A biology-driven deep generative model for cell-type annotation in cytometry.

Cytometry enables precise single-cell phenotyping within heterogeneous populations. These cell types are traditionally annotated via manual gating, but this method lacks reproducibility and sensitivity to batch effect. Also, the most recent cytometers-spectral flow or mass cytometers-create rich and high-dimensional data whose analysis via manual gating becomes challenging and time-consuming. To tackle these limitations, we introduce Scyan https://github.com/MICS-Lab/scyan, a Single-cell Cytometry Annotation Network that automatically annotates cell types using only prior expert knowledge about the cytometry panel. For this, it uses a normalizing flow-a type of deep generative model-that maps protein expressions into a biologically relevant latent space. We demonstrate that Scyan significantly outperforms the related state-of-the-art models on multiple public datasets while being faster and interpretable. In addition, Scyan overcomes several complementary tasks, such as batch-effect correction, debarcoding and population discovery. Overall, this model accelerates and eases cell population characterization, quantification and discovery in cytometry.

A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization.

Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to the experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides a robust batch-effect correction method that does not rely on prior knowledge of the cell types nor a specific normalization strategy while being adapted to any downstream analysis task. It compares to and even outperforms state-of-the-art methods in several scenarios: low signal-to-noise ratio, batch-specific cell types with few cells, and a multi-batches dataset with imbalanced batches and batch-specific cell types. Moreover, it best preserves the relative gene expression between cell types, yielding superior differential expression analysis results. Finally, in a more complex setting of a Leukemia cohort, our method preserved most of the underlying biological information for each patient while aligning the batches, improving the clustering metrics in the aggregated dataset.

Inferring the initiation and development of myeloproliferative neoplasms.

The developmental history of blood cancer begins with mutation acquisition and the resulting malignant clone expansion. The two most prevalent driver mutations found in myeloproliferative neoplasms-JAK2V617F and CALRm-occur in hematopoietic stem cells, which are highly complex to observe in vivo. To circumvent this difficulty, we propose a method relying on mathematical modeling and statistical inference to determine disease initiation and dynamics. Our findings suggest that CALRm mutations tend to occur later in life than JAK2V617F. Our results confirm the higher proliferative advantage of the CALRm malignant clone compared to JAK2V617F. Furthermore, we illustrate how mathematical modeling and Bayesian inference can be used for setting up early screening strategies.

Comorbidities, mortality and metabolic profile in individuals with primary biliary cholangitis-A Phenome-Wide-Association-Study.

BACKGROUND AND AIMS: Primary biliary cholangitis (PBC) is a chronic, immune-mediated liver disease that can lead to fibrosis and cirrhosis. In this cohort study, we aimed to investigate morbidity and mortality in conjunction with metabolomic changes of PBC in a UK population-based cohort. METHODS: 454 participants with PBC and 908 propensity score (age, sex, BMI, ethnicity) matched controls without liver disease were included in the study. A subset of participants with PBC and controls were analysed for their metabolomic profile. Further, PBC-associated comorbidities were investigated by PheWAS analysis. Lastly, we assessed causes of death in individuals with PBC using a Fine and Grey competing-risks regression model. RESULTS: Compared to the control group, various pathways associated with the metabolism of amino acids, lipids, and liver biochemistry were significantly enriched in individuals with PBC. We found reduced levels of S-HDL-cholesterol and Glycoprotein Acetyls in individuals with PBC as well as an association with diseases of the circulatory system. Notably, PBC individuals had a higher prevalence of digestive diseases, autoimmune diseases, cardiovascular diseases, anaemias, mental disorders, and urinary tract infections compared to the control group. Strikingly, the overall mortality was almost three times higher in the PBC group compared to the control group, with diseases of the digestive system accounting for a significant elevation of the death rate. A subsequent analysis, enhanced by propensity score matching that included the APRI score, demonstrated that the observed morbidity could not be exclusively attributed to advanced hepatic disease. CONCLUSIONS: Our study provides a detailed perspective on the morbidity of individuals with PBC. The exploration of potential effects of disease state on morbidity suggest that early detection and early treatment of PBC could enhance patient prognosis and prevent the onset of comorbid diseases. Finally, the metabolomic alterations could represent a link between the pathophysiological processes underlying PBC development, progression, and associated morbidity.

Machine learning uncovers manganese as a key nutrient associated with reduced risk of steatotic liver disease.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 20%-30% of the general population and is linked to high-caloric western style diet. However, there are little data that specific nutrients might help to prevent steatosis. METHODS: We analysed the UK Biobank (ID 71300) 24 h-nutritional assessments and investigated the association between nutrient intake calculated from food questionnaires and hepatic steatosis indicated by imaging or ICD10-coding. The effect of manganese (Mn) on subgroups with risk single nucleotide polymorphism carriage as well as the effect on metabolomics was investigated. All analyses are corrected for age, sex, body mass index, Townsend index for socioeconomic status, kcal, alcohol, protein intake, fat intake, carbohydrate intake, energy from beverages, diabetes, physical activity and for multiple testing. RESULTS: We used a random forest classifier to analyse the feature importance of 63 nutrients and imaging-proven steatosis in a cohort of over 25 000 UK Biobank participants. Increased dietary Mn intake was associated with a lower likelihood of MRI-diagnosed steatosis. Subsequently, we conducted a cohort study in over 200 000 UK Biobank participants to explore the relationship between Mn intake and hepatic or cardiometabolic outcomes and found that higher Mn intake was associated with a lower risk of ICD-10 coded steatosis (OR = .889 [.838-.943], p < .001), independent of other potential confounders. CONCLUSION: Our study provides evidence that higher Mn intake may be associated with lower odds of steatosis in a large population-based sample. These findings underline the potential role of Mn in the prevention of steatosis, but further research is needed to confirm these findings and to elucidate the underlying mechanisms.

