Carotenoids are the phytochemicals known for their biological activities.They are found in nature in the form of plants,algae,fungi and in microorganisms.This is the major group having two diferent structure one with ...Carotenoids are the phytochemicals known for their biological activities.They are found in nature in the form of plants,algae,fungi and in microorganisms.This is the major group having two diferent structure one with oxygen and without oxygen.The Present article aims to present these molecules as a new therapeutic agent,as it has unrealized efciency to prevent and reduce the symptoms of many diseases like cancer,neurodegenerative diseases such as Alzheimer,cerebral ischemia,diabetes associated with obesity and hypertension,ophthalmic diseases and many more.It can be utilized in the form of dietary supplement as nutraceutical and pharmaceutical compounds.Yet more research and developing test knowledge is needed to make it available to the humans.In this article its sources,biosynthesis,properties,applicability and commercialization of pigments from naturally produced sources are discussed.展开更多
Neuropsychological disorders(e.g.,dementia,epilepsy,brain cancer,autism,stroke,and multiple sclerosis)ad-versely affect the quality of life of patients and their families;moreover,in some instances,they may lead to lo...Neuropsychological disorders(e.g.,dementia,epilepsy,brain cancer,autism,stroke,and multiple sclerosis)ad-versely affect the quality of life of patients and their families;moreover,in some instances,they may lead to loss of life.The primary aim was to evaluate and compare the use of machine learning in neuropsychological research in contrast to traditional approaches such as through case studies.This was achieved by referring to earlier studies on this subject.This article presented the use of support vector machines(SVMs)and convolu-tional neural networks(CNN)for detecting and predicting neuropsychological diseases,such as dementia and Alzheimer’s disease.Challenges in using these models include data availability,quality,variability,model inter-pretability,and validation.Experimental findings have demonstrated the potential of these models in this field.It has been shown that SVM models are robust and efficient in processing and classifying data,particularly in neuroimaging applications,such as magnetic resonance imaging(MRI).CNNs have excelled in handling visual input;thus,they have been used in neuroimaging segregation,recognition,and classification,with applications in brain tumor segmentation,radiation therapy,robotic neurosurgery,and disease prediction.Future research will explore asymmetric differences among left-and right-handed patients,incorporate longitudinal studies,and utilize larger sample sizes.The use of machine learning models has the potential to revolutionize the diagnosis and treatment of neuropsychological diseases,allowing for early detection and intervention.This approach could offer significant advantages to healthcare,such as cost-effective diagnosis and treatment,to help save lives and preserve the quality of life of patients.展开更多
文摘Carotenoids are the phytochemicals known for their biological activities.They are found in nature in the form of plants,algae,fungi and in microorganisms.This is the major group having two diferent structure one with oxygen and without oxygen.The Present article aims to present these molecules as a new therapeutic agent,as it has unrealized efciency to prevent and reduce the symptoms of many diseases like cancer,neurodegenerative diseases such as Alzheimer,cerebral ischemia,diabetes associated with obesity and hypertension,ophthalmic diseases and many more.It can be utilized in the form of dietary supplement as nutraceutical and pharmaceutical compounds.Yet more research and developing test knowledge is needed to make it available to the humans.In this article its sources,biosynthesis,properties,applicability and commercialization of pigments from naturally produced sources are discussed.
文摘Neuropsychological disorders(e.g.,dementia,epilepsy,brain cancer,autism,stroke,and multiple sclerosis)ad-versely affect the quality of life of patients and their families;moreover,in some instances,they may lead to loss of life.The primary aim was to evaluate and compare the use of machine learning in neuropsychological research in contrast to traditional approaches such as through case studies.This was achieved by referring to earlier studies on this subject.This article presented the use of support vector machines(SVMs)and convolu-tional neural networks(CNN)for detecting and predicting neuropsychological diseases,such as dementia and Alzheimer’s disease.Challenges in using these models include data availability,quality,variability,model inter-pretability,and validation.Experimental findings have demonstrated the potential of these models in this field.It has been shown that SVM models are robust and efficient in processing and classifying data,particularly in neuroimaging applications,such as magnetic resonance imaging(MRI).CNNs have excelled in handling visual input;thus,they have been used in neuroimaging segregation,recognition,and classification,with applications in brain tumor segmentation,radiation therapy,robotic neurosurgery,and disease prediction.Future research will explore asymmetric differences among left-and right-handed patients,incorporate longitudinal studies,and utilize larger sample sizes.The use of machine learning models has the potential to revolutionize the diagnosis and treatment of neuropsychological diseases,allowing for early detection and intervention.This approach could offer significant advantages to healthcare,such as cost-effective diagnosis and treatment,to help save lives and preserve the quality of life of patients.
