Evidence map›Paper›PMID 40087323›Full record

ArticleScientific reports2025

Analysis of time-fractional cancer-tumor immunotherapy model using modified He-Laplace algorithm.

Mubashir Qayyum, Sidra Nayab, Imran Siddique, Abdullatif Ghallab

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mubashir QayyumDepartment of Sciences and Humanities, National University of Computer and Emerging Sciences, Lahore, Pakistan.
Sidra NayabDepartment of Sciences and Humanities, National University of Computer and Emerging Sciences, Lahore, Pakistan.
Imran SiddiqueDepartment of Mathematics, University of Sargodha, Sargodha, 40100, Pakistan.
Abdullatif GhallabDepartment of Computer Science, University of Science and Technology, P.O. Box: 13064, Sana'a, Yemen. ghallab@ust.edu.ye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer encompasses various diseases characterized by the uncontrolled growth of abnormal cells, which can invade healthy tissues and spread throughout the body, making it the second leading cause of death worldwide. This study presents a fractional cancer treatment model with immunotherapy to enhance understanding of cancer's mathematical framework and behavior. The model comprises fractional differential equations analyzed using the Caputo-fractional derivative, aiming to control cancer growth while considering cell population metrics. A framework integrating various homotopies and Laplace transforms is developed to explore cancer's complexities. Simultaneous solution profiles for effector immune cells and tumor cells illustrate their mutual influence. The model examines parameters such as the death rate of immune cells, natural tumor growth rate, rate of immune cells killing fractional tumor cells and numerous others graphically for clarity. The fractional parameter β is visually represented through 2D, 3D, and contour plots. This comprehensive analysis validates the proposed approach, suggesting its applicability to other complex cancer treatment models for better decision-making in cancer treatment.

Indexed as

AlgorithmsImmunotherapyModels, BiologicalNeoplasmsHumansModels, TheoreticalCancer treatment modelCaputo fractional derivativeHe-Laplace algorithmImmune cellsImmunotherapyTumor cells

Identifiers

PMID40087323
PMCPMC11909156

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Registered trials

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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.