Subjective Evaluation of Defocus and Astigmatism Combinations Using Image Simulation in Presbyopes.

SIGNIFICANCE: Image simulation is a useful and efficient tool to explore the impact of defocus and astigmatism combinations on visual acuity and image quality score when accommodation is taken into account. PURPOSE: The goal of this experiment was to determine if a simulation is able to predict visual acuity and image quality score (IQS) with defocus and astigmatism combinations in presbyopes. METHODS: We measured visual acuity and IQS in five defocus and astigmatism combinations in either real or simulated conditions. In real conditions, the subjects viewed a stimulus through an ophthalmic lens or a deformable mirror. In simulated conditions, subjects viewed images of the same stimulus with simulated blur. The amounts of defocus and astigmatism combinations of a progressive addition lens in near vision were generated through a static correction of the subject's aberrations. We simulated three levels of accommodation: subject could not accommodate (FOC0), subject could accommodate to the less hyperopic focal point (FOC1), or subject could accommodate to the circle of least confusion (FOC2). RESULTS: Visual acuity or IQS did not differ between mirror and progressive addition lens conditions. Visual acuity measured in real blur conditions differed significantly from that in FOC0 simulated blur condition but were similar to that in FOC1 and FOC2 simulated blur conditions. Image quality score obtained in real conditions were between scores measured with the FOC0 and FOC1 simulated conditions, suggesting that the subjects were able to produce a low level of accommodation. CONCLUSIONS: Accommodation may play a role when comparing optical and simulated defocus and astigmatism combinations. Presbyopic subjects are able to produce a low level of accommodation that may counterbalance a part of the deleterious effect of the astigmatism on image quality. Simulation remains a useful tool if the correct accommodation state is taken into account.

Population Pharmacokinetics of Levobupivacaine During Transversus Abdominis Plane Block in Children.

BACKGROUND: Levobupivacaine is commonly used during transversus abdominis plane (TAP) block in pediatric patients. However, the dosing regimen is still empirical, and the pharmacokinetic properties of levobupivacaine are not considered. Here, the pharmacokinetics of levobupivacaine during an ultrasound-guided TAP block were evaluated to optimize dosing regimen, regarding the between-subject variability (BSV) and the volume of levobupivacaine injected. METHOD: The clinical trial (prospective, randomized, double-blind study protocol) was conducted in 40 children aged 1-5 years, who were scheduled for inguinal surgery. Each patient received 0.4 mg/kg of levobupivacaine with a volume of local anesthesia solution adjusted to 0.2 mL/kg of 0.2% or 0.4 mL/kg of 0.1% levobupivacaine. Blood samples were collected at 5, 15, 20, 25, 30, 45, 60, and 75 minutes after the block injection. The population pharmacokinetic analysis was performed using the NONMEM software. RESULTS: From the pharmacokinetic parameters obtained, median Cmax, tmax,, and area under the concentration versus time curve were 0.315 mg/L, 17 minutes, and 41 mg/L·min, respectively. BSV of clearance was explained by weight. At the dose regimen of 0.4 mg/kg, none of the infants showed signs of toxicity, but in 13 patients, TAP block failed. After analysis, BSV for absorption rate constant, distribution volume, and clearance were 81%, 47%, and 41%, respectively. Residual unexplained variability was estimated to be 14%. CONCLUSIONS: For improved efficiency in the pediatric population, the dose of levobupivacaine should be greater than 0.4 mg/kg. Children's weight should be considered to anticipate any risk of toxicity.

Effect of Simulated and Real Spherical and Astigmatism Defocus on Visual Acuity and Image Quality Score.

