Faculty Bibliography

Homocysteine is an amino acid derived from methionine which is metabolized via vitamin B₆ (pyridoxine)- and vitamin B₁₂ (cobalamin)-dependent pathways. Supplementation of B vitamins has been shown to effectively reduce plasma homocysteine levels. Previous research has also demonstrated an association between lower plasma homocysteine levels and decreased risk of myocardial infarction, stroke, and venous thromboembolism. However, whether supplementation of B vitamins is associated with risk reduction in thromboembolic events and confers clinical benefits remains inconclusive. This review examines clinical trials published over the past 29 years to assess the effects of B vitamin supplementation on thrombotic risk reduction and homocysteine metabolism. The findings from these studies are inconsistent, and the impact of B vitamins on thrombosis prevention remains uncertain. Given the conflicting evidence, further clinical and translational research is necessary to clarify the role of B vitamin supplementation in thrombosis risk reduction.

The COVID-19 pandemic profoundly impacted global health, with disproportionate consequences for healthcare workers (HCWs). Religious beliefs and practices may improve psychological resilience by fostering community, providing purpose and giving meaning to hardships. Yet, how religiosity impacts HCWs during a time of crisis is unclear. We therefore performed a cross-sectional study to investigate how religiosity contributes to resilience among HCWs who were routinely exposed to high levels of stress during the pandemic, through a physiological measure (the Auditory Sustained Attention Test; ASAT) and psychological self-reports. Forty-two HCWs were recruited from COVID-19 units and 44 HCWs from general internal medicine units during June and July 2022. COVID-19 HCWs showed significantly elevated emotional and attentional dysregulation with the ASAT, as measured by acoustic startle and prepulse inhibition, that was undetectable with self-reports. Furthermore, after dividing the HCWs into a 'high' and 'low' religiosity group, those in the 'low' group showed higher emotional and attentional dysregulation with the ASAT. Findings suggest that the ASAT has greater sensitivity at detecting emotional and attentional dysregulations than self-reports. Moderate or high religiosity may lead to better performance on the ASAT which could suggest greater resilience to mental health problems in the face of a crisis.

The optimal treatment of patients with diffuse large B-cell lymphoma (DLBCL) or Hodgkin lymphoma (HL) with preexisting cardiomyopathy is uncertain. An anonymous, electronic survey was distributed by e-mail to three US lymphoma cooperative groups, two community hospitals, and twelve academic medical systems, and distributed at one international lymphoma meeting. Fifty hematology-oncology providers caring for patients with lymphoma were included. In response to a vignette of a 67-yo with Stage III DLBCL with LVEF of 40-45%, 15 (30%) would use non-anthracycline regimens, 13 (26%) R-CHOP with liposomal doxorubicin instead of doxorubicin, 11 (22%) R-CHOP without modification and 6 (12%) R-CHOP with a continuous doxorubicin infusion. In a second vignette of a patient with HL in remission after frontline treatment with doxorubicin cumulative dose 300 mg/m², 16 (32%) would order an echocardiogram after treatment. There was substantial variability in preferred treatment regimens with preexisting cardiomyopathy and in cardiac monitoring after anthracycline.

Macrophages are essential immune cells that play crucial roles in inflammation and tissue homeostasis, and are important regulators of metabolic processes, such as the metabolism of glucose, lipids, and amino acids. The regulation of macrophage metabolism by circadian clock genes has been emphasized in many studies. Changes in metabolic profiles occurring after the perturbation of macrophage circadian cycles may underlie the etiology of several diseases. Specifically, chronic inflammatory disorders, such as atherosclerosis, diabetes, cardiovascular diseases, and liver dysfunction, are associated with poor macrophage metabolism. Developing treatment approaches that target metabolic and immunological ailments requires an understanding of the complex relationships among clock genes, disease etiology, and macrophage metabolism. This review explores the molecular mechanisms through which clock genes regulate lipid, amino acid, and glucose metabolism in macrophages, and discusses their potential roles in the development and progression of metabolic disorders. The findings underscore the importance of maintaining circadian homeostasis in macrophage function as a promising avenue for therapeutic intervention in diseases involving metabolic dysregulation, given its key roles in inflammation and tissue homeostasis. Moreover, reviewing the therapeutic implications of circadian rhythm in macrophages can help minimize the side effects of treatment. Novel strategies may be beneficial in treating immune-related diseases cause by shifted and blunted circadian rhythms via light exposure, jet lag, seasonal changes, and shift work or disruption to the internal clock (such as stress or disease).

RATIONALE AND OBJECTIVES/OBJECTIVE:Cognitive decline is common in End-Stage Renal Disease (ESRD) patients, yet its neural mechanisms are poorly understood. This study investigates structural and functional brain network reconfiguration in ESRD patients transitioning to Mild Cognitive Impairment (MCI) and evaluates its potential for predicting MCI risk. METHODS:We enrolled 90 ESRD patients with 2-year follow-up, categorized as MCI converters (MCI_C, n=48) and non-converters (MCI_NC, n=42). Brain networks were constructed using baseline rs-fMRI and high angular resolution diffusion imaging, focusing on regional structural-functional coupling (SFC). A Support Vector Machine (SVM) model was used to identify brain regions associated with cognitive decline. Mediation analysis was conducted to explore the relationship between kidney function, brain network reconfiguration, and cognition. RESULTS:MCI_C patients showed decreased network efficiency in the structural network and compensatory changes in the functional network. Machine learning models using multimodal network features predicted MCI with high accuracy (AUC=0.928 for training set, AUC=0.903 for test set). SHAP analysis indicated that reduced hippocampal SFC was the most significant predictor of MCI_C. Mediation analysis revealed that altered brain network topology, particularly hippocampal SFC, mediated the relationship between kidney dysfunction and cognitive decline. CONCLUSION/CONCLUSIONS:This study provides new insights into the link between kidney function and cognition, offering potential clinical applications for structural and functional MRI biomarkers.

