المؤلفون: Amy P. Webster, Simone Ecker, Ismail Moghul, Xiaohong Liu, Pawan Dhami, Sarah Marzi, Dirk S. Paul, Michelle Kuxhausen, Stephanie J. Lee, Stephen R. Spellman, Tao Wang, Andrew Feber, Vardhman Rakyan, Karl S. Peggs, Stephan Beck

المصدر: Frontiers in Genetics, Vol 15 (2024)

مصطلحات موضوعية: DNA methylation, haematopoietic stem cell transplant, epigenetics, machine learning, biomarker identification and validation, HCT (hematopoietic cell transplant), Genetics, QH426-470

الوصف: Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fgene.2024.1242636/fullTest; https://doaj.org/toc/1664-8021Test

الوصول الحر: https://doaj.org/article/951e2cf5b5514896b6ba6afd2a1f92dbTest

المؤلفون: Webster, Amy P, Ecker, Simone, Moghul, Ismail, Liu, Xiaohong, Dhami, Pawan, Marzi, Sarah, Paul, Dirk S, Kuxhausen, Michelle, Lee, Stephanie J, Spellman, Stephen R, Wang, Tao, Feber, Andrew, Rakyan, Vardhman, Peggs, Karl S, Beck, Stephan

المصدر: Frontiers in Genetics , 15 , Article 1242636. (2024)

مصطلحات موضوعية: DNA methylation, haematopoietic stem cell transplant, epigenetics, machine learning, biomarker identification and validation, HCT (hematopoietic cell transplant)

الوصف: Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.

وصف الملف: text

العلاقة: https://discovery.ucl.ac.uk/id/eprint/10190831/1/Moghul_Donor%20whole%20blood%20DNA%20methylation%20is%20not%20a%20strong%20predictor%20of%20acute%20graft%20versus%20host%20disease%20in%20unrelated%20donor%20allogeneic%20haematopoietic%20cell%20transplantation_VoR.pdfTest; https://discovery.ucl.ac.uk/id/eprint/10190831Test/

الإتاحة: https://discovery.ucl.ac.uk/id/eprint/10190831/1/Moghul_Donor%20whole%20blood%20DNA%20methylation%20is%20not%20a%20strong%20predictor%20of%20acute%20graft%20versus%20host%20disease%20in%20unrelated%20donor%20allogeneic%20haematopoietic%20cell%20transplantation_VoR.pdfTest
https://discovery.ucl.ac.uk/id/eprint/10190831Test/

المؤلفون: Vau, Bernard, Bourlès, Henri

المساهمون: iXBlue Bonneuil - FR, Systèmes et Applications des Technologies de l'Information et de l'Energie (SATIE), École normale supérieure - Rennes (ENS Rennes)-Conservatoire National des Arts et Métiers CNAM (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)-Université Gustave Eiffel-CY Cergy Paris Université (CY), Conservatoire National des Arts et Métiers CNAM (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)

المصدر: https://hal.science/hal-04528898Test ; 2024.

مصطلحات موضوعية: Identification Model validation Model performance assessment Fit, Identification, Model validation, Model performance assessment, Fit, [SPI]Engineering Sciences [physics], [MATH]Mathematics [math]

الوصف: A paraître dans les actes de : 20th Sysid 2024, July 17-July 19, 2024 ; International audience ; It is shown that some usual criteria evaluating the performances of an identified model with respect to experimental data, like the FIT criterion, can be not well-suited to fast sampled systems. This leads to propose some generalized criteria where the signals are filtered by transfer functions belonging to an orthonormal basis. An interpretation of this filtering in the frequency domain is provided. The basis poles selection is equivalent to making a specification about the criterion in function of the expected use of the identified model.

العلاقة: hal-04528898; https://hal.science/hal-04528898Test; https://hal.science/hal-04528898/documentTest; https://hal.science/hal-04528898/file/sysid_24_FIT-1.pdfTest

الإتاحة: https://hal.science/hal-04528898Test
https://hal.science/hal-04528898/documentTest
https://hal.science/hal-04528898/file/sysid_24_FIT-1.pdfTest

المؤلفون: Ozaslan, Basak, Aiello, Eleonora M., III, Francis J. Doyle, Dassau, Eyal

المساهمون: Ozaslan, Basak, Aiello, Eleonora M., Iii, Francis J. Doyle, Dassau, Eyal

