Energy expenditure in the critically ill performing early physical therapy

Purpose: Resting energy expenditure (REE) determination is of high relevance to avoid both overfeeding and underfeeding. We conducted an observational study to determine the impact of early exercise on energy requirements to adjust caloric intake accordingly in critically ill patients. Methods: This was a prospective observational study conducted in an intensive care unit in 49 hemodynamically stable critically ill patients and 15 healthy volunteers. Indirect calorimetry (IC) was performed for 15 min at baseline during resting conditions, and then continuously recorded during 30 min of cycling at 0, 3, or 6 watts (W), followed by a 15-min resting period. REE determined by IC was compared with predictive formulas and correlated with several biomarkers. The energy cost of early exercise was compared between critically ill patients and healthy volunteers. Results: In patients, REE determined by IC was higher than predicted by Harris-Benedict (29 ± 31 %, p < 0.001) and Fleisch equations (23 ± 31 %, p < 0.001) but lower than predicted by the Faisy-Fagon equation for ventilated patients (16 ± 19 %, p < 0.05). Differences between Harris-Benedict predictions and IC determination were positively correlated with C-reactive protein (CRP) in patients with sepsis (r = 0.51, p = 0.003). During a similar exercise, VO 2 increase in patients was higher when compared with healthy volunteers at 3 W, close to significant at 6 W, and not present in the passive group. Conclusions: The critically ill have increased REE according to inflammation defined by CRP. Increased energy requirement for physical activity was only present for active exercise and seems to differ from that in the healthy population. For the exercise duration and intensity tested, nutritional adjustment is not indicated. © 2014 Springer-Verlag Berlin Heidelberg and ESICM

Vancomycin-intermediate Staphylococcus aureus selected during vancomycin therapy of experimental endocarditis are not detected by culture-based diagnostic procedures and persist after treatment arrest.

OBJECTIVES: Laboratory detection of vancomycin-intermediate Staphylococcus aureus (VISA) and their heterogeneous VISA (hVISA) precursors is difficult. Thus, it is possible that vancomycin failures against supposedly vancomycin-susceptible S. aureus are due to undiagnosed VISA or hVISA. We tested this hypothesis in experimental endocarditis.¦METHODS: Rats with aortic valve infection due to the vancomycin-susceptible (MIC 2 mg/L), methicillin-resistant S. aureus M1V2 were treated for 2 days with doses of vancomycin that mimicked the pharmacokinetics seen in humans following intravenous administration of 1 g of the drug every 12 h. Half of the treated animals were killed 8 h after treatment arrest and half 3 days thereafter. Population analyses were done directly on vegetation homogenates or after one subculture in drug-free medium to mimic standard diagnostic procedures.¦RESULTS: Vancomycin cured 14 of 26 animals (54%; P<0.05 versus controls) after 2 days of treatment. When vegetation homogenates were plated directly on vancomycin-containing plates, 6 of 13 rats killed 8 h after treatment arrest had positive cultures, 1 of which harboured hVISA. Likewise, 6 of 13 rats killed 3 days thereafter had positive valve cultures, 5 of which harboured hVISA. However, one subculture of vegetations in drug-free broth was enough to revert all the hVISA phenotypes to the susceptible pattern of the parent. Thus, vancomycin selected for hVISA during therapy of experimental endocarditis due to vancomycin-susceptible S. aureus. These hVISA were associated with vancomycin failure. The hVISA phenotype persisted in vivo, even after vancomycin arrest, but was missed in vitro after a single passage of the vegetation homogenate on drug-free medium.¦CONCLUSIONS: hVISA might escape detection in clinical samples if they are subcultured before susceptibility tests

Energy expenditure in the critically ill performing early physical therapy

Purpose: Resting energy expenditure (REE) determination is of high relevance to avoid both overfeeding and underfeeding. We conducted an observational study to determine the impact of early exercise on energy requirements to adjust caloric intake accordingly in critically ill patients. Methods: This was a prospective observational study conducted in an intensive care unit in 49 hemodynamically stable critically ill patients and 15 healthy volunteers. Indirect calorimetry (IC) was performed for 15 min at baseline during resting conditions, and then continuously recorded during 30 min of cycling at 0, 3, or 6 watts (W), followed by a 15-min resting period. REE determined by IC was compared with predictive formulas and correlated with several biomarkers. The energy cost of early exercise was compared between critically ill patients and healthy volunteers. Results: In patients, REE determined by IC was higher than predicted by Harris-Benedict (29 ± 31 %, p < 0.001) and Fleisch equations (23 ± 31 %, p < 0.001) but lower than predicted by the Faisy-Fagon equation for ventilated patients (16 ± 19 %, p < 0.05). Differences between Harris-Benedict predictions and IC determination were positively correlated with C-reactive protein (CRP) in patients with sepsis (r = 0.51, p = 0.003). During a similar exercise, VO 2 increase in patients was higher when compared with healthy volunteers at 3 W, close to significant at 6 W, and not present in the passive group. Conclusions: The critically ill have increased REE according to inflammation defined by CRP. Increased energy requirement for physical activity was only present for active exercise and seems to differ from that in the healthy population. For the exercise duration and intensity tested, nutritional adjustment is not indicated. © 2014 Springer-Verlag Berlin Heidelberg and ESICM

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.