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BACKGROUND AND PURPOSE Opioids and benzodiazepines are frequently combined in medical as well as in non-medical contexts. At high doses, such combinations often result in serious health complications attributed to pharmacodynamics interactions. Here, we investigate the contribution of the metabolic interactions between oxycodone, diazepam and diclazepam (a designer benzodiazepine) in abuse/overdose conditions through ex vivo, in vivo and in silico approaches. EXPERIMENTAL APPROACH A preparation of pooled human liver microsomes was used to study oxycodone metabolism in the presence or absence of diazepam or diclazepam. In mice, diazepam or diclazepam were concomitantly administered with oxycodone to mimic acute intoxication. Diclazepam was introduced on day 10 in mice continuously infused with oxycodone for 15 days to mimic chronic intoxication. In silico modelling was used to study the molecular interactions of the three drugs with CYP3A4 and 2D6. KEY RESULTS In mice, in acute conditions, both diazepam and diclazepam inhibited the metabolism of oxycodone. In chronic conditions and at pharmacologically equivalent doses, diclazepam drastically enhanced the production of oxymorphone. In silico, the affinity of benzodiazepines for CYP3A4 was higher than oxycodone, inhibiting oxycodone metabolism through CYP3A4. Oxycodone metabolism is likely to be diverted towards CYP2D6. CONCLUSION AND IMPLICATIONS Acute doses of diazepam or diclazepam results in the accumulation of oxycodone, whereas chronic administration induces the accumulation of oxymorphone, the toxic metabolite. This suggests that overdoses of opioids in the presence of benzodiazepines are partly due to metabolic interactions, which in turn rationalize the patterns of toxicity dependent on usage.

OBJECTIVE: To test the hypothesis that telomere shortening and/or loss are risk factors for infertility.DESIGN: Retrospective analysis of the telomere status in patients with infertility using conventional cytogenetic data collected prospectively.SETTING: Academic centers.PATIENT(S): Cytogenetic slides with cultured peripheral lymphocytes from 50 patients undergoing fertility treatment and 150 healthy donors, including 100 donors matched for age.INTERVENTION(S): Cytogenetic slides were used to detect chromosomal and telomere aberrations.MAIN OUTCOME MEASURE(S): Telomere length and telomere aberrations were analyzed after telomere and centromere staining.RESULT(S): The mean telomere length of patients consulting for infertility was significantly less than that of healthy donors of similar age. Moreover, patients with infertility showed significantly more extreme telomere loss and telomere doublet formation than healthy controls. Telomere shortening and/or telomere aberrations were more pronounced in patients with structural chromosomal aberrations. Dicentric chromosomes were identified in 6/13 patients, with constitutional chromosomal aberrations leading to chromosomal instability that correlated with chromosomal end-to-end fusions.CONCLUSION(S): Our findings demonstrate the feasibility of analyzing telomere aberrations in addition to chromosomal aberrations, using cytogenetic slides. Telomere attrition and/or dysfunction represent the main common cytogenetic characteristic of patients with infertility, leading to potential implications for fertility assessment. Pending further studies, these techniques that correlate the outcome of assisted reproduction and telomere integrity status may represent a novel and useful diagnostic and/or prognostic tool for medical care in this field.

BACKGROUND Immunosuppressant Bayesian Dose Adjustment (ISBA) is an online expert system that estimates the area under the curve (AUC) of immunosuppressive drugs through pharmacokinetic modelling and Bayesian estimation to propose dose adjustments to reach predefined exposure targets. The ISBA database was retrospectively analyzed to describe tacrolimus pharmacokinetics and exposure, evaluate the efficiency of ISBA dose recommendations, and propose tacrolimus AUC0-12h target ranges for pediatric renal allograft recipients treated with immediate release tacrolimus. METHODS The database included 1935 tacrolimus dose adjustment requests from 419 patients

BACKGROUND In children with cystic fibrosis (CF), the currently recommended amikacin dose ranges between 30 and 35 mg/kg/d; however, data supporting this dosing efficacy are lacking. In this article, the objectives were to develop a nonparametric pharmacokinetic population model for amikacin in children with CF and investigate the efficacy and toxicity at different dose rates for distinct minimum inhibitory concentration (MIC) clinical breakpoints using Monte Carlo simulations. METHODS Data from 94 children with CF (613 serum concentrations) from the Bordeaux University Hospital's CF-centre were analyzed. After determination of nonparametric pharmacokinetic population model parameters and associated influent covariates in Pmetrics, 1000 Monte Carlo simulations were performed for 7 different dose rates between 30 and 60 mg/kg/d, to predict the probability of obtaining peak serum amikacin ≥10 × MIC and trough level ≤2.5 mg/L, for MIC values between 1 and 16 mg/L. RESULTS The median (min-max) age and weight were 10 (0.3-17) years and 29 (6-71) kg, respectively, with only 2 children younger than 1 year of age. Body weight and creatinine clearance significantly impacted the amikacin volume of distribution and clearance. The mean relative bias/root mean squared error between observed and individual predicted concentrations was -0.68%/8.1%. Monte Carlo simulations showed that for sensitive bacteria with MICs ≤ 4, 30 mg/kg/d was most appropriate for a 100% success rate; for bacteria with MICs ≥ 8 [eg, Pseudomonas aeruginosa (MICamikacin = 8)], a dose of at least 40 mg/kg/d allowed a high success probability (90%), with a trough level below 2.5 mg/L. CONCLUSIONS For intermediate pathogens, a dose of at least 40 mg/kg/d can improve efficacy, with an acceptable calculated residual trough level in cases of normal or hyperfiltration. Because amikacin undergoes renal clearance, which is immature until 1 year of age, dosing recommendations for this age group may be markedly high, warranting cautious interpretation.

Given a high pharmacokinetic inter-individual variability and a low exposure target achievement, ganciclovir (GCV) therapeutic drug monitoring is sometimes used in children. We aimed to develop and validate Bayesian estimators based on limited sampling strategies for the estimation of GCV area under the concentration–time curve from 0 to 24 h in pediatric transplant recipients treated with valganciclovir (VGCV) or GCV. Solid organ transplant or stem-cell transplant recipients who received GCV or VGCV and had available GCV concentrations per standard of care were retrospectively included in this study for pharmacokinetic modeling and development of Bayesian estimators using the iterative two-stage Bayesian method. Validation datasets included additional child recipients of a solid organ transplant or stem-cell transplant, and child recipients of a kidney or liver transplant enrolled in a previous study. Various combinations of three or two sampling times, applicable in clinical practice, were assessed based on the relative mean bias, standard deviation, and the root mean square error in a development dataset and three independent validation datasets. In the development dataset, the mean bias/standard deviation/root mean square error for the 1 h/2 h/3 h and 1 h/3 h limited sampling strategies were − 1.4%/9.3%/9.1% and − 3.5%/12.2%/12.3%, respectively for GCV, while for VGCV, the mean bias/standard deviation/root mean square error for the 1 h/2 h/6 h and 1 h/6 h limited sampling strategies were 0.7%/13.5%/13.3% and − 0.1%/12.1%/11.8%, respectively. In the independent validation datasets, seven (13%) and five (14%) children would have had misclassifications of their exposure using these Bayesian estimators and limited sampling strategies for VGCV and GCV, respectively. Three plasma samples collected at 1 h/2 h/3 h and 1 h/2 h/6 h post-dose for GCV and VGCV respectively, are sufficient to accurately determine GCV area under the concentration–time curve from 0 to 24 h for pharmacokinetic-enhanced therapeutic drug monitoring.

