Evidence map›Paper›PMID 39704022›Full record

ReviewAnnals of medicine2025

Bioengineering and omics approaches for Type 1 diabetes practical research: advancements and constraints.

Xi Peng, Ling Li, Yihua Peng, Guangju Zhou, Zhenmei An

Abstract readReview
In one paragraph

Review in Annals of medicine, 2025. 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
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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

5 authors.

Xi PengDepartment of Endocrinology and Metabolism, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, Sichuan, China.
Ling LiDepartment of Endocrinology and Metabolism, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, Sichuan, China.
Yihua PengDepartment of Endocrinology and Metabolism, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, Sichuan, China.
Guangju ZhouDepartment of Endocrinology and Metabolism, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, Sichuan, China.
Zhenmei AnDepartment of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Insulin dependency arises from autoimmunity that targets the β cells of the pancreas, resulting in Type 1 diabetes (T1D). Despite the fact that T1D patients require insulin for survival, insulin does not provide a cure for this disease or prevent its complications. Despite extensive genetic, molecular, and cellular research on T1D over the years, the translation of this understanding into effective clinical therapies continues to pose a significant obstacle. It is therefore difficult to develop effective clinical treatment strategies without a thorough understanding of disease pathophysiology. Pancreatic tissue bioengineering models of human T1D offer a valuable approach to examining and controlling islet function while tackling various facets of the condition. And in recent years, due to advances in high-throughput omics analysis, the genotypic and molecular profiles of T1D have become finer tuned. The present article will examine recent progress in these areas, along with their utilization and constraints in the realm of T1D.

Indexed as

BioengineeringDiabetes Mellitus, Type 1AnimalsGenomicsHumansInsulinInsulin-Secreting CellsIslets of LangerhansProteomicsInsulinBioengineeringin vitro modelsomicstype 1 diabetes

Identifiers

PMID39704022
PMCPMC11703378

What Socratic holds

Textmetadata
LicenceCC BY-NC
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Registered trials

None linked

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 Socratic graph.