Evidence map›Paper›PMID 38863706›Full record

ReviewFrontiers in immunology2024

Advancements in ovarian cancer immunodiagnostics and therapeutics via phage display technology.

Yang Li, Xiao-Meng Li, Kai-di Yang, Wei-Hua Tong

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 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. Review
  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

4 authors.

Yang LiObstetrics and Gynecology Center, First Hospital of Jilin University, Changchun, Jilin, China.
Xiao-Meng LiDepartment of Rehabilitation, School of Nursing, Jilin University, Changchun, China.
Kai-di YangDepartment of Rehabilitation, School of Nursing, Jilin University, Changchun, China.
Wei-Hua TongObstetrics and Gynecology Center, First Hospital of Jilin University, Changchun, Jilin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer, ranking as the seventh most prevalent malignancy among women globally, faces significant challenges in diagnosis and therapeutic intervention. The difficulties in early detection are amplified by the limitations and inefficacies inherent in current screening methodologies, highlighting a pressing need for more efficacious diagnostic and treatment strategies. Phage display technology emerges as a pivotal innovation in this context, utilizing extensive phage-peptide libraries to identify ligands with specificity for cancer cell markers, thus enabling precision-targeted therapeutic strategies. This technology promises a paradigm shift in ovarian cancer management, concentrating on targeted drug delivery systems to improve treatment accuracy and efficacy while minimizing adverse effects. Through a meticulous review, this paper evaluates the revolutionary potential of phage display in enhancing ovarian cancer therapy, representing a significant advancement in combating this challenging disease. Phage display technology is heralded as an essential instrument for developing effective immunodiagnostic and therapeutic approaches in ovarian cancer, facilitating early detection, precision-targeted medication, and the implementation of customized treatment plans.

Indexed as

Cell Surface Display TechniquesOvarian NeoplasmsPeptide LibraryAnimalsBiomarkers, TumorFemaleHumansImmunotherapyBiomarkers, TumorPeptide Libraryimmunodiagnosticsovarian cancerpersonalized therapeutic strategiesphage display technologytargeted drug delivery

Identifiers

PMID38863706
PMCPMC11165035

What OpenQuestion holds

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LicenceCC BY
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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 OpenQuestion graph.