Patients with chronic lymphocytic leukemia (CLL) may be more susceptible to Coronavirus disease 2019 (COVID-19) due to age, disease, and treatment-related immunosuppression. We aimed to assess risk factors of outcome and elucidate the impact of CLL-directed treatments on the course of COVID-19. We conducted a retrospective, international study, collectively including 941 patients with CLL and confirmed COVID-19. Data from the beginning of the pandemic until March 16, 2021, were collected from 91 centers. The risk factors of case fatality rate (CFR), disease severity, and overall survival (OS) were investigated. OS analysis was restricted to patients with severe COVID-19 (definition: hospitalization with need of oxygen or admission into an intensive care unit). CFR in patients with severe COVID-19 was 38.4%. OS was inferior for patients in all treatment categories compared to untreated (p

AIMS Ganciclovir (GCV) and its prodrug valganciclovir (VGCV) are first-line agents to prevent and treat cytomegalovirus in transplant recipients. There is high pharmacokinetic (PK) interindividual variability and PK data are scarce, especially in paediatric stem cell transplant (SCT) recipients. We sought to determine the optimal GCV and VGCV dosing in transplanted children. METHODS We conducted a single-centre retrospective population PK (POPPK) study of IV GCV and enteral VGCV in paediatric solid organ transplant (SOT) and SCT recipients. We included children who were transplanted and had available plasma GCV concentrations, done per standard of care. POPPK analysis was performed using a nonlinear mixed effects modelling approach with NONMEM. Optimal dosing was determined based on the achievement of the surrogate efficacy target: GCV 24 h area under the concentration-time curve (AUC0-24h ) of 40-60 mg.h.L-1 . RESULTS Fifty children with a median [range] age of 7.5 years [0.5-17.4] contributed 580 PK samples. A two-compartment model with first-order absorption with a lag time and first-order elimination fit the data well. Creatinine clearance and body weight (WT) were significant covariates for GCV clearance (CL); and WT for the volumes of distribution. IV GCV 15-20 mg.kg-1 .day-1 divided every 12 hours achieved the highest probability of target achievement (PTA) (33.0-33.8%). Enteral VGCV 30 and 40 mg.kg-1 .day-1 divided every 12 hours in children 0

The aim of this work is to estimate the area-under the blood concentration curve of tacrolimus (TAC) following b.i.d. or q.d. dosing in organ transplant patients, using Xgboost machine learning (ML) models. A total of 4,997 and 1,452 TAC interdose area under the curves (AUCs) from patients on b.i.d. and q.d. TAC, sent to our Immunosuppressant Bayesian Dose Adjustment expert system (www.pharmaco.chu-limoges.fr/) for AUC estimation and dose recommendation based on TAC concentrations measured at least at 3 sampling times (predose, ~ 1 and 3 hours after dosing) were used to develop 4 ML models based on 2 or 3 concentrations. For each model, data splitting was performed to obtain a training set (75%) and a test set (25%). The Xgboost models in the training set with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment were evaluated in the test set and in 6 independent full-pharmacokinetic (PK) datasets from renal, liver, and heart transplant patients. ML models based on two or three concentrations, differences between these concentrations, relative deviations from theoretical times of sampling, and four covariates (dose, type of transplantation, age, and time between transplantation and sampling) yielded excellent AUC estimation performance in the test datasets (relative bias < 5% and relative RMSE < 10%) and better performance than maximum a posteriori Bayesian estimation in the six independent full-PK datasets. The Xgboost ML models described allow accurate estimation of TAC interdose AUC and can be used for routine TAC exposure estimation and dose adjustment. They will soon be implemented in a dedicated web interface.

Background and methods This opinion paper expanded on the WHO “six-step approach to optimal pharmacotherapy,” by detailed exploration of the underlying pharmacological and pathophysiological principles. This exercise led to the identification of a large number of domains of research that should be addressed to make clinical pharmacology progress toward “precision clinical pharmacology,” as a prerequisite for precision medicine. Result In order to improve clinical efficacy and safety in patient groups (to guide drug development) as well as in individuals (to guide therapeutic options and optimize clinical outcome), developments in clinical pharmacology should at least tackle the following: (1) molecular diagnostic assays to guide drug design and development and allow physicians to identify the optimal targets for therapy in the individual patient in a quick and precise manner (to guide selection of the right drug for the right patient); (2) the setting up and validation of biomarkers of target engagement and modification as predictors of clinical efficacy and safety; (3) integration of physiological PK/PD models and intermediate markers of pharmacological effects with the natural evolution of the disease to predict the drug dose that most effectively improves clinical outcome in patient groups and individuals, making use of advanced modeling technologies (building on deterministic models, machine-learning, and deep learning algorithms); (4) methodology to validate human or humanized in vitro, ex vivo, and in vivo models for their ability to predict clinical outcome with investigational therapies, including nucleic acids or recombinant genes together with vectors (including viruses or nanoparticles), cell therapy, or therapeutic vaccines; (5) methodological complements to the gold-standard, large Phase 3 randomized clinical trial to provide clinically relevant and reliable data on the efficacy and safety of all treatment options at the population level (pragmatic clinical trials), as well as in small groups of patients (as low as n = 1); (6) regulatory science, so as to optimize the ethical review process, documentation, and monitoring of clinical trials, improve efficiency, and reduce costs of clinical drug development; (7) interventions to effectively improve patient compliance and to rationalize polypharmacy for the reduction of adverse effects and the enhancement of therapeutic interactions; and (8) appraisal of the ecological and societal impact of drug use to safeguard against environmental hazards (following the “One Health” concept) and to reduce drug resistance. Discussion and conclusion As can be seen, precision clinical pharmacology aims at being highly translational, which will require very large panels of complementary skills. Interdisciplinary collaborations, including non-clinical pharmacologists, will be key to achieve such an ambitious program.

