Displaying publications 1 - 20 of 28 in total

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  1. Fulltext Low vision rehabilitation and ocular problems among industrial workers in a developing country
    Omar R, Knight VF, Aziz Mohammed MA
    Malays Fam Physician, 2014;9(3):27-33.
    PMID: 26425302 MyJurnal
    Work-related ocular injuries and illnesses were among the major causes of job absenteeism. This study was conducted to determine if low vision rehabilitation was provided following work-related ocular problems among industrial workers in a developing country. This was a retrospective analysis of case records.
  2. Characterisation of microcrystalline cellulose from oil palm fibres for food applications
    Xiang LY, P Mohammed MA, Samsu Baharuddin A
    Carbohydr Polym, 2016 09 05;148:11-20.
    PMID: 27185110 DOI: 10.1016/j.carbpol.2016.04.055
    Microcrystalline cellulose (MCC) extracted from empty fruit bunches (EFB), stalk and spikelet were characterised through physicochemical and microstructure analyses. Raw stalk fibres yielded the highest cellulose content (42.43%), followed by EFB (32.33%) and spikelet (18.83%). Likewise, lowest lignin and residual oil content was reported in raw stalk fibres compared to EFB and spikelet. SEM revealed significant changes on fibres' surface morphology throughout the extraction process. FTIR analysis showed that main characteristic peaks of hemicellulose and lignin was absent on the extracted MCC. The crystallinity index for MCC extracted from EFB (82.5%), stalk (82.2%) and spikelet (86.5%) was comparable to commercial MCC (81.9%). Results suggested stalk fibres is more preferable for the production of MCC compared to EFB and spikelet. Further rheological studies showed viscoelastic behaviour with no significant differences between commercial and stalk-based MCC, while modelling work showed ability to simulate complex deformation of the MCC-hydrogel/food mixture during processing/handling stage.
  3. Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images
    Abdulhay E, Mohammed MA, Ibrahim DA, Arunkumar N, Venkatraman V
    J Med Syst, 2018 Feb 17;42(4):58.
    PMID: 29455440 DOI: 10.1007/s10916-018-0912-y
    Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.
  4. Prevalence of sodium-glucose transporter 2 inhibitor-associated diabetic ketoacidosis in real-world data: A systematic review and meta-analysis
    Al-Hindi B, Mohammed MA, Mangantig E, Martini ND
    J Am Pharm Assoc (2003), 2023 Oct 14.
    PMID: 37844733 DOI: 10.1016/j.japh.2023.10.010
    BACKGROUND: The U.S. Food and Drug Administration revised the labels of sodium-glucose transporter 2 (SGLT2) inhibitors in December 2015 to inform users regarding the risk of diabetic ketoacidosis (DKA). As more drugs of this class are approved and their indications are expanded, this serious adverse effect has been increasingly reported.

    OBJECTIVE: This review evaluated observational studies to inform the prevalence of SGLT2-inhibitor-associated DKA compared with other antihyperglycemic agents.

    METHODS: A systematic review was conducted in PubMed and EMBASE until 19 July 2022 (PROSPERO: CRD42022385425). We included published retrospective cohort active comparator/new user (ACNU) and prevalent new user studies assessing SGLT2-inhibitor-associated DKA prevalence in adult patients with type 2 diabetes mellitus (T2DM) against active comparators. We excluded studies which lacked 1:1 propensity score matching. The JBI Checklist for Cohort Studies guided the risk-of-bias assessments. Meta-analysis was conducted based on the inverse variance method in R software.

    RESULTS: Sixteen studies with a sample of 2,956,100 non-unique patients met the inclusion criteria. Most studies were conducted in North America (n = 9) and adopted the ACNU design (n = 15). Meta-analysis of 14 studies identified 33% higher DKA risk associated with SGLT2 inhibitors (HR = 1.33, 95% CI: 1.14-1.55, p < 0.01). Meta-regression analysis identified the study location (p = 0.02), analysis principle (p < 0.001), exclusion of chronic comorbidities (p = 0.007), and canagliflozin (p = 0.04) as significant moderator variables.

    CONCLUSIONS: Despite limitations related to heterogeneity, generalisability, and misclassification, the results of this study show that SGLT2 inhibitors increase the prevalence of DKA among adult T2DM patients in the real world. The findings supplement evidence from randomised controlled trials and call for continued vigilance.

