UCSF

Congenital heart disease (CHD), a structural malformation of the heart or great vessels, is associated with brain abnormalities and an elevated risk of neurodevelopmental deficits. The mechanisms underlying these abnormalities remain incompletely characterized. The glymphatic system, the primary waste clearance system for the central nervous system, is driven by arterial pulsatility and dependent on systemic venous pressure. The glymphatic system may be impaired in CHD due to altered vascular pulsatility and systemic venous pressure resulting from abnormal cardiac physiology and surgical palliations. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) is an MRI-based non-invasive metric that is used to evaluate glymphatic function and has not been previously applied to adolescents with CHD. This study includes 34 adolescents with critical CHD (Transposition of great arteries (TGA): n = 17, Single-ventricle physiology (SVP): n = 9, Other CHD: n = 8) and 99 healthy adolescent controls. DTI-ALPS indices were calculated using a publicly available diffusion MRI processing pipeline (alps.sh). The expected results of this study included lower DTI-ALPS indices in subjects with CHD compared to controls, with subjects with SVP expected to have significantly lower ALPS indices compared to those with TGA. Statistical analysis included Bland-Altman analysis, group comparisons using two-tailed t-tests, and multivariable linear regression adjusting for age. DTI-ALPS mean indices were significantly lower in the CHD group compared to controls (1.348 vs. 1.499; β = -0.152, 95% CI: -0.216 - -0.089, p < 0.001), and this difference remained significant after adjusting for age (β = -0.083, 95% CI: -0.118 - -0.049, p < 0.001). Additionally, each CHD sub-type demonstrated significant reduction in DTI-ALPS mean indices compared against healthy controls. No significant difference was found between TGA and SVP subgroups (p = 0.671). No significant relationship was found between DTI-ALPS index and age or sex within either CHD or control group. Overall, these findings represent preliminary evidence of potential glymphatic dysfunction in CHD patients, with DTI-ALPS being a novel non-invasive biomarker for brain health and potential future neuroprotective interventions. Further research with larger cohorts and neurodevelopmental outcomes is needed to determine the clinical implications of these findings.

Diffuse midline gliomas (DMGs) have infiltrative, poorly defined boundaries that make tumor measurement difficult and variable. Volumetric assessment may be more informative than bidimensional response measurements, but manual volumetry is time-consuming and automated methods typically provide a single volume without indicating reliability. We evaluated whether evidential deep learning (EDL) with model ensembling could generate calibrated 95% uncertainty intervals for automated DMG volumes.Two five-model EDL ensembles based on SegResNet and nnU-Net v2 were evaluated. Voxel-wise probability distributions were propagated into tumor-volume estimates and 95% intervals. Analyses included voxel-wise calibration, true-volume coverage, relative interval width, and comparison of SegResNet uncertainty with physician difficulty ratings and spatial uncertainty annotations.The EDL nnU-Net v2 Ensemble approached 95% coverage but produced intervals likely too broad for clinical use, whereas the EDL SegResNet Ensemble provided the strongest balance between coverage and precision. SegResNet intervals widened with physician-rated difficulty, while aleatoric and epistemic uncertainty localized near physician-identified uncertain regions and tumor boundaries. These findings suggest evidential ensembling can provide automated DMG volumetry with calibrated uncertainty estimates that reflect clinically recognizable sources of difficulty.

Plasma neurofilament light chain (NfL) is a sensitive blood-based biomarker of neuroaxonal injury with potential value for predicting Alzheimer’s disease (AD)-related neurodegeneration. However, plasma NfL is influenced by physiological factors, including age, body mass index (BMI), and renal function. Besides, it remains unclear whether adjusted NfL measurements can predict subsequent structural brain changes during the preclinical stage of AD. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), this study developed individualized plasma NfL reference distributions and evaluated their ability to predict longitudinal MRI-measured neurodegeneration. Generalized Additive Models for Location, Scale, and Shape were fitted in CU Aβ− reference participants with valid eGFR data (n=378) to derive age-, BMI-, and eGFR-adjusted NfL Z-scores. Linear mixed-effects models were then used to test whether baseline and longitudinal NfL Z-scores were associated with changes in hippocampal volume, AD-signature cortical thickness, and lateral ventricular volume among Aβ+ participants. Expected plasma NfL increased with age and lower eGFR and decreased with higher BMI. Adjusted NfL Z-scores increased progressively across the AD continuum. However, baseline NfL Z-score did not significantly predict longitudinal change in any MRI outcome in the CU Aβ+, cognitively impaired Aβ+, or Early AD Continuum cohorts. In an exploratory subset, a greater annualized increase in NfL Z-score was associated with faster hippocampal volume decline (β = −0.225, P = 0.045), although the association did not remain significant after multiple-comparison correction. These findings support physiological adjustment for individualized interpretation of plasma NfL. Although a single baseline measurement did not predict subsequent MRI change, longitudinal NfL dynamics may provide a more informative signal of ongoing neurodegeneration and warrant further evaluation in larger cohorts with more frequent repeated measurements.

