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Fulltext A framework for designing interactive mobile training course content using augmented reality

Al-Amri S, Hamid S, Noor NFM, Gani A

Multimed Tools Appl, 2023 Feb 24.
PMID: 36855614 DOI: 10.1007/s11042-023-14561-4

Because mobile technology and the widespread usage of mobile devices have swiftly and radically evolved, several training centers have started to offer mobile training (m-training) via mobile devices. Thus, designing suitable m-training course content for training employees via mobile device applications has become an important professional development issue to allow employees to obtain knowledge and improve their skills in the rapidly changing mobile environment. Previous studies have identified challenges in this domain. One important challenge is that no solid theoretical framework serves as a foundation to provide instructional design guidelines for interactive m-training course content that motivates and attracts trainees to the training process via mobile devices. This study proposes a framework for designing interactive m-training course content using mobile augmented reality (MAR). A mixed-methods approach was adopted. Key elements were extracted from the literature to create an initial framework. Then, the framework was validated by interviewing experts, and it was tested by trainees. This integration led us to evaluate and prove the validity of the proposed framework. The framework follows a systematic approach guided by six key elements and offers a clear instructional design guideline checklist to ensure the design quality of interactive m-training course content. This study contributes to the knowledge by establishing a framework as a theoretical foundation for designing interactive m-training course content. Additionally, it supports the m-training domain by assisting trainers and designers in creating interactive m-training courses to train employees, thus increasing their engagement in m-training. Recommendations for future studies are proposed.
Fulltext Surface Electromyography (sEMG) Activity of Masticatory Muscle (Masseter and Temporalis) with Three Different Types of Orthodontic Bracket

Nishi SE, Rahman NA, Basri R, Alam MK, Noor NFM, Zainal SA, et al.

Biomed Res Int, 2021;2021:6642254.
PMID: 33969121 DOI: 10.1155/2021/6642254

Objective: This pre-post study is aimed at determining the effects of masticatory muscle activity (masseter and temporalis) measured via sEMG between conventional, self-ligating, and ceramic bracket after six months of orthodontic treatment.
Methods: A total of eighteen (18) malocclusion patients were identified. Malocclusion patients were subdivided into 3 groups based on the bracket selection (conventional, self-ligating, and ceramic bracket) with 6 patients for each group. sEMG of muscles were done using a two-channel electromyography device, where pregelled and self-adhesive electrodes (bilateral) were applied. Chewing and clenching of masseter and temporalis muscle activity were recorded for 20 s pre and 6 months of orthodontic treatment using sEMG (frequency 60 Hz). The data were analysed by using repeated measures ANOVA in IBM SPSS Statistics Version 24.0.
Results: Chewing and clenching for masseter muscle showed no significant difference (P > 0.05) in sEMG activity of three types of the brackets. However, for temporalis muscle, there was a significant difference found in sEMG activity during chewing (P < 0.05) and clenching (P < 0.05) between these three brackets.
Conclusion: The activity of temporalis muscle showed significant changes in chewing and clenching, where the conventional group demonstrated better muscle activity pre and at six months of fixed appliances.
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.
The Most Common Relationship of a Midface Fracture in Maxillofacial Trauma Study

Ahmad WMAW, Noor NFM, Shaari R, Nawi MAA, Ghazali FMM, Aleng NA, et al.

J Craniofac Surg, 2021 Jun 01;32(4):1500-1503.
PMID: 33852515 DOI: 10.1097/SCS.0000000000007435

ABSTRACT: Oral and maxillofacial fractures are the most common injuries among multiple trauma. About 5% to 10% of trauma patients having facial fractures. The objectives of this case study are to focus the most common mid-face fractures types' and to determine the relationship of the midface fracture in maxillofacial trauma among the patient who attended the outpatient clinic in a Hospital Universiti Sains Malaysia. In this research paper, an advanced statistical tool was chosen through the multilayer perceptron neural network methodology (MLPNN). Multilayer perceptron neural network methodology was applied to determine the most associated predictor important toward maxillary bone injury. Through the predictor important classification analysis, the relationship of each bone will be determined, and sorting according to their contribution. After sorting the most associated predictor important toward maxillary bone injury, the validation process will be applied through the value of training, testing, and validation. The input variables of MLPNN were zygomatic complex fracture, orbital wall fracture, nasal bone fracture, frontal bone fracture, and zygomatic arch fracture. The performance of MLPNN having high accuracy with 82.2%. As a conclusion, the zygomatic complex fracture is the most common fracture trauma among the patient, having the most important association toward maxillary bone fracture. This finding has the highest potential for further statistical modeling for education purposes and the decision-maker among the surgeon.
Fulltext The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation

Ahmad WMAW, Yaqoob MA, Noor NFM, Ghazali FMM, Rahman NA, Tang L, et al.

