Meso Scale Discovery (United States)

• The intervention did not reduce homelessness for most participants. • Aboriginal participants experienced worse outcomes than non-Aboriginal participants. • Previously homeless youth benefited more than peers without such history. • Participants found intervention more acceptable than support they received during OOHC. • Aftercare planning practice was a major barrier to implementation. Young people transitioning from out-of-home care (OOHC) face elevated risks of adverse outcomes across multiple domains. Existing interventions have shown small or null effects when rigorously evaluated, highlighting the need to identify effective approaches to supporting care leavers. Evaluate the impact of the Premier’s Youth Initiative (PYI), an accommodation and support intervention, on homelessness outcomes for care leavers and explore its implementation. 295 eligible care leavers who received PYI in New South Wales, Australia between 2017–2020 and a matched comparison group drawn from locations where PYI was unavailable. We undertook a hybrid type-1 implementation-effectiveness study that used linked administrative data from child welfare and homelessness services. Propensity score methods were applied to estimate the average treatment effect on the treated on ten measures of homelessness between ages 18–19. Implementation was explored through focus groups with participants, service providers and the funder to explore intervention acceptability as well as barriers and facilitators. Overall PYI had no impact on any assessed homelessness measures—with treatment effects consistently near zero. Subgroup analyses showed that Aboriginal participants experienced worse outcomes than non-Aboriginal peers. Participants who experienced homelessness during OOHC appeared to benefit more from PYI, relative to those who did not, but estimates were inconsistent. Implementation analysis identified high acceptability among participants but revealed substantial barriers, particularly inadequate planning for leaving care. Interventions like PYI may need to commence earlier, be provided at greater intensity, or be targeted more effectively to the most vulnerable care leavers.

Blood-based biomarkers for Alzheimer's disease (AD) have been widely studied, but direct comparisons of several biomarkers in clinical settings remain limited. In this cross-sectional study, plasma biomarkers from 197 participants in the BIODEGMAR cohort (Hospital del Mar, Barcelona) were analyzed. Participants were classified based on AD cerebrospinal fluid (CSF) core biomarkers. We assessed the ability of plasma p-tau181, p-tau217, p-tau231, t-tau, and Aβ42/40 to classify Aβ pathology status. Plasma p-tau biomarkers had a greater diagnostic performance and larger effect sizes compared to t-tau and Aβ42/40 assays in detecting Aβ pathology. Among them, plasma p-tau217 consistently outperformed the others, demonstrating superior area under the curves. Furthermore, p-tau217 showed the strongest correlation between plasma and CSF levels, underscoring its potential as a reliable surrogate for CSF biomarkers. Several plasma biomarkers, targeting different epitopes and using different platforms, demonstrated high performance in detecting AD in a memory clinic setting. Plasma p-tau biomarkers demonstrated higher diagnostic performance and larger effect sizes than t-tau and Aβ42/40 assays in detecting Alzheimer's disease. Among the p-tau biomarkers, p-tau217 assays consistently outperformed the others, providing superior classification of Aβ pathology status across different phosphorylation sites. p-tau217 assays showed the strongest correlation between plasma and CSF levels, indicating its potential as a reliable surrogate for CSF biomarkers. Several plasma p-tau biomarkers can be used in a specialized memory clinic to accurately detect Alzheimer's disease.

Abstract BackgroundNeurofilament light chain (NfL) is a key biomarker for axonal injury providing diagnostic and prognostic value in many neurodegenerative diseases. The sensitivity of electrochemiluminescence (ECL) immunoassays has previously not been adequate to quantify plasma NfL levels in Alzheimer’s disease (AD). Here we performed clinical evaluation of a recently developed ultra‐sensitive plasma NfL ECL S‐PLEX® immunoassay in individuals with AD and other neurodegenerative diseases.MethodWe measured NfL in banked plasma samples from 541 participants in the Massachusetts ADRC’s longitudinal cohort. Of these, 262 had a diagnosis of AD, 121 non‐AD neurodegenerative diseases (Ndgen), 46 other neurological diseases (OND), and 112 were controls (CON). The clinical diagnoses (AD/no AD) were verified by AD biomarkers (neuropathology, amyloid PET, CSF amyloid‐β 42/40 ratio, and/or plasma pTau181). Longitudinal analysis identified 223 participants with mild cognitive impairment (CDR 0.5) at baseline and 4 years of follow up. Of these, 131 remained stable with unchanged CDR, and 92 declined with worsening cognitive impairment (CDR³1.0). NfL was measured in all participants using an ECL immunoassays from Meso Scale Discovery (MSD) and in 72 participants using a SIMOA assay from Quanterix.ResultNfL levels measured using the two assays were highly correlated. Cross‐sectional analysis using the MSD assay showed the expected differences between the diagnostic groups with highest NfL levels in the Ndgen group, followed by AD, and the two control groups. NfL levels correlated with disease severity measured by cognitive testing (MoCA, global CDR, CDR SOB, and FAQ) at the time of blood collection. NfL levels at baseline were furthermore higher in participants who declined during follow‐up compared to stable participants both when studying the whole cohort and when limiting the analysis to participants with a final diagnosis of AD.ConclusionThe ultrasensitive ECL plasma NfL assay demonstrated utility for detection of AD and correlated with disease severity. Moreover, baseline plasma levels of NfL reflect cognitive decline over the next 4 years, providing prognostic information that may have utility in both clinical practice and clinical trial populations.

