13C NMR based profiling unveils different α-ketoglutarate pools involved into glutamate and lysine synthesis in the milk yeast Kluyveromyces lactis

Background: The construction of efficient cell factories for the production of metabolites requires the rational improvement/engineering of the metabolism of microorganisms. The subject of this paper is directed towards the quantitative understanding of the respiratory/fermentative Kluyveromyces lactis yeast metabolism and its rag8 casein kinase mutant, taken as a model for all rag gene mutations. Methods: 13C-NMR spectroscopy and [1,2-13C2]glucose was used as metabolic stable-isotope tracer to define the metabolic profiling of a K. lactis yeast and its derivative mutants. Results: Rag8 showed a decrease of all 13C glutamate fractional enrichments, except for [4- 13C]glutamate that was higher than wild type ones. A decrease of TCA cycle flux in rag8 mutants and a contribute of a [4-13C]ketoglutarate pool not originating from mitochondria was suggested. 13C lysine enrichments confirmed the presence of two compartmentalized α-ketoglutarate (α-KG) pools participating to glutamate and lysine synthesis. Moreover, an increased transaldolase, as compared to transketolase activity, was observed in the rag8 mutant by 13C-NMR isotopomer analysis of alanine. Conclusions: 13C NMR-based isotopomer analysis showed the existence of different α-KG metabolic pools for glutamate and lysine biosynthesis. In the rag8 mutant, 13C labeled pentose phosphate intermediates participated in the synthesis of this compartmentalised α-KG pool. General significance: A compartmentalization of the α-KG pools involved in lysine biosynthesis has been revealed for the first time in K. lactis. Given its great impact in metabolic engineering field, its existence should be validated/compared with other yeasts and/or fungal species

Continuous quality improvement in intensive care medicine. The GiViTI Margherita project - Report 2005

Aim. The assessment of the quality of intensive care medicine is mandatory in the modern healthcare system. In Italy, the GiViTI (Gruppo Italiano per la Valutazione degli Interventi in Terapia Intensiva) network is working in this field since 1991 and it now involves 295 out of the about 450 Italian intensive care units (ICU). In 2002 GiViTI launched a project for the continuous quality assessment and improvement that is now joined by 180 ICUs. Data collected in 2005 are analyzed and presented. Methods. All admitted patients were entered in a validated software, which performs a multitude of validity checks during the data entry. Data were further reviewed by the co-ordinating center; patients admitted in months with more than 10% of incomplete or inconsistent records in each ICU were excluded from the analysis. Each year, a multivariate logistic regression model is fitted to identify predictors of hospital mortality. Starting from the SAPS 2 and the 2004 GiViTI model predictions of hospital mortality, two calibration tables and curves are presented. Results. In 2005, 180 Italian ICUs collected data on 55 246 patients. After excluding those admitted in months with an unjustified lower recruitment rate or with less than 90% of complete and consistent data, we had 52 816 (95.6%) valid cases. Although the rough hospital mortality in 2005 was 1% higher than in 2004 (22.6% vs 21.5%), the adjusted mortality shows a statistically significant 4% reduction (obser-ved-to-expected ratio: 0.96; 95% CI: 0.94-0.97). Conclusion. Italian ICUs in 2005 performed better than in 2004, at a parity of patient severity

Continuous quality improvement in intensive care medicine. The GiViTI Margherita project - Report 2005

Aim. The assessment of the quality of intensive care medicine is mandatory in the modern healthcare system. In Italy, the GiViTI (Gruppo Italiano per la Valutazione degli Interventi in Terapia Intensiva) network is working in this field since 1991 and it now involves 295 out of the about 450 Italian intensive care units (ICU). In 2002 GiViTI launched a project for the continuous quality assessment and improvement that is now joined by 180 ICUs. Data collected in 2005 are analyzed and presented. Methods. All admitted patients were entered in a validated software, which performs a multitude of validity checks during the data entry. Data were further reviewed by the co-ordinating center; patients admitted in months with more than 10% of incomplete or inconsistent records in each ICU were excluded from the analysis. Each year, a multivariate logistic regression model is fitted to identify predictors of hospital mortality. Starting from the SAPS 2 and the 2004 GiViTI model predictions of hospital mortality, two calibration tables and curves are presented. Results. In 2005, 180 Italian ICUs collected data on 55 246 patients. After excluding those admitted in months with an unjustified lower recruitment rate or with less than 90% of complete and consistent data, we had 52 816 (95.6%) valid cases. Although the rough hospital mortality in 2005 was 1% higher than in 2004 (22.6% vs 21.5%), the adjusted mortality shows a statistically significant 4% reduction (obser-ved-to-expected ratio: 0.96; 95% CI: 0.94-0.97). Conclusion. Italian ICUs in 2005 performed better than in 2004, at a parity of patient severity

