Cortistain is expressed in a distinct subset of cortical interneurons

Cortistatin is a presumptive neuropeptide that shares 11 of its 14 amino acids with somatostatin. In contrast to somatostatin, administration of cortistatin into the rat brain ventricles specifically enhances slow wave sleep, apparently by antagonizing the effects of acetylcholine on cortical excitability. Here we show that preprocortistatin mRNA is expressed in a subset of GABAergic cells in the cortex and hippocampus that partially overlap with those containing somatostatin. A significant percentage of cortistatin-positive neurons is also positive for parvalbumin. In contrast, no colocalization was found between cortistatin and calretinin, cholecystokinin, or vasoactive intestinal peptide. During development there is a transient increase in cortistatin-expressing cells in the second postnatal week in all cortical areas and in the dentate gyrus. A transient expression of preprocortistatin mRNA in the hilar region at P16 is paralleled by electrophysiological changes in dentate granule cells. Together, these observations suggest mechanisms by which cortistatin may regulate cortical activity

Assessment of FIV-C infection of cats as a function of treatment with the protease inhibitor, TL-3

BACKGROUND: The protease inhibitor, TL-3, demonstrated broad efficacy in vitro against FIV, HIV and SIV (simian immunodeficiency virus), and exhibited very strong protective effects on early neurologic alterations in the CNS of FIV-PPR infected cats. In this study, we analyzed TL-3 efficacy using a highly pathogenic FIV-C isolate, which causes a severe acute phase immunodeficiency syndrome, with high early mortality rates. RESULTS: Twenty cats were infected with uncloned FIV-C and half were treated with TL-3 while the other half were left untreated. Two uninfected cats were used as controls. The general health and the immunological and virological status of the animals was monitored for eight weeks following infection. All infected animals became viremic independent of TL-3 treatment and seven of 20 FIV-C infected animals developed severe immunodepletive disease in conjunction with significantly (p ≤ 0.05) higher viral RNA loads as compared to asymptomatic animals. A marked and progressive increase in CD8(+ )T lymphocytes in animals surviving acute phase infection was noted, which was not evident in symptomatic animals (p ≤ 0.05). Average viral loads were lower in TL-3 treated animals and of the 6 animals requiring euthanasia, four were from the untreated cohort. At eight weeks post infection, half of the TL-3 treated animals and only one of six untreated animals had viral loads below detection limits. Analysis of protease genes in TL-3 treated animals with higher than average viral loads revealed sequence variations relative to wild type protease. In particular, one mutant, D105G, imparted 5-fold resistance against TL-3 relative to wild type protease. CONCLUSIONS: The findings indicate that the protease inhibitor, TL-3, when administered orally as a monotherapy, did not prevent viremia in cats infected with high dose FIV-C. However, the modest lowering of viral loads with TL-3 treatment, the greater survival rate in symptomatic animals of the treated cohort, and the lower average viral load in TL-3 treated animals at eight weeks post infection is indicative of a therapeutic effect of the compound on virus infection

Lifetime and 10-year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE-T1D model

AIMS: To develop and externally validate the LIFE-T1D model for the estimation of lifetime and 10-year risk of cardiovascular disease (CVD) in individuals with type 1 diabetes. MATERIALS AND METHODS: A sex-specific competing risk-adjusted Cox proportional hazards model was derived in individuals with type 1 diabetes without prior CVD from the Swedish National Diabetes Register (NDR), using age as the time axis. Predictors included age at diabetes onset, smoking status, body mass index, systolic blood pressure, glycated haemoglobin level, estimated glomerular filtration rate, non-high-density lipoprotein cholesterol, albuminuria and retinopathy. The model was externally validated in the Danish Funen Diabetes Database (FDDB) and the UK Biobank. RESULTS: During a median follow-up of 11.8 years (interquartile interval 6.1-17.1 years), 4608 CVD events and 1316 non-CVD deaths were observed in the NDR (n = 39 756). The internal validation c-statistic was 0.85 (95% confidence interval [CI] 0.84-0.85) and the external validation c-statistics were 0.77 (95% CI 0.74-0.81) for the FDDB (n = 2709) and 0.73 (95% CI 0.70-0.77) for the UK Biobank (n = 1022). Predicted risks were consistent with the observed incidence in the derivation and both validation cohorts. CONCLUSIONS: The LIFE-T1D model can estimate lifetime risk of CVD and CVD-free life expectancy in individuals with type 1 diabetes without previous CVD. This model can facilitate individualized CVD prevention among individuals with type 1 diabetes. Validation in additional cohorts will improve future clinical implementation

Selective Serotonin Reuptake Inhibitor Use Is Associated with Right Ventricular Structure and Function: The MESA-Right Ventricle Study

PURPOSE:Serotonin and the serotonin transporter have been implicated in the development of pulmonary hypertension (PH). Selective serotonin reuptake inhibitors (SSRIs) may have a role in PH treatment, but the effects of SSRI use on right ventricular (RV) structure and function are unknown. We hypothesized that SSRI use would be associated with RV morphology in a large cohort without cardiovascular disease (N = 4114). METHODS:SSRI use was determined by medication inventory during the Multi-Ethnic Study of Atherosclerosis baseline examination. RV measures were assessed via cardiac magnetic resonance imaging. The cross-sectional relationship between SSRI use and each RV measure was assessed using multivariable linear regression; analyses for RV mass and end-diastolic volume (RVEDV) were stratified by sex. RESULTS:After adjustment for multiple covariates including depression and left ventricular measures, SSRI use was associated with larger RV stroke volume (RVSV) (2.75 mL, 95% confidence interval [CI] 0.48-5.02 mL, p = 0.02). Among men only, SSRI use was associated with greater RV mass (1.08 g, 95% CI 0.19-1.97 g, p = 0.02) and larger RVEDV (7.71 mL, 95% 3.02-12.40 mL, p = 0.001). SSRI use may have been associated with larger RVEDV among women and larger RV end-systolic volume in both sexes. CONCLUSIONS:SSRI use was associated with higher RVSV in cardiovascular disease-free individuals and, among men, greater RV mass and larger RVEDV. The effects of SSRI use in patients with (or at risk for) RV dysfunction and the role of sex in modifying this relationship warrant further study

Enhancing discrete-event simulation with big data analytics: a review

This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic.

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.I recieved an honorarium from Roche Diagnostics for my participation in the advisory panel meeting leading to this pape

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery