Heterozygous variants in TBCK cause a mild neurologic syndrome in humans and mice

TBCK-related encephalopathy is a rare pediatric neurodegenerative disorder caused by biallelic loss-of-function variants in the TBCK gene. After receiving anecdotal reports of neurologic phenotypes in both human and mouse TBCK heterozygotes, we quantified if TBCK haploinsufficiency causes a phenotype in mice and humans. Using the tbck^+/− mouse model, we performed a battery of behavioral assays and mTOR pathway analysis to investigate potential alterations in neurophysiology. We conducted as well a phenome-wide association study (PheWAS) analysis in a large adult biobank to determine the presence of potential phenotypes associated to this variant. The tbck^+/− mouse model demonstrates a reduction of exploratory behavior in animals with significant sex and genotype interactions. The concurrent PheWAS analysis of 10,900 unrelated individuals showed that patients with one copy of a TBCK loss-of-function allele had a significantly higher rate of acquired toe and foot deformities, likely indicative of a mild peripheral neuropathy phenotype. This study presents an example of what may be the underappreciated occurrence of mild neurogenic symptoms in heterozygote individuals of recessive neurogenetic syndromes.     

A scoping review of pediatric transplant education

Background

Education is crucial for pediatric patients and caregivers throughout the transplant continuum, yet data are lacking around which interventions are effective and in what circumstances.

Methods

We undertook a scoping review with the objectives of (a) describing the types, effects, and outcomes of patient-focused educational interventions before and after pediatric transplant and (b) understanding the educational experiences of patients and caregivers. Five scientific databases were explored for relevant literature using the JBI methodology. Educational interventions published in English, targeting pediatric solid organ transplant patients (0–25 years) and their caregivers were included. Relevant data from eligible articles (n = 27) were extracted and summarized.

Results

Eighteen articles describing 17 educational interventions were identified for objective A, and nine articles qualitatively assessing patient or parental learning needs were identified for objective B. Most interventions were directed toward teenage patients and their caregivers post kidney transplant, primarily focusing on medication self-management and adherence, or providing general information on transplant using multicomponent delivery formats. Most interventions achieved statistically significant improvements in knowledge (n = 8/9) and patients or caregivers expressed satisfaction with the intervention (n = 7/7) but health-related outcomes such as medication adherence (n = 2/6) or behavior change (n = 1/3) rarely achieved statistically significant results. In objective B, patients and caregivers described the transplant process as overwhelming, but indicated that social supports and education helped them cope. Participants consistently wanted more information than they received.

Conclusion

Caregivers and pediatric patients value transplant education, but high-quality studies are limited. Since education is a fundamental part of the transplant process, future research in this area should be prioritized.     

Correlates of SARS-CoV-2 anti-RBD IgG antibody titers among persons experiencing homelessness in Los Angeles

Objectives

People experiencing homelessness (PEH) have been especially impacted by the COVID-19 pandemic, likely due to increased vulnerabilities stemming from chronic diseases, substance use, and mental health conditions.

Design

A case–control study to assess the presence of antibodies against SARS-CoV-2 among PEH and associations with key variables.

Sample

A convenience sample of 97 PEH in Skid Row, Los Angeles.

Measurements

A structured questionnaire assessing socio-demographic, mental health, drug and alcohol use, health care access, pandemic stress, and other COVID-19-specific questions.

Results

We found high anti-receptor binding domain (RBD) IgG titers among five of 15 PEH who reported no prior COVID-19 diagnosis or being vaccinated, suggesting undiagnosed and/or asymptomatic COVID-19. While anti-RBD IgG titers across vaccination categories were not statistically significant (p = .069), participants vaccinated with Janssen had the lowest mean anti-RBD IgG titers. In multivariable analysis, we found negative associations between level of SARS-CoV-2 antibody titers with the Janssen vaccine and depression; thus, a need for integrated care for PEH with depression and COVID-19.

Conclusions

Further research is warranted to confirm the immune response, initial and over time, to SARS-CoV-2 infection and to COVID-19 vaccinations, particularly among PEH whose immune systems may be impacted by multiple health conditions.     

Disciplined appetites: Reimagining food and nutrition programs for people living with HIV and AIDS

Aims

Medical nutrition therapy is recommended for people living with HIV/AIDS to improve health and wellness; however, there is a lack of food and nutrition programs for people living with HIV/AIDS in Nova Scotia, Canada. The aim of this study was to explore the beliefs, values, and experiences of people living with HIV/AIDS in relation to food and nutrition programs.

Methods

A critical social theory lens with two disciplinary contexts: critical health geography and critical dietetics guided this research. Semi-structured interviews were conducted with 12 people living with HIV/AIDS and analysed for themes.

Results

The three main themes were identified: (1) intersections of social determinants of health, wellness, and food security; (2) discursive shaping of food and nutrition in relation to HIV; and (3) the dynamic nature of HIV care.

Conclusions

Participants offered recommendations on how food and nutrition programs might be reimagined to be more accessible, inclusive, and effective for people living with HIV/AIDS.     

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department

Introduction

Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting.

Objective

Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for prevalent delirium in patients being transferred from the ED to inpatient units.

Methods

This was a retrospective cohort study to develop and validate a risk model to detect delirium using patient data available from prior visits and ED encounter. Electronic health records were extracted for patients hospitalized from the ED between January 1, 2014, and December 31, 2020. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded within 72 h of hospitalization. Six machine learning models were developed to estimate the risk of delirium using clinical variables including demographic features, physiological measurements, medications administered, lab results, and diagnoses.

Results

A total of 28,531 patients met the inclusion criteria with 8057 (28.4%) having a positive delirium screening within the outcome observation period. Machine learning models were compared using the area under the receiver operating curve (AUC). The gradient boosted machine achieved the best performance with an AUC of 0.839 (95% CI, 0.837–0.841). At a 90% sensitivity threshold, this model achieved a specificity of 53.5% (95% CI 53.0%–54.0%) a positive predictive value of 43.5% (95% CI 43.2%–43.9%), and a negative predictive value of 93.1% (95% CI 93.1%–93.2%). A random forest model and L1-penalized logistic regression also demonstrated notable performance with AUCs of 0.837 (95% CI, 0.835–0.838) and 0.831 (95% CI, 0.830–0.833) respectively.

Conclusion

This study demonstrated the use of machine learning algorithms to identify a combination of variables that enables an estimation of risk of positive delirium screens early in hospitalization to develop prevention or management protocols.