ABSTRACT
Background: Infectious illnesses are a serious health concern in Indonesia. Widespread use of self-medication by the community increases the risk of developing multi-drug resistant (MDR) bacteria. This study assessed the potential of sappan wood as an inhibitor of extended-spectrum beta-lactamase (ESBL) encoded by blaSHV, blaTEM and blaCTX-M genes. Method: In silico testing was conducted to develop an effective and economical starting strategy. Thereby, this study significantly advances the development of novel treatments to combat antibiotic resistance. Using clavulanic acid as the benchmark medicine, the potency of the beta-lactamase inhibitor brazilein was predicted. Using the Molegro Virtual Docker computer tool, docking was performed to estimate the chemical and physical properties of the compounds, as well as the biological activity of brazilein toward the required receptor. The receptors used were SHV-1 beta-lactamase, PDB code: 2H0T; TEM-1 beta-lactamase, PDB code: 4OQG and CTX-M-14 beta-lactamase, PDB code: 6VHS. Data analysis was performed by comparing the binding energies of the docking results between the ligands and the target receptor. The more stable the bond that formed between the ligand and the target receptor, the lower the bond energy. Results: The in silico test results on the blaSHV gene were as follows: binding energy of ligand MA4_400[A] = -100.699, brazilein = -82.206, clavulanic acid = -79.3704; in the blaTEM gene: ligand bond energy 2UL_301[B] = -107.681, brazilein = -82.0296, clavulanic acid = -103.3; in the blaCTX-M gene: X57_301[A] ligand bond energy = -86.6197, and brazilein = -88.1586, clavulanic acid = -101.933. Conclusion: The findings of this study demonstrate the significant potential of brazilein sappan wood to block the beta-lactamase activity of blaCTX-M.
ABSTRACT
Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites' stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.
ABSTRACT
BACKGROUND: Indonesia's hospital-based Severe Acute Respiratory Infection (SARI) surveillance system, Surveilans Infeksi Saluran Pernafasan Akut Berat Indonesia (SIBI), was established in 2013. While respiratory illnesses such as SARI pose a significant problem, there are limited incidence-based data on influenza disease burden in Indonesia. This study aimed to estimate the incidence of influenza-associated SARI in Indonesia during 2013-2016 at three existing SIBI surveillance sites. METHODS: From May 2013 to April 2016, inpatients from sentinel hospitals in three districts of Indonesia (Gunung Kidul, Balikpapan, Deli Serdang) were screened for SARI. Respiratory specimens were collected from eligible inpatients and screened for influenza viruses. Annual incidence rates were calculated using these SIBI-enrolled influenza-positive SARI cases as a numerator, with a denominator catchment population defined through hospital admission survey (HAS) to identify respiratory-coded admissions by age to hospitals in the sentinel site districts. RESULTS: From May 2013 to April 2016, there were 1527 SARI cases enrolled, of whom 1392 (91%) had specimens tested and 199 (14%) were influenza-positive. The overall estimated annual incidence of influenza-associated SARI ranged from 13 to 19 per 100 000 population. Incidence was highest in children aged 0-4 years (82-114 per 100 000 population), followed by children 5-14 years (22-36 per 100 000 population). CONCLUSIONS: Incidence rates of influenza-associated SARI in these districts indicate a substantial burden of influenza hospitalizations in young children in Indonesia. Further studies are needed to examine the influenza burden in other potential risk groups such as pregnant women and the elderly.