SIGNIFICANCE: Image simulation is a useful and efficient tool to explore the impact of spherical and astigmatic blur on visual acuity (VA) and image gradation. It could help to design new optical corrections more efficiently and rapidly. PURPOSE: The purpose of this study was to compare the effects of simulated (convolution by an artificial eye) and real spherical and astigmatic defocus on VA and image gradation. METHODS: Experiments were performed under highly controlled conditions: dynamic correction of the subjects' aberrations at 1 Hz and application of an artificial pupil. In experiment 1, Landolt C VA was measured in various conditions of spherical and astigmatism defocus. The amounts of spherical or positive astigmatic defocus oriented at 45° that gives a Landolt C VA of 0.0, 0.2, and 0.5 logMAR were measured in experiment 2. In experiment 3, the subjects scored the quality of the perceived image (three high-contrast 0.4 logMAR letters) with a five-item continuous grading scale. RESULTS: Simulated blur was always more detrimental than optical blur. We measured a difference of 0.08 ± 0.03 and 0.11 ± 0.05 logMAR between both conditions, respectively, in presence of spherical and astigmatism defocus. An average ± standard deviation difference of 0.16 ± 0.06 D (i.e., spherical defocus) and 0.24 ± 0.15 D (i.e., astigmatism defocus) was observed between simulated and real optics blur to provide a given VA. The differences of image quality score between both conditions were, respectively, 15.13 ± 9.63 and 13.33 ± 4.83 for spherical and astigmatism defocus. Most of the differences were statistically significant. CONCLUSIONS: We observed a difference of about 20 and 35% between simulated and real optics blur, respectively, in presence of spherical and astigmatism blur. However, the difference between both methods remains equal to or below the clinically significant difference.

Impact of single-nucleotide polymorphisms in DNA repair pathway genes on response to chemoradiotherapy in rectal cancer patients: Results from ACCORD-12/PRODIGE-2 phase III trial.

We examined whether 66 germline single-nucleotide polymorphisms (SNPs) in 10 candidate genes would predict clinical outcome in 316 patients with resectable locally advanced rectal cancer (LARC) enrolled in the ACCORD-12 phase III trial who were randomly treated with preoperative radiotherapy plus capecitabine (CAP45; n = 155) or dose-intensified radiotherapy plus capecitabine and oxaliplatin (CAPOX50; n = 161). The primary endpoint was tumor response according to the Dworak score. Multivariate logistic regression models adjusted on treatment arm and T stage determined the SNPs prognostic and predictive values for tumor response. In univariate analysis, five SNPs in ERCC2, XPA, MTHFR and ERCC1 were associated with the Dworak score in the CAPOX50 arm. In the overall population, interaction with treatment arm was significant for ERCC2 rs1799787 (pinteraction = 0.05) and XPA rs3176683 (pinteraction = 0.008), suggesting a predictive effect for response to oxaliplatin-based chemoradiotherapy (CRT). All but XPA rs3176683 had a prognostic effect on tumor response. In a multivariate model, interaction remained significant for XPA rs3176683 ([OR 7.33, 95% CI 1.40-38.23], pinteraction = 0.018) and the prognostic effect significant for ERCC2 rs1799787 ([OR 0.55, 95%CI 0.32-0.93], p = 0.027) and ERCC1 rs10412761 ([OR 0.57, 95%CI 0.34-0.98], p = 0.042). Patients with the T/G haplotype of rs1799787 and rs10412761 had a 60% decrease in odds of response (p < 0.001). None of the five SNPs were associated with toxicity, overall and disease-free survival. These data suggest that genetic variation in DNA repair genes influences response to preoperative CRT in LARC and identify patients who benefit from the addition of oxaliplatin to CRT.

Haploidentical Hematopoietic Stem Cell Transplantation with Post-Transplant Cyclophosphamide for Primary Immunodeficiencies and Inherited Disorders in Children.

