The combination of breast cancer PDO and mini-PDX platform for drug screening and individualized treatment.

The majority of advanced breast cancers exhibit strong aggressiveness, heterogeneity, and drug resistance, and currently, the lack of effective treatment strategies is one of the main challenges that cancer research must face. Therefore, developing a feasible preclinical model to explore tailored treatments for refractory breast cancer is urgently needed. We established organoid biobanks from 17 patients with breast cancer and characterized them by immunohistochemistry (IHC) and next generation sequencing (NGS). In addition, we in the first combination of patient-derived organoids (PDOs) with mini-patient-derived xenografts (Mini-PDXs) for the rapid and precise screening of drug sensitivity. We confirmed that breast cancer organoids are a high-fidelity three-dimension (3D) model in vitro that recapitulates the original tumour's histological and genetic features. In addition, for a heavily pretreated patient with advanced drug-resistant breast cancer, we combined PDO and Mini-PDX models to identify potentially effective combinations of therapeutic agents for this patient who were alpelisib + fulvestrant. In the drug sensitivity experiment of organoids, we observed changes in the PI3K/AKT/mTOR signalling axis and oestrogen receptor (ER) protein expression levels, which further verified the reliability of the screening results. Our study demonstrates that the PDO combined with mini-PDX model offers a rapid and precise drug screening platform that holds promise for personalized medicine, improving patient outcomes and addressing the urgent need for effective therapies in advanced breast cancer.

Resonance of fatty acid metabolism and immune infiltration in anti-PD-1 monotherapy for breast cancer.

The interaction between tumor fatty acid metabolism and immune microenvironment is a novel topic in oncology research, and the relationship of lipid-derived factors with immune editing in tumor is unclear. The breast cancer samples from the TCGA database were used as the training set, and samples from GSE42568 were employed as the validation set for constructing a model to identify a signature associated with fatty acid metabolism through Lasso Cox regression. And the changes in immune related signatures and risk score before and after anti-PD-1 monotherapy were caught by the differential analysis in GSE225078. A 14-gene prognostic risk scoring model identifying by fatty acid metabolism relevant signature was conducted, and the high risk group had shorter overall survival and progression free survival than low risk group. Many metabolism-related pathways were enriched in the high risk group, and many immune-related pathways were enriched in low risk group. The crucial differentially expressed genes between the high/low risk groups, CYP4F8 and CD52, were found to be strongly associated with SUCLA2 and ACOT4 of 14-gene model, and strongly related to immune infiltration. Immune related signatures, fatty acid metabolism-risk score and the expression level of ALDH1A1 (in 14-gene-model) changed after anti-PD-1 monotherapy. And the mice model results also showed anti-PD-1 mAb could significantly reduce the expression level of ALDH1A1 (p < 0.01). These results brought up the crosstalk between immune components and fatty acid metabolism in breast cancer microenvironment, which provided a new possibility of targeting fatty acid metabolism for combination therapy in breast cancer immunotherapy.

Dual-level clustering ensemble algorithm with three consensus strategies.

Clustering ensemble (CE), renowned for its robust and potent consensus capability, has garnered significant attention from scholars in recent years and has achieved numerous noteworthy breakthroughs. Nevertheless, three key issues persist: (1) the majority of CE selection strategies rely on preset parameters or empirical knowledge as a premise, lacking adaptive selectivity; (2) the construction of co-association matrix is excessively one-sided; (3) the CE method lacks a more macro perspective to reconcile the conflicts among different consensus results. To address these aforementioned problems, a dual-level clustering ensemble algorithm with three consensus strategies is proposed. Firstly, a backward clustering ensemble selection framework is devised, and its built-in selection strategy can adaptively eliminate redundant members. Then, at the base clustering consensus level, taking into account the interplay between actual spatial location information and the co-occurrence frequency, two modified relation matrices are reconstructed, resulting in the development of two consensus methods with different modes. Additionally, at the CE consensus level with a broader perspective, an adjustable Dempster-Shafer evidence theory is developed as the third consensus method in present algorithm to dynamically fuse multiple ensemble results. Experimental results demonstrate that compared to seven other state-of-the-art and typical CE algorithms, the proposed algorithm exhibits exceptional consensus ability and robustness.

Mussel-inspired dynamic facet-selective capping approach to highly uniform α-calcium sulfate hemihydrate crystals.