The role of the intensive care unit in real-time surveillance of emerging pandemics: the Italian GiViTI experience

The prompt availability of reliable epidemiological information on emerging pandemics is crucial for public health policy-makers. Early in 2013, a possible new H1N1 epidemic notified by an intensive care unit (ICU) to GiViTI, the Italian ICU network, prompted the re-activation of the real-time monitoring system developed during the 2009-2010 pandemic. Based on data from 216 ICUs, we were able to detect and monitor an outbreak of severe H1N1 infection, and to compare the situation with previous years. The timely and correct assessment of the severity of an epidemic can be obtained by investigating ICU admissions, especially when historical comparisons can be made

The prognostic importance of chronic end-stage diseases in geriatric patients admitted to 163 Italian ICUs

BACKGROUND: The number of elderly patients undergoing major surgical interventions and then needing admission to intensive care unit (ICU) grows steadily. We investigated this issue in a cohort of 232,278 patients admitted in five years (2011-2015) to 163 Italian general ICUs. METHODS: Surgical patients older than 75 registered in the GiViTI MargheritaPROSAFE project were analyzed. The impact on hospital mortality of important chronic conditions (severe COPD, NYHA class IV, dementia, end-stage renal disease, cirrhosis with portal hypertension) was investigated with two prognostic models developed yearly on patients staying in the ICU less or more than 24 hours. RESULTS: 44,551 elderly patients (19.2%) underwent emergency (47.3%) or elective surgery (52.7%). At least one severe comorbidity was present in 14.6% of them, yielding a higher hospital mortality (32.4%, vs. 21.1% without severe comorbidity). In the models for patients staying in the ICU 24 hours or more, cirrhosis, NYHA class IV, and severe COPD were constant independent predictors of death (adjusted odds ratios [ORs] range 1.67-1.97, 1.54-1.91, and 1.34-1.50, respectively), while dementia was statistically significant in four out of five models (adjusted ORs 1.23-1.28). End-stage renal disease, instead, never resulted to be an independent prognostic factor. For patients staying in the ICU less than 24 hours, chronic comorbidities were only occasionally independent predictors of death. CONCLUSIONS: Our study confirms that elderly surgical patients represent a relevant part of all ICUs admissions. About one of seven bear at least one severe chronic comorbidity, that, excluding end-stage renal disease, are all strong independent predictors of hospital death

Prosafe: a european endeavor to improve quality of critical care medicine in seven countries

BACKGROUND: long-lasting shared research databases are an important source of epidemiological information and can promote comparison between different healthcare services. Here we present ProsaFe, an advanced international research network in intensive care medicine, with the focus on assessing and improving the quality of care. the project involved 343 icUs in seven countries. all patients admitted to the icU were eligible for data collection. MetHoDs: the ProsaFe network collected data using the same electronic case report form translated into the corresponding languages. a complex, multidimensional validation system was implemented to ensure maximum data quality. individual and aggregate reports by country, region, and icU type were prepared annually. a web-based data-sharing system allowed participants to autonomously perform different analyses on both own data and the entire database. RESULTS: The final analysis was restricted to 262 general ICUs and 432,223 adult patients, mostly admitted to Italian units, where a research network had been active since 1991. organization of critical care medicine in the seven countries was relatively similar, in terms of staffing, case mix and procedures, suggesting a common understanding of the role of critical care medicine. conversely, icU equipment differed, and patient outcomes showed wide variations among countries. coNclUsioNs: ProsaFe is a permanent, stable, open access, multilingual database for clinical benchmarking, icU self-evaluation and research within and across countries, which offers a unique opportunity to improve the quality of critical care. its entry into routine clinical practice on a voluntary basis is testimony to the success and viability of the endeavor