<div class="section abstract"><div class="htmlview paragraph">Particle Number (PN) measurement testing has for long been conducted by using Condensation Particle Counter (CPC) based technology. While accurate at low concentrations, CPC has nevertheless several drawbacks for in-field use, such as the use of a working fluid, the need for dilution, the delicate optical components and the sensitivity to contamination. Diffusion Charging (DC) based particle counting technologies have often been disregarded as a valid alternative to CPC based methods due to their intrinsic particle size dependent counting efficiency and lower sensitivity. However, Dekati’s novel ePNC PN technology has brought DC technology to the next level. Due to its patented technology, the Dekati ePNC’s particle counting efficiency is nearly size independent, turning DC as a competing technology for CPC, especially for demanding field applications, such as Periodic Technical Inspection (PTI), Portable Emission Measurement Systems (PEMS) for Real Driving Emissions (RDE), and brake and tire wear measurements. These applications require the ability to continuously measure elevated particle concentrations with sufficient accuracy in often harsh environments such as garages or during on-road testing. In this study we will look closer into the ePNC technology and show the results of recent measurement campaigns that prove the suitability of the Dekati ePNC’s DC technology as an alternative to CPC methods. For example, during vehicle type approval style chassis dynamometer measurement ePNC technology was found to produce comparable results against a PMP reference (7% difference in emission factor).</div></div>

Certain demyelinating disorders, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) exhibit serum autoantibodies against aquaporin-4 (αAQP4) and myelin oligodendrocyte glycoprotein (αMOG). The variability of the autoantibody presentation warrants further research into subtyping each case. To elucidate the relationship between astroglial and neuronal protein concentrations in the peripheral circulation with occurrence of these autoantibodies, 86 serum samples were analyzed using immunoassays. The protein concentration of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL) and tau protein was measured in 3 groups of subcategories of suspected NMOSD: αAQP4 positive (n = 20), αMOG positive (n = 32) and αMOG/αAQP4 seronegative (n = 34). Kruskal-Wallis analysis, univariate predictor analysis, and multivariate logistic regression with ROC curves were performed. GFAP and NFL concentrations were significantly elevated in the αAQP4 positive group (p = 0.003; p = 0.042, respectively), and tau was elevated in the αMOG/αAQP4 seronegative group (p < 0.001). A logistic regression model to classify serostatus was able to separate αAQP4 seropositivity using GFAP + tau, and αMOG seropositivity using tau. The areas under the ROC curves (AUCs) were 0.77 and 0.72, respectively. Finally, a combined seropositivity versus negative status logistic regression model was generated, with AUC = 0.80. The 3 markers can univariately and multivariately classify with moderate accuracy the samples with seropositivity and seronegativity for αAQP4 and αMOG.

Extracellular vesicles (EVs) are released from different cell types in the central nervous system (CNS) and play roles in regulating physiological and pathological functions. Although brain‐derived EVs (bdEVs) have been successfully collected from brain tissue, there is not yet a “bdEV Atlas” of EVs from different brain regions. To address this gap, we separated EVs from eight anatomical brain regions of a single individual and subsequently characterized them by count, size, morphology, and protein and RNA content. The greatest particle yield was from cerebellum, while the fewest particles were recovered from the orbitofrontal, postcentral gyrus, and thalamus regions. EV surface phenotyping indicated that CD81 and CD9 were more abundant than CD63 in all regions. Cell‐enriched surface markers varied between brain regions. For example, putative neuronal markers NCAM, CD271, and NRCAM were more abundant in medulla, cerebellum, and occipital regions, respectively. These findings, while restricted to tissues from a single individual, suggest that additional studies are warranted to provide more insight into the links between EV heterogeneity and function in the CNS.

This paper studies how RAID (redundant array of independent disks) could take full advantage of modern SSDs (solid-state drives) with built-in transparent compression. In current practice, RAID users are forced to choose a specific RAID level (e.g., RAID 10 or RAID 5) with a fixed storage cost vs. speed performance trade-off. The commercial market is witnessing the emergence of a new family of SSDs that can internally perform hardware-based lossless compression on each 4KB LBA (logical block address) block, transparent to host OS and user applications. Beyond straightforwardly reducing the RAID storage cost, such modern SSDs make it possible to relieve RAID users from being locked into a fixed storage cost vs. speed performance trade-off. In particular, RAID systems could opportunistically leverage higher-than-expected runtime user data compressibility to enable dynamic RAID level conversion to improve the speed performance without compromising the effective storage capacity. This paper presents techniques to enable and optimize the practical implementation of such elastic RAID systems. We implemented a Linux software-based elastic RAID prototype that supports dynamic conversion between RAID 5 and RAID 10. Compared with a baseline software-based RAID 5, under sufficient runtime data compressibility that enables the conversion from RAID 5 to RAID 10 over 60% of user data, the elastic RAID could improve the 4KB random write IOPS (I/O per second) by 42% and 4KB random read IOPS in degraded mode by 46%, while maintaining the same effective storage capacity.

&lt;p&gt;Research in the field of automatic human emotion recognition has shown that changes in emotions can lead to physiological responses in humans. Typically, sensors placed on the body are used to collect physiological data for automatic emotion recognition. However, recent studies have demonstrated that heart rate can be estimated from human face videos by extracting photoplethysmographic (PPG) signals based on the RGB color changes in the face during the cardiac cycle. This study proposes a novel framework for classifying human emotions using contactless PPG signals. The study evaluated PPG signal extraction methods using a popular emotional database, and for classification, a deep learning architecture combining a 1D convolutional neural network (1DCNN) and a long short-term memory (LSTM) was adopted after normalization and signal segmentation. The proposed method achieved a recognition rate of 68.9\% and 54.2\% for binary classification of valence and arousal, respectively, with a signal segmentation of 5 seconds. It is important to note that the main aim of this study is to introduce a new approach to automatic emotion recognition using deep learning techniques based on contactless sensing of physiological signals.&lt;/p&gt;