Detection of Vancomycin Resistant Genes in Intrinsically Antibiotic Resistant Bacteria from the Gut Microbiota of Indonesian Individuals.

Background: Antibiotic resistance is a global public health concern that has been exacerbated by the overuse and misuse of antibiotics, leading to the emergence of resistant bacteria. The gut microbiota, often influenced by antibiotic usage, plays a crucial role in overall health. Therefore, this study aimed to investigate the prevalence of antibiotic resistant genes in the gut microbiota of Indonesian coastal and highland populations, as well as to identify vancomycin-resistant bacteria and their resistant genes. Methods: Stool samples were collected from 22 individuals residing in Pacet, Mojokerto, and Kenjeran, Surabaya Indonesia in 2022. The read count of antibiotic resistant genes was analyzed in the collected samples, and the bacterium concentration was counted by plating on the antibiotic-containing agar plate. Vancomycin-resistant strains were further isolated, and the presence of vancomycin-resistant genes was detected using a multiplex polymerase chain reaction (PCR). Results: The antibiotic resistant genes for tetracycline, aminoglycosides, macrolides, beta-lactams, and vancomycin were found in high frequency in all stool samples (100%) of the gut microbiota. Meanwhile, those meant for chloramphenicol and sulfonamides were found in 86% and 16% of the samples, respectively. Notably, vancomycin-resistant genes were found in 16 intrinsically resistant Gram-negative bacterial strains. Among the detected vancomycin-resistant genes, vanG was the most prevalent (27.3%), while vanA was the least prevalent (4.5%). Conclusion: The presence of multiple vancomycin resistance genes in intrinsically resistant Gram-negative bacterial strains demonstrated the importance of the gut microbiota as a reservoir and hub for the horizontal transfer of antibiotic resistant genes.

Enhanced leachate phytodetoxification test combined with plants and rhizobacteria bioaugmentation.

Plant combination and rhizobacterial bioaugmentation are the modification of constructed wetlands (CWs) to promote the detoxification of leachate. In this study, characterization of leachate was carried out to ensure the maximum concentration of leachate that did not affect the plant's growth. Herein, the identification of leachate-resistant rhizobacteria is used to determine the type of bacteria that is resistant and has the potential for leachate processing in the next step. The phytodetoxification test is carried out by comparing the addition of rhizobacteria and without the addition of rhizobacteria to detox leachate parameter Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), Total Suspended Solid (TSS), Total Nitrogen (TN), Cadmium (Cd), and Mercury (Hg). Results showed that used plants could still live in the largest leachate concentration of 100%. The rhizobacteria that were identified and bioaugmented in the reactor were Bacillus cereus, Nitrosomonas communis, and Pseudomonas aeruginosa. Phytodetoxification test by a single plant showed the efficiency ranged between 40% and 70%. The addition of rhizobacterial bioaugmentation and plant combination can improve the percentage of COD 80.47%, BOD 84.05%, TSS 80.05%, TN 75.58%, Cd 99.96%, and Hg 90%. These modifications are very influential for leachate detoxification through plant uptake and rhizodegradation processes.

Electronic nose homogeneous data sets for beef quality classification and microbial population prediction.

OBJECTIVES: In recent years, research on the use of electronic noses (e-nose) has developed rapidly, especially in the medical and food fields. Typically, e-nose is coupled with machine learning algorithms to detect or predict multiple sensory classes in a given sample. In many cases, comprehensive and complete experiments are required to ensure the generalizability of the predictive model. For this reason, homogeneous data sets are important to use. Homogeneous data sets refer to the data sets obtained from different observations in almost similar environmental condition. In this data article, e-nose homogeneous data sets are provided for beef quality classification and microbial population prediction. DATA DESCRIPTION: This data set is originated from 12 type of beef cuts. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors for 2220 min. The formal standards, issued by the Meat Standards Committee, are used as a reference in labeling beef quality. Based on the number of microbial populations, meat quality was grouped into four classes, namely excellent, good, acceptable, and spoiled. The data set is formatted in "xlsx" file. Each sheet represents one beef cut. Moreover, data sets are good cases for feature selection algorithm stability test, especially to solve sensor array optimization problems.

Electronic nose dataset for beef quality monitoring in uncontrolled ambient conditions.

In recent years, the development of a rapid, simple, and low-cost meat assessment system using an electronic nose (e-nose) has been the concern of researchers. In this data article, we provide a time series dataset that was obtained from a beef quality monitoring experiment using an e-nose in uncontrolled ambient conditions. The availability of this dataset will enable discussion on how to deal with noisy e-nose signals and non-optimum sensor array in beef quality monitoring. Hence, the development of proper signal processing and robust machine learning algorithm are several challenges that must be faced. Furthermore, this dataset can also be useful as a comparison dataset for similar e-nose applications, such as air quality monitoring, smart packaging system, and food quality monitoring.