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Your search keyword '"Gonzales, Jose Luis Arias"' showing total 10 results
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10 results on '"Gonzales, Jose Luis Arias"'

1. miR-199a-3p suppresses neuroinflammation by directly targeting MyD88 in a mouse model of bone cancer pain

Author

Saadh, Mohamed J., Rashed, Amera Bekhatroh, Jamal, Azfar, Castillo-Acobo, Roxana Yolanda, Kamal, Mohammad Azhar, Cotrina-Aliaga, Juan Carlos, Gonzáles, José Luis Arias, Alothaim, Abdulaziz S., Alhoqail, Wardah A., Ahmad, Fuzail, Lakshmaiya, Natrayan, Amin, Ali H., Younus, Dhuha Ghassan, Rojas, Gregorio Gilmer Rosales, Bahrami, Abolfazl, and Akhavan-Sigari, Reza

Published
2023
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2. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer

Author

Sudhakar K., Saravanan D., Hariharan G., Sanaj M. S., Kumar Santosh, Shaik Maznu, Gonzales Jose Luis Arias, and Aurangzeb Khursheed

Subjects
cervical cancer detection, convolutional neural network, accuracy, gray level co-occurrence matrix, feature selection, pep test images, Biology (General), QH301-705.5
Abstract

Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has increased. Implementing routine screenings for cervical cancer is something that has to be done to protect the health of women. Cervical cancer is famously difficult to diagnose and cure due to the slow rate at which it spreads and develops into more advanced stages of the disease. Screening for cervical cancer using a Pap smear, more often referred to as a Pap test, has the potential to detect the illness in its earlier stages. For the purpose of selecting features for this article, a gray level co-occurrence matrix (GLCM) technique was used. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine, and auto encoder. According to the findings of this experiment, the GLCM-CNN classifier proved to be the one with the highest degree of precision.

Published
2023
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10. Application of Porous Nanomaterials for Sustained and Targeted Drug Release

Author

Ansari, Mohammad Javed, Unnisa, Aziz, Singh, Anshul, Verma, Devvret, Kanaoujiya, Rahul, and Gonzales, Jose Luis Arias

Abstract

Patients must take significant doses of drugs to acquire the therapeutic effects required for disease therapy due to the absence of selectivity and accessibility of medicinal molecules. Drugs contain a range of drug carriers that are available to transport therapeutic chemicals to the targeted issues in the body. Mesoporous materials are choice for overcoming the aforementioned issues and producing effects in a predictable and long-term way. Because of its chemical characteristics, thermal stability, & biocompatibility, mesophoric nanoparticles are commonly utilized as release reagents. The innovative silica mesophore technology allows for efficient drug loading and administration after the target site has been reached. The additives used to manufacture MSNs can affect the property of mesoporous materials, including pore width, porosity, drug load, and surface characteristics. The need for an active surface provides for surface treatment as well as the coupling of therapeutic substances. They are widely employed in the bio-medical industry for diagnosis, target medication administration, bio-sensing, cellular absorption, and so on. The purpose of this study is, to sum up the existing level of information about mesoporous nanomaterials and their applications in diverse healthcare sectors.

Published
2022
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