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Real-world data on heterologous boosting with messenger RNA (mRNA)-1273 (Moderna) after inactivated COVID-19 vaccination are limited. We report mRNA-1273 boosting in heavily SARS-CoV-2-exposed Indonesian health-care workers who received a two-dose CoronaVac 6 months prior. Between August and November 2021, we measured SARS-CoV-2 spike-specific IgG binding antibody (Bab) titers in all 304 participants, and neutralizing antibody titers in a random subset of 71 participants, on stored paired serum samples taken before and 28 days after a full-dose (100-μg) mRNA-1273 booster. At the time of the mRNA-1273 boost, 107 participants (35.2%) were not previously infected (naive vaccinated), 42 (13.8%) were infected before CoronaVac (infected vaccinated), and 155 (51.0%) were infected after CoronaVac (mostly during the Delta wave; vaccinated infected). At time of the mRNA-1273 boost, neutralizing antibodies could still be detected in 83% of participants (59 of 71) overall, 60% of naive-vaccinated participants (15 of 25), 95.7% of vaccinated-infected participants (22 of 23), and 95.7% of infected vaccinated participants (22 of 23). After the mRNA-1273 boost, 100% of participants (71 of 71) had neutralizing antibody activity, with increases in median Bab and neutralizing antibody serum titers of 9.3- and 27.0-fold overall, 89.1- and 2,803.4-fold in naive-vaccinated participants, 15.9- and 19.9-fold in infected-vaccinated participants, and 2.2- and 18.4-fold in vaccinated-infected participants. In the multivariable analysis, Bab titers after the mRNA-1273 boost were greatest in individuals who had a previous virus breakthrough post-CoronaVac, and when a longer time period (> 4 months) had elapsed since the most recent prior "spike antigen exposure" (either second CoronaVac or virus breakthrough). Overall, adverse reactions were mild and short-lived. In conclusion, a full-dose mRNA-1273 booster after CoronaVac was well tolerated and immunogenic after 28 days, including in those with very low antibody levels.

In systems connected to smart grids, smart meters with fast and efficient responses are very helpful in detecting anomalies in realtime. However, sending data with a frequency of a minute or less is not normal with today's technology because of the bottleneck of the communication network and storage media. Because mitigation cannot be done in realtime, we propose prediction techniques using Deep Neural Network (DNN), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN). In addition to these techniques, the prediction timestep is chosen per day and wrapped in sliding windows, and clustering using Kmeans and intersection Kmeans and HDBSCAN is also evaluated. The predictive ability applied here is to predict whether anomalies in electricity usage will occur in the next few weeks. The aim is to give the user time to check their usage and from the utility side, whether it is necessary to prepare a sufficient supply. We also propose the latency reduction to counter higher latency as in the traditional centralized system by adding layer Edge Meter Data Management System (MDMS) and Cloud-MDMS as the inference and training model. Based on the experiments when running in the Raspberry Pi, the best solution is choosing DNN that has the shortest latency 1.25 ms, 159 kB persistent file size, and at 128 timesteps.

The human renin gene has been widely known to be involved in essential hypertension (EH) pathogenesis. Genetic variant C-5434T of REN enhancer contributed to renin gene transcription and serum renin regulation. However, the mechanism associated with the transcription level changes remains unknown, and only a few reports exist that discussed serum renin levels on C-5434T of REN. Thus, this study aims to investigate the relationship between genetic variant C-5434T of REN enhancer and serum renin levels in Indonesian hypertensive patients. SNP of C-5434T was genotyped in 56 hypertensive patients by using RFLP. The data showed that serum renin is slightly higher in hypertensive patients with the TT genotype (39 ± 10.3) than patients with the CC genotype (33 ± 10.6) but the difference was not statistically significant (p = 0.35). Here, we also present a docking approach for predicting interaction between genetic variant -5434C/T and STAT3 (Signal Transducers and Activators Transcription 3), the predicted transcription factor that regulates renin gene enhancer. The results showed that STAT3-DNA allele T more favorably binds to DNA than STAT3-DNA allele C. These data suggest that the presence of genetic variant C-5434T has changed the binding pattern of STAT3 to REN enhancer. This is likely to influence STAT3 activity to stimulate the expression of renin gene in producing renin.