MyMedR

Displaying all 11 publications

Abstract:

Sort:

Fulltext Experimental Investigation and Performance Evaluation of Modified Viscoelastic Surfactant (VES) as a New Thickening Fracturing Fluid

Chieng ZH, Mohyaldinn ME, Hassan AM, Bruining H

Polymers (Basel), 2020 Jun 30;12(7).
PMID: 32629958 DOI: 10.3390/polym12071470

In hydraulic fracturing, fracturing fluids are used to create fractures in a hydrocarbon reservoir throughout transported proppant into the fractures. The application of many fields proves that conventional fracturing fluid has the disadvantages of residue(s), which causes serious clogging of the reservoir's formations and, thus, leads to reduce the permeability in these hydrocarbon reservoirs. The development of clean (and cost-effective) fracturing fluid is a main driver of the hydraulic fracturing process. Presently, viscoelastic surfactant (VES)-fluid is one of the most widely used fracturing fluids in the hydraulic fracturing development of unconventional reservoirs, due to its non-residue(s) characteristics. However, conventional single-chain VES-fluid has a low temperature and shear resistance. In this study, two modified VES-fluid are developed as new thickening fracturing fluids, which consist of more single-chain coupled by hydrotropes (i.e., ionic organic salts) through non-covalent interaction. This new development is achieved by the formulation of mixing long chain cationic surfactant cetyltrimethylammonium bromide (CTAB) with organic acids, which are citric acid (CA) and maleic acid (MA) at a molar ratio of (3:1) and (2:1), respectively. As an innovative approach CTAB and CA are combined to obtain a solution (i.e., CTAB-based VES-fluid) with optimal properties for fracturing and this behaviour of the CTAB-based VES-fluid is experimentally corroborated. A rheometer was used to evaluate the visco-elasticity and shear rate & temperature resistance, while sand-carrying suspension capability was investigated by measuring the settling velocity of the transported proppant in the fluid. Moreover, the gel breaking capability was investigated by determining the viscosity of broken VES-fluid after mixing with ethanol, and the degree of core damage (i.e., permeability performance) caused by VES-fluid was evaluated while using core-flooding test. The experimental results show that, at pH-value ( 6.17 ), 30 (mM) VES-fluid (i.e., CTAB-CA) possesses the highest visco-elasticity as the apparent viscosity at zero shear-rate reached nearly to 10 6 (mPa·s). Moreover, the apparent viscosity of the 30 (mM) CTAB-CA VES-fluid remains 60 (mPa·s) at (90 ∘ C) and 170 (s - 1 ) after shearing for 2-h, indicating that CTAB-CA fluid has excellent temperature and shear resistance. Furthermore, excellent sand suspension and gel breaking ability of 30 (mM) CTAB-CA VES-fluid at 90 ( ∘ C) was shown; as the sand suspension velocity is 1.67 (mm/s) and complete gel breaking was achieved within 2 h after mixing with the ethanol at the ratio of 10:1. The core flooding experiments indicate that the core damage rate caused by the CTAB-CA VES-fluid is ( 7.99 % ), which indicate that it does not cause much damage. Based on the experimental results, it is expected that CTAB-CA VES-fluid under high-temperature will make the proposed new VES-fluid an attractive thickening fracturing fluid.
Protective effects of melatonin and N-acetyl cysteine against oxidative stress induced by microcystin-LR on cardiac muscle tissue

Ait Abderrahim L, Taïbi K, Abderrahim NA, Alomery AM, Abdellah F, Alhazmi AS, et al.

Toxicon, 2019 Aug 26;169:38-44.
PMID: 31465783 DOI: 10.1016/j.toxicon.2019.08.005

Microcystin Leucine-Arginine (MC-LR) is a toxin produced by the cyanobacteria Microcystis aeruginosa. It is the most encountered and toxic type of cyanotoxins. Oxidative stress was shown to play a role in the pathogenesis of microcystin LR by the induction of intracellular reactive oxygen species (ROS) formation that oxidize and damage cellular macromolecules. In the present study we examined the effect of acute MC-LR dose on the cardiac muscle of BALB/c mice. Afterwards, melatonin and N-acetyl cysteine (NAC) were assayed and evaluated as potential protective and antioxidant agents against damages generated by MC-LR. For this purpose, thirty mice were assigned into six groups of five mice each. The effect of MC-LR was first compared to the control group supplied with distilled water, then compared to the other groups supplied with melatonin and NAC. The experiment lasted 10 days after which animals were euthanized. Biomarkers of toxicity such as alkaline phosphatase activity, lipid peroxidation, protein carbonyl content, reduced glutathione content, serum lactate dehydrogenase and serum sorbitol dehydrogenase were assayed. Results showed that toxin treated mice have experienced significant oxidative damage in their myocardial tissue as revealed by noticeable levels of oxidative stress biomarkers and by the reduction in alkaline phosphatase activity. Whereas, melatonin and NAC treated mice manifested lesser oxidative damages. Our findings suggest a potential therapeutic use of melatonin and N-acetyl cysteine as antioxidant protective agents against oxidative damage induced by MC-LR.
Fulltext Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Podder KK, Chowdhury MEH, Tahir AM, Mahbub ZB, Khandakar A, Hossain MS, et al.

