Evidence map›Paper›PMID 38239705›Full record

ReviewCancer drug resistance (Alhambra, Calif.)2023

Autophagy-related mechanisms for treatment of multiple myeloma.

Gül Kozalak, Ali Koşar

Open access · diamondAbstract readReview
In one paragraph

Review in Cancer drug resistance (Alhambra, Calif.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 11% 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.

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

7 citing papers in PubMed, 10 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

2 authors at 1 institution in 1 country.

Gül KozalakFaculty of Engineering and Natural Science, Sabancı University, Istanbul 34956, Turkey.
Ali KoşarFaculty of Engineering and Natural Science, Sabancı University, Istanbul 34956, Turkey.
Sabancı Üniversitesi · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple myeloma (MM) is a type of hematological cancer that occurs when B cells become malignant. Various drugs such as proteasome inhibitors, immunomodulators, and compounds that cause DNA damage can be used in the treatment of MM. Autophagy, a type 2 cell death mechanism, plays a crucial role in determining the fate of B cells, either promoting their survival or inducing cell death. Therefore, autophagy can either facilitate the progression or hinder the treatment of MM disease. In this review, autophagy mechanisms that may be effective in MM cells were covered and evaluated within the contexts of unfolded protein response (UPR), bone marrow microenvironment (BMME), drug resistance, hypoxia, DNA repair and transcriptional regulation, and apoptosis. The genes that are effective in each mechanism and research efforts on this subject were discussed in detail. Signaling pathways targeted by new drugs to benefit from autophagy in MM disease were covered. The efficacy of drugs that regulate autophagy in MM was examined, and clinical trials on this subject were included. Consequently, among the autophagy mechanisms that are effective in MM, the most suitable ones to be used in the treatment were expressed. The importance of 3D models and microfluidic systems for the discovery of new drugs for autophagy and personalized treatment was emphasized. Ultimately, this review aims to provide a comprehensive overview of MM disease, encompassing autophagy mechanisms, drugs, clinical studies, and further studies.

Indexed as

apoptosisAutophagybone marrow microenvironmentDNA repair and transcriptional regulationdrug resistancehypoxiamultiple myelomaunfolded protein response

Identifiers

PMID38239705
PMCPMC10792488
OpenAlexW4390196764

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.