Evidence map›Paper›PMID 39601766›Full record

ArticlePharmacology research & perspectives2024

Enhancing Student Comprehension of Glucose Metabolism Visualization Through Virtual Simulation Platform: An Educational Approach.

Luna Ge, Tingting Feng, Shuo Cao, Hang Yin, Yuang Zhang, Jihong Pan, Wei Chen, Kai Cheng, Lin Wang

Abstract read
In one paragraph

Article in Pharmacology research & perspectives, 2024. 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
–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

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

9 authors.

Luna GeBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.
Tingting FengBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.
Shuo CaoBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.
Hang YinBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.
Yuang ZhangBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.
Jihong PanBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.
Wei ChenBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.
Kai ChengDepartment of PET/CT Center, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.ORCID 0000-0001-9572-9360
Lin WangBiomedical Sciences College & Shandong Medicinal Biotechnology Centre, Shandong First Medical University, Shandong Academy of Medical Sciences, Ji'nan, Shandong, China.ORCID 0000-0002-5478-0212

Funding

Academic Promotion Programme of Shandong First Medical University 2019LJ001The Youth Innovation Technology Plan of Shandong University 2019KJK003
6 · The paper itself

Abstract

With the rapid progression of biotechnology, the significance translational research on glycolysis in molecular pharmacology has become increasingly evident. To deepen students' understanding of glycolytic processes and facilitate their comprehension of drug action mechanisms, we have developed a visual virtual simulation platform dedicated glycolysis. The educational approach commenced with theoretical lectures on glycolysis, followed by practical laboratory sessions where students measured glycolysis-related parameters such as hexokinase, pyruvate kinase, and lactate. Students then engaged with the virtual simulation training platform to explore glycolytic stress tests and positron emission tomography/computed tomography (PET/CT) imaging, with their progress tracked through an assessment mode. The study involved 67 s-year undergraduate students majoring in biomedical sciences, all of whom had received instruction in glucose metabolism theories and completed the associated questionnaires. The results showed that the students gained a deeper understanding of glycolysis and the clinical application of PET/CT imaging in the context of glycolysis. The majority also agreed that the integration of scientific and clinical cases in teaching is beneficial and that the project sparked their interest in scientific research. These findings align with existing literature that emphasizes the importance of innovative educational tools in enhancing student engagement and understanding of the underlying theories of the curriculum. This project designed an innovative glycolytic metabolism teaching system encompassing the monitoring of traditional glycolytic indicators, glycolytic stress tests, and PET/CT imaging based on glycolysis. The visual virtual simulation platform for glycolysis can serve as an innovative educational tool in the molecular pharmacology curriculum or other courses involving glycolysis, assisting students in deeply understanding the molecular mechanisms of glycolysis and its significance in disease and drug action.

Indexed as

GlucoseGlycolysisPositron Emission Tomography Computed TomographyComprehensionComputer SimulationCurriculumHumansVirtual RealityGlucoseexperimental trainingglucose metabolismglycolysismolecular pharmacologyvirtual simulation platform

Identifiers

PMID39601766
PMCPMC11600983

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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.