مصطلحات موضوعية: Identification and validation, Quantification of physiological parameter, Grey box modeling, Physiological ModelType 1 Diabetes

الوصف: A major challenge in fitting models to glucose metabolism in people with type 1 diabetes is incomplete data as its collection partially relies on self-reporting and does not include all relevant events. We develop a method for identifying optimal input corrections to reestablish a correct input-output relationship in the data while jointly identifying personalized model parameters. The unreported or misreported parts in the data are reconciled by adding sparse corrections via mixed-integer quadratic programming leading to an improved identification of the model parameters. We conduct numerical experiments with incomplete in-silico training data and show that models obtained from our method are able to provide more accurate predictions on test data than models obtained from standard methods. The performance of our methodology is similar to that attained with the standard method when trained on data with complete information.

العلاقة: ispartofbook:22nd IFAC World Congress; IFAC WC 2023; volume:56; issue:2; firstpage:6518; lastpage:6524; numberofpages:7; serie:IFAC-PAPERSONLINE; https://hdl.handle.net/11572/402010Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85184960474; https://www.sciencedirect.com/science/article/pii/S2405896323006547?via=ihubTest

الإتاحة: https://doi.org/10.1016/j.ifacol.2023.10.299Test
https://hdl.handle.net/11572/402010Test
https://www.sciencedirect.com/science/article/pii/S2405896323006547?via=ihubTest

المؤلفون: Xinlin Xie, Shuiqin Li, Hang Liu, Qiang Xu, Huaping Tang, Yang Mu, Mei Deng, Qiantao Jiang, Guoyue Chen, Pengfei Qi, Wei Li, Zhien Pu, Ahsan Habib, Yuming Wei, Youliang Zheng, Xiujin Lan, Jian Ma

المصدر: BMC Genomics, Vol 23, Iss 1, Pp 1-13 (2022)

مصطلحات موضوعية: Kernel length, BSA-660 K SNP array, QTL identification and validation, F3 biparental populations, Biotechnology, TP248.13-248.65, Genetics, QH426-470

الوصف: Abstract Background High yield and quality are essential goals of wheat (Triticum aestivum L.) breeding. Kernel length (KL), as a main component of kernel size, can indirectly change kernel weight and then affects yield. Identification and utilization of excellent loci in wheat genetic resources is of great significance for cultivating high yield and quality wheat. Genetic identification of loci for KL has been performed mainly through genome-wide association study in natural populations or QTL mapping based on genetic linkage map in high generation populations. Results In this study, an F3 biparental population derived from the cross between an EMS mutant BLS1 selected from an EMS-induced wheat genotype LJ2135 (derived from the hybrid progeny of a spelt wheat (T. spelta L.) and a common wheat) mutant bank and a local breeding line 99E18 was used to rapidly identify loci controlling KL based on Bulked Segregant Analysis (BSA) and the wheat 660 K single-nucleotide polymorphism (SNP) array. The highest ratio of polymorphic SNPs was located on chromosome 4A. Linkage map analysis showed that 33 Kompetitive Allele Specific PCR markers were linked to the QTL for KL (Qkl.sicau-BLE18-4A) identified in three environments as well as the best linear unbiased prediction (BLUP) dataset. This QTL explained 10.87—19.30% of the phenotypic variation. Its effect was successfully confirmed in another F3 population with the two flanking markers KASP-AX-111536305 and KASP-AX-110174441. Compared with previous studies and given that the of BLS1 has the genetic background of spelt wheat, the major QTL was likely a new one. A few of predicted genes related to regulation of kernel development were identified in the interval of the detected QTL. Conclusion A major, novel and stable QTL (Qkl.sicau-BLE18-4A) for KL was identified and verified in two F3 biparental populations across three environments. Significant relationships among KL, kernel width (KW) and thousand kernel weight (TKW) were identified. Four predicted genes related to kernel growth regulation were detected in the interval of Qkl.sicau-BLE18-4A. Furthermore, this study laid foundation on subsequent fine mapping work and provided a possibility for breeding of elite wheat varieties.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/1471-2164Test

الوصول الحر: https://doaj.org/article/03fc8ab774d24e3a80fdf7adecda650fTest

المؤلفون: Furió-Novejarque, Clara, Sala-Mira, Iván, Ranjan, Ajenthen G., Nørgaard, Kirsten, Díez, José Luis, Jørgensen, John Bagterp, Bondia, Jorge

