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Indiana's 2024 Behavioral Health and Human Services Workforce Snapshot: Licensed Marriage and Family Therapists
No full textThis document is a 2024 data snapshot of actively practicing Licensed Marriage and Family Therapists (LMFTs) in Indiana within the Behavioral Health and Human Services workforce. It reports the total number of active LMFTs (628) and identifies primary practice settings, showing that a majority practice in private practice, with smaller proportions providing services via telehealth or in mental health clinics. The document also highlights core services provided, particularly mental health services, general counseling, and crisis counseling, and identifies key populations served, with an overwhelming emphasis on adults and families. Additional information is included on educational background and geographic training locations
RAZOR: a database of PCR primers targeting human respiratory viruses
No full textRespiratory viruses like SARS-CoV-2, Influenza A, and others represent a considerable threat to public health, infecting millions of people annually. Previous respiratory virus outbreaks have demonstrated the value of polymerase chain reaction (PCR) testing as a gold standard for definitive virus identification; however, existing database resources for viral PCR primer design are either outdated or restricted to a small number of species. To address the need for updated and comprehensive viral PCR resources, we introduce RAZOR, a database of nearly 20 000 primers covering 20 different respiratory virus species. We created genome-wide template sets for each virus and used them to design quantitative PCR (qPCR) and standard PCR primer pairs with Primer3. Detailed primer information, including sequence coordinates, melting and annealing temperatures, GC content, and hairpin structure probabilities, is provided through a user-friendly Integrative Genomics Viewer (IGV) interactive display. Validated primers, including a group of SARS-CoV-2 primers tested by our group, are also showcased in a dedicated section of the IGV. RAZOR stands out as a valuable tool for investigators designing targeted PCR approaches for respiratory virus detection. Database URL: https://razor-razor.webapps.iu.edu
Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study
No full textBackground: Artificial intelligence (AI)-enabled devices are increasingly used in health care. However, there has been limited research on patients' informational preferences, including which elements of AI device labeling enhance patient understanding, trust, and acceptance. Clear and effective patient-facing communication is essential to address patient concerns and support informed decision-making regarding AI-enabled care.
Objective: We evaluated 3 aims using simulated AI device labels in a cardiovascular context. First, we identified key information elements that influence patient trust and acceptance of an AI device. Second, we examined how these effects varied based on patient characteristics. Third, we explored how patients evaluated informational content of AI labels and their perceived effectiveness of the AI labels in informing decision-making about the use of AI device, building trust in the device, and shaping their intention to use it in their health care.
Methods: We recruited 340 US patients from ResearchMatch.org to participate in a web-based survey that contained 2 experiments. In the discrete choice experiment, participants indicated preferences in terms of trust and acceptance regarding 16 pairs of simulated AI device labels that varied across 8 types of information needs identified in our previous qualitative work. In the single profile factorial experiment, participants evaluated 4 randomly assigned label prototypes regarding the label's legibility, comprehensibility, information overload, credibility, and perceived effectiveness in informing about the AI device, as well as participants' trust in the AI device and intention to use the device in their health care. Data were analyzed using mixed effects binary or ordinal logistic regression.
Results: The discrete choice experiment showed that information about regulatory approval, high device performance, provider oversight, and AI's value added to usual care significantly increased the likelihood of patient trust by 14.1%-19.3% and acceptance by 13.3%-17.9%. Subgroup analyses revealed variations based on patient characteristics such as familiarity with AI, health literacy, and recency of last medical checkup. The single profile factorial experiment showed that patients reported good label comprehension, and that information about provider oversight, regulatory approval, device performance, and AI's added value improved perceived credibility and effectiveness of the AI label (odds ratio [OR] range: 1.35-2.05), reduced doubts in the AI device (OR range: 0.61-0.77), and increased trust and intention to use the AI device (OR range: 1.47-1.73). However, information about data privacy and safety management protocols was less influential.
Conclusions: Patients value information about an AI device's performance, provider oversight, regulatory status, and added value during decision-making. Providing transparent, easily understandable information about these aspects is critical to support patient determinations of trust and acceptance of AI-enabled health care. Information elements' impact on patient trust and acceptance varies by patient characteristics, highlighting the need for a tailored approach to address the concerns of diverse patient groups about AI in health care
Indiana Emergency Medical Services Workforce Data Management Documentation
No full textThis document outlines the data governance framework and management procedures supporting Indiana’s EMS workforce data collection initiatives. It details the roles of the IDHS EMS Commission as data owner, the Commission and Bowen Center as gatekeepers, and the Bowen Center as data steward, defining responsibilities for data security, quality assurance, and reporting. The document describes the development and implementation of REDCap-based surveys for EMS students and EMS professionals, including integration within the ACADIS system. It specifies procedures for data extraction, secure storage on designated directories, recoding of demographic variables—particularly multi‑select race and ethnicity fields—and preparation of analytic datasets using SAS and Excel. The documentation further explains the creation of standardized GIS maps with ArcGIS Pro and the compilation of renewal-year datasets for interactive Tableau dashboards published through the IU Tableau server. Overall, it provides a structured protocol for consistent, secure, and reproducible EMS workforce data processing and reporting
Relationship between age and severity of cognitive impairment at diagnosis for early-onset and late-onset Alzheimer's disease: Comparison of LEADS and ADNI
No full textIntroduction: Recent work has identified unique cognitive profiles for early-onset Alzheimer's disease (EOAD) relative to late-onset Alzheimer's disease (LOAD), however, examination has been limited in determining whether the association between age and cognitive severity at presentation also differs across conditions.
