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Introduction Anxiety is a common symptom for those experiencing dementia and is associated with worse outcomes. The aim of the study was to examine which anxiety tools have been validated compared with a gold standard diagnostic criterion in persons with dementia. Methods We completed a systematic review of the literature, which was registered a priori with PROSPERO (CRD42016042123). Three databases were searched, MEDLINE, EMBASE, and PsycINFO, as well as the gray literature. Abstracts and full text were searched in duplicate for inclusion. Risk of bias was assessed in duplicate. Results We identified 9626 citations from all sources after duplicates were removed. Many excluded studies used tools for anxiety, for which no diagnostic accuracy study was identified. Four articles were included in the final synthesis. Included articles had between 32 to 101 participants with mild to moderate dementia. The gold standard criteria focused on either generalized anxiety or all anxiety subtypes. The prevalence of anxiety was between 27.7% and 63.4%. Three tools were examined, the Geriatric Anxiety Inventory, Penn State Worry Questionnaire, and the Rating Anxiety in Dementia (RAID) scale. Sensitivity varied but was the highest in the RAID at 90% and lowest in the self-rated version of the Geriatric Anxiety Inventory (58%). Discussion Given how burdensome the symptoms of anxiety are to persons with dementia, valid tools are needed to help identify symptoms. We identified three validated tools, but further validation of these and other tools are needed. Practitioners should consider the use of tools with high sensitivity such as the RAID in persons with dementia.

Objective Depressive disorders are common in long-term care (LTC), however, there is no one process used to detect depressive disorders in this setting. Our goal was to describe the diagnostic accuracy of depression detection tools used in LTC settings. Methods We conducted a systematic review and meta-analysis of diagnostic accuracy measures. The databases PubMed, EMBASE, PsycINFO and CINAHL were searched from inception to 10 September 2021. Studies involving persons living in LTC, assisted living residences or facilities, comparing diagnostic accuracy of depression tools with a reference standard, were included. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess risk of bias. Results We identified 8,463 citations, of which 20 studies were included in qualitative synthesis and 19 in meta-analysis. We identified 23 depression detection tools (including different versions) that were validated against a reference standard. At a cut-off point of 6 on the Geriatric Depression Scale-15 (GDS-15), the pooled sensitivity was 73.6% (95% confidence interval (CI) 43.9%–76.5%), specificity was 76.5% (95% CI 62.9%–86.7%), and an area under the curve was 0.83. There was significant heterogeneity in these analyses. There was insufficient data to conduct meta-analysis of other screening tools. The Nursing Homes Short Depression Inventory (NH-SDI) had a sensitivity ranging from 40.0% to 98.0%. The 4-item Cornell Scale for Depression in Dementia (CSDD) had the highest sensitivity (67.0%–90.0%) for persons in LTC living with dementia. Conclusions There are 23 tools validated for detection of depressive disorders in LTC, with the GDS-15 being the most studied. Tools developed specifically for use in LTC settings include the NH-SDI and CSDD-4, which provide briefer options to screen for depression. However, more studies of both are needed to examine tool accuracy using meta-analyses.

Biological interactions are prevalent in the functioning organisms. Correspondingly, statistical geneticists developed various models to identify genetic interactions through genotype-phenotype association mapping. The current standard protocols in practice test single variants or single regions (that contain multiple local variants) sequentially along the genome, followed by functional annotations that involve various aspects including interactions. The testing of genetic interactions upfront is rare in practice due to the burden of testing a huge number of combinations, which lead to the multiple-test problem and the risk of overfitting. In this work, we developed interaction-integrated linear mixed model (ILMM), a novel model that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems associated with combination searching.Three dimensional (3D) genomic interactions assessed by Hi-C experiments have led to unprecedented biological discoveries. However, the contribution of 3D genomic interactions to the genetic basis of complex diseases has yet to be quantified. Using 3D interacting regions as a priori information, we conducted both simulations and real data analysis to test ILMM. By applying ILMM to whole genome sequencing data for Autism Spectrum Disorders, or ASD (MSSNG) and transcriptome sequencing data (GTEx), we revealed the 3D-genetic basis of ASD and 3D-eQTLs for a substantial proportion of gene expression in brain tissues. Moreover, we have revealed a potential mechanism involving distal regulation between FOXP2 and DNMT3A conferring the risk of ASD.Software is freely available in our GitHub: https://github.com/theLongLab/Jawamix5