Faculty Bibliography

Non-linear least squares (NLS) methods are commonly used for quantitative magnetic resonance imaging (MRI), especially for multi-exponential T1ρ mapping, which provides precise parameter estimation for different relaxation models in tissues, such as mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. However, NLS may suffer from problems like sensitivity to initial guesses, slow convergence speed, and high computational cost. While deep learning (DL)-based T1ρ fitting methods offer faster alternatives, they often face challenges such as noise sensitivity and reliance on NLS-generated reference data for training. To address these limitations of both approaches, we propose the HDNLS, a hybrid model for fast multi-component parameter mapping, particularly targeted for T1ρ mapping in the knee joint. HDNLS combines voxel-wise DL, trained with synthetic data, with a few iterations of NLS to accelerate the fitting process, thus eliminating the need for reference MRI data for training. Due to the inverse-problem nature of the parameter mapping, certain parameters in a specific model may be more sensitive to noise, such as the short component in the BE model. To address this, the number of NLS iterations in HDNLS can act as a regularization, stabilizing the estimation to obtain meaningful solutions. Thus, in this work, we conducted a comprehensive analysis of the impact of NLS iterations on HDNLS performance and proposed four variants that balance estimation accuracy and computational speed. These variants are Ultrafast-NLS, Superfast-HDNLS, HDNLS, and Relaxed-HDNLS. These methods allow users to select a suitable configuration based on their specific speed and performance requirements. Among these, HDNLS emerges as the optimal trade-off between performance and fitting time. Extensive experiments on synthetic data demonstrate that HDNLS achieves comparable performance to NLS and regularized-NLS (RNLS) with a minimum of a 13-fold improvement in speed. HDNLS is just a little slower than DL-based methods; however, it significantly improves estimation quality, offering a solution for T1ρ fitting that is fast and reliable.

Pregnancy is a vital period affecting both maternal and fetal health, with impacts on maternal metabolism, fetal growth, and long-term development. While the maternal metabolome undergoes significant changes during pregnancy, longitudinal shifts in maternal urine have been largely unexplored. In this study, we applied liquid chromatography-mass spectrometry-based untargeted metabolomics to analyze 346 maternal urine samples collected throughout pregnancy from 36 women with diverse backgrounds and clinical profiles. Key metabolite changes included glucocorticoids, lipids, and amino acid derivatives, indicating systematic pathway alterations. We also developed a machine learning model to accurately predict gestational age using urine metabolites, offering a non-invasive pregnancy dating method. Additionally, we demonstrated the ability of the urine metabolome to predict time-to-delivery, providing a complementary tool for prenatal care and delivery planning. This study highlights the clinical potential of urine untargeted metabolomics in obstetric care.

OBJECTIVE/UNASSIGNED:Policy ramifications of the COVID-19 pandemic shape the concurrent housing and overdose crises in the USA. Housing insecurity is a known risk factor for overdose, yet how residential eviction may influence fatal overdose risk is understudied. We sought to evaluate the spatiotemporal relationship between neighbourhood-level residential eviction rates and overdose mortality in Rhode Island (RI) before and during a statewide eviction moratorium in response to COVID-19. METHODS/UNASSIGNED:We conducted an ecological study at the census tract level in RI (N=240) by modelling the association between quintiles of eviction rates and fatal overdose rates from 2016 to 2021. We applied a Bayesian spatiotemporal approach using an integrated nested Laplace approximation and adjusted for an a priori determined set of time-varying demographic and policy covariates. RESULTS/UNASSIGNED:Descriptively, we observed a direct, dose-response relationship between quintiles of eviction incidence rates over the full study period and fatal overdose. Prior to the implementation of a statewide eviction moratorium, census tracts in the highest eviction quintile had increased rates of overdose mortality, relative to those in the lowest quintile (posterior mean relative rate = 1.49, 95% credible interval: 1.05 to 2.13). Associations during the periods of eviction moratorium were non-significant. CONCLUSION/UNASSIGNED:This work highlights the neighbourhood-level relationship between residential eviction and fatal overdose risk in the absence of an eviction moratorium. Enhanced investment in eviction prevention policies, such as rent relief and limitations to the circumstances under which landlords can file for eviction, may complement harm reduction efforts to reduce neighbourhood-level overdose inequalities.

