Mendeley Data Showcase

The Knight Alzheimer Disease Research Center (ADRC) Data Freeze Ending 2025-09-30 is a longitudinal compilation of harmonized, processed research data collected from August 1, 1979 through September 30, 2025. This Data Freeze represents a fixed, versioned snapshot of curated datasets maintained by the ADRC and is intended to support reproducible secondary analyses under approved data use agreements. The Data Freeze includes data contributed by multiple ADRC Cores, including clinical assessments, cognitive testing, neuropsychological measures, neuroimaging data summaries, fluid biomarker data, neuropathology variables, and related research measures. Modules are compiled, quality controlled, and integrated according to ADRC Data Management and Sharing procedures prior to freeze finalization. This dataset reflects the full longitudinal structure of ADRC participant data available as of the official cutoff date (September 30, 2025). No data collected after this date are included in this release. Each subsequent Data Freeze constitutes a new versioned dataset with its own DOI. The purpose of this Data Freeze is to: 1. Provide a stable, citable dataset snapshot for approved secondary analyses 2. Support NIH Data Management and Sharing (DMS) policy compliance 3. Enable transparency, reproducibility, and longitudinal tracking of dataset versions This repository record provides metadata describing the Data Freeze and assigns a persistent DOI to this version. The underlying human subject data are stored in a secure Research Infrastructure Services (RIS) environment and are not publicly downloadable. Access to this dataset is controlled and requires submission of a formal data request through the Knight ADRC Request Center. Approved investigators must complete the appropriate Data Use Agreement prior to receiving access. Upon approval, access to the static dataset corresponding to this DOI will be granted through ADRC-managed secure infrastructure. Investigators using this Data Freeze must cite this dataset DOI in all resulting publications and acknowledge Knight ADRC funding as specified in the Data Use Agreement. This dataset was generated and curated by the Knight ADRC Data Management and Statistics Core in collaboration with contributing ADRC Cores. For information about submitting a data request, please visit: https://knightadrc.wustl.edu/professionals-clinicians/request-center-resources/submit-a-request/

Post-COVID-19 syndrome has unveiled intricate connections between inflammation, depressive psychopathology, and cognitive impairment. This study investigates these relationships in 101 COVID-19 survivors, focusing on sex-specific variations. Utilizing path modelling techniques, we analysed the interplay of one-month 48-biomarker inflammatory panel, on three-month depressive symptoms and cognitive performance. The findings indicate that cognitive impairment is influenced by both inflammation and depression in the overall cohort. However, sex-specific differences emerged prominently. In females, residual dysregulated immune-inflammatory response significantly affects cognitive functioning also showing a trend of association with depressive burden thus suggesting that a mixed anti- and pro-inflammatory profile could foster these outcomes. Conversely, in males, inflammation was inversely associated with depression severity, with protective effects from regulatory mediators (IL-2, IL-4, IL-6, IL-15, LIF, TNF-α, β-NGF) on depression. Cognitive impairment in males was primarily influenced by depression, not inflammation. These results highlight distinct sex-specific pathways in immune and inflammatory responses post-COVID-19, potentially shaped by endocrine mechanisms. The findings emphasize the persistent impact of inflammation on the brain and underscore the need for sex-tailored therapeutic strategies to mitigate the long-term neuropsychiatric burden of COVID-19.

This Supplementary Information contains Figures S1–S4 and Tables S1–S6.

Multiple sclerosis (MS) is a chronic, autoimmune, inflammatory disease of the central nervous system (CNS) characterized by demyelination and neurodegeneration. Among the various pathogenic mechanisms, oxidative stress plays a critical role in driving both inflammation and neuronal damage, and emerging evidence suggests that mitochondrial dysfunction may significantly contribute to disease progression. Whole mtDNA was sequenced from blood-derived DNA using long-range PCR and Illumina® Nextera XT kit. Somatic mutations were defined based on heteroplasmy levels between 1–5%. Linear regression models were used to assess the association between age and mutation rate. We observed a significant age-dependent increase in low-frequency nonsynonymous mtDNA mutations in MS. Analyses stratified by disease course revealed this effect was driven by PPMS patients, while no association was seen in RRMS, suggesting course-specific mitochondrial trajectories. Furthermore, fast-progressing patients showed a positive linear relationship between age and mtDNA mutations rate, while slow-progressing ones showed a negative trend. These findings support the existence of a differential age-related accumulation of somatic mtDNA mutations across MS courses and underline the importance of mitochondrial genome instability in disease progression.

Data and code to replicate Baker, Balthrop, Johnson, Kotter, and Pisciotta (JFE): Gambling Away Stability: Sports Betting's Impact on Vulnerable Households.

This dataset includes soil physical, chemical, and spectral properties used for the estimation of soil water retention curve parameters. The data were collected from soil samples and can be used for developing and evaluating predictive models of soil hydraulic properties.

Causative Medications in DRESS patients with certain medication cause (n=48)

This file contains the supplementary tables for the manuscript “Trends in cancer-related hospital utilization and mortality in Mexico by access to social security, 2004–2024: implications for policymaking”, submitted to Salud Pública de México. The tables present annual percent changes (APC) and 95% confidence intervals derived from time-series cross-sectional marginal models used to assess trends in hospital utilization and mortality for breast, cervical, prostate, colorectal cancer, and leukemia, stratified by sex and social security status. Models were estimated using Prais–Winsten regression with predefined spline terms to account for policy-related changes and temporal autocorrelation. Sensitivity analyses excluding 2020–2021 were also performed to evaluate the potential influence of the COVID-19 pandemic. These supplementary materials provide the detailed numerical results underlying the graphical findings presented in the main manuscript.

This study performs a spatiotemporal wetland change analysis for the Sangamon River Watershed, Illinois, using Landsat 5 TM between 2000–2008, Sentinel-2 Surface Reflectance, Sentinel-1 Synthetic Aperture Radar (SAR) between 2017–2025, and terrain data through an integrated machine learning (ML) and deep learning (DL) frameworks in Google Earth Engine (GEE), Google Colab (Python3) and ArcGIS Pro 3.6. We conducted a comparative assessment of DL Dense Neural Networks (DNN) and U-Net semantic segmentation, as well as three ML models including Random Forest (RF), Gradient Tree Boosting (GTB), and Support Vector Machine (SVM) through pixel-based and object-based image analysis (OBIA) methods. Reclassified National Land Cover Database (NLCD) datasets were used for training and validation using stratified random sampling for five categories namely Wetlands (1), Forest (2), Agriculture/ Grassland/ Barren land (3), Urban/Developed (4) and Water (5).

Table S1. Dietary formulation information for camel milk from Northern Xinjiang (Altay City, Xinjiang), Southern Xinjiang (Aksu City, Xinjiang), and Inner Mongolia (Alxa League, Inner Mongolia). Notes: XJNC: camel milk from northern Xinjiang; XJSC: camel milk from southern Xinjiang; IMC: camel milk from Inner Mongolia. Table S2. Dietary formulation information for bovine milk from Northern Xinjiang (Altay City, Xinjiang), Southern Xinjiang (Aksu City, Xinjiang), and Inner Mongolia (Alxa League, Inner Mongolia). Notes: XJNB: bovine milk from northern Xinjiang; XJSB: bovine milk from southern Xinjiang; IMB: bovine milk from Inner Mongolia. Supplementary Fig.1. Standard curves for the quantification of K, Ca, Na, Mg, P, Zn, Fe, Mn, Ba, Co, Sr, and Cu (R²＞0.999). Table S3. ARRIVE Guidelines 2.0 author checklist.