Arsenic speciation, the abundance of arsenite-oxidizing bacteria and microbial community structures in groundwater, surface water, and soil from a gold mine.

The arsenic speciation, the abundance of arsenite-oxidizing bacteria, and microbial community structures in the groundwater, surface water, and soil from a gold mining area were explored using the PHREEQC model, cloning-ddPCR of the aioA gene, and high-throughput sequencing of the 16S rRNA gene, respectively. The analysis of the aioA gene showed that arsenite-oxidizing bacteria retrieved from groundwater, surface water, and soil were associated with Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria. In groundwaters from the mining area, there were relatively high ratios of aioA/total 16S rRNA gene copies and the dominance of As5+, which suggested the presence and activity of arsenite-oxidizing bacteria. Metagenomic analysis revealed that the majority of the soil and surface water microbiomes were Proteobacteria, Actinobacteria, Bacteroidetes, and Chloroflexi, whereas the groundwater microbiomes were dominated exclusively by Betaproteobacteria and Alphaproteobacteria. Geochemical factors influencing the microbial structure in the groundwater were As, residence time, and groundwater flowrate, while those showing a positive correlation to the microbial structure in the surface water were TOC, ORP, and DO. This study provides insights into the groundwater, surface water, and soil microbiomes from a gold mine and expands the current understanding of the diversity and abundance of arsenite-oxidizing bacteria, playing a vital role in global As cycling.

Automating the Generation of Notifiable Bacterial Disease Reports: Proof-of-Concept Study and Implementation in Six Hospitals in Thailand.

Information on notifiable bacterial diseases (NBD) in low- and middle-income countries (LMICs) is frequently incomplete. We developed the AutoMated tool for the Antimicrobial resistance Surveillance System plus (AMASSplus), which can support hospitals to analyze their microbiology and hospital data files automatically (in CSV or Excel format) and promptly generate antimicrobial resistance surveillance and NBD reports (in PDF and CSV formats). The NBD reports included the total number of cases and deaths after Brucella spp., Burkholderia pseudomallei, Corynebacterium diphtheriae, Neisseria gonorrhoeae, Neisseria meningitidis, nontyphoidal Salmonella spp., Salmonella enterica serovar Paratyphi, Salmonella enterica serovar Typhi, Shigella spp., Streptococcus suis, and Vibrio spp. infections. We tested the tool in six hospitals in Thailand in 2022. The total number of deaths identified by the AMASSplus was higher than those reported to the national notifiable disease surveillance system (NNDSS); particularly for B. pseudomallei infection (134 versus 2 deaths). This tool could support the NNDSS in LMICs.

Frequency and mortality rate following antimicrobial-resistant bloodstream infections in tertiary-care hospitals compared with secondary-care hospitals.

There are few studies comparing proportion, frequency, mortality and mortality rate following antimicrobial-resistant (AMR) infections between tertiary-care hospitals (TCHs) and secondary-care hospitals (SCHs) in low and middle-income countries (LMICs) to inform intervention strategies. The aim of this study is to demonstrate the utility of an offline tool to generate AMR reports and data for a secondary data analysis. We conducted a secondary-data analysis on a retrospective, multicentre data of hospitalised patients in Thailand. Routinely collected microbiology and hospital admission data of 2012 to 2015, from 15 TCHs and 34 SCHs were analysed using the AMASS v2.0 (www.amass.website). We then compared the burden of AMR bloodstream infections (BSI) between those TCHs and SCHs. Of 19,665 patients with AMR BSI caused by pathogens under evaluation, 10,858 (55.2%) and 8,807 (44.8%) were classified as community-origin and hospital-origin BSI, respectively. The burden of AMR BSI was considerably different between TCHs and SCHs, particularly of hospital-origin AMR BSI. The frequencies of hospital-origin AMR BSI per 100,000 patient-days at risk in TCHs were about twice that in SCHs for most pathogens under evaluation (for carbapenem-resistant Acinetobacter baumannii [CRAB]: 18.6 vs. 7.0, incidence rate ratio 2.77; 95%CI 1.72-4.43, p<0.001; for carbapenem-resistant Pseudomonas aeruginosa [CRPA]: 3.8 vs. 2.0, p = 0.0073; third-generation cephalosporin resistant Escherichia coli [3GCREC]: 12.1 vs. 7.0, p<0.001; third-generation cephalosporin resistant Klebsiella pneumoniae [3GCRKP]: 12.2 vs. 5.4, p<0.001; carbapenem-resistant K. pneumoniae [CRKP]: 1.6 vs. 0.7, p = 0.045; and methicillin-resistant Staphylococcus aureus [MRSA]: 5.1 vs. 2.5, p = 0.0091). All-cause in-hospital mortality (%) following hospital-origin AMR BSI was not significantly different between TCHs and SCHs (all p>0.20). Due to the higher frequencies, all-cause in-hospital mortality rates following hospital-origin AMR BSI per 100,000 patient-days at risk were considerably higher in TCHs for most pathogens (for CRAB: 10.2 vs. 3.6,mortality rate ratio 2.77; 95%CI 1.71 to 4.48, p<0.001; CRPA: 1.6 vs. 0.8; p = 0.020; 3GCREC: 4.0 vs. 2.4, p = 0.009; 3GCRKP, 4.0 vs. 1.8, p<0.001; CRKP: 0.8 vs. 0.3, p = 0.042; and MRSA: 2.3 vs. 1.1, p = 0.023). In conclusion, the burden of AMR infections in some LMICs might differ by hospital type and size. In those countries, activities and resources for antimicrobial stewardship and infection control programs might need to be tailored based on hospital setting. The frequency and in-hospital mortality rate of hospital-origin AMR BSI are important indicators and should be routinely measured to monitor the burden of AMR in every hospital with microbiology laboratories in LMICs.

