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Avibactam displays potent inhibition of extended-spectrum, AmpC, KPC and some OXA β-lactamases. We examined the combinations of avibactam with ceftazidime, ceftaroline and aztreonam by the broth microdilution method against Gram-negative bacteria harboring molecularly-characterized β-lactamase genes collected in Toho University, Japan. Bacterial isolates included: Ambler class A β-lactamase-producing Enterobacteriaceae (n = 26); class C β-lactamase-producing Enterobacteriaceae (n = 9) and class D β-lactamase-producing Acinetobacter baumannii (n = 9) and Enterobacteriaceae (n = 3). Ceftazidime-avibactam, ceftaroline-avibactam ands aztreonam-avibactam were active against the strains with an extended-spectrum β-lactamase (ESBL) or AmpC enzymes, but combination with avibactam did not reduce β-lactam MICs against A. baumannii with OXA β-lactamases including carbapenemases, such as OXA-40 and -69.
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http://dx.doi.org/10.1016/j.jiac.2014.08.028 | DOI Listing |
JAMA Netw Open
September 2025
Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.
Importance: Patients with advanced cancer frequently receive broad-spectrum antibiotics, but changing use patterns across the end-of-life trajectory remain poorly understood.
Objective: To describe the patterns of broad-spectrum antibiotic use across defined end-of-life intervals in patients with advanced cancer.
Design, Setting, And Participants: This nationwide, population-based, retrospective cohort study used data from the South Korean National Health Insurance Service database to examine broad-spectrum antibiotic use among patients with advanced cancer who died between July 1, 2002, and December 31, 2021.
Brief Bioinform
August 2025
School of Computer Science, Xi'an Polytechnic University, 710048, Xi'an, China.
Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering.
View Article and Find Full Text PDFNanoscale
September 2025
School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
The challenge of photocatalytic hydrogen production has motivated a targeted search for MXenes as a promising class of materials for this transformation because of their high mobility and high light absorption. High-throughput screening has been widely used to discover new materials, but the relatively high cost limits the chemical space for searching MXenes. We developed a deep-learning-enabled high-throughput screening approach that identified 14 stable candidates with suitable band alignment for water splitting from 23 857 MXenes.
View Article and Find Full Text PDFJ Behav Med
September 2025
Department of Psychology, University of Wisconsin-La Crosse, La Crosse, WI, USA.
Latent profile analysis (LPA) is in the finite mixture model analysis family and identifies subgroups by participants' responses to continuous variables (i.e., indicators); participants' probable membership in each subgroup is based on the similarity between the subgroup's prototypical responses and the person's unique responses.
View Article and Find Full Text PDFEnviron Monit Assess
September 2025
Indira Gandhi Conservation Monitoring Centre, World Wide Fund-India, New Delhi, 110003, India.
Understanding the intricate relationship between land use/land cover (LULC) transformations and land surface temperature (LST) is critical for sustainable urban planning. This study investigates the spatiotemporal dynamics of LULC and LST across Delhi, India, using thermal data from Landsat 7 (2001), Landsat 5 (2011) and Landsat 8 (2021) resampled to 30-m spatial resolution, during the peak summer month of May. The study aims to target three significant aspects: (i) to analyse and present LULC-LST dynamics across Delhi, (ii) to evaluate the implications of LST effects at the district level and (iii) to predict seasonal LST trends in 2041 for North Delhi district using the seasonal auto-regressive integrated moving average (SARIMA) time series model.
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