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

Background and Objective: Premature ventricular complex (PVC) burden exhibits one of three circadian types, classified as fast-type, slow-type, and independent-type PVC. It is unknown whether PVC circadian types have different heart rate variability (HRV) parameter values. Therefore, this study aimed to evaluate differences in HRV circadian rhythm among fast-, slow-, and independent-type PVC. Methods: This cross-sectional observational study consecutively recruited 65 idiopathic PVC subjects (23 fast-, 20 slow-, and 22 independent-type) as well as five control subjects. Each subject underwent a 24-hour Holter to examine PVC burden and HRV. HRV analysis included components that primarily reflect global, parasympathetic, and sympathetic activities. Repeated measures analysis of variance was used to compare differences in HRV circadian rhythm by PVC type. Results. The average PVC burden was 15.7%, 8.4%, and 13.6% in fast-, slow-, and independent-type idiopathic PVC subjects, respectively. Global, parasympathetic nervous system, and sympathetic nervous system HRV parameters were significantly lower in independenttype PVC versus fast- and slow-type PVC throughout the day and night. Furthermore, we unexpectedly found that tendency towards sympathetic activity dominance during nighttime was only in independent-type PVC. Conclusion The HRV parameters are reduced in patients with independent-type PVC compared to fast- and slowtype PVC. Future research is warranted to determine possible differences in the prognosis between the three PVC types.
Ventricular Premature Complexes ; Circadian Rhythm ; Autonomic Nervous System