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Article ; Online: Multi-Disease Detection Using a Prism-Based Surface Plasmon Resonance Sensor: A TMM and FEM Approach.

Rumi, Rabeya Bosrin / Paul, Alok Kumar / Alyami, Salem A / Moni, Mohammad Ali

IEEE transactions on nanobioscience

2024 Volume 23, Issue 1, Page(s) 51–62

Abstract: This research introduces a surface plasmon resonance (SPR)-based biosensor with multilayered structures for telecommunication wavelength in order to detect multiple diseases. The malaria and the chikungunya viruses are taken into account and the presence ...

Abstract	This research introduces a surface plasmon resonance (SPR)-based biosensor with multilayered structures for telecommunication wavelength in order to detect multiple diseases. The malaria and the chikungunya viruses are taken into account and the presence of these viruses are determined by examining several blood components in healthy and affected phases. Here, two distinct configurations (Al-BTO-Al-MoS2 and Cu-BTO-Cu-MoS2) are proposed and contrasted for the detection of numerous viruses. The performance characteristics of this work have been analyzed using Transfer Matrix Method (TMM) method and Finite Element Method (FEM) method under angle interrogation technique. From the TMM and FEM solutions, it is evident that the Al-BTO-Al-MoS2 structure provides the highest sensitivities of ~270 deg./RIU for malaria and ~262 deg./RIU for chikungunya viruses, with satisfactory detection accuracy of ~1.10 for malaria, ~1.64 for chikungunya, and quality factor of ~204.40 for malaria, ~208.20 for chikungunya. In addition, the Cu-BTO-Cu MoS2 structure offers the highest sensitivities of ~310 deg./RIU for malaria and ~298 deg./RIU for chikungunya, with satisfactory detection accuracy of ~0.40 for malaria, ~0.58 for chikungunya, and quality factor of ~89.85 for malaria, ~86.38 for chikungunya viruses. Therefore, the performance of the proposed sensors is analyzed using two distinct methods and gives around similar results. In a sum, this research could be utilized as a theoretical foundation and first step in the development of a real sensor.
MeSH term(s)	Humans ; Surface Plasmon Resonance ; Chikungunya Fever/diagnosis ; Molybdenum ; Biosensing Techniques/methods ; Malaria
Chemical Substances	Molybdenum (81AH48963U)
Language	English
Publishing date	2024-01-03
Publishing country	United States
Document type	Journal Article
ISSN	1558-2639
ISSN (online)	1558-2639
DOI	10.1109/TNB.2023.3286269
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model.

Asadi, Shirin / Tartibian, Bakhtyar / Moni, Mohammad Ali

Scientific reports

2023 Volume 13, Issue 1, Page(s) 8207

Abstract: One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood cells (WBCs) ...

Abstract	One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood cells (WBCs) during exercise can help to identify the appropriate intensity and duration. Therefore, this study was designed to predict leukocyte levels during exercise with the application of a machine-learning model. We used a random forest (RF) model to predict the number of lymphocytes (LYMPH), neutrophils (NEU), monocytes (MON), eosinophils, basophils, and WBC. Intensity and duration of exercise, WBCs values before exercise training, body mass index (BMI), and maximal aerobic capacity (VO
MeSH term(s)	Humans ; Machine Learning ; Exercise ; Body Mass Index ; Neutrophils
Language	English
Publishing date	2023-05-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-34974-3
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Article ; Online: Determination of optimum intensity and duration of exercise based on the immune system response using a machine-learning model

Shirin Asadi / Bakhtyar Tartibian / Mohammad Ali Moni

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 10

Abstract: Abstract One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood ... ...

