Evidence map›Paper›PMID 40143053›Full record

ReviewPharmaceutics2025

Nanotechnology-Based Therapies for Preventing Post-Surgical Adhesions.

Zi Yi Teo, Samyuktha Dhanalakshmi Senthilkumar, Dinesh Kumar Srinivasan

Abstract readReview
In one paragraph

Review in Pharmaceutics, 2025. 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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

3 authors.

Zi Yi TeoYong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Samyuktha Dhanalakshmi SenthilkumarYong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
Dinesh Kumar SrinivasanDepartment of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117594, Singapore.ORCID 0000-0001-6535-5441

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adhesions are the body's natural response to various inflammatory causes, with surgery being the most common cause. However, the formation of postoperative adhesions can lead to significant complications, including intestinal obstruction and chronic pain. To prevent such postoperative complications associated with adhesions, developing effective strategies for adhesion prevention has been a major focus of research. Currently, several therapeutic models have been developed to achieve this objective. These include pharmaceuticals, inert polymers, functional biomaterials, and nanotherapeutics. Among the various strategies developed, nanotherapeutics, though still in its early stages, has shown promise as a potential approach. Other therapeutic models are associated with adverse side effects and complications related to their application. On the other hand, nanotherapeutic models are able to overcome the limitations of the other strategies and provide their own set of unique advantages. Hence, nanotherapeutics represents a promising area for further research. Further efforts should be made to refine existing nanotherapeutics for clinical application while also addressing associated safety and ethical concerns related to their use in medical practice. Therefore, this article aims to review the various nanotherapeutic approaches developed for the prevention of postoperative adhesions, explore their regulatory pathways, and discuss associated safety and ethical concerns.

Indexed as

adhesionhydrogelsnanocompositesnanofibersnanotherapeuticspost-operation

Identifiers

PMID40143053
PMCPMC11944804

What OpenQuestion 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 OpenQuestion graph.