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Interferon-γ inducible protein-10 and interleukin 28B gene polymorphism as predictive markers for genotype 4 hepatitis C virus treatment response

Abd El-Maksoud E, Salem AM, Maher AM, Hegazy MGA

Trop Biomed, 2020 Dec 01;37(4):1083-1092.
PMID: 33612760 DOI: 10.47665/tb.37.4.1083

HCV genotype 4 dominates the HCV epidemic in Egypt. Drug resistance was the most serious side effect that reflects bad clinical outcome. Several studies had demonstrated that baseline serum interferon-γ-inducible-protein 10 (IP-10) levels and interleukin 28B polymorphisms were associated with the resistance to the standard of care pegylated interferon alpha and ribavirin (PEG-IFNα/RBV) therapy and development of post-treatment relapse. Our purpose was to assess the predictive value of combining IP-10 levels and IL28B genotypes to PEG-IFNα/RBV therapy response in Egyptian chronic HCV infection patients with genotype 4. Ninety Egyptian patients chronically infected by HCV genotype-4 treated with pegylated interferon alpha and ribavirin (PEG-IFNα/RBV) therapy were enrolled. Serum IP-10 levels were determined by enzyme linked immunosorbent assay pre- and post- treatment. IL-28B (rs12979860 and rs8099917) polymorphisms were performed by PCR-RFLP in all patients. Overall, 38 patients (42.2%) achieved sustained virologic response (SVR) and 52 (57.8%) patients have non-viral response (NVR). Pretreatment serum IP-10 mean levels were significantly lower in patients who achieved SVR than in NVR (P<0.05). CC genotype in IL28B polymorphism (rs12979860) was the favorable genotype as 65.8% achieved SVR, while TT genotype in IL-28B polymorphism (rs8099917) was the favorable genotype as 81.5% achieved SVR. Baseline IP-10 was significantly correlated to genotypes CC in rs12979860 and TT in rs8099917. Combined use of serum baseline IP-10 levels with IL-28B polymorphisms could improve the prediction of SVR to PEG-IFNα/RBV therapy in Egyptian chronic HCV infection patients with genotype 4.
Fulltext Kinetic and Thermodynamic Influence of NaCl on Methane Hydrate in an Oil-Dominated System

Almashwali AA, Idress M, Lal B, Salem A, Jin QC

ACS Omega, 2023 Nov 28;8(47):44796-44803.
PMID: 38046291 DOI: 10.1021/acsomega.3c05866

This experimental study reports the kinetic and thermodynamic inhibition influence of sodium chloride (NaCl) on methane (CH4) hydrate in an oil-dominated system. To thoroughly examine the inhibition effect of NaCl on CH4 hydrate formation, kinetically by the induction time and relative inhibition performance and thermodynamically by the hydrate liquid-vapor equilibrium (HLwVE) curve, enthalpy (ΔHdiss) and suppression temperature are used to measure the NaCl inhibition performance through this experimental study. All kinetic experiments are performed at a concentration of 1 wt % under a pressure and temperature of 8 MPa and 274.15K, respectively, whereby for the thermodynamic study, the concentration was 3 wt % by using the isochoric T-cycle technique at the selected range of pressures and temperatures of 4.0-9.0 MPa and 276.5-286.0K, respectively; both studies were conducted using a high-pressure reactor cell. Results show that kinetically, NaCl offers slightly to no inhibition in both systems with/without oil; however, the presence of drilling oil contributes positively by increasing the induction time; thermodynamically, NaCl contributes significantly in shifting the equilibrium curve to higher pressures and lower temperatures in both systems. In the oil system, the contribution of the THI to the equilibrium curve increases the pressure with a range of 0.04-0.15 MPa and reduces the temperature with a range of 1-3 K, which is due to the NaCl presence in the systems that reduces the activity of water molecules by increasing the ionic strength of the solution. At a high pressure of 9 MPa, the NaCl inhibition performance was greater than that at lower pressures <5.5 MPa because, at the high pressure, NaCl increases the activity of water, which means that more water molecules are available to form hydrate cages around gas molecules.
Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Islam MN, Sulaiman N, Farid FA, Uddin J, Alyami SA, Rashid M, et al.

