Analgesic and anti-inflammatory activities of mangiferin gel for musculoskeletal injuries in cancer patients
Bibliographic record
Abstract
OBJECTIVE: Musculoskeletal injuries, a global public health concern, are among the most significant causes of long-lasting disability and meager performance in activities of daily living (ADLs). METHODS: Mangiferin (5%) was used to formulate a gel, extracted from aqueous-methanolic (30:70) extracts of M. indica leaf. Participants (n = 200) diagnosed with musculoskeletal injuries were separated into four groups (n = 50/group). Group I and II received phonophoresis with 5% mangiferin gel and 1% diclofenac gel, respectively, while Group III and IV received superficial massage with the same gels. Color, stability test, pH, spreadability test, pain, onset of pain relief, stiffness, ADLs were evaluated through the Numeric Pain Rating Scale (NPRS), Global Pain Relief Scale (GPRS), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) Scale. RESULTS: NPRS was relieved in Group-I, while WOMAC was also reduced in Group-I, along with ADLs and stiffness measures; this improvement was greater than that in Group-II for all measures. Also, the NPRS of Group III was reduced along with WOMAC and ADLs scores and stiffness, more effectively that the same measures in Group IV. CONCLUSION: Mangiferin gel 5% has been proven more effective than diclofenac diethyl-ammonium gel 1% in treating human musculoskeletal injuries. Phonophoresis enhanced the effect of both gels, strongly suggesting that the topical application of mangiferin gel combined with phonophoresis could be a valuable therapeutic alternative to reduce inflammation and relieve pain significantly. The formulation of mangiferin gel is nature-based, cost-effective, eco-friendly, and prepared easily.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".