ESC derived human cortical neurons harboring the NACC1 c.892C > T p.R298W missense mutation exhibit molecular differences from controls that influence neuronal maturation.

A de novo mutation in the transcription factor Nucleus accumbens associated protein 1 (NACC1) gene (c.892C > T p.R298W) causes a rare, severe neurodevelopmental disorder which manifests postnatally.

Retrieval augmented generation based dynamic prompting for few-shot biomedical named entity recognition using large language models.

Biomedical named entity recognition (NER) is a high-utility natural language processing (NLP) task, and large language models (LLMs) show promise particularly in few-shot settings (i.e., limited training data).

A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation.

Background: Identifying neuroinfectious disease (NID) cases using International Classification of Diseases billing codes is often imprecise, while manual chart reviews are labor-intensive. Machine learning models can leverage unstructured electronic health records to detect subtle NID indicators, process large data volumes efficiently, and reduce misclassification. While accurate NID classification is needed for research and clinical decision support, using unstructured notes for this purpose remains underexplored.

Bridging AI, NLP, and Digital Health: A Bi-Directional Learning Experience at Emory University.

Digital health, natural language processing, and artificial intelligence collectively possess considerable promise, with the potential to transform healthcare particularly within resource-limited settings. This manuscript discusses a research fellowship at the Department of Biomedical Informatics at Emory University, focusing on the outcomes of applying AI and NLP to address healthcare challenges in low- and middle-income countries (LMICs) and the bi-directional learning between academia and real-world field application. The fellowship explored multilingual generative language models, gender bias in NLP, and conversational AI, demonstrating its application through the enhanced development of AvivaAI, a mobile conversational chatbot for cervical cancer awareness in Nigeria.

The normalizing properties of intracranial volume across race and sex.

In volumetric analysis of the human brain with MRI, intracranial volume is an important covariate as it has a strong correlation with the volume of regions of interest in the brain. Therefore, accurate adjustment for intracranial volume (e.g.

Dichotomous transcriptome divergence at the cell-type level between the male and female germlines.

Gene expression evolution in reproductive organs plays a central role in species divergence, yet cell-type-resolved patterns in invertebrates remain poorly understood. We used single-nucleus RNA-sequencing to profile testis and ovary transcriptomes from the sibling species and . Despite conserved cellular composition, we observed highly cell-type-specific expression and coexpression network divergence between species.

A GPT-4o-powered framework for identifying cognitive impairment stages in electronic health records.

Alzheimer's Disease and Related Dementias (ADRD) pose a major public health challenge, with a critical need for accurate and scalable tools for detecting cognitive impairment (CI). Readily available electronic health records (EHRs) contain valuable cognitive health data, but much of it is embedded in unstructured clinical notes. To address this problem, we developed a GPT-4o-powered framework for CI stage classification, leveraging longitudinal patient history summarization, multi-step reasoning, and confidence-aware decision-making.

Brain MRI signatures across sex and CSF Alzheimer's disease biomarkers.

The relationship between cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease and neurodegenerative effects is not fully understood. This study investigates neurodegeneration patterns across CSF Alzheimer's disease biomarker groups, the association of brain volumes with CSF amyloid and tau status and sex differences in these relationships in a clinical neurology sample. MRI and CSF Alzheimer's disease biomarkers data were analysed in 306 patients of the Mass General Brigham healthcare system aged 50+ (mean age = 68.

Comparative Analysis of Susceptibility Profile of Dermatophytes Against 8 Antifungal Agents: A Cross-Sectional Study.

Background: The currently reported prevalence of dermatophytosis (superficial mycoses) in India ranges from 6.09%-61.5%.

High-Throughput Phenotyping of the Symptoms of Alzheimer Disease and Related Dementias Using Large Language Models: Cross-Sectional Study.

Background: Alzheimer disease and related dementias (ADRD) are complex disorders with overlapping symptoms and pathologies. Comprehensive records of symptoms in electronic health records (EHRs) are critical for not only reaching an accurate diagnosis but also supporting ongoing research studies and clinical trials. However, these symptoms are frequently obscured within unstructured clinical notes in EHRs, making manual extraction both time-consuming and labor-intensive.

Cell-death pathways and tau-associated neuronal vulnerability in Alzheimer's disease.

Neuronal loss is the ultimate driver of neural system dysfunction in Alzheimer's disease (AD). We used single-nucleus RNA sequencing and neuropathological phenotyping to elucidate mechanisms of neurodegeneration in AD by identifying vulnerable neuronal populations and probing for their differentially expressed genes. Evidenced by transcriptomic analyses and quantitative tau immunoassays of human AD and non-AD brain tissue, we identified a neuronal population especially vulnerable to tau pathology.

"Recon-all-clinical": Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI.

Surface-based analysis of the cerebral cortex is ubiquitous in human neuroimaging with MRI. It is crucial for tasks like cortical registration, parcellation, and thickness estimation. Traditionally, such analyses require high-resolution, isotropic scans with good gray-white matter contrast, typically a T1-weighted scan with 1 mm resolution.

Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records.

Objectives: Unstructured and structured data in electronic health records (EHR) are a rich source of information for research and quality improvement studies. However, extracting accurate information from EHR is labor-intensive. Timely and accurate identification of patients with Alzheimer's Disease, related dementias (ADRD), or mild cognitive impairment (MCI) is critical for improving patient outcomes through early intervention, optimizing care plans, and reducing healthcare system burdens.

Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study.

Background: Congestive heart failure (CHF) is a common cause of hospital admissions. Medical records contain valuable information about CHF, but manual chart review is time-consuming. Claims databases (using International Classification of Diseases [ICD] codes) provide a scalable alternative but are less accurate.

Natural Language Processing for Digital Health in the Era of Large Language Models.

Objectives: Large language models (LLMs) are revolutionizing the natural language pro-cessing (NLP) landscape within healthcare, prompting the need to synthesize the latest ad-vancements and their diverse medical applications. We attempt to summarize the current state of research in this rapidly evolving space.

Methods: We conducted a review of the most recent studies on biomedical NLP facilitated by LLMs, sourcing literature from PubMed, the Association for Computational Linguistics Anthology, IEEE Explore, and Google Scholar (the latter particularly for preprints).

Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study.

Background: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes.

Objective: We aimed to use unsupervised learning techniques on electronic health records (EHRs) from memory clinic patients to identify ADRD subtypes.

Analysis of human urinary extracellular vesicles reveals disordered renal metabolism in myotonic dystrophy type 1.

Chronic kidney disease (CKD) and the genetic disorder myotonic dystrophy type 1 (DM1) each are associated with progressive muscle wasting, whole-body insulin resistance, and impaired systemic metabolism. However, CKD is undocumented in DM1 and the molecular pathogenesis driving DM1 is unknown to involve the kidney. Here we use urinary extracellular vesicles (EVs), RNA sequencing, droplet digital PCR, and predictive modeling to identify downregulation of metabolism transcripts Phosphoenolpyruvate carboxykinase-1, 4-Hydroxyphenylpyruvate dioxygenase, Dihydropyrimidinase, Glutathione S-transferase alpha-1, Aminoacylase-1, and Electron transfer flavoprotein B in DM1.

Heterogeneous treatment effects of BCG vaccine on Alzheimer's disease risk.

Background: This project has investigated the role of the Bacillus Calmette-Guérin (BCG) vaccine as a potential treatment against Alzheimer's disease (AD) and related dementias (ADRD).

Objective: To further establish that BCG treatment results in lower risk of ADRD through novel machine learning methods and to analyze the heterogeneity of treatment effects.

Methods: This retrospective cohort study was conducted from May 28, 1987 to May 6, 2021, in patients who were 50 years or older and were diagnosed with non-muscle-invasive bladder cancer (NMIBC).

Molecular profiling of frontal and occipital subcortical white matter hyperintensities in Alzheimer's disease.

White matter hyperintensities (WMHs) are commonly detected on T2-weighted magnetic resonance imaging (MRI) scans, occurring in both typical aging and Alzheimer's disease (AD). Despite their frequent appearance and their association with cognitive decline in AD, the molecular factors contributing to WMHs remain unclear. In this study, we investigated the transcriptomic profiles of two commonly affected brain regions with coincident AD pathology-frontal subcortical white matter (frontal-WM) and occipital subcortical white matter (occipital-WM)-and compared with age-matched cognitively intact controls.

Two-Layer Retrieval-Augmented Generation Framework for Low-Resource Medical Question Answering Using Reddit Data: Proof-of-Concept Study.

Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.

Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.

CARE-SD: classifier-based analysis for recognizing provider stigmatizing and doubt marker labels in electronic health records: model development and validation.

Objective: To detect and classify features of stigmatizing and biased language in intensive care electronic health records (EHRs) using natural language processing techniques.

Materials And Methods: We first created a lexicon and regular expression lists from literature-driven stem words for linguistic features of stigmatizing patient labels, doubt markers, and scare quotes within EHRs. The lexicon was further extended using Word2Vec and GPT 3.

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's disease.

The pathogenesis of Alzheimer's disease (AD) depends on environmental and heritable factors, with its molecular etiology still unclear. Here we present a spatial transcriptomic (ST) and single-nucleus transcriptomic survey of late-onset sporadic AD and AD in Down syndrome (DSAD). Studying DSAD provides an opportunity to enhance our understanding of the AD transcriptome, potentially bridging the gap between genetic mouse models and sporadic AD.

Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer's disease.

Astrocytes are crucial to brain homeostasis, yet their changes along the spatiotemporal progression of Alzheimer's disease (AD) neuropathology remain unexplored. Here we performed single-nucleus RNA sequencing of 628,943 astrocytes from five brain regions representing the stereotypical progression of AD pathology across 32 donors spanning the entire normal aging to severe AD continuum. We mapped out several unique astrocyte subclusters that exhibited varying responses to neuropathology across the AD-vulnerable neural network (spatial axis) or AD pathology stage (temporal axis).

Using Apple Watches to Monitor Health and Behaviors of Individuals With Cognitive Impairment: A Case Series Study.

Background: This study explores the potential of developing digital biomarkers from wearables for monitoring individuals with Alzheimer's Disease and Related Dementias, focusing on the feasibility of using Apple Watches for tracking health and behaviors in older adults with cognitive impairment.

Methods: Data collection used the Amissa Health technology stack, which passively collects time-series data from smartwatches and provides a high-frequency cloud database for secure data storage, query, and visualization by clinicians and researchers. The platform consists of (i) AmissaWear, a software app that runs on smartwatches and sends information to a cloud database using a secure API; and (ii) AmissaOrbis, a centralized cloud portal for the collected data.