المصدر: Furió-Novejarque , C , Sala-Mira , I , Ranjan , A G , Nørgaard , K , Díez , J L , Jørgensen , J B & Bondia , J 2023 , ' Validation of a model of glucagon action including glucagon receptor dynamics under consecutive doses in low and high-carb diets ' , IFAC-PapersOnLine , vol. 56 , no. 2 , pp. 9666-9671 . https://doi.org/10.1016/j.ifacol.2023.10.275Test

مصطلحات موضوعية: Artificial Pancreas, Biomedical system modeling, Diabetes, Identification and validation, Nonlinear system identification, Physiological Model, /dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being, name=SDG 3 - Good Health and Well-being

الوصف: Accurate mathematical models are needed to simulate and validate dual-hormone control algorithms for artificial pancreas systems. Glucagon receptors are a key component for glucagon to have effect on glucose. However, they are not usually taken into consideration in the development of models for type 1 diabetes (T1D). In previous work, a model proposal integrating glucagon receptor dynamics into endogenous glucose production (EGP) was successfully validated under single glucagon boluses. In this work, this model is further validated using a dataset of clinical data where two glucagon doses (100 and 500 µg) are consecutively administered to 10 patients with T1D in two different settings: low and high-carb diets (total of 20 datasets). The model performance is compared to three other EGP models from literature under different identification criteria. The receptors model achieved the lowest root mean squared error regardless of the diet and the individualization method.

وصف الملف: application/pdf

العلاقة: https://orbit.dtu.dk/en/publications/d21c1b9a-a113-4c0c-af4b-99c17d25bb07Test

الإتاحة: https://doi.org/10.1016/j.ifacol.2023.10.275Test
https://orbit.dtu.dk/en/publications/d21c1b9a-a113-4c0c-af4b-99c17d25bb07Test
https://backend.orbit.dtu.dk/ws/files/353616057/1-s2.0-S2405896323006262-main.pdfTest

المؤلفون: Matthew J. Winter, Yosuke Ono, Jonathan S. Ball, Anna Walentinsson, Erik Michaelsson, Anna Tochwin, Steffen Scholpp, Charles R. Tyler, Steve Rees, Malcolm J Hetheridge, Mohammad Bohlooly-Y

المصدر: Frontiers in Pharmacology, Vol 13 (2022)

مصطلحات موضوعية: CRISPR/Cas9, zebrafish, heart failure, drug target identification and validation, human whole exome sequencing, Therapeutics. Pharmacology, RM1-950

الوصف: The clinical heterogeneity of heart failure has challenged our understanding of the underlying genetic mechanisms of this disease. In this respect, large-scale patient DNA sequencing studies have become an invaluable strategy for identifying potential genetic contributing factors. The complex aetiology of heart failure, however, also means that in vivo models are vital to understand the links between genetic perturbations and functional impacts as part of the process for validating potential new drug targets. Traditional approaches (e.g., genetically-modified mice) are optimal for assessing small numbers of genes, but less practical when multiple genes are identified. The zebrafish, in contrast, offers great potential for higher throughput in vivo gene functional assessment to aid target prioritisation, by providing more confidence in target relevance and facilitating gene selection for definitive loss of function studies undertaken in mice. Here we used whole-exome sequencing and bioinformatics on human patient data to identify 3 genes (API5, HSPB7, and LMO2) suggestively associated with heart failure that were also predicted to play a broader role in disease aetiology. The role of these genes in cardiovascular system development and function was then further investigated using in vivo CRISPR/Cas9-mediated gene mutation analysis in zebrafish. We observed multiple impacts in F0 knockout zebrafish embryos (crispants) following effective somatic mutation, including changes in ventricle size, pericardial oedema, and chamber malformation. In the case of lmo2, there was also a significant impact on cardiovascular function as well as an expected reduction in erythropoiesis. The data generated from both the human in silico and zebrafish in vivo assessments undertaken supports further investigation of the potential roles of API5, HSPB7, and LMO2 in human cardiovascular disease. The data presented also supports the use of human in silico genetic variant analysis, in combination with zebrafish crispant phenotyping, as a powerful approach for assessing gene function as part of an integrated multi-level drug target validation strategy.

