Evidence map›Paper›PMID 39286723›Full record

ReviewCureus2024

Navigating the Genetic Landscape: A Comprehensive Review of Novel Therapeutic Strategies for Retinitis Pigmentosa Management.

Yuga B Pawar, Archana R Thool

Abstract readReview
In one paragraph

Review in Cureus, 2024. 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. Review
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

2 authors.

Yuga B PawarOphthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND.
Archana R ThoolOphthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Retinitis pigmentosa (RP) is a collection of retinal disorders characterized by the progressive degeneration of photoreceptor cells, leading to significant visual impairment and, in severe cases, blindness. RP affects individuals worldwide and can be inherited through various genetic patterns, making it a genetically diverse condition. Despite considerable advancements in diagnostic methods and supportive therapies, there is currently no cure for RP. The focus of existing management strategies is on slowing the progression of the disease and improving the quality of life for those affected. This comprehensive review explores the latest therapeutic approaches in the management of RP, highlighting advancements in genetic therapies, such as gene augmentation and editing, as well as cell-based treatments including stem cell transplantation and induced pluripotent stem cell (iPSC) technologies. Emerging methods like optogenetics and pharmacological interventions designed to preserve retinal function are also discussed. Additionally, the review examines technological innovations, including retinal prosthetics and the use of artificial intelligence, which hold the potential to revolutionize RP treatment. The challenges and limitations associated with these novel therapies, such as safety concerns, accessibility issues, and regulatory hurdles, are critically evaluated. By providing an overview of current research and future directions, this review aims to inform clinicians and researchers about the state of the art in RP treatment and the prospects for achieving significant therapeutic advancements.

Indexed as

cell-based treatmentsgenetic therapiesinnovative therapiesoptogeneticsretinal prostheticsretinitis pigmentosa

Identifiers

PMID39286723
PMCPMC11405069

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.