Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

<p>Abstract</p> <p>Background</p> <p>While progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps.</p> <p>Results</p> <p>We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features.</p> <p>Conclusions</p> <p>We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be possible to replace this with other texture identifiers, and we plan to explore this in future work.</p

Adjunctive rifampicin for Staphylococcus aureus bacteraemia (ARREST): a multicentre, randomised, double-blind, placebo-controlled trial.

BACKGROUND: Staphylococcus aureus bacteraemia is a common cause of severe community-acquired and hospital-acquired infection worldwide. We tested the hypothesis that adjunctive rifampicin would reduce bacteriologically confirmed treatment failure or disease recurrence, or death, by enhancing early S aureus killing, sterilising infected foci and blood faster, and reducing risks of dissemination and metastatic infection. METHODS: In this multicentre, randomised, double-blind, placebo-controlled trial, adults (≥18 years) with S aureus bacteraemia who had received ≤96 h of active antibiotic therapy were recruited from 29 UK hospitals. Patients were randomly assigned (1:1) via a computer-generated sequential randomisation list to receive 2 weeks of adjunctive rifampicin (600 mg or 900 mg per day according to weight, oral or intravenous) versus identical placebo, together with standard antibiotic therapy. Randomisation was stratified by centre. Patients, investigators, and those caring for the patients were masked to group allocation. The primary outcome was time to bacteriologically confirmed treatment failure or disease recurrence, or death (all-cause), from randomisation to 12 weeks, adjudicated by an independent review committee masked to the treatment. Analysis was intention to treat. This trial was registered, number ISRCTN37666216, and is closed to new participants. FINDINGS: Between Dec 10, 2012, and Oct 25, 2016, 758 eligible participants were randomly assigned: 370 to rifampicin and 388 to placebo. 485 (64%) participants had community-acquired S aureus infections, and 132 (17%) had nosocomial S aureus infections. 47 (6%) had meticillin-resistant infections. 301 (40%) participants had an initial deep infection focus. Standard antibiotics were given for 29 (IQR 18-45) days; 619 (82%) participants received flucloxacillin. By week 12, 62 (17%) of participants who received rifampicin versus 71 (18%) who received placebo experienced treatment failure or disease recurrence, or died (absolute risk difference -1·4%, 95% CI -7·0 to 4·3; hazard ratio 0·96, 0·68-1·35, p=0·81). From randomisation to 12 weeks, no evidence of differences in serious (p=0·17) or grade 3-4 (p=0·36) adverse events were observed; however, 63 (17%) participants in the rifampicin group versus 39 (10%) in the placebo group had antibiotic or trial drug-modifying adverse events (p=0·004), and 24 (6%) versus six (2%) had drug interactions (p=0·0005). INTERPRETATION: Adjunctive rifampicin provided no overall benefit over standard antibiotic therapy in adults with S aureus bacteraemia. FUNDING: UK National Institute for Health Research Health Technology Assessment

Recent disclosures of clinical drug candidates

On December 6, 2007, the Society for Medicines Research held a one-day meeting entitled Recent Disclosures of Clinical Drug Candidates. The meeting brought together speakers from around the world representing both the pharmaceutical industry and academia. The meeting provided an overview of some of the latest approaches being taken in a range of therapeutic areas such as oncology, immunology, central nervous system disease, gastroenterology and antiviral research. Copyright 2008 Prous Science, S.A.U. or its licensors. All rights reserved

Pharmacologic Profile of OC000459, a Potent, Selective, and Orally Active D Prostanoid Receptor 2 Antagonist That Inhibits Mast Cell-Dependent Activation of T Helper 2 Lymphocytes and Eosinophils

D prostanoid receptor 2 (DP2) [also known as chemoattractant receptor-homologous molecule expressed on T helper 2 (Th2) cells (CRTH2)] is selectively expressed by Th2 lymphocytes, eosinophils, and basophils and mediates recruitment and activation of these cell types in response to prostaglandin D-2 (PGD(2)). (5-Fluoro-2-methyl-3-quinolin-2-ylmethylindo-1-yl)-acetic acid (OC000459) is an indole-acetic acid derivative that potently displaces [H-3]PGD(2) from human recombinant DP2 (K-i = 0.013 mu M), rat recombinant DP2 (K-i = 0.003 mu M), and human native DP2 (Th2 cell membranes; K-i = 0.004 mu M) but does not interfere with the ligand binding properties or functional activities of other prostanoid receptors (prostaglandin E1-4 receptors, D prostanoid receptor 1, thromboxane receptor, prostacyclin receptor, and prostaglandin F receptor). OC000459 inhibited chemotaxis (IC50 = 0.028 mu M) of human Th2 lymphocytes and cytokine production (IC50 = 0.019 mu M) by human Th2 lymphocytes. OC000459 competitively antagonized eosinophil shape change responses induced by PGD(2) in both isolated human leukocytes (pK(B) = 7.9) and human whole blood (pK(B) = 7.5) but did not inhibit responses to eotaxin, 5-oxo-eicosatetraenoic acid, or complement component C5a. OC000459 also inhibited the activation of Th2 cells and eosinophils in response to supernatants from IgE/anti-IgE-activated human mast cells. OC000459 had no significant inhibitory activity on a battery of 69 receptors and 19 enzymes including cyclooxygenase 1 (COX1) and COX2. OC000459 was found to be orally bio-available in rats and effective in inhibiting blood eosinophilia induced by 13,14-dihydro-15-keto-PGD(2) (DK-PGD(2)) in this species (ED50 = 0.04 mg/kg p.o.) and airway eosinophilia in response to an aerosol of DK-PGD(2) in guinea pigs (ED50 = 0.01 mg/kg p.o.). These data indicate that OC000459 is a potent, selective, and orally active DP2 antagonist that retains activity in human whole blood and inhibits mast cell-dependent activation of both human Th2 lymphocytes and eosinophils.Peer reviewe

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers