An autonomous debating system.

Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activities, and common to all human societies. The development of computational argumentation technologies is therefore an important emerging discipline in AI research1. Here we present Project Debater, an autonomous debating system that can engage in a competitive debate with humans. We provide a complete description of the system's architecture, a thorough and systematic evaluation of its operation across a wide range of debate topics, and a detailed account of the system's performance in its public debut against three expert human debaters. We also highlight the fundamental differences between debating with humans as opposed to challenging humans in game competitions, the latter being the focus of classical 'grand challenges' pursued by the AI research community over the past few decades. We suggest that such challenges lie in the 'comfort zone' of AI, whereas debating with humans lies in a different territory, in which humans still prevail, and for which novel paradigms are required to make substantial progress.

Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer.

BACKGROUND: 5-Fluorouracil (5-FU) has been used as a standard first-line treatment for colorectal cancer (CRC) patients. Although 5-FU-based chemotherapy and immune checkpoint blockade (ICB) have achieved success in treating CRC, drug resistance and low response rates remain substantial limitations. Thus, it is necessary to construct a 5-FU resistance-related signature (5-FRSig) to predict patient prognosis and identify ideal patients for chemotherapy and immunotherapy. METHODS: Using bulk and single-cell RNA sequencing data, we established and validated a novel 5-FRSig model using stepwise regression and multiple CRC cohorts and evaluated its associations with the prognosis, clinical features, immune status, immunotherapy, neoadjuvant therapy, and drug sensitivity of CRC patients through various bioinformatics algorithms. Unsupervised consensus clustering was performed to categorize the 5-FU resistance-related molecular subtypes of CRC. The expression levels of 5-FRSig, immune checkpoints, and immunoregulators were determined using quantitative real-time polymerase chain reaction (RT‒qPCR). Potential small-molecule agents were identified via Connectivity Map (CMap) and molecular docking. RESULTS: The 5-FRSig and cluster were confirmed as independent prognostic factors in CRC, as patients in the low-risk group and Cluster 1 had a better prognosis. Notably, 5-FRSig was significantly associated with 5-FU sensitivity, chemotherapy response, immune cell infiltration, immunoreactivity phenotype, immunotherapy efficiency, and drug selection. We predicted 10 potential compounds that bind to the core targets of 5-FRSig with the highest affinity. CONCLUSION: We developed a valid 5-FRSig to predict the prognosis, chemotherapeutic response, and immune status of CRC patients, thus optimizing the therapeutic benefits of chemotherapy combined with immunotherapy, which can facilitate the development of personalized treatments and novel molecular targeted therapies for patients with CRC.

Lp-PLA2 inhibition prevents Ang II-induced cardiac inflammation and fibrosis by blocking macrophage NLRP3 inflammasome activation.

Macrophage-mediated inflammation plays an important role in hypertensive cardiac remodeling, whereas effective pharmacological treatments targeting cardiac inflammation remain unclear. Lipoprotein-associated phospholipase A2 (Lp-PLA2) contributes to vascular inflammation-related diseases by mediating macrophage migration and activation. Darapladib, the most advanced Lp-PLA2 inhibitor, has been evaluated in phase III trials in atherosclerosis patients. However, the role of darapladib in inhibiting hypertensive cardiac fibrosis remains unknown. Using a murine angiotensin II (Ang II) infusion-induced hypertension model, we found that Pla2g7 (the gene of Lp-PLA2) was the only upregulated PLA2 gene detected in hypertensive cardiac tissue, and it was primarily localized in heart-infiltrating macrophages. As expected, darapladib significantly prevented Ang II-induced cardiac fibrosis, ventricular hypertrophy, and cardiac dysfunction, with potent abatement of macrophage infiltration and inflammatory response. RNA sequencing revealed that darapladib strongly downregulated the expression of genes and signaling pathways related to inflammation, extracellular matrix, and proliferation. Moreover, darapladib substantially reduced the Ang II infusion-induced expression of nucleotide-binding oligomerization domain-like receptor with pyrin domain 3 (NLRP3) and interleukin (IL)-1ß and markedly attenuated caspase-1 activation in cardiac tissues. Furthermore, darapladib ameliorated Ang II-stimulated macrophage migration and IL-1ß secretion in macrophages by blocking NLRP3 inflammasome activation. Darapladib also effectively blocked macrophage-mediated transformation of fibroblasts into myofibroblasts by inhibiting the activation of the NLRP3 inflammasome in macrophages. Overall, our study identifies a novel anti-inflammatory and anti-cardiac fibrosis role of darapladib in Lp-PLA2 inhibition, elucidating the protective effects of suppressing NLRP3 inflammasome activation. Lp-PLA2 inhibition by darapladib represents a novel therapeutic strategy for hypertensive cardiac damage treatment.

Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking.

Background: Left ventricular hypertrophy (LVH) is a common consequence of hypertension and can lead to heart failure. The immune response plays an important role in hypertensive LVH; however, there is no comprehensive method to investigate the mechanistic relationships between immune response and hypertensive LVH or to find novel therapeutic targets. This study aimed to screen hub immune-related genes involved in hypertensive LVH as well as to explore immune target-based therapeutic drugs. Materials and methods: RNA-sequencing data from a mouse model generated by angiotensin II infusion were subjected to weighted gene co-expression network analysis (WGCNA) to identify core expression modules. Machine learning algorithms were applied to screen immune-related LVH characteristic genes. Heart structures were evaluated by echocardiography and cardiac magnetic resonance imaging (CMRI). Validation of hub genes was conducted by RT-qPCR and western blot. Using the Connectivity Map database and molecular docking, potential small-molecule drugs were explored. Results: A total of 1215 differentially expressed genes were obtained, most of which were significantly enriched in immunoregulation and collagen synthesis. WGCNA and multiple machine learning strategies uncovered six hub immune-related genes (Ankrd1, Birc5, Nuf2, C1qtnf6, Fcgr3, and Cdca3) that may accurately predict hypertensive LVH diagnosis. Immune analysis revealed that fibroblasts and macrophages were closely correlated with hypertensive LVH, and hub gene expression was significantly associated with these immune cells. A regulatory network of transcription factor-mRNA and a ceRNA network of miRNA-lncRNA was established. Notably, six hub immune-related genes were significantly increased in the hypertensive LVH model, which were positively linked to left ventricle wall thickness. Finally, 12 small-molecule compounds with the potential to reverse the high expression of hub genes were ruled out as potential therapeutic agents for hypertensive LVH. Conclusion: This study identified and validated six hub immune-related genes that may play essential roles in hypertensive LVH, providing new insights into the potential pathogenesis of cardiac remodeling and novel targets for medical interventions.

Developing an m5C regulator-mediated RNA methylation modification signature to predict prognosis and immunotherapy efficacy in rectal cancer.

Background: Currently, a very small number of patients with colorectal cancer (CRC) respond to immune checkpoint inhibitor (ICI) treatment. Therefore, there is an urgent need to investigate effective biomarkers to determine the responsiveness to ICI treatment. Recently, aberrant 5-methylcytosine (m5C) RNA modification has emerged as a key player in the pathogenesis of cancer. Thus, we aimed to explore the predictive signature based on m5C regulator-related genes for characterizing the immune landscapes and predicting the prognosis and response to therapies. Methods: The Cancer Genome Atlas (TCGA) cohort was used as the training set, while GEO data sets, real-time quantitative PCR (RT-qPCR) analysis from paired frozen tissues, and immunohistochemistry (IHC) data from tissue microarray (TMA) were used for validation. We constructed a novel signature based on three m5C regulator-related genes in patients with rectal adenocarcinoma (READ) using a least absolute shrinkage and selection operator (LASSO)-Cox regression and unsupervised consensus clustering analyses. Additionally, we correlated the three-gene signature risk model with the tumor immune microenvironment, immunotherapy efficiency, and potential applicable drugs. Results: The m5C methylation-based signature was an independent prognostic factor, where low-risk patients showed a stronger immunoreactivity phenotype and a superior response to ICI therapy. Conversely, the high-risk patients had enriched pathways of cancer hallmarks and presented immune-suppressive state, which demonstrated that they are more insensitive to immunotherapy. Additionally, the signature markedly correlated with drug susceptibility. Conclusions: We developed a reliable m5C regulator-based risk model to predict the prognosis, clarify the molecular and tumor microenvironment status, and identify patients who would benefit from immunotherapy or chemotherapy. Our study could provide vital guidance to improve prognostic stratification and optimize personalized therapeutic strategies for patients with rectal cancer.

Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients.

Background: Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism. Methods: CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR. Results: We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility. Conclusion: We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

Background  Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods  Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results  The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions  While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 3).

Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer.

Aberrant sialylation plays a key biological role in tumorigenesis and metastasis, including tumor cell survival and invasion, immune evasion, angiogenesis, and resistance to therapy. It has been proposed as a possible cancer biomarker and a potential therapeutic target of tumors. Nevertheless, the prognostic significance and biological features of sialylation-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC) remain unclear. This study aimed to develop a novel sialylation-related lncRNA signature to accurately evaluate the prognosis of patients with CRC and explore the potential molecular mechanisms of the sialylation-related lncRNAs. Here, we identified sialylation-related lncRNAs using the Pearson correlation analysis on The Cancer Genome Atlas (TCGA) dataset. Univariate and stepwise multivariable Cox analysis were used to establish a signature based on seven sialylation-related lncRNAs in the TCGA dataset, and the risk model was validated in the Gene Expression Omnibus dataset. Kaplan-Meier curve analysis revealed that CRC patients in the low-risk subgroup had a better survival outcome than those in the high-risk subgroup in the training set, testing set, and overall set. Multivariate analysis demonstrated that the sialylation-related lncRNA signature was an independent prognostic factor for overall survival, progression-free survival, and disease-specific survival prediction. The sialylation lncRNA signature-based nomogram exhibited a robust prognostic performance. Furthermore, enrichment analysis showed that cancer hallmarks and oncogenic signaling were enriched in the high-risk group, while inflammatory responses and immune-related pathways were enriched in the low-risk group. The comprehensive analysis suggested that low-risk patients had higher activity of immune response pathways, greater immune cell infiltration, and higher expression of immune stimulators. In addition, we determined the sialylation level in normal colonic cells and CRC cell lines by flow cytometry combined with immunofluorescence, and verified the expression levels of seven lncRNAs using real-time quantitative polymerase chain reaction. Finally, combined drug sensitivity analysis using the Genomics of Drug Sensitivity in Cancer, Cancer Therapeutics Response Portal, and Profiling Relative Inhibition Simultaneously in Mixtures indicated that the sialylation-related lncRNA signature could serve as a potential predictor for chemosensitivity. Collectively, this is the first sialylation lncRNA-based signature for predicting the prognosis, immune landscape, and chemotherapeutic response in CRC, and may provide vital guidance to facilitate risk stratification and optimize individualized therapy for CRC patients.

Prognostic Values and Underlying Regulatory Network of Cohesin Subunits in Esophageal Carcinoma.

Background: Cohesin is a highly conserved and ubiquitously expressed protein complex. While increasing evidence suggests that cohesin dysregulation is vital in the carcinogenesis of numerous malignancies, little is known about the prognostic values and potential mechanisms of cohesin subunits and direct regulators in esophageal carcinoma (ESCA). Methods: RNA-sequencing data from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) were used. The subunits and regulators of cohesin affecting the prognosis of ESCA were screened by Kaplan-Meier survival analysis; univariate and multivariate Cox regression analyses were performed; and the receiver-operating characteristic (ROC) curve was determined. The ESCA hazard model and nomogram map were constructed by integrating the clinical data. We used functional analysis and protein-protein interaction (PPI) networks to explore underlying pathways. Finally, immunohistochemistry was performed to examine the expression levels of cohesin subunits in tissue microarray (TMA). Results: Transcriptome data from multiple ESCA patient datasets showed cohesin subunits SMC1A, SMC1B, SMC3, STAG1, STAG2, RAD21, and cohesin regulators including ESCO2, NIPBL, MAU2, WAPL, PDS5A and PDS5B were all upregulated in ESCA tissues compared to normal tissues. Survival analysis demonstrated that high STAG2 expression was significantly associated with poorer overall survival (OS) and progression-free survival (PFS) in esophageal adenocarcinoma (EAC). In contrast, high RAD21 expression was significantly correlated with better OS in esophageal squamous cell carcinoma (ESCC). Moreover, STAG2 and RAD21 were identified as independent prognostic factors and predictive biomarkers in EAC and ESCC, respectively. Functional enrichment analysis further revealed that STAG2 and RAD21 were mainly involved in the mitotic nuclear division, DNA repair, angiogenesis, epithelial-mesenchymal transition (EMT), and oncogenic signaling pathways. PPI analysis illustrated that STAG2 and RAD21 could cross-talk through cancer-associated modules and performed the core roles of the whole PPI network. Using TMA, STAG2 protein expression positively correlated with lymph node metastasis and advanced clinical stage of EAC patients, whereas there was a negative correlation between RAD21 protein expression and the malignant clinicopathological parameters in ESCC. Conclusion: These findings suggest that STAG2 and RAD21 can be used as predictive biomarkers for risk assessment and prognostic stratification in ESCA, which provide potential novel insights into molecular targets of ESCA.