وصف الملف: electronic resource

العلاقة: https://www.frontiersin.org/articles/10.3389/fphar.2022.827686/fullTest; https://doaj.org/toc/1663-9812Test

الوصول الحر: https://doaj.org/article/c5241e27f188422cbb46be887827542aTest

المؤلفون: Webster, Amy P, Ecker, Simone, Moghul, Ismail, Liu, Xiaohong, Dhami, Pawan, Marzi, Sarah, Paul, Dirk S, Kuxhausen, Michelle, Lee, Stephanie J, Spellman, Stephen R, Wang, Tao, Feber, Andrew, Rakyan, Vardhman, Peggs, Karl S, Beck, Stephan

المصدر: Front Genet ; ISSN:1664-8021 ; Volume:15

مصطلحات موضوعية: DNA methylation, HCT (hematopoietic cell transplant), biomarker identification and validation, epigenetics, haematopoietic stem cell transplant, machine learning

الوصف: Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.

العلاقة: https://doi.org/10.3389/fgene.2024.1242636Test; https://pubmed.ncbi.nlm.nih.gov/38633407Test; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11021570Test/

الإتاحة: https://doi.org/10.3389/fgene.2024.1242636Test
https://pubmed.ncbi.nlm.nih.gov/38633407Test
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11021570Test/

المؤلفون: Pironet, Antoine, Dauby, Pierre, Chase, J. Geoffrey, Revie, James A., Docherty, Paul D., Desaive, Thomas

مصطلحات موضوعية: Quantification of physiological parameters for diagnosis and treatment assessment, Identification and validation, Biomedical system modeling, simulation and visualization, Human health sciences :: Cardiovascular & respiratory systems, Sciences de la santé humaine :: Systèmes cardiovasculaire & respiratoire, Engineering, computing & technology :: Multidisciplinary, general & others, Ingénierie, informatique & technologie :: Multidisciplinaire, généralités & autres

جغرافية الموضوع: international

الوصف: A simple experimentally validated cardiovascular system model has been shown to be able to track the evolution of various diseases. The model has previously been made patient-specific by adjustment of its parameters on the basis of a minimal set of hemodynamic measurements. However, this model has not yet been shown to be structurally identifiable, which means that the adjusted model parameters may not be unique. The model equations were manipulated to show that, from a theoretical point of view, all of their parameters can be exactly retrieved from a restricted set of model outputs. However, this set of model outputs is still too large for a clinical application, because it includes left and right ventricular pressures. Consequently, further hypotheses that determine some model parameter values have to be made for the model to be clinically applicable.

العلاقة: 19th World Congress of the International Federation of Automatic Control (du 24 au 29 août 2014)

الوصول الحر: https://orbi.uliege.be/handle/2268/171875Test

المؤلفون: Pironet, Antoine, Desaive, Thomas, Chase, J. Geoffrey, MORIMONT, Philippe, Dauby, Pierre

مصطلحات موضوعية: Quantification of physiological parameters for diagnosis and treatment assessment, Identification and validation, Healthcare management, disease control, critical care, Engineering, computing & technology :: Multidisciplinary, general & others, Ingénierie, informatique & technologie :: Multidisciplinaire, généralités & autres, Human health sciences :: Cardiovascular & respiratory systems, Sciences de la santé humaine :: Systèmes cardiovasculaire & respiratoire

جغرافية الموضوع: international

الوصف: Total stressed blood volume is an important parameter for both doctors and engineers. From a medical point of view, it has been associated with the success or failure of fluid resuscitation therapy, which is a treatment for cardiac failure. From an engineering point of view, this parameter dictates the cardiovascular system's dynamic behavior. Current methods to determine this parameter involve repeated phases of circulatory arrests followed by fluid administration. In this work, a method is developed to compute stressed blood volume from preload reduction experiments. A simple six-chamber cardiovascular system model is used and its parameters are adjusted to pig experimental data. The parameter adjustment process has three steps: (1) compute nominal values for all model parameters; (2) determine the most sensitive parameters; and (3) adjust only these sensitive parameters. Stressed blood volume was determined sensitive for all datasets, which emphasizes the importance of this parameter. The model was able to track experimental trends with a maximal mean squared error of 11.77 %. Stressed blood volume has been computed to range between 450 and 963 ml, or 15 to 28 ml/kg, which matches previous independent experiments on pigs, dogs and humans. Consequently, the method proposed in this work provides a simple way to compute total stressed blood volume from usual hemodynamic data.

العلاقة: 19th World Congress of the International Federation of Automatic Control (du 24 au 29 août 2014)

الوصول الحر: https://orbi.uliege.be/handle/2268/172767Test