Upregulation of ICAM-1 expression on J774.2 macrophages by endotoxin involves activation of NF-kappaB but not protein tyrosine kinase: comparison to induction of iNOS.

This study compares the signal transduction pathway which leads to the upregulation of intercellular adhesion molecule-1 (ICAM-1) expression with that of the increase in the expression of inducible nitric oxide synthase (iNOS) protein and activity caused by endotoxin in cultured J774.2 macrophages. Treatment of J774.2 cells with lipopolysaccharide E. coli (LPS) induced a concentration-dependent increase in the expression of ICAM-1 on the cell surface within 4 h and an increase in iNOS protein and activity at 24 h. The upregulation of ICAM-1 expression on J774.2 macrophages caused by LPS was significantly inhibited by pretreatment of the cells with inhibitors of the activation of the nuclear transcription factor NF-kappaB, such as L-1-tosylamido-2-phenylethylchloromethyl ketone (TPCK), pyrrolidine dithiocarbamate (PDTC), rotenone or calpain inhibitor I, but not by the tyrosine kinase inhibitors, tyrphostin AG126 or genistein. In contrast, genistein or tyrphostin AG126 also prevented the induction of iNOS protein and activity in J774.2 macrophages elicited by LPS. Thus, the increase in the expression of ICAM-1 on J774.2 macrophages by endotoxin involves the activation of NFkappaB, but not of protein tyrosine kinase

Prevalence of inflammatory versus neoplastic lesions in dogs with chronic gastrointestinal signs undergoing gastroduodenoscopy: 195 cases (2007–2015)

Objectives: Prevalence of inflammatory enteropathy versus lymphoma in dogs undergoing gastroduodenoscopy has not been evaluated. This retrospective study assessed outcome from 195 client-owned dogs scheduled to undergo upper gastrointestinal endoscopy as the next diagnostic step. Material and methods: Cases were grouped into the following diagnoses according to WSAVA guidelines: lymphoplasmacytic enteritis (LPE), eosinophilic enteritis (EE), mixed-cell enteritis (ME), histologically normal biopsies (N), and lymphoma (L). Clinical signs, and preendoscopic results from laboratory and ultrasonography examinations, were compared among groups. Results: LPE was diagnosed in 133 (68%), EE in 17 (9%), ME in 9 (5%), 32 (16%) dogs had histologically normal biopsies. Four (2%) dogs were diagnosed with lymphoma. Vomiting was the most frequent clinical sign (61%), followed by weight loss (43%), and diarrhea (39%). Vomiting also predominated when looking at individual histological disease categories, however clinical signs did not differ significantly between groups. Dogs with lymphoma were more likely to have ultrasonographic abnormalities, had significantly lower haematocrit, albumin and total protein concentrations compared to dogs with LPE and histologically normal biopsies. Clinical significance: Lymphoma was rarely found in this group of dogs with nonspecific results of pre-endoscopic work-up. Our results provide first reference for clinicians when discussing the possibility of a step-up therapeutic approach (such as multiple dietary trials) with owners before pursuing endoscopy. Understanding the likelihood of finding lymphoma is important in that histologic documentation of inflammatory enteropathy alone has limited therapeutic consequences. Future studies are needed to validate these findings in dogs undergoing combined upper and lower gastrointestinal endoscopy and biopsies

Introduction to quantitative susceptibility mapping and susceptibility weighted imaging.

Quantitative Susceptibility Mapping (QSM) and Susceptibility Weighted Imaging (SWI) are magnetic resonance imaging techniques that measure and display differences in the magnetisation that is induced in tissues, i.e. their magnetic susceptibility, when placed in the strong external magnetic field of an MRI system. SWI produces images in which the contrast is heavily weighted by the intrinsic tissue magnetic susceptibility. It has been applied in a wide range of clinical applications. QSM is a further advancement of this technique that requires sophisticated post-processing in order to provide quantitative maps of tissue susceptibility. This review explains the steps involved in both SWI and QSM as well as describing some of their uses in both clinical and research applications.Mr Ruetten is funded by a Medical Research Council/Sackler Stipend. The project was supported by the Addenbrooke’s Charitable Trust and the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust]

Counting and integrating readout for direct conversion X-ray imaging: Concept, realization and first prototype measurements

A novel signal processing concept for X-ray imaging with directly converting pixelated semiconductor sensors is presented. The novelty of this approach compared to existing concepts is the combination of charge integration and photon counting in every single pixel. Simultaneous operation of both signal processing chains extends the dynamic range beyond the limits of the individual schemes and allows determination of the mean photon energy. Medical applications such as X-ray computed tomography can benefit from this additional spectral information through improved contrast and the ability to determine the hardening of the tube spectrum due to attenuation by the scanned object. A prototype chip in 0.35-micrometer technology has been successfully tested. The pixel electronics are designed using a low-swing differential current mode logic. Key element is a configurable feedback circuit for the charge sensitive amplifier which provides continuous reset, leakage current compensation and replicates the input signal for the integrator. This paper will discuss measurement results of the prototype structures and give details on the circuit design

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: rationale and design of the epidemiological studies within the IMI DIRECT Consortium

Aims/hypothesis The DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT. Methods Prediabetic participants (target sample size 2,200-2,700) and patients with newly diagnosed type 2 diabetes (target sample size similar to 1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires. Conclusinos/interpretation DIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes

Predicting and elucidating the etiology of fatty liver disease : A machine learning modeling and validation study in the IMI DIRECT cohorts

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n= 795) or at high risk of developing the disease (n= 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (= 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86;p = 5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see:) and made it available to the community.Peer reviewe

Profiles of glucose metabolism in different prediabetes phenotypes, classified by fasting glycemia, 2-hour OGTT, glycated hemoglobin, and 1-hour OGTT:An IMI DIRECT study

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P &lt; 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P &lt; 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio &gt;2, P &lt; 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.</p

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes:An IMI-DIRECT study

AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments