Background: The innate immune response to tuberculosis infection may involve the increased production of nitric oxide and cathelicidin due to the up-regulated expression of the vitamin D receptor (VDR), though this proposed mechanism remains controversial. The aim of this study was to determine how the exposure of human monocytes to Mycobacterium tuberculosis (M. tuberculosis) DNA affects the production of nitric oxide and cathelicidin, as well as the expression of VDR. Methods: This study was performed using monocytes obtained from healthy donors. After 24 h incubation, monocytes were stimulated with M. tuberculosis DNA for 18 h to determine the expression of VDR mRNA and the production of nitric oxide and cathelicidin versus non-stimulated cells (the control group). Results: The expression of VDR mRNA was higher in the monocytes exposed to M. tuberculosis DNA compared to the control group (P = 0.020). Monocytes exposed to M. tuberculosis DNA also showed significantly increased production of nitric oxide and cathelicidin compared to the control group (P = 0.0001; P = 0.028). Conclusion: The stimulation of human monocytes with M. tuberculosis DNA increases the expression of the VDR mRNA and the production of nitric oxide and cathelicidin.

Background Coronary artery fistula (CAF) is a rare anomaly.Transcatheter CAF closure has been introduced using various materials,but only few data are available on the Guglielmi detachable coil (GDC).The advantage of using GDC for transcatheter CAF closure is more controllable,therefore much safer when compared to other coils.This report is about our experience in transcatheter closure of CAF using fibered GDC in our hospital.Methods & Results From 2002 to 2007,there were 10 patients with CAFs (age range:28 to 56 year-old,7 males) who underwent transcatheter CAF closure.There were a total of 19 CAFs which originated from right coronary (n =5),left circumflex (n =3),left anterior descending artery (n =10) and left main trunk (n =1).Median number of coil deployment for each fistula was 3 (range:1 to 6).The pulmonary artery was the most common site of the distal communication of CAFs (n =14),followed by right atrium (n =3),left atrium (n =1) and left ventricle (n =1).Immediate coronary angiography after GDC deployment revealed no residual shunt in 12 (63.2％) CAFs,significant reduction of the flow in 5 (26.3％),while 2 (10.5％) could not be closed due to small size.Nine (90％) patients underwent a repeated angiography within 3 to 8 months.Among 12 CAFs that were occluded immediately post-deployment,there were 2 CAFs with insignificant residual flow.Among 6 CAFs with significantly decreased flow immediately post-deployment,2 were occluded totally in the follow-up angiography.In total,12 (70.5％) CAFs were occluded completely and 5 (29.5％) CAFs still had insignificant residual flow,which did not need any additional coil deployment.During a mean follow up of 4.3 ± 0.7 year,all patients remained symptom and complication free.Conclusions The fibered GDC is a safe and effective method for percutaneous closure of the CAFs.

Background: During pregnancy, the balanced dominance of the T helper17 response shifts to a Th2 response that is characterised by the production of IL-10, following the completion of the implantation process. Transforming growth factor-β (TGF-β) expression is associated with the completion of trophoblast invasion and placental growth. This study assessed the effect of malaria infection on the levels of IL-17, IL-10, and TGF-β in the plasma of pregnant mice with malaria. Methods: Seventeen pregnant BALB/C mice were divided into two groups: mice infected with Plasmodium berghei (treatment group) and uninfected mice (control group). The mice were sacrificed on day 18 post-mating. Parasitemia was measured by Giemsa staining. The levels of IL-17, IL-10, and TGF-β were measured by ELISA. Results: Using independent t test, the IL-17 levels in the treatment group were higher than those in the control group (P = 0.040). The IL-10 levels in the treatment group were lower than those in the control group (P = 0.00). There was no significant difference in the TGF-β levels (P = 0.055) between two groups. However, using SEM analysis the degree of parasitemia decreased the plasma TGF-β levels (tcount = 5.148; ≥ ttable = 1.96). SEM analysis showed that a high degree of parasitemia increased the IL-17 levels and decreased the IL-10 and TGF-β levels. Conclusion: Malaria infection during pregnancy interferes with the systemic balance by increasing the IL-17 levels and decreasing the IL-10 and TGF-β levels.

Objectives: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. Methods: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Results: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. Conclusions The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Objectives: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. Methods: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Results: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. Conclusions The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Objectives: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. Methods: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Results: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. Conclusions The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

Objectives: In Indonesia, the poor prognosis and high hospital readmission rates of patients with heart failure (HF) have yet to receive focused attention. However, machine learning (ML) approaches can help to mitigate these problems. We aimed to determine which ML models best predicted HF severity and hospital readmissions and could be used in a patient self-monitoring mobile application. Methods: In a retrospective cohort study, we collected the data of patients admitted with HF to the Siloam Diagram Heart Center in 2020, 2021, and 2022. Data was analyzed using the Orange data mining classification method. ML support algorithms, including artificial neural network (ANN), random forest, gradient boosting, Naïve Bayes, tree-based models, and logistic regression were used to predict HF severity and hospital readmissions. The performance of these models was evaluated using the area under the curve (AUC), accuracy, and F1-scores. Results: Of the 543 patients with HF, 3 (0.56%) were excluded due to death on admission. Hospital readmission occurred in 138 patients (25.6%). Of the six algorithms tested, ANN showed the best performance in predicting both HF severity (AUC = 1.000, accuracy = 0.998, F1-score = 0.998) and readmission for HF (AUC = 0.998, accuracy = 0.975, F1-score = 0.972). Other studies have shown variable results for the best algorithm to predict hospital readmission in patients with HF. Conclusions The ANN algorithm performed best in predicting HF severity and hospital readmissions and will be integrated into a mobile application for patient self-monitoring to prevent readmissions.