Methods: A series of linear spline regression models was conducted across baseline cognitive data from 325 EOAD and 314 LOAD participants, after accounting for education, sex, and apolipoprotein ε4 status.
Results: Significant differences existed in the relationship between baseline age and cognitive performance between EOAD and LOAD samples for Processing Speed/Attention, Executive Functioning, and Episodic Immediate Memory. Younger participants from both EOAD and LOAD groups performed disproportionately worse on non-amnestic cognitive domains, with this occurring to a greater extent in EOAD than LOAD.
Discussion: In the age of disease-modifying treatments, results highlight the importance of assessing for cognitive declines in individuals starting much earlier than age 65.
Highlights: Early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD) participants each displayed cognitive impairments relative to same-aged peers across most domains. Both groups displayed positive relationships between impairment among non-amnestic cognitive domains and baseline age. This relationship displayed a significantly greater effect in EOAD than LOAD, with domains of Processing Speed/Attention and Executive Functioning skills being the most pronounced. Of those participants developing AD, age displayed a disproportionate impact on their symptom onset
Next‐generation Alzheimer's therapeutics: target assessment and enablement at the Indiana University School of Medicine–Purdue University TREAT‐AD Center
No full textThe incidence of Alzheimer's disease (AD) continues to increase, despite decades of effort to develop disease-modifying therapies. In response, the National Institute on Aging (NIA) established the TaRget Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT-AD) centers to address the gap between basic research and translational drug discovery. Situated within a robust AD research environment, the Indiana University School of Medicine (IUSM)-Purdue University TREAT-AD Center is one of two National Institutes of Health (NIH)-supported centers funded to accomplish this mission. With a focus on novel biological targets beyond amyloid and tau, our center has assembled the necessary components of a drug discovery engine: project and data management, bioinformatics and computational science, structural biology and biochemistry, assay development and pharmacology, and molecular design and synthesis of small molecules, antibodies, and oligonucleotides. Our objective is to deliver Target Enabling Packages (TEPs) within an open science framework, making data, methods, and research tools broadly accessible through the AD Knowledge Portal. HIGHLIGHTS: The Indiana University School of Medicine (IUSM)-Purdue TREAT-AD Center develops Target Enabling Packages (TEPs) to advance novel targets for the treatment of Alzheimer's disease (AD). The center is overseen by an administrative core and operates through four technical cores - bioinformatics, structural biology, assay development, and medicinal chemistry - within a milestone-driven and open science framework. Multi-omics, systems biology, and machine learning (ML) approaches guide the nomination of high-priority targets beyond amyloid and tau. Cross-core workflows provide structural insights into novel biological targets, validated assays, biomarkers, and molecular probes that enable lead optimization. All data, methods, and tools are openly shared through the AD Knowledge Portal to accelerate global efforts in AD drug discovery
Characterization of Lysine Methylation During Neuronal Differentiation of LUHMES cells
No full textOver one-third of human lysine methyltransferases (KMTs) and lysine demethylases (KDMs)-the enzymes responsible for adding or removing methylation on lysine residues within proteins-are linked to neurodevelopmental disorders (NDDs). Consequently, several studies have explored the roles of specific KMTs or KDMs in neuronal differentiation, and alterations in histone methylation patterns have been identified. It is now widely recognized that KMTs and KDMs also target non-histone proteins, yet knowledge of how non-histone lysine methylation changes during neuronal differentiation remains limited. Here, we address this gap using quantitative mass spectrometry-based proteomics to identify and measure changes in non-histone lysine methylation at three different stages in the Lund human mesencephalic (LUHMES) neuronal differentiation model. We identify 74 lysine methylation sites with significant differences in abundance across differentiation. Our analysis reveals lysine methylation on many non-histone proteins involved in neuronal differentiation and neurodevelopment, including signaling molecules, cytoskeletal proteins, RNA splicing factors, and transcription factors. Overall, this work broadens the understanding of non-histone lysine methylation in a neuronal differentiation model and offers a valuable resource of lysine methylation sites on proteins of biological and clinical significance for future research
Integrative Multi‐Omics Approach with Graph Attention Network and Cross‐Attention to Uncover Alzheimer's Disease Subtypes
No full textBackground:
Distinguishing Alzheimer's Disease (AD) subtypes can improve disease diagnosis, treatment, and management. This study uses Graph Attention Networks (GAT) and a cross‐attention algorithm to integrate multi‐omics data and characterize distinct AD subtypes.