Introduction:Recently there has been a renewal of therapeutic tools for the treatment of lymphoid neoplasms to increase the antitumor efficacy and reduce the toxicity generated by conventional chemotherapies, which adds to the intrinsic immunological dysfunction of the disease itself. To date, few data are published about infection risk of these new drugs, and the need for infectious prophylaxis is unknown. The aim of the study is to analyze the infectious complications in patients with LPD treated with monoclonal antibodies (obinutuzumab, ofatumumab, brentuximab, nivolumab and pembrolizumab), BTK inhibitors (ibrutinib, acalabrutinib) and PI3K inhibitors (idelalisib). Methods: Multicenter retrospective study in patients with LPD treated with targeted therapies (single agents or combination) in 18 Hematology centers in Spain, from the time of their commercial availability to March 2020. Patients in clinical trials were excluded as well as patients with active infections at the beginning of treatment. Results:During the study period, 380 patients were included.Baseline characteristics of the entire cohort are shown in Table 1.Median follow-up was 17.3 months (range 0-103), the longest follow-up corresponding to CLL patients (24 months, range 0-98) and the shortest to LBCL (5 months, range 0-25). Median exposure to target drugs was 8 months (range 0-72).Ibrutinib was administered to 219 patients(1 FL, 147 CLL, 27 MCL, 10 DLBCL, 1 TL and 32 WM, 1 HL),Brentuximab to 49(31 HL, 14 TL and 4 DLBCL) andIdelalisibto 35 patients (16 affected by chronic lymphocytic leukemia - CLL, 15 FL and 1 DLBCL, 1 WM, 1MCL, 1HL).Obinutuzumabcombinations were used in 10 (6 CLL, 3 FL, 1 MCL) and 5 HL patients (of which 4/5 underwent previous BMT) receivedNivolumab. A total number of 237 infectious events occurred in 148/380 patients (38.9%), 39% of which were grade 3 and 54/148 (36.4%) experienced 2 or more infective episodes: of those 54, 21 (38%) had underwent 3 or more lines of therapy and 28 (51%) had hypogammaglobulinemia. Hospitalization was required in 59.2% events. A bacterial cause of infection was reported in 40% of cases, and viral in 16%, including 11/237 (4,6%) SARS-CoV-2 infection. Invasive fungal infection (IFI) occurred in 3.3% (8/237). Noteworthy, no case of PJP was identified. Lung was the most frequent site of infection in 24% of cases (57/237) while the upper respiratory tract was involved in 17% of events (41/237). Urinary tract infections were diagnosed in 10% (24/237). Other sites involved were skin and soft tissue 7%, gastrointestinal tract 5,4%, bloodstream infections 3% and catheter related infections 2,5%. Considering drugs individually, 86 patients that receivedIbrutinib(39.2 %)experienced a total of 137 infectious episodes: 30% bacterial, 19% viral, 5% fungal and 45% clinical and image-based infections; the 17(34.6%of those who received Brentuximab, experienced a total of 16 infectious episodes: 56% bacterial, 37.5% viral infections and one catheter-related sepsis. Of those who receivedIdelalisib,18 (51.4%)experienced a total of 28 episodes: 42% bacterial, 14% viral and 7% fungal. Four patients treated withObinutuzumabcombinations (40%) experienced one infection during treatment (25% bacterial and 75% viral). Only one patient treated withNivolumabexperienced more than three infections, he was also under corticosteroid treatment. Focusing on IFI (Table 2): 7/8 infections were identified in CLL patients, 6 out 7 being on ibrutinib treatment and 1/7 on Idelalisib.Aspergilluswas the fungus most frequently isolated. The targeted drug was discontinued temporarily in 4 patients and indefinitely in 3. Twenty three (6%) patients died due to infection in our series. Conclusions: 1. We identified 38.7% infections in our LPD patients treated with targeted drugs, with a median drug-exposure time of 8 months (range 0-72), with a non-negligible incidence of bacterial infections. 2. The highest rates of infection were found in patients treated with with Idelalisib and Ibrutinib (51.4% and 39.2% respectively). 3. IFI (3.3%) occurred with low frequency, mostly in CLL patients during ibrutinib treatment, leading to its temporal discontinuation in most of the cases. 4. No case of PJP was identified in our cohort. 5. An analysis to determine risk factors for infection and the optimal monitoring and prophylaxis for these patients is ongoing. Disclosures Hernandez-Rivas: Janssen:Membership on an entity's Board of Directors or advisory committees;Abbvie:Membership on an entity's Board of Directors or advisory committees;Roche:Membership on an entity's Board of Directors or advisory committees;AstraZeneca:Membership on an entity's Board of Directors or advisory committees;Gilead:Membership on an entity's Board of Directors or advisory committees;Celgene/BMS:Membership on an entity's Board of Directors or advisory committees;Rovi:Membership on an entity's Board of Directors or advisory committees.Lopez-Guillermo:novartis:Consultancy;celgene:Consultancy, Research Funding;roche:Consultancy, Research Funding;gilead:Consultancy, Research Funding.

National audience; Recently, the International Association of Therapeutic Drug Monitoring (IATDMCT), that is the learning society for biological pharmacology and toxicology, issued recommendations on the therapeutic drug monitoring (TDM) of tacrolimus. This is the second consensus after the one issued in 2009. In this document, the role of tacrolimus TDM for the four principal transplanted organs is discussed. The analytical aspects, pharmacogenetics, TDM alternative approaches and the positioning of biomarkers are also presented. Stronger recommendations are about trough concentration targets in kidney and liver transplantation and for other indication of tacrolimus use. For the first time, an area under the curve of tacrolimus concentrations target is recommended for recipients management. Eventually, another set of recommendations are proposed for pharmacodynamic biomarkers used in patients’ follow-up.

This work aims to evaluate whether a machine learning approach is appropriate to estimate the glomerular filtration rate in intensive care unit patients based on sparse iohexol pharmacokinetic data and a limited number of predictors. Eighty-six unstable patients received 3250 mg of iohexol intravenously and had nine blood samples collected 5, 30, 60, 180, 360, 540, 720, 1080, and 1440 min thereafter. Data splitting was performed to obtain a training (75%) and a test set (25%). To estimate the glomerular filtration rate, 37 candidate potential predictors were considered and the best machine learning approach among multivariate-adaptive regression spline and extreme gradient boosting (Xgboost) was selected based on the root-mean-square error. The approach associated with the best results in a ten-fold cross-validation experiment was then used to select the best limited combination of predictors in the training set, which was finally evaluated in the test set. The Xgboost approach yielded the best performance in the training set. The best combination of covariates comprised iohexol concentrations at times 180 and 720 min; the relative deviation from these theoretical times; the difference between these two concentrations; the Simplified Acute Physiology Score II; serum creatinine; and the fluid balance. It resulted in a root-mean-square error of 6.2 mL/min and an r2 of 0.866 in the test set. Interestingly, the eight patients in the test set with a glomerular filtration rate

Metropolitan university campuses (MUCs) are of interest for policymaking given their general highly car-oriented nature, and the subsequent need to promote policies that enhance sustainable accessi...

BACKGROUND Renal failure is predictive of mortality in the early postliver-transplantation period and calcineurin inhibitors toxicity is a main challenge. Our aim is to assess the impact of longitudinal tacrolimus exposure (TLE) and other variables on chronic kidney disease (CKD)-free 1-year-survival. METHODS Retrospective data of consecutive patients transplanted between 2011 and 2016 and treated with tacrolimus were collected. TLE and all relevant pre- and post-liver transplantation (LT) predictive factors of CKD were tested and included in a time-to-event model. CKD was defined by repeated estimated glomerular filtration rate (eGFR) values below 60 mL/min/1.73m2 at least for the last 3 months before M12 post-LT. RESULTS Data from 180 patients were analyzed. CKD-free survival was 74.5% and was not associated with TLE. Pre-LT acute kidney injury (AKI) and eGFR at 1-month post-LT (eGFRM1)

Background Recently a peripheral blood 8-gene expression assay was developed for non-invasive detection of antibody-mediated rejection (ABMR) after kidney transplantation. Its value has not yet been evaluated in detail in clinical scenarios with different baseline disease probability [human leucocyte antigen donor-specific antibodies (HLA-DSA)-positive versus HLA-DSA-negative cases at the time of stable graft function versus graft dysfunction]. Methods Here we investigated the diagnostic accuracy of the 8-gene expression assay for histology of ABMR (ABMRh) with or without HLA-DSA in a cross-sectional cohort study of 387 blood samples with a concomitant graft biopsy. Results In patients with HLA-DSA (n = 64), the 8-gene expression assay discriminated DSA-positive ABMRh (DSAposABMRh) cases (n = 16) with good diagnostic performance {area under the receiver operating characteristic curve [AUROC] 83.1% [95% confidence interval (CI) 70.8–95.3]}. Also, in HLA-DSA-negative samples (n = 323), a clinically relevant diagnostic performance for DSAnegABMRh cases was found (n = 30) with an AUROC of 75.8% (95% CI 67.4–84.4). The 8-gene assay did not discriminate DSAposABMRh cases from DSAnegABMRh cases. There was a net benefit for clinical decision-making when adding the 8-gene expression assay to a clinical model consisting of estimated glomerular filtration rate, proteinuria, HLA-DSA and age. Conclusion The 8-gene expression assay shows great potential for implementation in the clinical follow-up of high-risk HLA-DSA-positive patients and clinical relevance in HLA-DSA-negative cases.

