Search

Dental caries is a chronic disease that mostly interferes with oral health worldwide. It is caused by acidic bacteria on the enamel surface, mainly Streptococcus mutans, forming an oral biofilm that can be transmitted among people. The spread behavior and population dynamics of bacteria must be studied to control this disease, which can be approached through mathematical modeling. In this review, we aimed to identify the utilization of compartmental models in studying bacterial behavior. The aim was to explore compartmental model utilization and reveal the research gaps. This study was conducted with the PRISMA guidelines for scoping reviews to identify an existing mathematical model to study the phenomenon. Four databases, Scopus, ScienceDirect, PubMed, and Dimensions, were used to search for related studies. Our results showed that compartmental model utilization for studying bacteria's role in dental caries is sparse and poorly explained. Moreover, the constructed models have not yet considered more intervention efforts. A study is needed to explore this phenomenon by developing a mathematical model considering some situations. When conducted, it will result in more insights into controlling the spread of bacteria to prevent dental caries. [ABSTRACT FROM AUTHOR]

Introduction LDL is the acronym used to denote low-density lipoproteins. When the level of LDL is high, it leads to the accumulation of cholesterol in the arteries, and as a result, it is commonly known as "bad" cholesterol. Recognizing the significance, a study is conducted on computational and statistical modelling for parameter optimization of LDL-cholesterol levels. In recent years, there has been an increase in the application of precise statistical analysis methodologies. Consequently, scientists are more focused on producing reliable results and are more determined to provide credible findings. Objective This study aims to develop and validate a proposed hybrid method that combines Multilayer Feedforward Neural Network (MLFFNN), and Multiple Linear Model (MLR) and to present the R-syntax applications of the proposed hybrid method with clinical study data. Material and Methods The proposed hybrid model will be built using the generalized mixture model, which is formulated using an MLFFNN framework and Linear Model (LM). The performance of the hybrid model will be assessed utilizing the predicted mean square error neural network (MSE.net) and the predicted mean square error (P.MSE), which will serve as a benchmark for precision and efficacy. Results: The result indicates that hybrid method modelling is superior, with the highest R-squared value and the lowest MSE.net value. The hybrid model technique was found to produce a more precise forecast of the outcome when the data is separated into training and testing datasets. The R-square score in this report demonstrates that the LM model is a good fit (84.97%), and the MSE.net value of 0.00529 indicates that the model is both accurate and predictive. Conclusion The research concludes that the hybrid model method proposed is preferable. This critical conclusion helps us comprehend the hybrid method's proportional contribution to this illustration's result. [ABSTRACT FROM AUTHOR]

Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%-30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia. Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping. Results: Variable validation was performed by using the well-established bootstrap-integrated MLR technique. All variables affected the hazard ratio as follows: total cholesterol (ß1: -0.00664; p < 0.25), diabetes status (ß2: 0.62332; p < 0.25), diastolic reading (ß3: 0.08160; p < 0.25), height measurement (ß4: -0.05411; p < 0.25), coronary heart disease incidence (ß5: 1.42544; p < 0.25), triglyceride reading (ß6: 0.00616; p < 0.25), and waist reading (ß7: -0.00158; p < 0.25). Conclusions: A hybrid approach was developed and extensively tested. The hybrid technique is superior to other standalone techniques and allows an improved understanding of the influence of variables on outcomes. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study is to find propotion of fractured orbital walls in the maxillofacial trauma cases and its associated maxillofacial fracture treated in the Oral Maxillofacial Clinic Oral Maxillofacial ward and operation theatre of Hospital USM in Kelantan, Malaysia. Materials and methods: From July 2013 to June 2018, records of patients who sustained maxillofacial fractures and presented them to the Accident and Emergency Department, Oral Maxillofacial Clinic, Hospital USM were reviewed, recorded, and analyzed. There are 294 patients whose data has been collected because they met the inclusion criteria. Each patient with a complete medical record was reviewed. Data were collected under the variables: Zygomatic Complex, Zygomatic Arch, Nasal, Maxillary Sinus, Le Fort I, Le Fort II, Le Fort III, Orbital Wall, Alveolar Process, Symphysis of Mandible, Condyle of Mandible, Ramus of Mandible, Maxillary Bone and Mandibular Bone of maxillofacial fracture. The fractured orbital walls in these cases was reviewed. At the first stage, all the selected variables will be screened for their important clinical point of view. The SPSS software version 26.0 was used to determine all possible factors contributing to orbital wall fracture. Results: This was a retrospective cross-sectional analysis of the medical records of 294 patients with maxillofacial fracture treated in the Oral Maxillofacial Clinic and Oral Maxillofacial ward, Hospital USM. There were 228 (77.3%) men and 66 (22.4%) women included in this study. The most common age range is 11-20 years (39.8%), 21-30 years (26.2%). Maxillary Bone Fracture (0.371; p <0.05), Maxillary Sinus Fracture (0.180; p <0.05), Zygomatic Arch Fracture (0.127; p <0.05) were found to be the most affected site, which had a positive correlation with an orbital fracture of the maxillofacial trauma cases. A path analysis based on the Spearman correlation was developed by taking into account significant correlations at the level of 0.05. Conclusion: Using the matrix spearman correlation, multiple response analysis (MRA), path analysis, we discovered a clear connection between orbital wall fracture and several other factors. This discovery will aid in the understanding of the most common fracture and the causes of orbital wall fracture in maxillofacial trauma. The Zygomatic Arch Fracture, Maxillary Sinus Fracture, and Maxillary Bone Fracture were found to have a significant relationship with the orbital wall when the significance level was set at 0.05. [ABSTRACT FROM AUTHOR]

Background and Objective: The incidence of type 2 diabetes has been steadily rising over the past few decades, which has contributed significantly to the rise in the prevalence of diabetes (DM). Statistics from the World Health Organization show that more than 422 million adults worldwide had diabetes in 2014, and an ongoing rise in DM prevalence is anticipated. This study aims to create a method that can use to predict and manage diabetes cases in light of the importance of statistical modeling in diabetes. Decision trees and ordinal regression were the two methods used in this study. With some modification and extension, both methods will be harmonized in the R syntax. Materials and Methods: In this paper, we developed a method for analyzing decision trees using R syntax and embedding classification predictions. The classification for prediction with accuracy will indicate a successful classification analysis. This study illustrated the development method using diabetes data consisting of one thousand observations. Before further testing, the clinical relevance and significance of each preselected variable will be assessed. The decision tree will be used to evaluate nine variables. The selected variables are body mass index, total cholesterol, diabetes status, glucose reading, high-density lipoprotein, patient height, hip circumference, hypertension status, smoking status, and triglycerides. The classification obtained will be used as an input for the ordinal regression modeling. Result: It has been discovered that the status of diabetes can be determined by the level of glucose during fasting, which is consistent with the most recent research that has been published. one variable was chosen and used for the input of the ordinal regression. The suggested variables will apply to the ordered logistic regression, and the developed syntax will be used to assess the goodness of measurement and the significance level is set at a 0.05 level. Conclusion: Our proposed method achieves the highest level of forecasting precision possible. The methodology offers a precise evaluation of the fit of the final model. The superior performance of the model resulted in improved outcomes and efficient decision-making management. [ABSTRACT FROM AUTHOR]

