Evidence map›Paper›PMID 39391007›Full record

ReviewCell insight2024

Modeling respiratory tract diseases for clinical translation employing conditionally reprogrammed cells.

Danyal Daneshdoust, Kai He, Qi-En Wang, Jenny Li, Xuefeng Liu

Abstract readReview
In one paragraph

Review in Cell insight, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
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.

Danyal DaneshdoustComprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Kai HeComprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Qi-En WangComprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Jenny LiComprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Xuefeng LiuComprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.

Funding

Developing Functional Human Cell Models to Study Initiation and Progression of Prostate Cancer between AA and EA menR01CA276474 · NCI · OHIO STATE UNIVERSITY · PI Xuefeng Liu · 2023 to 2026
$2.1M
Conditionally Reprogrammed Cell Model for Castration-Resistant Prostate Cancer (CRPC)R01CA222148 · NCI · OHIO STATE UNIVERSITY · PI LIU, XUEFENG · 2019 to 2022
$1.9M
Evaluation of Pre-Analytical Factors of Urine Samples for Urine Cancer Cell Cultures (UCCC) --A Non-Invasive Biomarker – in Monitoring Response and Recurrence of Bladder CancerU01CA278927 · NCI · OHIO STATE UNIVERSITY · PI LIU, XUEFENG · 2023 to 2025
$1.8M
Validating Urine Derived Cancer Cells (UDCC) -- Non-Invasive and Living Liquid Biopsies -- in Bladder Cancer ClinicsR33CA258016 · NCI · OHIO STATE UNIVERSITY · PI LIU, XUEFENG · 2021 to 2023
$1.2M
NCI NIH HHS R01 CA222148NCI NIH HHS R01 CA276474NCI NIH HHS R33 CA258016NCI NIH HHS U01 CA278927
6 · The paper itself

Abstract

Preclinical models serve as indispensable tools in translational medicine. Specifically, patient-derived models such as patient-derived xenografts (PDX), induced pluripotent stem cells (iPSC), organoids, and recently developed technique of conditional reprogramming (CR) have been employed to reflect the host characteristics of diseases. CR technology involves co-culturing epithelial cells with irradiated Swiss-3T3-J2 mouse fibroblasts (feeder cells) in the presence of a Rho kinase (ROCK) inhibitor, Y-27632. CR technique facilitates the rapid conversion of both normal and malignant cells into a "reprogrammed stem-like" state, marked by robust in vitro proliferation. This is achieved without reliance on exogenous gene expression or viral transfection, while maintaining the genetic profile of the parental cells. So far, CR technology has been used to study biology of diseases, targeted therapies (precision medicine), regenerative medicine, and noninvasive diagnosis and surveillance. Respiratory diseases, ranking as the third leading cause of global mortality, pose a significant burden to healthcare systems worldwide. Given the substantial mortality and morbidity rates of respiratory diseases, efficient and rapid preclinical models are imperative to accurately recapitulate the diverse spectrum of respiratory conditions. In this article, we discuss the applications and future potential of CR technology in modeling various respiratory tract diseases, including lung cancer, respiratory viral infections (such as influenza and Covid-19 and etc.), asthma, cystic fibrosis, respiratory papillomatosis, and upper aerodigestive track tumors. Furthermore, we discuss the potential utility of CR in personalized medicine, regenerative medicine, and clinical translation.

Indexed as

AsthmaConditional reprogrammingCovid-19Cystic fibrosisLung cancerPreclinical modelsRespiratory diseasesRespiratory papillomatosisRespiratory viral infectionsTranslational medicine

Identifiers

PMID39391007
PMCPMC11462205

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.