We report herein a dynamic facet-selective capping (dFSC) strategy for α-calcium sulfate hemihydrate crystal growth from dihydrate gypsum in the presence of a catechol-derived PEI capping agent (DPA-PEI) with inspiration by the biomineralization process of mussel. The crystal shape is controllable and varies from long and pyramid-tipped prisms to thin hexagonal plate. The highly uniform truncated crystals have extremely high compression and bending strengths after hydration molding.

Waste-biomass-derived activated carbon supported Co-Cu-P nanocatalysts for hydrolytic dehydrogenation of ammonia borane.

Hydrolytic dehydrogenation of ammonia borane is a significant and promising approach for on-site hydrogen production at ambient conditions, and developing highly efficient and low-cost catalysts has attracted considerable attention. Herein, waste-biomass-derived activated carbon (AC) was prepared by hydrothermal carbonization and alkali-assisted activation, and non-precious bimetal phosphides (Co-Cu-P) nanocatalysts with a series of different Co/Cu ratios were synthesized on the AC surface through in situ phosphidation method. Owing to the synergetic effects, the optimal Co0.8Cu0.2P/AC presents an outstanding turnover frequency of 26.5 min-1 (25 °C), which is much higher than that of many reported catalysts. The reaction activation energy was measured to be 34.6 kJ mol-1. Benefiting from the ferromagnetic nature of the phosphides, the Co0.8Cu0.2P/AC can be magnetically separated and reused again. After recycling six times, the catalyst still retains 72% of the initial activity, thus indicating great potential for practical applications.

Influence of Analytic Processing on Divergent and Convergent Thinking Tasks: The Role of Rational and Experiential Thinking Styles.

Scientific interest in the relationship between analytic processing and creativity has increased in recent years. However, there is conflicting evidence on whether analytic processing reduces or enhances creativity. We hypothesize that differences in creativity measurement paradigms (divergent or convergent thinking tasks) and the research orientation of analytic processing (dispositional or situational) may explain the conflicting findings. The present study aims to investigate how priming analytic processing affects individuals' performance on divergent and convergent thinking tasks and the moderating role of thinking styles. In Study 1 (N = 155), participants were assigned to either an analytic processing group or a control group and performed convergent thinking (Remote Associates Task) and divergent thinking (Alternative Uses Test) tasks after priming. In Study 2 (N = 119), we conducted a priming paradigm of analytic processing that differed from Study 1, and a personal experiential-rational thinking style was introduced as a moderator. Results showed that priming analytic processing promoted convergent thinking performance but decreased fluency and flexibility scores on the divergent thinking task (Study 1). Notably, the effect of priming analytic processing on divergent thinking performance was significant only for participants with higher levels of rational thinking style (Study 2). These results suggest that thinking styles and dimensions of creativity should be considered in the relationship between analytic processing and creativity.

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

A cohort study of circulating progenitor cells after ST-segment elevation and non-ST segment elevation myocardial infarction in non-diabetic and diabetic patients.

Background: Myocardial infarction induces elevation of progenitor cells in the circulation, a reparative response inhibited by type-2 diabetes. Objectives: Determine if myocardial infarct severity and diabetes interactively influence the migratory activity of CD34+/CXCR4+ progenitor cells and if the migratory test predicts cardiac outcomes. Materials and methods: A longitudinal study was conducted on patients with or without diabetes with a STEMI or NSTEMI. CD34+/CXCR4+ cells were measured in the peripheral blood using flow cytometry, and migratory activity was tested in vitro on cells isolated from samples collected on days 0 and 4 post-infarct. Cardiac function was assessed at three months using cardiac MRI. Results: Of 1,149 patients screened, 71 (6.3%) were eligible and consented. Fifty had STEMI (16 with diabetes) and 21 NSTEMI (8 with diabetes). The proportion of CD34+/CXCR4+ cells within blood mononuclear cells was 1.96 times higher after STEMI compared with NSTEMI (GMR = 1.96, 95% CI 0.87, 4.37) and 1.55 times higher in patients with diabetes compared to patients without diabetes (GMR = 1.55, 95% CI 0.77, 3.13). In the latter, STEMI was associated with a 2.42-times higher proportion of migrated CD34 + /CXCR4 + cells compared with NSTEMI (GMR = 2.42, 95% CI 0.66, 8.81). In patients with diabetes, the association was the opposite, with a 55% reduction in the proportion of migrated CD34+/CXCR4+ cells. No statistically significant associations were observed between the frequency in peripheral blood or in vitro migration capacity of CD34+/CXCR4+ cells and MRI outcomes. Conclusion: We document the interaction between infarct and diabetes on the migratory activity of CD34+/CXCR4+ cells. The test did not predict functional outcomes in the studied cohort.

Robust and adhesive lignin hybrid hydrogel as an ultrasensitive sensor.

The fabrication of hydrogel for sensing purposes remains to be a challenge since the hydrogel needs to have both good mechanical strength and adhesiveness. This work reports a robust and adhesive hydrogel mainly constructed with AgNPs@lignin, polyacrylamide (PAM) and sodium alginate (SA). The silver nanoparticles (AgNPs) were in-situ generated via the reaction between lignin and silver ammonia ([Ag(NH3)2]+). The resultant lignin hybrid hydrogel exhibited a stress, strain and tearing energy up to 0.055 MPa, 1000% and 250 J·m-2, respectively. Furthermore, the hydrogel adhered to different materials with an adhesion energy of higher than 230 J·m-2. This hydrogel was demonstrated to be an ideal sensing material since it could detect both large-scale motions and tiny physiological signals including breathing and pulse. The hydrogel also exhibited good antibacterial performance and biocompatibility. This work provides a good example to design a lignin-based high-performance hydrogel material for sensing purposes.

Unraveling the composition and succession of microbial community and its relationship to flavor substances during Xin-flavor baijiu brewing.

A novel baijiu type, Xin-flavor, has been developed via combining Qu and craftsmanship from three famous baijiu types including light-flavor, sauce-flavor, and strong-flavor. However, the microbial community and its relationship to physicochemical factors and flavor substances were little known during the three-stage brewing processes including saccharification, stacking, and fermentation. In this study, 21 phyla, 407 genera, and 615 species distributed in bacteria and fungi were identified via high-throughput sequencing (HTS). For bacterial community, the dominant bacteria at saccharification stage were Weissella and Leuconostoc, which were superseded by Acetobacter and Gluconobacter at stacking stage, then quickly replaced by Lactobacillus at fermentation stage. For fungal community, Rhizopus, dominant at saccharification stage, was partially replaced by Thermomyces at stacking stage, then was completely superseded by Thermoascus and three yeasts (Saccharomyces, Kazachstania and Apiotrichum) at fermentation stage. The moisture, ethanol, and acidity, as key factors affecting the microbial community, explained this microbial succession. Interestingly, Apiotrichum was firstly detected in fermented grains. Furthermore, the rapid accumulation of ethanol mainly occurred in the first 10 days of fermentation stage, and highly correlated with Saccharomyces, while Kazachstania and Apiotrichum were related to the formation of flavor substances (acetic acid, lactic acid, caproic acid, ethyl lactate and ethyl caproate). Therefore, this study provides fundamental basis for the rational control of baijiu production and improving the craft and quality of Xin-flavor baijiu.

DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction.

Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed and demonstrated with their excellent performances for peptide-HLA class I binding prediction. However, there is still a lack of effective binding prediction models for HLA class II protein binding with peptides due to its inherent challenges. We present a novel sequence-based pan-specific neural network structure, DeepSeaPanII, for peptide-HLA class II binding prediction in this work. Our model is an end-to-end neural network model without the need for pre-or post-processing on input samples compared with existing pan-specific models. Besides state-of-the-art performance in binding affinity prediction, DeepSeqPanII can also extract biological insight on the binding mechanism over the peptide by its attention mechanism-based binding core prediction capability. The leave-one-allele-out cross-validation and benchmark evaluation results show that our proposed network model achieved state-of-the-art performance in HLA-II peptide binding. The source code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPanII.

Distinctive Prognostic Value and Cellular Functions of Osteopontin Splice Variants in Human Gastric Cancer.

