Yoxas/statistical_literacy huggingface dataset:
Alzheimer's disease (AD) is the leading cause of dementia, resulting in impairments in memory, cognition, decision-making, and social skills. Thus, accurate preclinical diagnosis of Alzheimer's disease is paramount. The identification of biomarkers for Alzheimer's disease through magnetic resonance spectroscopy (MRS) represents a novel adjunctive diagnostic approach. This study conducted a meta-analysis of the diagnostic results of this technology to explore its feasibility and accuracy. PubMed, Cochrane Library, EMBASE, and Web of Science databases were searched without restrictions, with the search period extending up to July 31, 2022. The search strategy employed a combination of subject headings and keywords. All retrieved documents underwent screening by two researchers, who selected them for meta-analysis. The included literature was analyzed using Review Manager 5.4 software, with corresponding bias maps, forest plots, and summary receiver operating characteristic (SROC) curves generated and analyzed. A total of 344 articles were retrieved initially, with 11 articles meeting the criteria for inclusion in the analysis. The analysis encompassed data from approximately 1766 patients. In the forest plot, both sensitivity (95% CI) and specificity (95% CI) approached 1. Examining the true positive rate, false positive rate, true negative rate, and false negative rate, all studies on the summary receiver operating characteristic (SROC) curve clustered in the upper left quadrant, suggesting a very high accuracy of biomarkers detected by MRS for diagnosing Alzheimer's disease. The detection of biomarkers by MRS demonstrates feasibility and high accuracy in diagnosing AD. This technology holds promise for widespread adoption in the clinical diagnosis of AD in the future.
Alzheimer's disease (AD) is the most common form of age-related dementia, leading to a decline in memory, reasoning, and social skills. While numerous studies have investigated the genetic risk factors associated with AD, less attention has been given to identifying a brain imaging-based measure of AD risk. This study introduces a novel approach to assess mild cognitive impairment MCI, as a stage before AD, risk using neuroimaging data, referred to as a brain-wide risk score (BRS), which incorporates multimodal brain imaging. To begin, we first categorized participants from the Open Access Series of Imaging Studies (OASIS)-3 cohort into two groups: controls (CN) and individuals with MCI. Next, we computed structure and functional imaging features from all the OASIS data as well as all the UK Biobank data. For resting functional magnetic resonance imaging (fMRI) data, we computed functional network connectivity (FNC) matrices using fully automated spatially constrained independent component analysis. For structural MRI data we computed gray matter (GM) segmentation maps. We then evaluated the similarity between each participant's neuroimaging features from the UK Biobank and the difference in the average of those features between CN individuals and those with MCI, which we refer to as the brain-wide risk score (BRS). Both GM and FNC features were utilized in determining the BRS. We first evaluated the differences in the distribution of the BRS for CN vs MCI within the OASIS-3 (using OASIS-3 as the reference group). Next, we evaluated the BRS in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (using OASIS-3 as the reference group), showing that the BRS can differentiate MCI from CN in an independent data set. Subsequently, using the sMRI BRS, we identified 10 distinct subgroups and similarly, we identified another set of 10 subgroups using the FNC BRS. For sMRI and FNC we observed results that mutually validate each other, with certain aspects being complementary. For the unimodal analysis, sMRI provides greater differentiation between MCI and CN individuals than the fMRI data, consistent with prior work. Additionally, by utilizing a multimodal BRS approach, which combines both GM and FNC assessments, we identified two groups of subjects using the multimodal BRS scores. One group exhibits high MCI risk with both negative GM and FNC BRS, while the other shows low MCI risk with both positive GM and FNC BRS. Moreover, in the UKBB we have 46 participants diagnosed with AD showed FNC and GM patterns similar to those in high-risk groups, defined in both unimodal and multimodal BRS. Finally, to ensure the reproducibility of our findings, we conducted a validation analysis using the ADNI as an additional reference dataset and repeated the above analysis. The results were consistently replicated across different reference groups, highlighting the potential of FNC and sMRI-based BRS in early Alzheimer's detection.
