Target prediction of compounds on jamu formula using nearest profile method

Jamu is one of Indonesia's cultural heritage, which consists of several plants that have been practiced for centuries in Indonesian society to maintain health and treat diseases. One of the scientification efforts of Jamu to reveal its mechanism is to predict the target-protein of the active ingredients of the Jamu. In this study, the prediction of the target compound for Jamu was carried out using a supervised learning approach involving conventional medicinal compounds as training data. The method used in this study is the closest profile method adopted from the nearest neighbor algorithm. This method is implemented in drug compound data to construct a learning model. The AUC value for measuring performance of the three implemented models is 0.62 for the fixed compound model, 0.78 for the fixed target model, and 0.83 for the mixed model. The fixed compound model is then used to construct a prediction model on the herbal medicine data with an optimal threshold value of 0.91. The model produced 10 potential compounds in the herbal formula and its 44 unique protein targets. Even though it has many limitations in obtaining a good performance, the closest profile method can be used to predict the target of the herbal compound whose target is not yet known

PRELIMINARY STUDY OF MICRODEBRIS CONTAMINATION IN SEDIMENT FROM THREE ESTUARIES ON THE PANGKAJENE RIVER

Studies on microdebris contamination in sediments at the estuary of the Pangkajene River were carried out at three stations. This study aims to observe the abundance and characteristics of microdebris in the estuary sediments of the Pangkajene River. The samples of sediment were collected using a core sampler with a diameter of 8 cm. Microdebris was extracted using the flotation method and vacuum filtration system. The particles were observed with a stereomicroscope then classified based on shape (form), size, and color. The number of particles found ranged from 2.83±2.04-4.00±1.87 item/100gr. The analysis of variance (ANOVA) showed p>0.05, therefore the abundance of microdebris between the three stations did not show a significant difference. Microdebris in Fragment form had the highest percentage compared to fibers and granules. Blue and black particles of microdebris were the most common colors. The dominant size of particles was found in the size class <100µm and 100µm-500µm. This preliminary study revealed that the microdebris occurs in the Pangkajene estuary sediments. Currently, we have not classified the types of microdebris found as microplastic, semi-synthetic debris, or natural origin. Therefore, further research is needed to verify particles using an FT-IR Microscope to determine the type of microdebris polymer

Predicting Obesity in Adults Using Machine Learning Techniques: An Analysis of Indonesian Basic Health Research 2018

Obesity is strongly associated with multiple risk factors. It is significantly contributing to an increased risk of chronic disease morbidity and mortality worldwide. There are various challenges to better understand the association between risk factors and the occurrence of obesity. The traditional regression approach limits analysis to a small number of predictors and imposes assumptions of independence and linearity. Machine Learning (ML) methods are an alternative that provide information with a unique approach to the application stage of data analysis on obesity. This study aims to assess the ability of ML methods, namely Logistic Regression, Classification and Regression Trees (CART), and Naïve Bayes to identify the presence of obesity using publicly available health data, using a novel approach with sophisticated ML methods to predict obesity as an attempt to go beyond traditional prediction models, and to compare the performance of three different methods. Meanwhile, the main objective of this study is to establish a set of risk factors for obesity in adults among the available study variables. Furthermore, we address data imbalance using Synthetic Minority Oversampling Technique (SMOTE) to predict obesity status based on risk factors available in the dataset. This study indicates that the Logistic Regression method shows the highest performance. Nevertheless, kappa coefficients show only moderate concordance between predicted and measured obesity. Location, marital status, age groups, education, sweet drinks, fatty/oily foods, grilled foods, preserved foods, seasoning powders, soft/carbonated drinks, alcoholic drinks, mental emotional disorders, diagnosed hypertension, physical activity, smoking, and fruit and vegetables consumptions are significant in predicting obesity status in adults. Identifying these risk factors could inform health authorities in designing or modifying existing policies for better controlling chronic diseases especially in relation to risk factors associated with obesity. Moreover, applying ML methods on publicly available health data, such as Indonesian Basic Health Research (RISKESDAS) is a promising strategy to fill the gap for a more robust understanding of the associations of multiple risk factors in predicting health outcomes

Obesity Risk-Factor Variation Based on Island Clusters: A Secondary Analysis of Indonesian Basic Health Research 2018