Safeguarding patients from emerging infectious diseases demands strategies that prioritise patient well-being and protection. Immunobridging is an established trial methodology which has been increasingly employed to ensure patient protection and provide clinicians with swift access to vaccines. It uses immunological markers to infer the effectiveness of a new drug through a surrogate measure of efficacy. Recently, this method has also been employed to authorise novel drugs, such as COVID-19 vaccines, and this article explores the concepts behind immunobridging trials, their advantages, issues, and significance in the context of COVID-19 and other infectious diseases. Our goal is to improve awareness among clinicians, patient groups, regulators, and health leaders of the opportunities and issues of immunobridging, so that fewer patients are left without protection from infectious diseases, particularly from major pathogens that may emerge.

INTRODUCTION/UNASSIGNED:Scaling up interventions targeting non-communicable diseases (NCDs) is a global health priority, and implementation research can contribute to that effort. In 2019, the Global Alliance for Chronic Diseases funded 27 implementation research studies to improve evidence for scaling up interventions targeting prevention and/or control of hypertension and/or diabetes in low-resource settings. We examined these studies to improve the understanding of the implementation factors, including challenges and facilitators, that influence the early implementation phase of scale-up research projects targeting NCDs. METHODS/UNASSIGNED:This qualitative study was undertaken between August 2020 and July 2021. 43 semi-structured interviews were conducted with project investigators, implementers and policymakers, across 19 diverse scale-up projects, being implemented in 20 countries. The transcripts were inductively, open-coded using thematic analysis. Generated themes were mapped systematically to four out of five domain categorisations of the Consolidated Framework for Implementation Research (CFIR); the innovation domain fell outside the scope of this study. RESULTS/UNASSIGNED:Highlighted findings using CFIR are: (i) outer setting: influence of politics, lack of coordination between government departments and differing agendas towards NCDs hindered implementation while reliable and trustworthy government connections proved useful; (ii) inner setting: commitment of resources for implementation was a challenge while research capacity, work culture and trustworthy networks facilitated implementation; (iii) individuals: high-level stakeholder support and leadership was essential; (iv) process: extensive time and efforts required for stakeholder engagement towards local contextualisation was challenging, while collaborating, joint reflection, effective communication and adaptation facilitated. COVID-19 provided both challenges and opportunities and these varied depending on the intervention characteristics and study objectives. CONCLUSION/UNASSIGNED:Researchers supporting the scale-up of complex interventions targeting NCDs need to leverage on existing trusting relationships and foster equitable stakeholder partnerships through research. Interpersonal skills and good communication are essential complements to research expertise and must be considered during capacity building.

BACKGROUND/UNASSIGNED:Placenta accreta spectrum (PAS) disorder is a source of severe obstetric morbidity and mortality worldwide. The objective of this paper was to evaluate the potential relationship between social vulnerability and severe maternal morbidity in a cohort of patients delivering a pregnancy complicated by PAS. METHODS/UNASSIGNED:A retrospective review of 323 deliveries at three academic medical institutions between January 2013 and June 2022 was included in the analyses. Patients were those with a histopathologically confirmed case of PAS. The composite morbidity outcome included such maternal complications as mechanical ventilation, injury to organs and transfusion of 4+units of red blood cells. Social vulnerability was measured by assigning subjects a value of the Childhood Opportunity Index based on their home zip code. Logistic regression models were employed and adjusted for potential confounders. RESULTS/UNASSIGNED:73% of our sample experienced composite severe maternal morbidity at the time of their delivery. There were no statistically significant associations between social vulnerability and severe surgical morbidity, either as a composite or individually, within the multivariate regression models. CONCLUSION/UNASSIGNED:Our results do not support the hypothesis that social vulnerability is associated with severe maternal morbidity in deliveries complicated by PAS. The present study suggests that the relationship between social vulnerability and obstetrical surgical morbidity is more complicated than can be assessed by the present linear regression models.

The emergence of liquid biopsy technologies holds great promise in the cancer setting, including in pediatric central nervous system (CNS) tumors. In contrast to broad lower-depth sequencing, commonly referred to as low pass whole genome sequencing (WGS), targeted platforms with a higher depth of coverage have also been established. Here, we review targeted liquid biopsy techniques with applicability to pediatric CNS tumors. These include polymerase chain reaction (PCR), both droplet digital PCR and reverse transcription-based PCR, Sanger sequencing, and next-generation sequencing approaches that incorporate amplicon- and hybrid capture-based methods. The goal of this paper is to facilitate an understanding of these targeted techniques and provide a context for clinical relevance within disease categories, as well as a discussion on optimizing real-world implementation for pediatric CNS tumors.