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(författare)
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- 2021
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swepub:Mat__t
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| 2. |
- Tabiri, S, et al.
(författare)
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- 2021
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| 3. |
- Niemi, MEK, et al.
(författare)
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- 2021
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- Glasbey, JC, et al.
(författare)
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- 2021
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- Kanai, M, et al.
(författare)
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- 2023
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- Khatri, C, et al.
(författare)
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Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study
- 2021
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 11:11, s. e050830-
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Tidskriftsartikel (refereegranskat)abstract
- Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis.SettingProspective, international, multicentre, observational cohort study.ParticipantsPatients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative).Primary outcome30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality.ResultsThis study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p<0.001), age >80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787).ConclusionsPatients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups.Trial registration numberNCT04323644
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| 20. |
- Adcox, K, et al.
(författare)
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PHENIX detector overview
- 2003
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Ingår i: Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment. - 0167-5087. ; 499:2-3, s. 469-479
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Tidskriftsartikel (refereegranskat)abstract
- The PHENIX detector is designed to perform a broad study of A-A, p-A, and p-p collisions to investigate nuclear matter under extreme conditions. A wide variety of probes, sensitive to all timescales, are used to study systematic variations with species and energy as well as to measure the spin structure of the nucleon. Designing for the needs of the heavy-ion and polarized-proton programs has produced a detector with unparalleled capabilities. PHENIX measures electron and muon pairs, photons, and hadrons with excellent energy and momentum resolution. The detector consists of a large number of subsystems that are discussed in other papers in this volume. The overall design parameters of the detector are presented. (C) 2002 Elsevier Science B.V. All rights reserved.
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| 32. |
- Adler, SS, et al.
(författare)
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PHENIX on-line systems
- 2003
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Ingår i: Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment. - 0167-5087. ; 499:2-3, s. 560-592
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Tidskriftsartikel (refereegranskat)abstract
- The PHENIX On-Line system takes signals from the Front End Modules (FEM) on each detector subsystem for the purpose of generating events for physics analysis. Processing of event data begins when the Data Collection Modules (DCM) receive data via fiber-optic links from the FEMs. The DCMs format and zero suppress the data and generate data packets. These packets go to the Event Builders (EvB) that assemble the events in final form. The Level-1 trigger (LVL1) generates a decision for each beam crossing and eliminates uninteresting events. The FEMs carry out all detector processing of the data so that it is delivered to the DCMs using a standard format. The FEMs also provide buffering for LVL1 trigger processing and DCM data collection. This is carried out using an architecture that is pipelined and deadtimeless. All of this is controlled by the Master Timing System (MTS) that distributes the RHIC clocks. A Level-2 trigger (LVL2) gives additional discrimination. A description of the components and operation of the PHENIX On-Line system is given and the solution to a number of electronic infrastructure problems are discussed. (C) 2002 Elsevier Science B.V. All rights reserved.
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| 33. |
- Alcorn, J, et al.
(författare)
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Basic instrumentation for Hall A at Jefferson Lab
- 2004
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Ingår i: Nuclear Instruments & Methods in Physics Research. Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment. - : Elsevier BV. - 0167-5087 .- 0168-9002. ; 522:3, s. 294-346
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Tidskriftsartikel (refereegranskat)abstract
- The instrumentation in Hall A at the Thomas Jefferson National Accelerator Facility was designed to study electro-and photo-induced reactions at very high luminosity and good momentum and angular resolution for at least one of the reaction products. The central components of Hall A are two identical high resolution spectrometers, which allow the vertical drift chambers in the focal plane to provide a momentum resolution of better than 2 x 10(-4). A variety of Cherenkov counters, scintillators and lead-glass calorimeters provide excellent particle identification. The facility has been operated successfully at a luminosity well in excess of 10(38) CM-2 s(-1). The research program is aimed at a variety of subjects, including nucleon structure functions, nucleon form factors and properties of the nuclear medium. (C) 2003 Elsevier B.V. All rights reserved.
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| 49. |
- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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