scholarly journals Transnasal delivery of anticancer drugs to the brain tumor. A new strategy for brain tumor chemotherapy.

1999 ◽  
Vol 14 (5) ◽  
pp. 365-371 ◽  
Author(s):  
Tomotaka Shingaki ◽  
Toshiyasu Sakane ◽  
Shinji Yamashita ◽  
Hitoshi Sezaki ◽  
Yoshiharu Tokunaga ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Anticancer Drugs ◽  
New Strategy ◽  
The Brain ◽  
Brain Tumor Chemotherapy

Transnasal Delivery of Methotrexate to Brain Tumors in Rats: A New Strategy for Brain Tumor Chemotherapy

2010 ◽  
Vol 7 (5) ◽  
pp. 1561-1568 ◽  
Author(s):  
Tomotaka Shingaki ◽  
Daisuke Inoue ◽  
Tomoyuki Furubayashi ◽  
Toshiyasu Sakane ◽  
Hidemasa Katsumi ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
New Strategy ◽  
Brain Tumor Chemotherapy

scholarly journals Integrated Pharmacokinetic-Driven Approach to Screen Candidate Anticancer Drugs for Brain Tumor Chemotherapy

2012 ◽  
Vol 15 (1) ◽  
pp. 250-257 ◽  
Author(s):  
Hua Lv ◽  
Xiaoping Zhang ◽  
Jyoti Sharma ◽  
M. V. Ramana Reddy ◽  
E. Premkumar Reddy ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Anticancer Drugs ◽  
Brain Tumor Chemotherapy

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Novel Phospholipid-Based Labrasol Nanomicelles Loaded Flavonoids for Oral Delivery with Enhanced Penetration and Anti-Brain Tumor Efficiency

2020 ◽  
Vol 17 (3) ◽  
pp. 229-245
Author(s):  
Gang Wang ◽  
Junjie Wang ◽  
Rui Guan
Keyword(s):  
Drug Delivery ◽  
Brain Tumor ◽  
Oral Delivery ◽  
Safe Delivery ◽  
Drug Delivery Vehicles ◽  
Therapeutic Benefits ◽  
Delivery Vehicles ◽  
The Brain

Background: Owing to the rich anticancer properties of flavonoids, there is a need for their incorporation into drug delivery vehicles like nanomicelles for safe delivery of the drug into the brain tumor microenvironment. Objective: This study, therefore, aimed to prepare the phospholipid-based Labrasol/Pluronic F68 modified nano micelles loaded with flavonoids (Nano-flavonoids) for the delivery of the drug to the target brain tumor. Methods: Myricetin, quercetin and fisetin were selected as the initial drugs to evaluate the biodistribution and acute toxicity of the drug delivery vehicles in rats with implanted C6 glioma tumors after oral administration, while the uptake, retention, release in human intestinal Caco-2 cells and the effect on the brain endothelial barrier were investigated in Human Brain Microvascular Endothelial Cells (HBMECs). Results: The results demonstrated that nano-flavonoids loaded with myricetin showed more evenly distributed targeting tissues and enhanced anti-tumor efficiency in vivo without significant cytotoxicity to Caco-2 cells and alteration in the Trans Epithelial Electric Resistance (TEER). There was no pathological evidence of renal, hepatic or other organs dysfunction after the administration of nanoflavonoids, which showed no significant influence on cytotoxicity to Caco-2 cells. Conclusion: In conclusion, Labrasol/F68-NMs loaded with MYR and quercetin could enhance antiglioma effect in vitro and in vivo, which may be better tools for medical therapy, while the pharmacokinetics and pharmacodynamics of nano-flavonoids may ensure optimal therapeutic benefits.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

Brain Tumor Detection via Asymmetry Quantification across Mid Sagittal Plane

2020 ◽  
Vol 13 ◽  
Author(s):  
Shoaib Amin Banday ◽  
Mohammad Khalid Pandit
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Sagittal Plane ◽  
Early Stage ◽  
Imaging Techniques ◽  
Complex Structure ◽  
Magnetic Resonance Images ◽  
Absolute Difference ◽  
Brain Hemispheres ◽  
The Brain