Abstract	Abstract One of the important concerns in the field of exercise immunology is determining the appropriate intensity and duration of exercise to prevent suppression of the immune system. Adopting a reliable approach to predict the number of white blood cells (WBCs) during exercise can help to identify the appropriate intensity and duration. Therefore, this study was designed to predict leukocyte levels during exercise with the application of a machine-learning model. We used a random forest (RF) model to predict the number of lymphocytes (LYMPH), neutrophils (NEU), monocytes (MON), eosinophils, basophils, and WBC. Intensity and duration of exercise, WBCs values before exercise training, body mass index (BMI), and maximal aerobic capacity (VO2 max) were used as inputs and WBCs values after exercise training were assessed as outputs of the RF model. In this study, the data was collected from 200 eligible people and K-fold cross-validation was used to train and test the model. Finally, model efficiency was assessed using standard statistics (root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative square error (RRSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency coefficient (NSE)). Our findings revealed that the RF model performed well for predicting the number of WBC with RMSE = 0.94, MAE = 0.76, RAE = 48.54, RRSE = 48.17, NSE = 0.76, and R2 = 0.77. Furthermore, the results showed that intensity and duration of exercise are more effective parameters than BMI and VO2 max to predict the number of LYMPH, NEU, MON, and WBC during exercise. Totally, this study developed a novel approach based on the RF model using the relevant and accessible variables to predict WBCs during exercise. The proposed method can be applied as a promising and cost-effective tool for determining the correct intensity and duration of exercise in healthy people according to the body’s immune system response.
Keywords	Medicine ; R ; Science ; Q
Subject code	796
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Systems biology approach discovers comorbidity interaction of Parkinson's disease with psychiatric disorders utilizing brain transcriptome.

Nashiry, Md Asif / Sumi, Shauli Sarmin / Alyami, Salem A / Moni, Mohammad Ali

Frontiers in molecular neuroscience

2023 Volume 16, Page(s) 1232805

Abstract: Several studies found that most patients with Parkinson's disorder (PD) appear to have psychiatric symptoms such as depression, anxiety, hallucination, delusion, and cognitive dysfunction. Therefore, recognizing these psychiatrically symptoms of PD ... ...

Abstract	Several studies found that most patients with Parkinson's disorder (PD) appear to have psychiatric symptoms such as depression, anxiety, hallucination, delusion, and cognitive dysfunction. Therefore, recognizing these psychiatrically symptoms of PD patients is crucial for both symptomatic therapy and better knowledge of the pathophysiology of PD. In order to address this issue, we created a bioinformatics framework to determine the effects of PD mRNA expression on understanding its relationship with psychiatric symptoms in PD patients. We have discovered a significant overlap between the sets of differentially expressed genes from PD exposed tissue and psychiatric disordered tissues using RNA-seq datasets. We have chosen Bipolar disorder and Schizophrenia as psychiatric disorders in our study. A number of significant correlations between PD and the occurrence of psychiatric diseases were also found by gene set enrichment analysis, investigations of the protein-protein interaction network, gene regulatory network, and protein-chemical agent interaction network. We anticipate that the results of this pathogenetic study will provide crucial information for understanding the intricate relationship between PD and psychiatric diseases.
Language	English
Publishing date	2023-08-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452967-9
ISSN	1662-5099
ISSN	1662-5099
DOI	10.3389/fnmol.2023.1232805
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Article ; Online: Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke.

Islam, Tania / Rahman, Md Rezanur / Khan, Asaduzzaman / Moni, Mohammad Ali

Journal of biomedical informatics

2023 Volume 141, Page(s) 104345

Abstract: Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify ... ...

Abstract	Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; P
MeSH term(s)	Humans ; Systems Biology ; Genome-Wide Association Study ; Phenotype ; Stroke/diagnosis ; Stroke/genetics ; Genetic Markers ; Genetic Predisposition to Disease ; Polymorphism, Single Nucleotide ; NIMA-Related Kinases/genetics ; High-Temperature Requirement A Serine Peptidase 1/genetics ; Acyl-CoA Dehydrogenase/genetics
Chemical Substances	Genetic Markers ; NEK6 protein, human (EC 2.7.11.1) ; NIMA-Related Kinases (EC 2.7.11.1) ; HTRA1 protein, human (EC 3.4.21.-) ; High-Temperature Requirement A Serine Peptidase 1 (EC 3.4.21.-) ; ACAD10 protein, human (EC 1.3.8.-) ; Acyl-CoA Dehydrogenase (EC 1.3.8.7)
Language	English
Publishing date	2023-03-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2023.104345
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Article: Ensemble Learning for Disease Prediction: A Review.