The sequestration of infected erythrocytes in the placenta can activate the syncytiotrophoblast to release cytokines that affect the micro-environment and influence the delivery of nutrients and oxygen to fetus. The high level of IL-10 has been reported in the intervillous space and could prevent the pathological effects. There is still no data of Th17 involvement in the pathogenesis of placental malaria. This study was conducted to reveal the influence of placental IL-17 and IL-10 levels on fetal weights in malaria placenta. Seventeen pregnant BALB/C mice were divided into control (8 pregnant mice) and treatment group (9 pregnant mice infected by Plasmodium berghei). Placental specimens stained with hematoxylin and eosin were examined to determine the level of cytoadherence by counting the infected erythrocytes in the intervillous space of placenta. Levels of IL-17 and IL-10 in the placenta were measured using ELISA. All fetuses were weighed by analytical balance. Statistical analysis using Structural Equation Modeling showed that cytoadherence caused an increased level of placental IL-17 and a decreased level of placental IL-10. Cytoadherence also caused low fetal weight. The increased level of placental IL-17 caused low fetal weight, and interestingly low fetal weight was caused by a decrease of placental IL-10. It can be concluded that low fetal weight in placental malaria is directly caused by sequestration of the parasites and indirectly by the local imbalance of IL-17 and IL-10 levels.
Animals ; Female ; *Fetal Weight ; Humans ; Interleukin-10/*analysis/metabolism ; Interleukin-17/*analysis/metabolism ; Malaria/*metabolism/parasitology/physiopathology ; Male ; Mice ; Mice, Inbred BALB C ; Placenta/*chemistry/metabolism ; Plasmodium berghei/*physiology ; Pregnancy ; Pregnancy Complications, Parasitic/*metabolism/parasitology/physiopathology

Introduction: As the important part of tooth, enamel must be treated well in order to keep its function. The purpose of study is provide tooth composition for various condition and at different ages. Methods: Children (1-2 years), adults (13-40 years), and parents (41-70 years) tooth, aquabidest, abrasive paper, tooth bor, caliper, trimmer, Machines for Hardness test, compressive test and XRD. Each test sample will be cut, mashed, washed and soaked in aquabidest in refrigerator and then to be examined. Results: Micro test results were showed that the main components of the teeth are Hydroxy apatite, Fluorine apatite, and Calcium phosphate hydrate. Transition glass occurs in the age range 1-12 years old at 340o C and in the age range 13-40 years old at a temperature of 400o C , however at the age of 41-70 years old at temperatures of 360o C based on the thermal test results, melting point and boiling point. Mechanical test was showed the level of violence has increased from the age of 1-40 years old then later has decreased, as well as for compressive strength. Hydroxy apatite, Fluorine apatite increase, and Calcium phosphate hydrate decreases in line with age. Tooth enamel also tends to be able to withstand heat in line with age. Conclusion: The level of hardness and tooth strength tends to increase temporarily in older people’s teeth as the calcium content decreases, thus its mechanical strength decreases.

Introduction: The quality of Zinc Oxide Eugenol-Ethoxy Benzoic Acid (ZOE-EBA) dental cement could be improved by the addition of Aluminum Oxide (Al2 O3 ). It was caused by the characteristic of alumina which are easy on fabrication process, resistant on corrosion, endurance usage, bioinert, and biocompatible. The purpose of this study was to determine the effect of the addition of Al2 O3 in ZOE-EBA cement. Methods: Nanoparticle of ZnO (zinc oxide), Al2 O3 , MgO (magnesium oxide),eugenol liquid and EBA (Ethoxy Benzoic Acid) fluid. The variations of Al2 O3 were 24%, 26%, 28%, 30%. First is the sintering on 1000°C and tested by XRD. Sintered powder was mixed with liquid, with a ratio of powder: liquid 7:1. The mechanical characteristic are compressive strength and hardness. Results: XRD test is showed that ZnO has dominant phase on the sample and there was new phase on cement powder such as Zinc-Aluminium oxide (ZnAl2 O4 ). The best result was shown on the addition of 26% of Al2 O3 composition in the 3 type test because the sample had ZnAl2 O4 phase volume fewer than 28% and 30% of Al2 O3 . This result was supported by the compressive strength and hardness which showed the optimum value at concentrations of 26%, which were 64.49 MPa and hardness of 69.33 VHN. Conclusion: Based on the result, it was found that Al2 O3 variation gives the best results in the teeth ZOE-EBA cement was 26%.