  5. Prosthetic Rehabilitation of Microstomia Patients: A Systematic Review of Published Case Reports and Case Series
    Patil S, Raj AT, Sarode SC, Sarode GS, Menon RK, Bhandi S, et al.
    J Contemp Dent Pract, 2019 Apr 01;20(4):508-515.
    PMID: 31308286
    STATEMENT OF PROBLEM: Prosthetic techniques commonly employed for the rehabilitation of edentulous patients might not be adequate in the treatment of patients with microstomia.

    PURPOSE: The purpose of this paper is to systematically review all the prosthetic techniques that have been used in the oral rehabilitation of patients with microstomia.

    MATERIALS AND METHODS: Data sources, including PubMed, Google Scholar, SCOPUS and Web of Science, were searched for case reports and case series published through September 2017. Three investigators reviewed and verified the extracted data. Only case reports and case series on prosthetic rehabilitation in microstomia patients published in the English language were considered eligible.

    RESULTS: A total of 212 records were identified from the database search. Forty duplicate records were removed. The remaining 172 articles were assessed for eligibility, and 139 articles were removed because they did not satisfy the inclusion criteria. A total of 34 cases (including 32 case reports and 1 case series) were finally included in the qualitative analysis. The review revealed the use of a modified impression technique with flexible and sectional trays to record impressions in patients with microstomia. Modified forms of oral prostheses ranging from sectional, flexible, collapsible and hinged dentures to implant-supported prosthesis were fabricated to overcome the limited mouth opening. The success of the prosthetic technique primarily depended on the extent of the microstomia and the nature of the cause of the microstomia.

    CONCLUSION: Even though the patient acceptance of the prosthetic techniques summarized in the systematic review were high, long-term success rates for each option could not be assessed because of the short follow-up time in most of the included case reports and series.

