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Record W3153632671 · doi:10.15414/afz.2021.24.01.9-15

Evaluation of medicinal potential and antibacterial activity of selected plants against Streptococcus mutans

2021· article· en· W3153632671 on OpenAlexaboutno aff
Archita Sahoo

Bibliographic record

VenueActa fytotechnica et zootechnica/Acta fytotechnica et zootechnica · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects of Medicinal Plants
Canadian institutionsnot available
Fundersnot available
KeywordsStreptococcus mutansTanninTraditional medicinePhytochemicalSaponinChemistryAntibacterial activityPolyphenolTerpenoidBark (sound)Condensed tanninAntioxidantTerminaliaCombretaceaeFood scienceBacteriaBiologyProanthocyanidinBiochemistryMedicine

Abstract

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Article Details: Received: 2020-06-15 | Accepted: 2020-09-28 | Available online: 2021-03-31 https://doi.org/10.15414/afz.2021.24.01.9-15 The aim of the study is to screen the bioactive compounds (saponin, tannin, phenolic compounds, terpenoid & steroid) present in selected ethnomedicinal plants, Terminalia bellirica (fruits), Smilax zeylanica (leaves) and Dioscorea oppositifolia (fruits) from Odisha state, India. The single formulation was prepared using the selected plants parts in the ratio 1 : 6 : 3 respectively for quantitative analysis of tannin & total phenol, antioxidant activity and analysis of MIC (Minimum Inhibitory Concentration) against Streptococcus mutans causing bacteria of tooth decay. Results revealed that selected plant parts are rich source of bioactive compounds like tannin, phenolic compounds and saponin. The quantitative analysis of secondary metabolites showed highest concentration of tannin. It was noted that antioxidant activity is highest in methanol extract as compared to aqueous and acetone. MIC analysis also revealed that formulated powder had excellent antibacterial activity against S. mutans and it was observed the lowest values (450 µg ml-1) showed aqueous & methanol followed by acetone. The herbal formulation might be used to formulate new herbal products against tooth decay in near future. Keywords: antibacterial activity, antioxidant activity, ethnomedicinal plants, secondary metabolites, tooth decay References ANDERSON, T. (2004). Dental treatment in medieval England. British Dental Journal, 197(7), 419–425. DESHMUKH, M.A. and THENG, M.A. (2018). Phytochemical screening, quantitative analysis of primary secondary metabolites of Acacia aeabica bark. International Journal of Current Pharmaceutical Research, 10(2), 35–37. DHANYA, S.V.S., et al. (2018). Preliminary phytochemical activity of Smilax zeylanica L. (Smilaceaceae). Journal of Drug Delivery and Therapeutics, 8(4), 237–243. FERRAZ, E.G. et al. (2012). The oral manifestations of celiac disease: information for the pediatric dentist. Pediatric Dentistry, 34(7), 485–488. FERRAZZANO, G.F. et al. (2011). Plant polyphenols and their anti-cariogenic properties: a review. Molecules, 16(2), 1486–1507. GIUCA, M.R. et al. (2010). Oral signs in the diagnosis of celiac disease: review of the literature. Minerva Stomatologica, 59(1– 2), 33–43. GOUDA, S. et al. (2013). Free radical scavenging potential of extracts of Gracilaria verrucosa (L) (Harvey). An economically important seaweed from Chilika lake, India. Journal of Pharm Pharm Sciences, 6, 707–710. GUPTA, V. et al. (2015). Folklore herbal remedies used in dental care in Northern India and their pharmacological potential. American Journal of Ethnomedicine, 2(6), 365–72. HAINES, H.H. (1922). The Botany of Bihar and Orissa. Adlard & Son & West Newman, UK. HARBORNE, J.B. (1973). Phytochemicals methods. London. Chapman and Hall Ltd, 49–188. HAZRA, K. (2019). Phytochemical investigation of Terminalia bellirica fruit inside. Asian Journal of Pharmaceutical and Clinical Research, 12(8), 191–194. JYOTHI, T., et al. (2012). Phytochemical evaluation of Smilax zeylanica Linn. Soushrutam, 1(1), 1–14. KANDUTI, D. (2016). Fluoride: a review of use and effects on health. Mater Sociomed, 28, 133–137. MAST, P. et al. (2013). Understanding MIH: definition, epidemiology, differential diagnosis and new treatment guidelines. European Journal of Paediatrics Dent, 14(3), 204–8. MEJÁRE, I. and MJÖR, I.A. (2003). Dental caries: The Disease and its Clinical Management. Wiley-Blackwell. MOORE, W.J. (1983). The role of sugar in the aetiology of dental caries. 1. Sugar and the antiquity of dental caries. Journal of Dentist,11(3), 189–190. NATIONS, M.K. and NUTO, S.D.A.S. (2002). Tooth worms: poverty tattoos and dental care conflicts in Northeast Brazil. Social Sciences & Medicines, 54(2), 229–244. NEVILLE, B.W. and Day, T.A. (2002). Oral cancer and precancerous lesions. CA: A Cancer Journal for Clinicians, 52(4), 195–215. RAAMAN, N. (2006). Qualitative phytochemical screening and Phytochemical Techniques. New Delhi Publishing. RAI, A. et al. (2010). Antibiotic mediated synthesis of gold nanoparticles with potent antimicrobial activity and their application in antimicrobial coatings. Journal of Materials Chemistry, 20(32), 6789–6798. SAXENA, H.O. and BRAHMAM, M. (1994). The flora of Orissa. Regional Research Laboratory; Orissa Forest Development Corporation, pp. 437–439. SHARMA, D. et al. (2018). Role of plant extract in the inhibition of dental caries. International Journal of Life Science & Pharma Research, 8(2), 9–23. SHEKARCHIZADEH, H. et al. (2013). Oral health of drugs abusers: a review of health effects and care. Iranian Journal of Public Health, 42(9), 929–940. SMITH, R.E. et al. (2002). Maternal risk indicators for childhood caries in an inner city population. Community Dentistry and Oral Epidemiology, 30(3), 176–181. SOFOWORA, A. (1993). Medicinal plants and traditional medicine in Africa. Spectrum Books limited. Ibadan. TREASE, G.E. and EVANS, W.C. (1989). Pharmacognosy. WB Scanders Company Ltd., 89–300. WONG, C.Y. et al. (2013). Experimental and computational modeling of solid particle erosion in a pipe annular cavity. Wear, 303(1–2), 109–129. YOUNG, D.A. et al. (2009). Curing the silent epidemic: caries management in the 21st century and beyond. Ontario Dentist, 86(2), 681–685.

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)0.011
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.341
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; both teacher heads agree on what is shown here.

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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Citations2
Published2021
Admission routes1
Has abstractyes

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