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Fulltext Deep Learning Approach for Robust Prediction of Reservoir Bubble Point Pressure

Alakbari FS, Mohyaldinn ME, Ayoub MA, Muhsan AS

ACS Omega, 2021 Aug 24;6(33):21499-21513.
PMID: 34471753 DOI: 10.1021/acsomega.1c02376

The bubble point pressure (P b) is a crucial pressure-volume-temperature (PVT) property and a primary input needed for performing many petroleum engineering calculations, such as reservoir simulation. The industrial practice of determining P b is by direct measurement from PVT tests or prediction using empirical correlations. The main problems encountered with the published empirical correlations are their lack of accuracy and the noncomprehensive data set used to develop the model. In addition, most of the published correlations have not proven the relationships between the inputs and outputs as part of the validation process (i.e., no trend analysis was conducted). Nowadays, deep learning techniques such as long short-term memory (LSTM) networks have begun to replace the empirical correlations as they generate high accuracy. This study, therefore, presents a robust LSTM-based model for predicting P b using a global data set of 760 collected data points from different fields worldwide to build the model. The developed model was then validated by applying trend analysis to ensure that the model follows the correct relationships between the inputs and outputs and performing statistical analysis after comparing the most published correlations. The robustness and accuracy of the model have been verified by performing various statistical analyses and using additional data that was not part of the data set used to develop the model. The trend analysis results have proven that the proposed LSTM-based model follows the correct relationships, indicating the model's reliability. Furthermore, the statistical analysis results have shown that the lowest average absolute percent relative error (AAPRE) is 8.422% and the highest correlation coefficient is 0.99. These values are much better than those given by the most accurate models in the literature.
Molecular basis of the anti-diabetic properties of camel milk through profiling of its bioactive peptides on dipeptidyl peptidase IV (DPP-IV) and insulin receptor activity

Ashraf A, Mudgil P, Palakkott A, Iratni R, Gan CY, Maqsood S, et al.

J Dairy Sci, 2021 Jan;104(1):61-77.
PMID: 33162074 DOI: 10.3168/jds.2020-18627

The molecular basis of the anti-diabetic properties of camel milk reported in many studies and the exact active agent are still elusive. Recent studies have reported effects of camel whey proteins (CWP) and their hydrolysates (CWPH) on the activities of dipeptidyl peptidase IV (DPP-IV) and the human insulin receptor (hIR). In this study, CWPH were generated, screened for DPP-IV binding in silico and inhibitory activity in vitro, and processed for peptide identification. Furthermore, pharmacological action of intact CWP and their selected hydrolysates on hIR activity and signaling and on glucose uptake were investigated in cell lines. Results showed inhibition of DPP-IV by CWP and CWPH and their positive action on hIR activation and glucose uptake. Interestingly, the combination of CWP or CWPH with insulin revealed a positive allosteric modulation of hIR that was drastically reduced by the competitive hIR antagonist. Our data reveal for the first time the profiling and pharmacological actions of CWP and their derived peptides fractions on hIR and their pathways involved in glucose homeostasis. This sheds more light on the anti-diabetic properties of camel milk by providing the molecular basis for the potential use of camel milk in the management of diabetes.
Fulltext A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

Alakbari FS, Mohyaldinn ME, Ayoub MA, Muhsan AS, Hussein IA

PLoS One, 2021;16(4):e0250466.
PMID: 33901240 DOI: 10.1371/journal.pone.0250466

Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model's reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.
An investigative study on the zoonotic potential of Helicobacter pylori

Shaaban SI, Talat D, Khatab SA, Nossair MA, Ayoub MA, Ewida RM, et al.

BMC Vet Res, 2023 Jan 21;19(1):16.
PMID: 36670434 DOI: 10.1186/s12917-023-03572-w

BACKGROUND: Helicobacter pylori is one of the most common bacterial infections and is widespread globally. It causes a variety of gastrointestinal disorders, though a great proportion of infections are asymptomatic. A total of 143 fresh stool samples were collected from apparently healthy farm and pet animals (43 cattle, 50 buffaloes, 50 sheep, 50 dogs, and 50 cats), in addition to 768 human stool samples. The samples were examined using stool antigen and rapid antibody tests, and further confirmation of glmM "human antigen-positive samples and animal milk samples" was conducted by polymerase chain reaction (PCR).
RESULTS: The prevalence rates of H. pylori infection in animals were 22.2% and 16% in antibody and stool antigen tests, respectively. The detection rates were 28%, 24%, 12%, 10%, and 4.7% in cats, dogs, buffaloes, sheep, and cattle, respectively. On the other hand, the prevalence rate of H. pylori infection in human stool samples was 74.8%, and a statistically significant association was observed between prevalence and several factors, such as sex, age, and locality. PCR was performed to detect the glmM gene of H. pylori, and this gene was found in 21 of 27 human antigen-positive samples and 5 of 13 animal milk samples.
CONCLUSIONS: H. pylori was detected in both human and animal samples. Furthermore, glmM was found in milk and human samples. Our findings suggest that pet and farm animals could transmit H. pylori infection to humans.
Camel Milk-Derived Bioactive Peptides and Diabetes: Molecular View and Perspectives

Ayoub MA, Yap PG, Mudgil P, Khan FB, Anwar I, Muhammad K, et al.