Allogeneic hematopoietic stem cell transplantation (HSCT) is a potentially curative treatment for some inherited disorders, including selected primary immunodeficiencies (PIDs). In the absence of a well-matched donor, HSCT from a haploidentical family donor (HIFD) may be considered. In adult recipients high-dose post-transplant cyclophosphamide (PTCY) is increasingly used to mitigate the risks of graft failure and graft-versus-host disease (GVHD). However, data on the use of PTCY in children (and especially those with inherited disorders) are scarce. We reviewed the outcomes of 27 children transplanted with an HIFD and PTCY for a PID (n = 22) or osteopetrosis (n = 5) in a single center. The median age was 1.5 years (range, .2 to 17). HSCT with PTCY was a primary procedure (n = 21) or a rescue procedure after graft failure (n = 6). The conditioning regimen was myeloablative in most primary HSCTs and nonmyeloablative in rescue procedures. After a median follow-up of 25.6 months, 24 of 27 patients had engrafted. Twenty-one patients are alive and have been cured of the underlying disease. The 2-year overall survival rate was 77.7%. The cumulative incidences of acute GVHD grade ≥ II, chronic GVHD, and autoimmune disease were 45.8%, 24.2%, and 29.6%, respectively. There were 2 cases of grade III acute GVHD and no extensive cGVHD. The cumulative incidences of blood viral replication and life-threatening viral events were 58% and 15.6%, respectively. There was evidence of early T cell immune reconstitution. In the absence of an HLA-identical donor, HIFD HSCT with PTCY is a viable option for patients with life-threatening inherited disorders.

Phenotypic similarity for rare disease: Ciliopathy diagnoses and subtyping.

Rare diseases are often hard and long to be diagnosed precisely, and most of them lack approved treatment. For some complex rare diseases, precision medicine approach is further required to stratify patients into homogeneous subgroups based on the clinical, biological or molecular features. In such situation, deep phenotyping of these patients and comparing their profiles based on subjacent similarities are thus essential to help fast and precise diagnoses and better understanding of pathophysiological processes in order to develop therapeutic solutions. In this article, we developed a new pipeline of using deep phenotyping to define patient similarity and applied it to ciliopathies, a group of rare and severe diseases caused by ciliary dysfunction. As a French national reference center for rare and undiagnosed diseases, the Necker-Enfants Malades Hospital (Necker Children's Hospital) hosts the Imagine Institute, a research institute focusing on genetic diseases. The clinical data warehouse contains on one hand EHR data, and on the other hand, clinical research data. The similarity metrics were computed on both data sources, and were evaluated with two tasks: diagnoses with EHRs and subtyping with ciliopathy specific research data. We obtained a precision of 0.767 in the top 30 most similar patients with diagnosed ciliopathies. Subtyping ciliopathy patients with phenotypic similarity showed concordances with expert knowledge. Similarity metrics applied to rare disease offer new perspectives in a translational context that may help to recruit patients for research, reduce the length of the diagnostic journey, and better understand the mechanisms of the disease.

Gene expression profiling of cytochromes P450, ABC transporters and their principal transcription factors in the amygdala and prefrontal cortex of alcoholics, smokers and drug-free controls by qRT-PCR.

1. Ethanol consumption and smoking alter the expression of certain drug-metabolizing enzymes and transporters, potentially influencing the tissue-specific effects of xenobiotics. 2. Amygdala (AMG) and prefrontal cortex (PFC) are brain regions that modulate the effects of alcohol and smoking, yet little is known about the expression of cytochrome P450 enzymes (P450s) and ATP-binding cassette (ABC) transporters in these tissues. 3. Here, we describe the first study on the expression of 19 P450s, their redox partners, three ABC transporters and four related transcription factors in the AMG and PFC of smokers and alcoholics by quantitative RT-PCR. 4. CYP1A1, CYP1B1, CYP2B6, CYP2C8, CYP2C18, CYP2D6, CYP2E1, CYP2J2, CYP2S1, CYP2U1, CYP4X1, CYP46, adrenodoxin and NADPH-P450 reductase, ABCB1, ABCG2, ABCA1, and transcription factors aryl hydrocarbon receptor AhR and proliferator-activated receptor α were quantified in both areas. CYP2A6, CYP2C9, CYP2C19, CYP3A4, CYP3A5, adrenodoxin reductase and the nuclear receptors pregnane X receptor and constitutive androstane receptor were detected but below the limit of quantification. CYP1A2 and CYP2W1 were not detected. 5. Adrenodoxin expression was elevated in all case groups over controls, and smokers showed a trend toward higher CYP1A1 and CYP1B1 expression. 6. Our study shows that most xenobiotic-metabolizing P450s and associated redox partners, transporters and transcription factors are expressed in human AMG and PFC.

