Evidence map›Paper›PMID 35312676›Full record

ArticlePLoS computational biology2022

Chemotherapy-induced cachexia and model-informed dosing to preserve lean mass in cancer treatment.

Suzan Farhang-Sardroodi, Michael A La Croix, Kathleen P Wilkie

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 24% 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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
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  5. Article
  6. Effects of Voluntary Wheel Running Exercise on Chemotherapy-Impaired Cognitive and Motor Performance in Mice.International journal of environmental research and public health · 2023
    Article
  7. Article
  8. 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 at 2 institutions in 1 country.

Suzan Farhang-SardroodiModelling Infection, and Immunity Lab, Department of Mathematics and Statistics, York University, Toronto, Canada.ORCID 0000-0001-7654-8804
Michael A La CroixDepartment of Mathematics, Ryerson University, Toronto, Canada.ORCID 0000-0003-3536-7286
Kathleen P WilkieDepartment of Mathematics, Ryerson University, Toronto, Canada.ORCID 0000-0002-3226-724X
Toronto Metropolitan University · CAYork University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although chemotherapy is a standard treatment for cancer, it comes with significant side effects. In particular, certain agents can induce severe muscle loss, known as cachexia, worsening patient quality of life and treatment outcomes. 5-fluorouracil, an anti-cancer agent used to treat several cancers, has been shown to cause muscle loss. Experimental data indicates a non-linear dose-dependence for muscle loss in mice treated with daily or week-day schedules. We present a mathematical model of chemotherapy-induced muscle wasting that captures this non-linear dose-dependence. Area-under-the-curve metrics are proposed to quantify the treatment's effects on lean mass and tumour control. Model simulations are used to explore alternate dosing schedules, aging effects, and morphine use in chemotherapy treatment with the aim of better protecting lean mass while actively targeting the tumour, ultimately leading to improved personalization of treatment planning and improved patient quality of life.

Indexed as

Antineoplastic AgentsNeoplasmsAnimalsCachexiaHumansMiceMuscle, SkeletalQuality of LifeTreatment OutcomeAntineoplastic Agents

Identifiers

PMID35312676
PMCPMC8989307
OpenAlexW4220895081

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.