The reference methods used for sickle cell disease (SCD) screening usually include two analytical steps: a first tier for differentiating haemoglobin S (HbS) heterozygotes, HbS homozygotes and β-thalassemia from other samples, and a confirmatory second tier. Here, we evaluated a first-tier approach based on a fully automated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) platform with automated sample processing, a laboratory information management system and NeoSickle® software for automatic data interpretation. A total of 6701 samples (with high proportions of phenotypes homozygous (FS) or heterozygous (FAS) for the inherited genes for sickle haemoglobin and samples from premature newborns) were screened. The NeoSickle® software correctly classified 98.8% of the samples. This specific blood sample collection was enriched in qualified difficult samples (premature newborns, FAS samples, late and very late samples, etc.). In this study, the sensitivity of FS sample detection was found to be 100% on the Lille MS facility and 99% on the Dijon MS facility, and the specificity of FS sample detection was found to be 100% on both MS facilities. The MALDI-MS platform appears to be a robust solution for first-tier use to detect the HbS variant: it is reproducible and sensitive, it has the power to analyze 600–1000 samples per day and it can reduce the unit cost of testing thanks to maximal automation, minimal intervention by the medical team and good overall practicability. The MALDI-MS approach meets today’s criteria for the large-scale, cost-effective screening of newborns, children and adults.

The objective of this paper is to provide an illustrative feedback on development of Open Source software among several partners. We describe the first stage of the design of a specific software package, namely a customized Laboratory Information Management System (LIMS) for biology applications. This software package is structured in several modules which are reusable and can be customized for other applications. In this paper, we address the problem of multi-licensing for the same software tools due to the participation of several partners, the reuse of code source, and the subsequent distribution of this produced software.

Biomedical information systems (BIS) require consideration of three types of variability: data variability induced by new high throughput technologies, schema or model variability induced by large scale studies or new fields of research, and knowledge variability resulting from new discoveries. Beyond data heterogeneity, managing variabilities in the context of BIS requires extensible and dynamic integration process. In this paper, we focus on data and schema variabilities and we propose an integration framework based on ontologies, master data, and semantic annotations. The framework addresses issues related to: 1) collaborative work through a dynamic integration process; 2) variability among studies using an annotation mechanism; and 3) quality control over data and semantic annotations. Our approach relies on two levels of knowledge: BIS-related knowledge is modeled using an application ontology coupled with UML models that allow controlling data completeness and consistency, and domain knowledge is described by a domain ontology, which ensures data coherence. A system build with the eClims framework has been implemented and evaluated in the context of a proteomic platform.

Abstract. Biomedical domain and proteomics in particular are faced with an increasing volume of data. The heterogeneity of data sources implies heterogeneity in the representation and in the content of data. Data may also be incorrect, implicate errors and can compromise the analysis of experiments results. Our approach aims to ensure the initial quality of data during import into an information system dedicated to proteomics. It is based on the joint use of models, which represent the system sources, and ontologies, which are use as mediators between them. The controls, we propose, ensure the validity of values, semantics and data consistency during import process.

No abstract

Previous research has shown that a MALDI-MS technique can be used to screen for sickle cell disease (SCD), and that a system combining automated sample preparation, MALDI-MS analysis and classification software is a relevant approach for first-line, high-throughput SCD screening. In order to achieve a high-throughput "plug and play" approach while detecting "non-standard" profiles that might prompt the misclassification of a sample, we have incorporated various sets of alerts into the decision support software. These included "biological alert" indicators of a newborn's clinical status (e. g., detecting samples with no or low HbA), and "technical alerts" indicators for the most common non-standard profiles, i.e., those which might otherwise lead to sample misclassification. We evaluated these alerts by applying them to two datasets (produced by different laboratories). Despite the random generation of abnormal spectra by one-off technical faults or due to the nature and quality of the samples, the use of alerts fully secured the process of automatic sample classification. Firstly, cases of β-thalassemia were detected. Secondly, after a visual check on the tagged profiles and reanalysis of the corresponding biological samples, all the samples were correctly reclassified without prompting further alerts. All of the samples for which the results were not tagged were well classified (i.e., sensitivity and specificity = 1). The alerts were mainly designed for detecting false-negative classifications; all the FAS samples misclassified by the software as FA (a false negative) were marked with an alert. The implementation of alerts in the NeoScreening ® Laboratory Information Management System's decision support software opens up perspectives for the safe, reliable, automated classification of samples, with a visual check solely on abnormal results or samples. It should now be possible to evaluate the combination of the NeoSickle ® analytical solution and the NeoScreening ® Laboratory Information Management System in a real-life, prospective study of first-line SCD screening.

No abstract

No abstract