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Fulltext Modeling of Knowledge Toward Herbal Medicine for Oral Health Using Multiple Linear Regression and Neural Network

Tabnjh A, W Ahmad WMA, Hasan R

Cureus, 2023 Jul;15(7):e41790.
PMID: 37575818 DOI: 10.7759/cureus.41790

Background and goals Herbal medicine is used to treat a variety of oral health problems. Therefore, it is essential to comprehend it fully. To determine whether the amount used is risky, it is crucial to understand the dosages of medicinal plants. Before performing multiple linear regression (MLR) modeling, this paper uses the multilayer feedforward (MLFF) neural network (NN) technique to propose the variable selection. A data set with socio-demographic variables for dental staff and herbal medicine related to oral health knowledge score (KS) was chosen to demonstrate the design-build methodology. Materials and methods It was discovered that the KS is significantly related to the sex, age, income, occupation, and practice score (PS) at the first stage of the selection process, where all the variables were screened for their clinical importance. These five variables are chosen and used as inputs for the MLFF model by considering the level of significance, alpha = 0.05. Then, using the best variable discovered by the MLFF process, the MLR is applied. Results The performance of MLFF was evaluated using the mean squared error (MSE). MSE measures how far our estimates are off from the actual results. The MLFF's smallest MSE indicates the model's ideal combination of variable selection. Conclusion This study showed that using MLFF would help confirm the selected independent variables for MLR. In addition, KS level is more correlated with occupation, PS, and sex than with age and income. Moreover, this model could be used practically for any dataset. with the same criteria.
Translation and Validation of a Culturally Adapted Arabic Version of the Beverage Intake Questionnaire (BEVQ)

Alkinani AA, Rohana AJ, Hasan R, W Ahmad WMA, Al-Badri SA

Cureus, 2024 Oct;16(10):e71710.
PMID: 39553075 DOI: 10.7759/cureus.71710

BACKGROUND: The prevalence of sugar-sweetened beverage (SSB) consumption is on the rise among Arabic adolescents, with a notable increase observed in Iraq. However, no validated tool currently exists to assess SSB consumption apart from the frequency of SSB intake within this population. The objective of this study is to evaluate the beverage consumption patterns of Arabic-speaking adolescents using a validated Beverage Intake Questionnaire (BEVQ) specifically designed for this population.
METHODOLOGY: The BEVQ has been authorized by the original author and was meticulously translated through a 10-step protocol. The content validity of the BEVQ was rigorously evaluated by four independent experts using the item-level content validity index (I-CVI), scale-level content validity index average (S-CVI/Ave), sum of the content validity index/universal agreement (S-CVI/UA), and the modified kappa statistic (κ m). The face validity was also conducted on 30 adolescents, ensuring clarity and comprehensive validation.
RESULTS: The translation process required minor modifications to ensure linguistic and cultural equivalence to the original questionnaire. The Arabic version of the BEVQ (BEVQ-A) achieved S-CVI/Ave scores ranging from 0.90 to 0.98 and S-CVI/UA scores ranging from 0.75 to 1.00. The modified kappa statistic (κ m) indicated that the majority of items were categorized as good to excellent. These scores confirmed that the BEVQ-A possessed robust content validity. Additionally, the BEVQ-A demonstratedcomprehensive and clear face validity, with a sum of face validity index (S-FVI) score of 0.97.
CONCLUSION: In conclusion, the Arabic-translated version of the BEVQ is a valid and reliable instrument for assessing total beverage consumption among Arabic-speaking adolescents.
A Study of Creatinine Level among Patients with Dyslipidemia and Type 2 Diabetes Mellitus using Multilayer Perceptron and Multiple Linear Regression

Ghazali FMM, W Ahmad WMA, Srivastava KC, Shrivastava D, Noor NFM, Akbar NAN, et al.

J Pharm Bioallied Sci, 2021 Jun;13(Suppl 1):S795-S800.
PMID: 34447203 DOI: 10.4103/jpbs.JPBS_778_20

Background and Objective: Dyslipidemia is one of the most important risk factors for coronary heart disease with diabetes mellitus. Diabetic dyslipidemia is correlated with reduced concentrations of high-density lipoprotein cholesterol, elevated concentrations of plasma triglycerides, and increased concentrations of dense small particles of low-density lipoprotein cholesterol. Furthermore, dyslipidemia is one of the factors that accelerate renal failure in patients with nephropathy that is observed to be higher in these patients. This paper aims to propose the variable selection using the multilayer perceptron (MLP) neural network methodology before performing the multiple linear regression (MLR) modeling. Dataset consists of patient with Dyslipidemia, and Type 2 Diabetes Mellitus was selected to illustrate the design-build methodology. According to clinical expert's opinion and based on their assessment, these variables were chosen, which comprises the level of creatinine, urea, total cholesterol, uric acid, sodium, and HbA1c.
Materials and Methods: At the first stage, all the selected variables will be a screen for their clinical important point of view, and it was found that creatinine has a significant relationship to the level of urea reading, a total of cholesterol reading, and the level of uric acid reading. By considering the level of significance, α = 0.05, these three variables are being selected and used for the input of the MLP model. Then, the MLR is being applied according to the best variable obtained through MLP process.
Results: Through the testing/out-sample mean squared error (MSE), the performance of MLP was assessed. MSE is an indication of the distance from the actual findings from our estimates. The smallest MSE of the MLP shows the best variable selection combination in the model.
Conclusion: In this research paper, we also provide the R syntax for MLP better illustration. The key factors associated with creatinine were urea, total cholesterol, and uric acid in patients with dyslipidemia and type 2 diabetes mellitus.
Fulltext Biomarkers for Temporomandibular Disorders: Current Status and Future Directions

Zwiri A, Al-Hatamleh MAI, W Ahmad WMA, Ahmed Asif J, Khoo SP, Husein A, et al.

Diagnostics (Basel), 2020 May 15;10(5).
PMID: 32429070 DOI: 10.3390/diagnostics10050303

Numerous studies have been conducted in the previous years with an objective to determine the ideal biomarker or set of biomarkers in temporomandibular disorders (TMDs). It was recorded that tumour necrosis factor (TNF), interleukin 8 (IL-8), IL-6, and IL-1 were the most common biomarkers of TMDs. As of recently, although the research on TMDs biomarkers still aims to find more diagnostic agents, no recent study employs the biomarker as a targeting point of pharmacotherapy to suppress the inflammatory responses. This article represents an explicit review on the biomarkers of TMDs that have been discovered so far and provides possible future directions towards further research on these biomarkers. The potential implementation of the interactions of TNF with its receptor 2 (TNFR2) in the inflammatory process has been interpreted, and thus, this review presents a new hypothesis towards suppression of the inflammatory response using TNFR2-agonist. Subsequently, this hypothesis could be explored as a potential pain elimination approach in patients with TMDs.