Recent research uses an index to measure economic resilience, but the index is inadequate because it is impossible to determine which disturbance factors have the greatest impact on the economic resilience of cities. This study aims to develop a new methodology to measure the economic resilience of a city by simultaneously examining unwanted conditions and disturbance factors. The ratio of regional original income to the number of poor people is known as Z and is identified as a measure of economic resilience in Indonesia. Resilience is measured by Z's position in relation to the unwanted area following a specific level of disturbance. If Z is in the unwanted condition, the city's per capita income will decrease, and the city will be considered economically not resilient. The results of the analysis show that six levels of economic resilience have been successfully distinguished based on research on 514 cities in Indonesia involving nine indicators of disturbance and one variable of economic resilience during the five-year observation period, 2015–2019. Only 3.11 percent of cities have economic resilience level 1, while 69.18 percent have level 0. Economically resilient cities consist of 4.24 percent of cities at level 2, as much as 3.39 percent at level 3, as much as 3.39 percent at level 4, and as much as 16.69 percent at level 5. The novelty of this research is to provide a new methodology for measuring the economic resilience of cities by integrating unwanted conditions as necessary conditions and disturbance factors as sufficient conditions. The measurement of a city's economic resilience is critical to help the city government assess the security of the city so the government can take preventive actions to avoid the cities falling into unwanted conditions. [ABSTRACT FROM AUTHOR]

Background: Software for text analysis and data mining is called IBM SPSS Modeler. Predictive models were made using this software, along with other kinds of analysis. This work presented the multilayer perceptron analysis and simple linear regression analysis. Using this methodology, which finds the relationship between the objective variable and the predictor, the researchers may evaluate the usefulness of neural networks as a support tool for estimating the prevalence of the bacterium Lactobacillus Salivarius. Objectives: In this paper, we are going to determine the association between days of the culture and the rate of bacteria growth using simple logistic regression by improving it using multilayer perceptron. Methods: Simple Logistic Regression (SLR) and Multilayer Perceptron (MLP) were selected statistical tools for the factor determine as for the Lactobacillus. The significant analysis can be determined by SLR the next analysis is to validate the relationship between the dependent and independent variables using the MLP procedure. We tested the variables by evaluating the dataset using SLR by looking at the P value and R squared that showed the relationship between variables and the effectiveness of the dataset attributes. MLP model was created by using the holdout regression by partitioning the dataset into training and testing. This showed the mean absolute error and variance of the model, deeming it accurate due to its low error rate. The days of the culture and the rate of bacteria growth Lactobacillus are related each other in linear and exponentially. Conclusions: This study help researchers to understand the specific growth rate(s) which can be used to best grow the organism. [ABSTRACT FROM AUTHOR]

Introduction: The number of patients who present with facial injuries every year is on the rise. Most admission requires combined intervention by neurosurgery and maxillofacial team due to frontal bone fractures associated with various types of brain injury. The most common form of skull bone fracture is a frontal bone fracture. A high-impact head injury can fracture the frontal bone and other nearby bones. Objectives and Method: There is a retrospective study of patients with maxillofacial trauma at Hospital Universiti Sains Malaysia (USM) over five years (1 January 2012 to 31 December 2016). The hospital records of patients who sustained these fractures were analyzed using the newly developed R syntax. This study aims to determine which facial bone fractures are associated with a frontal bone fracture in maxillofacial trauma that occurs at the same time. Therefore, this study proposes an application of Artificial Neural Networks (ANNs) through a feed-forward network toward clinical study data on craniofacial fractures. The most associated bones related to the frontal bone fracture will be determined and will be the input for the multiple logistic regression (MLR). The analysis will be conducted entirely using developed R syntax. The generated syntax is divided into three major sections: Bootstrap (B), Multilayer Perceptron (MLP), and Multiple Logistic Regression. Results: This type of fracture occurred in 218 patients, with 80.7% male and 19.3% female. There is four variable which was Gender (ß1 =1.031; p 0.25; 95% CI:1.028,7.658), Le Fort III fracture (2 ß =1.175; p 0.25; 95% CI: 0.831,12.628), mandibular symphysis fracture (3 ß = -0.935; p 0.25; 95% CI: 0.115,1.342), and mandibular condylar fracture (4 = -1.485; p 0.25; 95% CI: 0.028,1.844). The above MLP gave the lowest mean absolute deviance (0.0007179404). The accuracy obtained is about 99.928%. Conclusions: A Multilayer Feed-Forward Neural Network (MLFF) with multiple logistics regression for the modeling and prediction purpose of collected data is a good approach. The result obtained is being tested and checked from an important clinical point of view. This approachable technique was discovered to have superiority in the variable selection for multiple logistic regression modeling. In real life, many of the relationships between inputs and outputs are non-linear as well as complex relationships. As a result, using MLFF for variable selection, especially for modeling purposes, is a very good strategy and was discovered to have superiority of the variable selection for multiple logistic regression modeling. [ABSTRACT FROM AUTHOR]

