Search Results (10)

Light is an electromagnetic radiation that has visible and invisible wavelength spectrums. Visible light can only be detected by the eyes through the optic pathways. With the presence of the scalp, cranium, and meninges, the brain is seen as being protected from direct exposure to light. For that reason, the brain can be viewed as a black body lying inside a black box. In physics, a black body tends to be in thermal equilibrium with its environment and can tightly regulate its temperature via thermodynamic principles. Therefore, a healthy brain inside a black box should not be exposed to light. On the contrary, photobiomodulation, a form of light therapy for the brain, has been shown to have beneficial effects on some neurological conditions. The proposed underlying mechanisms are multiple. Herein, we present our intraoperative findings of rapid electrocorticographic brainwave changes when the brain was shone directly with different wavelengths of light during awake brain surgery. Our findings provide literature evidence for light’s ability to influence human brain energy and function. Our proposed mechanism for these rapid changes is the presence of plasma-like energy inside the brain, which causes fast brain activities that are akin to lightning strikes. Full article

Working memory, language and speech abilities, motor skills, and visual abilities are often impaired in children with brain tumours. This is because tumours can invade the brain’s functional areas and cause alterations to the neuronal networks. However, it is unclear what the mechanism of tumour invasion is and how various treatments can cause cognitive impairment. Therefore, this study aims to systematically evaluate the effects of tumour invasion on the cognitive, language, motor, and visual abilities of paediatric patients, as well as discuss the alterations and modifications in neuronal networks and anatomy. The electronic database, PubMed, was used to find relevant studies. The studies were systematically reviewed based on the type and location of brain tumours, cognitive assessment, and pre- and post-operative deficits experienced by patients. Sixteen studies were selected based on the inclusion and exclusion criteria following the guidelines from PRISMA. Most studies agree that tumour invasion in the brain causes cognitive dysfunction and alteration in patients. The effects of a tumour on cognition, language, motor, and visual abilities depend on the type of tumour and its location in the brain. The alteration to the neuronal networks is also dependent on the type and location of the tumour. However, the default mode network (DMN) is the most affected network, regardless of the tumour type and location.Furthermore, our findings suggest that different treatment types can also contribute to patients’ cognitive function to improve or deteriorate. Deficits that persisted or were acquired after surgery could result from surgical manipulation or the progression of the tumour’s growth. Meanwhile, recovery from the deficits indicated that the brain has the ability to recover and reorganise itself. Full article

Although traumatic brain injury (TBI) is a global public health issue, not all injuries necessitate additional hospitalisation. Thinking, memory, attention, personality, and movement can all be negatively impacted by TBI. However, only a small proportion of nonsevere TBIs necessitate prolonged observation. Clinicians would benefit from an electroencephalography (EEG)-based computational intelligence model for outcome prediction by having access to an evidence-based analysis that would allow them to securely discharge patients who are at minimal risk of TBI-related mortality. Despite the increasing popularity of EEG-based deep learning research to create predictive models with breakthrough performance, particularly in epilepsy prediction, its use in clinical decision making for the diagnosis and prognosis of TBI has not been as widely exploited. Therefore, utilising 60s segments of unprocessed resting-state EEG data as input, we suggest a long short-term memory (LSTM) network that can distinguish between improved and unimproved outcomes in moderate TBI patients. Complex feature extraction and selection are avoided in this architecture. The experimental results show that, with a classification accuracy of 87.50 ± 0.05%, the proposed prognostic model outperforms three related works. The results suggest that the proposed methodology is an efficient and reliable strategy to assist clinicians in creating an automated tool for predicting treatment outcomes from EEG signals. Full article

Background: Mobile health (mHealth) has been considered as a prominent concept in digital health and is widely used and easily accessible. Periodic follow-up visits, previously planned procedures, and rehabilitation services for stroke survivors have been cut down during the recent COVID-19 pandemic. Therefore, in this qualitative study we aimed to explore the need for a mobile application in stroke management by informal caregivers. Methods: A phenomenological qualitative study was conducted from November 2020 to June 2021. Thirteen respondents were recruited from two public rehabilitation centers in Kota Bharu, Kelantan, Malaysia. In-depth interviews were conducted. A comprehensive representation of perspectives from the respondents was achieved through purposive sampling. The interviews were conducted in the Kelantanese dialect, recorded, transcribed, and analyzed by using thematic analysis. Results: Thirteen participants were involved in the interviews. All of them agreed with the need for a mobile application in stroke management. They believed the future stroke application will help them to seek information, continuous stroke home care, and help in the welfare of caregivers and stroke patients. Conclusions: The current study revealed two themes with respective subthemes that were identified, namely, self-seeking for information and reasons for using a stroke mobile application in the future. This application helps in reducing healthcare costs, enhancing the rehabilitation process, facilitating patient engagement in decision making, and the continuous monitoring of patient health. Full article

Forensic facial approximation was applied to a 5000-year-old female skull from a shell midden in Guar Kepah, Malaysia. The skull was scanned using a computed tomography (CT) scanner in the Radiology Department of the Hospital Universiti Sains Malaysia using a Light Speed Plus scanner with a 1 mm section thickness in spiral mode and a 512 × 512 matrix. The resulting images were stored in Digital Imaging and Communications in Medicine (DICOM) format. A three-dimensional (3D) model of the skull was obtained from the CT scan data using Blender’s 3D modelling and animation software. After the skull was reconstructed, it was placed on the Frankfurt plane, and soft tissue thickness markers were placed based on 34 Malay CT scan data of the nose and lips. The technique based on facial approximation by data extracted from facial measurements of living individuals showed greater anatomical coherence when combined with anatomical deformation. The facial approximation in this study will pave the way towards understanding face prediction based on skull structures, soft tissue prediction rules, and soft tissue thickness descriptors. Full article

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUC${}_{\delta }$ = 0.75, AUC ${}_{\alpha }$ = 0.73 and AUC${}_{\theta }$ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model. Full article

The concept of wholeness or oneness refers to not only humans, but also all of creation. Similarly, consciousness may not wholly exist inside the human brain. One consciousness could permeate the whole universe as limitless energy; thus, human consciousness can be regarded as limited or partial in character. According to the limited consciousness concept, humans perceive projected waves or wave-vortices as a waveless item. Therefore, human limited consciousness collapses the wave function or energy of particles; accordingly, we are only able to perceive them as particles. With this “limited concept”, the wave-vortex or wave movement comes into review, which also seems to have a limited concept, i.e., the limited projected wave concept. Notably, this wave-vortex seems to embrace photonic light, as well as electricity and anything in between them, which gives a sense of dimension to our brain. These elements of limited projected wave-vortex and limitless energy (consciousness) may coexist inside our brain as electric (directional pilot wave) and quantum (diffused oneness of waves) brainwaves, respectively, with both of them giving rise to one brain field. Abnormality in either the electrical or the quantum field or their fusion may lead to abnormal brain function. Full article

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision. Full article

The aim was to isolate cellulose nanocrystals (CNC) from commercialized oil palm empty fruit bunch cellulose nanofibre (CNF) through sulphuric acid hydrolysis and explore its safeness as a potential nanocarrier. Successful extraction of CNC was confirmed through a field emission scanning electron microscope (FESEM) and attenuated total reflection Fourier transmission infrared (ATR-FTIR) spectrometry analysis. For subsequent cellular uptake study, the spherical CNC was covalently tagged with fluorescein isothiocyanate (FITC), resulting in negative charged FITC-CNC nanospheres with a dispersity (Ð) of 0.371. MTT assay revealed low degree cytotoxicity for both CNC and FITC-CNC against C6 rat glioma and NIH3T3 normal fibroblasts up to 50 µg/mL. FITC conjugation had no contribution to the particle’s toxicity. Through confocal laser scanning microscope (CLSM), synthesized FITC-CNC manifested negligible cellular accumulation, indicating a poor non-selective adsorptive endocytosis into studied cells. Overall, an untargeted CNC-based nanosphere with less cytotoxicity that posed poor selectivity against normal and cancerous cells was successfully synthesized. It can be considered safe and suitable to be developed into targeted nanocarrier. Full article

Insulin-like growth factor 1 (IGF-1) enhances cellular proliferation and reduces apoptosis during the early differentiation of bone marrow derived mesenchymal stem cells (BMSCs) into neural progenitor-like cells (NPCs) in the presence of epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). BMSCs were differentiated in three groups of growth factors: (A) EGF + bFGF, (B) EGF + bFGF + IGF-1, and (C) without growth factor. To unravel the molecular mechanisms of the NPCs derivation, microarray analysis using GeneChip^® miRNA arrays was performed. The profiles were compared among the groups. Annotated microRNA fingerprints (GSE60060) delineated 46 microRNAs temporally up-regulated or down-regulated compared to group C. The expressions of selected microRNAs were validated by real-time PCR. Among the 46 microRNAs, 30 were consistently expressed for minimum of two consecutive time intervals. In Group B, only miR-496 was up-regulated and 12 microRNAs, including the let-7 family, miR-1224, miR-125a-3p, miR-214, miR-22, miR-320, miR-708, and miR-93, were down-regulated. Bioinformatics analysis reveals that some of these microRNAs (miR-22, miR-214, miR-125a-3p, miR-320 and let-7 family) are associated with reduction of apoptosis. Here, we summarize the roles of key microRNAs associated with IGF-1 in the differentiation of BMSCs into NPCs. These findings may provide clues to further our understanding of the mechanisms and roles of microRNAs as key regulators of BMSC-derived NPC maintenance. Full article