  6. Commuter motorcycle crashes in Malaysia: An understanding of contributing factors
    Oxley J, Yuen J, Ravi MD, Hoareau E, Mohammed MA, Bakar H, et al.
    Ann Adv Automot Med, 2014 1 11;57:45-54.
    PMID: 24406945
    In Malaysia, two-thirds of reported workplace-related fatal and serious injury incidents are the result of commuting crashes (especially those involving motorcyclists), however, little is known about the contributing factors to these collisions. A telephone survey of 1,750 motorcyclists (1,004 adults who had been involved in a motorcycle commuting crash in the last 2 years and 746 adult motorcyclists who had not been involved in a motorcycle crash in the last 2 years) was undertaken. The contributions of a range of behavioural, attitudinal, employment and travel pattern factors to collision involvement were examined. The findings revealed that the majority of participants were licensed riders, rode substantial distances (most often for work purposes), and reported adopting safe riding practices (helmet wearing and buckling). However, there were some concerning findings regarding speeding behaviour, use of mobile phones while riding, and engaging in other risky behaviours. Participants who had been involved in a collision were younger (aged 25-29 years), had higher exposure (measured by distances travelled, frequency of riding, and riding on high volume and higher speed roads), reported higher rates of riding for work purposes, worked more shift hours and had a higher likelihood of riding at relatively high speeds compared with participants who had not been involved in a collision. Collisions generally occurred during morning and early evening hours, striking another vehicles, and during normal traffic flow. The implications of these findings for policy decisions and development of evidence-based behavioural/training interventions addressing key contributing factors are discussed.
  7. Gasification of oil palm empty fruit bunches: a characterization and kinetic study
    Mohammed MA, Salmiaton A, Wan Azlina WA, Mohamad Amran MS
    Bioresour Technol, 2012 Apr;110:628-36.
    PMID: 22326334 DOI: 10.1016/j.biortech.2012.01.056
    Empty fruit bunches (EFBs), a waste material from the palm oil industry, were subjected to pyrolysis and gasification. A high content of volatiles (>82%) increased the reactivity of EFBs, and more than 90% decomposed at 700°C; however, a high content of moisture (>50%) and oxygen (>45%) resulted in a low calorific value. Thermogravimetric analysis demonstrated that the higher the heating rate and the smaller the particle size, the higher the peak and final reaction temperatures. The least squares estimation for a first-order reaction model was used to study the degradation kinetics. The values of activation energy increased from 61.14 to 73.76 and from 40.06 to 47.99kJ/mol when the EFB particle size increased from 0.3 to 1.0mm for holocellulose and lignin degradation stages, respectively. The fuel characteristics of EFB are comparable to those of other biomasses and EFB can be considered a good candidate for gasification.
  8. Fulltext Fatal occupational injuries among non-governmental employees in Malaysia
    Abas AB, Mohd Said DA, Aziz Mohammed MA, Sathiakumar N
    Am. J. Ind. Med., 2013 Jan;56(1):65-76.
    PMID: 22544443 DOI: 10.1002/ajim.22056
    BACKGROUND: In Malaysia, surveillance of fatal occupational injuries is fragmented. We therefore analyzed an alternative data source from Malaysia's Social Security organization, the Pertubuhan Keselamatan Sosial (PERKESO).
    METHODS: We conducted a secondary data analysis of the PERKESO database comprised of 7 million employees from 2002 to 2006.
    RESULTS: Overall, the average annual incidence was 9.2 fatal occupational injuries per 100,000 workers. During the 5-year period, there was a decrease in the absolute number of fatal injuries by 16% and the incidence by 34%. The transportation sector reported the highest incidence of fatal injuries (35.1/100,000), followed by agriculture (30.5/100,000) and construction (19.3/100,000) sectors. Persons of Indian ethnicity were more likely to sustain fatal injuries compared to other ethnic groups.
    CONCLUSIONS: Government and industry should develop rigorous strategies to detect hazards in the workplace, especially in sectors that continuously record high injury rates. Targeted interventions emphasizing worker empowerment coupled with systematic monitoring and evaluation is critical to ensure success in prevention and control measures.
  9. Molecular phylogenetic and evolutionary analyses of Muar strain of Japanese encephalitis virus reveal it is the missing fifth genotype
    Mohammed MA, Galbraith SE, Radford AD, Dove W, Takasaki T, Kurane I, et al.
    Infect Genet Evol, 2011 Jul;11(5):855-62.
    PMID: 21352956 DOI: 10.1016/j.meegid.2011.01.020
    Japanese encephalitis virus (JEV) is the most important cause of epidemic encephalitis worldwide but its origin is unknown. Epidemics of encephalitis suggestive of Japanese encephalitis (JE) were described in Japan from the 1870s onwards. Four genotypes of JEV have been characterised and representatives of each genotype have been fully sequenced. Based on limited information, a single isolate from Malaysia is thought to represent a putative fifth genotype. We have determined the complete nucleotide and amino acid sequence of Muar strain and compared it with other fully sequenced JEV genomes. Muar was the least similar, with nucleotide divergence ranging from 20.2 to 21.2% and amino acid divergence ranging from 8.5 to 9.9%. Phylogenetic analysis of Muar strain revealed that it does represent a distinct fifth genotype of JEV. We elucidated Muar signature amino acids in the envelope (E) protein, including E327 Glu on the exposed lateral surface of the putative receptor binding domain which distinguishes Muar strain from the other four genotypes. Evolutionary analysis of full-length JEV genomes revealed that the mean evolutionary rate is 4.35 × 10(-4) (3.4906 × 10(-4) to 5.303 × 10(-4)) nucleotides substitutions per site per year and suggests JEV originated from its ancestral virus in the mid 1500s in the Indonesia-Malaysia region and evolved there into different genotypes, which then spread across Asia. No strong evidence for positive selection was found between JEV strains of the five genotypes and the E gene has generally been subjected to strong purifying selection.