Introduction: General anaesthesia (GA) dental care is one of the clinical strategies used to treat non-cooperative those, patients with chronic medical problems or with specialised and comprehensive treatment by some paediatric dentists. Objective: The purpose of this retrospective research was to analyse cases of general anaesthesia in paediatric dentistry at Hospital Universiti Sains Malaysia (USM), Kubang Kerian, Kelantan. Methods: A total of 298 patients reports were collected for data processing from 2015 to 2018. Results: About 54% of patients in the Malay ethnic community were male and the mean age was 5 years. The highest treatment is on the fissure sealant restoration, 100(33.6%) and follows by extraction of deciduous teeth 218(73.2%). The lowest treatment was found in Sandwich Technique Restoration 1(0.3%), excision of chronic mucocele, which is about 2(0.7%), and the treatment based on GIC Fuji IV 2(0.7%). The next analysis is focusing on the type of treatment. The result from multiple responses shows that patients with a combination of three treatment having 61%, this is the highest percentage. While patients with four types of treatment are the second highest, 59 cases or 25.4% and the third highest comes from the category of patients with two types of treatment. Conclusion: An annual rise in referred cases for dental care under GA has been observed. it is believed that the number of patients receiving dental treatment under GA is likely will continue to show an upward trend, and for the specific finding it was found that the extraction deciduous teeth are the highest case which is about 31.2%, fissure sealant restoration about 14.3% and stainless steel crown which is 13.3%. [ABSTRACT FROM AUTHOR]

Objective: This study aims to find the association of fractured orbital walls with other possible fractures reported in the maxillofacial trauma cases in the Oral Maxillofacial Clinic Oral Maxillofacial ward, Hospital USM Kelantan, Malaysia. Materials and methods: From 2013 to June 2018, records of patients who sustained maxillofacial fractures and presented them to the Accident and Emergency Department, Oral Maxillofacial Clinic, Hospital USM were reviewed, recorded, and analyzed. Data were obtained from 294 patients who met the study's eligibility requirements. The medical records of every patient with a comprehensive medical history were reviewed. The following factors were studied: age, gender, zygomatic arch, maxillary sinus, orbital wall, symphysis of the mandible, parasymphysis, and the condyle. The broken orbital walls in these patients were examined in detail. In the first stage, all of the variables that have been picked will be assessed for their significance from a clinical standpoint. All potential factors contributing to the orbital wall fracture were analyzed using the SPSS and R studio programs. As a result of meeting the inclusion criteria, 294 patients' data has been gathered. Each patient who had a complete medical record was subjected to an examination. In these patients, the cracked orbital walls were examined in greater depth. All chosen variables will be tested in the first stage to see if they are clinically significant. Results: The participants in this study were 228 men (77.6%) and 66 women (22.4%). It was found that the most common age ranges are 11-20 years (39.8%), 21-30 years, and 31-40 years (26.2%). According to Spearman correlation, all of the studied variables have a significant accosiation, with a p-value of less than 0.05. According to the findings of the multiple logistic regression, it was discovered that gender is significant, [0.2652 (0.1761); p < 0.25], Zygomatic Arch fracture, [ 3 β (SE)= -0.4511(0.2403); p < 0.25], Maxillary Sinus, [ 4 β (SE)= -0.5917 (0.2403); p < 0.25], Symphysis of the mandible, [ 5 β (SE)= 2.4826 (0.7298); p < 0.05], the condyle of the mandible, [ 5 β (SE) = 0.9479 (0.4315); exp (0.9479) = 2.58 = 3 times], the body of the mandible, [ 5 β (SE)= 0.4893 (0.4315); p < 0.25] and the angle of the mandible, [ 5 β (SE) = 0.6911 (0.4286); p < 0.25]. The validation of the factor through the Multilayer Neural Network (MLNN) and the accuracy obtained 97.71% with the predicted mean square error (PMSE) 0.159%. Conclusion: The matrix spearman correlation, multiple logistic regression, and neural network uncovered a clear association between orbital wall fracture and several other parameters. This discovery will help researchers understand the most common orbital wall fracture causes in maxillofacial trauma. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to develop and illustrate an optimum variable selection approach using Multiple Linear Regression (MLR) and validation using Multi-Layer Feed Forward Neural Network (MLFF), also known as Multilayer Perceptron Neural Network (MLP) while taking bootstrapping into account. All of the factors specified will be evaluated to determine if they have a meaningful association. Objective:The goal of this study was to create a new method in making predictions for oral health data by combining a few statistical techniques. This would make the model more accurate. Material and Methods:A set of medical data that consists 30 observations was used to develop the methodology. The data descriptions of variables being used in this retrospective research including Creatinine, Fasting Blood Sugar, Haemoglobin A1C, and Urea were evaluated using advanced computational statistical modelling approaches. The medical data are used to test the R syntax that is developed in this study. The statistics for each sample are calculated using a model that incorporates bootstrapping and multiple linear regression techniques. Results:The statistical strategy which combines Bootstrap, MLFF and MLR is better than regular statistical method being used for this type of data. The hybrid model technique's accuracy was increased when the data was separated into training and testing dataset. Four variables are taken into account in this case: fasting blood sugar, creatinine, haemoglobin A1C, and urea. Fasting Blood Sugar (β1: 0.461889; p< 0.05), Creatinine (β3: 0.029761; p< 0.05) and Urea (β2:-0.454766; p< 0.05) are all significant, whereas MLFF and MLRhave the predicted mean square error (PMSE) values of 0.01226 and 0.3531 respectively. Conclusion: The value of PMSE is mainly used to diagnose the performance of MLR and MLFF. The MLFF is used to determine how close predicted values are to real data, while the model that has low PMSE value indicates that the model is accurate. The R syntax for combining Bootstrap, MLR, and MLFF is also included in this research article. In a word, this research establishes the superiority of the hybrid model method. [ABSTRACT FROM AUTHOR]

