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- Drake, TM, et al.
(författare)
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Surgical site infection after gastrointestinal surgery in children: an international, multicentre, prospective cohort study
- 2020
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Ingår i: BMJ global health. - : BMJ. - 2059-7908. ; 5:12
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Tidskriftsartikel (refereegranskat)abstract
- Surgical site infection (SSI) is one of the most common healthcare-associated infections (HAIs). However, there is a lack of data available about SSI in children worldwide, especially from low-income and middle-income countries. This study aimed to estimate the incidence of SSI in children and associations between SSI and morbidity across human development settings.MethodsA multicentre, international, prospective, validated cohort study of children aged under 16 years undergoing clean-contaminated, contaminated or dirty gastrointestinal surgery. Any hospital in the world providing paediatric surgery was eligible to contribute data between January and July 2016. The primary outcome was the incidence of SSI by 30 days. Relationships between explanatory variables and SSI were examined using multilevel logistic regression. Countries were stratified into high development, middle development and low development groups using the United Nations Human Development Index (HDI).ResultsOf 1159 children across 181 hospitals in 51 countries, 523 (45·1%) children were from high HDI, 397 (34·2%) from middle HDI and 239 (20·6%) from low HDI countries. The 30-day SSI rate was 6.3% (33/523) in high HDI, 12·8% (51/397) in middle HDI and 24·7% (59/239) in low HDI countries. SSI was associated with higher incidence of 30-day mortality, intervention, organ-space infection and other HAIs, with the highest rates seen in low HDI countries. Median length of stay in patients who had an SSI was longer (7.0 days), compared with 3.0 days in patients who did not have an SSI. Use of laparoscopy was associated with significantly lower SSI rates, even after accounting for HDI.ConclusionThe odds of SSI in children is nearly four times greater in low HDI compared with high HDI countries. Policies to reduce SSI should be prioritised as part of the wider global agenda.
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- Paradela, C., et al.
(författare)
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High-accuracy determination of the 238U/235U fission cross section ratio up to ~1 GeV at n_TOF at CERN
- 2015
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Ingår i: Physical Review C. Nuclear Physics. - 0556-2813 .- 1089-490X. ; 91, s. 024602-
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Tidskriftsartikel (refereegranskat)abstract
- The U238 to U235 fission cross section ratio has been determined at n_TOF up to ≈1 GeV, with two different detection systems, in different geometrical configurations. A total of four datasets has been collected and compared. They are all consistent to each other within the relative systematic uncertainty of 3–4%. The data collected at n_TOF have been suitably combined to yield a unique fission cross section ratio as a function of neutron energy. The result confirms current evaluations up to 200 MeV. Good agreement is also observed with theoretical calculations based on the INCL++/Gemini++ combination up to the highest measured energy. The n_TOF results may help solve a long-standing discrepancy between the two most important experimental datasets available so far above 20 MeV, while extending the neutron energy range for the first time up to ≈1 GeV.
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- Praena, J., et al.
(författare)
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Preparation and characterization of 33S samples for 33S(n,alpha)30Si cross-section measurements at the n_TOF facility at CERN
- 2018
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Ingår i: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier BV. - 0168-9002 .- 1872-9576. ; 890, s. 142-147
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Tidskriftsartikel (refereegranskat)abstract
- Thin 33S samples for the study of the 33S(n,alpha)30Si cross-section at the n_TOF facility at CERN were made by thermal evaporation of 33S powder onto a dedicated substrate made of kapton covered with thin layers of copper, chromium and titanium. This method has provided for the first time bare sulfur samples a few centimeters in diameter. The samples have shown an excellent adherence with no mass loss after few years and no sublimation in vacuum at room temperature. The determination of the mass thickness of 33S has been performed by means of Rutherford backscattering spectrometry. The samples have been successfully tested under neutron irradiation.
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- Angelini, G., et al.
(författare)
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Upper gut heat shock proteins HSP70 and GRP78 promote insulin resistance, hyperglycemia, and non-alcoholic steatohepatitis
- 2022
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- A high-fat diet increases the risk of insulin resistance, type-2 diabetes, and non-alcoholic steato-hepatitis. Here we identified two heat-shock proteins, Heat-Shock-Protein70 and Glucose-Regulated Protein78, which are increased in the jejunum of rats on a high-fat diet. We demonstrated a causal link between these proteins and hepatic and whole-body insulin-resistance, as well as the metabolic response to bariatric/metabolic surgery. Long-term continuous infusion of Heat-Shock-Protein70 and Glucose-Regulated Protein78 caused insulin-resistance, hyperglycemia, and non-alcoholic steato-hepatitis in rats on a chow diet, while in rats on a high-fat diet continuous infusion of monoclonal antibodies reversed these phenotypes, mimicking metabolic surgery. Infusion of these proteins or their antibodies was also associated with shifts in fecal microbiota composition. Serum levels of Heat-Shock-Protein70 and Glucose-Regulated Protein78were elevated in patients with non-alcoholic steato-hepatitis, but decreased following metabolic surgery. Understanding the intestinal regulation of metabolism may provide options to reverse metabolic diseases.
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- Saux, Patrick, et al.
(författare)
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Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study.
- 2023
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Ingår i: The Lancet. Digital health. - 2589-7500. ; 5:10
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Tidskriftsartikel (refereegranskat)abstract
- Weight loss trajectories after bariatric surgery vary widely between individuals, and predicting weight loss before the operation remains challenging. We aimed to develop a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery.In this multinational retrospective observational study we enrolled adult participants (aged ≥18 years) from ten prospective cohorts (including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year follow-up after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band. Patients with a previous history of bariatric surgery or large delays between scheduled and actual visits were excluded. The training cohort comprised patients from two centres in France (ABOS and BAREVAL). The primary outcome was BMI at 5 years. A model was developed using least absolute shrinkage and selection operator to select variables and the classification and regression trees algorithm to build interpretable regression trees. The performances of the model were assessed through the median absolute deviation (MAD) and root mean squared error (RMSE) of BMI.10231 patients from 12 centres in ten countries were included in the analysis, corresponding to 30602 patient-years. Among participants in all 12 cohorts, 7701 (75·3%) were female, 2530 (24·7%) were male. Among 434 baseline attributes available in the training cohort, seven variables were selected: height, weight, intervention type, age, diabetes status, diabetes duration, and smoking status. At 5 years, across external testing cohorts the overall mean MAD BMI was 2·8 kg/m2 (95% CI 2·6-3·0) and mean RMSE BMI was 4·7 kg/m2 (4·4-5·0), and the mean difference between predicted and observed BMI was -0·3 kg/m2 (SD 4·7). This model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery.We developed a machine learning-based model, which is internationally validated, for predicting individual 5-year weight loss trajectories after three common bariatric interventions.SOPHIA Innovative Medicines Initiative 2 Joint Undertaking, supported by the EU's Horizon 2020 research and innovation programme, the European Federation of Pharmaceutical Industries and Associations, Type 1 Diabetes Exchange, and the Juvenile Diabetes Research Foundation and Obesity Action Coalition; Métropole Européenne de Lille; Agence Nationale de la Recherche; Institut national de recherche en sciences et technologies du numérique through the Artificial Intelligence chair Apprenf; Université de Lille Nord Europe's I-SITE EXPAND as part of the Bandits For Health project; Laboratoire d'excellence European Genomic Institute for Diabetes; Soutien aux Travaux Interdisciplinaires, Multi-établissements et Exploratoires programme by Conseil Régional Hauts-de-France (volet partenarial phase 2, project PERSO-SURG).
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