  10. Fulltext Non-fatal occupational injuries among non-governmental employees in Malaysia
    Abas AB, Said AR, Mohammed MA, Sathiakumar N
    Int J Occup Environ Health, 2011 Jan-Mar;17(1):38-48.
    PMID: 21344818
    We analyzed data on non-fatal occupational injuries reported to Malaysia's social security organization from 2002 to 2006. There was a decrease in both the absolute number and the incidence rates of these injuries over time. About 40% of cases occurred in the manufacturing sector followed by the service (17%) and trading (17%) sectors. The agriculture sector reported the highest incidence rate (24.1/1,000), followed by the manufacturing sector subcategories of wood-product manufacturing (22.1/1,000) and non-metallic industries (20.8/1,000). Men age 40 to 59 and persons of Indian ethnicity had a greater tendency to sustain injuries. Government and non-governmental organizations should strive to develop strategies to reduce the occupational injuries targeting vulnerable groups. Enforcement of safety measures will further play an important role to ensure that both employees and employers take special precautions to address workplace hazards.
  11. Occupational disease among non-governmental employees in Malaysia: 2002-2006
    Abas AB, Said AR, Mohammed MA, Sathiakumar N
    Int J Occup Environ Health, 2008 Oct-Dec;14(4):263-71.
    PMID: 19043913
    In the absence of systematic occupational disease surveillance, other data collected by governmental agencies or industry is useful in the identification of occupational diseases and their control. We examined data on occupational diseases reported by non-governmental employees to the national workers' social security organization in Malaysia, 2002-2006. The overall incidence rate of occupational disease was 2.8 per 100,000 workers. There was an increase in the annual number and rates of occupational disease over time. The most frequently reported conditions were hearing impairment (32%) and musculoskeletal disorders (28%). Workers in the non-metallic manufacturing industry had the highest average incidence rate of hearing impairment (12.7 per 100,000 workers) and musculoskeletal disorders (3.5 per 100,000 workers), compared to all other industries. Preventive measures should focus on safety education, engineering control and workplace ergonomics. Enforcing workplace standards and incorporating an ongoing surveillance system will facilitate the control and reduction of occupational disease.
  12. Mechanical characterisation of lignocellulosic fibres using toy bricks tensile tester
    Talib AT, P Mohammed MA, Baharuddin AS, Mokhtar MN, Wakisaka M
    J Mech Behav Biomed Mater, 2019 09;97:58-64.
    PMID: 31100486 DOI: 10.1016/j.jmbbm.2019.05.010
    This paper demonstrates the potential use of toy-bricks as the building block of a mechanical tensile testing instrument for the mechanical characterisation of natural fibres. A table-top tensile testing instrument was developed using LEGO parts (Mindstorms EV3 and Technics) and a 2 kg capacity load cell, whereas deformation modes were programmed in an open source programming language. Experimental work was conducted on oil palm fibres under different tensile modes (i.e. constant deformation, triple-twisted-tension and deformation-relaxation modes), which showed anisotropic-viscoelastic behaviour, and microstructural damages due to deformation.
  13. Fulltext Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring
    Mutlag AA, Ghani MKA, Mohammed MA, Lakhan A, Mohd O, Abdulkareem KH, et al.
    Sensors (Basel), 2021 Oct 19;21(20).
    PMID: 34696135 DOI: 10.3390/s21206923
    In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
  14. Fulltext COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images
    Hasoon JN, Fadel AH, Hameed RS, Mostafa SA, Khalaf BA, Mohammed MA, et al.
    Results Phys, 2021 Dec;31:105045.
    PMID: 34840938 DOI: 10.1016/j.rinp.2021.105045
    The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.
  15. A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application
    Mostafa SA, Mustapha A, Mohammed MA, Ahmad MS, Mahmoud MA
    Int J Med Inform, 2018 04;112:173-184.
    PMID: 29500017 DOI: 10.1016/j.ijmedinf.2018.02.001
    Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls.
  16. Fulltext A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models
    Sarra RR, Dinar AM, Mohammed MA, Ghani MKA, Albahar MA
    Diagnostics (Basel), 2022 Nov 22;12(12).
    PMID: 36552906 DOI: 10.3390/diagnostics12122899
    Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.
  17. Fulltext Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model
    Shamim S, Awan MJ, Mohd Zain A, Naseem U, Mohammed MA, Garcia-Zapirain B
    J Healthc Eng, 2022;2022:6566982.
    PMID: 35422980 DOI: 10.1155/2022/6566982