Introduction: Brain tumor is among the major causes of morbidity and mortality rates worldwide. According to National Brain Tumor Foundation (NBTS), the death rate has nearly increased by as much as 300% over last couple of decades. Tumors can be categorized as benign (non-cancerous) and malignant (cancerous). The type of the brain tumor significantly depends on various factors like the site of its occurrence, its shape, the age of the subject etc. On the other hand, Computer Aided Detection (CAD) has been improving significantly in recent times. The concept, design and implementation of these systems ascend from fairly simple ones to computationally intense ones. For efficient and effective diagnosis and treatment plans in brain tumor studies, it is imperative that an abnormality is detected at an early stage as it provides a little more time for medical professionals to respond. The early detection of diseases has predominantly been possible because of medical imaging techniques developed from past many decades like CT, MRI, PET, SPECT, FMRI etc. The detection of brain tumors however, has always been a challenging task because of the complex structure of the brain, diverse tumor sizes and locations in the brain. Method: This paper proposes an algorithm that can detect the brain tumors in the presence of the Radio-Frequency (RF) inhomoginiety. The algorithm utilizes the Mid Sagittal Plane as a landmark point across which the asymmetry between the two brain hemispheres is estimated using various intensity and texture based parameters. Result: The results show the efficacy of the proposed method for the detection of the brain tumors with an acceptable detection rate. Conclusion: In this paper, we have calculated three textural features from the two hemispheres of the brain viz: Contrast (CON), Entropy (ENT) and Homogeneity (HOM) and three parameters viz: Root Mean Square Error (RMSE), Correlation Co-efficient (CC), and Integral of Absolute Difference (IAD) from the intensity distribution profiles of the two brain hemispheres to predict any presence of the pathology. First a Mid Sagittal Plane (MSP) is obtained on the Magnetic Resonance Images that virtually divides brain into two bilaterally symmetric hemispheres. The block wise texture asymmetry is estimated for these hemispheres using the above 6 parameters.

scholarly journals Development of a New Polymeric Nanocarrier Dedicated to Controlled Clozapine Delivery at the Dopamine D2-Serotonin 5-HT1A Heteromers

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1000
Author(s):  
Sylwia Łukasiewicz
Keyword(s):  
Physicochemical Parameters ◽  
Modern Science ◽  
Dopamine D2 ◽  
Targeted Transport ◽  
First Choice ◽  
New Strategy ◽  
Cell Components ◽  
Second Generation Antipsychotic ◽  
The Brain ◽  
Polymeric Nanocarrier

Clozapine, the second generation antipsychotic drug, is one of the prominent compounds used for treatment of schizophrenia. Unfortunately, use of this drug is still limited due to serious side effects connected to its unspecific and non-selective action. Nevertheless, clozapine still remains the first-choice drug for the situation of drug-resistance schizophrenia. Development of the new strategy of clozapine delivery into well-defined parts of the brain has been a great challenge for modern science. In the present paper we focus on the presentation of a new nanocarrier for clozapine and its use for targeted transport, enabling its interaction with the dopamine D2 and serotonin 5-HT1A heteromers (D2-5-HT1A) in the brain tissue. Clozapine polymeric nanocapsules (CLO-NCs) were prepared using anionic surfactant AOT (sodium docusate) as an emulsifier, and bio-compatible polyelectrolytes such as: poly-L-glutamic acid (PGA) and poly-L-lysine (PLL). Outer layer of the carrier was grafted by polyethylene glycol (PEG). Several variants of nanocarriers containing the antipsychotic varying in physicochemical parameters were tested. This kind of approach may enable the availability and safety of the drug, improve the selectivity of its action, and finally increase effectiveness of schizophrenia therapy. Moreover, the purpose of the manuscript is to cover a wide scope of the issues, which should be considered while designing a novel means for drug delivery. It is important to determine the interactions of a new nanocarrier with many cell components on various cellular levels in order to be sure that the new nanocarrier will be safe and won’t cause undesired effects for a patient.

scholarly journals Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN)

2021 ◽  
Vol 11 (1) ◽  
pp. 380-390
Author(s):  
Pradipta Kumar Mishra ◽  
Suresh Chandra Satapathy ◽  
Minakhi Rout
Keyword(s):  
Brain Tumor ◽  
Tumor Segmentation ◽  
Gray Wolf ◽  
Deep Convolutional Neural Networks ◽  
Brain Tumor Segmentation ◽  
Swarm Optimization ◽  
Brain Image ◽  
Mri Brain ◽  
Whale Optimization ◽  
The Brain

Abstract Segmentation of brain image should be done accurately as it can help to predict deadly brain tumor disease so that it can be possible to control the malicious segments of brain image if known beforehand. The accuracy of the brain tumor analysis can be enhanced through the brain tumor segmentation procedure. Earlier DCNN models do not consider the weights as of learning instances which may decrease accuracy levels of the segmentation procedure. Considering the above point, we have suggested a framework for optimizing the network parameters such as weight and bias vector of DCNN models using swarm intelligent based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The simulation results reveals that the WOA optimized DCNN segmentation model is outperformed than other three optimization based DCNN models i.e., GA-DCNN, PSO-DCNN, GWO-DCNN.

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