Obesity has become a rising global health problem affecting quality of life for adults. The objective of this study is to describe the prevalence of obesity in Indonesian adults based on the cluster of islands. The study also aims to identify the risk factors of obesity in each island cluster. This study analyzes the secondary data of Indonesian Basic Health Research 2018. Data for this analysis comprised 618,910 adults (≥18 years) randomly selected, proportionate to the population size throughout Indonesia. We included 20 variables for the socio-demographic and obesity-related risk factors for analysis. The obesity status was defined using Body Mass Index (BMI) ≥ 25 kg/m2. Our current study defines 7 major island clusters as the unit analysis consisting of 34 provinces in Indonesia. Descriptive analysis was conducted to determine the characteristics of the population and to calculate the prevalence of obesity within the provinces in each of the island clusters. Multivariate logistic regression analyses to calculate the odds ratios (ORs) was performed using SPSS version 27. The study results show that all the island clusters have at least one province with an obesity prevalence above the national prevalence (35.4%). Six out of twenty variables, comprising four dietary factors (the consumption of sweet food, high-salt food, meat, and carbonated drinks) and one psychological factor (mental health disorders), varied across the island clusters. In conclusion, there was a variation of obesity prevalence of the provinces within and between island clusters. The variation of risk factors found in each island cluster suggests that a government rethink of the current intervention strategies to address obesity is recommended

Geospatial analysis of type 2 diabetes mellitus and hypertension in South Sulawesi, Indonesia

Abstract The spatial variation of type 2 diabetes mellitus (T2DM) and hypertension and their potential linkage were explored in South Sulawesi Province, Indonesia. The Global Moran’s I and regression analysis were utilized to identify the characteristics involved. The methods were performed based on T2DM and hypertension data from 2017 and 2018 acquired from Social Health Insurance Administration in Indonesia. The spatial variation of T2DM and hypertension showed that the prevalence rate of T2DM and hypertension tends to occur randomly (p = 0.678, p = 0.711, respectively). By utilizing Generalized Poisson Regression Analysis, our study showed a significant relationship between T2DM and hypertension (p ≤ 0.001). This research could help policy makers to plan and support projects with the aim of overcoming the risk of T2DM and hypertension

Influence of patient-related factors on intraoperative blood loss during double opposing Z-plasty Furlow palatoplasty and buccal fat pad coverage: A prospective study

Background: Surgical procedures including palatoplasty have a risk for complications. The aim of this study was to investigate the intraoperative and early postoperative blood loss using the buccal fat pad (BFP) during cleft lip and/or cleft palate (CL/P) surgery. Material and Methods: This prospective study included a total of 109 patients with cleft palate (CP) during a three-month period of treatment at Hasanuddin University Dental Hospital (permanent center) and charity trips in rural parts of Eastern Indonesia. All patients were treated with DOZ Furlow technique combined with BFP graft. Before and after surgery, the total amount of intraoperative blood loss was calculated by measuring the weight differences of the gauze swabs that were used to control the surgical bleeding followed by a complete blood count at three days postoperatively. Results: The difference in the amount of blood loss based on age categories in charity groups was found to be significant (P<0.05). Overall, we found that high body weight and operation time significantly contributed to increased blood loss (P<0.05). Conclusions: Weight and operative time can contribute to more blood loss during palatoplasty

Influence of patient-related factors on intraoperative blood loss during double opposing Z-plasty Furlow palatoplasty and buccal fat pad coverage: A prospective study

Background: Surgical procedures including palatoplasty have a risk for complications. The aim of this study was to investigate the intraoperative and early postoperative blood loss using the buccal fat pad (BFP) during cleft lip and/or cleft palate (CL/P) surgery. Material and Methods: This prospective study included a total of 109 patients with cleft palate (CP) during a three-month period of treatment at Hasanuddin University Dental Hospital (permanent center) and charity trips in rural parts of Eastern Indonesia. All patients were treated with DOZ Furlow technique combined with BFP graft. Before and after surgery, the total amount of intraoperative blood loss was calculated by measuring the weight differences of the gauze swabs that were used to control the surgical bleeding followed by a complete blood count at three days postoperatively. Results: The difference in the amount of blood loss based on age categories in charity groups was found to be significant (P<0.05). Overall, we found that high body weight and operation time significantly contributed to increased blood loss (P<0.05). Conclusions: Weight and operative time can contribute to more blood loss during palatoplasty