Evidence map›Paper›PMID 40033587›Full record

ArticleCurrent radiopharmaceuticals2025

The Central Composite Design and Artificial Neural Network Coupled with Genetic Algorithm in Optimization and Modeling of the Radiolabeling Process of

Sima Attar Nosrati, Maryam Salahinejad, Mohammad Reza Aboudzadeh, Mojtaba Amiri, Ali Roozbahani

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

5 authors.

Sima Attar NosratiRadiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Maryam SalahinejadRadiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.ORCID 0000-0002-7825-5981
Mohammad Reza AboudzadehRadiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Mojtaba AmiriRadiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Ali RoozbahaniRadiation Application Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionA promising material used in radiation synovectomy of small joints is hydroxyapatite, labeled with

methodsIn this investigation, a central composite design based on response surface methodology and artificial neural networks modeling coupled with genetic algorithm technique is applied to build predictive models and explore key parameters' effect in hydroxyapatite's radiolabeling process with

resultsBased on the validation data set, the statistical values demonstrate that the artificial neural networks model performs better than the response surface methodology model. The artificial neural networks model has a small mean squared error (9.08 artificial neural networks < 12.36 response surface methodology) and a high coefficient of determination (R

conclusionThe ability to generate more data with fewer experiments for optimization and improved production is a pertinent advantage of multivariate optimization methods over traditional methods in radiation-related activities. The central composite design and artificial neural network- genetic algorithm optimization approaches are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with

Indexed as

DurapatiteLutetiumNeural Networks, ComputerRadioisotopesRadiopharmaceuticalsAlgorithmsGenetic AlgorithmsIsotope LabelingDurapatiteLutetiumLutetium-177RadioisotopesRadiopharmaceuticals177Lu-hydroxyapatiteartificial neural networks (ANN)central composite design (CCD).genetic algorithm (GA)radiochemical yieldRadiolabelingresponse surface methodology (RSM)

Identifiers

PMID40033587

What Socratic holds

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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 Socratic graph.