Gr1+IL-4-producing innate cells are induced in response to Th2 stimuli and suppress Th1-dependent antibody responses

Alum is used as a vaccine adjuvant and induces T<sub>h</sub>2 responses and T<sub>h</sub>2-driven antibody isotype production against co-injected antigens. Alum also promotes the appearance in the spleen of Gr1+IL-4+ innate cells that, via IL-4 production, induce MHC II-mediated signaling in B cells. To investigate whether these Gr1+ cells accumulate in the spleen in response to other T<sub>h</sub>2-inducing stimuli and to understand some of their functions, the effects of injection of alum and eggs from the helminth, Schistosoma mansoni, were compared. Like alum, schistosome eggs induced the appearance of Gr1+IL-4+ cells in spleen and promoted MHC II-mediated signaling in B cells. Unlike alum, however, schistosome eggs did not promote CD4 T cell responses against co-injected antigens, suggesting that the effects of alum or schistosome eggs on splenic B cells cannot by themselves explain the T cell adjuvant properties of alum. Accordingly, depletion of IL-4 or Gr1+ cells in alum-injected mice had no effect on the ability of alum to improve expansion of primary CD4 T cells. However, Gr1+ cells and IL-4 played some role in the effects of alum, since depletion of either resulted in antibody responses to antigen that included not only the normal T<sub>h</sub>2-driven isotypes, like IgG1, but also a T<sub>h</sub>1-driven isotype, IgG2c. These data suggest that alum affects the immune response in at least two ways: one, independent of Gr1+ cells and IL-4, that promotes CD4 T cell proliferation and another, via Gr1+IL-4+ cells, that participates in the polarization of the response

High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID)

BACKGROUND: We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. RESULTS: We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of 'false discovery rate' multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories. CONCLUSION: High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. For illustration, we show an application of this new tool to the interpretation of altered gene expression patterns in Common Variable Immune Deficiency (CVID). High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound

Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks

Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis

Methylation of hMLH1 promoter correlates with the gene silencing with a region-specific manner in colorectal cancer

Microsatellite instability is present in over 80% of the hereditary non-polyposis colorectal carcinoma and about 15–20% of the sporadic cancer. Microsatellite instability is caused by the inactivation of the mismatch repair genes, such as primarily hMLH1, hMSH2. To study the mechanisms of the inactivation of mismatch repair genes in colorectal cancers, especially the region-specific methylation of hMLH1 promoter and its correlation with gene expression, we analysed microsatellite instability, expression and methylation of hMLH1 and loss of heterozygosity at hMLH1 locus in these samples. Microsatellite instability was present in 17 of 71 primary tumours of colorectal cancer, including 14 of 39 (36%) mucinous cancer and three of 32 (9%) non-mucinous cancer. Loss of hMLH1 and hMSH2 expression was detected in nine and three of 16 microsatellite instability tumours respectively. Methylation at CpG sites in a proximal region of hMLH1 promoter was detected in seven of nine tumours that showed no hMLH1 expression, while no methylation was present in normal mucosa and tumours which express hMLH1. However, methylation in the distal region was observed in all tissues including normal mucosa and hMLH1 expressing tumours. This observation indicates that methylation of hMLH1 promoter plays an important role in microsatellite instability with a region-specific manner in colorectal cancer. Loss of heterozygosity at hMLH1 locus was present in four of 17 cell lines and 16 of 54 tumours with normal hMLH1 status, while loss of heterozygosity was absent in all nine cell lines and nine tumours with abnormal hMLH1 status (mutation or loss of expression), showing loss of heterozygosity is not frequently involved in the inactivation of hMLH1 gene in sporadic colorectal cancer

Deficient mismatch repair system in patients with sporadic advanced colorectal cancer

A deficient mismatch repair system (dMMR) is present in 10–20% of patients with sporadic colorectal cancer (CRC) and is associated with a favourable prognosis in early stage disease. Data on patients with advanced disease are scarce. Our aim was to investigate the incidence and outcome of sporadic dMMR in advanced CRC. Data were collected from a phase III study in 820 advanced CRC patients. Expression of mismatch repair proteins was examined by immunohistochemistry. In addition microsatellite instability analysis was performed and the methylation status of the MLH1 promoter was assessed. We then correlated MMR status to clinical outcome. Deficient mismatch repair was found in only 18 (3.5%) out of 515 evaluable patients, of which 13 were caused by hypermethylation of the MLH1 promoter. The median overall survival in proficient MMR (pMMR), dMMR caused by hypermethylation of the MLH1 promoter and total dMMR was 17.9 months (95% confidence interval 16.2–18.8), 7.4 months (95% CI 3.7–16.9) and 10.2 months (95% CI 5.9–19.8), respectively. The disease control rate in pMMR and dMMR patients was 83% (95% CI 79–86%) and 56% (30–80%), respectively. We conclude that dMMR is rare in patients with sporadic advanced CRC. This supports the hypothesis that dMMR tumours have a reduced metastatic potential, as is observed in dMMR patients with early stage disease. The low incidence of dMMR does not allow drawing meaningful conclusions about the outcome of treatment in these patients

University–industry collaboration: using meta-rules to overcome barriers to knowledge transfer

This is the final version of the article. Available from Springer Verlag via the DOI in this record.University–industry knowledge transfer is an important source wealth of creation for all partners; however, the practical management of this activity within universities is often hampered by procedural rigidity either through the absence of decision-making protocols to reconcile conflicting priorities or through the inconsistent implementation of existing policies. This is problematic, since it can impede operational effectiveness, prevent inter-organisational knowledge-creation and hamper organisational learning. This paper addresses this issue by adopting a cross-discipline approach and presenting meta-rules as a solution to aid organisational decision making. It is proposed that meta-rules can help resolve tensions arising from conflicting priorities between academics, knowledge transfer offices and industry and help facilitate strategic alignment of processes and policies within and between organisations. This research contributes to the growing debate on the strategic challenges of managing knowledge transfer and presents meta-rules as a practical solution to facilitate strategic alignment of internal and external stakeholder tensions. Meta-rules has previously only been applied in a computer intelligence context however, this research proves the efficacy of meta rules in a university–industry knowledge transfer context. This research also has practical implications for knowledge transfer office managers who can use meta-rules to help overcome resource limitations, conflicting priorities and goals of diverse internal and external stakeholders

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy