From putative brain tumor marker to high cognitive abilities: Emerging roles of a disintegrin and metalloprotease (ADAM) 12 in 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.

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

Current Drug Delivery ◽

10.2174/1567201817666200210120950 ◽

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.

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

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190903144419 ◽

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.

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Brain Tumor Detection via Asymmetry Quantification across Mid Sagittal Plane

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200831104047 ◽

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.

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Effect of Environmental Enrichment on the Brain and on Learning and Cognition by Animals

Animals ◽

10.3390/ani11040973 ◽

2021 ◽

Vol 11 (4) ◽

pp. 973

Author(s):

Thomas R. Zentall

Keyword(s):

Cognitive Abilities ◽

Environmental Enrichment ◽

Behavioral Effect ◽

Enriched Environment ◽

Main Concern ◽

Accurate Assessment ◽

Housing Conditions ◽

Complex Tasks ◽

Learning And Cognition ◽

The Brain

The humane treatment of animals suggests that they should be housed in an environment that is rich in stimulation and allows for varied activities. However, even if one’s main concern is an accurate assessment of their learning and cognitive abilities, housing them in an enriched environment can have an important effect on the assessment of those abilities. Research has found that the development of the brain of animals is significantly affected by the environment in which they live. Not surprisingly, their ability to learn both simple and complex tasks is affected by even modest time spent in an enriched environment. In particular, animals that are housed in an enriched environment are less impulsive and make more optimal choices than animals housed in isolation. Even the way that they judge the passage of time is affected by their housing conditions. Some researchers have even suggested that exposing animals to an enriched environment can make them more “optimistic” in how they treat ambiguous stimuli. Whether that behavioral effect reflects the subtlety of differences in optimism/pessimism or something simpler, like differences in motivation, incentive, discriminability, or neophobia, it is clear that the conditions of housing can have an important effect on the learning and cognition of animals.

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

Open Computer Science ◽

10.1515/comp-2020-0166 ◽

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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A bigger brain for a more complex environment

Reviews in the Neurosciences ◽

10.1515/revneuro-2020-0041 ◽

2020 ◽

Vol 31 (8) ◽

pp. 803-816

Author(s):

Umberto di Porzio

Keyword(s):

Human Brain ◽

Cognitive Abilities ◽

Molecular Genetic ◽

Adult Size ◽

Genetic Changes ◽

Cultural Challenges ◽

Birth Canal ◽

Newborn Brain ◽

Neuronal Interactions ◽

The Brain

AbstractThe environment increased complexity required more neural functions to develop in the hominin brains, and the hominins adapted to the complexity by developing a bigger brain with a greater interconnection between its parts. Thus, complex environments drove the growth of the brain. In about two million years during hominin evolution, the brain increased three folds in size, one of the largest and most complex amongst mammals, relative to body size. The size increase has led to anatomical reorganization and complex neuronal interactions in a relatively small skull. At birth, the human brain is only about 20% of its adult size. That facilitates the passage through the birth canal. Therefore, the human brain, especially cortex, develops postnatally in a rich stimulating environment with continuous brain wiring and rewiring and insertion of billions of new neurons. One of the consequence is that in the newborn brain, neuroplasticity is always turned “on” and it remains active throughout life, which gave humans the ability to adapt to complex and often hostile environments, integrate external experiences, solve problems, elaborate abstract ideas and innovative technologies, store a lot of information. Besides, hominins acquired unique abilities as music, language, and intense social cooperation. Overwhelming ecological, social, and cultural challenges have made the human brain so unique. From these events, as well as the molecular genetic changes that took place in those million years, under the pressure of natural selection, derive the distinctive cognitive abilities that have led us to complex social organizations and made our species successful.

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Aggregation-and-Attention Network for brain tumor segmentation

BMC Medical Imaging ◽

10.1186/s12880-021-00639-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chih-Wei Lin ◽

Yu Hong ◽

Jinfu Liu

Keyword(s):

Brain Tumor ◽

Semantic Information ◽

Spatial Relationship ◽

Tumor Segmentation ◽

Computer Assisted ◽

Brain Tumor Segmentation ◽

Attention Network ◽

Multi Scale ◽

Assisted Diagnosis ◽

The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

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