An Integrated Pipeline for Phenotypic Characterization, Clustering and Visualization of Patient Cohorts in a Rare Disease-Oriented Clinical Data Warehouse.

Rare diseases pose significant challenges due to their heterogeneity and lack of knowledge. This study develops a comprehensive pipeline interoperable with a document-oriented clinical data warehouse, integrating cohort characterization, patient clustering and interpretation. Leveraging NLP, semantic similarity, machine learning and visualization, the pipeline enables the identification of prevalent phenotype patterns and patient stratification. To enhance interpretability, discriminant phenotypes characterizing each cluster are provided. Users can visually test hypotheses by marking patients exhibiting specific keywords in the EHR like genes, drugs and procedures. Implemented through a web interface, the pipeline enables clinicians to navigate through different modules, discover intricate patterns and generate interpretable insights that may advance rare diseases understanding, guide decision-making, and ultimately improve patient outcomes.

Impact of genotypic variation and cultivation conditions on the techno-functional characteristics and chemical composition of 25 new Canadian quinoa cultivars.

The utilization of quinoa in food production requires comprehensive information on its processing characteristics. Twenty-five new quinoa cultivars developed by the Northern Quinoa Breeding Program, grown in three Canadian locations over two seasons, were characterized for their proximate composition, pasting properties, thermal properties, water absorption index, water solubility index, foaming capacity, foaming stability, oil holding capacity, and emulsion activity crucial for potential food applications. Results showed significant variations in the proximate composition among the cultivars, which was also influenced by the growing location and harvest year. Significant differences (p < 0.05) were also observed in the pasting properties, thermal stability, hydration properties, foaming properties, oil holding capacity, and emulsion activity. The hierarchical cluster and principal component analyses were associated with five distinct clusters of quinoa cultivars, each with unique techno-functional attributes, suggesting their potential for different food applications. These findings emphasize the need for further research to explore the performance of quinoa flours in specific food products and their impact on end-product quality.

Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.

BACKGROUND: Rare diseases affect approximately 400 million people worldwide. Many of them suffer from delayed diagnosis. Among them, NPHP1-related renal ciliopathies need to be diagnosed as early as possible as potential treatments have been recently investigated with promising results. Our objective was to develop a supervised machine learning pipeline for the detection of NPHP1 ciliopathy patients from a large number of nephrology patients using electronic health records (EHRs). METHODS AND RESULTS: We designed a pipeline combining a phenotyping module re-using unstructured EHR data, a semantic similarity module to address the phenotype dependence, a feature selection step to deal with high dimensionality, an undersampling step to address the class imbalance, and a classification step with multiple train-test split for the small number of rare cases. The pipeline was applied to thirty NPHP1 patients and 7231 controls and achieved good performances (sensitivity 86% with specificity 90%). A qualitative review of the EHRs of 40 misclassified controls showed that 25% had phenotypes belonging to the ciliopathy spectrum, which demonstrates the ability of our system to detect patients with similar conditions. CONCLUSIONS: Our pipeline reached very encouraging performance scores for pre-diagnosing ciliopathy patients. The identified patients could then undergo genetic testing. The same data-driven approach can be adapted to other rare diseases facing underdiagnosis challenges.

Does visual acuity predict visual preference in progressive addition lenses?

