Citations

Objectives/Goals: Longitudinal studies capture dynamic host–environment interactions crucial to microbiome research. Commonly used mixed-effects models can struggle with small clinical samples, like many endometrial cancer studies. We will compare them to simpler fixed-effects models, aiming to develop a new fixed-effects differential abundance alternative. Methods/Study Population: Starting with simulated data for linear models with a single treatment effect, we will incrementally increase complexity to reflect real-world microbiome data. To observe conditions of misspecification, simulated data of repeated measures will be generated using different distributions, while varying parameters and inter-class correlation. In each condition, linear fixed- and mixed-effects models will be fitted 1000 times to empirically determine p-value, type 1 error rate, and power. We will increase the verisimilitude of our approach by using endometrial cancer data from our lab as a real background, while adding simulated signals. This data will be used to assess extant differential abundance models against our novel fixed-effects model. Results/Anticipated Results: Our initial findings indicate that fixed-effects models maintain control over type 1 error rates in small samples, while mixed-effects models do not perform as reliably. We are now exploring the effects of confounders on type 1 error rates and power in linear models, while our next step will evaluate generalized linear models in a differential abundance context. We expect that the fixed-effects models will continue to be as reliable as mixed-effects models, while being less computationally complex. After establishing our theoretical basis, our novel model should perform comparably to mixed-effects models on pseudo-simulated endometrial cancer data and better at small sample sizes. We will finally apply our model to real endometrial cancer data to identify microbial markers of disease predisposition. Discussion/Significance of Impact: By characterizing and improving these statistical tools, we seek to increase the robustness and power of microbiome study results (especially at small sample sizes), ultimately supporting better-informed translational research decisions. This work underscores the importance of statistical methods in advancing microbiome research.

The interplay between intracellular and intravascular lipolysis is crucial for maintaining circulating lipid levels and systemic energy homeostasis. Adipose triglyceride lipase (ATGL) and lipoprotein lipase (LPL), the primary triglyceride (TG) lipases responsible for these two spatially separate processes, are highly expressed in adipose tissue. Yet the mechanisms underlying their coordinated regulation remain undetermined. Here, we demonstrate that genetic ablation of G0S2, a specific inhibitory protein of ATGL, completely abolished diet-induced hypertriglyceridemia and significantly attenuated atherogenesis in mice. These effects were attributable to enhanced whole-body TG clearance, not altered hepatic TG secretion. Specifically, G0S2 deletion increased circulating LPL concentration and activity, predominantly through LPL production from white adipose tissue (WAT). Strikingly, transplantation of G0S2-deficient WAT normalized plasma TG levels in mice with hypertriglyceridemia. In conjunction with improved insulin sensitivity and decreased ANGPTL4 expression, the absence of G0S2 enhanced the stability of LPL protein in adipocytes, a phenomenon that could be reversed upon ATGL inhibition. Collectively, these findings highlight the pivotal role of adipocyte G0S2 in regulating both intracellular and intravascular lipolysis, and the possibility of targeting G0S2 as a viable pharmacological approach to reducing levels of circulating TGs.

Abstract The development of targeted therapy for patients with multiple myeloma (MM) is hampered by the low frequency of actionable genetic abnormalities. Gain or amplification of chromosome 1q (1q+) is the most frequent arm-level copy number gain in patients with MM and is associated with higher risk of progression and death despite recent therapeutic advances. Thus, developing targeted therapy for patients with MM with 1q+ stands to benefit a large portion of patients in need of more effective management. Here, we used large-scale dependency screens and drug screens to systematically characterize the therapeutic vulnerabilities of MM with 1q+ and displayed increased sensitivity to myeloid cell leukemia-1 (MCL1) and phosphatidyl inositol 3-kinase (PI3K) inhibitors. Using single-cell RNA sequencing, we compared subclones with and without 1q+ within the same patient tumors and demonstrated that 1q+ is associated with higher levels of MCL1 and the PI3K pathway. Furthermore, by isolating isogenic clones with different copy number profiles for part of the chromosome 1q arm, we observed increased sensitivity to MCL1 and PI3K inhibitors with arm-level gain. Lastly, we demonstrated synergy between MCL1 and PI3K inhibitors and dissected their mechanism of action in MM with 1q+, uncovering a cytostatic effect. In conclusion, this study highlights that MM with 1q+ may present enhanced sensitivity to MCL1 and PI3K inhibitors, enabling their use at lower doses without sacrificing efficacy, and may thus accelerate the development of targeted therapy for patients with MM and 1q+.