The ionotropic GABAA receptors represent the main target for different groups of widely used drugs having hypnotic and anxiolytic effects. So far, most approaches used to assess GABA activity invol...

Introduction Antibody-mediated rejection (ABMR) impacts kidney allograft outcome. The diagnosis is made based on findings from invasive kidney transplant biopsy specimens. The aim of this study was to identify a noninvasive urinary protein biomarker for ABMR after kidney transplantation. Methods We performed a multicenter case-control study to identify a urinary biomarker for ABMR (training cohort, n = 249) and an independent, prospective multicenter cohort study for validation (n = 391). We used concomitant biopsies to classify the samples according to the Banff classification. After untargeted protein identification and quantification, we used a support vector machine to train the model in the training cohort. The primary endpoint was the diagnostic accuracy of the urinary biomarker for ABMR in the validation cohort. Results We identified a set of 10 urinary proteins that accurately discriminated patients with (n = 60) and without (n = 189) ABMR in the training cohort with an area under the curve (AUC) of 0.98 (95% confidence interval [CI], 0.96–1.00). The diagnostic accuracy was maintained in the validation cohort (AUC, 0.88; 95% CI, 0.8–0.93) for discriminating the presence (n = 43) from the absence (n = 348) of ABMR. The negative predictive value of the 10-protein marker set for exclusion of ABMR was 0.99, and the positive predictive value was 0.33. The diagnostic accuracy was independent of the reason for performing the biopsy, time after transplantation, and better than the accuracy of gross proteinuria (AUC, 0.76). Conclusions We identified and validated a urinary protein biomarker set that can be used to exclude ABMR., Graphical abstract

Aims Intravenous mycophenolate mofetil (IV MMF), a prodrug of mycophenolic acid (MPA), is used during nonmyeloablative and reduced-intensity conditioning haematopoetic stem cell transplantation (HCT) to improve engraftment and reduce graft-versus-host disease. The aims of this study were to develop population pharmacokinetic models and Bayesian estimators based on limited sampling strategies to allow for individual dose adjustment of intravenous mycophenolate mofetil administered by infusion in haematopoietic stem cell transplant patients. Methods Sixty-three MPA concentration-time profiles (median [min-max] = 6 [4-7] samples) were collected from 34 HCT recipients transplanted for 14 (1-45) days and administered IV MMF every 8 hours, concomitantly with cyclosporine. The database was split into development (75%) and validation (25%) datasets. Pharmacokinetic models characterized by a single compartment with first-order elimination, combined with two gamma distributions to describe the transformation of MMF into mycophenolic acid, were developed using in parallel nonparametric (Pmetrics) and parametric (ITSIM) approaches. The performances of the models and the derived Bayesian estimators were evaluated in the validation set. Results The best limited sampling strategy led to a bias (min, max), root mean square error between observed and modeled interdose areas under the curve in the validation dataset of -11.72% (-31.08%, 5.00%), 14.9% for ITSIM and -2.21% (-23.40%, 30.01%), 12.4% for Pmetrics with three samples collected at 0.33, 2 and 3 hours post dosing. Conclusion Population pharmacokinetic models and Bayesian estimators for IV MMF in HCT have been developed and are now available online (https://pharmaco.chu-limoges.fr) for individual dose adjustment based on the interdose area under the curve.

We present a low-cost, 3D-printed, and biocompatible fluidic device, engineered to produce laminar and homogeneous flow over a large field-of-view. Such a fluidic device allows us to perform multiplexed temporal monitoring of cell cultures compatible with the use of various pharmacological protocols. Therefore, specific properties of each of the observed cell cultures can be discriminated simultaneously during the same experiment. This was illustrated by monitoring the agonists-mediated cellular responses, with digital holographic microscopy, of four different cell culture models of cystic fibrosis. Quantitatively speaking, this multiplexed approach provides a time saving factor of around four to reveal specific cellular features.

Background The issue of contact precautions as contributory factors for reducing Pseudomonas aeruginosa (Pa) infections in intensive care units (ICUs) remains questioned. We evaluated the impact of the addition of contact precautions to standard precautions for Pa-positive patients on incidence of ICU-acquired Pa infections. Methods In this multicenter, cluster-randomized crossover trial, 10 French ICUs were randomly assigned (1:1) to sequence 0–1 (6-month control period [CP]/3-month washout period/6-month intervention period [IP]) or sequence 1–0 (6-month IP/3-month washout period/6-month CP). A surveillance screening program for Pa was implemented. Competing-risks regression models were built with death and discharge without the occurrence of ICU-acquired Pa infection (the primary outcome) as competing events. Models were adjusted for within-ICU correlation and patient- and ICU-level covariates. The Simpson diversity index (SDI) and transmission index (TI) of Pa isolates were derived from pulsed-field gel electrophoresis typing. Results Within recruited ICUs, the cumulative incidence and incidence rate of ICU-acquired Pa infections were 3.38% (55/1625) versus 3.44% (57/1658) and 3.31 versus 3.52 per 1000 patient-days at risk during the CP and IP, respectively. Multivariable models indicated that the intervention did not significantly change the cumulative incidence (subdistribution hazard ratio, .91; 95% confidence interval [CI], .49–1.67; P = .76) or rate (cause-specific hazard ratio, 1.36; 95% CI, .71–2.63; P = .36) of the primary outcome. SDI and TI did not significantly differ between CP and IP. Conclusions The addition of contact precautions to standard precautions for Pa-positive patients with a surveillance screening program does not significantly reduce ICU-acquired Pa infections in non-outbreak situations. Clinical Trials Registration. ISRCTN92710225.

BackgroundDespite the availability of whole exome (WES) and genome sequencing (WGS), chromosomal microarray (CMA) remains the first-line diagnostic test in most rare disorders diagnostic workup, looking for copy number variations (CNVs), with a diagnostic yield of 10%–20%. The question of the equivalence of CMA and WES in CNV calling is an organisational and economic question, especially when ordering a WGS after a negative CMA and/or WES.MethodsThis study measures the equivalence between CMA and GATK4 exome sequencing depth of coverage method in detecting coding CNVs on a retrospective cohort of 615 unrelated individuals. A prospective detection of WES-CNV on a cohort of 2418 unrelated individuals, including the 615 individuals from the validation cohort, was performed.ResultsOn the retrospective validation cohort, every CNV detectable by the method (ie, a CNV with at least one exon not in a dark zone) was accurately called (64/64 events). In the prospective cohort, 32 diagnoses were performed among the 2418 individuals with CNVs ranging from 704 bp to aneuploidy. An incidental finding was reported. The overall increase in diagnostic yield was of 1.7%, varying from 1.2% in individuals with multiple congenital anomalies to 1.9% in individuals with chronic kidney failure.ConclusionCombining single-nucleotide variant (SNV) and CNV detection increases the suitability of exome sequencing as a first-tier diagnostic test for suspected rare Mendelian disorders. Before considering the prescription of a WGS after a negative WES, a careful reanalysis with updated CNV calling and SNV annotation should be considered.