Mahajan, Palak / Uddin, Shahadat / Hajati, Farshid / Moni, Mohammad Ali

Healthcare (Basel, Switzerland)

2023 Volume 11, Issue 12

Abstract: Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single ... ...

Abstract	Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016-2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.
Language	English
Publishing date	2023-06-20
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2721009-1
ISSN	2227-9032
ISSN	2227-9032
DOI	10.3390/healthcare11121808
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Article ; Online: Ensemble Learning for Disease Prediction

Palak Mahajan / Shahadat Uddin / Farshid Hajati / Mohammad Ali Moni

Healthcare, Vol 11, Iss 1808, p

A Review

2023 Volume 1808

Abstract	Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses ...
Keywords	machine learning ; bagging ; boosting ; stacking ; voting ; disease prediction ; Medicine ; R
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Road networks and socio-demographic factors to explore COVID-19 infection during its different waves.

Uddin, Shahadat / Khan, Arif / Lu, Haohui / Zhou, Fangyu / Karim, Shakir / Hajati, Farshid / Moni, Mohammad Ali

Scientific reports

2024 Volume 14, Issue 1, Page(s) 1551

Abstract: The COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although ... ...

Abstract	The COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although lockdowns, social distancing and business closures generally slowed the case growth, there is a growing concern about these restrictions' social, economic and psychological impact, especially on the disadvantaged and poorer segments of society. While we are all in this together, these segments often take the heavier toll of the pandemic and face harsher restrictions or get blamed for community transmission. This study proposes a road-network-based networked approach to model mobility patterns between localities during lockdown stages. It utilises a panel regression method to analyse the effects of mobility in transmitting COVID-19 in an Australian context, together with a close look at a suburban population's characteristics like their age, income and education. Firstly, we attempt to model how the local road networks between the neighbouring suburbs (i.e., neighbourhood measure) and current infection count affect the case growth and how they differ between delta and omicron variants. We use a geographic information system, population and infection data to measure road connections, mobility and transmission probability across the suburbs. We then looked at three socio-demographic variables: age, education and income and explored how they moderate independent and dependent variables (infection rates and neighbourhood measures). The result shows strong model performance to predict infection rate based on neighbourhood road connection. However, apart from age in the delta variant context, the other variables (income and education level) do not seem to moderate the relationship between infection rate and neighbourhood measure. The results indicate that suburbs with a more socio-economically disadvantaged population do not necessarily contribute to more community transmission. The study findings could be potentially helpful for stakeholders in tailoring any health decision for future pandemics.
MeSH term(s)	Humans ; Australia/epidemiology ; COVID-19/epidemiology ; Communicable Disease Control ; Pandemics ; SARS-CoV-2 ; Demography
Language	English
Publishing date	2024-01-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51610-w
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Article ; Online: Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities.

Nayan, Saidul Islam / Rahman, Md Habibur / Hasan, Md Mehedi / Raj, Sheikh Md Razibul Hasan / Almoyad, Mohammad Ali Abdullah / Liò, Pietro / Moni, Mohammad Ali

Life sciences

2023 Volume 335, Page(s) 122244

Abstract: High blood sugar and insulin insensitivity causes the lifelong chronic metabolic disease called Type 2 diabetes (T2D) which has a higher chance of developing different malignancies. T2D with comorbidities like Cancers can make normal medications for ... ...