PeerJ Comput Sci, 2021;7:e638.
PMID: 34712786 DOI: 10.7717/peerj-cs.638

Hearing deficiency is the world's most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain's cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method's performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.
Fulltext BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients

Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, et al.

Sci Rep, 2023 Dec 18;13(1):22874.
PMID: 38129433 DOI: 10.1038/s41598-023-48486-7

Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
Fulltext Improving management of hospitalised patients with COVID-19: algorithms and tools for implementation and measurement

Salem A, Elamir H, Alfoudri H, Shamsah M, Abdelraheem S, Abdo I, et al.

BMJ Open Qual, 2020 Nov;9(4).
PMID: 33199287 DOI: 10.1136/bmjoq-2020-001130

BACKGROUND: The COVID-19 pandemic represents an unprecedented challenge to healthcare systems and nations across the world. Particularly challenging are the lack of agreed-upon management guidelines and variations in practice. Our hospital is a large, secondary-care government hospital in Kuwait, which has increased its capacity by approximately 28% to manage the care of patients with COVID-19. The surge in capacity has necessitated the redeployment of staff who are not well-trained to manage such conditions. There was a great need to develop a tool to help redeployed staff in decision-making for patients with COVID-19, a tool which could also be used for training.
METHODS: Based on the best available clinical knowledge and best practices, an eight member multidisciplinary group of clinical and quality experts undertook the development of a clinical algorithm-based toolkit to guide training and practice for the management of patients with COVID-19. The team followed Horabin and Lewis' seven-step approach in developing the algorithms and a five-step method in writing them. Moreover, we applied Rosenfeld et al's five points to each algorithm.
RESULTS: A set of seven clinical algorithms and one illustrative layout diagram were developed. The algorithms were augmented with documentation forms, data-collection online forms and spreadsheets and an indicators' reference sheet to guide implementation and performance measurement. The final version underwent several revisions and amendments prior to approval.
CONCLUSIONS: A large volume of published literature on the topic of COVID-19 pandemic was translated into a user-friendly, algorithm-based toolkit for the management of patients with COVID-19. This toolkit can be used for training and decision-making to improve the quality of care provided to patients with COVID-19.
Fulltext Investigating a hybrid extreme learning machine coupled with Dingo Optimization Algorithm for modeling liquefaction triggering in sand-silt mixtures

Hameed MM, Masood A, Srivastava A, Abd Rahman N, Mohd Razali SF, Salem A, et al.

Sci Rep, 2024 May 11;14(1):10799.
PMID: 38734717 DOI: 10.1038/s41598-024-61059-6

Liquefaction is a devastating consequence of earthquakes that occurs in loose, saturated soil deposits, resulting in catastrophic ground failure. Accurate prediction of such geotechnical parameter is crucial for mitigating hazards, assessing risks, and advancing geotechnical engineering. This study introduces a novel predictive model that combines Extreme Learning Machine (ELM) with Dingo Optimization Algorithm (DOA) to estimate strain energy-based liquefaction resistance. The hybrid model (ELM-DOA) is compared with the classical ELM, Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means (ANFIS-FCM model), and Sub-clustering (ANFIS-Sub model). Also, two data pre-processing scenarios are employed, namely traditional linear and non-linear normalization. The results demonstrate that non-linear normalization significantly enhances the prediction performance of all models by approximately 25% compared to linear normalization. Furthermore, the ELM-DOA model achieves the most accurate predictions, exhibiting the lowest root mean square error (484.286 J/m3), mean absolute percentage error (24.900%), mean absolute error (404.416 J/m3), and the highest correlation of determination (0.935). Additionally, a Graphical User Interface (GUI) has been developed, specifically tailored for the ELM-DOA model, to assist engineers and researchers in maximizing the utilization of this predictive model. The GUI provides a user-friendly platform for easy input of data and accessing the model's predictions, enhancing its practical applicability. Overall, the results strongly support the proposed hybrid model with GUI serving as an effective tool for assessing soil liquefaction resistance in geotechnical engineering, aiding in predicting and mitigating liquefaction hazards.
ISPAD Clinical Practice Consensus Guidelines: Fasting during Ramadan by young people with diabetes

Deeb A, Elbarbary N, Smart CE, Beshyah SA, Habeb A, Kalra S, et al.

Pediatr Diabetes, 2020 02;21(1):5-17.
PMID: 31659852 DOI: 10.1111/pedi.12920
Enhancing photosynthesis and root development for better fruit quality, aroma, and lessening of radioactive materials in key lime (Citrus aurantifolia) using Trichoderma harzianum and Bacillus thuringiensis

Salem A, Khandaker MM, Mahmud K, Alsufyani SJ, Majrashi AA, Rashid ZM, et al.

Plant Physiol Biochem, 2024 Jan;206:108295.
PMID: 38154296 DOI: 10.1016/j.plaphy.2023.108295

The present study was conducted to investigate the effects of Trichoderma harzianum and Bacillus thuringiensis alone or with gradual levels of NPK on photosynthesis, growth, fruit quality, aroma improvement and reduced radionuclides of key lime fruits. The lemon seedlings were treated with (T0) without fertilizers as control, (T1) 100g of NPK at 100%, (T2) 5 g of Trichoderma. harzianum at 50% + 50 g of NPK at 50%, (T3) 5 g of Bacillus thuringiensis at 50% + 50 g of NPK at 50 %, (T4) 7.5 g of Trichoderma harzianum at 75% + 25 g of NPK at 25 %, (T5) 7.5 g of Bacillus thuringiensis at 75% + 25 g of NPK at 25 %, (T6) 10 g of Trichoderma harzianum at 100 % and (T7)10 g of Bacillus thuringiensis at 100 %. The results showed that T2 increased net photosynthetic rate, stomatal conductance, transpiration rate, internal CO2 concentration, fresh and dry root biomass by 209%, 74%, 56%, 376%, 69.4% and 71.6%, while, T5 increased root volume, root length, and root tip number by 27.1%, 167%, and 67%, respectively over the control trees. The microbial treatments developed cortex, vascular cylinder and tracheal elements of the root. Fruit number, length, diameter, weight, pulp thickness, pulp/peel ratio, juice, total soluble solids (TSS), pigment contents and antioxidant activity increased significantly in the T2 treatment. Vitamin C, total phenols, total flavonoids, and total sugar content increased by 1.59-, 1.66-, 1.44- and 2.07- fold in T5 treated fruits compared to the control. The two microbes increased volatile compounds and decreased radionucleotides in the fruit, moreover, 27 identified and 2 (two) unmatched volatile compounds were identified by GCMS analysis. It is concluded that T. harzianum and B. thuringiensis with 25-50 g NPK treatments improved photosynthesis, root structure, fruit growth, fruit quality, aroma and lessened radionuclides in key lime fruits.
Fulltext Perspective and practice of root caries management: A multicountry study - Part II: A deeper dive into risk factors

Salem A, Aouididi R, Delatorre Bronzato J, Al-Waeli H, Abufadalah M, Shaikh S, et al.

J Conserv Dent, 2021;24(2):163-168.
PMID: 34759583 DOI: 10.4103/jcd.jcd_20_21

BACKGROUND: The potential of an improved understanding to prevent and treat a complex oral condition such as root caries is important, given its correlation with multiple factors and the uncertainty surrounding the approach/material of choice. Deeper insights into risk factors may improve the quality of treatment and reduce the formation of root surface caries.
AIM: The present work aims to gain knowledge about dentists' opinions and experiences on assessing the risk factor related to the development of root caries and to help identify any overlooked factors that may contribute to less efficacious clinical outcomes.
METHODOLOGY: A questionnaire related to root surface caries was distributed among practicing dentists in nine different countries, namely the United Kingdom, Libya, Jordan, Saudi Arabia, Egypt, Brazil, India, Malaysia, and Iraq. Questionnaire responses were analyzed, and the results were compared among the groups.
RESULTS: Dentists around the world ranked the oral hygiene status of patients as the most important factor in the development of root surface caries. Patients with poor oral hygiene, active periodontal disease, reduced salivary flow, and gingival recession are perceived to have a higher risk of developing new root surface caries. There is a greater focus on prevention in the UK and greater levels of untreated dental disease in other countries, especially those recovering from civil wars.
CONCLUSION: This work identified some overlooked factors that may have contributed to the less efficacious clinical outcomes reported in the literature. It is hoped that this deep dive into risk factors coupled with the findings presented in Part I of this study will be used as a basis for a more comprehensive investigation into the management of patients with root surface caries.