J Dairy Sci, 2023 Sep 12.
PMID: 37709024 DOI: 10.3168/jds.2023-23733

In dairy science, camel milk (CM) constitutes a center of interest for scientists due to its known beneficial impact on diabetes as demonstrated in many in vitro, in vivo, and clinical studies and trials. Overall, CM had positive effects on various parameters related to glucose transport and metabolism as well as the structural and functional properties of the pancreatic β-cells and insulin secretion. Thus, CM consumption may help manage diabetes, however, such a recommendation will become rationale and clinically conceivable only if the exact molecular mechanisms and pathways involved at the cellular levels are well understood. Moreover, the application of CM as an alternative antidiabetic tool may first require the identification of the exact bioactive molecule(s) behind such antidiabetic properties. In this review, we describe the advances in our knowledge of the molecular mechanisms reported to be involved in the beneficial effects of CM in managing diabetes using different in vitro and in vivo models. This mainly includes the effects of CM on the different molecular pathways controlling (i) insulin receptor signaling and glucose uptake, (ii) the pancreatic β-cell structure and function, and (iii) the activity of key metabolic enzymes in glucose metabolism. Moreover, we described the current status of the identification of CM-derived bioactive peptides and their structure-activity relationship study and characterization in the context of molecular markers related to diabetes. Such an overview will not only enrich our scientific knowledge of the plausible mode of action of CM in diabetes but should ultimately rationalize the claim of the potential application of CM against diabetes. This will pave the way toward new directions and ideas for developing a new generation of antidiabetic products taking benefits from the chemical composition of CM.
A decision tree model for accurate prediction of sand erosion in elbow geometry

Alakbari FS, Mohyaldinn ME, Ayoub MA, Salih AA, Abbas AH

Heliyon, 2023 Jul;9(7):e17639.
PMID: 37539270 DOI: 10.1016/j.heliyon.2023.e17639

Erosion of piping components, e.g., elbows, is a hazardous phenomenon that frequently occurs due to sand flow with fluids during petroleum production. Early prediction of the sand's erosion rate (ER) is essential for ensuring a safe flow process and material integrity. Some models have been applied to determine the ER of the sand in the literature. However, these models have been created based on specific data to require a model for application to wide-range data. Moreover, the previous models have not studied relationships between independent and dependent variables. Thus, this research aims to use machine learning techniques, namely linear regression and decision tree (DT), to predict the ER robustly. The optimum model, the DT model, was evaluated using various trend analysis and statistical error analyses (SEA) techniques, namely the correlation coefficient (R). The evaluation results proved proper physical behavior for all independent variables, along with high accuracy and the DT model robustness. The proposed DT method can accurately predict the ER with R of 0.9975, 0.9911, 0.9761, and 0.9908, AAPRE of 5.0%, 6.27%, 6.26%, and 5.5%, RMSE of 2.492E-05, 6.189E-05, 9.310E-05, and 5.339E-05, and STD of 13.44, 6.66, 8.01, and 11.44 for the training, validation, testing, and whole datasets, respectively. Hence, this study delivers an effective, robust, accurate, and fast prediction tool for ER determination, significantly saving the petroleum industry's cost and time.
A comparative investigation into novel cholesterol esterase and pancreatic lipase inhibitory peptides from cow and camel casein hydrolysates generated upon enzymatic hydrolysis and in-vitro digestion

Mudgil P, Baba WN, Kamal H, FitzGerald RJ, Hassan HM, Ayoub MA, et al.

Food Chem, 2022 Jan 15;367:130661.
PMID: 34348197 DOI: 10.1016/j.foodchem.2021.130661

Cow (CwC) and camel casein (CaC) hydrolysates were generated using Alcalase™ (CwCA and CaCA) and Pronase-E (CwCP and CaCP) each for 3 and 6 h, and investigated for their potential to inhibit key lipid digesting enzymes i.e., pancreatic lipase (PL) and cholesteryl esterase (CE). Results revealed stronger PL and CE inhibition by CaC hydrolysates compared to CwC. Potent hydrolysates (CwCP-3 h and CaCA-6 h) upon simulated gastrointestinal digestion (SGID) showed significant improvement in inhibition of both PL and CE. However, both the SGID hydrolysates showed similar extent of PL and CE inhibition and were further sequenced for peptide identification. Peptides MMML, FDML, HLPGRG from CwC and AAGF, MSNYF, FLWPEYGAL from CaC hydrolysates were predicted to be most active PL inhibitory peptides. Peptide LP found in both CwC and CaC hydrolysates was predicted as active CE inhibitor. Thus, CwC and CaC could be potential source of peptides with promising CE and PL inhibitory properties.
Fulltext 2D nanostructures: Potential in diagnosis and treatment of Alzheimer's disease

Tufail S, Sherwani MA, Shamim Z, Abdullah, Goh KW, Alomary MN, et al.

