This study characterized the fecal indicator bacteria (FIB), including Escherichia coli (E. coli) and Enteroccocus (ENT), disseminated over time in the Bay of Vidy, which is the most contaminated area of Lake Geneva. Sediments were collected from a site located at ∼500 m from the present waste water treatment plant (WWTP) outlet pipe, in front of the former WWTP outlet pipe, which was located at only 300 m from the coastal recreational area (before 2001). E. coli and ENT were enumerated in sediment suspension using the membrane filter method. The FIB characterization was performed for human Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) and human specific bacteroides by PCR using specific primers and a matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). Bacterial cultures revealed that maximum values of 35.2 × 10(8) and 6.6 × 10(6)CFU g(-1) dry sediment for E. coli and ENT, respectively, were found in the sediments deposited following eutrophication of Lake Geneva in the 1970s, whereas the WWTP started operating in 1964. The same tendency was observed for the presence of human fecal pollution: the percentage of PCR amplification with primers ESP-1/ESP-2 for E. faecalis and E. faecium indicated that more than 90% of these bacteria were from human origin. Interestingly, the PCR assays for specific-human bacteroides HF183/HF134 were positive for DNA extracted from all isolated strains of sediment surrounding WWPT outlet pipe discharge. The MALDI-TOF MS confirmed the presence of general E. coli and predominance E. faecium in isolated strains. Our results demonstrated that human fecal bacteria highly increased in the sediments contaminated with WWTP effluent following the eutrophication of Lake Geneva. Additionally, other FIB cultivable strains from animals or adapted environmental strains were detected in the sediment of the bay. The approaches used in this research are valuable to assess the temporal distribution and the source of the human fecal pollution in aquatic environments.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.ecoenv.2011.11.005 | DOI Listing |
J Med Chem
December 2024
Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas 75390, United States.
Malaria remains a serious global health challenge, yet treatment and control programs are threatened by drug resistance. Dihydroorotate dehydrogenase (DHODH) was clinically validated as a target for treatment and prevention of malaria through human studies with DSM265, but currently no drugs against this target are in clinical use. We used structure-based computational tools including free energy perturbation (FEP+) to discover highly ligand efficient, potent, and selective pyrazole-based DHODH inhibitors through a scaffold hop from a pyrrole-based series.
View Article and Find Full Text PDFLancet Infect Dis
December 2024
Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Background: Pneumococcal conjugate vaccines (PCVs) that are ten-valent (PCV10) and 13-valent (PCV13) became available in 2010. We evaluated their global impact on invasive pneumococcal disease (IPD) incidence in all ages.
Methods: Serotype-specific IPD cases and population denominators were obtained directly from surveillance sites using PCV10 or PCV13 in their national immunisation programmes and with a primary series uptake of at least 50%.
Lancet Oncol
December 2024
International Atomic Energy Agency, Vienna, Austria.
Global efforts to highlight cancer and non-communicable diseases (NCDs) as a growing burden were first raised in 2005 World Health Assembly Resolution 58.22 and reinforced with Resolution 70.12 and the Global NCD action plan in 2017.
View Article and Find Full Text PDFNat Biomed Eng
December 2024
Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!