Citations

Abstract Alzheimer's disease and related dementias (ADRD) significantly impact patients and their caregivers, causing emotional stress, lack of training, and financial strain for the latter. Addressing the need for effective support, this research developed and validated ADQueryAid, a conversational AI system designed to empower ADRD caregivers. Built on a Large Language Model (LLM) and enriched with authoritative ADRD information through a knowledge graph, ADQueryAid uses Retrieval Augmented Generation (RAG) techniques to retrieve relevant information from both structured and unstructured data sources. Prompt engineering and conversation policies ensure the delivery of informative, empathetic, and personalized responses. Evaluated via a Blind Within-Subjects Design study with 20 ADRD caregivers interacting with both ADQueryAid and a baseline model (ChatGPT 3.5) in fictional caregiving scenarios, the system's usability was assessed using the Chatbot Usability Questionnaire (CUQ). Results showed ADQueryAid significantly outperformed the baseline model across all CUQ metrics, offering more contextually relevant information, accurate guidance, and emotional support, which enhanced the caregiving experience. This study highlights the potential of AI to support ADRD caregivers by providing personalized assistance, with future research focusing on expanding the knowledge base, refining conversational strategies, and exploring the long-term impact on caregiver well-being and patient outcomes.

Abstract Metabolic disorders such as insulin resistance and hypertension are potential risk factors for aging and neurodegenerative diseases. These conditions are reversed in Chromogranin A knockout (CgA-KO) mice. This study investigates the role of CgA in Alzheimer’s disease (AD) and corticobasal degeneration (CBD). CgA ablation in tauopathy mice (hTau) (CgA-KO/hTau) exhibited reduced tau aggregation, spreading, extended lifespan, and improved cognitive function. Transcriptomic and metabolite analysis of mouse cortices revealed altered alpha1-adrenergic receptors (Adra1) and high epinephrine (EPI) levels in hTau mice compared to WT mice, mirroring observations in AD and CBD patients. CgA-KO/hTau mice exhibited a reversal of EPI levels in the cortex and the expression of Adra1, nearly returning them to WT levels. Treatment of hippocampal slices with EPI or Adra1 agonist intensified, while an Adra1 antagonist inhibited tau hyperphosphorylation and aggregation. These findings highlight the interplay between the EPI-Adra signaling system and CgA in tauopathy.

AbstractMulti‐energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single‐energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user‐selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task‐specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single‐energy CT, the substantial differences between single‐energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

7011 Background: Monoclonal B-cell lymphocytosis (MBL) is a precursor to chronic lymphocytic leukemia (CLL) but also a risk factor for other lymphoid malignancies. Clonal hematopoiesis (CH), originally defined as the presence of mutations in myeloid driver genes, is a precursor to myeloid malignancies. Recently, CH that includes genes associated with lymphoid malignancies has been reportedly associated with risk of lymphoid malignancies, and particularly CLL. Little is known about the association between CH and MBL and their joint effects on risk of hematological malignancies (HM). Methods: Study participants were from the Mayo Clinic Biobank, a large-scale biorepository of patients who provided a peripheral blood sample. CH at enrollment was determined using whole-exome sequencing (50x coverage). Myeloid-CH (M-CH) was based on mutations located in 56 genes associated with myeloid malignancies. Lymphoid-CH (L-CH) was based on 235 genes associated with lymphoid malignancies. Individuals were screened for MBL using eight-color flow cytometry in two different cohorts of Biobank participants. Cohort 1 (N=3883) included participants who had available sample for MBL screening at enrollment. Cohort 2 (N=5684) were participants recontacted to provide a sample for MBL screening. Incident HM were identified using ICD codes and confirmed via medical record review. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Cox regression was used to estimate hazard ratios (HR), with time defined as date between MBL sample and the first of incident HM, death, or 9/30/2023. Analyses were adjusted for age at MBL screening and sex. Results: In total, 9567 individuals (39% male, median age 66 years) were screened for MBL with available CH, of whom 17% were positive for MBL, 7% were positive for M-CH, and 2% were positive for L-CH. We found no evidence of an association between M-CH and MBL (OR=1.11, 95% CI:0.91-1.34) nor L-CH and MBL (OR=1.05, 95% CI:0.70-1.55). When subset to Cohort 1, where the same sample was used for both CH and MBL screening, the results held (M-CH OR=1.11, 95% CI: 0.83-1.48; L-CH OR=0.82, 95% CI:0.40-1.53). Next, we investigated the effect of each of these precursors with incident HM. Median follow-up was 4 years, and 78 individuals developed incident HM (31 myeloid, 49 lymphoid overall, 8 CLL). When modeling both precursors, both L-CH (HR=6.23, 95% CI:2.46-15.79) and MBL (HR=3.89, 95% CI:2.17-6.99) were independently associated with incident lymphoid malignancy. When excluding CLL events, the association held (L-CH: HR=5.94, 95% CI:2.11-16.77; MBL: HR=2.95, 95% CI:1.54-5.66). Only M-CH was significantly associated with incident myeloid malignancies. Conclusions: In the largest cohort with MBL and CH precursors measured, we found no evidence of an association between them. However, both MBL and L-CH were strong independent risk factors for incident lymphoid malignancies.