Introduction: One of the major risk factors for cardiovascular disease in diabetes mellitus is dyslipidaemia. The characteristic of diabetic dyslipidaemia includes high plasma triglycerides concentration, low HDL cholesterol concentration and increased the concentration of small dense LDL-cholesterol particles. Most dyslipidaemias are hyperlipidemias; which is an increase in lipids in the blood that occur in developed countries. This is often caused by diet and lifestyle. Prolonged levels of insulin can also cause dyslipidaemia. Objective: The aim of this paper is to determine the factor that contributes to dyslipidaemia disease among Type 2 Diabetes Mellitus attending the outpatient clinic in Hospital Universiti Sains Malaysia. Method: The first method approach for the factor determination is through binary logistic regression (BLR). After obtaining the list of factors, the validation process will take the place. The validation of the factor will be obtained through multilayer perceptron (MLP) procedure where the training and testing procedure will be applied. Results: The accuracy and the significant factor will be determined and used for educational purposes and for the decision maker. Through the both methodology it was found that gender (β3 = -2.643, p < 0.25, 95% CI: 0.004, 1.353), sodium (β2 = -0.661, p < 0.25, 95% CI: 0.313, 0.850), and creatinine, (β1 = 0.043, p < 0.25, 95% CI: 0.997, 1.093), were the factor that contribute most to the dyslipidaemia disease. Conclusion: From these two methodologies, it was found that creatinine, sodium and gender factor are the most dominating factor which contributes to dyslipidaemia disease. These three factors were validated through the multilayer perceptron (MLP) procedure. [ABSTRACT FROM AUTHOR]