Local and timely antimicrobial resistance data for local and national actions: the early implementation of an automated tool for data analysis at local hospital level in Thailand.

Background: In low- and middle-income countries (LMICs), hospitals can rarely utilize their own antimicrobial resistance (AMR) data in a timely manner. Objectives: To evaluate the utility of local AMR data generated by an automated tool in the real-world setting. Methods: From 16 December 2022 to 10 January 2023, on behalf of the Health Administration Division, Ministry of Public Health (MoPH) Thailand, we trained 26 public tertiary-care and secondary-care hospitals to utilize the AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) with their own microbiology and hospital admission data files via two online meetings, one face-to-face meeting and online support. All meetings were recorded on video, and feedback was analysed. Results: Twenty-five hospitals successfully generated and shared the AMR reports with the MoPH by 28 February 2023. In 2022, the median frequency of hospital-origin bloodstream infections (BSIs) caused by carbapenem-resistant Escherichia coli (CREC) was 129 (range 0-1204), by carbapenem-resistant Klebsiella pneumoniae (CRKP) was 1306 (range 0-5432) and by carbapenem-resistant Acinetobacter baumannii (CRAB) was 4472 (range 1460-11 968) per 100 000 patients tested for hospital-origin BSI. The median number of all-cause in-hospital deaths with hospital-origin AMR BSI caused by CREC was 1 (range 0-18), by CRKP was 10 (range 0-77) and by CRAB was 56 (range 7-148). Participating hospitals found that the data obtained could be used to support their antimicrobial stewardship and infection prevention control programmes. Conclusions: Local and timely AMR data are crucial for local and national actions. MoPH Thailand is inviting all 127 public tertiary-care and secondary-care hospitals to utilize the AMASS. Using any appropriate analytical software or tools, all hospitals in LMICs that have electronic data records should analyse and utilize their data for immediate actions.

Microbial community structure in aquifers associated with arsenic: analysis of 16S rRNA and arsenite oxidase genes.

The microbiomes of deep and shallow aquifers located in an agricultural area, impacted by an old tin mine, were explored to understand spatial variation in microbial community structures and identify environmental factors influencing microbial distribution patterns through the analysis of 16S rRNA and aioA genes. Although Proteobacteria, Cyanobacteria, Actinobacteria, Patescibacteria, Bacteroidetes, and Epsilonbacteraeota were widespread across the analyzed aquifers, the dominant taxa found in each aquifer were unique. The co-dominance of Burkholderiaceae and Gallionellaceae potentially controlled arsenic immobilization in the aquifers. Analysis of the aioA gene suggested that arsenite-oxidizing bacteria phylogenetically associated with Alpha-, Beta-, and Gamma proteobacteria were present at low abundance (0.85 to 37.13%) and were more prevalent in shallow aquifers and surface water. The concentrations of dissolved oxygen and total phosphorus significantly governed the microbiomes analyzed in this study, while the combination of NO3 --N concentration and oxidation-reduction potential significantly influenced the diversity and abundance of arsenite-oxidizing bacteria in the aquifers. The knowledge of microbial community structures and functions in relation to deep and shallow aquifers is required for further development of sustainable aquifer management.