    The coronavirus (COVID-19) pandemic has had a terrible impact on human lives globally, with far-reaching consequences for the health and well-being of many people around the world. Statistically, 305.9 million people worldwide tested positive for COVID-19, and 5.48 million people died due to COVID-19 up to 10 January 2022. CT scans can be used as an alternative to time-consuming RT-PCR testing for COVID-19. This research work proposes a segmentation approach to identifying ground glass opacity or ROI in CT images developed by coronavirus, with a modified structure of the Unet model having been used to classify the region of interest at the pixel level. The problem with segmentation is that the GGO often appears indistinguishable from a healthy lung in the initial stages of COVID-19, and so, to cope with this, the increased set of weights in contracting and expanding the Unet path and an improved convolutional module is added in order to establish the connection between the encoder and decoder pipeline. This has a major capacity to segment the GGO in the case of COVID-19, with the proposed model being referred to as "convUnet." The experiment was performed on the Medseg1 dataset, and the addition of a set of weights at each layer of the model and modification in the connected module in Unet led to an improvement in overall segmentation results. The quantitative results obtained using accuracy, recall, precision, dice-coefficient, F1score, and IOU were 93.29%, 93.01%, 93.67%, 92.46%, 93.34%, 86.96%, respectively, which is better than that obtained using Unet and other state-of-the-art models. Therefore, this segmentation approach proved to be more accurate, fast, and reliable in helping doctors to diagnose COVID-19 quickly and efficiently.
  18. Fulltext Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach
    Awan MJ, Rahim MSM, Salim N, Mohammed MA, Garcia-Zapirain B, Abdulkareem KH
    Diagnostics (Basel), 2021 Jan 11;11(1).
    PMID: 33440798 DOI: 10.3390/diagnostics11010105
    The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
  19. Ground improvement and its role in carbon dioxide reduction: a review
    Mohammed MA, Mohd Yunus NZ, Hezmi MA, Abang Hasbollah DZ, A Rashid AS
    Environ Sci Pollut Res Int, 2021 Feb;28(8):8968-8988.
    PMID: 33443736 DOI: 10.1007/s11356-021-12392-0
    Environmental global issues affecting global warming, such as carbon dioxide (CO2), have attracted the attention of researchers around the world. This paper reviews and discusses the ground improvement and its contribution to reducing CO2 in the atmosphere. The approach is divided into three parts: the Streamlined Energy and Emissions Assessment Model (SEEAM), the replacement of soil stabilisation materials that lead to the emission of a large amount of CO2 with alternatives and mineral carbonation. A brief discussion about the first two is reviewed in this paper and a detailed discussion about mineral carbonation and its role in enhancing soil strength while absorbing a large amount of CO2. It is emphasised that natural mineral carbonation requires a very long time for a material to reach its full capacity to form CO2; as a result, different acceleration processes can be done from increasing pressure, temperature, the concentration of CO2 and the addition of various additives. In conclusion, it was found that magnesium is more attractive than calcium, and calcium is complicated in terms of strength behaviour. Magnesium has a larger capacity for CO2 sequestration and it has a greater potential to increase soil strength than calcium.
  20. Induction of ROS‑mediated cell death and activation of the JNK pathway by a sulfonamide derivative
    Ahmad R, Vaali-Mohammed MA, Elwatidy M, Al-Obeed O, Al-Khayal K, Eldehna WM, et al.
    Int J Mol Med, 2019 Jul 23.
    PMID: 31364730 DOI: 10.3892/ijmm.2019.4284
    The emergence of colorectal cancer in developed nations can be attributed to dietary habits, smoking, a sedentary lifestyle and obesity. Several treatment regimens are available for primary and metastatic colorectal cancer; however, these treatment options have had limited impact on cure and disease‑free survival, and novel agents need to be developed for treating colorectal cancer. Thus, the objective of this study was to explore the anticancer mechanism of a benzo(1,3)dioxol‑based derivative of sulfonamide. The compound's inhibitory effect on cell proliferation was determined using the MTT assay and the xCelligence RTDP machine. Alternations in the expression of Bcl‑2 and inhibitor of apoptosis protein families were detected by western blotting. Apoptotic marker protein expression, including cytochrome c and cleaved poly(ADP‑ribose)polymerase was measured in the cytosolic extract of cells. Apoptosis and necrosis were detected by flow cytometry and immunofluorescence. Reactive oxygen species (ROS), and activation of caspase‑3 and caspase‑7 were measured using flow cytometry. Activation of the JNK pathway was detected by western blotting. We investigated the molecular mechanism of action of the sulfonamide derivative on colorectal cancer cells and found that the compound possesses a potent anticancer effect, which is primarily exerted by inducing apoptosis and necrosis. Interestingly, this compound exhibited little antiproliferative effect against the normal colonic epithelial cell line FHC. Furthermore, our results showed that the compound could significantly increase ROS production. Apoptosis induction could be attenuated by the free oxygen radical scavenger N‑acetyl cysteine (NAC), indicating that the antiproliferative effect of this compound on colorectal cancer cells is at least partially dependent on the redox balance. In addition, JNK signaling was activated by treatment with this derivative, which led to the induction of apoptosis. On the contrary, a JNK inhibitor could suppress the cell death induced by this compound. Our findings thus suggested a novel anticancer mechanism of a benzo(1,3)dioxol‑based derivative of sulfonamide for colorectal cancer cells and may have therapeutic potential for the treatment of colorectal cancer; however, further investigation is required.
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