Method:
Three omics datasets, including transcriptomics, proteomics, DNA methylation, and clinical information from 156 Religious Orders Study and Rush Memory and Aging Project (ROSMAP) patients and controls, were integrated using a Graph Attention Network (GAT) and a cross‐attention mechanism. GAT encoders generated embeddings for each omics graph, which were integrated via pairwise cross‐attention and combined with clinical data through projection layers. A multi‐task loss combining cross‐entropy and reconstruction losses was used for training, yielding integrated embeddings representing the molecular complexity of AD (Figure 1). Pseudotime for all patients was calculated using the Partition‐based Graph Abstraction (PAGA) method to compare disease progression trajectories across subtypes identified by KMeans. Differentially expressed genes (DEGs) and clinical differences between AD‐enriched clusters were evaluated to characterize AD subtypes, and gene set enrichment analysis identified molecular functions, biological processes, and cellular components enriched among DEGs.
Result:
Four clusters were identified: two enriched in controls and two in AD (Figure 2). This study focuses on comparing the two AD subtypes. Pseudotime analysis showed significant trajectory differences among all clusters, particularly between the AD subtypes (adjusted p 0.7), along with 126 significant molecular processes, 3 biological functions, and 38 cell components were identified, with adjusted p‐values < 0.1 (Figure 3).
Conclusion:
The pipeline integrated multimodal omics data and successfully identified two AD subtypes with distinct disease progression. The 75 DEGs are enriched cellular response to copper ions, hormone activity, and mitochondrial respiratory chain complex I and clathrin‐sculpted vesicles, which we believe play important roles in AD pathogenesis and differentiate the two subtypes
Evaluating School‐Based Substance Use Services: Insights From a Systematic Review
No full textBackground: Substance use among youth can have lifelong consequences and therefore requires early and targeted services for those at risk. Schools possess a unique opportunity to provide substance use services to youth for both prevention and intervention. However, limited research exists on the school-based substance use services and their effectiveness.
Methods: Using PRISMA guidelines, online databases were searched for studies done between 2004 and 2024 on school-based substance use services, their outcomes, and the characteristics of those administering them.
Findings: Results showed school-based substance use services being offered in multiple settings. Screening and intervention were the most common services provided. Although specific outcomes varied by study, including academic performance, perceptions, and actions, most were positive.
Implications for school health policy, practice, and equity: Schools should create strategic plans for feasible and sustainable substance use services. Use of the screening, brief intervention, and referral to treatment (SBIRT) framework can be used to organize these efforts. Establishing robust referral networks is of particular importance for schools.
Conclusions: This review highlights opportunities for schools to focus on screening and brief intervention for in-school services while also building a strong referral network for times when treatment is necessary
Associations of circulating c-reactive protein levels with central Alzheimer's disease biomarkers
No full textBackground: C-reactive protein (CRP) is well-known marker of inflammation and immune response. Its potential role in Alzheimer's disease (AD) pathophysiology remains unclear, particularly in relation to central AD biomarkers, including beta-amyloid (Aβ), tau, and neurodegeneration.
Objectives: To investigate the associations between circulating CRP levels and central AD biomarkers-including Aβ deposition, tau, and AD-signature neurodegeneration-in nondemented older adults.
Design, setting, participants: This cross-sectional observational study analyzed data from a Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer Disease conducted from 2014 to 2020. A total of 417 nondemented older adults underwent comprehensive evaluations, including blood sampling and multimodal neuroimaging for measuring of Aβ and AD-signature neurodegeneration. A subset of participants (N = 123) also underwent tau positron emission tomography (PET) scan.
Measurements: The primary outcomes were A/T/N biomarkers of AD, including brain Aβ and tau deposition measured via amyloid and tau PET, as well as AD-signature neurodegeneration measured by fluorodeoxyglucose (FDG)-PET. Associations between CRP levels and these biomarkers were analyzed while adjusting for CRP-decreasing allele scores, as well as other confounders, including age, sex, vascular risk score, body mass index, nonsteroidal anti-inflammatory drug (NSAID) usage, smoking status, and APOE ε4 carrier status.
Results: The mean (SD) age of participants was 70.57 (8.00) years, with 179 (42.9 %) females. Circulating CRP levels showed non-linear associations with A/T/N biomarkers of AD, showing a U-shaped relationship with Aβ and tau deposition and an inverted U-shaped association with neurodegeneration. Threshold effect analyses revealed that CRP was inversely associated with Aβ deposition (B = -0.081; 95 % CI, -0.153 to -0.007; p = 0.031) below 0.63 mg/L, after adjusting for all confounding variables. In contrast, higher CRP levels were associated with lower cerebral glucose metabolism in AD-signature regions, indicative of greater AD-related neurodegeneration, when above 2.15 mg/L (B = -0.056; 95 % CI, -0.112 to -0.001; p= 0.042).
Conclusions: Our study revealed differential associations between circulating CRP levels and central AD biomarkers that varied according to the CRP concentration. Further studies are necessary to elucidate the mechanisms underlying the inverse relationship between circulating CRP and brain Aβ within the clinically normal range, as well as potential aggravating effects of elevated CRP on Aβ-independent neurodegeneration