No abstract

Autologous Preparation Rich in Growth Factors (PRGF), a small volume of plasma enriched in platelets, is a novel therapeutic strategy for the acceleration of the wound healing of a wide range of tissues because of the continuous release of multiple growth factors, including PDGF-AB, TGF-beta1, IGF-I, HGF, VEGF-A, and EGF. In this article, we have characterized the PRGF preparation and designed a randomized open-label controlled pilot trial to evaluate the effectiveness of PRGF in the treatment of chronic cutaneous ulcers. Results showed that at 8 weeks, the mean percentage of surface healed in the PRGF group was 72.94% +/- 22.25% whereas it was 21.48% +/- 33.56% in the control group (p < 0.05). These results, with the limitations of a pilot study, suggest that topical application of PRGF is more effective than standard therapy in helping a chronic ulcer to heal.

This is the first time different formulations of this product have been evaluated, and the results suggest that PRGF-Endoret could be used in the fight against postoperative and wound infections.

No abstract

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.