PURPOSE: We aimed to determine if visual acuity (VA) could differentiate the quality of vision with two ophthalmic lenses with unwanted astigmatism. METHODS: Twenty presbyopic subjects (48 to 62 years old; VA better than 0.0 logMAR) graded the magnitude of their preference between two progressive addition lenses (plano addition 2.00D) and their visual acuities were measured with both lenses at various eccentricities from -12 to +12 mm from the near vision point every 3 mm in controlled conditions. RESULTS: The Lens with the least peripheral astigmatism was preferred by 75% of the subjects. VA measured at the near vision point was statistically worse (p<0.01) with this lens whereas the contrary was observed in the periphery (± 12 and -9 mm of eccentricity). The Friedman test shows that the eccentricity (p<0.001) has a significant effect on visual acuity. However, the lens did not show any significant effect (p=0.76). The choice of the favorite lens was predicted for only 35% when considering central VA (up to 6mm) and 80% of the subjects when considering peripheral VA (9 to 12mm). However, the magnitude of the difference could be predicted by peripheral VA in only 60% of the subjects. CONCLUSION: High contrast Visual acuity was clearly able to differentiate the 2 lens designs tested in our experiment. However, even under the controlled conditions of this study, it was not possible to predict the quality of vision, as measured by a subjective appreciation, through progressive addition lenses at various eccentricities from the near vision with an addition of 2.0D.

Improving Patient Similarity Using Different Modalities of Phenotypes Extracted from Clinical Narratives.

In the context of medical concept extraction, it is critical to determine if clinical signs or symptoms mentioned in the text were present or absent, experienced by the patient or their relatives. Previous studies have focused on the NLP aspect but not on how to leverage this supplemental information for clinical applications. In this paper, we aim to use the patient similarity networks framework to aggregate different phenotyping modalities. NLP techniques were applied to extract phenotypes and predict their modalities from 5470 narrative reports of 148 patients with ciliopathies (a group of rare diseases). Patient similarities were computed using each modality separately for aggregation and clustering. We found that aggregating negated phenotypes improved patient similarity, but further aggregating relatives' phenotypes worsened the result. We suggest that different modalities of phenotypes can contribute to patient similarity, but they should be aggregated carefully and with appropriate similarity metrics and aggregation models.

Diagnosis and management of hyponatremia in cancer patients.

Hyponatremia, a common electrolyte abnormality in oncology practice, may be a negative prognostic factor in cancer patients based on a systematic analysis of published studies. The largest body of evidence comes from small-cell lung cancer (SCLC), for which hyponatremia was identified as an independent risk factor for poor outcome in six of 13 studies. Hyponatremia in the cancer patient is usually caused by the syndrome of inappropriate antidiuretic hormone (SIADH), which develops more frequently with SCLC than with other malignancies. SIADH may be driven by ectopic production of arginine vasopressin (AVP) by tumors or by effects of anticancer and palliative medications on AVP production or action. Other factors may cause hypovolemic hyponatremia, including diarrhea and vomiting caused by cancer therapy. Hyponatremia may be detected on routine laboratory testing before or during cancer treatment or may be suggested by the presence of mostly neurological symptoms. Treatment depends on several factors, including symptom severity, onset timing, and extracellular volume status. Appropriate diagnosis is important because treatment differs by etiology, and choosing the wrong approach can worsen the electrolyte abnormality. When hyponatremia is caused by SIADH, hypertonic saline is indicated for acute, symptomatic cases, whereas fluid restriction is recommended to achieve a slower rate of correction for chronic asymptomatic hyponatremia. Pharmacological therapy may be necessary when fluid restriction is insufficient. The orally active, selective AVP receptor 2 (V(2))-receptor antagonist tolvaptan provides a mechanism-based option for correcting hyponatremia caused by SIADH or other conditions with inappropriate AVP elevations. By blocking AVP effects in the renal collecting duct, tolvaptan promotes aquaresis, leading to a controlled increase in serum sodium levels.

Consideration of oral health in rare disease expertise centres: a retrospective study on 39 rare diseases using text mining extraction method.

