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Record W4312366734 · doi:10.20527/dentin.v6i1.6228

PENGARUH EKSTRAK KULIT JERUK SIAM BANJAR (Citrus reticulata) TERHADAP KADAR ION FOSFAT PADA GIGI DESIDUI

2022· article· en· W4312366734 on OpenAlexaff
Nurul Hidayah, Renie Kumala Dewi, Amy Nindia Carabelly

Bibliographic record

VenueDentin · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMedicinal Plant Research
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsOrange (colour)DemineralizationRemineralisationChemistryDentistryPhosphateSignificant differenceRhizomeAnimal scienceNuclear chemistryFood scienceTraditional medicineEnamel paintMedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACTBackground: Barito Kuala district has the highest prevalence of cavities in South Kalimantan, namely 59.67%. Barito Kuala Regency is a wetland area with an acidic pH of 3.65 on average. The low pH will cause the enamel crystals to dissolve, resulting in a demineralization process. Minerals lost due to demineralization can be recovered by a remineralization using materials containing phosphate and calcium. Siam banjar orange peel contains 19.9% phosphate and 37.1% calcium. Objective: To analyze the effect of siam banjar orange peel extract on the increase in phosphate ion levels in deciduous teeth after demineralization in wetland water pH 4.5 in the Barito Kuala district. Methods: This research was a true experimental research with post test only with control group design which divided 20 extracted mandibular primary incisors into 4 treatment groups, namely immersion in the extract of Siam Banjar orange peel with concentrations of 100%, 75%, 50%, And 25% and 1 control group that is soaking in wetland water pH 4.5. Then measured the levels of phosphate ions in the teeth using UV-Vis spectrophotometry. Results: The results of the One Way ANOVA statistical test showed the value of p = 0.065 (p>0.05), which means that there was no statistically significant difference between groups. Conclusion: There was no significant effect on immersion of the teeth in the extract of the orange peel of Siam Banjar (Citrus reticulata) on the increase in phosphate ion levels in primary teeth. Keywords: Citrus reticulata, Phosphate, RemineralizationABSTRAKLatar Belakang: Kabupaten Barito Kuala mempunyai prevalensi gigi berlubang tertinggi di Kalimantan Selatan yaitu 59,67%. Kabupaten Barito Kuala merupakan daerah lahan basah dengan pH asam yaitu rata-rata 3,65. Rendahnya pH tersebut akan menyebabkan kristal enamel larut sehingga terjadi proses demineralisasi. Mineral yang hilang akibat demineralisasi dapat dikembalikan dengan proses remineralisasi menggunakan bahan yang mengandung fosfat dan kalsium. Kulit jeruk siam banjar mengandung fosfor sebanyak 19,9% dan kalsium 37,1%. Tujuan: Menganalisis pengaruh ekstrak kulit jeruk siam banjar terhadap peningkatan kadar ion fosfat pada gigi desidui setelah dilakukan demineralisasi pada air lahan basah pH 4,5 di daerah Kabupaten Barito Kuala. Metode: Penelitian ini merupakan eksperimental murni dengan post test only with control group design yang membagi 20 gigi desidui insisivus rahang bawah yang sudah di ekstraksi dalam 4 kelompok perlakuan, yaitu perendaman pada ekstrak kulit jeruk siam banjar konsentrasi 100%, 75%, 50%, dan 25% dan 1 kelompok kontrol yaitu perendaman pada air lahan basah pH 4,5. Kemudian dilakukan pengukuran kadar ion fosfat pada gigi dengan menggunakan alat spektrofotometri UV-Vis. Hasil: Hasil uji statistik One Way ANOVA menunjukkan nilai p = 0,065 (p>0,05) yang berarti tidak ada perbedaan yang bermakna antar kelompok secara statistik. Kesimpulan: Tidak terdapat pengaruh yang signifikan pada perendaman gigi dalam ekstrak kulit jeruk siam banjar (Citrus reticulata) terhadap peningkatan kadar ion fosfat pada gigi desidui.Kata Kunci: Citrus reticulata, Fosfat, Remineralisasi.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.030
GPT teacher head0.234
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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