The study presents the preparation and characterization of new scaffolds based on bacterial cellulose and keratin hydrogel which were seeded with adipose stem cells. The bacterial cellulose was obtained by developing an Acetobacter xylinum culture and was visualized using SEM (scanning electron microscopy) and elementally determined through EDAX (dispersive X-ray analysis) tests. Keratin species (β–keratose and γ-keratose) was extracted by hydrolytic degradation from non-dyed human hair. SEM, EDAX and conductometric titration tests were performed for physical–chemical and morphological evaluation. Cytocompatibility tests performed in vitro confirmed the material non-toxic effect on cells. The scaffolds, with and without stem cells, were grafted on the burned wounds on the rabbit’s dorsal region and the grafts were monitored for 21 days after the application on the wounds. The clinical monitoring of the grafts and the histopathological examination demonstrated the regenerative potential of the bacterial cellulose–keratin scaffolds, under the test conditions.
Osteoporosis is a systemic skeletal disease characterized by low bone strength, which leads to an increased risk of fracture. The primary objective of osteoporosis treatment is the prevention of fragility fractures and the secondary objective is their rapid healing if they occur. Strontium ranelate is an antiosteoporotic therapeutic agent with a double action mechanism: the increase of bone formation and the decrease of bone resorption, contributing thus to the improvement of bone healing. Preclinical studies have demonstrated the efficacy of strontium ranelate for the improvement of bone healing and bone microarhitecture, as well as of the osteointegration of implants. Some clinical cases have been reported regarding the efficacy of strontium ranelate in the healing of long bone fractures complicated by nonunion or delayed union. In the present study we have reported 2 clinical cases that demonstrate the effectiveness of the treatment with strontium ranelate (Osseor) for 3-6 months in the healing of complicated long bone fractures with delayed union. Our cases confirm the results of the open label study CL3-12911-036 (delayed union and non-union fracture study), where the treatment with 2 g/day of strontium ranelate improved healing and led to a better quality of life. Even if there are some cardiovascular contraindications, strontium ranelate is proven to reduce vertebral and non-vertebral fracture risk in osteoporosis and in the same time improves bone microarchitecture and accelerates fracture healing.
Pancreatic cancer is one of the most aggressive malignant diseases due high rate of recurrence and the lack effective medical therapy. Surgery remains the only option for curable treatment but unfortunately, less than 20% of patients are eligibles at the time of diagnosis therefore identifying the risk factors represent a big step for cancer research. Pancreatic cancer is frequently associated with diabetes or glucose intolerance. There are two hypotheses at the base of this observation: either the diabetes cause pancreatic cancer or is a concequences of the cancer. In these theses we studied the patients diagnosticated with pancreatic cancer and with diabetes mellitus type 2. A total of 256 pancreatic cancer cases were identified and 71 patients had diabetes mellitus and 21 patients had glucose intolerance. Mean age 62.2 years, 81% cases were male and in 71% cancer originated form the pancreatic head. In 51.4% cases the diagnosis was in stage IV of the disease. Patients with pancreatic cancer and diabetes mellitus had reduced survival compared with those without diabetes but the difference was not statistically significant. Diabetes mellitus is associated with a decreased survival among patients with pancreatic cancer and reveal a link between chronic glucose intolerance and pancreatic cancer survival. The complex relationship between pancreatic cancer and diabetes requires more clinical research in order to developed new therapeutical posibilities.
The aim of this study is to evaluate the changes related to diabetic retinopathy (DR) (no changes, small or moderate changes) in patients with glaucoma and diabetes using artificial intelligence instruments: Support Vector Machines (SVM) in combination with a powerful optimization algorithm—Differential Evolution (DE). In order to classify the DR changes and to make predictions in various situations, an approach including SVM optimized with DE was applied. The role of the optimizer was to automatically determine the SVM parameters that lead to the lowest classification error. The study was conducted on a sample of 52 patients: particularly, 101 eyes with glaucoma and diabetes mellitus, in the Ophthalmology Clinic I of the “St. Spiridon” Clinical Hospital of Iaşi. The criteria considered in the modelling action were normal or hypertensive open-angle glaucoma, intraocular hypertension and associated diabetes. The patients with other types of glaucoma pseudoexfoliation, pigment, cortisone, neovascular and primitive angle-closure, and those without associated diabetes, were excluded. The assessment of diabetic retinopathy changes were carried out with Volk lens and Fundus Camera Zeiss retinal photography on the dilated pupil, inspecting all quadrants. The criteria for classifying the DR (early treatment diabetic retinopathy study—ETDRS) changes were: without changes (absence of DR), mild forma nonproliferative diabetic retinopathy (the presence of a single micro aneurysm), moderate form (micro aneurysms, hemorrhages in 2–3 quadrants, venous dilatations and soft exudates in a quadrant), severe form (micro aneurysms, hemorrhages in all quadrants, venous dilatation in 2–3 quadrants) and proliferative diabetic retinopathy (disk and retinal neovascularization in different quadrants). Any new clinical element that occurred in subsequent checks, which led to their inclusion in severe nonproliferative or proliferative forms of diabetic retinopathy, was considered to be the result of the progression of diabetic retinopathy. The results obtained were very good; in the testing phase, a 95.23% accuracy has been obtained, only one sample being wrongly classified. The effectiveness of the classification algorithm (SVM), developed in optimal form with DE, and used in predictions of retinal changes related to diabetes, was demonstrated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.