Abstract	High blood sugar and insulin insensitivity causes the lifelong chronic metabolic disease called Type 2 diabetes (T2D) which has a higher chance of developing different malignancies. T2D with comorbidities like Cancers can make normal medications for those disorders more difficult. There may be a significant correlation between comorbidities and have an impact on one another's health. These associations may be due to a number of direct and indirect mechanisms. Such molecular mechanisms that underpin T2D and cancer are yet unknown. However, the large volumes of data available on these diseases allowed us to use analytical tools for uncovering their interrelated pathways. Here, we tried to present a system for investigating potential comorbidity relationships between T2D and Cancer disease by looking at the molecular processes involved, analyzing a huge number of freely accessible transcriptomic datasets of various disorders using bioinformatics. Using semantic similarity and gene set enrichment analysis, we created an informatics pipeline that evaluates and integrates Gene Ontology (GO), expression of genes, and biological process data. We discovered genes that are common in T2D and Cancer along with molecular pathways and GOs. We compared the top 200 Differentially Expressed Genes (DEGs) from each selected T2D and cancer dataset and found the most significant common genes. Among all the common genes 13 genes were found most frequent. We also found 4 common GO terms: GO:0000003, GO:0000122, GO:0000165, and GO:0000278 among all the common GO terms between T2d and different cancers. Using these genes and GO term semantic similarity, we calculated the distance between these two diseases. The semantic similarity results of our study showed a higher association of Liver Cancer (LiC), Breast Cancer (BreC), Colorectal Cancer (CC), and Bladder Cancer (BlaC) with T2D. Furthermore we found KIF4A, NUSAP1, CENPF, CCNB1, TOP2A, CCNB2, RRM2, HMMR, NDC80, NCAPG, and IGFBP5 common hub proteins among different cancers correlated to T2D. AGE-RAGE signaling pathway in diabetic complications, Osteoclast differentiation, TNF signaling pathway, IL-17 signaling pathway, p53 signaling pathway, MAPK signaling pathway, Human T-cell leukemia virus 1 infection, and Non-alcoholic fatty liver disease are the 8 most significant pathways found among 18 common pathways between T2D and selected cancers. As a result of our technique, we now know more about disease pathways that are critical between T2D and cancer.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/genetics ; Liver Neoplasms/pathology ; Gene Expression Profiling/methods ; Transcriptome ; Comorbidity ; Computational Biology/methods ; Kinesins/genetics
Chemical Substances	KIF4A protein, human (EC 3.6.1.-) ; Kinesins (EC 3.6.4.4)
Language	English
Publishing date	2023-11-10
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	3378-9
ISSN	1879-0631 ; 0024-3205
ISSN (online)	1879-0631
ISSN	0024-3205
DOI	10.1016/j.lfs.2023.122244
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Article: Severity of COVID-19 patients with coexistence of asthma and vitamin D deficiency.

Islam, M Babul / Chowdhury, Utpala Nanda / Nashiry, Md Asif / Moni, Mohammad Ali

Informatics in medicine unlocked

2022 Volume 34, Page(s) 101116

Abstract: Coronavirus disease 2019 (COVID-19)-driven global pandemic triggered innumerable health complications, imposing great challenges in managing other respiratory diseases like asthma. Furthermore, increases in the underlying inflammation involved in the ... ...

Abstract	Coronavirus disease 2019 (COVID-19)-driven global pandemic triggered innumerable health complications, imposing great challenges in managing other respiratory diseases like asthma. Furthermore, increases in the underlying inflammation involved in the fatality of COVID-19 have been linked with lack of vitamin D. In this research work, we intend to investigate the possible genetic linkage of asthma and vitamin D deficiency with the severity and fatality of COVID-19 using a network-based approach. We identified and analysed 41 and 14 differentially expressed genes (DEGs) of COVID-19 being common with asthma and vitamin D deficiency, respectively, through the comparative differential gene expression analysis and their footprints on signalling pathways. Gene set enrichment analysis for GO terms and signalling pathways reveals key biological activities, including inflammatory response-related pathways (e.g., cytokine- and chemokine-mediated signalling pathways, IL-17, and TNF signalling pathways). Besides, the Protein-Protein Interaction network analysis of those DEGs reveals hub proteins, some of which are reported as inflammatory antiviral interferon-stimulated biomarkers that potentially drive the cytokine storm leading to COVID-19 severity and fatality, and contributes in the early stage of viral replication, respectively. Moreover, the regulatory network analysis found these DEGs associated with antiviral and tumour inhibitory transcription factors and micro-RNAs. Finally, drug-target enrichment analysis yields
Language	English
Publishing date	2022-10-28
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2022.101116
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