Double-averaged velocity profiles over fixed dune shapes

Peer reviewedPublisher PD

Essential role for proteinase-activated receptor-2 in arthritis

Using physiological, pharmacological, and gene disruption approaches, we demonstrate that proteinase-activated receptor-2 (PAR-2) plays a pivotal role in mediating chronic inflammation. Using an adjuvant monoarthritis model of chronic inflammation, joint swelling was substantially inhibited in PAR-2-deficient mice, being reduced by more than fourfold compared with wild-type mice, with virtually no histological evidence of joint damage. Mice heterozygous for PAR-2 gene disruption showed an intermediate phenotype. PAR-2 expression, normally limited to endothelial cells in small arterioles, was substantially upregulated 2 weeks after induction of inflammation, both in synovium and in other periarticular tissues. PAR-2 agonists showed potent proinflammatory effects as intra-articular injection of ASKH95, a novel synthetic PAR-2 agonist, induced prolonged joint swelling and synovial hyperemia. Given the absence of the chronic inflammatory response in the PAR-2-deficient mice, our findings demonstrate a key role for PAR-2 in mediating chronic inflammation, thereby identifying a novel and important therapeutic target for the management of chronic inflammatory diseases such as rheumatoid arthritis

Isotopic composition ( 238 U/ 235 U) of some commonly used uranium reference materials

Abstract We have determined 238 U/ 235 U ratios for a suite of commonly used natural (CRM 112a, SRM 950a, and HU-1) and synthetic (IRMM 184 and CRM U500) uranium reference materials by thermal ionisation mass-spectrometry (TIMS) using the IRMM 3636 233 U-236 U double spike to accurately correct for mass fractionation. Total uncertainty on the 238 U/ 235 U determinations is estimated to be <0.02% (2r). These natural 238 U/ 235 U values are different from the widely used 'consensus' value (137.88), with each standard having lower 238 U/ 235 U values by up to 0.08%. The 238 U/ 235 U ratio determined for CRM U500 and IRMM 184 are within error of their certified values; however, the total uncertainty for CRM U500 is substantially reduced (from 0.1% to 0.02%). These reference materials are commonly used to assess mass-spectrometer performance and accuracy, calibrate isotope tracers employed in U, U-Th and U-Pb isotopic studies, and as a reference for terrestrial and meteoritic 238 U/ 235 U variations. These new 238 U/ 235 U values will thus provide greater accuracy and reduced uncertainty for a wide variety of isotopic determinations

Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas.

PURPOSE: To use perfusion and magnetic resonance (MR) spectroscopy to compare the diffusion tensor imaging (DTI)-defined invasive and noninvasive regions. Invasion of normal brain is a cardinal feature of glioblastomas (GBM) and a major cause of treatment failure. DTI can identify invasive regions. MATERIALS AND METHODS: In all, 50 GBM patients were imaged preoperatively at 3T with anatomic sequences, DTI, dynamic susceptibility perfusion MR (DSCI), and multivoxel spectroscopy. The DTI and DSCI data were coregistered to the spectroscopy data and regions of interest (ROIs) were made in the invasive (determined by DTI), noninvasive regions, and normal brain. Values of relative cerebral blood volume (rCBV), N-acetyl aspartate (NAA), myoinositol (mI), total choline (Cho), and glutamate + glutamine (Glx) normalized to creatine (Cr) and Cho/NAA were measured at each ROI. RESULTS: Invasive regions showed significant increases in rCBV, suggesting angiogenesis (invasive rCBV 1.64 [95% confidence interval, CI: 1.5-1.76] vs. noninvasive 1.14 [1.09-1.18]; P < 0.001), Cho/Cr (invasive 0.42 [0.38-0.46] vs. noninvasive 0.35 [0.31-0.38]; P = 0.02) and Cho/NAA (invasive 0.54 [0.41-0.68] vs. noninvasive 0.37 [0.29-0.45]; P = < 0.03), suggesting proliferation, and Glx/Cr (invasive 1.54 [1.27-1.82] vs. noninvasive 1.3 [1.13-1.47]; P = 0.028), suggesting glutamate release; and a significantly reduced NAA/Cr (invasive 0.95 [0.85-1.05] vs. noninvasive 1.19 [1.06-1.31]; P = 0.008). The mI/Cr was not different between the three ROIs (invasive 1.2 [0.99-1.41] vs. noninvasive 1.3 [1.14-1.46]; P = 0.68). In the noninvasive regions, the values were not different from normal brain. CONCLUSION: Combining DTI to identify the invasive region with perfusion and spectroscopy, we can identify changes in invasive regions not seen in noninvasive regions.This study was funded from a National Institutes of Health Research Clinician Scientist FellowshipThis is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/jmri.2499

