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Record W2780444046 · doi:10.4103/jpbs.jpbs_141_17

Assessment of Total Antioxidant Capacity and Antimicrobial Activity of Glycyrrhiza glabra in Saliva of HIV-Infected Patients

2017· article· en· W2780444046 on OpenAlexaff
Eby Aluckal, Asif Ismail, Anoopa Paulose, Sanju Lakshmanan, Manikandan Balakrishnan, Benoy Mathew, Abraham Kunnilathu

Bibliographic record

VenueJournal of Pharmacy And Bioallied Sciences · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacological Effects of Natural Compounds
Canadian institutionsWestern University
Fundersnot available
KeywordsGlycyrrhizaSalivaAntioxidant capacityTraditional medicineMedicineAntimicrobialPost-hoc analysisAnalysis of varianceAntioxidantOxidative stressMicrobiologyChemistryBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Objectives: The objectives of this study were to evaluate the antimicrobial activity and total antioxidant capacity (TAC) of licorice in Saliva of HIV/AIDS patients. Materials and Methods: Saliva specimens were collected from 20 people living with HIV infection, with CD4 count <500 cells/mm 3 from people infected with HIV/AIDS in Mangalore city, India. A combination of amoxicillin-clavulanic acid and nystatin was taken as the positive control and normal saline as negative control. Results were compared using one-way analysis of variance followed by Tukey's post hoc analysis in SPSS 19. Results: The TAC was evaluated spectrophotometrically at 695nm using the phosphomolybdenum method. Glycyrrhiza glabra showed a statistically significant reduction ( P < 0.05) in total Candida count. The TAC of G. glabra was found to be 4.467 mM/L. Conclusions: G. glabra extracts showed good anticandidal activity and also high antioxidant property which reduces the oxidative stress of HIV-infected people.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.104
GPT teacher head0.445
Teacher spread0.342 · 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 designObservational
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

Citations8
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of Pharmacy And Bioallied SciencesSame topicPharmacological Effects of Natural CompoundsFrench-language works237,207