Anas Mohd Noor
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Preferred name
Anas Mohd Noor
Official Name
Anas, Mohd Noor
Alternative Name
Mohd Noor, Anas
Main Affiliation
Scopus Author ID
57190394249
Researcher ID
DVM-0848-2022

Research Output
Now showing 1 - 10 of 24
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A wearable 3D printed microfluidic device for sweat-sensing application

2024-12 , Nur Fatin Adini Ibrahim , Anas Mohd Noor , Norhayati Sabani , Shazlina Johari

This study focuses on developing a wearable microfluidic device (WMD) using stereolithography (SLA) 3D printing for sweat collection. The use of the SLA technique, particularly in achieving rapid fabrication, printing smooth surfaces, and creation of channels with dimensions below 1 mm. However, it is quite challenging to integrate the SLA 3D printed WMD with a sensor for real-time sweat analysis using a traditional bonding method. In addition, an SLA conventional resin is non-water-washable and is made from a polymer material that tends to cause a hydrophobic effect on the microchannel surface. In this work, a reversible bonding method through mechanical clamping was applied to enable easy assembly and disassembly of the WMD integrated with a sensor. A water-washable clear resin was used to provide a hydrophilic surface, allowing for effective fluid handling. The fluid delivery into the sensor's channel was efficient, taking only 0.06 s after the fluid flowed out at the outlet channel, and it sufficiently covered the entire surface of the sensor. This work also found that closed channels can be created up to 0.6 mm after fine-tuning the minimum achievable using the SLA printer. The dimensions of the printed WMD resulted in a size tolerance difference of 0.05–0.35 mm compared to the 3D model design, indicating a discrepancy of less than 1%. These capabilities promise to advance WMD and enable cutting-edge research in sweat analysis and related fields.

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A comprehensive review of the recent developments in wearable Sweat-Sensing Devices

2022-10-01 , Nur Fatin Adini Ibrahim , Norhayati Sabani , Shazlina Johari , Asrulnizam Abd Manaf , Asnida Abdul Wahab , Zulkarnay Zakaria , Anas Mohd Noor

Sweat analysis offers non-invasive real-time on-body measurement for wearable sensors. However, there are still gaps in current developed sweat-sensing devices (SSDs) regarding the concerns of mixing fresh and old sweat and real-time measurement, which are the requirements to ensure accurate the measurement of wearable devices. This review paper discusses these limitations by aiding model designs, features, performance, and the device operation for exploring the SSDs used in different sweat collection tools, focusing on continuous and non-continuous flow sweat analysis. In addition, the paper also comprehensively presents various sweat biomarkers that have been explored by earlier works in order to broaden the use of non-invasive sweat samples in healthcare and related applications. This work also discusses the target analyte’s response mechanism for different sweat compositions, categories of sweat collection devices, and recent advances in SSDs regarding optimal design, functionality, and performance.

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An Overview of Medical Applications in Meningitis Detection

2020-07-09 , Abdulrahman Ahmed A. , Zulkarnay Zakaria , Hamood Ali M. , Pusppanathan J. , Zarina Mohd Mhji S. , Anas Mohd Noor , Mohd Hafiz Fazalul Rahiman , Muhamad Khairul Ali Hassan , Muhammad Juhairi Aziz Safar , Ahmad Faizal Salleh

Meningitis remains one of the common infections among young children with high morbidity and mortality rates. In Southeast Asian, only few studies were reported published which evaluated meningitis clinically in the last two decades. Similarly, few studies in Malaysia evaluated meningitis among adolescents and children. Globally, more than one million cases with 135,000 deaths has been recorded yearly, and in Malaysia, severe neurological complications occurs in 9-25% of cases which affirms the most serious risk manifests from bacterial meningitis. Therefore, early detection and effective treatment are required before the irreversible damages occur. This paper reviews the current states and perspectives of diagnostic techniques on meningitis detection. Currently, there are three diagnostic techniques available for meningitis detection, such as blood cultures, spinal tap (lumbar puncture), and imaging techniques (CT scan, MRI, EIT, Ultrasonography, Nuclear imaging and X-ray). However, these techniques have limitations that may limit the chances of carrying out the early detection of the disease. The essence of this review is that meningitis requires an effective technique that is capable of carrying out the early detection of the disease by differentiating normal people and Meningitis infected patients so as to promote longevity worldwide. In this review magnetic induction tomography (MIT) technique is proposed to diagnose meningitis earlier as it is non-intrusive, non-invasive, contactless, and electrode-less imaging technique which does not expose the patients to a harmful radiation.