Abstract BACKGROUND Medulloblastoma (MB) is a highly malignant childhood cerebellar tumor comprising a collection of molecularly and clinically distinct entities. Group 3 and Group 4-MB subgroups originate from overlapping progenitor pools of the developing rhombic lip when neuronal differentiation hierarchies are subverted through somatic alterations. Mutations suspected to deregulate the activity of chromatin-modifying genes are pervasive in Group 3 and Group 4-MB. However, the molecular consequences of these alterations remain largely undefined. METHODS We characterized the chromatin landscape of fifty-two primary MBs using chromatin immunoprecipitation followed by sequencing (ChIP-seq) for six histone modifications and performed multi-modal integration with sample-matched somatic lesions, genome-wide DNA methylation, and transcriptomic profiles. RESULTS Analysis of differential chromatin states across MB subgroups using a Hidden Markov Model revealed a significant enrichment of the bivalent enhancer state (EnhBiv; marked by coincident H3K4me1 and H3K27me3) in Group 4-MB which localized to promoters of neurodevelopmental genes and transcription factors. To functionally dissect the pathogenetic significance of the enriched EnhBiv state in Group 4-MB, we sought to identify key regulators that confer this state. Integrative bioinformatics coupled with CUT&RUN analysis revealed significant enrichment of KDM2B, a histone lysine demethylase overexpressed in Group 4-MB, in gene promoters marked by the EnhBiv state. CRISPR gene targeting or targeted protein degradation of KDM2B selectively suppressed growth of Group 3 and Group 4-MB models in vitro and in vivo. Mechanistically, inhibition of KDM2B disrupted chromatin bivalency by blocking PRC2 complex recruitment and reducing H3K27me3 deposition, profoundly derepressing PRC2 targets involved in neuronal differentiation. CONCLUSIONS Our comprehensive characterization of the MB chromatin landscape in a large cohort of primary tumors provides unprecedented insights into the epigenetic basis of MB subgroups, implicating novel mechanisms of tumorigenesis, and nominating KDM2B as a selective dependency in Group 3/4-MB that should be prioritized as an attractive therapeutic target.

AbstractNucleic acid amplification testing (NAAT) remains one of the most reliable methods for pathogen identification. However, conventional bulk NAATs may not be sufficiently fast or sensitive enough for the detection of clinically‐relevant pathogens in point‐of‐care testing. Here, we have developed a digital droplet RT‐LAMP (ddRT‐LAMP) assay that rapidly and quantitatively detects the SARS‐CoV‐2 viral E gene in microfluidic drops. Droplet partitioning using ddRT‐LAMP significantly accelerates detection times across a wide range of template concentrations compared to bulk RT‐LAMP assays. We discover that a reduction in droplet diameter decreases assay times up to a certain size, upon which surface adsorption of the RT‐LAMP polymerase reduces reaction efficiency. Optimization of drop size and polymerase concentration enables rapid, sensitive, and quantitative detection of the SARS‐CoV‐2 E gene in only 8 min. These results highlight the potential of ddRT‐LAMP assays as an excellent platform for quantitative point‐of‐care testing.

Abstract Objectives Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes—real-time search and pretrained model utilization—it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. Materials and Methods RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics (“cancer immunotherapy and target therapy” and “LLMs in medicine”) were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. Results The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions—relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). Discussion RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. Conclusion By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.