2024-03-29T07:06:17Z
https://www.journalbe.com/?_action=export&rf=summon&issue=12625
Journal of Bioengineering Research
2645-5633
2645-5633
2019
1
3
Comparing size particle, release study and cytotoxicity activity of PHMB encapsulated in different liposomal formulations: neutral and cationic liposomes
Elnaz
Ahani
Tayebeh
Toliyat
Mahnaz
Mahmoudi Rad
Charges in liposome composition plays a critical role, so it has significant outcome on the manner of liposome in vitro and in vivo. Neutral and cationic liposomes (LPFS and CLPFS) were prepared and characteristics of LPFS and CLPFS were studied after PHMB encapsulation. According to results, CLPFS exhibited higher cytotoxicity activity, while kept similar size distribution, encapsulation efficiency, lower prolonged retention release profile in comparison to LPFS. CLFPS represented more stable release due to its mutual repulsive force. MTT assay of the produced formulation was examined versus normal primary human skin fibroblast cells and free PHMB presented stronger cytotoxic activity when compared to both cationic and neutral liposomes. It was concluded that PHMB are more toxic to the body but encapsulation liposome specially in neutral liposome can reduce the toxic effect of them.
2019
09
01
1
6
https://www.journalbe.com/article_93677_44378214d890350db193bfddf9e644b7.pdf
Journal of Bioengineering Research
2645-5633
2645-5633
2019
1
3
A Proposed Algorithm for the Detection of Thyroid Cancer based on Image Processing
Mehrad
Jaloli
Mohammad
Fathi
Seyed Mohsen
Mohammadi
Reza
Abbasi Kesbi
Introduction: Today's world allows digital images to be downloaded and stored. Computer image processing systems have been developed to make these actions faster and more accurate, especially in medical engineering. Many diseases, including brain tumors, cancers are identified and estimated by benefiting of the image processing. Objective: In this paper, a proposed algorithm for detection the thyroid cancer is presented. Material and Methods: To this end, a few images of thyroid cancer are collected, and then these images are converted to jpeg format. After that, these images are called and converted into a matrix using MATLAB software. The obtained matrix is normalized and then, the noise of these images are removed. Next, the highlighted areas are defined by a threshold value. By summing up this highlighted value and negation of the original images, the cancerous area is determined. Results: The results of the five images illustrate that the accuracy of the algorithm for the detection of thyroid cancer is 93.5% that show an improvement of 3-7 % than other works. Furthermore, sensitivity, specificity, and F-score, Mathew Correlation Coefficient (MCC) are 62%, 93.2%, 92.2%, and 86.4%, respectively, for the proposed method. Conclusions: The method is a simple way and owns an acceptable accuracy that can be used for the detection of thyroid cancer in a portable computer. As the images show the cancerous parts have lighter pixels than non-cancerous parts that the feature is used for separating the cancerous part of the thyroid gland from non-cancerous parts.
Thyroid cancer
image processing
rotational mean filter
static parameters
2019
09
01
7
14
https://www.journalbe.com/article_93089_b3e62e0ad1fb9dc7d572abd062007272.pdf