Occupational therapy practice addressing functional cognition reduces hospital readmission rates. But no widely accepted performance-based functional cognition screen exists for inpatient rehabilitation. The aim was to determine how occupational therapy practitioners perceive the Menu Task's (MT's) utility for addressing functional cognition impairment. This study is a qualitative interpretive constructionist design with a phenomenological approach using semi-structured interviews with nine inpatient rehabilitation occupational therapy practitioners. Three themes emerged: (a) the screen's focus on ability, highlighting what the patient can do; (b) convenient administration, emphasizing the screen's ease of use; and (c) room to grow, focusing on areas for screen improvement. The Menu Task is convenient to administer and informs occupational therapy practice by revealing functional cognition ability. Although needing improvement, the Menu Task aligns with occupational therapy practice tenets by highlighting occupational participation. Occupational therapy practitioners indicated that inclusion of the Menu Task enhanced their clinical practice in inpatient rehabilitation, addressing functional cognition.

BACKGROUND:Childhood environmental factors back to the prenatal environment can contribute to MS risk. Childhood adversity, which causes biological, behavioral, and epigenetic changes that can be passed down through families, has been understudied in MS. Here, we emphasize the need to understand the role that intergenerational adversity may play among families affected by MS. OBJECTIVE:To evaluate the frequency and types of adverse childhood experiences among parents of children with MS. METHODS:Individuals with pediatric MS (n = 68) were enrolled in a longitudinal study of cognition. At enrollment, the patient and one caregiver or parent completed questionnaires. As the pediatric participants were under age 18 at time of enrollment, one parent completed the Adverse Childhood Experiences (ACEs, a 10-item self-report measure) about the parents' own childhood. Results from the ACE questionnaire among parents of pediatric healthy controls (n = 96) and adults in a national cohort are also reported for comparison. RESULTS:Over half of pediatric MS parents reported at least one ACE exposure. Of parents that did have ACE exposures, the exposures were broad in terms of abuse, neglect, and household dysfunction. Over 10 % of parents reported total ACE scores of 7 or above. CONCLUSION/CONCLUSIONS:Over half of pediatric MS parents experienced some degree of childhood adversity. The impact of intergenerational adversity on the development of pediatric onset MS warrants further study.

BACKGROUND/UNASSIGNED:Attention Deficit Hyperactivity Disorder (ADHD) among children younger than 6 years is quite impairing, nearly half these youth with ADHD experience school exclusion from mainstream preschool classes due to related emotional and behavioral outbursts. While a range of behavior rating scales and subjective measures are used to assess these youth, objective methods of assessment and prediction derived from technology have potential to improve therapeutic and academic interventions outcomes for these youths. We hypothesized that biometric sensors would provide objective, highly sensitive and specific information regarding the physiological status of children prior to an impulsive outburst and could be feasibly implemented using a wearable device in the special education classroom. METHODS/UNASSIGNED: = 5 youth from the first grade) of a specialized therapeutic day-school for youth with ADHD and other psychiatric and developmental disorders to examine feasibility of obtaining continuous physiological data associated with behavioral and emotional outbursts through smartwatch use. Children wore a sensor watch during their daily classroom activities for two weeks and trained observers collected data using behavioral logs. Using Ecological Momentary Assessment methodology, to examine correlations between objective sensor data and observer observation. Data collected from parents regarding prior night's sleep was also examined. RESULTS/UNASSIGNED:All participants completed the study. With a few tolerability or palatability issues. Associations were found between physiological and behavioral/questionnaire data. The methodology holds promise for reliably measuring behavioral and emotional outbursts in young children. CONCLUSIONS/UNASSIGNED:among severely dysregulated pre-school aged youth throughout a full school day. This study established the feasibility of utilizing sensor derived physiological data as an objective biomarker of ADHD within the special education therapeutic classroom. Further research with larger samples is required to build a more robust and personalized AI predictive model.

BACKGROUND:Rates of childhood mental health problems are increasing in the UK. Early identification of childhood mental health problems is challenging but critical to children's future psychosocial development. This is particularly important for children with social care contact because earlier identification can facilitate earlier intervention. Clinical prediction tools could improve these early intervention efforts. AIMS/OBJECTIVE:Characterise a novel cohort consisting of children in social care and develop effective machine learning models for prediction of childhood mental health problems. METHOD/METHODS:We used linked, de-identified data from the Secure Anonymised Information Linkage Databank to create a cohort of 26 820 children in Wales, UK, receiving social care services. Integrating health, social care and education data, we developed several machine learning models aimed at predicting childhood mental health problems. We assessed the performance, interpretability and fairness of these models. RESULTS:Risk factors strongly associated with childhood mental health problems included age, substance misuse and being a looked after child. The best-performing model, a gradient boosting classifier, achieved an area under the receiver operating characteristic curve of 0.75 (95% CI 0.73-0.78). Assessments of algorithmic fairness showed potential biases within these models. CONCLUSIONS:Machine learning performance on this prediction task was promising. Predictive performance in social care settings can be bolstered by linking diverse routinely collected data-sets, making available a range of heterogenous risk factors relating to clinical, social and environmental exposures.