Longitudinal clinical and biomarker characteristics of non-manifesting LRRK2 G2019S carriers in the PPMI cohort

We examined 2-year longitudinal change in clinical features and biomarkers in LRRK2 non-manifesting carriers (NMCs) versus healthy controls (HCs) enrolled in the Parkinson’s Progression Markers Initiative (PPMI). We analyzed 2-year longitudinal data from 176 LRRK2 G2019S NMCs and 185 HCs. All participants were assessed annually with comprehensive motor and non-motor scales, dopamine transporter (DAT) imaging, and biofluid biomarkers. The latter included cerebrospinal fluid (CSF) Abeta, total tau and phospho-tau; serum urate and neurofilament light chain (NfL); and urine bis(monoacylglycerol) phosphate (BMP). At baseline, LRRK2 G2019S NMCs had a mean (SD) age of 62 (7.7) years and were 56% female. 13% had DAT deficit (defined as <65% of age/sex-expected lowest putamen SBR) and 11% had hyposmia (defined as ≤15th percentile for age and sex). Only 5 of 176 LRRK2 NMCs developed PD during follow-up. Although NMCs scored significantly worse on numerous clinical scales at baseline than HCs, there was no longitudinal change in any clinical measures over 2 years or in DAT binding. There were no longitudinal differences in CSF and serum biomarkers between NMCs and HCs. Urinary BMP was significantly elevated in NMCs at all time points but did not change longitudinally. Neither baseline biofluid biomarkers nor the presence of DAT deficit correlated with 2-year change in clinical outcomes. We observed no significant 2-year longitudinal change in clinical or biomarker measures in LRRK2 G2019S NMCs in this large, well-characterized cohort even in the participants with baseline DAT deficit. These findings highlight the essential need for further enrichment biomarker discovery in addition to DAT deficit and longer follow-up to enable the selection of NMCs at the highest risk for conversion to enable future prevention clinical trials

Dry Sliding Wear Behavior of Austenitic Stainless Steel Material by Gas Nitriding Process

In industries, components must operate under extreme conditions such as high load, speed, temperature and chemical environment. Materials are selected according to commercial availability, cost and their properties such as strength, hardness, etc. AISI 904L is a high-alloy stainless steel with low carbon content, poor surface hardness and wear characteristics. Many engineering failures are caused by fatigue, corrosion, and poor wear resistance, begins at the surface level. This causes cracks in the surface, reducing the material’s life. The surfaces of the materials were subjected to severe thermal, chemical, and shock loads. The selected AISI 904L materials for this work were subjected to gas nitriding process and processed with 3 different time parameters such as 12 hours, 18 hours and 24 hours respectively and named as GN1, GN2 and GN3. The treatments were done at a constant temperature of 650°C. Gas nitriding, in comparison to other nitriding processes such as plasma and liquid nitriding, provides good dimensional stability, reduced deformation, and uniform case depth regardless of the size and shape of the specimen. To analyze the wear properties, a pin on a disc machine is used. Finally, metallographic studies were performed by scanning electron microscopy

Predicting progression in Parkinson's Disease using baseline and 1-Year change measures

Background: Improved prediction of Parkinson’s disease (PD) progression is needed to support clinical decision-making and to accelerate research trials. Objectives: To examine whether baseline measures and their 1-year change predict longer-term progression in early PD. Methods: Parkinson’s Progression Markers Initiative study data were used. Participants had disease duration ≤2 years, abnormal dopamine transporter (DAT) imaging, and were untreated with PD medications. Baseline and 1-year change in clinical, cerebrospinal fluid (CSF), and imaging measures were evaluated as candidate predictors of longer-term (up to 5 years) change in Movement Disorders Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) score and DAT specific binding ratios (SBR) using linear mixed-effects models. Results: Among 413 PD participants, median follow-up was 5 years. Change in MDS-UPDRS from year-2 to last follow-up was associated with disease duration (β= 0.351; 95% CI = 0.146, 0.555), male gender (β= 3.090; 95% CI = 0.310, 5.869), and baseline (β= –0.199; 95% CI = –0.315, –0.082) and 1-year change (β= 0.540; 95% CI = 0.423, 0.658) in MDS-UPDRS; predictors in the model accounted for 17.6% of the variance in outcome. Predictors of percent change in mean SBR from year-2 to last follow-up included baseline rapid eye movement sleep behavior disorder score (β= –0.6229; 95% CI = –1.2910, 0.0452), baseline (β= 7.232; 95% CI = 2.268, 12.195) and 1-year change (β= 45.918; 95% CI = 35.994,55.843) in mean striatum SBR, and 1-year change in autonomic symptom score (β= –0.325;95% CI = –0.695, 0.045); predictors in the model accounted for 44.1% of the variance. Conclusions: Baseline clinical, CSF, and imaging measures in early PD predicted change in MDS-UPDRS and dopamine-transporter binding, but the predictive value of the models was low. Adding the short-term change of possible predictors improved the predictive value, especially for modeling change in dopamine-transporter binding