ENPP1 homozygous stop-loss variant causing generalized arterial cal cifications of infancy: About a severe neonatal clinical case

Generalized Arterial Calcifications of Infancy (GACI) is an extremely rare autosomal recessive genetic condition, mostly due to pathogenic variations in the ENPP1 gene (GACI1, MIM # 208000, ENPP1, MIM *173335). To date 46 likely pathogenic or pathogenic distinct variations in ENPP1 have been described, including nonsense, frameshift, missense, splicing variations, and large deletions. Here we report a case of GACI in a male newborn with a homozygous stop-loss variant in ENPP1 treated in Nancy Regional University Maternity Hospital. Based on proband main clinical signs, clinical exome sequencing was performed and showed a deletion of one nucleotide leading to frameshift and stop-loss (NM_006208.3 (ENPP1):c.2746del,p.(Thr916Hisfs*23)). Clinical presentation is characterized by primary neonatal arterial hypertension resulting in hypertrophic cardiomyopathy decompensated by three cardiogenic shocks and a neonatal deep right sylvian stroke. The child died at 24 days of life. This is the first report of a pathogenic stop-loss variant in ENPP1. It is an opportunity to remind clinicians of GACI disease, a rare and severe etiology in neonates with severe hypertension, and possibility of bisphosphonates therapy.

Ontology for Overcrowding Management in Emergency Department

Emergency department (ED) overcrowding is an ongoing problem worldwide. Scoring systems are available for the detection of this problem. This study aims to combine a model that allows the detection and management of overcrowding. Therefore, it is crucial to implement a system that can reason model, rank ED resources and ED performance indicators based on environmental factors. Thus, we propose in this paper a new domain ontology (EDOMO) based on a new overcrowding estimation score (OES) to detect critical situations, specify the level of overcrowding and propose solutions to deal with these situations. Our approach is based on a real database created during more than four years from the Lille University Hospital Center (LUHC) in France. The resulting ontology is capable of modeling complete domain knowledge to enable semantic reasoning based on SWRL rules. The evaluation results show that the EDOMO is complete that can enhance the functioning of the ED.

Exploring sexualized substance use, sexual behaviors and HIV-screening practices in people attending erotic industry shows

Previous studies showed that Erotic industry sShows (ES) were appropriate events for sexual health promotion and testing interventions. A cross-sectional survey exploring screening practices, sexual behaviors, substance use, and sexual motives for substance use was conducted in ES in December 2017 and completed by 781 respondents. Overall, . Eighteen18% percent reported substance use in the last 3 months (51% alcohol), 26%. Twenty-six percent reported a sexual purpose for substance use. Main sexual partners were spouse (68%), regular (21%), unknown (18%) and several (17%) partners. Main sexual practices were libertinism (22%), partner swapping (15%) and threesome (15%). Twenty-seven percent of respondents reported cContactless sex was reported by 27% of the respondents. 18% reported no previous HIV test. Univariate analysis showed that having or not previous HIV test was linked to male sex (76.8% vs. 54.5%, p < 10-3), alcohol consumption in the last three months (58.7% vs. 49.4%, p = .043), number of drugs in a lifetime (1.3% vs. 1.6%, p = .022), sexual partnership with spouse/long-term partner (57.3% vs. 70.5%; p = .002), at least one multiple-partner sexual practice (23.1% vs. 31.8%, p = .040) and type of sexual attraction (p = <10-3). Results contribute to establishing the usefulness of HIV-testing and awareness campaigns in ES eventsand informing potential combined risk behaviors and related interventions.

The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based "white-box" classification algorithm for medical data

BACKGROUND

Multi-drug resistant (MDR) bacteria are a major health concern. In this retrospective study, a rule-based classification algorithm, MOCA-I (Multi-Objective Classification Algorithm for Imbalanced data) is used to identify hospitalized patients at risk of testing positive for multidrug-resistant (MDR) bacteria, including Methicillin-resistant Staphylococcus aureus (MRSA), before or during their stay.

METHODS

Applied to a data set of 48,945 hospital stays (including known cases of carriage) with up to 16,325 attributes per stay, MOCA-I generated alert rules for risk of carriage or infection. A risk score was then computed from each stay according to the triggered rules.Recall and precision curves were plotted.

RESULTS

The classification can be focused on specifically detecting high risk of having a positive test, or identifying large numbers of at-risk patients by modulating the risk score cut-off level. For a risk score above 0.85,recall (sensitivity) is 62 % with 69 % precision (confidence) for MDR bacteria, recall is 58 % with 88 % precision for MRSA. In addition, MOCA-I identifies 38 and 21 cases of previously unknown MDR and MRSA respectively.

CONCLUSIONS

MOCA-I generates medically pertinent alert rules. This classification algorithm can be used to detect patients with high risk of testing positive to MDR bacteria (including MRSA). Classification can be modulated by appropriately setting the risk score cut-off level to favor specific detection of small numbers of patients at very high risk or identification of large numbers of patients at risk. MOCA-I can thus contribute to more adapted treatments and preventive measures from admission, depending on the clinical setting or management strategy.

Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems

Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. Specifically, we compare three AAC approaches: one considering only the hypervolume indicator, a second optimising the weighted sum of hypervolume and spread, and a third that simultaneously optimises these complementary indicators, using a genuinely multi-objective approach. We assess these approaches by applying them to a highly-parametric local search framework for two widely studied multi-objective optimisation problems, the bi-objective permutation flowshop and travelling salesman problems. Our results show that multi-objective algorithms are indeed best configured using a multi-objective configurator.