Alzheimer's disease and mild cognitive impairment (MCI) progress silently, making early diagnosis challenging, especially in less educated populations. The visual paired comparison (VPC) task, utilizing eye-tracking movement (ETM) technology, offers a promising alternative for early detection of memory decline. This systematic review and meta-analysis evaluated the efficacy of the VPC task, utilizing ETM as a tool for assessing age-related cognitive changes. A comprehensive search across five databases and grey literature focused on healthy and impaired memory participants assessed through the ETM-based VPC task. The primary outcomes were novelty preference scores and eye movement metrics. The risk of bias of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Random-effects meta-analyses calculated Hedges' g effect size. Sensitivity and specificity of the VPC were meta-analytically pooled. The systematic review included 12 articles, involving 1,022 participants (aged 18 to 90 years, with education ranging from 6.5 to 20.0 years), with a low risk of bias and minimal applicability concerns across all items. Five studies contributed to the meta-analysis, revealing a significant effect favoring the VPC task for recognition memory detection (k = 9, g = -1.03). Pooled sensitivity and specificity analyses demonstrated VPC effectiveness as a recognition memory assessment tool (0.84 and 0.75, respectively). The VPC task, utilizing ETM, may serve as a biomarker for early memory decline detection. Its use as a digital eye-tracking tool presents a possible alternative to traditional tests, warranting further research for application in neurodegenerative disease diagnosis.
Alzheimer's disease and related dementia (ADRD) is a major cause of death in the United States. While effective interventions have been developed to deliver palliative care to nursing home residents with ADRD, little work has identified effective interventions to reach assisted living (AL) residents with dementia. One hundred and eighteen AL residents with dementia from 10 different ALs in Florida participated. A pilot study using a cluster randomized trial was conducted, with 6 sites randomized to receive a palliative care educational intervention for staff (N = 23) to deliver care to residents; 4 sites were usual care. The feasibility of the intervention was assessed by examining recruitment, retention, and treatment fidelity at 6 months. Cohen's d statistic was used to calculate facility-level treatment effect sizes on key outcomes (documentation of advance care planning [ACP] discussions, hospice admission, and documentation of pain screening). The intervention proved feasible with high ratings of treatment fidelity. The intervention also demonstrated preliminary evidence for efficacy of the intervention, with effect sizes for the treatment group over 0.80 for increases in documentation of ACP discussions compared to the control group. Hospice admissions had a smaller effect size (0.16) and documentation of pain screenings had no effect. The pilot results suggest that the intervention shows promise as a resource for educating and empowering AL staff on implementing person-centered palliative care delivery to persons with dementia in AL. A larger, fully powered randomized trial is needed to test for its efficacy.
Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants. This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies. A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models. Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability. Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening. User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.
Ambient levels of volatile organic compounds (VOCs) released from nearby industrial plants have shown positive associations with increased colorectal cancer (CRC) rates. The objective of this study is to analyze the distribution of CRC in the context of socioeconomic status and its correlation with community environmental data. A retrospective study analyzed CRC patients from 2021 to 2023. The census tracts of the patients' residential addresses were obtained, and CRC rates were calculated for each census tract. Socioeconomic data was gathered on these communities. Environmental VOC measurements were obtained from the National Scale Air Toxics Assessment. All datapoints were compared to statewide levels. Three census tracts in the county had higher CRC cases comparatively. These areas exhibited higher incidence rates and localized clusters of CRC cases, higher distribution of Black or African Americans, lower household incomes, lower home values, and lower educational attainment. VOC measurements in these census tracts had higher levels compared to county and state averages: specifically, 10.68% higher than county and 48.07% higher than state benzene levels (0.52 µg/m3 clusters vs 0.47µg/m3 county vs 0.35 µg/m3 state), 10.84% and 129.15% higher toluene (1.65 µg/m3 vs 1.49 vs 0.72 µg/m3), and 15.64% and 141.87% higher butadiene (0.048 µg/m3 vs 0.041 µg/m3 vs 0.020 µg/m3). This study illustrates a positive correlation between higher ambient exposure to VOCs with increased CRC incidence. These findings underscore the potential interplay of environmental factors, socioeconomic determinants, and environmental injustice when considering strategies to address health disparities and CRC incidence.
