Parkinson's disease biomarkers: perspective from the NINDS Parkinson's Disease Biomarkers Program

Biomarkers for Parkinson's disease (PD) diagnosis, prognostication and clinical trial cohort selection are an urgent need. While many promising markers have been discovered through the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP) and other mechanisms, no single PD marker or set of markers are ready for clinical use. Here we discuss the current state of biomarker discovery for platforms relevant to PDBP. We discuss the role of the PDBP in PD biomarker identification and present guidelines to facilitate their development. These guidelines include: harmonizing procedures for biofluid acquisition and clinical assessments, replication of the most promising biomarkers, support and encouragement of publications that report negative findings, longitudinal follow-up of current cohorts including the PDBP, testing of wearable technologies to capture readouts between study visits and development of recently diagnosed (de novo) cohorts to foster identification of the earliest markers of disease onset

Influence of Site on Course of Intracerebral Hemorrhage

The Influence of Site of Hemorrhage on Presentation, Clinical Profile, Hospital Course, and Outcome Was Examined in 225 Patients with Intracerebral Hemorrhage in the NINDS Stroke Data Bank. Mode of Presentation Differed by Hemorrhage Site (Coma at Onset Was Most Typical of Pontine Hemorrhage and Headache with Vomiting Was Most Typical of Cerebellar Hemorrhage, Whereas Onset of Focal Deficit Sometimes with Headache Was Typical of Lobar and And Basal Ganglionic Hemorrhages). Distinct Clinical Profiles Were Found for Cerebellar (Ataxia, Drowsiness, and Horizontal Gaze Paresis), Pontine (Quadriparesis, Coma, Vertical and Horizontal Gaze Paresis), and Caudate Hemorrhages (Drowsiness and Hemiparesis). Putaminal, Thalamic, and Lobar Hemorrhages Presented Similarly with Hemiparesis, Sensory Loss, and Higher Cortical Function Deficits. However, Thalamic Hemorrhages Had More Sensory Loss, Putaminal Hemorrhages Had More Weakness, and Lobar Hemorrhages Had More Higher Cortical Function Deficits. Hemorrhage Volume Was Greatest for the Lobar and Putaminal Hemorrhages and Smallest for the Pontine and Cerebellar Hemorrhages. Clot Evacuations Were Performed for 28.9% of the Lobar Hemorrhages and 48.2% of the Cerebellar Hemorrhages. Few Basal Ganglionic Hemorrhages or Pontine Hemorrhages Had Clot Evacuations. Thirty-Day Survival Was Lowest for Caudate Hemorrhage (46.2%) and Highest for Cerebellar Hemorrhage (81.5%). Hydrocephalus, Intraventricular Blood, Larger Size, and Mass Effect Were Adverse Predictors of Survival at Most But Not All Hemorrhage Sites. History of Hypertension Was the Most Prevalent Risk Factor for Hemorrhage (64.0% of the Patients). Other Risk Factors for Hemorrhage Included Anticoagulants, Platelet Antiaggregating Drugs, Aneurysms, Arteriovenous Malformations, Pregnancy, Alcohol Use, Amyloid Angiopathy, Thrombocytopenia, Renal and Liver Failure, and Cocaine Use. the Most Common Medical Complications Were Pneumonia (15.5%), Urinary Tract Infection (15.0%), Arrhythmias (8.4%), and Seizures (8.0%). © 1993, National Stroke Association. All Rights Reserved

Reef sharks exhibit site-fidelity and higher relative abundance in marine reserves on the Mesoamerican Barrier Reef.

Carcharhinid sharks can make up a large fraction of the top predators inhabiting tropical marine ecosystems and have declined in many regions due to intense fishing pressure. There is some support for the hypothesis that carcharhinid species that complete their life-cycle within coral reef ecosystems, hereafter referred to as "reef sharks", are more abundant inside no-take marine reserves due to a reduction in fishing pressure (i.e., they benefit from marine reserves). Key predictions of this hypothesis are that (a) individual reef sharks exhibit high site-fidelity to these protected areas and (b) their relative abundance will generally be higher in these areas compared to fished reefs. To test this hypothesis for the first time in Caribbean coral reef ecosystems we combined acoustic monitoring and baited remote underwater video (BRUV) surveys to measure reef shark site-fidelity and relative abundance, respectively. We focused on the Caribbean reef shark (Carcharhinus perezi), the most common reef shark in the Western Atlantic, at Glover's Reef Marine Reserve (GRMR), Belize. Acoustically tagged sharks (N = 34) were detected throughout the year at this location and exhibited strong site-fidelity. Shark presence or absence on 200 BRUVs deployed at GRMR and three other sites (another reserve site and two fished reefs) showed that the factor "marine reserve" had a significant positive effect on reef shark presence. We rejected environmental factors or site-environment interactions as predominant drivers of this pattern. These results are consistent with the hypothesis that marine reserves can benefit reef shark populations and we suggest new hypotheses to determine the underlying mechanism(s) involved: reduced fishing mortality or enhanced prey availability

Observed and predicted presence or absence of Caribbean reef sharks based on a model including only log-distance and days sampled.

<p>Observed and predicted presence or absence of Caribbean reef sharks based on a model including only log-distance and days sampled.</p

Number of BRUV deployments out of 50 per site in which one (solid portion of bars) or more (open portion of bars) Caribbean reef sharks were recorded at GRMR (reserve), CCAR (reserve), TU (fished) and SWC (fished).

<p>Number of BRUV deployments out of 50 per site in which one (solid portion of bars) or more (open portion of bars) Caribbean reef sharks were recorded at GRMR (reserve), CCAR (reserve), TU (fished) and SWC (fished).</p

Observed and predicted presence or absence of Caribbean reef sharks based on the AIC best model (days+ldist+habitat+ldist×shark).

<p>Observed and predicted presence or absence of Caribbean reef sharks based on the AIC best model (days+ldist+habitat+ldist×shark).</p

The AIC and BIC values for models with random effects (in bold).

<p>The AIC and BIC values for models with random effects (in bold).</p

Analysis of deviance for the AIC best model of presence or absence of Caribbean reef sharks by receiver with fixed effects only.

<p>“Days” refers to days sampled by each receiver; “ldist” is log(distance).</p