Comprehensive Analysis of a Cancer-Immunity Cycle-Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer.

Immune checkpoint blockade (ICB) has been recognized as a promising immunotherapy for colorectal cancer (CRC); however, most patients have little or no clinical benefit. This study aimed to develop a novel cancer-immunity cycle-based signature to stratify prognosis of patients with CRC and predict efficacy of immunotherapy. CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the RNA data from Gene Expression Omnibus (GEO) data sets and real-time quantitative PCR (RT-qPCR) data from paired frozen tissues were used for validation. We built a least absolute shrinkage and selection operator (LASSO)-Cox regression model of the cancer-immunity cycle-related gene signature in CRC. Patients who scored low on the risk scale had a better prognosis than those who scored high. Notably, the signature was an independent prognostic factor in multivariate analyses, and to improve prognostic classification and forecast accuracy for individual patients, a scoring nomogram was created. The comprehensive results revealed that the low-risk patients exhibited a higher degree of immune infiltration, a higher immunoreactivity phenotype, stronger expression of immune checkpoint-associated genes, and a superior response to ICB therapy. Furthermore, the risk model was closely related to the response to multiple chemotherapeutic drugs. Overall, we developed a reliable cancer-immunity cycle-based risk model to predict the prognosis, the molecular and immune status, and the immune benefit from ICB therapy, which may contribute greatly to accurate stratification and precise immunotherapy for patients with CRC.

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy.

Left ventricular hypertrophy (LVH) is a pivotal manifestation of hypertensive organ damage associated with an increased cardiovascular risk. However, early diagnostic biomarkers for assessing LVH in patients with hypertension (HT) remain indefinite. Here, multiple bioinformatics tools combined with an experimental verification strategy were used to identify blood biomarkers for hypertensive LVH. GSE74144 mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database to screen candidate biomarkers, which were used to perform weighted gene co-expression network analysis (WGCNA) and establish the least absolute shrinkage and selection operator (LASSO) regression model, combined with support vector machine-recursive feature elimination (SVM-RFE) algorithms. Finally, the potential blood biomarkers were verified in an animal model. A total of 142 hub genes in peripheral blood leukocytes were identified between HT with LVH and HT without LVH, which were mainly involved in the ATP metabolic process, oxidative phosphorylation, and mitochondrial structure and function. Notably, lysosomal associated transmembrane protein 5 (LAPTM5) was identified as the potential diagnostic marker of hypertensive LVH, which showed strong correlations with diverse marker sets of reactive oxygen species (ROS) and autophagy. RT-PCR validation of blood samples and cardiac magnetic resonance imaging (CMRI) showed that the expression of LAPTM5 was significantly higher in the HT with LVH model than in normal controls, LAPTM5 demonstrated a positive association with the left ventricle wall thickness as well as electrocardiogram (ECG) parameters widths of the QRS complex and QTc interval. In conclusion, LAPTM5 may be a potential biomarker for the diagnosis of LVH in patients with HT, and it can provide new insights for future studies on the occurrence and the molecular mechanisms of hypertensive LVH.

Sample preparation method for isolation of single-cell types from mouse liver for proteomic studies.

It becomes increasingly clear that separation of pure cell populations provides a uniquely sensitive and accurate approach to protein profiling in biological systems and opens up a new area for proteomic analysis. The method we described could simultaneously isolate population of hepatocytes (HCs), hepatic stellate cells (HSCs), Kupffer cells (KCs) and liver sinusoidal endothelial cells (LSECs) by a combination of collagenase-based density gradient centrifugation and magnetic activated cell sorting with high purity and yield for the first time. More than 98% of the isolated HCs were positive for cytokeratin 18, with a viability of 91%. Approximately 97% of the isolated HSCs expressed glial fibrillary acidic protein with a viability of 95%. Nearly 98% of isolated KCs expressed F4/80 with a viability of 94%. And the purity of LSECs reached up to 91% with a viability of 94%. And yield for HCs, HSCs, LSECs and KCs were 6.3, 1.3, 2.6 and 5.0 million per mouse. This systematic isolation method enables us to study the proteome profiling of different types of liver cells with high purity and yield, which is especially useful for sample preparation of Human Liver Proteome Project.

SEMA6B Overexpression Predicts Poor Prognosis and Correlates With the Tumor Immunosuppressive Microenvironment in Colorectal Cancer.

Background: Semaphorin 6b (SEMA6B) is a member of the semaphorin axon-guidance family and has been demonstrated to both induce and inhibit tumor progression. However, the role of SEMA6B in colorectal cancer (CRC) has remained unclear. This study sought to explore the promising prognostic biomarker for CRC and to understand the expression pattern, clinical significance, immune effects, and biological functions of SEMA6B. Methods: SEMA6B expression in CRC was evaluated via multiple gene and protein expression databases and we identified its prognostic value through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Correlations between SEMA6B expression and components of the tumor immune microenvironment were analyzed by packages implemented in R, Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), and Tumor-Immune System Interactions database (TISIDB). RNA interference was performed to silence the expression of SEMA6B to explore its biological roles in the colon cancer cell lines HCT116 and LoVo. Results: The messenger RNA (mRNA) level of SEMA6B and the protein expression were higher in CRC tissues than adjacent normal tissues from multiple CRC datasets. High SEMA6B expression was significantly associated with dismal survival. Multivariate Cox regression analysis demonstrated that SEMA6B was an independent prognostic factor for progression-free survival (PFS). The nomogram showed a favorable predictive ability in PFS. Functional enrichment analysis and the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm revealed that the gene cluster associated with the high SEMA6B group were prominently involved in immune responses and inflammatory activities. Notably, SEMA6B expression was positively correlated with infiltrating levels of CD4+ T cells, macrophages, myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), neutrophils, and dendritic cells. Moreover, SEMA6B expression displayed strong correlations with diverse marker sets of immunosuppressive cells in CRC. Integrative analysis revealed that immunosuppressive molecules and immune checkpoints were markedly upregulated in CRC samples with high SEMA6B expression. Furthermore, knockdown of SMEA6B in colon cancer cells significantly inhibited cell proliferation, migration, invasion and reduced the mRNA levels of immunosuppressive molecules. Conclusion: Our findings provide evidence that high SEMA6B expression correlated with adverse prognosis and the tumor immunosuppressive microenvironment in CRC patients. Therefore, SEMA6B may serve as a novel prognostic biomarker for CRC, which offers further insights into developing CRC-targeted immunotherapies.

Outcome Prediction from Behaviour Change Intervention Evaluations using a Combination of Node and Word Embedding.

Findings from randomized controlled trials (RCTs) of behaviour change interventions encode much of our knowledge on intervention efficacy under defined conditions. Predicting outcomes of novel interventions in novel conditions can be challenging, as can predicting differences in outcomes between different interventions or different conditions. To predict outcomes from RCTs, we propose a generic framework of combining the information from two sources - i) the instances (comprised of surrounding text and their numeric values) of relevant attributes, namely the intervention, setting and population characteristics of a study, and ii) abstract representation of the categories of these attributes themselves. We demonstrate that this way of encoding both the information about an attribute and its value when used as an embedding layer within a standard deep sequence modeling setup improves the outcome prediction effectiveness.

Extraction and purification of a lectin from red kidney bean and preliminary immune function studies of the lectin and four Chinese herbal polysaccharides.

Reversed micelles were used to extract lectin from red kidney beans and factors affecting reverse micellar systems (pH value, ionic strength and extraction time) were studied. The optimal conditions were extraction at pH 4-6, back extraction at pH 9-11, ion strength at 0.15 M NaCl, extraction for 4-6 minutes and back extraction for 8 minutes. The reverse micellar system was compared with traditional extraction methods and demonstrated to be a time-saving method for the extraction of red kidney bean lectin. Mitogenic activity of the lectin was reasonably good compared with commercial phytohemagglutinin (extracted from Phaseolus vulgaris) Mitogenic properties of the lectin were enhanced when four Chinese herbal polysaccharides were applied concurrently, among which 50 µg/mL Astragalus mongholicus polysaccharides (APS) with 12.5 µg/mL red kidney bean lectin yielded the highest mitogenic activity and 100 mg/kg/bw APS with 12.5 mg/kg/bw red kidney bean lectin elevated mouse nonspecific immunity.

High SEMA4C expression promotes the epithelial-mesenchymal transition and predicts poor prognosis in colorectal carcinoma.

Semaphorin 4C (SEMA4C), is an important regulator of axonal guidance and aggravates tumor development. However, the roles and prognostic value of SEMA4C in colorectal cancer (CRC) remain unclear. Here, bioinformatics analyses of transcriptome data from multiple CRC patient datasets and immunohistochemical staining of a CRC tissue microarray (TMA) (n=83) showed that SEMA4C mRNA and protein expression were higher in CRC tissues than normal colorectal tissues. SEMA4C mRNA and protein expression correlated with pathologic stage and metastasis in CRC patients. Higher SEMA4C expression was associated with shorter overall survival, consensus molecular subtype 4 (CMS4), and DNA hypomethylation of SEMA4C in CRC patients. Multivariate Cox regression analyses revealed that SEMA4C expression was an independent prognostic predictor in CRC patients. Gene set expression analysis (GSEA) illustrated that SEMA4C expression had remarkable correlations with epithelial-mesenchymal transition (EMT) as well as hedgehog, Wnt/ß-catenin, TGF-ß, and Notch signaling pathways. Receiver operating characteristic (ROC) curve analysis demonstrated that SEMA4C expression accurately distinguished between the CMS4 and CMS1-3 subtypes of CRC patients. By inhibiting EMT, SEMA4C silencing reduced in vitro proliferation, migration, and invasion by CRC cells. These findings suggest that SEMA4C is a CMS4-associated gene that enhances CRC progression by inducing EMT.

Indoleamine 2, 3-Dioxygenase 1 and CD8 Expression Profiling Revealed an Immunological Subtype of Colon Cancer With a Poor Prognosis.

BACKGROUND: The Immunoscore method, based on the distribution of the quantification of cytotoxic and memory T cells, provides an indicator of tumor recurrence for colon cancer. However, recent evidence has suggested that immune checkpoint expression represents a surrogate measure of tumor-infiltrating T cell exhaustion, and therefore may serve as a more accurate prognostic biomarker for colon cancer. Indoleamine 2, 3-dioxygenase 1 (IDO1), a potent immunosuppressive molecule, has been strongly associated with T-cell infiltration, but it lacks universal prognostic significance among all of the cancer subtypes. Our aim was to elucidate the prognostic significance of the combination of IDO1 and CD8A expression in colon cancer. METHODS: Gene expression and clinical survival data were analyzed using The Cancer Genome Atlas (TCGA) data set and validated using NCBI Gene Expression Omnibus (NCBI-GEO) cohort. Hierarchical clustering, functional enrichment analyses, and immune infiltration analysis were applied to evaluate the distinctive immune statuses in colon cancer risk subgroups stratified by IDO1 and CD8A expression. Moreover, Multivariate Cox regression analysis and Receiver Operating Characteristic (ROC) analyses were conducted to determine the prognostic value of IDO1/CD8A stratification. The IDO1/CD8A classifier may be suitable for use in the prediction of cancer development. It was validated via an in vivo murine model. RESULTS: The stratification analysis demonstrated that the colon cancer subtype with the CD8AhighIDO1high* tumor resulted in the worst survival despite high levels of CD8 infiltrates. Its poor prognosis was associated with high levels of immune response, checkpoint genes, and Th1/IFN-γ gene signatures, regardless of CMS classification. Moreover, the IDO1/CD8A stratification was identified as an independent prognostic factor of overall survival (OS) and a useful predictive biomarker in colon cancer. In vivo data revealed the CD8AhighIDO1high group showed strong correlations with late-stage metastasis of colon carcinoma cells and upregulation of immune checkpoints. CONCLUSIONS: The findings indicate that the proposed IDO1/CD8A stratification has exact and independent prognostic implications beyond CD8 T cell alone and CMS classification. As a result, it may represent a promising tool for risk stratification in colon cancer and improve the development of immunotherapies for patients with colon cancer in the future.