Biomed Pharmacother, 2024 Jan;170:116070.
PMID: 38163396 DOI: 10.1016/j.biopha.2023.116070

Two-dimensional (2D) nanomaterials have garnered enormous attention seemingly due to their unusual architecture and properties. Graphene and graphene oxide based 2D nanomaterials remained the most sought after for several years but the quest to design superior 2D nanomaterials which can find wider application gave rise to development of non-graphene 2D materials as well. Consequently, in addition to graphene based 2D nanomaterials, 2D nanostructures designed using macromolecules (such as DNAs, proteins, peptides and peptoids), transition metal dichalcogenides, transition-metal carbides and/or nitrides (MXene), black phosphorous, chitosan, hexagonal boron nitrides, and graphitic carbon nitride, and covalent organic frameworks have been developed. Interestingly, these 2D nanomaterials have found applications in diagnosis and treatment of various diseases including Alzheimer's disease (AD). Although AD is one of the most debilitating neurodegenerative conditions across the globe; unfortunately, there remains a paucity of effective diagnostic and/or therapeutic intervention for it till date. In this scenario, nanomaterial-based biosensors, or therapeutics especially 2D nanostructures are emerging to be promising in this regard. This review summarizes the diagnostic and therapeutic platforms developed for AD using 2D nanostructures. Collectively, it is worth mentioning that these 2D nanomaterials would seemingly provide an alternative and intriguing platform for biomedical interventions.
Fulltext Multifaceted Pharmacological Potentials of Curcumin, Genistein, and Tanshinone IIA through Proteomic Approaches: An In-Depth Review

Khan FB, Singh P, Jamous YF, Ali SA, Abdullah, Uddin S, et al.

Cancers (Basel), 2022 Dec 30;15(1).
PMID: 36612248 DOI: 10.3390/cancers15010249

Phytochemicals possess various intriguing pharmacological properties against diverse pathological conditions. Extensive studies are on-going to understand the structural/functional properties of phytochemicals as well as the molecular mechanisms of their therapeutic function against various disease conditions. Phytochemicals such as curcumin (Cur), genistein (Gen), and tanshinone-IIA (Tan IIA) have multifaceted therapeutic potentials and various efforts are in progress to understand the molecular dynamics of their function with different tools and technologies. Cur is an active lipophilic polyphenol with pleiotropic function, and it has been shown to possess various intriguing properties including antioxidant, anti-inflammatory, anti-microbial, anticancer, and anti-genotoxic properties besides others beneficial properties. Similarly, Gen (an isoflavone) exhibits a wide range of vital functions including antioxidant, anti-inflammatory, pro-apoptotic, anti-proliferative, anti-angiogenic activities etc. In addition, Tan IIA, a lipophilic compound, possesses antioxidant, anti-angiogenic, anti-inflammatory, anticancer activities, and so on. Over the last few decades, the field of proteomics has garnered great momentum mainly attributed to the recent advancement in mass spectrometry (MS) techniques. It is envisaged that the proteomics technology has considerably contributed to the biomedical research endeavors lately. Interestingly, they have also been explored as a reliable approach to understand the molecular intricacies related to phytochemical-based therapeutic interventions. The present review provides an overview of the proteomics studies performed to unravel the underlying molecular intricacies of various phytochemicals such as Cur, Gen, and Tan IIA. This in-depth study will help the researchers in better understanding of the pharmacological potential of the phytochemicals at the proteomics level. Certainly, this review will be highly instrumental in catalyzing the translational shift from phytochemical-based biomedical research to clinical practice in the near future.
Fulltext Illuminating the Molecular Intricacies of Exosomes and ncRNAs in Cardiovascular Diseases: Prospective Therapeutic and Biomarker Potential

Khan FB, Uddin S, Elderdery AY, Goh KW, Ming LC, Ardianto C, et al.

Cells, 2022 Nov 18;11(22).
PMID: 36429092 DOI: 10.3390/cells11223664

Cardiovascular diseases (CVDs) are one of the leading causes of death worldwide. Accumulating evidences have highlighted the importance of exosomes and non-coding RNAs (ncRNAs) in cardiac physiology and pathology. It is in general consensus that exosomes and ncRNAs play a crucial role in the maintenance of normal cellular function; and interestingly it is envisaged that their potential as prospective therapeutic candidates and biomarkers are increasing rapidly. Considering all these aspects, this review provides a comprehensive overview of the recent understanding of exosomes and ncRNAs in CVDs. We provide a great deal of discussion regarding their role in the cardiovascular system, together with providing a glimpse of ideas regarding strategies exploited to harness their potential as a therapeutic intervention and prospective biomarker against CVDs. Thus, it could be envisaged that a thorough understanding of the intricacies related to exosomes and ncRNA would seemingly allow their full exploration and may lead clinical settings to become a reality in near future.

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