BackgroundIschemia-related injury during the pre-implantation period impacts kidney graft outcome. Evaluating these lesions by a non-invasive approach before transplantation could help to understand the mechanisms of graft injury and identify potential biomarkers predictive of graft outcomes. This study aims to determine metabolomic content of graft perfusion fluids and its dependence on preservation time and to explore whether tubular transporters are possibly involved in the metabolomics variations observed.MethodsKidneys were stored on hypothermic perfusion machines. We evaluated the metabolomic profiles of perfusion fluids (n=35) using Liquid Chromatography coupled with tandem Mass Spectrometry and studied the transcriptional expression of tubular transporters on pre-implantation biopsies (n=26). We used univariate and multivariate analyses to assess the impact of perfusion time on these parameters and their relationship with graft outcome.ResultsSeventy-two metabolites were found in preservation fluids at the end of perfusion, of which 40% were already present in the native conservation solution. We observed an increase of 23 metabolites with longer perfusion time and a decrease for 8. The predictive model for time-dependent variation of metabolomics content showed good performance (R2= 76%, Q2= 54%, accuracy= 41%, permutations test significant). Perfusion time had no effect on the mRNA expression of transporters. We found no correlation between metabolomics and transporters expression. Neither the metabolomics profile nor the transporters expression were predictive of graft outcome.ConclusionOur results open the way for further studies, focusing on both intra- and extra-tissue metabolome, to investigate whether transporter alterations can explain the variations observed in pre-implantation period.

BackgroundThe Banff classification standardizes the diagnoses of kidney transplant rejection based on histological criteria. Clinical decisions are generally made after integration of the Banff diagnoses in the clinical context. However, interpretation of the biopsy cases is still heterogeneous among pathologists or clinicians. Machine Learning (ML) algorithms may be trained from expertly assessed cases to provide clinical decision support.MethodsThe ML technique of Extreme Gradient Boosting learned from two large training datasets from the European programs BIOMARGIN and ROCKET (n= 631 and 304), in which biopsies were read centrally and consensually interpreted by a group of experts and used as a reference for untargeted biomarker screenings. The model was then externally validated in three independent datasets (n= 3744, 589 and 360).ResultsIn the three validation datasets, the algorithm yielded a ROC curve AUC of mean (95% CI) 0.97 (0.92-1.00), 0.97 (0.96-0.97) and 0.95 (0.93-0.97) for antibody-mediated rejection (ABMR); 0.94 (0.91-0.96), 0.94 (0.92-0.95) and 0.91 (0.88-0.95) for T cell-mediated rejection; >0.96 (0.90-1.00) in all three for interstitial fibrosis - tubular atrophy (IFTA). Finally, using the largest validation cohort, we developed an additional algorithm to discriminate active and chronic active ABMR with an accuracy of 0.95.ConclusionWe built an Artificial Intelligence algorithm able to interpret histological lesions together with a few routine clinical data with very high sensitivity and specificity. This algorithm should be useful in routine or clinical trials to help pathologists and clinicians and increase biopsy interpretation homogeneity.

BACKGROUND The significance of discrepant findings between histology (BMB) and flow cytometry (FC) in bone marrow (BM) examination at diffuse large B-cell lymphoma (DLBCL) diagnosis is uncertain. METHODS We performed a 5-year retrospective single-center study of patients diagnosed by DLBCL not otherwise specified (n = 82), divided into three groups according to BM infiltration at diagnosis: BMB-/FC- (75.6%), BMB+/FC+ (13.4%), and BMB-/FC+ (11%). RESULTS Median infiltration by FC analysis of the BMB-/FC+ group was 0.8% and if we considered BM infiltration as positive in all cases, 4/9 would be upstaged. Median follow was 33 months. Event-free survival (EFS) after 18 months was 82, 23, and 27% for BMB-/FC-, BMB-/FC+, and BMB+/FC+, respectively (p

Aims The objective of this study is to develop a generic model for tacrolimus pharmacokinetics modelling using a meta-analysis approach, that could serve as a first step towards a prediction tool to inform pharmacokinetics-based optimal dosing of tacrolimus in different populations and indications. Methods A systematic literature review was performed and a meta-model developed with NONMEM software using a top-down approach. Historical (previously published) data were used for model development and qualification. In-house individual rich and sparse tacrolimus blood concentration profiles from adult and paediatric kidney, liver, lung and heart transplant patients were used for model validation. Model validation was based on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness of fit with respect to measured blood concentrations with no indication of bias, and acceptable performance of visual predictive checks. External validation was performed by fitting the model to independent data from 3 external cohorts and remaining previously published studies. Results A total of 76 models were found relevant for meta-model building from the literature and the related parameters recorded. The meta-model developed using patient level data was structurally a 2-compartment model with first-order absorption, absorption lag time and first-time varying elimination. Population values for clearance, intercompartmental clearance, central and peripheral volume were 22.5 L/h, 24.2 L/h, 246.2 L and 109.9 L, respectively. The absorption first-order rate and the lag time were fixed to 3.37/h and 0.33 hours, respectively. Transplanted organ and time after transplantation were found to influence drug apparent clearance whereas body weight influenced both the apparent volume of distribution and the apparent clearance. The model displayed good results as regards the internal and external validation. Conclusion A meta-model was successfully developed for tacrolimus in solid organ transplantation that can be used as a basis for the prediction of concentrations in different groups of patients, and eventually for effective dose individualization in different subgroups of the population.

Background Since its introduction in 2006, SOPARC (Systematic Observation of Play and Recreation in Communities) has become a fundamental tool to quantify park visitor behaviors and characteristics. We tested SOPARC reliability when assessing race/ethnicity, physical activity, contextual conditions at the time of observation, and settings of target areas to understand its utility when trying to account for individual characteristics of users. Methods We used 4725 SOPARC observations completed simultaneously by two independent observers to evaluate intraclass correlation and agreement rate between the two observers when trying to assess sex, age group, race/ethnicity, and level of physical activity of urban park users in different park settings. Observations were in 20 New York City parks during Spring and Summer 2017 within the PARC3 project. Results Observers counted 25,765 park users with high interobserver reliability (ICC = .94; %Agreement.75). Reliability scores were negatively affected by the population being observed, the intensity of physical activity, and the contextual conditions and settings of the target area at the time of observation. Specific challenges emerged when assessing the combination of physical activity and race/ethnicity. Conclusions SOPARC training should aim to improve reliability when assessing concurrent measures such as physical activity, race/ethnicity, age, and sex. Similarly, observing crowded park areas with many active users areas may require more observation practice hours.

Background: Selection of expected phenotypes (ie, expressers/non-expressers) is currently used in CYP3A5*3 genotype-based tacrolimus dosing. The authors assessed whether a dosing regimen based on the 3 CYP3A5 genotypes may reduce the occurrence of inadequate exposure. Methods: Tacrolimus whole blood trough levels ( C 0) were retrieved from a retrospective cohort of 100 kidney transplant recipients treated with a starting dose of 0.15 (non-expressers) or 0.30 (expressers) mg/kg/d. The authors evaluated the occurrence of overexposures (12 < C 0 < 20 ng/mL) or toxic concentrations ( C 0 ≥ 20 ng/mL). These results were used to set up a new strategy based on the 3 distinct CYP3A5 genotypes, which relevance was evaluated in a prospective cohort of 107 patients. Results: In the retrospective cohort, non-expressers exhibited frequent overexposure (63.6%) or toxic C 0 (20.8%). Among expressers, none of the homozygous *1 carriers exhibited overexposure contrary to 25% of the heterozygotes. Based on these results, new tacrolimus starting doses were set at 0.10, 0.20, and 0.30 mg/kg/d for CYP3A5*3/*3, CYP3A5*1/*3, and CYP3A5*1/*1 genotypes, respectively. Tacrolimus overexposure was reduced in the CYP3A5*3/*3 group (63.6% vs 40%, P = .0038). None of the heterozygous patients exhibited toxic tacrolimus C 0. Clinical outcomes were not different between the 2 periods, whatever the genotype. Our results indicate that the best tacrolimus exposure was obtained for doses of 0.10, 0.20, and 0.20 mg/kg/d for CYP3A5*3/3, CYP3A5*1/*3, and CYP3A5*1/*1, respectively. Conclusions: Our results confirm that selecting tacrolimus dosing regimen according to the expected phenotype is appropriate, but that lower than currently recommended doses may be preferable.