Sensors (Basel), 2022 Jan 12;22(2).
PMID: 35062533 DOI: 10.3390/s22020574

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.
Fulltext Quality of Life and Illness Acceptance among End-Stage Renal Disease (ESRD) Patients on Hemodialysis: The Moderating Effect of Death Anxiety during COVID-19 pandemic

Mushtaque I, Awais-E-Yazdan M, Zahra R, Anas M

Omega (Westport), 2024 Jun;89(2):567-586.
PMID: 35254867 DOI: 10.1177/00302228221075202

The purpose of the study was to examine the quality of life and illness acceptance among ESRD patients with the moderating effects of death anxiety. The cross-sectional design was incorporated. The sample was comprised of 240 participants. Individuals with ESRD on hemodialysis were approached above 20 years of age. A self-administered questionnaire was used for data collection. The results revealed that COVID-19 has a significant impact on the quality of life of patients and their illness acceptance. Covid-19 affected the general health of patients, their psychological health, as well as their social relationships. The results also confirmed that death anxiety negatively moderates the relationship between quality of life and illness acceptance among ESRD patients. This study will shed light on the need to provide appropriate psychosocial care as well as supportive therapies to people with end-stage renal disease who are experiencing mental distress during and after the COVID-19 outbreak.
Fulltext A Systematic Approach to the Design and Characterization of A Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

Tahir AM, Chowdhury MEH, Khandakar A, Al-Hamouz S, Abdalla M, Awadallah S, et al.

Sensors (Basel), 2020 Feb 11;20(4).
PMID: 32053914 DOI: 10.3390/s20040957

Gait analysis is a systematic study of human locomotion, which can be utilized in variousapplications, such as rehabilitation, clinical diagnostics and sports activities. The various limitationssuch as cost, non-portability, long setup time, post-processing time etc., of the current gait analysistechniques have made them unfeasible for individual use. This led to an increase in research interestin developing smart insoles where wearable sensors can be employed to detect vertical groundreaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortablefor gait analysis, and can monitor plantar pressure frequently through embedded sensors thatconvert the applied pressure to an electrical signal that can be displayed and analyzed further.Several research teams are still working to improve the insoles' features such as size, sensitivity ofinsoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait bydetecting various complications providing recommendation to enhance walking performance. Eventhough systematic sensor calibration approaches have been followed by different teams to calibrateinsoles' sensor, expensive calibration devices were used for calibration such as universal testingmachines or infrared motion capture cameras equipped in motion analysis labs. This paper providesa systematic design and characterization procedure for three different pressure sensors: forcesensitiveresistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that canbe used for detecting vGRF using a smart insole. A simple calibration method based on a load cellis presented as an alternative to the expensive calibration techniques. In addition, to evaluate theperformance of the different sensors as a component for the smart insole, the acquired vGRF fromdifferent insoles were used to compare them. The results showed that the FSR is the most effectivesensor among the three sensors for smart insole applications, whereas the piezoelectric sensors canbe utilized in detecting the start and end of the gait cycle. This study will be useful for any researchgroup in replicating the design of a customized smart insole for gait analysis.
Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs

Rahman A, Chowdhury MEH, Khandakar A, Tahir AM, Ibtehaz N, Hossain MS, et al.

Comput Biol Med, 2022 Mar;142:105238.
PMID: 35077938 DOI: 10.1016/j.compbiomed.2022.105238

Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has become a potential field of research in recent years. Although several studies have been conducted, still there are some vital challenges present in the deployment of EEG-based biometrics, which is stable and capable of handling the real-world scenario. One of the key challenges is the large signal variability of EEG when recorded on different days or sessions which impedes the performance of biometric systems significantly. To address this issue, a session invariant multimodal Self-organized Operational Neural Network (Self-ONN) based ensemble model combining EEG and keystroke dynamics is proposed in this paper. Our model is tested successfully on a large number of sessions (10 recording days) with many challenging noisy and variable environments for the identification and authentication tasks. In most of the previous studies, training and testing were performed either over a single recording session (same day) only or without ensuring appropriate splitting of the data on multiple recording days. Unlike those studies, in our work, we have rigorously split the data so that train and test sets do not share the data of the same recording day. The proposed multimodal Self-ONN based ensemble model has achieved identification accuracy of 98% in rigorous validation cases and outperformed the equivalent ensemble of deep CNN models. A novel Self-ONN Siamese network has also been proposed to measure the similarity of templates during the authentication task instead of the commonly used simple distance measure techniques. The multimodal Siamese network reduces the Equal Error Rate (EER) to 1.56% in rigorous authentication. The obtained results indicate that the proposed multimodal Self-ONN model can automatically extract session invariant unique non-linear features to identify and authenticate users with high accuracy.
Lupus manifestations in children with primary immunodeficiency diseases: Comprehensive phenotypic and genetic features and outcome

Al-Mayouf SM, Alreefi HA, Alsinan TA, AlSalmi G, AlRowais A, Al-Herz W, et al.