Pair Production Tomography (P2T) is an emerging imaging modality that detects pair production events from high-energy photon beams, providing unique atomic-number-based contrast. A fundamental challenge in P2T image reconstruction is positron range — the finite distance positrons travel before annihilating — which causes spatial blurring between the true pair production distribution and the reconstructed annihilation image. Unlike in Positron Emission Tomography (PET), the directed photon beam in P2T breaks the isotropy of positron emission, requiring a beam-direction-dependent correction approach. No positron range correction method has previously been developed for P2T.In this work, two positron range correction methods are developed and evaluated for P2T imaging: Richardson-Lucy deconvolution and a total variation (TV)-regularized optimization framework solved using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Beamlet-specific positron range kernels were constructed from Geant4 Monte Carlo simulations using a 10MV bremsstrahlung source, capturing the joint distribution of transverse positron displacement and emission angle relative to the beam direction. Kernels were constructed at three resolutions — 2mm, 1mm, and 0.5mm — to evaluate the effect of kernel resolution on correction performance.Corrections were applied to all 46 beamlets of a simulated computed tomography (CT)-based phantom containing three high-Z inserts. Both methods consistently improved beam profile spatial accuracy across all 46 beamlets, reducing average beam full width at half maximum (FWHM) from 8.5mm to approximately 6mm. At the full image level, pseudo-high resolution reconstruction at 1mm followed by rebinning to the intrinsic 2mm detector resolution was found to be the optimal pipeline. Both Richardson-Lucy and FISTA reduced insert FWHM from 18mm to 16mm with the 1mm resolution kernel correction. Direct 2mm reconstruction and correction showed no improvement in insert FWHM, demonstrating the importance of kernel resolution for accurate correction.These results demonstrate that positron range correction is feasible for P2T imaging and can meaningfully improve spatial resolution. The current pipeline represents a practical and effective approach, providing a foundation for future development of spatially varying kernels and expansion to full-view phantom and patient data.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder, currently affecting more than 7 million Americans aged 65 years and older, with disproportionately higher incidence rates observed among women and Black and Latinx populations. AD is a complex, polygenic, and multifactorial disease that arises through the interaction of genomic susceptibility and exposomic factors, including clinical, lifestyle, and environmental risk exposures experienced across the life course. Recent advances in biomarker research have enabled earlier detection of AD-related processes through plasma and cerebrospinal fluid assays and neuroimaging modalities, supporting preventative intervention during preclinical and prodromal stages. However, the predictive accuracy and generalizability of biomarkers and existing AD risk prediction tools often vary across demographic groups, highlighting the need for analytical validation of these tools in diverse populations. Addressing this gap is critical for developing equitable precision medicine approaches for AD screening and intervention. The present study describes a two-phase investigation aimed at benchmarking genetic, clinical, and environmental measures of AD liability and quantifying the extent to which these risk domains contribute to underlying AD pathology. Collectively, this body of work provides analytical insights into the improvement of risk prediction and assessment strategies in diverse populations while also advancing mechanistic understanding of how genetic, clinical, and environmental factors influence AD pathological processes.

Lysosomes are membrane-bound organelles that maintain cellular homeostasis by degrading intracellular waste as the endpoint of autophagic pathways. Impaired lysosomal function contributes to the disruption of proteostasis that characterizes aging and neurodegenerative diseases, and a growing body of evidence indicates that lysosomal alkalinization is a common feature of these conditions. Because lysosomal hydrolases require an acidic luminal environment for optimal activity, dysregulation of lysosomal pH is increasingly recognized as an important contributor to disease pathogenesis. Chapter 1 of this thesis reviews current understanding of lysosomal pH dysregulation in aging and neurodegeneration, with emphasis on the mechanisms underlying lysosomal alkalinization and the effects of restoring lysosomal acidity. Chapter 2 describes a phenotypic high-throughput screen that ultimately identified two novel lysosomal acidification compounds, WNK463 and WNK-IN-11, that are inhibitors to the WNK family of kinases, which had not previously been linked to lysosomal pH regulation. Collectively, this work advances our understanding of lysosomal pH dysregulation and supports lysosomal acidification as a promising therapeutic strategy for aging and neurodegenerative diseases.

