Hybrid PCL/chitosan-PEO nanofibrous scaffolds incorporated with A. euchroma extract for skin tissue engineering application.

Skin tissue engineering is an advanced method to repair and regenerate skin injuries. Recent research is focused on the development of scaffolds that are safe, bioactive, and cytocompatible. In this work, a new hybrid nanofibrous scaffold composed of polycaprolactone/chitosan-polyethylene oxide (PCL/Cs-PEO) incorporated with Arnebia euchroma (A. euchroma) extract were synthesized by the two-nozzle electrospinning method. Then the synthesized scaffold was characterized for morphology, sustainability, chemical structure and properties. Moreover, to verify their potential in the burn wound healing process, biodegradation rate, contact angle, swelling properties, water vapor permeability, mechanical properties, antibacterial activity and drug release profile were measured. Furthermore, cytotoxicity and biocompatibility tests were performed on human dermal fibroblasts cell line via XTT and LDH assay. It is shown that the scaffold improved and increased proliferation during in-vitro studies. Thus, results confirm the efficacy and potential of the hybrid nanofibrous scaffold for skin tissue engineering.

Curcumin-loaded nanofibers for targeting endometriosis in the peritoneum of a mouse model.

Endometriosis is a common, chronic gynecological disorder associated with ongoing pelvic pain, infertility, and adhesions in reproductive age women. Current therapeutic strategies are not effective and the recurrent nature of endometriosis makes it difficult to treat. In this study, we have designed a drug delivery system to control sustained and prolonged release of curcumin in the peritoneum and pelvic cavity of a mouse model of endometriosis. Poly ε-Caprolactone (PCL) and poly ethylene glycol (PEG) polymers were used to synthesize curcumin loaded nanofibers. After scanning electron microscopy (SEM) observation of the nanofiber's morphology, we evaluated the drug release profile and in vitro degradation rate of the curcumin-loaded nanofibers. Next, we tested these nanofibers in vivo in the peritoneum of an endometriosis mouse model to determine their anti-endometriosis effects. Histological evaluations were also performed. Curcumin loaded nanofibers were successfully synthesized in the 8 and 10 wt% polymers. The release test of the curcumin-loaded nanofibers showed that approximately 23% of the loaded curcumin was released during 30 min, 35% at 24 h, and 50% at 30 days. Endometriosis was successfully induced in Balb/c mice, as noted by the observed characteristics of endometriosis in all of the mice and confirmation of endometriosis by hematoxylin and eosin (H&E) staining. In vivo experiments showed the ability of these implanted curcumin loaded nanofibers to mitigate endometriosis. We observed a considerable reduction in the endometrial glands and stroma, along with significant reduction in infiltration of inflammatory cells. Implantable curcumin loaded nanofibers successfully mitigated intraperitoneal endometriosis.

Investigating the parameters affecting the stability of superparamagnetic iron oxide-loaded nanoemulsion using artificial neural networks.

Nanoemulsions are increasingly being investigated for their fascinating capability of loading both hydrophobic and hydrophilic molecules while their stability is still an issue, being affected by various factors. In this study, to evaluate the dominant factors affecting the stability of nanoemulsions, artificial neural networks (ANNs) were implemented. Nanoemulsions of almond oil in water containing oleic acid-coated superparamagnetic iron oxide nanoparticles were prepared using a mixture of Tween 80 and Span 80 as surfactant system and ethanol as a co-surfactant. The ratio of transparency of the samples at 30 min and 7 days after preparation was taken as an indication of the stability of samples. Four independent variables, namely, concentration of nanoparticle, surfactant, oil, and alcohol were investigated to find their relations with the dependent variable (i.e., transparency ratio). Using ANNs modeling, it was concluded that the stability is affected by all variables, with all variables showing reverse effect on the stability beyond an optimum amount.