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Telomere length and bipolar disorder
Variation in telomere length is heritable and is currently considered a promising biomarker of susceptibility for neuropsychiatric disorders, particularly because of its association with memory function and hippocampal morphology. Here, we investigate telomere length in connection to familial risk and disease expression in bipolar disorder (BD). We used quantitative polymerase chain reactions and a telomere-sequence to single-copy-gene-sequence ratio method to determine telomere length in genomic DNA extracted from buccal smears from 63 patients with BD, 74 first-degree relatives (49 relatives had no lifetime psychopathology and 25 had a non-BD mood disorder) and 80 unrelated healthy individuals. Participants also underwent magnetic resonance imaging to determine hippocampal volumes and cognitive assessment to evaluate episodic memory using the verbal paired associates test. Telomere length was shorter in psychiatrically-well relatives (p=0.007) compared to unrelated healthy participants. Telomere length was also shorter in relatives (regardless of psychiatric status; p<0.01) and patients with BD not on lithium (p=0.02) compared to lithium-treated patients with BD. In the entire sample, telomere length was positively associated with left and right hippocampal volume and with delayed recall. This study provides evidence that shortened telomere length is associated with familial risk for BD. Lithium may have neuroprotective properties that require further investigation using prospective designs
The association of telomere length with substance use disorders: systematic review and meta-analysis protocol
BACKGROUND: The present protocol was designed for a systematic review and meta-analysis aimed at determining the association of telomere length with substance use disorders with the exclusion of nicotine addiction, and to identify potential moderators of the effect of telomere length. Such methodological information may provide guidance to improve the quality of future research on this important topic. METHODS: Potential studies will be identified through electronic databases (PubMed/MEDLINE, EMBASE, PsycINFO, and Web of Science) up from inception onwards. The inclusion criteria will include published or unpublished observational studies (cohort, case-control, and cross-sectional studies) reporting telomere length in adult patients with substance use disorder compared with a control group. Non-human studies or other study designs such as reviews, case-only, family-based, and/or population studies with only healthy participants will be excluded, as well as those focused solely on nicotine addiction. The main outcome will be telomere length in adults with substance use disorder (primary) and, specifically, in those with alcohol use disorder (secondary). Two investigators will independently evaluate the preselected studies for possible inclusion and will extract data following a standardized protocol. Disagreements will be resolved by consensus. The risk of bias of all included studies will be assessed using the Newcastle-Ottawa Quality Assessment Scale for non-randomized studies. Data will be converted into standardized mean differences as effect size index, and random-effects models will be used for the meta-analysis. Cochran's Q statistic, I(2) index, and visual inspection of the forest plot will be used to verify study heterogeneity. Subgroup analyses and meta-regressions will be conducted to ascertain heterogeneity. Several sensitivity analyses will be conducted to address the influence of potential confounding factors. Publication bias will be examined using the "funnel plot" method with Duval and Tweedie's trim-and-fill method and Egger test. DISCUSSION: This systematic review will assess the association of telomere length with substance use disorders aside from nicotine addiction. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number CRD42019119785
Real-time multimodal feedback with the CPR tutor
We developed the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects mistakes using recurrent neural networks for real-time time-series classification. From a multimodal data stream consisting of kinematic and electromyographic data, the CPR Tutor system automatically detects the chest compressions, which are then classified and assessed according to five performance indicators. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. To test the validity of the CPR Tutor, we first collected the data corpus from 10 experts used for model training. Hence, to test the impact of the feedback functionality, we ran a user study involving 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: 1) an architecture design, 2) a methodological approach to design multimodal feedback and 3) a field study on real-time feedback for CPR training.Virtual/online event due to COVID-19 Accepted author manuscriptWeb Information System