Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors.
Logan BraseDepartment of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America.ORCID 0000-0002-7175-3208
Yanbo YuDepartment of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America.ORCID 0000-0002-6687-9850
Eric McDadeDepartment of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.ORCID 0000-0002-6764-3866
Dominantly Inherited Alzheimer Network (DIAN)
Oscar HarariDepartment of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America.ORCID 0000-0002-2635-6975
Bruno A BenitezDepartment of Neurology and Neuroscience, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.ORCID 0000-0002-2699-3878
Funding
Alzheimer's Disease Genetics ConsortiumU01AG032984 · NIA · UNIVERSITY OF PENNSYLVANIA · PI SCHELLENBERG, GERARD DAVID · 2009 to 2024
$60.4M
Imaging CoreU19AG032438 · NIA · WASHINGTON UNIVERSITY · PI BATEMAN, RANDALL J · 2010 to 2025
$53.9M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1M
EPIDEMIOLOGY OF NEURAL RESERVE AND NEUROBIOLOGY IN AGINGR01AG017917 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 2001 to 2023
$43.3M
RISK FACTORS, PATHOLOGY, AND CLINICAL EXPRESSIONS OF ADR01AG015819 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1998 to 2024
$21.4M
Multi-omic network-directed proteoform discovery, dissection and functional validation to prioritize novel AD therapeutic targetsU01AG061356 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2018 to 2022
$13.7M
Pathway discovery, validation and compound identification for Alzheimer's disease - SupplementU01AG046152 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BENNETT, DAVID ALAN, DE JAGER, PHILIP L · 2013 to 2017
$13.6M
Investigating the Functional Impact of AD Risk Genes on Neuro-Vascular InteractionsU01AG072464 · NIA · REGENERATIVE RESEARCH FOUNDATION · PI HARARI, OSCAR, KAMPMANN, MARTIN · 2021 to 2025
$8.7M
Discovery of Novel Proteomic Targets in Alzheimer's DiseaseU01AG046161 · NIA · EMORY UNIVERSITY · PI BENNETT, DAVID ALAN, GESCHWIND, DANIEL H · 2014 to 2018
$8.4M
Systems Genetics Analysis of Sex Differences in Alzheimer's DiseaseR01AG074012 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HARARI, OSCAR, HOHMAN, TIMOTHY J · 2021 to 2025
$6.2M
Genetic Epidemiology of Cognitive Decline in an Aging Population SampleR01AG030146 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI EVANS, DENIS A · 2007 to 2018
$6.2M
MENDELIAN RANDOMIZATION FOR UNBIAS BIOMARKER DISCOVERY FOR AD AND OTHER COMPLEX TRAITSR01AG057777 · NIA · WASHINGTON UNIVERSITY · PI HARARI, OSCAR · 2018 to 2022
Background: Single-cell technologies have unveiled various transcriptional states in different brain cell types. Transcription factors (TFs) regulate the expression of related gene sets, thereby controlling these diverse expression states. Apolipoprotein E ( Methods: We used pySCENIC to construct state-specific gene regulatory networks (GRNs) for resting and activated cell states within microglia and astrocytes based on single-nucleus RNA sequencing data from AD patients' cortices from the Knight ADRC-DIAN cohort. We then identified replicating TF using data from the ROSMAP cohort. We identified sets of genes co-regulated with Results: We identified an average of 96 replicating regulators for each microglial and astrocyte cell state. Our analysis identified the CEBP, JUN, FOS, and FOXO TF families as key regulators of microglial APOE expression. The steroid/thyroid hormone receptor families, including the THR TF family, consistently regulated APOE across astrocyte states, while CEBP and JUN TF families were also involved in resting astrocytes. AD GWAS-associated genes ( Conclusions: Our findings reveal a novel perspective on the transcriptional regulation of
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Comparative gene regulatory networks modulating · full record | OpenQuestion