BACKGROUND: Antibody-mediated rejection, a leading cause of renal allograft graft failure, is diagnosed by histological assessment of invasive allograft biopsies. Accurate non-invasive biomarkers are not available. METHODS: In the multicentre, prospective BIOMARGIN study, blood samples were prospectively collected at time of renal allograft biopsies between June 2011 and August 2016 and analyzed in three phases. The discovery and derivation phases of the study (N = 117 and N = 183 respectively) followed a case-control design and included whole genome transcriptomics and targeted mRNA expression analysis to construct and lock a multigene model. The primary end point was the diagnostic accuracy of the locked multigene assay for antibody-mediated rejection in a third validation cohort of serially collected blood samples (N = 387). This trial is registered with ClinicalTrials.gov, number NCT02832661. FINDINGS: We identified and locked an 8-gene assay (CXCL10, FCGR1A, FCGR1B, GBP1, GBP4, IL15, KLRC1, TIMP1) in blood samples from the discovery and derivation phases for discrimination between cases with (N = 49) and without (N = 134) antibody-mediated rejection. In the validation cohort, this 8-gene assay discriminated between cases with (N = 41) and without antibody-mediated rejection (N = 346) with good diagnostic accuracy (ROC AUC 79·9%; 95% CI 72·6 to 87·2, p

In critically ill patients treated with meropenem, the proposed pharmacokinetics/pharmacodynamics (PK/PD) efficacy index is to keep the free drug concentration 4–5 times above the minimum inhibitory concentration (MIC) of the germ isolated, for 100% of the interval regimen. The objectives were to design a population pharmacokinetics model for meropenem in critically ill patients and to evaluate different dosage schemes that achieve the optimal PK/PD objectives. This retrospective, observational, single-centre study included 80 critically ill patients (154 samples) treated with meropenem between May 2011 and December 2017. Patient data, concentrations, treatment and bacteriological variables were collected from electronic medical records. Total and free concentrations of meropenem were modelled in Pmetrics. Monte Carlo simulations were performed to assess the probability of achieving the PK/PD target for different dosage regimens. For patients with available data, the number of patients with a free concentration 4 times higher or lower than the observed MIC for the P. aeruginosa and E. coli was investigated. A one-compartment model with first-order elimination adequately described serum total and free meropenem concentrations. The only variable that significantly influenced the elimination constant of meropenem was the creatinine clearance (CLcr) calculated using the CKD-EPI formula. The highest probability of achieving the pharmacodynamic objective was with 3-h infusion dosage regimens. Sixty percent and 89% of patients attained a free drug concentration 4 times above the MIC for P. aeruginosa and E. coli respectively. This study proposed different dosing regimens depending on renal clearance strata and the MIC of the germ targeted.

Stem cell chemo-resistance is still challenging the efficacy of the front-line temozolomide against glioblastoma. Novel therapies are urgently needed to fight those cells in order to control tumor relapse. Here, we report that anti-O-acetyl-GD2 adjuvant immunotherapy controls glioma stem-like cell-driven chemo-resistance. Using patient-derived glioblastoma cells we found that glioma stem-like cells over-expressed O-acetyl-GD2. As a result, monoclonal antibody 8B6 immunotherapy significantly increased temozolomide genotoxicity and tumor cell death in vitro by enhancing temozolomide tumor uptake. Furthermore, the combination therapy decreased the expression of the glioma stem-like cell markers CD133 and Nestin and compromised glioma stem-like cell self-renewal capabilities. When tested in vivo, adjuvant 8B6 immunotherapy prevented the extension of the temozolomide-resistant glioma stem-like cell pool within the tumor bulk in vivo and was more effective than the single agent therapies. This is the first report demonstrating that anti-O-acetyl-GD2 monoclonal antibody 8B6 targets glioblastoma in a manner that control temozolomide-resistance driven by glioma stem-like cells. Together our results offer a proof of concept for using anti-O-acetyl GD2 reagents in glioblastoma to develop more efficient combination therapies for malignant gliomas. This article is protected by copyright. All rights reserved.

Aims Tacrolimus is a narrow therapeutic range drug that requires fine dose adjustment, for which pharmacokinetic (PK) models have been amply proposed in renal, but not in liver, transplant recipients. This study aimed to build population PK models and Bayesian estimators (BEs) in adult de novo liver transplant patients receiving either the immediate-release (Prograf, twice daily, TD) or prolonged-release (Advagraf, once daily, OD) forms to help PK-guided dose individualization. Methods In total, 160 tacrolimus concentration-time profiles (1654 samples) were collected from 80 patients, at day 7 (D7) and week 6 (W6) post-transplant. Four population PK models were developed using in-parallel parametric and nonparametric approaches for each formulation and period post-transplant. The best limited sampling strategies for estimating the area-under-the-curve (AUC) were selected by comparing predicted values to an independent dataset. Finally, the doses required to reach AUC targets were estimated using each BE and compared to the doses obtained using the trapezoidal AUC. Results Tacrolimus PK was best described using a 1-compartmental model with first-order elimination and 2 γ-distributions to describe the absorption. In the validation datasets, Bayesian AUC estimates yielded mean bias/root mean squared prediction error of −5.06%/13.43% (OD D7), 2.25%/8.51% (OD W6), −2.36%/7.27% (TD D7) and 0.87%/9.07% (TD W6) for the in-parallel parametric approach; and 8.95%/17.84% (OD D7), −0.11%/10.13% (OD W6), 3.57%/18.40% (TD D7) and 4.48%/12.59% (TD W6) for the nonparametric approach. Conclusion The BEs and limited sampling strategies proposed here are able to predict accurately and precisely tacrolimus AUC in liver patients using only 3 plasma concentrations. The dosing methods are available on our ImmunoSuppressive Bayesian dose Adjustment website (www.pharmaco.chu-limoges.fr).

Objective The objective of this study was to develop a physiologically based pharmacokinetic model for meropenem using a retrograde approach, which could serve as a basis for prediction of the systemic and infection-site drug exposures in different populations and indications. We intended this model to be a useful tool to inform (local) pharmacokinetic-based optimal dosing of meropenem in different settings. Methods We developed a reduced physiologically based pharmacokinetic model with NONMEM software using a top-down approach. We used historical (previously published) data for model development and qualification. We used steady-state systemic and infection-site concentrations from 60 adult patients diagnosed with severe lung infection for model development and internal evaluation. The data included rich plasma and sparse epithelial lining fluid samples. We based the internal validation of the model on successful numerical convergence, adequate precision in parameter estimation, acceptable goodness-of-fit plot with no indication of bias, and acceptable performance of visual predictive checks. We performed external validation by fitting the model to independent data from five previously published studies: four studies in patients with pneumonia, with different grades of renal impairment, and one study in morbidly obese patients. Results We successfully fitted a reduced physiologically based pharmacokinetic model with six compartments (arterial and venous pools, infection site [lungs], liver, kidneys and rest of the body) to the data and adequately estimated model parameters. We successfully qualified the model (internally and externally) using established methods. Estimated values for tissue-to-plasma partition coefficients were 0.2629 and 0.1946 for lungs and non-fat tissues (kidneys and liver), respectively. Estimated total clearance was 8.174 L/h for a typical patient with a glomerular filtration rate of 65 mL/min. Consistent with the known mechanism of meropenem elimination and previously published models, renal clearance accounted for 70% of total clearance. The model had good predictive performances on data from five different sources including populations with different characteristics with regard to body size, renal function and morbidity. Conclusions We successfully developed a physiologically based pharmacokinetic model for meropenem in adult patients to be used as a basis for prediction of concentrations in different groups of patients, and eventually for effective dose individualisation in different subgroups of the population. Electronic supplementary material The online version of this article (10.1007/s40268-019-0268-x) contains supplementary material, which is available to authorized users.

