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Record W4392303895 · doi:10.4103/jpbs.jpbs_829_23

Evaluation of the Circulatory Levels of Heat Shock Protein 60 Levels in Periodontitis and Cardiovascular Disease Patients

2024· article· en· W4392303895 on OpenAlexaff
Rakshit K. Dalal, Manpreet Kaur, Komal Khatri, F.D. Patel, Heena Shaikh, Arifa Bakerywala

Bibliographic record

VenueJournal of Pharmacy And Bioallied Sciences · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsCARE Canada
Fundersnot available
KeywordsMedicinePeriodontitisHSP60DiseaseInflammationHeat shock proteinPeriodontal diseaseInternal medicineChronic periodontitisBiomarkerGastroenterologyImmunologyHsp70

Abstract

fetched live from OpenAlex

A BSTRACT Introduction: HSP is arguably the most thoroughly studied self-antigens connected to Cardio Vascular Diseases (CVD) and periodontal disease. Hence, the major goal of this analysis was to determine the amount of HSP60 in patients’ Chronic Periodontitis (CP) patients’ serum. Materials and Methods: The current investigation involved 100 patients in all. Based on the patients’ periodontal and cardiovascular health, the patients were divided. The patients were made aware that this research had no direct bearing on disease treatment or cure. Results: In contrast to periodontal disease, which had a mean serum HSP60 of 59.94 ng/dl, CVD had a mean serum HSP60 of 85.98 ng/dl. When compared to periodontal disease, the CVD increased significantly ( P < 0.05, 0.03). Discussion and Conclusion: We emphasize the function of HSP60 in the pathophysiology of individuals with chronic periodontitis based on the findings of the current investigation. Serum HSP60 concentrations can serve as a biomarker for periodontal inflammation. More longitudinal and interventional research with a larger sample size is required to validate the present findings. In periodontal therapies, targeting HSP60 may enhance results.

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)0.003
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 score0.328
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.070
GPT teacher head0.350
Teacher spread0.280 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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