BACKGROUND: Osteopontin (OPN) splice variants are identified as predictors of tumour progression and therapeutic resistance in certain types of solid tumours. However, their roles in gastric cancer (GC) remain poorly characterized. The current study sought to assess the prognostic value of the three OPN splice variants (namely OPN-a, OPN-b, and OPN-c) in gastric cancer and their potential functions within gastric cancer cells. METHODS: RNA extraction and reverse transcription were performed using our clinical cohort of gastric carcinomas and matched normal tissues (n = 324 matched pairs). Transcript levels were determined using real-time quantitative PCR. Three OPN splice variants overexpressed cell lines were created from the gastric cancer cell line HGC-27. Subsequently, biological functions, including cell growth, adhesion, migration, and invasion, were studied. The potential effects of OPN isoforms on cisplatin and 5-Fu were evaluated by detecting cellular reactive oxygen species (ROS) levels in the HGC-27-derived cell lines. RESULTS: Compared with normal tissues, the expression levels of three splice variants were all elevated in gastric cancer tissues in an order of OPN-a > OPN-b > OPN-c. The OPN-a level significantly increased with increasing TNM staging and worse clinical outcome. There appeared to be a downregulation for OPN-c in increasing lymph node status (p < 0.05), increasing TNM staging, and poor differentiation. High levels of OPN-a and OPN-b were correlated with short overall survival and disease-free survival of gastric cancer patients. However, the low expression of OPN-c was significantly associated with a poor prognosis. Functional analyses further showed that ectopic expression of OPN-c suppressed in vitro proliferation, adhesiveness, migration, and invasion properties of HGC-27 cells, while the opposite role was seen for OPN-a. Cellular ROS detection indicated that OPN-a and OPN-c significantly promoted ROS production after treatment with 5-Fu comparing to OPN-vector, while only OPN-a markedly induced ROS production after treatment with cisplatin. CONCLUSION: Our results suggest that OPN splice variants have distinguished potential to predict the prognosis of gastric cancer. Three OPN variants exert distinctive functions in gastric cancer cells. Focusing on specific OPN isoforms could be a novel direction for developing diagnostic and therapeutic approaches in gastric cancer.

Dual effects of targeting S100A11 on suppressing cellular metastatic properties and sensitizing drug response in gastric cancer.

BACKGROUND: S100A11 is a member of the S100 family of proteins containing two EF-hand calcium-binding motifs. The dysregulated expression of the S100A11 gene has been implicated in tumour metastasis. However, the role of S100A11 protein in tumour cell response to chemotherapeutic drugs has not been characterised. METHODS: Transcript levels of S100A11 in gastric cancer were evaluated using an in-house patient cohort. Protein expression of S100A11 in gastric cancer was estimated by immunohistochemistry of a tissue microarray. The stable gastric cancer cell lines were established using lentiviral shRNA vectors. The knockdown of S100A11 was validated by qRT-PCR, PCR, and Western blot. The cellular function of S100A11 was estimated by assays of cell adhesion, migration, and invasion. The cell cytotoxic assay was performed to investigate the response to chemotherapeutic drugs. An unsupervised hierarchical clustering and principal component analysis (HCPC) was applied to unveil the dimensional role of S100A11 among all S100 family members in gastric cancer. RESULTS: High expression of S100A11 is associated with poor survival of gastric cancer patients (p < 0.001, HR = 1.85) and is an independent prognostic factor of gastric cancer. We demonstrate that S100A11 plays its role as a tumour promoter through regulating the MMP activity and the epithelial-mesenchymal transition (EMT) process. The stable knockdown of S100A11 suppresses the metastatic properties of gastric cancer cells, which include enhancing cell adhesion, but decelerating cell migration and invasion. Furthermore, the knockdown of S100A11 gene expression dramatically induces the cellular response of gastric cancer cells to the first-line chemotherapeutic drugs fluoropyrimidine 5-fluorouracil (5-FU) and cisplatin. CONCLUSION: The present study identifies S100A11 as a tumour promoter in gastric cancer. More importantly, the S100A11-specific targeting potentially presents dual therapeutic benefits by not only controlling tumour progression but also sensitising chemotherapeutic cytotoxic response.

The G4 Resolvase DHX36 Possesses a Prognosis Significance and Exerts Tumour Suppressing Function Through Multiple Causal Regulations in Non-Small Cell Lung Cancer.

