Project description:The characterization of glioblastoma has provided invaluable data related to this molecularly heterogeneous disease. Recent advances in high-throughput microarrays have received extensive attention and made substantial progress in reconstructing the gene regulatory network of medical biology. Using microarray analysis, significant differences in gene expression between normal and disease tissues have been observed. However, as a result of the underlying shortcomings of microarray technology, such as small sample size, measurement error, and information insufficiency, unveiling this disease mechanism has remained a major challenge to glioblastoma research. Hence, GO, pathway information, network-based approaches and machine learning algorithms have been employed to identify the mechanisms underlying this disease. We identified the differentially expressed genes (DEGs) between 9 glioblastoma samples and 9 normal brain samples.
Project description:Samples used in the study originated from three UK sites: the Walton Centre for Neurology and Neurosurgery in Liverpool, the Salford Royal Hospital in Salford and the Southern General Hospital in Glasgow. We recruited patients with pharmacoresistant mesial temporal lobe epilepsy for whom a therapeutic temporal lobectomy was being undertaken. After surgery, the hippocampus was divided into two portions: (1) one portion was preserved for RNA isolation, and (2) the other portion underwent histological analysis by an experienced neuropathologist. Frozen post-mortem histologically-normal hippocampal samples from donors with no known brain diseases were obtained from the MRC Edinburgh Brain Bank (Edinburgh, UK) and the Queen Square Brain Bank (London, UK). Brain samples were disrupted and homogenized in an appropriate volume of QIAzol lysis reagent (Qiagen, Crawley, United Kingdom) by using a TissueRuptor handheld rotor-stator homogenizer (Qiagen, Crawley, United Kingdom). Total RNA was extracted from the homogenates using the RNeasy Lipid Tissue Mini Kit (Qiagen, Crawley, United Kingdom), according to the manufacturer’s instructions. RNA quality was examined by capillary electrophoresis on an Agilent Bioanalyzer 2100 (Agilent, Palo Alto, CA) and Agilent 2100 Expert software was used to calculate the RNA Integrity number (RIN) of each sample. Purity of the RNA sample was assessed using a NanoDrop1000 Spectrophotometer. Capillary electrophoresis traces were also examined. Samples with RNA integrity number scores (RIN) below 6, obvious RNA degradation, significant 18S or 28S ribosomal RNA degradation, ratio of absorbance at 260nm and 280nm <1.95, or with noticeable DNA or background contaminants did not pass QC, and were withheld from microarray analysis. The microarrays were processed at the Centre for Genomics Research in the University of Liverpool (http://www.liv.ac.uk/genomic-research/). 50ng of total RNA was amplified and labelled using the Agilent Low Input Quick Amp One-Colour Labeling Kit and labelled RNA was hybridized to Agilent SurePrint G3 Custom Exon 8x60K Microarrays designed to contain probes for each exon of 936 selected genes, including all known SLC genes. Standard Agilent protocols were followed. One array failed on five of the QC criteria and, hence, was excluded. Intensity data were extracted from the remaining arrays using the Feature Extraction Software, in line with the manufacturer’s recommendations. The uploaded files contain data both for a custom exon array designed to contain probes for each exon of 936 selected genes and for a custom gene expression array designed to contain gene expression probes for genes across the whole genome.
Project description:BackgroundMinimally invasive tissue sampling (MITS) is an alternative to complete autopsy for determining causes of death. Multiplex molecular testing performed on MITS specimens poses challenges of interpretation, due to high sensitivity and indiscriminate detection of pathogenic, commensal, or contaminating microorganisms.MethodsMITS was performed on 20 deceased children with respiratory illness, at 10 timepoints up to 88 hours postmortem. Samples were evaluated by multiplex molecular testing on fresh tissues by TaqMan® Array Card (TAC) and by histopathology, special stains, immunohistochemistry (IHC), and molecular testing (PCR) on formalin-fixed, paraffin-embedded (FFPE) tissues. Results were correlated to determine overall pathologic and etiologic diagnoses and to guide interpretation of TAC results.ResultsMITS specimens collected up to 3 days postmortem were adequate for histopathologic evaluation and testing. Seven different etiologic agents were detected by TAC in 10 cases. Three cases had etiologic agents detected by FFPE or other methods and not TAC; 2 were agents not present on TAC, and 2 were streptococci that may have been species other than those present on TAC. Result agreement was 43% for TAC and IHC or PCR, and 69% for IHC and PCR. Extraneous TAC results were common, especially when aspiration was present.ConclusionsTAC can be performed on MITS up to 3 days after death with refrigeration and provides a sensitive method for detection of pathogens but requires careful interpretation in the context of clinicoepidemiologic and histopathologic findings. Interpretation of all diagnostic tests in aggregate to establish overall case diagnoses maximizes the utility of TAC in MITS.
Project description:As radical gastrectomy with lymph node dissection is currently the best strategy to cure gastric cancer, the role of the surgeon remains quite important in conquering it. Dr. Sung Hoon Noh, a surgeon and surgical oncologist specializing in gastric cancer, has treated gastric cancer for 30 years and has conducted over 10000 cases of gastrectomy for gastric cancer. He first adapted an electrocautery device into gastric cancer surgery and has led standardization of surgical procedures, including spleen preserving gastrectomy. His procedures based on patient-oriented insights have become the basis of the concept of enhanced recovery after surgery. He has also contributed to improving patient's survival through adoption of a multidisciplinary approach: he proved the benefit of adjuvant chemotherapy after radical D2 gastrectomy for stage II/III gastric cancer in clinical trials, updating treatment guidelines throughout the world. Dr. Noh also opened the era of precision medicine for treating gastric cancer, as he developed and validated a mRNA expression based algorithm to predict prognosis and response to chemotherapy. This article reviews his contribution and long history of service in the field of gastric cancer. The perspectives of this master surgeon, based on his profound experience and insights, will outline directions for integrative multidisciplinary health care and how can surgeons prepare for the future.