Amblyopia is a common neurodevelopmental condition and leading cause of childhood visual impairment. Given the known association between neurodevelopmental impairment and cardiometabolic dysfunction in later life, we investigated whether children with amblyopia have increased risk of cardiometabolic disorders in adult life. This was a cross-sectional and longitudinal analysis of 126,399 United Kingdom Biobank cohort participants who underwent ocular examination. A subset of 67,321 of these received retinal imaging. Data analysis was conducted between November 1st 2021 and October 15th 2022. Our primary objective was to investigate the association between amblyopia and a number of components of metabolic syndrome and individual cardiometabolic diseases. Childhood amblyopia, dichotomised as resolved or persisting by adulthood, cardiometabolic disease and mortality were defined using ophthalmic assessment, self-reported, hospital admissions and death records. Morphological features of the optic nerve and retinal vasculature and sublayers were extracted from retinal photography and optical coherence tomography. Associations between amblyopia and cardiometabolic disorders as well as retinal markers were investigated in multivariable-adjusted regression models. Individuals with persisting amblyopia (n = 2647) were more likely to be obese (adjusted odds ratio (95% confidence interval): 1.16 (1.05; 1.28)), hypertensive (1.25 (1.13; 1.38)) and diabetic (1.29 (1.04; 1.59)) than individuals without amblyopia (controls, (n = 18,481)). Amblyopia was also associated with an increased risk of myocardial infarction (adjusted hazard ratio: 1.38 (1.11; 1.72)) and death (1.36 (1.15; 1.60)). On retinal imaging, amblyopic eyes had significantly increased venular caliber (0.29 units (0.21; 0.36)), increased tortuosity (0.11 units (0.03; 0.19)), but lower fractal dimension (-0.23 units (-0.30; -0.16)) and thinner ganglion cell-inner plexiform layer (mGC-IPL, -2.85 microns (-3.47; -2.22)). Unaffected fellow eyes of individuals with amblyopia also had significantly lower retinal fractal dimension (-0.08 units (-0.15; -0.01)) and thinner mGC-IPL (-1.14 microns (-1.74; -0.54)). Amblyopic eyes with a persisting visual deficit had smaller optic nerve disc height (-0.17 units (-0.25; -0.08)) and width (-0.13 units (-0.21; -0.04)) compared to control eyes. Although further research is needed to understand the basis of the observed associations, healthcare professionals should be cognisant of greater cardiometabolic dysfunction in adults who had childhood amblyopia. Differences in retinal features in both the amblyopic eye and the unaffected non-amblyopic suggest generalised versus local processes. Medical Research Council (MR/T000953/1) and the National Institute for Health and Care Research.
Amblyopia is a neurodevelopmental visual disorder that affects approximately 3-5% of children globally and it can lead to vision loss if it is not diagnosed and treated early. Traditional diagnostic methods, which rely on subjective assessments and expert interpretation of eye movement recordings presents challenges in resource-limited eye care centers. This study introduces a new approach that integrates the Gemini large language model (LLM) with eye-tracking data to develop a classification tool for diagnosis of patients with amblyopia. The study demonstrates: (1) LLMs can be successfully applied to the analysis of fixation eye movement data to diagnose patients with amblyopia; and (2) Input of medical subject matter expertise, introduced in this study in the form of medical expert augmented generation (MEAG), is an effective adaption of the generic retrieval augmented generation (RAG) approach for medical applications using LLMs. This study introduces a new multi-view prompting framework for ophthalmology applications that incorporates fine granularity feedback from pediatric ophthalmologist together with in-context learning to report an accuracy of 80% in diagnosing patients with amblyopia. In addition to the binary classification task, the classification tool is generalizable to specific subpopulations of amblyopic patients based on severity of amblyopia, type of amblyopia, and with or without nystagmus. The model reports an accuracy of: (1) 83% in classifying patients with moderate or severe amblyopia, (2) 81% in classifying patients with mild or treated amblyopia; and (3) 85% accuracy in classifying patients with nystagmus. To the best of our knowledge, this is the first study that defines a multi-view prompting framework with MEAG to analyze eye tracking data for the diagnosis of amblyopic patients.
Ambulatory central line-associated bloodstream infections (CLABSIs) cause significant morbidity and mortality, especially in pediatric oncology. Few studies have had interventions directed toward caregivers managing central lines (CL) at home to reduce ambulatory CLABSI rates. We aimed to reduce and sustain our ambulatory CLABSI rate by 25% within 3 years of the start of a quality improvement intervention. Plan-do-study-act cycles were implemented beginning April 2016. The main intervention was a family-centered CL care skill development curriculum for external CLs. Training began upon hospital CL insertion, followed by an ambulatory teach-back program to achieve home caregiver CL care independence. Other changes included: standardizing ambulatory nurse CL care practice (audits, a train the nurse trainer process, and workshops for independent home care agencies); developing aids for trainers and caregivers; providing supplies for clean surfaces; wide dissemination of the program; and minimizing opportunities of CLABSI (e.g., standardizing timing of CL removal). The outcome measure was the ambulatory CLABSI rate (excluding mucosal barrier injury laboratory-confirmed bloodstream infection), compared pre intervention (January 2015 to March 2016) to post intervention, including 2 years of sustainability (April 2016 to June 2023), using statistical process control charts. We estimated the total number of CLABSI and associated healthcare charges prevented. The ambulatory CLABSI rate decreased by 52% from 0.25 to 0.12 per 1000 CL days post intervention, achieved within 27 months; 117 CLABSI were prevented, with $4.2 million hospital charges and 702 hospital days avoided. Focusing efforts on home caregivers CL care may lead to reduction in pediatric oncology ambulatory CLABSI rates.
American Indian and Alaska Native (AI/AN) people have high rates of diabetes and limited access to nutrition education. The "What Can I Eat?" (WCIE) diabetes nutrition education program was culturally adapted for AI/AN adults. This analysis was designed to evaluate the reliability and validity of items developed to measure diabetes nutrition self-efficacy (i.e., confidence one can engage in specific behaviors) and diabetes nutrition behavior among participants in the WCIE program for AI/AN adults. This study was a secondary analysis of data from a randomized controlled trial designed to evaluate the WCIE program for AI/AN adults. Baseline data were used to assess the reliability and validity of the self-efficacy and behavior items, which were collected via survey. Due to COVID-19 safety protocols, the intervention was conducted via Zoom, and both survey and clinical data were collected at home by participants. The study was conducted from January to December 2021 with five AI/AN-serving health care programs in Oklahoma, Illinois, North Carolina, California, and New York. AI/AN adults with type 2 diabetes who spoke English and had Internet access were eligible. Sixty participated. Analyses examined validity and reliability of diabetes nutrition self-efficacy and behavior items. To test reliability, internal consistency and factor structures of the scales were examined. To evaluate convergent validity, Pearson correlations were computed to examine the association of the self-efficacy and behavior measures with each other and with clinical indicators (i.e., Body Mass Index, blood pressure, hemoglobin A1C). Two self-efficacy factors were identified. Each showed strong internal consistency (Cronbach alphas ≥ 0.85; McDonald omegas ≥ 0.88) and was directly associated with diabetes nutrition behavior (Ps < 0.001). The factor assessing Confidence in Using the Diabetes Plate was inversely associated with A1C (Pearson Correlation = -0.32, P = 0.0243). The behavior measure capturing Healthy Nutrition Behavior showed strong internal consistency (alpha = 0.89; omega = 0.92) and was inversely associated with A1C (Pearson Correlation = - 0.38, P = 0.0057). Diabetes nutrition self-efficacy and behavior items developed for the WCIE program for AI/AN adults are valid and reliable. These items can facilitate rigorous and consistent evaluation of the AI/AN WCIE program.
American Nurses Credentialing Center (ANCC) Accreditation in Nursing Continuing Professional Development (NCPD) emphasizes the importance of maintaining competence, ensuring patient safety, and supporting career advancement in nursing through learning and development. It underscores the shift toward competency-based and outcome-oriented education models, addressing challenges such as misconceptions about the demands of NCPD and focusing more on the impact. This column provides the "why" for organizations, leaders, and nurses to engage in ANCC accredited NCPD educational programs. It further explores a future where NCPD fosters a skilled, equitable, and diverse health care workforce, promoting transformational learning experiences. [J Contin Educ Nurs. 2024;55(4):153-156. ].
Amide-bond equilibrium probability density, Peq = exp(-u) (u, local potential), and associated conformational entropy, Sk = -∫Peq (ln Peq) dΩ ─ln ∫dΩ, are derived for the Rho GTPase binding domain of Plexin-B1 (RBD) as monomer and dimer from 1 μs MD simulations. The objective is to elucidate the effect of dimerization on the dynamic structure of the RBD. Dispersed (peaked) Peq functions indicate "flexibility" ("rigidity"; the respective concepts are used below in this context). The L1 and L3 loops are throughout highly flexible, the L2 loop and the secondary structure elements are generally rigid, and the L4 loop is flexible in the monomer and rigid in the dimer. Overall, many residues are more flexible in the dimer. These features, and their implications, are discussed. Unexpectedly, we find that monomer unit 1 of the dimer (in short, d1) is unusually flexible, whereas monomer unit 2 (in short, d2) is as rigid as the RBD monomer. This is revealed due to their engagement in slow-to-intermediate conformational exchange detected previously by 15N relaxation experiments. Such motions occur with rates on the order of 103-104 s-1; hence, they cannot be completely sampled over the course of 1 μs simulation. However, the extent to which rigid d2 is affected is small enough to enable physically relevant analysis. The entropy difference between d2 and the monomer yields an entropic contribution of -7 ± 0.7 kJ/mol to the free energy of RBD dimerization. In previous work aimed at similar objectives we used 50-100 ns MD simulations. Those results and the present result differ considerably. In summary, bond-vector Peq functions derived directly from long MD simulations are useful descriptors of protein structural dynamics and provide accurate conformational entropy. Within the scope of slow conformational exchange, they can be useful, even in the presence of incomplete sampling.