Abstract Purpose To evaluate the test–retest repeatability of a rapid, free‐breathing two‐dimensional (2D) MR elastography (MRE) technique and to assess the reliability of the liver‐stiffness measurements compared with conventional breath‐hold MREs. Methods Fifteen and 115 participants were enrolled in the technical repeatability and measurement equivalence assessment cohorts, respectively. All participants underwent rapid free‐breathing and conventional breath‐hold 2D MRE (twice in repeatability cohort) on 1.5T scanners. Both methods have four phase offsets over one harmonic motion cycle at 60 Hz; one and 10 cycles were collected and processed in breath‐hold and free‐breathing MREs, respectively. The liver stiffness measurements of free‐breathing (LS F ) and breath‐hold MRE (LS B ) were calculated from manually drawn regions of interest. The repeatability coefficients and Spearman correlation were used to assess technical repeatability and measurement agreement between LS F and LS B . Univariable and multivariable linear regressions were performed to identify potential influencing factors, including age, sex, body mass index, and fat fraction, in the measurement agreement between LS B and LS F . Results The repeatability coefficient of free‐breathing 2D MRE is comparable to breath‐hold MRE (LS F : 20.8%, LS B : 20.4%). LS F showed a strong agreement and significant correlation with LS B (LS F = 1.01 × LS B , ρ = 0.94, p < 0.001). The measurement agreement between LS F and LS B was only significantly affected by sex ( p = 0.047) after adjusting for confounding factors of age, body mass index, and fat fraction. Conclusions The nongated, free‐breathing, multislice 2D MRE technique can provide reliable liver stiffness measurements compared with conventional breath‐hold 2D MRE. It could provide a comfortable alternative method with reliable liver stiffness measurements for patients who have difficulty in suspending respiration.

Microglia actively survey the brain and dynamically interact with neurons to maintain brain homeostasis. Microglial Gi protein–coupled receptors (Gi-GPCRs) play a critical role in microglia-neuron communications. However, the impact of temporally activating microglial Gi signaling on microglial dynamics and neuronal activity in the homeostatic brain remains largely unknown. In this study, we used Gi-based designer receptors exclusively activated by designer drugs (Gi-DREADD) to selectively and temporally modulate microglial Gi signaling pathway. By integrating this chemogenetic approach with in vivo two-photon imaging, we observed that exogenous activation of microglial Gi signaling transiently inhibited microglial process dynamics, reduced neuronal activity, and impaired neuronal synchronization. These altered neuronal functions were associated with a decrease in interactions between microglia and neuron somata. Together, this study demonstrates that acute, exogenous activation of microglial Gi signaling regulates neuronal circuit function, offering a potential pharmacological target for the neuromodulation through microglia.

ABSTRACTBackgroundContrast‐enhanced magnetic resonance neurography (ceMRN) can enhance brachial plexus visualization and quality of imaging. However, the interval between contrast injection and scanning that provides the highest‐quality images is not known.MethodsFifteen patients underwent brachial plexus imaging using the 3D T2‐NerveView sequence with a scanning duration of 5 min. A consecutive six‐phase scan was initiated immediately at the start of contrast agent injection. Subsequently, all patients' images were classified into six groups according to the phases: group A (phase 1, delay 0 min), group B (phase 2, delay 5 min), group C (phase 3, delay 10 min), group D (phase 4, delay 15 min), group E (phase 5, delay 20 min), and group F (phase 6, delay 25 min). The image quality in each group was assessed based on nerve signal (signalnerve), muscle signal (signalmuscle), lymph node signal (signallymph node), background noise (BN), signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and subjective score.ResultsSignalnerve, signalmuscle, BN, and SNR did not significantly differ among the six groups (p > 0.05). However, significant differences (p < 0.05) were observed in signallymph node (F = 16.067), CNR (F = 9.495), and subjective score (χ2 = 23.586). As the scanning delay increased, signallymph node intensity gradually increased whereas the CNR gradually decreased. The subjective score was significantly higher in groups B (4.83 ± 0.24), C (4.90 ± 0.21), D (4.87 ± 0.30), E (4.83 ± 0.31), and F (4.83 ± 0.31) than in group A (4.47 ± 0.30).ConclusionWe recommend performing brachial plexus ceMRN 5 min after contrast injection. With this delay, the brachial plexus can be visualized optimally with minimal interference from background signals.