Prognostic models for surgical-site infection in gastrointestinal surgery: systematic review

BACKGROUND: Identification of patients at high risk of surgical-site infection may allow clinicians to target interventions and monitoring to minimize associated morbidity. The aim of this systematic review was to identify and evaluate prognostic tools for the prediction of surgical-site infection in gastrointestinal surgery.METHODS: This systematic review sought to identify original studies describing the development and validation of prognostic models for 30-day SSI after gastrointestinal surgery (PROSPERO: CRD42022311019). MEDLINE, Embase, Global Health, and IEEE Xplore were searched from 1 January 2000 to 24 February 2022. Studies were excluded if prognostic models included postoperative parameters or were procedure specific. A narrative synthesis was performed, with sample-size sufficiency, discriminative ability (area under the receiver operating characteristic curve), and prognostic accuracy compared.RESULTS: Of 2249 records reviewed, 23 eligible prognostic models were identified. A total of 13 (57 per cent) reported no internal validation and only 4 (17 per cent) had undergone external validation. Most identified operative contamination (57 per cent, 13 of 23) and duration (52 per cent, 12 of 23) as important predictors; however, there remained substantial heterogeneity in other predictors identified (range 2-28). All models demonstrated a high risk of bias due to the analytic approach, with overall low applicability to an undifferentiated gastrointestinal surgical population. Model discrimination was reported in most studies (83 per cent, 19 of 23); however, calibration (22 per cent, 5 of 23) and prognostic accuracy (17 per cent, 4 of 23) were infrequently assessed. Of externally validated models (of which there were four), none displayed 'good' discrimination (area under the receiver operating characteristic curve greater than or equal to 0.7).CONCLUSION: The risk of surgical-site infection after gastrointestinal surgery is insufficiently described by existing risk-prediction tools, which are not suitable for routine use. Novel risk-stratification tools are required to target perioperative interventions and mitigate modifiable risk factors.</p

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

Introduction Glioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivo method to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge. Methods A total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS). Results The MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p Discussion This study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.The research was supported by the National Institute for Health Research (NIHR) Brain Injury MedTech Cooperative based at Cambridge University Hospitals NHS Foundation Trust and University of Cambridge. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care (SJP, project reference NIHR/CS/009/011); CRUK core grant C14303/A17197 and A19274 (FM lab); Cambridge Trust and China Scholarship Council (CL & SW); the Chang Gung Medical Foundation and Chang Gung Memorial Hospital, Keelung, Taiwan (JLY); the Commonwealth Scholarship Commission and Cambridge Commonwealth Trust (NRB); CRUK & EPSRC Cancer Imaging Centre in Cambridge & Manchester (FM & TT, grant C197/A16465); and NIHR Cambridge Biomedical Research Centre (TM & SJP)

A molecular map of mesenchymal tumors

BACKGROUND: Bone and soft tissue tumors represent a diverse group of neoplasms thought to derive from cells of the mesenchyme or neural crest. Histological diagnosis is challenging due to the poor or heterogenous differentiation of many tumors, resulting in uncertainty over prognosis and appropriate therapy. RESULTS: We have undertaken a broad and comprehensive study of the gene expression profile of 96 tumors with representatives of all mesenchymal tissues, including several problem diagnostic groups. Using machine learning methods adapted to this problem we identify molecular fingerprints for most tumors, which are pathognomonic (decisive) and biologically revealing. CONCLUSION: We demonstrate the utility of gene expression profiles and machine learning for a complex clinical problem, and identify putative origins for certain mesenchymal tumors