Purpose: Addressing health inequities is an integral part of contemporary medical education (ME), yet traditional lecture-based formats often fail to develop students’ deeper understanding and engagement. This study examined how a project-based learning (PjBL) approach influenced students’ learning experiences related to health equity. Methods: This was a qualitative phenomenology study. We designed an elective course on health equity using the PjBL approach and active learning methods. All participating students were asked to complete a group project aimed at addressing a specific health inequity issue from the surrounding community. Data were collected through reflective writing at the end of the course and analyzed using deductive thematic analysis. Twenty-seven codings were identified from 259 meaningful quotes (interrater agreement 99.62%) and grouped into four categories: character, role, competence, and learning experience. Results: Three major themes emerged from data analysis: (1) key learning experiences during the ME course (active learning, role-modeling, collaborative learning, comprehensive learning, and affective learning); (2) topics that facilitated students’ understanding of health inequities and physicians’ roles, particularly in addressing health inequities in Indonesia; and (3) the influence of the ME course on students’ outlook and beliefs. Conclusion Although this study did not introduce a novel method of instruction, it underscores the value of PjBL in enhancing students’ capacity to understand and tackle health inequities.

Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Purpose: Addressing health inequities is an integral part of contemporary medical education (ME), yet traditional lecture-based formats often fail to develop students’ deeper understanding and engagement. This study examined how a project-based learning (PjBL) approach influenced students’ learning experiences related to health equity. Methods: This was a qualitative phenomenology study. We designed an elective course on health equity using the PjBL approach and active learning methods. All participating students were asked to complete a group project aimed at addressing a specific health inequity issue from the surrounding community. Data were collected through reflective writing at the end of the course and analyzed using deductive thematic analysis. Twenty-seven codings were identified from 259 meaningful quotes (interrater agreement 99.62%) and grouped into four categories: character, role, competence, and learning experience. Results: Three major themes emerged from data analysis: (1) key learning experiences during the ME course (active learning, role-modeling, collaborative learning, comprehensive learning, and affective learning); (2) topics that facilitated students’ understanding of health inequities and physicians’ roles, particularly in addressing health inequities in Indonesia; and (3) the influence of the ME course on students’ outlook and beliefs. Conclusion Although this study did not introduce a novel method of instruction, it underscores the value of PjBL in enhancing students’ capacity to understand and tackle health inequities.

Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Purpose: Addressing health inequities is an integral part of contemporary medical education (ME), yet traditional lecture-based formats often fail to develop students’ deeper understanding and engagement. This study examined how a project-based learning (PjBL) approach influenced students’ learning experiences related to health equity. Methods: This was a qualitative phenomenology study. We designed an elective course on health equity using the PjBL approach and active learning methods. All participating students were asked to complete a group project aimed at addressing a specific health inequity issue from the surrounding community. Data were collected through reflective writing at the end of the course and analyzed using deductive thematic analysis. Twenty-seven codings were identified from 259 meaningful quotes (interrater agreement 99.62%) and grouped into four categories: character, role, competence, and learning experience. Results: Three major themes emerged from data analysis: (1) key learning experiences during the ME course (active learning, role-modeling, collaborative learning, comprehensive learning, and affective learning); (2) topics that facilitated students’ understanding of health inequities and physicians’ roles, particularly in addressing health inequities in Indonesia; and (3) the influence of the ME course on students’ outlook and beliefs. Conclusion Although this study did not introduce a novel method of instruction, it underscores the value of PjBL in enhancing students’ capacity to understand and tackle health inequities.

Purpose: This study employed a convolutional neural network (CNN) algorithm to develop an automated dental age estimation method based on the London Atlas of Tooth Development and Eruption. The primary objectives were to create and validate CNN models trained on panoramic radiographs to achieve accurate dental age predictions using a standardized approach. Materials and Methods: A dataset of 801 panoramic radiographs from outpatients aged 5 to 15 years was used. A CNN model for dental age estimation was developed using a 16-layer CNN architecture implemented in Python with TensorFlow and Scikit-learn, guided by the London Atlas of Tooth Development. The model included 6 convolutional layers for feature extraction, each followed by a pooling layer to reduce the spatial dimensions of the feature maps. A confusion matrix was used to evaluate key performance metrics, including accuracy, precision, recall, and F1 score. Results: The proposed model achieved an overall accuracy, precision, recall, and F1 score of 74% on the validation set. The highest F1 scores were observed in the 10-year and 12-year age groups, indicating superior performancein these categories. In contrast, the 6-year age group demonstrated the highest misclassification rate, highlightingpotential challenges in accurately estimating age in younger individuals. Conclusion: Integrating a CNN algorithm for dental age estimation represents a significant advancement in forensic odontology. The application of AI improves both the precision and efficiency of age estimation processes, providing results that are more reliable and objective than those obtained via traditional methods.

