Evidence map›Paper›PMID 35418283›Full record

ArticleCombinatorial chemistry & high throughput screening2023

Unveiling the Time Course Mechanism of Bone Fracture Healing by Transcriptional Profiles.

Tong-Meng Jiang

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In one paragraph

Article in Combinatorial chemistry & high throughput screening, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 29% of its field
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.

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 11 citations in OpenAlex.

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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

1 author at 1 institution in 1 country.

Tong-Meng JiangSchool of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBone fracture healing is a time-consuming and high-priority orthopedic problem worldwide.

objectiveDiscovering the potential mechanism of bone healing at a time course and transcriptional level may better help manage bone fracture.

methodsIn this study, we analyze a time-course bone fracture healing transcriptional dataset in a rat model (GSE592, GSE594, and GSE1371) of Gene Expression Omnibus (GEO). RNA was obtained from female Sprague-Dawley rats with a femoral fracture at the initial time (day 3) as well as early (week 1), middle (week 2), and late (week 4) time periods, with nonfracture rats used as control. Gene Ontology (GO) functional analysis and pathway examinations were performed for further measurements of GSEA and hub genes.

resultsResults indicated that the four stages of bone fracture healing at the initial, early, middle, and late time periods represent the phases of hematoma formation, callus formation, callus molding, and mature lamellar bone formation, respectively. Extracellular organization was positively employed throughout the four stages. At the hematoma formation phase, the muscle contraction process was downregulated. Antibacterial peptide pathway was downregulated at all phases. The upregulation of Fn1 (initial, early, middle, and late time periods), Col3a1 (initial, early, and middle time periods), Col11a1 (initial and early time periods), Mmp9 (middle and late time periods), Mmp13 (early, middle, and late time periods) and the downregulation of RatNP-3b (initial, early, middle, and late time periods) were possible symbols for bone fracture healing and may be used as therapeutic targets.

conclusionThese findings suggest some new potential pathways and genes in the process of bone fracture healing and further provide insights that can be used in targeted molecular therapy for bone fracture healing.

Indexed as

Femoral FracturesFracture HealingAnimalsBony CallusFemaleRatsRats, Sprague-DawleybioinformaticsBone fracture healingcallus formationcallus moldinghematoma formationmature lamellar bone formation

Identifiers

PMID35418283
OpenAlexW4223914605

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Registered trials

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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.