Synthesis, characterization, and evaluation of cytotoxicity, antioxidant, antifungal, antibacterial, and cutaneous wound healing effects of copper nanoparticles using the aqueous extract of Strawberry fruit and L-Ascorbic acid

© 2020 Elsevier Ltd Fragaria ananassa, also known as “Strawberry” is a common species in Iran and widely used for its anti-inflammatory, anti-ulcer, astringent, anti-allergic, antibacterial, antifungal, and antidiarrheal activities and also in the treatment of skin wounds. The purpose of the study was chemical characterization and assessment of cytotoxicity, antioxidant, antifungal, antibacterial, and cutaneous wound healing properties of copper nanoparticles (CuNPs) using the aqueous extract of Strawberry fruit and L-Ascorbic acid as reducing and stabilizing agents. These nanoparticles were characterized by FT-IR, UV–visible spectroscopy, EDS, FE-SEM, and TEM analysis. TEM images exhibited a uniform spherical morphology and diameters of 10–30 nm for the biosynthesized nanoparticles. DPPH free radical scavenging test revealed similar antioxidant properties for Strawberry, CuNPs, and butylated hydroxytoluene. The Strawberry and synthesized CuNPs had great cell viability dose-dependently against HUVEC cell line. In the microbiological part of this study, CuNPs showed higher antibacterial and antifungal properties than all standard antibiotics (p ≤ 0.01). Also, CuNPs prevented the growth of all bacteria at 2–8 mg/mL concentrations and destroyed them at 2–16 mg/mL concentrations (p ≤ 0.01). In the case of antifungal property of CuNPs, they inhibited the growth of all fungi at 2–4 mg/mL concentrations and destroyed them at 2–8 mg/mL concentrations (p ≤ 0.01). In vivo design, the use of CuNPs ointment in the treatment groups substantially remarkably raised (p ≤ 0.01) the wound contracture, hydroxyl proline, hexosamine, hexuronic acid, fibrocyte, and fibrocytes/fibroblast rate and reduced (p ≤ 0.01) the wound area, total cells, neutrophil, macrophage, and lymphocyte compared to Strawberry, CuSO4, tetracycline, Eucerin basal, and untreated control groups. In conclusion, the results of chemical characterization confirm that the Strawberry fruit can be consumed to produce copper nanoparticles with a remarkable amount of remedial effects without any cytotoxicity against HUVECs

Expression, Purification and Characterization of Functional Teduglutide Using GST Fusion System in Prokaryotic Cells

Purpose: Teduglutide is the first and only FDA-approved drug for long-term treatment of short bowel syndrome (SBS). The current study aimed to present an approach for production of teduglutide using recombinant DNA technology. Methods: The coding gene for teduglutide was cloned into pGEX-2T vector, where coding sequence for factor Xa cleavage site was added between GST and teduglutide coding genes. The GST-teduglutide protein was overexpressed in E. coli BL21 (DE3) strain and affinity purified using glutathione sepharose affinity column. Results: On-column proteolytic activity of factor Xa followed by size exclusion chromatography resulted in the pure teduglutide. Circular dichroism (CD) spectropolarimetry showed that the produced teduglutide folds into mainly α-helical structure (>50%), as expected. In mass spectroscopy analysis, the fragments of teduglutide resulted by cyanogen bromide cleavage as well as those expected theoretically due to mass fragmentation were identified. The functionality of the produced peptide was evaluated by measuring its proliferative effect on Caco2 intestinal epithelial cells, and the results indicated that produced teduglutide induces cell proliferation by 19±0.30 and 33±7.82 % at 1.21 and 3.64 µM concentrations, respectively, compared to untreated cells. Conclusion: Teduglutide was successfully expressed and purified and its functionality and structural integrity were confirmed by in vitro experiments. We believe that the experimental-scale method presented in the current study can be useful for pilot-scale and also industrial-scale production of teduglutide

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

<p>Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.</p&gt