Article in bioRxiv : the preprint server for biology, 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.
Stepan NersisyanComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.
Phillipe LoherComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.
Iliza NazerajComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.
Zhiping ShaoCenter for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
John F FullardCenter for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
Georgios VoloudakisCenter for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
Kiran GirdharCenter for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
Panos RoussosCenter for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, USA.
Isidore RigoutsosComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA.ORCID 0000-0003-1529-8631
Funding
X-Ray Crystallography and Macromolecular CharacterizationP30CA056036 · NCI · THOMAS JEFFERSON UNIVERSITY · PI Claudio Guillermo Giraudo · 1995 to 2026
$94.8M
Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer DiseaseR01AG067025 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FINKBEINER, STEVEN M, HAROUTUNIAN, VAHRAM · 2019 to 2023
$11.8M
Higher Order Chromatin and Genetic Risk for Alzheimer's DiseaseR01AG050986 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ROUSSOS, PANAGIOTIS · 2015 to 2025
$11.2M
Understanding the protective and neuroinflammatory role of human brain immune cells in Alzheimer DiseaseR01AG065582 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI HAROUTUNIAN, VAHRAM, ROUSSOS, PANAGIOTIS · 2020 to 2024
$9.9M
Molecular Profiling of SchizophreniaR01MH110921 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CHESS, ANDREW J, ROUSSOS, PANAGIOTIS · 2016 to 2020
$6.9M
The 3D genome in transcriptional regulation across the postnatal life span, with implications for schizophrenia and bipolar disorderU01MH116442 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI AKBARIAN, SCHAHRAM, DRACHEVA, STELLA · 2018 to 2022
$5.9M
Multiethnic genomic epigenomic and transcriptomic fine-mapping and functional validation analysis of schizophrenia and bipolar disorder risk lociR01MH125246 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ROUSSOS, PANAGIOTIS · 2021 to 2025
$5.0M
Risk genetic variants and cis regulation of gene expression in Bipolar DisorderR01MH109677 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ROUSSOS, PANAGIOTIS · 2016 to 2020
$4.6M
Integrated Multiscale Networks in SchizophreniaR01MH109897 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BRENNAND, KRISTEN JENNIFER, ROUSSOS, PANAGIOTIS · 2016 to 2020
$3.0M
Specialized Tools and Auto-updatable Scalable Interactive Databases to Study isomiRs, tRFs and rRFs in Human and MouseR01HG012784 · NHGRI · THOMAS JEFFERSON UNIVERSITY · PI Isidore Rigoutsos · 2023 to 2026
We investigated small non-coding RNAs (sncRNAs) from the prefrontal cortex of 93 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) and 77 controls. We uncovered recurring complex sncRNA profiles, with 98% of all sncRNAs being accounted for by miRNA isoforms (60.6%), tRNA-derived fragments (17.8%), rRNA-derived fragments (11.4%), and Y RNA-derived fragments (8.3%). In SCZ, 15% of all sncRNAs exhibit statistically significant changes in their abundance. In BD, the fold changes (FCs) are highly correlated with those in SCZ but less acute. Non-templated nucleotide additions to the 3´-ends of many miRNA isoforms determine their FC independently of miRNA identity or genomic locus of origin. In both SCZ and BD, disease- and age-associated sncRNAs and mRNAs reveal accelerated aging. Co-expression modules between sncRNAs and mRNAs align with the polarities of SCZ changes and implicate sncRNAs in critical processes, including synaptic signaling, neurogenesis, memory, behavior, and cognition.
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.
Comprehensive profiling of small RNAs and their changes and linkages to mRNAs in schizophrenia and bipolar disorder. · full record | OpenQuestion