Lung cancer is one of the most prevalent cancers in both men and women worldwide. The nucleic acid G4 structures have been implicated in the transcriptional programmes of cancer-related genes in some cancers such as lung cancer. However, the role of the dominant G4 resolvase DHX36 in the progression of lung cancer remains unknown. In this study, by bioinformatic analysis of public datasets (TCGA and GEO), we find DHX36 is an independent prognosis indicator in non-small-cell lung carcinoma (NSCLC) with subtype dependence. The stable lentiviral knockdown of the DHX36 results in accelerated migration and aggregation of the S-phase subpopulation in lung cancer cells. The reduction of DHX36 level de-sensitises the proliferation response of lung cancer cells to chemotherapeutic drugs such as paclitaxel with cell dependence. The knockdown of this helicase leads to promoted tumour growth, demonstrated by a 3D fluorescence spheroid lung cancer model, and the stimulation of cell colony formation as shown by single-cell cultivation. High throughput proteomic array indicates that DHX36 functions in lung cancer cells through regulating multiple signalling pathways including activation of protein activity, protein autophosphorylation, Fc-receptor signalling pathway, response to peptide hormone and stress-activated protein kinase signalling cascade. A causal transcriptomic analysis suggests that DHX36 is significantly associated with mRNA surveillance, RNA degradation, DNA replication and Myc targets. Therefore, we unveil that DHX36 presents clinical significance and plays a role in tumour suppression in lung cancer, and propose a potentially new concept for an anti-cancer therapy based on helicase-specific targeting.

Emotional Creativity Improves Posttraumatic Growth and Mental Health During the COVID-19 Pandemic.

Emotional creativity refers to a set of cognitive abilities and personality traits related to the originality of emotional experience and expression. Previous studies have found that emotional creativity can positively predict posttraumatic growth and mental health. The outbreak of coronavirus disease 2019 (COVID-19) has posed great challenges to people's daily lives and their mental health status. Therefore, this study aims to address the following two questions: whether emotional creativity can improve posttraumatic growth and mental health during the COVID-19 pandemic and how it works. To do this, a multiple mediation model has been proposed, which supposes that emotional creativity is associated with posttraumatic growth and mental health through perceived social support and regulatory emotional self-efficacy. The study involved 423 participants from multiple regions with different COVID-19 involvement levels. Participants were asked to complete a questionnaire with six parts, which included Emotional Creativity Inventory (ECI), Regulatory Emotional Self-Efficacy Scale (RES), Stress-Related Growth Scale-Short Form (SRGS-SF), Multidimensional Scale of Perceived Social Support scale (MSPSS), Brief Symptom Inventory-18 scale (BSI-18), and COVID-19-related life events questionnaire. Path analysis used to examine the mediation model indicated that under the control of COVID-19-related life events and age, perceived social support mediated a positive association between emotional creativity and posttraumatic growth as well as a negative association between emotional creativity and all mental health problems, including somatization, depression, and anxiety. Regulatory emotional self-efficacy mediates the association between emotional creativity and posttraumatic growth, emotional creativity and anxiety, and emotional creativity and depression. The results suggest that emotional creativity plays an important role in coping with stressful events related to COVID-19. Furthermore, these results might provide a better understanding of the possible paths through which emotional creativity is related to psychological outcomes, such as mental health and posttraumatic growth.

Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.

Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better prediction performance than other approaches. However, most of the top algorithms are neural networks based black box models. Here, we propose DeepAttentionPan, an improved pan-specific model, based on convolutional neural networks and attention mechanisms for more flexible, stable and interpretable MHC-I binding prediction. With the attention mechanism, our ensemble model consisting of 20 trained networks achieves high and more stabilized prediction performance. Extensive tests on IEDB's weekly benchmark dataset show that our method achieves state-of-the-art prediction performance on 21 test allele datasets. Analysis of the peptide positional attention weights learned by our model demonstrates its capability to capture critical binding positions of the peptides, which leads to mechanistic understanding of MHC-peptide binding with high alignment with experimentally verified results. Furthermore, we show that with transfer learning, our pan model can be fine-tuned for alleles with few samples to achieve additional performance improvement. DeepAttentionPan is freely available as an open-source software at https://github.com/jjin49/DeepAttentionPan.

Lead DEAD/H box helicase biomarkers with the therapeutic potential identified by integrated bioinformatic approaches in lung cancer.

