The presence of methane and other hydrocarbons in domestic-use groundwater aquifers poses significant environmental and human health concerns. Isotopic measurements are often relied upon as indicators of groundwater aquifer contamination with methane. While these parameters are used to infer microbial metabolisms, there is growing evidence that isotopes present an incomplete picture of subsurface microbial processes. This study examined the relationships between microbiology and chemistry in groundwater wells located in the Denver-Julesburg Basin of Colorado, a rapidly urbanizing area with active oil and gas development. A primary goal was to determine if microbial data can reliably indicate the quantities and sources of groundwater methane. Comprehensive chemical and molecular analyses were performed on 39 groundwater well samples from five aquifers. Elevated methane concentrations were found in only one aquifer, and both isotopic and microbial data support a microbial origin. Microbial parameters had similar explanatory power as chemical parameters for predicting sample methane concentrations. Furthermore, a subset of samples with unique microbiology corresponded with unique chemical signatures that may be useful indicators of methane gas migration, potentially from nearby coal seams interacting with the aquifer. Microbial data may allow for more accurate determination of groundwater contamination and improved long-term water quality monitoring compared solely to isotopic and chemical data in areas with microbial methane.
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http://dx.doi.org/10.1021/acs.est.0c04228 | DOI Listing |
J Coll Physicians Surg Pak
January 2025
Department of Pathology, Jinnah Sindh Medical University, Karachi, Pakistan.
Objective: To determine the clinical microbial synergy in skin and soft tissue infections (SSTIs) based on bacterial groups and explore the likelihood ratios of clinical parameters.
Study Design: Descriptive cross-sectional study. Place and Duration of the Study: The study was conducted at the Department of Microbiology, University of Karachi in collaboration with Jinnah Postgraduate Medical Centre, and Jinnah Sindh Medical University, Karachi, Pakistan, from June 2023 to May 2024.
Cancer Cell Int
January 2025
Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197, Ruijin 2nd Road, Shanghai, 20025, China.
Background: Methyltransferase-like (METTL) family protein plays a crucial role in the progression of malignancies. However, the function of METTL17 across pan-cancers, especially in hepatocellular carcinoma (HCC) is still poorly understood.
Methods: All original data were downloaded from TCGA, GTEx, HPA, UCSC databases and various data portals.
BMC Microbiol
January 2025
Mycobacteriology Research Center, Institute of Health, Jimma University, Jimma, Oromia, Ethiopia.
Background: Early and accurate diagnosis of drug resistance, including resistance to second-line anti-tuberculosis (TB) drugs, is crucial for the effective control and management of pre-extensively drug-resistant TB (pre-XDR-TB) and extensively drug-resistant TB (XDR-TB). The Xpert MTB/XDR assay is the WHO recommended method for detecting resistance to isoniazid and second-line anti-TB drugs when rifampicin resistance is detected. Currently, the Xpert MTB/XDR assay is not yet implemented in Ethiopia, thus the MTBDRsl assay continues to be used.
View Article and Find Full Text PDFBMC Microbiol
January 2025
Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, SP, Brazil.
Amazonian Dark Earths (ADEs) are fertile soils from the Amazon rainforest that harbor microorganisms with biotechnological potential. This study aimed to investigate the individual and potential synergistic effects of a 2% portion of ADEs and Urochloa brizantha cv. Marandu roots (Brazil's most common grass species used for pastures) on soil prokaryotic communities and overall soil attributes in degraded soil.
View Article and Find Full Text PDFNat Microbiol
January 2025
School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China.
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptides (AMPs) and virtually modifies peptide sequences to produce more potent AMPs, akin to in silico directed evolution. We applied this model to peptides encoded in low-abundance human oral bacteria, resulting in the virtual evolution of 32 peptides into potent AMPs.
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