Modelling the Maternal Oral Health Knowledge, Age Group, Social-Economic Status, and Oral Health-Related Quality of Life in Stunting Children

The main themes are two main health problems affecting children under five in Indonesia, namely nutrition and oral health. Lack of nutrition in children can also affect their general health, and so does their oral health, leading to their quality of life. The study aimed to analyse the relationship between maternal oral health knowledge, maternal age group, social-economic status with the oral health-related of life in stunting children. This type of analytical research used a survey method on 86 mothers aged 2-5 years in one of 15 villages designated by the mayor of Bandung as a stunting locus. Maternal oral health knowledge, social-economic status, and oral health-related quality of life were assessed using a set of questionnaires that have been pre-tested to non-participant mothers. The hypotheses of the conceptual model were tested using structural equation modelling-partial least squares. The results showed that 16.7% of the variance in OHRQoL was explained by maternal oral health knowledge and the maternal age group. Social-economic status has an indirect relationship to OHRQoL by predicting the maternal oral health knowledge 10.6%. The path coefficient between maternal age group and OHRQoL was the strongest (b = -0.350, P = 0.000), followed by SES and maternal oral health knowledge (b = 0.325, P = 0.04) and to OHRQoL (b = 0.215, P=0.02). The overall predictive power of the model was 10.6%. This result indicated maternal oral health knowledge, social-economic status, and maternal age group related to children's oral health quality of life.

Proprioceptive Training to Improve Static and Dynamic Balance in Elderly

Background: The risk of falls is a major cause of disability in older adults. A single fall, for the elderly, increases the risk of frequent falls and often causes an increased fear of falling again, which can become debilitating. Objectives: The purpose of the present study was to test the effects of 12 weeks of proprioceptive training on the static and dynamic balance of older adults who have experienced at least one fall without compromising consequences. Method: The sample consisted of older adults, aged 60 to 80 years, randomly divided into two groups: an experimental group, which followed a proprioceptive training protocol at a physiotherapy studio, and a control group, which did not observe any treatment. Static and dynamic balance assessment was performed pre-and post-intervention. The tests administered were the Berg Balance Test for the evaluation of static balance, and the Four-Square Step Test, for dynamic balance. A questionnaire to assess confidence and fear of falling was administered at the end of the protocol. Independent sample t-test was performed to analyze differences between groups and two-way ANOVA to test the null hypothesis of no change different over time between groups (interaction intervention × time). A Chi-Square was performed to analyze perceptions. Conclusions: The results showed that 12 weeks of proprioceptive training effectively improved dynamic and static balance in older adults. The perceptions of the experimental group were more positive than the other one, in terms of the importance of physical activity to prevent the risk of falls, fear of falling again, and experience of falls during the last 12 weeks.

A Pragmatic Approach for Detecting nCOVID-19 using Pervasive Computing Based on Dual Diagnostic Measures

Our regular way of life has been disrupted by the COVID-19, and we have been obliged to accept the procedures that are in place under the new normal regime. It is envisaged that the standard diagnostic technique will evolve throughout the course of the procedure. As a help to this type of diagnostic technique, our research group is developing a tool. In this article, the group discusses the importance of employing two diagnostic metrics that have proven to be pivotal in many diagnoses for doctors, and how they might be used to their advantage. Together, natural language processing-based symptoms measures and a machine learning-based strategy that takes into account medical vitals can help to minimise the error percentage of detection by as much as 50%. The technique suggested in this study is the first of its type, and the authors have obtained findings that are satisfactory in terms of accuracy. A further justification for suggesting such a strategy is the manner in which a fusion algorithm might arrive at the correct results from two concurrent algorithms performing the same task. One of the group's other objectives was to give the doctor a valuable opinion in the form of such an architectural design. The suggested design may be employed at any point of care facility without the need for any additional infrastructure or escalation of the current amenities to accommodate the proposed architecture.