Mod Rheumatol, 2021 Nov;31(6):1171-1178.
PMID: 33563058 DOI: 10.1080/14397595.2021.1886627

OBJECTIVES: To report the phenotypic, genetic findings and outcome of children with lupus manifestations associated with primary immunodeficiency diseases (PIDs).
METHODS: Data are retrospectively collected on patients with lupus manifestations and PIDs seen between 1998 and 2019. Data comprised the clinical findings and genetic testing, the response to treatment and the accrual damage related to SLE.
RESULTS: A total of 39 patients (22 female) were reviewed. Thirty-four patients had lupus manifestations and six patients with SLE-like manifestations. Genetic analysis was performed in 25 patients. Complement deficiency was the most frequent PIDs; 26 patients were C1q deficient, three patients had C3 deficiency, two patients had C4 deficiency and one patient with heterozygous C8b variant. The other seven patients had different PIDs genetic defects that include SCID caused by PNP deficiency, CGD, CVID (PIK3CD), IL-2RB mutation, DNase II deficiency, STAT1 mutation, ISG15 mutation and Griscelli syndrome type 3. Mucocutaneous lesions, arthritis and lung involvement were the main clinical features. 84.1% experienced recurrent infections. The mean accrual damage was 2.7 ± 2.2. There were five deaths because of infection.
CONCLUSION: This study suggests that patients with lupus manifestations and early onset disease, family history of SLE or recurrent infections should undergo immunological work-up and genetic testing to rule out PIDs.
Fulltext Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images

Tahir AM, Qiblawey Y, Khandakar A, Rahman T, Khurshid U, Musharavati F, et al.

Cognit Comput, 2022;14(5):1752-1772.
PMID: 35035591 DOI: 10.1007/s12559-021-09955-1

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.
Fulltext A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

Mahmud S, Ibtehaz N, Khandakar A, Tahir AM, Rahman T, Islam KR, et al.

Sensors (Basel), 2022 Jan 25;22(3).
PMID: 35161664 DOI: 10.3390/s22030919

Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.
Fulltext Expanding the phenome and variome of skeletal dysplasia

Maddirevula S, Alsahli S, Alhabeeb L, Patel N, Alzahrani F, Shamseldin HE, et al.

Genet Med, 2018 12;20(12):1609-1616.
PMID: 29620724 DOI: 10.1038/gim.2018.50

PURPOSE: To describe our experience with a large cohort (411 patients from 288 families) of various forms of skeletal dysplasia who were molecularly characterized.
METHODS: Detailed phenotyping and next-generation sequencing (panel and exome).
RESULTS: Our analysis revealed 224 pathogenic/likely pathogenic variants (54 (24%) of which are novel) in 123 genes with established or tentative links to skeletal dysplasia. In addition, we propose 5 genes as candidate disease genes with suggestive biological links (WNT3A, SUCO, RIN1, DIP2C, and PAN2). Phenotypically, we note that our cohort spans 36 established phenotypic categories by the International Skeletal Dysplasia Nosology, as well as 18 novel skeletal dysplasia phenotypes that could not be classified under these categories, e.g., the novel C3orf17-related skeletal dysplasia. We also describe novel phenotypic aspects of well-known disease genes, e.g., PGAP3-related Toriello-Carey syndrome-like phenotype. We note a strong founder effect for many genes in our cohort, which allowed us to calculate a minimum disease burden for the autosomal recessive forms of skeletal dysplasia in our population (7.16E-04), which is much higher than the global average.
CONCLUSION: By expanding the phenotypic, allelic, and locus heterogeneity of skeletal dysplasia in humans, we hope our study will improve the diagnostic rate of patients with these conditions.
Fulltext Global injury morbidity and mortality from 1990 to 2017: results from the Global Burden of Disease Study 2017

James SL, Castle CD, Dingels ZV, Fox JT, Hamilton EB, Liu Z, et al.

Inj Prev, 2020 10;26(Supp 1):i96-i114.
PMID: 32332142 DOI: 10.1136/injuryprev-2019-043494

BACKGROUND: Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries.
METHODS: We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs).
FINDINGS: In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505).
INTERPRETATION: Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links