Background and Objectives: According to the global cancer situation, which is very alarming, with over 10 million new diagnoses and more than 6 million deaths each year globally, cancer is one of the most prominent causes of morbidity and mortality today. One of the cancers is oral cancer. Oral cancer is the irregular development of malignant cells in the oral cavity. The study's objective was to decide the mortality of cross-tabulation among patients treated for oral carcinoma from Hospital Universiti Sains Malaysia (USM), Kelantan, Malaysia. Materials and Methods: This chapter summarizes the medical history for 7 years from January 2011 to December 2018 of patients who have been treated for oral carcinoma in the Hospital USM, Oral and Maxillofacial Surgery (OMFS) Unit. Each patient's complete medical record was checked, and data gathered were based on age, gender, site lesion, clinical diagnosis, and mortality. Version 26.0 of the SPSS software was used to evaluate the correlation and distribution of patient survival. Results: This was a retrospective cross-sectional review of the medical evidence of 117 patients infected for oral carcinoma at OMFS (Hospital USM). Sixty-seven (57.26%) of the patients were male and fifty (42.74%) were female. Patient age ranged from 25 to 93 years. Malay has the highest prevalence (85.5%) in oral carcinoma, followed by a second ethnic group, Chinese (7.7%). The result indicates that the majority of oral carcinoma patients were over 60 years old. Cases of oral squamous cell carcinoma have proved to be the most prevalent malignant tumour in the mouth cavity. The largest number of cases collected is 91% of the data collected. Mucoepidermoid carcinoma (10%) is the second most common small salivary gland tumor. Conclusion: OSCC is the most prevalent kind of oral cancer. According to the data review, the most popular site for oral cancer is the tongue. [ABSTRACT FROM AUTHOR]

Background: COVID-19 outbreak is being studied throughout the world. Adding more analysis to date strengthening the information about the illness. Here, we analysis the data of Malaysian Ministry of Health from February 15, 2020 until January 10, 2021 was analysed using linear regression model statistical analysis with aim to forecast the trend. Materials and Methods: This study reviewed the data by Malaysia Ministry of Health from February 15, 2020, until January 10, 2021. Linear regression model statistical analysis was used for predictive modelling. The forecasting of the linear trend of the Covid-19 outbreak prediction is purposed to estimate the number of confirm cases according to the number of recoveries patients. Results: Malaysia is currently anticipating another lockdown restriction as new confirmed case of COVID-19 hit new record high. The cumulative confirmed Covid-19 cases in MCO predicted a sharp increase. At the first of March, 2021, the predicted cumulative confirmed Covid-19 cases are 319,477 cases. Conclusions: Covid-19 cases projected to 315766 by end of February 2021 with 3000-4000 daily cases predicted. Initiative and proactive measurement by Malaysian government hopefully can reduce the number of cases and flatten the infection curve. [ABSTRACT FROM AUTHOR]

Objective: To determine the satisfaction with the dentist-patient interaction and factors associated with patient satisfaction among patients attending the outpatient dental clinic Hospital Universiti Sains Malaysia (HUSM). Material and Methods: This cross-sectional study involved 229 patients who attended outpatient dental clinic Hospital USM that located in the East Coast region of Malaysia. A selfadministered Skala Kepuasan Interaksi Perubatan - 11 (SKIP-11) questionnaire was used to assess the satisfaction towards dentist-patient interaction. Systematic random sampling was applied in this study. The data were analyzed using simple logistic regression analysis to determine the factors associated with patient satisfaction with dentist-patient interaction. Results: The mean age of patients was 32.6 ± 13.9 years, 71.6% of them study up to tertiary level, 31.5% came to for dental check up and 23.6% of them had tooth decay. More than half (64.6%) of the patients were satisfactory with dentist-patient interaction. The satisfaction percentage in the distress relief domain was 60.7%, 56.8% in the rapport domain, and 53.7% in the interaction outcome domain. Satisfaction with dentist-patient interaction was significantly associated with the dentists' characteristics such as age (OR = 0.583, 95%CI 0.44-0.76, p=0.001), gender (OR = 0.386, 95% CI 0.22-0.69, p=0.001) and years of service (OR = 0.294, 95% CI 0.15-0.57, p=0.001). Conclusion: The result showed that slightly more than half of the patients who attended the outpatient dental clinic HUSM were satisfied with the dentist-patient interaction, which was found to be influenced by the characteristics of the dentists. Efforts to improve patient-dentist interaction are recommended to ensure delivery of good quality oral health care. [ABSTRACT FROM AUTHOR]