Purpose: Addressing health inequities is an integral part of contemporary medical education (ME), yet traditional lecture-based formats often fail to develop students’ deeper understanding and engagement. This study examined how a project-based learning (PjBL) approach influenced students’ learning experiences related to health equity. Methods: This was a qualitative phenomenology study. We designed an elective course on health equity using the PjBL approach and active learning methods. All participating students were asked to complete a group project aimed at addressing a specific health inequity issue from the surrounding community. Data were collected through reflective writing at the end of the course and analyzed using deductive thematic analysis. Twenty-seven codings were identified from 259 meaningful quotes (interrater agreement 99.62%) and grouped into four categories: character, role, competence, and learning experience. Results: Three major themes emerged from data analysis: (1) key learning experiences during the ME course (active learning, role-modeling, collaborative learning, comprehensive learning, and affective learning); (2) topics that facilitated students’ understanding of health inequities and physicians’ roles, particularly in addressing health inequities in Indonesia; and (3) the influence of the ME course on students’ outlook and beliefs. Conclusion Although this study did not introduce a novel method of instruction, it underscores the value of PjBL in enhancing students’ capacity to understand and tackle health inequities.

Background: Hypertension is associated with the improvement of cardiovascular disease and all-cause mortality. A healthy diet based on consuming natural foods can prevent and control hypertension. Objective: The aim of this study was to analyze the effectiveness of tomato and cucumber juice in reducing the blood pressure of hypertensive patients. Methods: The study used a quasi-experiment pretest-posttest control group design. The target population are people with hypertension Stage 1, people living in urban area - Cibiru Health Centre Work Area. Purposive sampling was used and the sample size was calculated using the average comparison formula with effect size=0.9, α=0.05, β =0.2. Forty-five subjects involved in the study were divided into three groups (15 subjects were given tomato juice, 15 subjects were given cucumber juice, and 15 subjects were given treatment with mineral water (control group)., This research used 100 grams of ripe red tomatoes, 100 grams of fresh cucumber, and 200 ml of water. The data collected were patient characteristics and blood pressure. The ANOVA analysis test and the Bonferroni Post Hoc test were used to analyze the data. Results: The results of the study showed a difference in blood pressure reduction in each group. The decrease in systolic blood pressure in the tomato juice group was 7.3+3.1, the cucumber juice group was 4.2+3.3, and the control group was -0.0+2.5 (p=0.0001). The decrease in diastolic blood pressure in the group given tomato juice was 9.2+3.1, the group given cucumber juice was 7.6+3.4, and the control group was 0.4+2.1 (p=0.0001). Conclusion There is a difference in blood pressure reduction between the group given tomato juice and the control group, and there is a difference in blood pressure between the group given cucumber juice and the control group.
Blood Pressure

Insufficient knowledge about the decomposition of microplastics from plastic waste in landfills hinders community involvement in waste management and sorting, posing a new threat to the environment and public health. The present study identifies, characterizes, and quantifies the microplastics in landfills soil sample to determine the latest threats posed by microplastics in the environment, particularly in landfills that are close to residential areas. This research is a descriptive study, with soil samples taken from six points in landfill site in Depok City. The abundance and shape of microplastics were characterized using a microscope, while the microplastic types were identified using Fourier Transform Infrared Spectroscopy (FTIR). The results showed that the abundance of microplastics in the Depok City landfill soil was 60,111.67 particles/kg, with the largest percentage being fragments at 63 %. FTIR functional group characterization showed the presence of plastic types, such as Polyethylene (PE), Polyvinyl Chloride (PVC), Polystyrene (PS), Polypropylene (PP), Polyethylene Terephthalate (PET), and Polyamide. The differences in waste types entering the Depok Landfill caused variations in the number, shape, and type of microplastic samples, and this study provides a foundation for mitigating and biodegrading microplastics in the landfill to minimize environmental impact and protect public health.

Purpose: The objective of this study was to determine the minimum number of teeth in the anterior dental arch that would yield accurate results for individual identification in forensic contexts. Materials and Methods: The study involved the analysis of 28 sets of 3-dimensional (3D) point cloud data, focused on the labial surface of the anterior teeth. These datasets were superimposed within each group in both genuine and imposter pairs. Group A incorporated data from the right to the left central incisor, group B from the right to the left lateral incisor, and group C from the right to the left canine. A comprehensive analysis was conducted, including the evaluation of root mean square error (RMSE) values and the distances resulting from the superimposition of dental arch segments. All analyses were conducted using CloudCompare version 2.12.4 (Telecom ParisTech and R&D, Kyiv, Ukraine). Results: The distances between genuine pairs in groups A, B, and C displayed an average range of 0.153 to 0.184 mm. In contrast, distances for imposter pairs ranged from 0.338 to 0.522 mm. RMSE values for genuine pairs showed an average range of 0.166 to 0.177, whereas those for imposter pairs ranged from 0.424 to 0.638. A statistically significant difference was observed between the distances of genuine and imposter pairs (P<0.05). Conclusion The exceptional performance observed for the labial surfaces of anterior teeth underscores their potential as a dependable criterion for accurate 3D dental identification. This was achieved by assessing a minimum of 4 teeth.