DEAD/H box helicases are implicated in lung cancer but have not been systematically investigated for their clinical significance and function. In this study, we aimed to evaluate the potential of DEAD/H box helicases as prognostic biomarkers and therapeutic targets in lung cancer by integrated bioinformatic analysis of multivariate large-scale databases. Survival and differential expression analysis of these helicases enabled us to identify four biomarkers with the most significant alterations. These were found to be the negative prognostic factors DDX11, DDX55 and DDX56, and positive prognostic factor DDX5. Pathway enrichment analysis indicates that MYC signalling is negatively associated with expression levels of the DDX5 gene while positively associated with that of DDX11, DDX55 and DDX56. High expression levels of the DDX5 gene is associated with low mutation levels of TP53 and MUC16, the two most frequently mutated genes in lung cancer. In contrast, high expression levels of DDX11, DDX55 and DDX56 genes are associated with high levels of TP53 and MUC16 mutation. The tumour-infiltrated CD8 + T and B cells positively correlate with levels of DDX5 gene expression, while negatively correlate with that of the other three DEAD box helicases, respectively. Moreover, the DDX5-associated miRNA profile is distinguished from the miRNA profiles of DDX11, DDX55 and DDX56, although each DDX has a different miRNA signature. The identification of these four DDX helicases as biomarkers will be valuable for prognostic prediction and targeted therapeutic development in lung cancer.

Activated leukocyte cell adhesion molecule (ALCAM)/CD166 in pancreatic cancer, a pivotal link to clinical outcome and vascular embolism.

Activated leukocyte cell adhesion molecule (ALCAM, or CD166) is a cell adhesion molecule and one of potential tumour metastasis 'soil' receptors that via homotypic and heterotypic interactions, mediates cancer cell adhesion. The present study investigated clinical, pathological and prognostic values of ALCAM in patients with pancreatic cancer. Human pancreatic cancer (PANC-1 and Mia PaCa-2) and human vascular endothelial cell lines were used to construct cell models differentially expressing levels of ALCAM. Tumour-endothelial interaction and tumour migration were assessed by a DiI-based method and electric cell-substrate impedance sensing (ECIS) assay. Pancreatic cancer tissues (n=223), collected immediately after surgery, were analysed for levels of the ALCAM transcripts, which were also analysed against clinical, pathological and clinical outcomes of the patients. ALCAM protein was assessed by immunohistochemistry on a tissue array. Our study demonstrate that pancreatic cancer tissues had significantly higher levels of ALCAM transcripts than normal tissues (P<0.00001). There were no significant differences with staging, differentiation and tumour locations. Tumours from patients who died of pancreatic cancer had significantly high levels of ALCAM compared with those who lived (P=0.018), and this finding was further supported by ROC analysis (P=0.016). Multivariant analysis showed that ALCAM is an independent prognosis factor for overall survival (HR=5.485), with both nodal status and TNM staging contributing to the model (HR=2.578 and 3.02, respectively). A surprising finding was the relationship between ALCAM expression and microvessel embolism of tumour cells (P=0.021, with vs without tumour embolism). Levels of ALCAM were found to be a determinant factor to adherence of the pancreatic cancer cells to vascular endothelial cells, as demonstrated by pancreatic cancer cell models genetically engineered to express differential levels of ALCAM. The tumour-endothelial interaction mediated by ALCAM was readily blocked by addition of soluble ALCAM. Our data supports the conclusion that ALCAM expression is aberrant in pancreatic cancer and its raised expression is an independent prognostic factor for the survival of the patients and the microvascular embolism by cancer cells. Our results suggest that ALCAM plays a key role in mediating tumour-endothelial cell interactions and enhancing tumour embolism in pancreatic cancer, and targeting ALCAM represents a potential therapeutic strategy for treating human pancreatic cancer.

The Impact of Mortality Salience on Purchase Intention and Creativity Evaluation on Products During COVID-19 Pandemic.

In the environment of COVID-19, people are faced with mortality salience (MS) and socioeconomic crisis. According to the terror management theory, the MS would lead to particular consumption attitudes and behaviors caused by the self-esteem and cultural worldview defense. The creativity as a potential value of products needs to be examined to explore how the MS changed the creativity evaluation of three types of products categorized into normal, renovative, and innovative products, based on the degree of originality (Zhang et al., 2019). Two experiments were conducted to examine (1) the MS effect on the creativity and purchase intention evaluation and (2) both MS and country-of-origin effect on the evaluations. The results show that usefulness and purchase intention are affected by both effects, and the novelty is mainly affected by MS.