Project description:BackgroundDNA sequencing is increasingly incorporated into the routine care of cancer patients, many of whom also carry inherited, moderate/high-penetrance variants associated with other diseases. Yet, the prevalence and consequence of such variants remain unclear.MethodsWe analyzed the germline genomes of 10,389 adult cancer cases in the TCGA cohort, identifying pathogenic/likely pathogenic variants in autosomal-dominant genes, autosomal-recessive genes, and 59 medically actionable genes curated by the American College of Molecular Genetics (i.e., the ACMG 59 genes). We also analyzed variant- and gene-level expression consequences in carriers.ResultsThe affected genes exhibited varying pan-ancestry and population-specific patterns, and overall, the European population showed the highest frequency of pathogenic/likely pathogenic variants. We further identified genes showing expression consequence supporting variant functionality, including altered gene expression, allelic specific expression, and mis-splicing determined by a massively parallel splicing assay.ConclusionsOur results demonstrate that expression-altering variants are found in a substantial fraction of cases and illustrate the yield of genomic risk assessments for a wide range of diseases across diverse populations.
Project description:ObjectiveThe accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome.MethodsWe performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-year postoperative seizure outcome on our data set. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N = 2000. Finally, we examined the impact of predictor selection on model performance.ResultsOur logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP = 67%-74%, AUCMLP = .70; 95% CIXGBoost own = 68%-75%, AUCXGBoost own = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR = .005, pMLP = .01, pXGBoost own = .01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection.SignificanceWe show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.
Project description:The distribution and appearance of nuclei are essential markers for the diagnosis and study of cancer. Despite the importance of nuclear morphology, there is a lack of large scale, accurate, publicly accessible nucleus segmentation data. To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process. We have generated nucleus segmentation results in 5,060 Whole Slide Tissue images from 10 cancer types in The Cancer Genome Atlas. One key component of our work is that we carried out a multi-level quality control process (WSI-level and image patch-level), to evaluate the quality of our segmentation results. The image patch-level quality control used manual segmentation ground truth data from 1,356 sampled image patches. The datasets we publish in this work consist of roughly 5 billion quality controlled nuclei from more than 5,060 TCGA WSIs from 10 different TCGA cancer types and 1,356 manually segmented TCGA image patches from the same 10 cancer types plus additional 4 cancer types.
Project description:We identified diffuse lesions made of BRAF V600E-mutant CD34-immunopositive stellar cells in human samples resected to cure drug-resistant focal epilepsy. We performed single-nuclei RNAseq 5' 10X on three human brain samples (two BRAF mutant samples and one BRAF wildtype sample as control) in order to identify the molecular phenotype of CD34+ cells.
Project description:Stenosis from venous neointimal hyperplasia is common in native arteriovenous fistulas (AVFs). However, the preexisting histologic characteristics of veins at fistula creation, and associations thereof with baseline patient factors, have not been well characterized. In this study, we conducted histologic analysis of a segment of the vein used for anastomosis creation, obtained during AVF creation from 554 of the 602 participants in the multicenter Hemodialysis Fistula Maturation Cohort Study. We quantified intimal and medial areas and lengths of the internal and external elastic lamina by morphometry and assessed venous wall cells by immunohistochemistry, extracellular matrix with Movat stain, and calcium deposition by alizarin red stain. We also studied a representative subset of veins for markers of monocyte/macrophage content, cell proliferation, apoptosis, and neoangiogenesis. Neointima occupied >20% of the lumen in 57% of fully circumferential vein samples, and neointimal hyperplasia associated positively with age and inversely with black race. The neointima was usually irregularly thickened, sometimes concentric, and contained α-smooth muscle actin-expressing cells of smooth muscle or myofibroblast origin. Proteoglycans admixed with lesser amounts of collagen constituted the predominant matrix in the neointima. In 82% of vein samples, the media of vessel walls contained large aggregates of collagen. A minority of veins expressed markers of inflammation, cell proliferation, cell death, calcification, or neoangiogenesis. In conclusion, we observed preexisting abnormalities, including neointimal hyperplasia and prominent accumulation of extracellular matrix, in veins used for AVF creation from a substantial proportion of this cohort.
Project description:Microbial monitoring of hospital surfaces can help identify target areas for improved infection prevention and control (IPCs). This study aimed to determine the levels and variations in the bacterial contamination of high-touch surfaces in five Kenyan hospitals and identify the contributing modifiable risk factors. A total of 559 high-touch surfaces in four departments identified as high risk of hospital-acquired infections were sampled and examined for bacterial levels of contamination using standard bacteriological culture methods. Bacteria were detected in 536/559 (95.9%) surfaces. The median bacterial load on all sampled surfaces was 6.0 × 104 CFU/cm2 (interquartile range (IQR); 8.0 × 103-1.0 × 106). Only 55/559 (9.8%) of the sampled surfaces had acceptable bacterial loads, <5 CFU/cm². Cleaning practices, such as daily washing of patient sheets, incident rate ratio (IRR) = 0.10 [95% CI: 0.04-0.24], providing hand wash stations, IRR = 0.25 [95% CI: 0.02-0.30], having running water, IRR = 0.19 [95% CI: 0.08-0.47] and soap for handwashing IRR = 0.21 [95% CI: 0.12-0.39] each significantly lowered bacterial loads. Transporting dirty linen in a designated container, IRR = 72.11 [95% CI: 20.22-257.14], increased bacterial loads. The study hospitals can best reduce the bacterial loads by improving waste-handling protocols, cleaning high-touch surfaces five times a day and providing soap at the handwash stations.