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Classification of electromyography signal from residual limb of hand amputees

2022-01-01 , Ahmad Nasrul Norali , Anas Mohd Noor , Zulkarnay Zakaria , Al-Mahdi Y.S.M. , Fook Chong Yen , Asyraf Hakimi Abu Bakar

Several researchers had worked on collecting electromyography (EMG) signal from amputees and come out with dataset that could be utilized for study in EMG signal processing and classification for decoding of amputee movement intention. This paper presents the work on classification of EMG signal based on the residual limb of amputees with intuitive hand movement based on interactive exercises. Dataset is obtained from NINAPRO public database website where 11 amputee subjects performed intuitive exercise of 17 hand gestures and EMG signal is acquired from the residual arm. Eight feature extraction methods are performed to obtain the EMG feature which are Mean, Minimum, Median, Skewness, Kurtosis, Approximate Entropy, Fuzzy Entropy and Kolmogorov Complexity. Two classifiers are used for EMG classification which are k-Nearest Neighbour and Ensemble classifier. Results shows average accuracy of 87.65% with Ensemble classifier for classification of movement exercise with all features of EMG is used as input to classifier.

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A Numerical Study of Collective Cell Migration in a Microchannel Driven by Surface Acoustic Wave (SAW) Device

2023-01-01 , Mazlee Mazalan , Anas Mohd Noor , Yufridin Wahab , Shazlina Johari , Mohd Rosydi Zakaria , Zaman W.S.W.K.

Collective cell migration is involved in a variety of biological contexts, including tissue morphogenesis, wound healing, and cancer invasion. Many studies have revealed that chemical, mechanical, and electrical stimulation all affect cell migration. Although an acoustic stimulus has been shown to influence cell migration in the past, the underlying mechanism is still unknown. A computational model that accounts for acoustic-structure interaction was constructed in this study to simulate the formation of a surface acoustic wave (SAW) field and the application of the acoustic pressure field on collective cell migration. A group of cells within a microchannel device and two ports of interdigitated transducers (IDTs) with different wavelengths were modeled. The stresses within cells were investigated as it was influenced by substrate displacement and pressure acoustic in the cell media generated by the SAW device. As a result, we observed the local stress within cells near the solid-fluid interfaces. For propagating SAW, the shorter wavelength of IDTs (600 μm) attributed to high stress at the cell's top and bottom as compared to the SAW device with the longer wavelength (1000 μm). The standing SAW occurred underneath collective cells. The results of standing SAW on cell stress at the bottom confirm that the SAW device can be useful to regulate the abnormalities cellular activities associated with cell migration.

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Classification of White Blood Cells Based on Surf Feature

2021-01-01 , Anas Mohd Noor , Zulkarnay Zakaria , Aishah Mohd Noor , Ahmad Nasrul Norali

Conventional blood analysis using blood smear image were performed manually by experts in hematology is tedious and highly depending on the level of experience. Currently, computer-assist technology is developed to reduce the time-consuming process and improved accuracy. As an example, various image processing techniques used to quantify such as white blood cells (WBCs) morphological conditions or classification in the blood smear image, which assist experts in developing confidence decision making in the analysis of cells conditions linked to the specific diseases. However, the WBCs shape features are arbitrary than the red blood cells (RBCs) because of the maturation state, cell orientations or positions, cell color variations, and the quality of the image captured influences the performance of classification accuracy. Therefore, we proposed a scale and rotation invariance feature for WBCs classification using speed up robust feature (SURF). SURF is suitable to be applied in identifying objects even though the orientation, scale, and position are varying, such as WBCs in microscopic blood smear images. We analyzed the classification performances using a support vector machine (SVM) and an artificial neural network (ANN) of WBCs types in the microscopic image based on the cell nucleus. The results show that the purposed SURF feature method has an excellent performance of accuracy for both methods and suitable to be utilized for the application of cell types classification.

