Antibacterial activity of Stachytarpheta jamaicensis (L.) Vahl roots extract on some bacteria proteins: An in silico and in vitro study

Context: Stachytarpheta jamaicensis (L.) Vahl plant is used for traditional therapy because of its content, including flavonoids, alkaloids, tannins, saponins, terpenoids, and coumarins. Aims: To determine the antibacterial ability of S. jamaicensis roots extract (SJRE) on some selected mouth bacteria through in vitro and in silico studies. Methods: Phytochemical analysis and liquid chromatography-high resolution mass spectrometry (LC-HRMS) were done to explore the active compounds on SJRE. Absorption, distribution, metabolism, excretion and toxicity prediction, molecular docking simulation and visualization of luvangetin, and xanthyletin as anti-inflammatory and antibacterial were investigated in silico. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of SJRE against Aggregatibacter actinomycetemcomitans, Enterococcus faecalis, and Actinomyces spp. were calculated. Results: Luvangetin and xanthyletin are good candidate drug molecules with low toxicity. Xanthyletin has higher binding activity than luvangetin to TNF-alpha, IL6, IL-10, peptidoglycan, flagellin, and dectin protein. SJRE exhibited a high antibacterial ability, and MIC. This extract inhibits the growth of A. actinomycetemcomitans, E. faecalis and Actinomyces spp. at various concentrations 2000, 8000, and 8000 mu g/mL, respectively, with statistically significant differences (p = 0.0001;p<0.05). Conclusions: SJRE has an antibacterial ability, and 2000 mu g/mL SJRE may act as an antibacterial agent in vitro. In addition, xanthyletin in SJRE has a potential role as an antibacterial and anti-inflammatory in silico.

Impact of the COVID-19 pandemic on admission of acute coronary syndrome patients: A local insight from iSTEMI registry

Aims: Despite the spike in COVID-19 hospitalizations, the number of acute coronary syndrome (ACS) admissions has declined significantly. It raises concerns about the long-term consequences of cardiovascular problems. This study aims to provide new insights into our awareness of the pandemic situation in ACS settings. Methods: We performed a single centre retrospective analysis of 397 patients from iSTEMI Registry between March-October 2019 (Pre-COVID-19 pandemic) and March-October 2020 (COVID-19 pandemic) with ACS (i.e., unstable angina (UA), non- ST-segment elevation myocardial infarction (NSTEMI), and STEMI). We analyzed case fatality rate, delay to First Medical Contact (FMC), and troponin findings. Results: The number of ACS patients admissions in March-October 2019 (254 patients) and 2020 (143 patients) had significantly reduced by 43.7%. Admission of ACS significantly dropped for STEMI (35.76%, p=0.048) and NSTEMI (65.70%, p=0.001), but not for UA (31.90%, p=0.262). There was non-significant case fatality rate between-group of STEMI (p=0.168), NSTEMI (p=0.388), or UA (p=0.343). We found a significant delay to FMC during pandemic (39.8%, p=0.000) and correlated with the higher troponin I level (30,6%, p=0.001). Conclusion: This study reveals that local conditions in our institution are similar to the other centers during the COVID-19 pandemic. In parallel to the COVID-19 burden, we will have to deal with the morbidity and mortality caused by delays in FMC and patients severity in the future. Further studies are needed to analyze the factors that decreased ACS patients' admission.

Development of Compound Non-Standard Word Dataset Using Crowdsourcing Method

During the COVID-19 Pandemic, many activities, such as communication and socialization are forced to be carried out through digital devices. Many communication applications have been developed to improve the users' communication experience, namely chatbots, auto-translation, sentiment analysis and many other applications. However, most Indonesian use non-standard words when communicating with each other. These non-standard words used caused the text processing software work to be nonoptimal. This research aims to create a corpus of standard and non-standard words. This corpus can then be used to normalize the non-standard word to the standard version. The crowdsourcing method was chosen to create the dataset. This research has successfully collected 371 records as final corpus data. The most common problem is the difference in respondents' perceptions in determining single or compound non-standard words. There were 5 problems found in the forming of corpus data, namely character repetition in non-standard words, various forms of loan words, typing errors, differences between respondent's answers, and expression word without standard. © 2021 IEEE.