Abstract Motivation Multiplex immunofluorescence (MxIF) enables the quantification of multiple protein markers at a single-cell level while preserving spatial information, offering a powerful tool for studying tissue microenvironments. However, the flexibility in MxIF panel design poses challenges in standardizing cell phenotyping. Results We present SpaFlow, an efficient, customizable pipeline for unsupervised clustering and classification of MxIF data, implemented using Nextflow. SpaFlow performs quality control, clustering, and postclustering analysis on segmented and quantified MxIF data, facilitating reproducible and scalable analyses across various computing platforms. The SpaFlow pipeline integrates three clustering and classification packages—Seurat, SCIMAP, and CELESTA—each providing unique methodologies for identifying cell types based on phenotypic markers. A novel “meta-clustering” approach condenses clusters across multiple regions of interest into common meta-clusters, streamlining the cell-type identification process in large datasets. SpaFlow’s robust quality control steps, including signal summation and cell density filtering, mitigate artifacts that may impact clustering accuracy. We demonstrate the utility of SpaFlow in a case study involving 297 ovarian tumor cores, where SpaFlow successfully identified biologically meaningful cell populations, including tumor-infiltrating lymphocytes, efficiently and rapidly. Additionally, SpaFlow’s reproducibility is validated using serial tonsil sections, confirming its capability to consistently identify distinctive cell populations across matched ROIs. Availability and implementation SpaFlow is freely available with detailed documentation and examples at https://github.com/dimi-lab/SpaFlow.

Background: The incidence of pediatric thyroid cancer has been increasing, and care varies due to socioeconomic disparities or differing practice patterns. Clinical guidelines call for care in multidisciplinary teams to minimize variance and provide protocols. Based on expert opinion, we hope to describe the form and function of such multidisciplinary teams for pediatric thyroid programs. Methods: A modified Delphi method to reach consensus statements over two rounds. Twenty-one experts with varying backgrounds responded to each statement on a 9-point Likert scale. Upon completion of the survey, the panel reviewed and shared the results and comments from participants and modified the statements accordingly. This process was repeated such that statements reached consensus, were deemed no consensus, or had no change in the mean. Results: There was an 88% and 83% completion rate for Rounds 1 and 2, respectively. A consensus was observed that there is a distinct definable model of care for pediatric thyroid patients. No consensus was reached for the age range of patients, but programs should care for children with medullary thyroid cancer, differentiated thyroid cancer, and patients with genetic predisposition syndromes. A comprehensive team includes, but is not limited to, a thyroid surgeon, a pediatric endocrinologist, a high-volume fine-needle aspiration (FNA) proceduralist, an oncologist, a nuclear medicine physician, a pediatric pathologist, a pediatric radiologist, and a nurse coordinator. Necessary support services involve care coordination, access to a multidisciplinary tumor board, ability to perform ultrasound-guided FNA, and access to molecular testing. The panel emphasized cross-institutional collaborative research prioritizing guidelines development, disease-specific outcomes, treatment toxicity, and the molecular landscape of thyroid cancer. Conclusions: These consensus statements can be beneficial in improving multidisciplinary care, by describing which elements of pediatric thyroid programs should be consistent across institutions. Overall, the panel agreed that pediatric thyroid centers should provide integrated care with defined team members, services, resources, and research priorities. This model has the potential to standardize various aspects of clinical care and enhance our ability to study patient outcomes, improve health care delivery, and increase scholarly collaboration.

AbstractCases of myelin oligodendrocyte glycoprotein (MOG) antibody‐associated disease (MOGAD) co‐occurring with neoplasms have been reported. In this international, retrospective cohort study in South Korea and the USA, 16 of 445 (3.6%) patients with MOGAD had concurrent neoplasm within 2 years of MOGAD onset, resulting in a standardized incidence ratio for neoplasm of 3.10 (95% confidence interval [CI], 1.77–4.81; P < 0.001) when compared to the age‐ and country‐adjusted incidence of neoplasm in the general population. However, none of the nine tumor tissues obtained demonstrated MOG immunostaining. The slightly increased frequency without immunohistopathological evidence suggest with true paraneoplastic MOGAD is extremely rare.

Abstract The prevalence of steatotic liver disease (SLD) is rising globally, presenting a significant public health challenge. While non-contrast computed tomography (CT) scans hold promise for the opportunistic detection of SLD, their full potential has not been systematically explored. Clinical indicators, such as serum data, are crucial for monitoring the progression of SLD. We introduce a novel Multimodal AI for Opportunistic Steatotic Liver Disease Screening (MAOSS), which integrates demographic data, serum tests, conventional CT biomarkers, and deep learning-derived CT image features to enhance model performance and explainability. MAOSS employs an innovative learning strategy, trained on a large-scale dataset (n = 1,783) encompassing both histopathologically-confirmed SLD and radiologically-reported steatosis cases. The model underwent extensive retrospective validation using steatosis grades derived from both histology and MRI-PDFF across internal and external datasets (n = 913), as well as real-world patient data (n = 18,504). It accurately detected mild to severe steatosis (≥ S1) with AUCs ranging from 0.903 to 0.929. Notably, MAOSS significantly improved the overall AUC for radiologists (n = 11) from 0.709 to 0.798 (p < 0.001). Furthermore, MAOSS was applied to optimize the standard clinical pathway for a simulated primary risk screening of SLD patients (n = 1,192), resulting in the identification of 31% more patients at risk of steatohepatitis and liver fibrosis progression, highlighting its potential to enhance SLD detection and management.