Prognostic significance of mutant-allele tumor heterogeneity in uterine corpus endometrial carcinoma.

BACKGROUND: Uterine corpus endometrial carcinoma (UCEC) is a clinically heterogeneous disease, and this heterogeneity is associated with tumor development, clinical characteristics, and prognostic outcomes. Mutant-allele tumor heterogeneity (MATH) is a novel, non-biased, quantitative measure to assess intra-tumor heterogeneity based on next-generation sequencing data. We aimed to explore the use of MATH as a measure for tumor heterogeneity and its prognostic role in UCEC patients. METHODS: We calculated MATH scores from the available data of 560 UCEC patients from The Cancer Genome Atlas (TCGA) and investigated their correlations with clinical characteristics, genetic alterations, and overall survival. Predictive accuracy was quantified using the area under the receiver operating characteristic curve (AUC) and the index of concordance (C-index). RESULTS: In total, 242 MATH scores were obtained from the UCEC cohort. MATH scores were significantly related to age, race, cancer type, clinical stage, histological grade, molecular type, targeted molecular therapy, and hormonal therapy. Furthermore, the genomic pattern on the basis of MATH scores showed that mutation rates of TP53 (tumor protein p53) and ARID1A (AT-rich interaction domain 1A) were independently associated with MATH scores. Correlation analysis revealed a significantly positive association of MATH scores with the fraction of somatic copy number alteration (SCNA). Importantly, a high MATH score was significantly associated with shorter overall survival [hazard ratio (HR), 2.342; 95% confidence interval (CI), 1.110-4.942]. Multivariate Cox regression combined with stratified analysis revealed that the MATH score is an independent prognostic factor in UCEC patients under 60 years old, and predictive quantification showed the MATH score had an AUC of 0.756 and a C-index of 0.845. CONCLUSIONS: Our results suggest that MATH, a practical and useful way to measure intra-tumor heterogeneity, may serve as a significant biomarker for the prognosis of patients with UCEC, enabling more accurate prediction of clinical outcomes.

Knowledge Extraction and Prediction from Behavior Science Randomized Controlled Trials: A Case Study in Smoking Cessation.

Due to the fast pace at which randomized controlled trials are published in the health domain, researchers, consultants and policymakers would benefit from more automatic ways to process them by both extracting relevant information and automating the meta-analysis processes. In this paper, we present a novel methodology based on natural language processing and reasoning models to 1) extract relevant information from RCTs and 2) predict potential outcome values on novel scenarios, given the extracted knowledge, in the domain of behavior change for smoking cessation.

Information Extraction of Behavior Change Intervention Descriptions.

We describe an information extraction (IE) approach for knowledge base population of behavior change scientific intervention findings. In this paper, we focus on building a system able to characterize the specific intervention techniques that are undertaken within behavior change intervention studies. We have investigated three different configurations of a general information retrieval based framework for information extraction: a) an unsupervised approach that hinges on specification of a query for each attribute to be extracted and a few parameters for rule-based post-processing; b) a semi-supervised approach, which uses a part of the ground-truth annotations as a training set to automatically learn optimal representation of the queries; and c) a supervised approach that replaces the rule-based post processing by a text classifier. To train and evaluate our system, we make use of a ground-truth data set annotated by behavior science experts. This dataset consists of a total of 226 research papers on smoking cessation.