<b><span style="font-family:Verdana;">Introduction: </span></b><span style="font-family:;" "=""><span style="font-family:Verdana;">Sickl...<b><span style="font-family:Verdana;">Introduction: </span></b><span style="font-family:;" "=""><span style="font-family:Verdana;">Sickle cell disease is a public health problem in the Republic of Congo where the prevalence of sickle cell trait is estimated at 1.25%. The objective of this study is to describe the variations of hematological and biochemical parameters of hemolysis in sickle cell patients in critical and inter-critical periods. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> This is a descriptive cross-sectional study including sickle cell patients followed regularly at the National Reference Center for Sickle Cell Disease (CNRDr) from November 2019 to March 2020. A sample of 167 patients (sickle cell subjects in crisis and in steady state as well as control subjects) was randomly selected during the study period. The blood count was performed using a Sysmex-XN 350 automated system and the biochemical parameters were determined using the Cobas e 311 automated system. Statistical analysis was performed with SPSS version 22 software. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> The study showed that the mean cholesterol level in controls was 4.16 ± 0.77 ul compared with 9.64 ± 4.34 ul in sickle cell crisis subjects. Hb and HCT levels were significantly higher in controls compared with sickle cell subjects in crisis. During crisis, total bilirubin, direct bilirubin, triglycerides, LDH, AST, and CRP were significantly elevated. Hematological parameters such as Hb and HCT were elevated in controls, while the mean WBC value and RET were higher in sickle cell patients in steady state. The mean values of the biochemical parameters were higher in sickle cell patients in steady state. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> Evaluation of the influence of sickle cell trait on biochemical and hematological parameters showed significant differences between sickle cell and control subjects.</span></span>展开更多
Density-functional theory with extended Hubbard functionals(DFT+U+V)provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements.It does so by mitigating self...Density-functional theory with extended Hubbard functionals(DFT+U+V)provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements.It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states.However,achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters.In practice,these are obtained either by semi-empirical tuning,requiring prior knowledge,or,more correctly,by using predictive but expensive first-principles calculations.Here,we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors,directly capturing the electronic structure,local chemical environment,and oxidation states of the system at hand.We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations,as implemented in density-functional perturbation theory(DFPT),and structural relaxations.Remarkably,when trained on data from 12 materials spanning various crystal structures and compositions,our model achieves mean absolute relative errors of 3%and 5%for Hubbard U and V parameters,respectively.By circumventing computationally expensive DFT or DFPT self-consistent protocols,our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead,while approaching the accuracy of DFPT.Moreover,owing to its robust transferability,the model facilitates accelerated materials discovery and design via high-throughput calculations,with relevance for various technological applications.展开更多
文摘<b><span style="font-family:Verdana;">Introduction: </span></b><span style="font-family:;" "=""><span style="font-family:Verdana;">Sickle cell disease is a public health problem in the Republic of Congo where the prevalence of sickle cell trait is estimated at 1.25%. The objective of this study is to describe the variations of hematological and biochemical parameters of hemolysis in sickle cell patients in critical and inter-critical periods. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> This is a descriptive cross-sectional study including sickle cell patients followed regularly at the National Reference Center for Sickle Cell Disease (CNRDr) from November 2019 to March 2020. A sample of 167 patients (sickle cell subjects in crisis and in steady state as well as control subjects) was randomly selected during the study period. The blood count was performed using a Sysmex-XN 350 automated system and the biochemical parameters were determined using the Cobas e 311 automated system. Statistical analysis was performed with SPSS version 22 software. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> The study showed that the mean cholesterol level in controls was 4.16 ± 0.77 ul compared with 9.64 ± 4.34 ul in sickle cell crisis subjects. Hb and HCT levels were significantly higher in controls compared with sickle cell subjects in crisis. During crisis, total bilirubin, direct bilirubin, triglycerides, LDH, AST, and CRP were significantly elevated. Hematological parameters such as Hb and HCT were elevated in controls, while the mean WBC value and RET were higher in sickle cell patients in steady state. The mean values of the biochemical parameters were higher in sickle cell patients in steady state. </span><b><span style="font-family:Verdana;">Conclusion:</span></b><span style="font-family:Verdana;"> Evaluation of the influence of sickle cell trait on biochemical and hematological parameters showed significant differences between sickle cell and control subjects.</span></span>
基金support by the NCCR MARVEL,a National Centre of Competence in Research,funded by the Swiss National Science Foundation(Grant number 205602)supported by a grant from the Swiss National Supercomputing Centre(CSCS)under project ID s1073(Piz Daint)and ID 465000416(LUMI-G)supported by MIAI@Grenoble Alpes,(ANR-19-P3IA-0003).
文摘Density-functional theory with extended Hubbard functionals(DFT+U+V)provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements.It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states.However,achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters.In practice,these are obtained either by semi-empirical tuning,requiring prior knowledge,or,more correctly,by using predictive but expensive first-principles calculations.Here,we present a machine learning model based on equivariant neural networks which uses atomic occupation matrices as descriptors,directly capturing the electronic structure,local chemical environment,and oxidation states of the system at hand.We target here the prediction of Hubbard parameters computed self-consistently with iterative linear-response calculations,as implemented in density-functional perturbation theory(DFPT),and structural relaxations.Remarkably,when trained on data from 12 materials spanning various crystal structures and compositions,our model achieves mean absolute relative errors of 3%and 5%for Hubbard U and V parameters,respectively.By circumventing computationally expensive DFT or DFPT self-consistent protocols,our model significantly expedites the prediction of Hubbard parameters with negligible computational overhead,while approaching the accuracy of DFPT.Moreover,owing to its robust transferability,the model facilitates accelerated materials discovery and design via high-throughput calculations,with relevance for various technological applications.
