The preeminent treatments for neurodegenerative disease are often unavailable due to the poor accessibility of therapeutic drugs. Moreover, the blood–brain barrier (BBB) effectively blocks the transfer of cells, particles and large molecules, ie, drugs, across the brain. The most important challenge in the treatment of neurodegenerative diseases is the development of targeted drug delivery system. Theranostic strategies are known to combine therapeutic and diagnostic capabilities together. The aim of this review was to record the response to treatment and thereby improve drug safety. Nanotechnology offers a platform for designing and developing theranostic agents that can be used as an efficient nano-carrier system. This is achieved by the manipulation of some of the properties of nanoparticles (NPs), thereby enabling the attachment of suitable drugs onto their surface. The results provide revolutionary treatments by stimulation and thus interaction with targeted sites to promote physiological response with minimum side effects. This review is a brief discussion of the administration of drugs across the brain and the advantages of using NPs as an effective theranostic platform in the treatment of Alzheimer’s, Parkinson’s, epilepsy and Huntington’s disease.
Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.
Magnetoencephalography (MEG) plays a pivotal role in the diagnosis of brain disorders. In this review, we have investigated potential MEG applications for analysing brain disorders. The signal-to-noise ratio (SNRMEG =2.2 db, SNREEG <1 db) and spatial resolution (SRMEG =2–3 mm, SREEG =7–10 mm) is higher for MEG than EEG, thus MEG potentially facilitates accurate monitoring of cortical activity. We found that the direct electrophysiological MEG signals reflected the physiological status of neurological disorders and play a vital role in disease diagnosis. Single-channel connectivity, as well as brain network analysis, using MEG data acquired during resting state and a given task has been used for the diagnosis of neurological disorders such as epilepsy, Alzheimer’s, Parkinsonism, autism, and schizophrenia. The workflow of MEG and its potential applications in the diagnosis of disease and therapeutic planning are also discussed. We forecast that computer-aided algorithms will play a prominent role in the diagnosis and prediction of neurological diseases in the future. The outcome of this narrative review will aid researchers to utilise MEG in diagnostics.
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