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The atmosphere is vastly underexplored as a habitable ecosystem for microbial organisms. In this study, we investigated 795 time-resolved metagenomes from tropical air, generating 2.27 terabases of data. Despite only 9 to 17% of the generated sequence data currently being assignable to taxa, the air harbored a microbial diversity that rivals the complexity of other planetary ecosystems. The airborne microbial organisms followed a clear diel cycle, possibly driven by environmental factors. Interday taxonomic diversity exceeded day-to-day and month-to-month variation. Environmental time series revealed the existence of a large core of microbial taxa that remained invariable over 13 mo, thereby underlining the long-term robustness of the airborne community structure. Unlike terrestrial or aquatic environments, where prokaryotes are prevalent, the tropical airborne biomass was dominated by DNA from eukaryotic phyla. Specific fungal and bacterial species were strongly correlated with temperature, humidity, and CO2 concentration, making them suitable biomarkers for studying the bioaerosol dynamics of the atmosphere.
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Microbiologia do Ar , Microbiota , Clima Tropical , Poluentes Atmosféricos/análise , Ritmo Circadiano , Ecossistema , Metagenoma , Modelos Biológicos , SingapuraRESUMO
BACKGROUND: As new technologies emerge, there is a significant shift in the way care is delivered on a global scale. Artificial intelligence (AI) technologies have been rapidly and inexorably used to optimize patient outcomes, reduce health system costs, improve workflow efficiency, and enhance population health. Despite the widespread adoption of AI technologies, the literature on patient engagement and their perspectives on how AI will affect clinical care is scarce. Minimal patient engagement can limit the optimization of these novel technologies and contribute to suboptimal use in care settings. OBJECTIVE: We aimed to explore patients' views on what skills they believe health care professionals should have in preparation for this AI-enabled future and how we can better engage patients when adopting and deploying AI technologies in health care settings. METHODS: Semistructured interviews were conducted from August 2020 to December 2021 with 12 individuals who were a patient in any Canadian health care setting. Interviews were conducted until thematic saturation occurred. A thematic analysis approach outlined by Braun and Clarke was used to inductively analyze the data and identify overarching themes. RESULTS: Among the 12 patients interviewed, 8 (67%) were from urban settings and 4 (33%) were from rural settings. A majority of the participants were very comfortable with technology (n=6, 50%) and somewhat familiar with AI (n=7, 58%). In total, 3 themes emerged: cultivating patients' trust, fostering patient engagement, and establishing data governance and validation of AI technologies. CONCLUSIONS: With the rapid surge of AI solutions, there is a critical need to understand patient values in advancing the quality of care and contributing to an equitable health system. Our study demonstrated that health care professionals play a synergetic role in the future of AI and digital technologies. Patient engagement is vital in addressing underlying health inequities and fostering an optimal care experience. Future research is warranted to understand and capture the diverse perspectives of patients with various racial, ethnic, and socioeconomic backgrounds.
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BACKGROUND: As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. OBJECTIVE: With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness. METHODS: After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). RESULTS: Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. CONCLUSIONS: This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.
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BACKGROUND: Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today's health care providers. OBJECTIVE: The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. METHODS: To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. RESULTS: The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. CONCLUSIONS: Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30940.
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BACKGROUND: Enterobacter cloacae complex (ECC) bacteria, such as E. cloacae, E. sichuanensis, E. kobei, and E. roggenkampii, have been emerging as nosocomial pathogens. Many strains isolated from medical clinics were found to be resistant to antibiotics, and in the worst cases, acquired multidrug resistance. We present the whole genome sequence of SGAir0282, isolated from the outdoor air in Singapore, and its relevance to other ECC bacteria by in silico genomic analysis. RESULTS: Complete genome assembly of E. sichuanensis strain SGAir0282 was generated using PacBio RSII and Illumina MiSeq platforms, and the datasets were used for de novo assembly using Hierarchical Genome Assembly Process (HGAP) and error corrected with Pilon. The genome assembly consisted of a single contig of 4.71 Mb and with a G+C content of 55.5%. No plasmid was detected in the assembly. The genome contained 4371 coding genes, 83 tRNA and 25 rRNA genes, as predicted by NCBI's Prokaryotic Genome Annotation Pipeline (PGAP). Among the genes, the antibiotic resistance related genes were included: Streptothricin acetdyltransferase (SatA), fosfomycin resistance protein (FosA) and metal-dependent hydrolases of the beta-lactamase superfamily I (BLI). CONCLUSION: Based on whole genome alignment and phylogenetic analysis, the strain SGAir0282 was identified to be Enterobacter sichuanensis. The strain possesses gene clusters for virulence, disease and defence, that can also be found in other multidrug resistant ECC type strains.
