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Record W4388732984 · doi:10.1002/jdd.13421

Thinking outside the box: Using homology models and interactive PowerPoints for active learning

2023· article· en· W4388732984 on OpenAlexaff
Nazlee Sharmin, Karen K Yee, Jaskaranjit Kaur Dhaliwal, A. Chow

Bibliographic record

VenueJournal of Dental Education · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related gene regulation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeneticsBiologyMutationGene

Abstract

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The molecular genetics of tooth development is a complex and rapidly evolving field of study. Tooth eruption, the last stage of tooth development, is one of the least understood areas.1 Dental students spend extensive time learning the genetics of tooth development through didactic lectures and textbooks, while genomic data rapidly expands with the advancement of gene sequencing technology. Students studying the genetics of tooth development in a traditional manner may not receive the most up-to-date knowledge on recently identified mutations and gene regulation. Moreover, opportunities for active learning are rare in the study of tooth development. A ‘Genomic Table’ with curated human genes involved in tooth eruption and mutations linked to eruption-related disorders (Table 1) A set of ‘Interactive PowerPoints’ with selected homology models of wildtype and mutant proteins (Figure 1) Gene name NCBI Cytogenetic location OMIM Encoded protein NCBI Homology model I-TASSER Genetic disorder OMIM Mutation OMIM PTH1R: Parathyroid hormone 1 receptor Gene ID: 5745 Truncation mutation: GLU155TER (Decker et al., 2008) Splice site mutation: IVS11AS, C-G, −3 (Decker et al.) IVS8DS, G-A, +1 (Decker et al.) RUNX2: RUNX family transcription factor 2 Gene ID: 860 Substitution mutation: ARG225GLN; ARG225TRP (Quack et al.) TER522SER (Machuca-Tzili et al.). ARG169PRO (Morava et al.). THR200ALA (Zhou et al.). MET175ARG; SER191ASN (Lee et al.). Insertion mutation: 16-BP INS; TRP283TER (Mundlos et al.). 1-BP INS, 1228C (Zheng et al.). 1-BP INS, 1206C (Quack et al., 1999). 1-BP INS, 1380C (Quack et al.). Duplication mutation: 30-BP DUP, ALANINE TRACT EXPANSION (Mundlos et al.). EDA: Ectodysplasin A Gene ID: 1896 Ectodermal dysplasia 1, hypohidrotic, X-linked Tooth agenesis, selective, X-linked Ectodermal dysplasia, hypohidrotic, X-linked Substitution mutation: TYR61HIS, ARG69LEU (Kere et al., 1996) GLU63LYS (Ferguson et al., 1998) ARG276CYS (Ferrier et al., 2009) ARG155CYS, ARG156CYS, ARG156HIS, PRO209LEU, GLY224ALA, ALA349THR, HIS252LEU (Monreal et al.). Truncation mutation: GLN23TER (Ferguson et al.) TYR61TER (Yotsumoto et al.) Deletion mutation: 36-BP DEL, 1-BP DEL (Visinoni et al.) Insertion mutation: 1-BP INS, 573T (Huang et al.) 2-BP INS, 913TA (Schneider and Muhle) Tooth agenesis, selective, X-linked Substitution mutation: ARG65GLY(Tao et al.) GLN358GLU (Tarpey et al.) THR338MET (Han et al.) ALA259GLU, ARG289CYS, ARG334HIS (Song et al.) Identifying wildtype and mutated proteins: Homology modeling: Creating interactive PowerPoints: ICM-Browser was used to create the 3D presentations of the protein models, highlighting the protein backbone, surface, important residues with side chains, and sites of mutations. The ActiveICM plugin enabled embedding the interactive 3D protein structures into PowerPoint.3 The ‘Genomic Table’ (Table 1) includes GenBank ID and cytogenetic locations for each gene and literature references for each mutation, allowing students to conduct self-directed learning. Homology modeling revealed significant structural changes between several wild-type and mutant proteins. The interactive PowerPoints, with embedded 3D protein structures (wild type and mutants), enable users to rotate, magnify, and toggle between structures and representations (Figure 1). This teaching and learning resource can help dental students comprehend the genetics of tooth eruption interactively instead of memorizing facts that are not easily retained and lack context. A demonstration of this tool is available in the supplementary video. Potential learning outcomes and related activities that can be conducted with this tooth-eruption learning resource are listed in Figure 2B. Such active learning approaches are supported by constructivist learning theory, which postulates that learning is a process of ‘making meaning,’ and active learning occurs when learners build their own understanding.4 We believe our endeavor will encourage other educators to develop similar resources for teaching complex proteins, mutations, and drug interactions. The authors have nothing to report. The authors declare no conflict of interest. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.326
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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