A Comparison of Multiple Machine Learning Algorithms to Predict Whole-Body Vibration Exposure of Dumper Operators in Iron Ore Mines in India

Background: This study deals with some factors that influence the exposure of whole-body vibration (WBV) of dumper operators in surface mines. The study also highlights the approach to improve the multivariate linear analysis outcomes when collinearity exists between certain factor pairs. Material and Methods: A total number of 130 vibration readings was taken from two adjacent surface iron ore mines. The frequency-weighted RMS acceleration was used for the WBV exposure assessment of the dumper operators. The factors considered in this study are age, weight, seat backrest height, awkward posture, the machine age, load tonnage, dumper speed and haul road condition. Four machine learning models were explored through the empirical training-testing approach. Results: The bootstrap linear regression model was found to be the best model based on performance and predictability when compared to multiple linear regression, LASSO regression, and decision tree. Results revealed that multiple factors influence WBV exposure. The significant factors are: weight of operators (regression coefficient β=-0.005, p<0.001), awkward posture (β=0.033, p<0.001), load tonnage (β=-0.026, p<0.05), dumper speed (β=0.008, p<0.001) and poor haul road condition (β=0.015, p<0.001). Conclusion: The bootstrap linear regression model produced efficient results for the dataset which was characterized by collinearity. WBV exposure is multifactorial. Regular monitoring of WBV exposure and corrective actions through appropriate prevention programs including the ergonomic design of the seat would increase the health and safety of operators.

The Normative Data for Sensorineural Acuity Level (SAL) Test among Young Adults: Comparisons Between B71 and B81 Bone Transducers

Introduction: The sensorineural acuity level (SAL) test is a valuable clinical assessment for hearing diagnosis, especially when the results of pure tone audiometry (PTA) are incomplete or questionable. Nevertheless, the literature on the SAL test is extremely limited and more research efforts are warranted to promote the application of this test in clinical settings. Essentially, the aim of the present study was to compare the normative data for the SAL test between two different bone vibrators (Radioear B71 and Radioear B81) among Malaysian participants. Materials and Methods: Forty-two healthy Malaysian adults (aged 19 to 27 years) participated in this repeated-measures study. The PTA and SAL test was carried out according to the recommended protocols. The SAL normative data produced by the two bone transducers were then compared at different frequencies. Paired t-test and Bayesian inference were used for data analyses. Results: The normative data for the SAL test gathered in the present study were descriptively higher than those reported among Caucasian adults. Relative to the Radioear B81 bone vibrator, the SAL normative data were significantly higher for the Radioear B71 bone transducer at all tested frequencies (p < 0.001, BF10 ≥ 48.67). Conclusions: The SAL normative data established by the two bone vibrators are statistically different. This information would be beneficial to provide clinicians with appropriate normative data when performing the SAL test on Malaysian patients.

Analysis of Recurrent Events with Associated Informative Censoring: Application to HIV Data

In this study, we adapt a Cox-based model for recurrent events; the Prentice, Williams and Peterson Total -Time (PWP-TT) that has largely, been used under the assumption of non-informative censoring and evaluate it under an informative censoring setting. Empirical evaluation was undertaken with the aid of the semi-parametric framework for recurrent events suggested by Huang [1] and implemented in R Studio software. For validation we used data from a typical HIV care setting in Kenya. Of the three models under consideration; the standard Cox Model had gender hazard ratio (HR) of 0.66 (p-value=0.165), Andersen-Gill had HR 0.46 (with borderline p-value=0.054) and extended PWP TT had HR 0.22 (p-value=0.006). The PWP-TT model performed better as compared to other models under informative setting. In terms of risk factors under informative setting, LTFU due to stigma; gender [base=Male] had HR 0.544 (p-value =0.002), age [base is < 37] had HR 0.772 (p-value=0.008), ART regimen [base= First line] had HR 0.518 (p-value= 0.233) and differentiated care model (Base=not on DCM) had HR 0.77(p-value=0.036). In conclusion, in spite of the multiple interventions designed to address incidences of LTFU among HIV patients, within-person cases of LTFU are usually common and recurrent in nature, with the present likelihood of a person getting LTFU influenced by previous occurrences and therefore informative censoring should be checked.