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Muscle Fatigue Assessment Using Multi-sensing Based on Electrical, Mechanical and Acoustic Properties

2021-01-01 , Anas Mohd Noor , Zulkarnay Zakaria , Ahmad Nasrul Norali

This paper shows that a multi-sensing technique using electromyogram (EMG), mechanomyogram (MMG), and acousticmyogram (AMG) used to monitor the status of rectus femoris muscle over three states; minimal stress, moderate fatigue, and severe muscle fatigue. Test subjects need to do the designed exercise protocol to simulate these state conditions. The sensors are located at the rectus femoris muscle, and signals were recorded simultaneously. Analysis of signals is based on root mean square (RMS), mean power frequency (MPF), and power spectral density (PSD) plot were compared between the muscle state conditions. Results show that the RMS values of the muscle are increased as the contraction occurs, and the MPF signal is decreased for all sensing properties. On the other hand, the frequency signal is shifted to the left in the PSD plot as the muscle undergoes fatigued for all sensors. In conclusion, multi-sensing using EMG, MMG, and AMG are useful for assessing muscle fatigue condition. It also provide advantages over the single-measurement muscle assessment method.

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Current development in interdigital transducer (IDT) surface acoustic wave devices for live cell in vitro studies: A review

2022-01-01 , Mazlee Mazalan , Anas Mohd Noor , Yufridin Wahab , Shuhaida Yahud , Zaman W.S.W.K.

Acoustics have a wide range of uses, from noise-cancelling to ultrasonic imaging. There has been a surge in interest in developing acoustic-based approaches for biological and biomedical applications in the last decade. This review focused on the application of surface acoustic waves (SAW) based on interdigital transducers (IDT) for live-cell investigations, such as cell manipulation, cell separation, cell seeding, cell migration, cell characteristics, and cell behaviours. The approach is also known as acoustofluidic, because the SAW device is coupled with a microfluidic system that contains live cells. This article provides an overview of several forms of IDT of SAW devices on recently used cells. Conclusively, a brief viewpoint and overview of the future application of SAW techniques in live-cell investigations were presented.

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Simulation of Single Channel Magnetic Induction Tomography for Meningitis Detection by Using COMSOL Multiphysics

2021-11-25 , Aiman Abdulrahman Ahmed , Zulkarnay Zakaria , Ali M.H. , Anas Mohd Noor , Siti Fatimah Abdul Halim , Ahmad Nasrul Norali , Pusppanathan J. , Rahim R.A.

Meningitis is a inflammation of the meninges and the most common central nervous system (CNS) due to bacterial infection. Numbers of children who have bacterial meningitis are still high in recent 15 years regardless of the availability of newer antibiotics and preventive strategies. This research focuses on simulation using COMSOL Multiphysics on the design and development of magnetic induction tomography (MIT) system that emphasizes on a single channel rotatable of brain tissue imaging. The purpose of this simulation is to test the capability of the developed MIT system in detecting the change in conductivity and to identify the suitable transmitter-receiver pair and the optimum frequency based on phase shift measurement technique for detecting the conductivity property distribution of brain tissues. The obtained result verified that the performance of the square coil with 12 number of turns (5Tx-12Rx) with 10MHz frequency has been identified as the suitable transmitter-receiver pair and the optimum frequency for detecting the conductivity property distribution of brain tissues.

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Classifying white blood cells from a peripheral blood smear image using a histogram of oriented gradient feature of nuclei shapes

2020-06-11 , Anas Mohd Noor , Haniza Yazid , Zulkarnay Zakaria , Aishah Mohd Noor

Researchers developed various methods and algorithms to classify white blood cells (WBCs) from blood smear images to assist hematologists and to develop an automatic system. Furthermore, the pathological and hematological conditions of WBCs are related to diseases that can be analyzed accurately in a short time. In this work, we proposed a simple technique for WBC classification from a peripheral blood smear image based on the types of cell nuclei. The developed algorithms utilized a histogram of oriented gradient (HOG) feature typically known for application in human disease detection. The segmentation of WBC nuclei utilizes a YCbCr color space and K-means clustering techniques. The HOG feature contains information about the cell nuclei shapes, which then is classified using a support vector machine (SVM) and backpropagation artificial neural network (ANN). The results show that the proposed HOG feature is useful for WBC classification based on the shapes of nuclei. We are able to categorize the type of a WBC based on its nucleus shape with more than 95% accuracy.

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