of the immobilized enzyme and the optimal catalytic conditions and reusability of the double-enzyme immobilization system. Compared to the free enzyme, the immobilized enzyme showed an improved optimal pH and temperature, maintaining higher relative activity across a wider range. The double-enzyme immobilization system was applied to catalyze the asymmetric reduction reaction of COBE, and the yield of (S)-CHBE reached 60.1% at 30 °C and pH 8.0. In addition, the double-enzyme immobilization system possessed better operational stability than the free enzyme, and maintained about 50% of the initial yield after six cycles. In summary, we show a simple and effective strategy for self-assembling SpyCatcher/SnoopCatcher and SpyTag/SnoopTag fusion proteins, which inspires building more cascade systems at the interface. It provides a new method for facilitating the rapid construction of in vitro immobilized multi-enzyme complexes from crude cell lysate.

N6-Methyladenosine-Modified KREMEN2 Promotes Tumorigenesis and Malignant Progression of High-Grade Serous Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) remains the most lethal female cancer by far. Herein, clinical HGSOC samples had higher N6-methyladenosine (m6A) modification than normal ovarian tissue, and its dysregulation had been reported to drive aberrant transcription and translation programs. However, Kringle-containing transmembrane protein 2 (KREMEN2) and its m6A modification have not been fully elucidated in HGSOC. In this study, the data from the high-throughput messenger RNA (mRNA) sequencing of clinical samples were processed using the weighted correlation network analysis and functional enrichment analysis. Results revealed that KREMEN2 was a driver gene in the tumorigenesis of HGSOC and a potential target of m6A demethylase fat-mass and obesity-associated protein (FTO). KREMEN2 and FTO levels were upregulated and downregulated, respectively, and correlation analysis showed a significant negative correlation in HGSOC samples. Importantly, upregulated KREMEN2 was remarkably associated with lymph node metastasis, distant metastasis, peritoneal metastasis, and high International Federation of Gynecology and Obstetrics stage (Ⅲ/Ⅳ), independent of the age of patients. KREMEN2 promoted the growth of HGSOC in vitro and in vivo, which was dependent on FTO. The methylated RNA immunoprecipitation qPCR and RNA immunoprecipitation assays were performed to verify the m6A level and sites of KREMEN2. FTO overexpression significantly decreased m6A modification in the 3' and 5' untranslated regions of KREMEN2 mRNA and downregulated its expression. In addition, we found that FTO-mediated m6A modification of KREMEN2 mRNA was recognized and stabilized by the m6A reader IGF2BP1 rather than by IGF2BP2 or IGF2BP3. This study highlights the m6A modification of KREMEN2 and extends the importance of RNA epigenetics in HGSOC.

Tumour-derived exosome SNHG17 induced by oestrogen contributes to ovarian cancer progression via the CCL13-CCR2-M2 macrophage axis.

Oestrogen is known to be strongly associated with ovarian cancer. There was much work to show the importance of lncRNA SNHG17 in ovarian cancer. However, no study has revealed the molecular regulatory mechanism and functional effects between oestrogen and SNHG17 in the development and metastasis of ovarian cancer. In this study, we found that SNHG17 expression was significantly increased in ovarian cancer and positively correlated with oestrogen treatment. Oestrogen could promote M2 macrophage polarization as well as ovarian cancer cells SKOV3 and ES2 cell exosomal SNHG17 expression. When exposure to oestrogen, exosomal SNHG17 promoted ovarian cancer cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) in vitro, and tumour growth and lung metastasis in vivo by accelerating M2-like phenotype of macrophages. Mechanically, exosomal SNHG17 could facilitate the release of CCL13 from M2 macrophage via the PI3K-Akt signalling pathway. Moreover, CCL13-CCR2 axis was identified to be involved in ovarian cancer tumour behaviours driven by oestrogen. There results demonstrate a novel mechanism that exosomal SNHG17 exerts an oncogenic effect on ovarian cancer via the CCL13-CCR2-M2 macrophage axis upon oestrogen treatment, of which SNHG17 may be a potential biomarker and therapeutic target for ovarian cancer responded to oestrogen.

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.

BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis. METHODS: To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features. RESULTS: The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs. CONCLUSIONS: The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.

Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer.

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC.

The p38 MAPK/snail signaling axis participates in cadmium-induced lung cancer cell migration and invasiveness.

To determine that p38 MAPK activation contributes to the migration and invasion of lung cancer cells caused by cadmium (Cd). A549 lung cancer cell migration and invasion were assessed using a transwell plate system, and the role of p38 was determined by knocking down p38 activity with two different inhibitors of p38. The activity of p38 was measured by western blot analysis using phospho-specific p38 antibodies and normalized to blots using antibodies directed to total p38 proteins. Snail transcripts were measured using qRT-PCR. The inhibition of p38 blocked Cd-induced migration and invasion, which correlated with an increased activation of p38 as a function of dose and time. Furthermore, Cd-induced activation of p38 MAPK controlled the increase of snail mRNA expression. The p38 MAPK/snail signaling axis was involved in Cd-induced lung cancer cell migration and invasion.