In one paragraphArticle in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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 registryThe 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.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
19 authors.
Tabitha Banks-TibbsDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Henry ThorpeBiostatistics Facility, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.ORCID 0000-0003-1580-3757 Jia-Jun LiuDepartment of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.ORCID 0009-0001-3677-613X Jinting YangDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Aidan F CarneyDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Halla M ShaikhDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Lauren M WoolleyDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Paul N JosephDepartment of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Guy A RutterCardiometabolic Axis, CR-CHUM and Department of Medicine, University of Montreal, QC, Canada.ORCID 0000-0001-6360-0343 Silvia LiuDepartment of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.ORCID 0000-0002-1840-9520 Lora L PlessMicrobial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA.
Funding
CLINICAL RESEARCH TRAINING IN LATE-LIFE MOOD DISORDERST32MH019986 · NIMH · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOWARD J AIZENSTEIN, Carmen Andreescu · 1997 to 2026
$6.9MUnderstanding putative beta-cell subtypesR01DK139630 · NIDDK · VAN ANDEL RESEARCH INSTITUTE · PI John Andrew Pospisilik · 2024 to 2026
$2.7MControl of insulin secretion by mitochondrial fusionR01DK135268 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Brett A Kaufman, Scott Soleimanpour · 2023 to 2026
$2.4MInterinstitutional Program in Cell and Molecular Biology: A Graduate Training Path to Promote Traditional and Non-Traditional Professional OutcomesT32GM133353 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BRODSKY, JEFFREY L., MURRAY, SANDRA ANN · 2020 to 2024
$1.5MNovel dopaminergic mechanisms of islet hormone secretion and antipsychotic drug-induced metabolic disturbancesR01DK124219 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI FREYBERG, ZACHARY · 2021 to 2023
$1.2MAdvanced computational approaches for single-cell multi-omics integrationR35GM159862 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Shuchang Silvia Liu · 2025 to 2026
$875kHigh-Throughput Computing for Genomics and Bioinformatics ResearchS10OD028483 · OD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI LEE, ADRIAN V · 2021 to 2021
$574kDissecting the functional relevance of unique subpopulations of striatal dopamine receptors in opioid use disorderR36DA057972 · NIDA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KOZEL, JENESIS · 2023 to 2024
$108kNIDA NIH HHS R36 DA057972NIDDK NIH HHS R01 DK124219NIDDK NIH HHS R01 DK135268NIDDK NIH HHS R01 DK139630NIGMS NIH HHS R35 GM159862NIGMS NIH HHS T32 GM133353NIH HHS S10 OD028483NIMH NIH HHS T32 MH019986
6 · The paper itselfAbstract
Human pancreatic islets exhibit greater anatomic and cellular heterogeneity than previously appreciated, raising fundamental questions about how their composition varies with age, sex, region, and islet size and how type 1 diabetes (T1D) alters these relationships. Yet these questions remained largely unresolved due to the bottleneck of manual tissue inspection. Here, we developed an integrated artificial intelligence (AI)-guided imaging, processing, and statistical pipeline enabling unbiased, high-throughput analysis of more than 2 million candidate islets from 106 non-diabetic (ND) and T1D donors. We identified age-, region-, sex-, and islet size-dependent differences in islet distribution and composition between ND and T1D donors. Profound β-cell loss in T1D was accompanied by reciprocal α-cell expansion, whereas δ-cells and pancreatic polypeptide cells were largely resilient. Cell area and pseudotime analyses uncovered regional and age-dependent trajectories of islet remodeling across T1D progression, along with distinct patterns of cytoarchitectural reorganization of the endocrine pancreas.
Indexed as
artificial intelligenceendocrine objectsimage analysisislet cell compositionpancreatic isletsType 1 diabetes
Identifiers
PMID42327161
PMCPMC13277830
What OpenQuestion holds
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