The aim of this study was to explore the exposure to green elements of residents in a compact and dense Mediterranean city, with a main focus on the exposure to traditional green elements such as parks and gardens and also less-explored elements such as public squares, boulevards, and beaches. Differences between walking and residential environments between morphologically distinct residential areas are also analysed. To achieve this, both residential and GPS-based exposure measures were obtained with GIS and smartphone tracking data for 129 adult individuals from Barcelona, Spain. Results have shown how using smartphone tracking data allows obtaining a higher density of green elements and the different types of these elements that residential buffers are not able to detect. Furthermore, research findings highlight the importance of urban form in the provision of green spaces in cities and that alternative greenness can be found in other types of open spaces beyond public parks.

Unidad de excelencia María de Maeztu MdM-2015-0552 document de l'ICTA Al postprint no apareix informació de subvenció, però a la versió publicada diu això: This research was carried out within the framework of the research project: "Movilidad cotidiana activa y saludable en entornos urbanos de proximidad. Enfoques multimetodologicos: tracking living labs, encuestas de movilidad y estudios cualitativos" funded by Ministerio de Economía y Competitividad, Subdirección General de Proyectos de Investigación (Ref: CSO2016-74904-R). No sé si ho he codificat bé... gra'cies! Being physically active in natural environments has been linked with multiple mental and physical health benefits. However, not all urban contexts can provide their residents the same access to green areas for walking and sport activities. Mediterranean cities provide open spaces for physical activity that differ from those of Northern European cities. This study explores both conventional spaces in the form of public parks and urban green spaces, i.e. beaches, tree-lined streets, boulevards and public squares, in relation to the daily walking levels of residents in a Mediterranean city, such as Barcelona by presenting findings based on examining and assessing spatio-temporal exposure levels measured with the help of smartphones and publicly available GIS layers. To achieve this, both exposure and daily walking time were measured from GIS and GPS-based smartphone tracking data for 127 adult individuals from Barcelona, Spain. Based on these measurements, it was determined that the presence of large-scale open spaces for physical activity, such as beaches or large parks in the participants' daily walking routes, proved to have the highest association with daily walking time. Also, underexplored forms of nature, such as street trees were also positively correlated with individual walking levels. Additionally, small-scale public spaces, such as public squares and boulevards, indicated a considerably negative association with walking time. The findings from this study confirm existing evidence on the health benefits of urban greenness and broaden the analytical focus on the role and impact of green space provision on physical health. Altogether, street trees and the presence of both blue and traditional green spaces proved to be significant factors of increased walking levels.

Nowadays, cruciate ligament injuries have increased in incidence, since practicing a sport or physical activity has become a trend in current societies. Although this lifestyle generates multiple benefits, as a consequence, injury has also increased. Due to its nature and complexity, the ligaments of the knee are those that are most frequently affected, mainly the ACL (anterior cruciate ligament). This tissue reacts to overexertion or movements out of range, either caused by the exercise itself or caused by trauma caused by the practice of physical activity, causing various degrees of sprain. Whatever the etiology of these injuries, they will require a therapy indicated for each degree of injury. This therapy initially entails immobilization of the affected area and later; physical therapy will be required to a lesser or greater degree. Commonly, in the physiotherapy of these injuries, rehabilitation exercises are prescribed, where the physiotherapist asks a patient to use equipment with an estimated weight. However, the effectiveness of a generalized therapy in this way does not always give the expected results. This is related to the fact that these therapies are standardized and do not consider some factors such as the remaining muscle fibres that are not directly affected by the sprain, which does not mean that they should not be considered. Therefore, in the present work, a biomodel of a human knee has been developed and used to evaluate numerically how the ACL acts under an external load, when there are different degrees of injuries, caused by trauma. Four case studies were considered: Case 1 (control case) where the ACL is healthy, Case 2 where the ACL presents a 1st-degree sprain, Case 3 where the ACL presents a 2nd-degree sprain, and finally Case 4 where the ACL presents a 3rd-sprain grade. After performing the analyses, in the control case, it was found that it presents a balance between tensile and compressive stresses. While in the 4th case, the most critical tensile stress decreases while compression stresses increase. This shows that the ligament, having considerable damage, no longer works as it should and can eventually damage the collateral structures. It was found that, when there was a sprain, where the continuity of the ligament is compromised, a second torsional moment occurs in the ACL which causes the tissue fibres not to act according to their normal physiology or in a healthy state. The results obtained from the present study provide the possibility of predicting where the following injuries will occur by considering the von Mises failure criterion. Likewise, they will allow to improve the therapeutic procedures considering not only the injured structure but also the system as a whole.

International audience; Most widely used Reynolds-averaged Navier-Stokes (RANS) models employ the Boussinesq approximation, which assumes a linear relationship between the turbulent Reynolds stresses and the mean-velocity gradient tensor. This assumption, which can be very stringent, is more suited for simple shear-flows and is regarded as an important shortcoming for the improvement of the representation of turbulence in complex geometries. Correction of the local turbulence length scales, as achieved, for example, by the introduction of a correction term in the eddy-viscosity equation of a Spalart-Allmaras model, then indeed only allows relatively small corrections to the base-line model and may not be sufficiently flexible to adapt to common situations where the Reynolds stresses are not aligned with the velocity gradient tensor. For the variational data-assimilation step, we consider a vectorial source correction term in the momentum equations (output quantity) together with the Spalart-Allmaras model, which allows full flexibility to adapt to any mean-flow topology. We show how the machine-learning framework should be adapted, in particular with respect to the vectorial nature of the correction term and given invariance properties. As for the input quantities, we discuss the impact of considering either local quantities for the nondimensionalization or global ones that characterize the configuration. We showcase the procedure on the periodic hill configuration, for which a rich direct numerical simulation database is available in the literature: in particular, the availability of the full mean-flow solution for a range of Reynolds numbers and geometries ensures identification of “physical” correction terms and allows learning of turbulence models that accurately extrapolate to new Reynolds numbers and geometries.

Safety development is of high stakes in work environment, as recent accidents remind us. Despite several paradigms have proposed approaches to characterize and develop safety practices, it appears that more complementarity is still possible based on the consideration of real work. From a case study in the aeronautics industry (metal additive manufacturing), the objective of this article is to present an operationalization of the concept of constructed safety practices.