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Brevundimonas sp. strain SGAir0440 was isolated from indoor air samples collected in Singapore. Its genome was assembled using single-molecule real-time sequencing data, resulting in one circular chromosome with a length of 3.1 Mbp. The genome consists of 3,033 protein-coding genes, 48 tRNAs, and 6 rRNA operons.
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Enterococcus faecalis strain SGAir0397 was isolated from a tropical air sample collected in Singapore. Its genome was assembled using single-molecule real-time sequencing data and comprises one circular chromosome with a length of 2.69 Mbp. The genome contains 2,595 protein-coding genes, 59 tRNAs, and 12 rRNAs.
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Bacillus megaterium strain SGAir0080 was isolated from a tropical air sample in Singapore. Its genome was assembled using single-molecule real-time (SMRT) sequencing and MiSeq reads. It has one chromosome of 5.06 Mbp and seven plasmids (average length, 62.8 kbp). It possesses 5,339 protein-coding genes, 130 tRNAs, and 35 rRNAs.
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Lysinibacillus sp. strain SGAir0095 was isolated from tropical air samples collected in Singapore, and its complete genome was sequenced with a hybrid strategy using single-molecule real-time sequencing and short reads. The genome consists of one chromosome of 4.14 Mbp and encompasses 3,885 protein-coding genes, 39 rRNAs, and 101 tRNAs.
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Nissabacter sp. strain SGAir0207 was isolated from a tropical air sample collected in Singapore. Its genome was assembled using a hybrid approach with long and short reads, resulting in one chromosome of 3.9 Mb and 7 plasmids. The complete genome consists of 4,403 protein-coding, 84 tRNA, and 22 rRNA genes.
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Brachybacterium sp. strain SGAir0954 was isolated from tropical air collected in Singapore, and its genome was sequenced and assembled using long reads generated by single-molecule real-time (SMRT) sequencing. The complete genome has a size of 3.41 Mb and consists of 2,955 protein coding genes, 50 tRNAs, and 9 rRNAs.
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Agrococcus sp. strain SGAir0287 was isolated from tropical air samples collected in Singapore. Assembled using single-molecule real-time (SMRT) sequencing and MiSeq reads, the genome consists of one circular chromosome of 3,084,767 bp. The entire genome has 2,870 protein-coding genes, 45 tRNAs, and 3 rRNAs.
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The Pontibacter bacterial genus has been detected in marine and soil environments. Here, we report the genome sequence of Pontibacter sp. strain SGAir0037, which was isolated from outdoor air samples collected in Singapore. The genome comprises one chromosome of 5.26 Mb and one plasmid of 127 kb.
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Microbacterium sp. strain SGAir0570 was isolated from air samples collected in Singapore. Its genome was assembled using single-molecule real-time sequencing and MiSeq short reads. It has one chromosome with a length of 3.38 Mb and one 59.2-kb plasmid. It contains 3,170 protein-coding genes, 48 tRNAs, and 6 rRNAs.
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Pseudomonas sp. strain SGAir0191 was isolated from an air sample collected in Singapore, and its genome was sequenced using a combination of long and short reads to generate a high-quality genome assembly. The complete genome is approximately 5.07 Mb with 4,370 protein-coding genes, 19 rRNAs, and 73 tRNAs.
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Serratia marcescens strain SGAir0764 was isolated from a tropical air sample collected in Singapore. The complete genome, sequenced on the PacBio RS II platform, consists of one chromosome with 5.1 Mb and one plasmid with 76.4 kb. Genome annotation predicts 4,723 protein-coding genes, 89 tRNAs, and 22 rRNAs.
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Bacillus velezensis strain SGAir0473 (Firmicutes) was isolated from tropical air collected in Singapore. Its genome was assembled using short reads and single-molecule real-time sequencing and comprises one chromosome with 4.18 Mb. The genome consists of 3,937 protein-coding genes, 86 tRNAs, and 27 rRNAs.
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Pantoea ananatis SGAir0210 was isolated from outdoor air collected in Singapore. The genome was assembled from long reads generated by single-molecule real-time sequencing complemented with short reads. The genome size was approximately 4.81 Mb, with 4,303 protein-coding genes, 80 tRNAs, and 22 rRNAs identified.
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Lelliottia nimipressuralis type strain SGAir0187 was isolated from tropical air samples collected in Singapore. The genome was assembled with an average coverage of 180-fold using Pacific Biosciences long reads and Illumina MiSeq paired-end reads. The genome measures 4.8 Mb and contains 4,424 protein-coding genes, 83 tRNAs, and 25 rRNAs.
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Acinetobacter indicus (Gammaproteobacteria) is a strict aerobic nonmotile bacterium. The strain SGAir0564 was isolated from air samples collected in Singapore. The complete genome is 3.1 Mb and was assembled using a combination of short and long reads. The genome contains 2,808 protein-coding genes, 80 tRNAs, and 21 rRNA subunits.