Despite more than sixty years of civil rights legislation prohibiting racial segregation, pregnancy care in the United States remains segregated by insurance, and de facto by race, ethnicity, and socioeconomic status. While previous research has documented racial segregation in hospitals and its consequences for health outcomes, less attention has been paid to the organizational processes that produce and sustain segregated pregnancy care or the role of academic medicine in these practices. This study examined how law, health care financing, and academic medicine co-constructed and sustained segregated pregnancy care in the United States. It also elucidated the experiences of health care providers and trainees who witness and participate in a system that stratifies care and resources based on race, ethnicity, and socioeconomic status. Drawing on genealogy, institutional theory, critical legal theory, the theory of racialized organizations, and biopower, I conducted a qualitative study of two academic medical centers with different models of prenatal care delivery: one that sorts pregnant patients by insurance into the resident and faculty practices and one that blinds insurance at intake and assigns patients to providers based on clinical risk. Data included 94 interviews with obstetricians, midwives, trainees, administrators, and national experts; 190 hours of observation of clinical settings and professional meetings and events; and review of legal, archival, and institutional documents. I argue that payer segregation is produced through reinforcing legal, financial, and administrative structures and operationalized through organizational processes that sort pregnant patients into different providers, clinical spaces, and standards of care based on insurance. These routine sorting processes reproduce racial and economic segregation through the delivery of pregnancy care. Health care providers and trainees reported experiencing moral injury as they witnessed and participated in segregated systems. This study contributes to medical sociology by identifying payer segregation as a mechanism through which racial and economic segregation are reproduced in health care settings and by theorizing patient sorting as the organizational process that operationalizes payer segregation in everyday clinical practice. It shows how seemingly race-neutral laws, policies, and organizational practices sustain segregation long after the end of de jure segregation. I conclude by proposing health system and organizational reforms to dismantle payer segregation and advance equity in pregnancy care.

Macrophage (MØ) identities are coupled to specialized functions and local niches of residence. The diverse roles of macrophages in the breast, including epithelial development and remodeling, immune surveillance, and angiogenesis, imply a spatial organization of distinct transcriptional states. Yet, how this heterogeneity is spatially arranged in the normal human breast at microanatomical resolution remains largely uncharacterized. Here, we leveraged Xenium in situ to delineate two discrete macrophage populations. We identify a TREM2+ population intercalated between basal-myoepithelial cells and analogous to previously identified ductal niche macrophages in the mouse mammary gland. FOLR2+ macrophages broadly distribute across the interlobular stroma, and unbiased niche analyses further resolved a periepithelial subpopulation that localized to the intralobular stroma. Spatially weighted communication inference highlights differentially enriched signaling patterns between TREM2+ and FOLR2+ macrophage subsets with their surrounding microenvironment. Orthogonal spatial co-expression analyses of ligand-receptor pairs converged on CX3CL1-CX3CR1, in which TREM2+ macrophages interact with the neighboring epithelia. Collectively, these findings show that macrophage heterogeneity in the normal human breast is organized across transcriptional and microanatomical axes, thereby providing a spatially resolved framework for the resident mammary immune microenvironment.

The nuclear periphery is critical to establishing eukaryotic cell fate. The inner nuclear membrane (INM) protein emerin has been shown to play a role in cardiac and skeletal muscle development and maintenance. Mutations in the protein lead to X-linked Emery-Dreifuss Muscular Dystrophy, a degenerative disease characterized by both muscle wasting and cardiomyopathy. However, emerin-null mice do not develop muscular dystrophy phenotypes, making in vivo murine models impractical. To gain insight into emerin’s functions in human cardiac and skeletal muscle, we need a tractable human model system. Here, we have adapted an induced skeletal muscle (iSM) model system to efficiently direct skeletal muscle differentiation from human induced pluripotent stem cells (hiPSCs).Chapter 1 provides an overview of the nuclear periphery, skeletal muscle development, and current understanding of emerin function. Chapter 2 describes the use of iSMs to define the role of emerin in myogenesis. Chapter 3 summarizes experiments investigating the mechanism by which emerin regulates the Wnt effector β-catenin. Lastly, Chapter 4 highlights the challenges of using combinatorial CRISPR screening to investigate redundancy among LEM family proteins. This work establishes emerin’s requirement during late myogenesis as well as the consequences of its loss in gene regulation and expression.

The termination of food and water consumption is traditionally thought to depend on gastrointestinal and post-absorptive feedback. However, sensory signals generated during ingestion begin regulating consumption before nutrients or water reach the stomach. The mechanisms by which these pre-gastric signals regulate satiation and interact with post-ingestive feedback remain poorly understood. In this dissertation, I investigated the neural basis of pre-gastric satiation using a sham ingestion paradigm in rats, which isolates pre-gastric signals from post-gastric feedback, combined with transgenic rat models, neural recordings, and optogenetic manipulations.I found that pre-gastric signals make a major contribution to rapid thirst quenching by inhibiting thirst-promoting neurons in the median preoptic nucleus. This inhibition progressively weakens when oral water cues are repeatedly uncoupled from rehydration, demonstrating that pre-gastric thirst quenching is learned rather than innate. I further show that this learning is bidirectional and reflects the predicted physiological consequences of ingestion. In parallel, I found that pre-gastric satiation during feeding is nutrient-specific and depends on oral detection of calories rather than sweetness alone, with evidence supporting a role for oral glucose sensing through SGLT1. Together, these findings demonstrate that pre-gastric satiation is a learned predictive process that links oral sensory cues with their post-ingestive consequences to tightly regulate ongoing consumption. This work provides new insights into the neural mechanisms underlying the brain's control of meal size and fluid intake.