Targeting Nanotechnology and Nutraceuticals in Obesity: An Updated Approach
Bibliographic record
Abstract
Hypothesis: This review article represents a brief layout of the risk factors and pathophysiology responsible for obesity, customary treatment strategies, and nanotechnology-based nutraceutical for therapeutics of obesity. Experiments: An exhaustive search of the literature was done for this purpose, using Google Scholar, PubMed, and ScienceDirect databases. A study of the literature was conducted using publications published in peer-reviewed journals between the years 2000 and 2022. Findings: This was revealed that risk factors responsible for obesity were genetic abnormalities, environmental, and socio-economic factors. Number of research articles published between 2000 and 2022 were based on phytoconstituents based nanoformulation for obesity therapeutics and therefore, have been systematically compiled in this review. Various nutraceuticals like Garcinia cambogia, quercetin, resveratrol, capsaicin, Capsicum, Curcuma longa, Camella Sinensis, Zingiber officinalis, Citrus aurantium, Aegle marmelos, Coffea canephora, Asparagus officinalis, Gardenia jasminoides, Catha edulis, Clusia nemroisa, Rosmarinus officinalis, Cirsium setidens, Betula platyphylla, Tripterygium wilfordi possessing anti-obesity actions are discussed in this review along with their patents, clinical trials as well as their nanoformulation available. Conclusion: This review illustrates that the nanotechnology have a great propensity to impart promising role in the delivery of phytochemicals and nutraceuticals in management of obesity conditions and other related disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".