The Caribbean ranks seventh among the world regions most affected by cervical cancer. HPV-prevalence and genotype distributions also differ from regions. Knowledge of HPV genotype profiles is important for patients care and HPV vaccination implementation. The objective of this study was to describe HPV genotype distribution and risk factors in a population-based cohort of women in Martinique. In this study, 1312 women were included and underwent cervical cancer screening with successful sample collection between 2009 and 2014. Sociodemographic and clinical variables were recorded. Cytological examination of cervical vaginal smear was performed and classified(Bethesda). Detection of HPV DNA was performed with the PapilloCheck© Kit from Greiner Bio-one. Genotypes were analyzed for18 high-risk HPV (hrHPV) and 6low-risk HPV(lrHPV) types. A total of 1075 women were included with a mean age of 49.1±10.5 years. HPV prevalence was 27.6% (297/1075) with 19.4% (209/1075) women with only hrHPV, 5.3% (57/1075) with only lrHPV. Multiple infections (hrHPV/lrHPV) were detected in 31/240 cases of hrHPV (12.9%). A total of 353 hrHPV genotypes were analyzed; the most common HPV types were HPV51 (11.0%), HPV68 (10.8%), HPV53 (9.1%) and HPV 52 (7.1%). HPV16 and HPV18 represented respectively 4.8% and 4.0% of hrHPV genotypes. Abnormal cytology was observed in 34 cases (3.2%), with 14 ASCUS (1.3%), 10 LSIL (0.9%), 5 HSIL (0.5%), 3 ASC-H (0.3%) and 2 AGC (0.2%). Fifteen (44.1%) were hrHPV and 4 (14.7%) lrHPV; 7 cases of hrPHV were in the age-group 25–34 years. Among 1041cases of normal cytology, 225 had positive hrHPV detection (21.6%). This is the first population-based study of HPV profiles in our country, and we found a high prevalence of hrHPV. The most common genotypes were HPV51, 68, 53. These results could serve for cancer vaccination strategies and HPV surveillance in Martinique.

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (LMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or marked amino acids from the context of entire sequence regions. Here, we explored how to benefit from learned protein LM representations (embeddings) to predict SAV effects. Although we have failed so far to predict SAV effects directly from embeddings, this input alone predicted residue conservation almost as accurately from single sequences as using multiple sequence alignments (MSAs) with a two-state per-residue accuracy (conserved/not) of Q2=80% (embeddings) vs. 81% (ConSeq). Considering all SAVs at all residue positions predicted as conserved to affect function reached 68.6% (Q2: effect/neutral; for PMD) without optimization, compared to an expert solution such as SNAP2 (Q2=69.8). Combining predicted conservation with BLOSUM62 to obtain variant-specific binary predictions, DMS experiments of four human proteins were predicted better than by SNAP2, and better than by applying the same simplistic approach to conservation taken from ConSeq. Thus, embedding methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. This allowed prediction of SAV effects for the entire human proteome (~20k proteins) within 17 minutes on a single GPU.

The modelling of biological structures has allowed great advances in Engineering, Biology, and Medicine. In turn, these advances are seen from the design of footwear and sports accessories, to the design of prostheses, accessories and rehabilitation treatments. The reproduction of the various tissues has gone through an important evolution thanks to the development of computer systems and programs. However, knowledge of the medical-biological and engineering areas continues to be required, and it involves a considerable investment of time and resources. The resulting biomodels still require great precision. The present work shows a methodology that allows to optimize computational resources and reduce elaboration time of biomodels. Through this methodology, it is possible to generate a biomodel of high biofidelity of a human knee. This biomodel is constituted by hard tissues (cortical and trabecular bones) and soft tissues (ligaments and meniscus) resulting in the modelling of the lower third of the femur, the tibial plateaus, the anterior cruciate ligament, posterior cruciate ligament, external lateral ligament, interior lateral ligaments, and the meniscus. With this model and methodology, it is possible to perform numerical analyses that will provide results very similar to those of real life. As, the methodology allows to assign the mechanical properties to each tissue and the anatomical structure.

Ganciclovir and valganciclovir are first choice drugs for the prevention and treatment of cytomegalovirus infection and disease in solid organ and stem cell transplant recipients. Only a few studies on the pharmacokinetics and exposure/efficacy or exposure/safety relationships of ganciclovir and valganciclovir in transplant recipients have been published so far, and there are still controversies about the exposure parameter to use for therapeutic drug monitoring (TDM). We performed an extensive literature review of the clinical pharmacokinetics data, the exposure/effect relationships in terms of efficacy and safety, and the available tools for valganciclovir and ganciclovir TDM in adults and pediatrics transplant recipients. The pharmacokinetics of ganciclovir and valganciclovir is well described in adults and children, and a high interindividual variability is commonly observed. In contrast, the drug pharmacodynamics has been poorly described in adults and barely in children. The average 24-hour area under the concentration-time curve (AUC0-24h ) seems to be the best predictor of efficacy and toxicity. The benefit of TDM remains controversial in adult patients but should be considered in children due to higher interindividual variability and lower probability of target attainment. Several bayesian estimators based on limited sampling strategies have been developed with this aim and may be used in clinical practice for the AUC-based individual dose adjustment of ganciclovir and valganciclovir.

Quantitative Phase Signal (QPS) stems from the difference between the refractive indices of the observed specimen and that of its surrounding environment (ne). Therefore, any change in ne will drastically affect the QPS. We have developed an original approach, based on both the manipulation and the measurement of ne that allows from the QPS the visualization of various structures inside the observed specimen as well as accurate measurements of their respective refractive indices. Such an approach performed with digital holographic microscopy on living cells reveals internal cell structures including organelles (nucleus, nucleolus, vesicles, etc.) and some components of the cytoskeleton.

We previously demonstrated that Machine learning (ML) algorithms can accurately estimate drug area under the curve (AUC) of tacrolimus or mycophenolate mofetil (MMF) based on limited information, as well as or even better than maximum a posteriori Bayesian estimation (MAP-BE). However, the major limitation in the development of such ML algorithms is the limited availability of large databases of concentration vs. time profiles for such drugs. The objectives of this study were: (i) to develop a Xgboost model to estimate tacrolimus inter-dose AUC based on concentration-time profiles obtained from a literature population pharmacokinetic (POPPK) model using Monte Carlo simulation; and (ii) to compare its performance with that of MAP-BE in external datasets of rich concentration-time profiles. The population parameters of a previously published PK model were used in the mrgsolve R package to simulate 9000 rich interdose tacrolimus profiles (one concentration simulated every 30 min) at steady-state. Data splitting was performed to obtain a training set (75%) and a test set (25%). Xgboost algorithms able to estimate tacrolimus AUC based on 2 or 3 concentrations were developed in the training set and the model with the lowest RMSE in a ten-fold cross-validation experiment was evaluated in the test set, as well as in 4 independent, rich PK datasets from transplant patients. ML algorithms based on 2 or 3 concentrations and a few covariates yielded excellent AUC estimation in the external validation datasets (relative bias 5% and relative RMSE 10%), comparable to those obtained with MAP-BE. In conclusion, Xgboost machine learning models trained on concentration-time profiles simulated using literature POPPK models allow accurate tacrolimus AUC estimation based on sparse concentration data. This study paves the way to the development of artificial intelligence at the service of precision therapeutic drug monitoring in different therapeutic areas.

We present the B-BOP instrument, a polarimetric camera on board the future ESA-JAXA SPICA far-infrared space observatory. B-BOP will allow the study of the magnetic field in various astrophysical environments thanks to its unprecedented ability to measure the linear polarization of the submillimeter light. The maps produced by B-BOP will contain not only information on total power, but also on the degree and the angle of polarization, simultaneously in three spectral bands (70, 200 and 350 microns). The B-BOP detectors are ultra-sensitive silicon bolometers that are intrinsically sensitive to polarization. Their NEP is close to 10E-18 W/sqrt(Hz). We will present the optical and thermal architectures of the instrument, we will detail the bolometer design and we will show the expected performances of the instrument based on preliminary lab work., 10 pages, 6 figures, SPIE proceeding: Astronomical Telescopes + Instrumentation 2020