Relationship between Pretreatment Serum Albumin Levels with the Risk of Malignant Pleural Mesothelioma

Background: Malignant Pleural Mesothelioma (MPM) is a very rare and aggressive form of cancer. Recently it was found that pretreatment Serum Albumin (SA), the main circulating protein in blood is a significant prognostic factor for MPM patients. The objective of this present article is to show the relationship between pretreatment Serum albumin (SA) levels with the risk of MPM. Methods: Generalized additive model (GAM), an advanced regression analysis method has been introduced here to find this mathematical relationship between the response variable (SA) and the cofactors. Results: The main determinates of SA are identified - asbestos exposure, hemoglobin, disease diagnosis status (patients having MPM) are the factors having significant association with SA, whereas duration of asbestos exposure, duration of disease symptoms, total protein (TP), Pleural lactic dehydrogenise (PLD), pleural protein (PP), pleural glucose (PG) and C-reactive protein (CRP) are the significant continuous variables for SA. The non-parametric estimation part of this model shows Lactate dehydrogenase (LDH) and Glucose level are the significant smoothing terms. Additionally it is also found that, second and third order interactions between cofactors are highly significant for SA. Conclusions: The findings of this present work can conclude that - serum albumin may play the role of a very significant prognostic factor for MPM disease and it has been established here through mathematical modeling. Few of the findings are already been exist in MPM research literature whereas some of the findings are completely new in the literature.

Survival Curves Projection and Benefit Time Points Estimation using a New Statistical Method

Survival analysis concerns the analysis of time-to-event data and it is essential to study in fields such as oncology, the survival function, S(t), calculation is usually used, but in the presence of competing risks (presence of competing events), is necessary introduce other statistical concepts and methods, as is the Cumulative incidence function CI(t). This is defined as the proportion of subjects with an event time less than or equal to. The present study describe a methodology that enables to obtain numerically a shape of CI(t) curves and estimate the benefit time points (BTP) as the time (t) when a 90, 95 or 99% is reached for the maximum value of CI(t). Once you get the numerical function of CI(t), it can be projected for an infinite time, with all the limitations that it entails. To do this task the R function Weibull.cumulative.incidence() is proposed. In a first step these function transforms the survival function (S(t)) obtained using the Kaplan–Meier method to CI(t). In a second step the best fit function of CI(t) is calculated in order to estimate BTP using two procedures, 1) Parametric function: estimates a Weibull growth curve of 4 parameters by means a non-linear regression (nls) procedure or 2) Non parametric method: using Local Polynomial Regression (LPR) or LOESS fitting. Two examples are presented and developed using Weibull.cumulative.incidence() function in order to present the method. The methodology presented will be useful for performing better tracking of the evolution of the diseases (especially in the case of the presence of competitive risks), project time to infinity and it is possible that this methodology can help identify the causes of current trends in diseases like cancer. We think that BTP points can be important in large diseases like cardiac illness or cancer to seek the inflection point of the disease, treatment associate or speculate how is the course of the disease and change the treatments at those points. These points can be important to take medical decisions furthermore.

COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification

Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem.

Life Expectancy and Healthy Life Expectancy of Adults in Oman: Does Women’s Longer Life Expectancy than Men Mean Success or Burden for Women?

Objectives: The objective of this study was to examine the life expectancy (LE) and healthy life expectancy (HLE) of Omani adults with age and gender differentials, focusing on whether the higher LE of women than men is a gain or burden for women. Method: Data for the study come from multiple sources such as the 2010 population census, the 2008 World Health Survey in Oman, and secondary data published in the Statistical Yearbook of Oman. The life table and the modified life table proposed by Sullivan were used for estimating the LE and HLE of adult people of age 20 and above, respectively. Results: LE in Oman reached 76 years for both sexes in recent times. However, since 2010 LE has been stalled in the vicinity of 76 years in Oman. Women had higher LE than men (79 years versus 74 years). In terms of HLE, men outweighed women in Oman. At the age of 20, the gap between male-female LE was found to be 4.7 years in favor of females, whereas the gap between male-female HLE was found to be 5.8 years in favor of males. Females spent a relatively long time in poor health status than males (20.8 years versus 10.8 years) and the proportion of life spent in poor health was greater for females than males (35.0% vs. 19.3%). This revealed the paradox of less mortality but higher morbidity among women, supporting the “Failure of Success” hypothesis. Conclusion: Appropriate health policy and strategy need to be taken to reduce the gender gap in LE and HLE in Oman.