BACKGROUND: Around 8000 rare diseases are currently defined. In the context of individual vulnerability and more specifically the one induced by rare diseases, ensuring oral health is a particularly important issue. The objective of the study is to evaluate the pattern of oral health care course for patients with any rare genetic disease. Description of oral phenotypic signs-which predict a theoretical dental health care course-and effective orientation into an oral healthcare were evaluated. MATERIALS AND METHODS: We set up a retrospective cohort study to describe the consideration of patient oral health and potential orientation to an oral health care course who have at least been seen once between 1 January 2017 and 1 January 2020 in Necker Enfants Malades Hospital. We recruited patients from this study using the data warehouse, Dr Warehouse® (DrWH), from Necker-Enfants Malades Hospital. RESULTS: The study sample included 39 rare diseases, 2712 patients, with 54.7% girls and 45.3% boys. In the sample studied, 27.9% of patients had an acquisition delay or a pervasive developmental disorder. Among the patient files studied, oral and dental phenotypic signs were described for 18.40% of the patients, and an orientation in an oral healthcare was made in 15.60% of patients. The overall "network" effect was significantly associated with description of phenotypic signs (corrected p = 1.44e-77) and orientation to an oral healthcare (corrected p = 23.58e-44). Taking the Defiscience network (rare diseases of cerebral development and intellectual disability) as a reference for the odd ratio analysis, OSCAR, TETECOU, FILNEMUS, FIMARAD, MHEMO networks stand out from the other networks for their significantly higher consideration of oral phenotypic signs and orientation in an oral healthcare. CONCLUSION: To our knowledge, no study has explored the management of oral health in so many rare diseases. The expected benefits of this study are, among others, a better understanding, and a better knowledge of the oral care, or at least of the consideration of oral care, in patients with rare diseases. Moreover, with the will to improve the knowledge on genetic diseases, oral heath must have a major place in the deep patient phenotyping. Therefore, interdisciplinary consultations with health professionals from different fields are crucial.

Using Deep Learning to Improve Phenotyping from Clinical Reports.

With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children's hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.

Enriching UMLS-Based Phenotyping of Rare Diseases Using Deep-Learning: Evaluation on Jeune Syndrome.

The wide adoption of Electronic Health Records (EHR) in hospitals provides unique opportunities for high throughput phenotyping of patients. The phenotype extraction from narrative reports can be performed by using either dictionary-based or data-driven methods. We developed a hybrid pipeline using deep learning to enrich the UMLS Metathesaurus for automatic detection of phenotypes from EHRs. The pipeline was evaluated on a French database of patients with a rare disease characterized by skeletal abnormalities, Jeune syndrome. The results showed a 2.5-fold improvement regarding the number of detected skeletal abnormalities compared to the baseline extraction using the standard release of UMLS. Our method can help enrich the coverage of the UMLS and improve phenotyping, especially for languages other than English.

Patient-Patient Similarity-Based Screening of a Clinical Data Warehouse to Support Ciliopathy Diagnosis.

A timely diagnosis is a key challenge for many rare diseases. As an expanding group of rare and severe monogenic disorders with a broad spectrum of clinical manifestations, ciliopathies, notably renal ciliopathies, suffer from important underdiagnosis issues. Our objective is to develop an approach for screening large-scale clinical data warehouses and detecting patients with similar clinical manifestations to those from diagnosed ciliopathy patients. We expect that the top-ranked similar patients will benefit from genetic testing for an early diagnosis. The dependence and relatedness between phenotypes were taken into account in our similarity model through medical concept embedding. The relevance of each phenotype to each patient was also considered by adjusted aggregation of phenotype similarity into patient similarity. A ranking model based on the best-subtype-average similarity was proposed to address the phenotypic overlapping and heterogeneity of ciliopathies. Our results showed that using less than one-tenth of learning sources, our language and center specific embedding provided comparable or better performances than other existing medical concept embeddings. Combined with the best-subtype-average ranking model, our patient-patient similarity-based screening approach was demonstrated effective in two large scale unbalanced datasets containing approximately 10,000 and 60,000 controls with kidney manifestations in the clinical data warehouse (about 2 and 0.4% of prevalence, respectively). Our approach will offer the opportunity to identify candidate patients who could go through genetic testing for ciliopathy. Earlier diagnosis, before irreversible end-stage kidney disease, will enable these patients to benefit from appropriate follow-up and novel treatments that could alleviate kidney dysfunction.

Identification of Similar Patients Through Medical Concept Embedding from Electronic Health Records: A Feasibility Study for Rare Disease Diagnosis.

To identify patients with similar clinical profiles and derive insights from the records and outcomes of similar patients can help fast and precise diagnosis and other clinical decisions for rare diseases. Similarity methods are required to take into account the semantic relations between medical concepts and also the different relevance of all medical concepts presented in patients' medical records. In this paper, we introduce the methods developed in the context of rare disease screening/diagnosis from clinical data warehouse using medical concept embedding and adjusted aggregations. Our methods provided better preliminary results than baseline methods, with a significant improvement of precision among the top ranked similar patients, which is encouraging for further fine-tuning and application on a large-scale dataset for new/candidate patient identification.

Health consequences of cocaine use in France: data from the French Addictovigilance Network.

As the number and severity of complications related to cocaine use reported to the French Addictovigilance Network have increased, the French health authorities requested a national epidemiologic study of the data collected by this network from 2010 to 2016. For this purpose, the spontaneous reports (SRs) linked to cocaine notified by health professionals were analyzed as well as the data from the pharmacoepidemiological surveys OPPIDUM (observation of illegal drugs and misuse of psychotropic medications) and DRAMES (deaths related to the abuse of licit and illicit psychoactive substances). In total, 1 265 SRs were analyzed (510% increase from 2010 to 2016). Users were mainly men (952/1 261; 75%), with a median age of 35.0 years [IQ25-75 : 28-42]. Cocaine was consumed through the intranasal route by 52% of users (416/797), followed by intravenous administration (32%, 253/797) and inhalation (24%, 190/797). The use of cocaine powder and crack cocaine was reported in 70% (475/674) and 23% (154/674) of SRs, respectively. Cocaine was consumed with other psychoactive substances and alcohol in 47% (603/1265) and 60% (387/649) of cases, respectively. The main cocaine-related complications were psychiatric complications (29%), neurologic complications (24%) and cardiovascular complications (23%). Analysis of the OPPIDUM survey data showed that in 2016, 15.9 and 2.4% of the included subjects consumed cocaine or crack cocaine the week preceding the survey, the highest rate for the 2006-2016 period. The DRAMES survey indicated that cocaine-related deaths increased by threefold from 2014 to 2016. These data confirm that cocaine use in France is worrying with an increase in the number of severe complications and deaths.

A simplified approach to disulfide connectivity prediction from protein sequences.

BACKGROUND: Prediction of disulfide bridges from protein sequences is useful for characterizing structural and functional properties of proteins. Several methods based on different machine learning algorithms have been applied to solve this problem and public domain prediction services exist. These methods are however still potentially subject to significant improvements both in terms of prediction accuracy and overall architectural complexity. RESULTS: We introduce new methods for predicting disulfide bridges from protein sequences. The methods take advantage of two new decomposition kernels for measuring the similarity between protein sequences according to the amino acid environments around cysteines. Disulfide connectivity is predicted in two passes. First, a binary classifier is trained to predict whether a given protein chain has at least one intra-chain disulfide bridge. Second, a multiclass classifier (plemented by 1-nearest neighbor) is trained to predict connectivity patterns. The two passes can be easily cascaded to obtain connectivity prediction from sequence alone. We report an extensive experimental comparison on several data sets that have been previously employed in the literature to assess the accuracy of cysteine bonding state and disulfide connectivity predictors. CONCLUSION: We reach state-of-the-art results on bonding state prediction with a simple method that classifies chains rather than individual residues. The prediction accuracy reached by our connectivity prediction method compares favorably with respect to all but the most complex other approaches. On the other hand, our method does not need any model selection or hyperparameter tuning, a property that makes it less prone to overfitting and prediction accuracy overestimation.