A Subgroup Analysis of Legal Needs Among Older Adults in Rural Communities

A legal needs assessment of older adults in Maine was conducted by surveying key populations of older adults who are often hard to reach through traditional outreach and service provision methods. The focus of this assessment was on basic demographics, use of LSE services, legal issues, preferred methods for receiving legal information, and the utility of various legal service options. Surveys were distributed via area agencies on aging, and concluded that the ley legal issues included financial scams, home repair problems, obtaining or retaining government benefits, debt collection, and accessing medical services. Sixty-seven percent of individuals aged 70 or older reported having legal issues in the previous year. Providing resources to local area agencies on aging is a critical component to allowing for information about resources to be distributed

An Analysis of Current and Projected Rural Older Adult Legal Services Needs

Maine is now the oldest state in the nation, and is one of the most rural states. A legal needs assessment of older adults in Maine was conducted by analyzing the findings from recent research conducted in six other states and service data from Maine Legal Services for the Elderly over a one-year time period. The six states analyzed were Kentucky, Michigan, Nevada, North Dakota, Ohio, and Utah. There were a total of over 7,300 older adults that responded to the legal needs surveys. The assessment concluded that the high-level service needs included the following: health insurance, government benefits, estate planning, and personal finances and consumer issues. Other needs included help with housing, abuse, employment, and family matters. This assessment was a crucial project in establishing means to plan to distribute a legal needs survey in Maine

Noncardiac genetic predisposition in sudden infant death syndrome.

PURPOSE: Sudden infant death syndrome (SIDS) is the commonest cause of sudden death of an infant; however, the genetic basis remains poorly understood. We aimed to identify noncardiac genes underpinning SIDS and determine their prevalence compared with ethnically matched controls. METHODS: Using exome sequencing we assessed the yield of ultrarare nonsynonymous variants (minor allele frequency [MAF] ≤0.00005, dominant model; MAF ≤0.01, recessive model) in 278 European SIDS cases (62% male; average age =2.7 ± 2 months) versus 973 European controls across 61 noncardiac SIDS-susceptibility genes. The variants were classified according to American College of Medical Genetics and Genomics criteria. Case-control, gene-collapsing analysis was performed in eight candidate biological pathways previously implicated in SIDS pathogenesis. RESULTS: Overall 43/278 SIDS cases harbored an ultrarare single-nucleotide variant compared with 114/973 controls (15.5 vs. 11.7%, p=0.10). Only 2/61 noncardiac genes were significantly overrepresented in cases compared with controls (ECE1, 3/278 [1%] vs. 1/973 [0.1%] p=0.036; SLC6A4, 2/278 [0.7%] vs. 1/973 [0.1%] p=0.049). There was no difference in yield of pathogenic or likely pathogenic variants between cases and controls (1/278 [0.36%] vs. 4/973 [0.41%]; p=1.0). Gene-collapsing analysis did not identify any specific biological pathways to be significantly associated with SIDS. CONCLUSIONS: A monogenic basis for SIDS amongst the previously implicated noncardiac genes and their encoded biological pathways is negligible

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Technical advance: autofluorescence-based sorting: rapid and nonperturbing isolation of ultrapure neutrophils to determine cytokine production

The technical limitations of isolating neutrophils without contaminating leukocytes, while concurrently minimizing neutrophil activation, is a barrier to determining specific neutrophil functions. We aimed to assess the use of FACS for generating highly pure quiescent neutrophil populations in an antibody-free environment. Peripheral blood human granulocytes and murine bone marrow-derived neutrophils were isolated by discontinuous Percoll gradient and flow-sorted using FSC/SSC profiles and differences in autofluorescence. Postsort purity was assessed by morphological analysis and flow cytometry. Neutrophil activation was measured in unstimulated-unsorted and sorted cells and in response to fMLF, LTB(4), and PAF by measuring shape change, CD62L, and CD11b expression; intracellular calcium flux; and chemotaxis. Cytokine production by human neutrophils was also determined. Postsort human neutrophil purity was 99.95% (sem=0.03; n=11; morphological analysis), and 99.68% were CD16(+ve) (sem=0.06; n=11), with similar results achieved for murine neutrophils. Flow sorting did not alter neutrophil activation or chemotaxis, relative to presorted cells, and no differences in response to agonists were observed. Stimulated neutrophils produced IL-1β, although to a lesser degree than CXCL8/IL-8. The exploitation of the difference in autofluorescence between neutrophils and eosinophils by FACS is a quick and effective method for generating highly purified populations for subsequent in vitro study

Lightweight, open source, easy-use algorithm and web service for paraprotein screening using spatial frequency domain analysis of electrophoresis studies

Introduction: Serum protein electrophoresis (SPEP) is commonly used to detect monoclonal paraproteins to meet laboratory diagnostic criteria for plasma cell neoplasms. We propose an automated screening method for paraprotein detection that uses minimal computational resources for training and deployment. Methods: A model screening for paraproteins based on the presence of high-frequency components in the spatial frequency spectrum of the SPEP densitometry curve was calibrated on a set of 330 samples, and evaluated on representative (n=110) and external (n=1,321) test sets. The model takes as input a patient’s serum densitometry curve and a standardized control curve and outputs a prediction of whether a paraprotein is present. We built an interactive web application allowing users to easily perform paraprotein screening given inputs for densitometry curves, as well as a macro-enabled spreadsheet for easy automated screening. Results: When tuned to maximize likelihood ratio with minimum sensitivity 0.90, the model achieved AUC 0.90, sensitivity 0.90, positive-predictive value 0.64, specificity 0.55, and accuracy 0.72 in the representative test set. In the external test set, the model achieved AUC 0.90, sensitivity 0.97, positive-predictive value 0.42, specificity 0.29, and accuracy 0.52. A subset analysis showed sensitivities of 0.90, 0.96, and 1.0 in detecting low (0.1–0.5 g/dL), medium (0.5–3.0 g/dL), and high paraprotein levels (≥3.0 g/dL), respectively. We have released a web service allowing users to score their own SPEP data, and also released the algorithm and application programming interface in an open-source package allowing users to customize the model to their needs. Conclusions: We developed a proof of concept for an automated method for paraprotein screening using only the characteristics of the SPEP curve. Future work should focus on testing the method with other laboratory data including immunofixation gels, as well as incorporation of outside data sources including clinical data