Biomed Res Int, 2021;2021:5436894.
PMID: 34904115 DOI: 10.1155/2021/5436894

Background: Cancer is primarily caused by smoking, alcohol, betel quit, a series of genetic alterations, and epigenetic abnormalities in signaling pathways, which result in a variety of phenotypes that favor the development of OSCC. Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for 80-90% of all oral malignant neoplasms. Oral cancer is relatively common, and it is frequently curable when detected and treated early enough. The tumor-node-metastasis (TNM) staging system is used to determine patient prognosis; however, geographical inaccuracies frequently occur, affecting management.
Objective: To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR).
Results: The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age (β 1: -0.006423; p < 2e - 16), treatment (β 2: -0.355389; p < 2e - 16), and distant metastasis (β 3: -0.355389; p < 2e - 16). There is a 0.003469102 MSE for the linear model in this scenario.
Conclusion: In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.
Fulltext The Photocatalytic Effects of Modified Hydrothermal Nanotitania Extraction on the Skin and Behavior of Sprague-Dawley Rats

Harun AM, Awang H, Noor NFM, Makhatar NM, Yusoff ME, Affandi NDN, et al.

Biomed Res Int, 2021;2021:6173143.
PMID: 34859102 DOI: 10.1155/2021/6173143

BACKGROUND: Potential antibacterial substances, such as titanium dioxide (TiO2), are being extensively studied throughout the research world. A modified hydrothermal nanotitania extraction was shown to inhibit Staphylococcus aureus growth in the laboratory. However, the toxicity effect of the extract on rats is unknown. In this study, we observed the effects of a modified hydrothermal nanotitania extraction on the skin and behavior of Sprague-Dawley rats.
METHODS: Sprague-Dawley (Rattus norvegicus) rats were used as the experimental animals. The skin around the dorsum of the tested animals was shaved and pasted with 0.1 mg and 0.5 mg of the nanotitania extraction. The color and condition of the pasted area and the behavior of the animals were observed.
RESULTS: 0.1 mg nanotitania extraction application on the dorsum of the rat produced no skin color changes at day 1, day 3, day 5, or day 7 postapplication. There were no changes in their behavior up to day 7 with no skin rashes or skin scratches seen or fur changes. However, 0.5 mg of nanotitania extraction resulted in redness and less fur regrowth at day 7.
CONCLUSIONS: A 0.1 mg modified nanotitania extraction was observed to have no effect on the skin of Sprague-Dawley rats.
Fulltext The Antimicrobial Properties of Nanotitania Extract and Its Role in Inhibiting the Growth of Klebsiella pneumonia and Haemophilus influenza

Harun AM, Noor NFM, Zaid A, Yusoff ME, Shaari R, Affandi NDN, et al.

Antibiotics (Basel), 2021 Aug 10;10(8).
PMID: 34439011 DOI: 10.3390/antibiotics10080961

Titanium dioxide (TiO2) is an antimicrobial agent which is considered of potential value in inhibiting the growth of multiple bacteria. Klebsiella pneumonia and Haemophilus influenza are two of the most common respiratory infection pathogens, and are the most. Klebsiella pneumonia causes fatal meningitis, while Haemophilus influenza causes mortality even in younger patients. Both are associated with bacteremia and mortality. The purpose of this study was to test a new antibacterial material, namely nanotitania extract combined with 0.03% silver that was developed at Universiti Malaysia Sabah (UMS) and tested against K. pneumonia and H. influenza. The nanoparticles were synthesized through a modified hydrothermal process, combined with molten salt and proven to have excellent crystallinity, with the band-gap energy falling in the visible light spectrum. The nanoparticle extract was tested using a macro-dilutional method, which involved combining it with 0.03% silver solution during the process of nanoparticle synthesis and then introducing it to the bacteria. A positive control containing the bacteria minus the nanoparticles extract was also prepared. 25 mg/mL, 12.5 mg/mL, and 6.25 mg/mL concentrations of the samples were produced using the macro dilution method. After adding the bacteria to multiple concentrations of nanoparticle extract, the suspensions were incubated for 24 h at a temperature of 37 °C. The suspensions were then spread on Mueller-Hinton agar (K. pneumonia) and chocolate blood agar (H. influenza), where the growth of bacteria was observed after 24 h. Nanoparticle extract in combination with silver at 0.03% was proven to have potential as an antimicrobial agent as it was able to inhibit H. influenza at all concentrations. Furthermore, it was also shown to be capable of inhibiting K. pneumonia at concentrations of 25 mg/mL and 50 mg/mL. In conclusion, the nanoparticle extract, when tested using a macro-dilutional method, displayed antimicrobial properties which were proven effective against the growth of both K. pneumonia and H. influenza.