Evidence map›Paper›PMID 42429834›Full record

ArticleNaunyn-Schmiedeberg's archives of pharmacology2026

Preclinical evaluation of silver-doped copper carbonate nanoparticles for antidiabetic and wound healing effects in mice.

Muhammad Zahir Tahir, Ali Umar, Waseem Ul Ghafoor, Misbah Ullah Khan, Muhammad Saleem Khan

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Article in Naunyn-Schmiedeberg's archives of pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Muhammad Zahir TahirDepartment of Zoology, Faculty of Life Sciences, University of Okara, Okara, 56130, Pakistan.
Ali UmarDepartment of Zoology, Faculty of Life Sciences, University of Okara, Okara, 56130, Pakistan.
Waseem Ul GhafoorDepartment of Zoology, Faculty of Life Sciences, University of Okara, Okara, 56130, Pakistan.
Misbah Ullah KhanCenter for Nanosciences, University of Okara, Okara, 56130, Pakistan.
Muhammad Saleem KhanDepartment of Zoology, Faculty of Life Sciences, University of Okara, Okara, 56130, Pakistan. samiikhan@uo.edu.pk.ORCID https://orcid.org/0000-0003-2653-402X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes mellitus is a chronic metabolic disorder characterized by persistent hyperglycemia and oxidative stress, leading to multi-organ dysfunction and impaired wound healing. This study evaluated the antidiabetic, antioxidant, hematological, biochemical, and wound-healing effects of silver-doped copper carbonate nanoparticles (Ag-CuCO₃ NPs) in alloxan-induced diabetic albino mice. Diabetes was induced via intraperitoneal injection of alloxan monohydrate (150 mg/kg) and animals were divided into control, diabetic control, and treatment groups receiving low dose (LD) or high dose (HD) of Ag-CuCO₃ NPs orally for 28 days. Hematological, biochemical, hormonal, electrolyte, and wound-healing parameters were assessed, and data were analyzed using one-way analysis of variance (ANOVA) followed by Tukey's post hoc test, with p < 0.05 considered statistically significant. Diabetic controls showed severe hyperglycemia (> 280 mg/dL), elevated LDL (64 ± 3 mg/dL), reduced RBC count (5.1 ± 0.3 × 10⁶/µL) and delayed wound closure beyond Day 20. Low-dose Ag-CuCO₃ NP treatment significantly reduced fasting blood glucose levels from diabetic values (> 280 mg/dL) toward near-normal levels and improved hematological indices, including a ~ 42% increase in RBC count (2.85 to 4.05 × 10⁶/µL), ~ 38% increase in hemoglobin (7.46 to 10.33 g/dL), and ~ 115% increase in hematocrit (10.43 to 22.43%) compared with diabetic controls. Insulin levels increased by ~ 53% (6.12 to 9.35 µIU/mL), while sodium levels improved by ~ 3% (136.13 to 140.03 mmol/L), indicating partial restoration of metabolic and renal balance. High-dose treatment produced a more pronounced improvement in oxidative status, with SOD activity increasing by ~ 100% (2.31 to 4.62 U/mg protein) and lipid peroxidation marker MDA decreasing by ~ 51% (6.41 to 3.12 nmol/mg protein). However, high-dose treatment was also associated with elevations in hepatic enzymes, including AST (~ 50% increase; 56.33 to 84.66 U/L) and ALT (~ 114% increase; 25.66 to 55 U/L), suggesting potential dose-related hepatic stress. While high-dose treatment achieved faster glycemic and oxidative stress correction, low-dose treatment provided more balanced therapeutic efficacy with minimal biochemical disturbances. Overall, these findings provide preliminary evidence of dose-dependent antidiabetic, antioxidant and wound-healing effects of Ag-CuCO₃ nanoparticles in a preclinical diabetic model, warranting further mechanistic and comparative investigations.

Indexed as

Antidiabetic therapyBimetallic nanoparticlesDiabetic wound modelNanomedicineReactive oxygen species

Identifiers

PMID42429834

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.