Introduction: Breastmilk alone is the best food and drink for an infant for the first six months of life. After six months, infants need other nutritious foods, in addition to breastfeeding up to two years and beyond, to meet their growth and development needs. Objective: This study is conducted to determine the knowledge, attitude, and future exclusive breastfeeding intention among final year medical and dental students in University Sains Malaysia Method: Multiple Response Analysis methodologies were used to determine the level of acceptance. The seven domains of knowledge were analyzed to determine the most dominant factor which leads to the acceptance of future intention towards exclusive breastfeeding. The co-occurrence frequency network analysis was used to determine the most frequent event between the studied factors. Results: All the findings from this analysis will be used as an indicator for the general knowledge and attitude towards exclusive breastfeeding focusing on young adults. Conclusion: This study found that most final year medical students in Universiti Sains Malaysia generally have positive future intentions to practice exclusive breastfeeding compared to dental students. [ABSTRACT FROM AUTHOR]

Background: Oral cancer is the sixth most common cancer and is associated with a mortality rate of up to 50%. This global burden has a diverse aetiology, which included smoking, tobacco, alcohol, betel-quid, human papillomavirus (HPV), and ultraviolet radiations. Worldwide, the prevalence of HPV infection in oral cavity cancer is 3%. This has a significant role in the management of oral squamous cell carcinoma (OSCC) as HPV related oral cancers have shown better prognosis. The objective of this study was to investigate the prevalence of HPV 16 in OSCC and its association with socio-demographic features of Kelantan population. Methods: Forty one tissue specimens were collected from the patients who were diagnosed with OSCC in Hospital Universiti Sains Malaysia from 2005-2015. Immunohistochemistry was performed using a p16 antibody to detect HPV 16. The cut-off value for p16 positivity staining was 70%. Two oral pathologists observed the interrater agreement of the p16 staining. Results: Out of forty one tissue specimens of oral squamous cell carcinoma examined, 4 (10%) were found to be human papillomavirus 16 positive with very strong agreement between the observers (κ = 0.84). No significant association was found between the HPV 16 positivity and age, gender, smoking, betel quid, and alcohol habit (P > 0.05). Conclusion: Higher prevalence of human papillomavirus 16 was seen in OSCC affecting Kelantan population as compared to three percent global prevalence in oral cavity reported by World Health Organization. There was no association between the presence of HPV 16 to socio-demographic features. [ABSTRACT FROM AUTHOR]

Introduction: Oral cancer is a disease resulting from an abnormal growth cell found in the mouth region such as in lips, tongue or throat. Furthermore, oral cancer commonly occurs at the buccal mucosa (cheek), tongue, the floor of the mouth and lip. The previous study shows that many studies focus especially on detecting the factors with the highest probability of cancer but the study which emphasizes the prediction of the tumor size category is still less from the point of computational biostatistical. Objective: The aim of this research paper is to build the methodology for computing ordinal regression model through SAS language by considering the clinical pathological prognostic factors in patients who attended Hospital Universiti Sains Malaysia (HUSM) from 2005 to 2015 (based on secondary data). Method: In this paper, there are three main methodologies proposed in this study. The first and second methodology is on the ordinal regression methodology and followed by bootstrap method, and the second is a nonlinear programming (NLP) methodology, which aims to obtain fuzzy regression modeling for the prediction purposed. Results: The result from ordinal regression had shown that smoking and nerve invasion factors contributing significantly to the growth of a tumor. The significant result from this finding (output based) can be used to educate people or stakeholder of how important this factor toward patients management. Conclusion: From a statistical point of view, the integration of computing methodology can expose and provide the researcher with an integrated method of analysis. [ABSTRACT FROM AUTHOR]