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Non-alcoholic fatty liver disease (NAFLD) could be diagnosed either by imaging or histology, and laboratory parameters from RFT such as AST, ALT, GGT, as well as UP. In this study, data were collected from Advanced Pathology Sdn. Bhd. The data collected from random population in the year of 2020 were used to identify the association between the parameters of AST, ALT, GGT and UP in presumed NAFLD patients, which are being compared by age, and gender. The results show that the level of GGT was having an insignificant association between gender (p >0.05), but there is a statistically significant association between the age group of respondents (p 0.05). While for ALT, it proved an insignificant correlation between the gender of respondents (p >0.05), but significant correlated with the age of respondents (p 0.05), which proved insignificant correlation between gender of respondents and presence of UP andp-value = 0.261 (p >0.05), which proved insignificant correlation between age of respondents and presence of UP. (Chi-square test, p-value

Background The revolutionary technology of smartphone-based retinal imaging has been consistently improving over the years. Smartphone-based retinal image acquisition devices are designed to be portable, and easy to use, besides being low-cost which enables eye care to be more widely accessible especially in geographically remote areas. This enables early disease detection for those who are in low- and middle- income population or just in general has very limited access to eye care. This study investigates the limitation of smartphone compatibility of existing smartphone-based retinal image acquisition devices. Additionally, this study aims to propose a universal adapter that is usable with an existing smartphone-based retinal image acquisition device, the PanOptic ophthalmoscope. This study also aims to simulate the reliability, validity, and performance overall of improved develop prototype. Existing studies have shown that the concept of smartphone-based retinal imaging is still limited to screening purposes only. Furthermore, existing smartphone-based devices also have a limited smartphone compatibility where it is only usable with specific smartphone models. Methods A literature review was conducted that identifies the limitation of smartphone compatibility among existing smartphone-based retinal image acquisition devices. Designing and modelling of proposed adapter was performed using the software AutoCAD 3D. For proposed performance evaluation, finite element analysis (FEA) in the software Autodesk Inventor and 5-point scale method were applied. Results It was identified how a universal adapter is beneficial in broadening the usability of existing smartphone-based retinal image acquisition devices as most of the devices that are available in the market have limited smartphone compatibility. A functional universal adapter was developed and found to be suitable with two smartphones that have different camera placement and dimensions. The proposed performance evaluation method was able to generate efficient stress analysis of the proposed adapter design. Conclusion The concept of a universal and suitable adapter for retinal imaging using the PanOptic ophthalmoscope was presented in this paper. Performance evaluation methods proposed were identified to be sufficient to analyze the behavior of proposed adapter when an external load is applied and determine its suitability with the PanOptic ophthalmoscope.

The plant species belonging to the Litsea genus are widely investigated due to their nutritional and medicinal purposes. In this regard, this study is another similar sincere effort in which the antioxidant property and phytochemical composition of Litsea garciae (L. garciae) bark’s hexane, chloroform, methanol, and aqueous extracts were evaluated to confirm its traditional benefits. The total flavonoid content (TFC) and total phenolic content (TPC) were determined first, followed by an assessment of in vitro antioxidant activity using the DPPH and FRAP assays. The composition of the secondary metabolites was determined using Ultra-High-Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS). As a result, methanol extract was recorded to have the highest TPC value aligned with its positive appearance in phytochemical screening. Its antioxidant capacity indicated the least IC50. The results indicated that the significant free radical scavenging activity was due to the methanolic extract's high phenolic content. The secondary metabolites found in the methanol extract varied significantly according to UHPLC-MS analysis. The major phenolic compounds were found including N-trans-feruloyl-4-O-methyldopamine, N-cis-feruloyltyramine, epicatechin-(4beta->6)-epicatechin-(2beta->7,4beta->8)-epicatechin, 7-Hydroxy-3-(4-methoxyphenyl)-4-propyl-2H-1-benzopyran-2-one and 9-O-Methylneodunol. In general, the results indicate that L. garciae bark may be a promising source of novel natural compounds with antioxidative properties.

Pain is a sensation a humans sense as a protective mechanism against physical injury. This sensation is closely related to inflammation. It ranges from mild to highly obnoxious. It is well-known that the levels of the inflammatory biomarker, C-reactive protein (CRP), increase manifold in acute inflammation and pain. Olive oil, known to have many phytochemicals, has been traditionally used to alleviate pain. Amongst major phenolic compounds in olive oil are oleuropein (OLE), hydroxytyrosol (HT), tyrosol, and oleocanthal. Whether the analgesic and anti-inflammatory properties in olive oil are due to any specific interections is not known. Therefore, this study aimed to elucidate the possible anti-inflammatory and anti-nociceptive properties in those major phenolic compounds by using molecular docking software MOE 2015, comparing the energy value and binding site of phenolic compounds to that of well-known synthetic non-steroidal anti-inflammatory drugs (NSAIDs) and phosphocholine. The docking experiment showed that all compounds could directly interact with CRP. Oleuropein had the most potent interaction with CRP (-7.7580), followed by indomethacin (-6.0775), oleocanthal (-5.5734), ibuprofen (-5.3857), phosphocholine (-4.3876), HT (-4.2782), and tyrosol (-4.2329). Interestingly, the present study found other phytochemicals in olive oil that can be exploited as potential, safe, and cost-effective lead compound(s) for analgesic and anti-inflammatory activity, as supported by its molecular docking data.

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.

Immediate treatment of a stroke can minimize long-term effects and even help reduce death risk. In the ischemic stroke cases, there are two zones of injury which are ischemic core and ischemic penumbra zone. The ischemic penumbra indicates the part that is located around the infarct core that is at risk of developing a brain infarction. Recently, various segmentation methods of infarct lesion from the MRI input images were developed and these methods gave a high accuracy in the extraction and detection of the infarct core. However, only some limited works have been reported to isolate the penumbra tissues and infarct core separately. The challenges exist in ischemic core identification are traditional approach prone to error, time-consuming and tedious for medical expert which could delay the treatment. In this paper, we study and analyse the segmentation algorithms for brain MRI ischemic of different categories. The focus of the review is mainly on the segmentation algorithms of infarct core with penumbra and infarct core only. We highlight the advantages and limitations alongside the discussion of the capabilities of these segmentation algorithms and its key challenges. The paper also devised a generic structure for automated stroke lesion segmentation. The performance of these algorithms was investigated by comparing different parameters of the surveyed algorithms. In addition, a new structure of the segmentation process for segmentation of penumbra is proposed by considering the challenges remains. The best accuracy for segmentation of infarct core and penumbra tissues is 82.1% whereas 99.1% for segmentation infarct core only. Meanwhile, the shortest average computational time recorded was 3.42 seconds for segmenting 10 slices of MR images. This paper presents an inclusive analysis of the discussed papers based on different categories of the segmentation algorithm. The proposed structure is important to enable a more robust and accurate assessment in clinical practice. This could be an opportunity for the medical and engineering sector to work together in designing a complete end-to-end automatic framework in detecting stroke lesion and penumbra.

Digital image processing is one of the most widely used computer vision technologies in biomedical engineering. In the present modern ophthalmological practice, biomarkers analysis through digital fundus image processing analysis greatly contributes to vision science. This further facilitates developments in medical imaging, enabling this robust technology to attain extensive scopes in biomedical engineering platform. Various diagnostic techniques are used to analyze retinal microvasculature image to enable geometric features measurements such as vessel tortuosity, branching angles, branching coefficient, vessel diameter, and fractal dimension. These extracted markers or characterized fundus digital image features provide insights and relates quantitative retinal vascular topography abnormalities to various pathologies such as diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack, neovascular glaucoma, and cardiovascular diseases. Apart from that, this noninvasive research tool is automated, allowing it to be used in large-scale screening programs, and all are described in this present review paper. This paper will also review recent research on the image processing-based extraction techniques of the quantitative retinal microvascular feature. It mainly focuses on features associated with the early symptom of transient ischemic attack or sharp stroke.