CustOmics: A versatile deep-learning based strategy for multi-omics integration.

The availability of patient cohorts with several types of omics data opens new perspectives for exploring the disease's underlying biological processes and developing predictive models. It also comes with new challenges in computational biology in terms of integrating high-dimensional and heterogeneous data in a fashion that captures the interrelationships between multiple genes and their functions. Deep learning methods offer promising perspectives for integrating multi-omics data. In this paper, we review the existing integration strategies based on autoencoders and propose a new customizable one whose principle relies on a two-phase approach. In the first phase, we adapt the training to each data source independently before learning cross-modality interactions in the second phase. By taking into account each source's singularity, we show that this approach succeeds at taking advantage of all the sources more efficiently than other strategies. Moreover, by adapting our architecture to the computation of Shapley additive explanations, our model can provide interpretable results in a multi-source setting. Using multiple omics sources from different TCGA cohorts, we demonstrate the performance of the proposed method for cancer on test cases for several tasks, such as the classification of tumor types and breast cancer subtypes, as well as survival outcome prediction. We show through our experiments the great performances of our architecture on seven different datasets with various sizes and provide some interpretations of the results obtained. Our code is available on (https://github.com/HakimBenkirane/CustOmics).

Optimizing IFN Alpha Therapy against Myeloproliferative Neoplasms.

Myeloproliferative neoplasms (MPNs) are hematologic malignancies that result from acquired driver mutations in hematopoietic stem cells (HSCs), causing overproduction of blood cells and an increased risk of thrombohemorrhagic events. The most common MPN driver mutation affects the JAK2 gene (JAK2V617F ). Interferon alpha (IFNα) is a promising treatment against MPNs by inducing a hematologic response and molecular remission for some patients. Mathematical models have been proposed to describe how IFNα targets mutated HSCs, indicating that a minimal dose is necessary for long-term remission. This study aims to determine a personalized treatment strategy. First, we show the capacity of an existing model to predict cell dynamics for new patients from data that can be easily obtained in clinic. Then, we study different treatment scenarios in silico for three patients, considering potential IFNα dose-toxicity relations. We assess when the treatment should be interrupted depending on the response, the patient's age, and the inferred development of the malignant clone without IFNα We find that an optimal strategy would be to treat patients with a constant dose so that treatment could be interrupted as quickly as possible. Higher doses result in earlier discontinuation but also higher toxicity. Without knowledge of the dose-toxicity relationship, trade-off strategies can be found for each patient. A compromise strategy is to treat patients with medium doses (60-120 µg/week) for 10-15 years. Altogether, this work demonstrates how a mathematical model calibrated from real data can help build a clinical decision-support tool to optimize long-term IFNα therapy for MPN patients. SIGNIFICANCE STATEMENT: Myeloproliferative neoplasms (MPNs) are chronic blood cancers. Interferon alpha (IFNα) is a promising treatment with the potential to induce a molecular response by targeting mutated hematopoietic stem cells. MPN patients are treated over several years, and there is a lack of knowledge concerning the posology strategy and the best timing for interrupting therapy. The study opens avenues for rationalizing how to treat MPN patients with IFNα over several years, promoting a more personalized approach to treatment.

Machine learning identifies a profile of inadequate responder to methotrexate in rheumatoid arthritis.

OBJECTIVES: Around 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers. METHODS: Models were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (± 6 months). Several models were compared on the training set using the AUROC. The best model was evaluated on an external validation cohort (tREACH). The model's predictions were explained using Shapley values to extract a biomarker of inadequate response. RESULTS: We included 493 therapeutic sequences from ESPOIR, 239 from EAC and 138 from tREACH. The model selected DAS28, Lymphocytes, Creatininemia, Leucocytes, AST, ALT, swollen joint count and corticosteroid co-treatment as predictors. The model reached an AUROC of 0.72 [95% CI (0.63, 0.80)] on the external validation set, where 70% of patients were responders to MTX. Patients predicted as inadequate responders had only 38% [95% CI (20%, 58%)] chance to respond and using the algorithm to decide to initiate MTX would decrease inadequate-response rate from 30% to 23% [95% CI: (17%, 29%)]. A biomarker was identified in patients with moderate or high activity (DAS28 > 3.2): patients with a lymphocyte count superior to 2000 cells/mm3 are significantly less likely to respond. CONCLUSION: Our study highlights the usefulness of machine learning in unveiling subgroups of inadequate responders to MTX to guide new therapeutic strategies. Further work is needed to validate this approach.

Inferring the dynamics of mutated hematopoietic stem and progenitor cells induced by IFNα in myeloproliferative neoplasms.

Classical BCR-ABL-negative myeloproliferative neoplasms (MPNs) are clonal disorders of hematopoietic stem cells (HSCs) caused mainly by recurrent mutations in genes encoding JAK2 (JAK2), calreticulin (CALR), or the thrombopoietin receptor (MPL). Interferon α (IFNα) has demonstrated some efficacy in inducing molecular remission in MPNs. To determine factors that influence molecular response rate, we evaluated the long-term molecular efficacy of IFNα in patients with MPN by monitoring the fate of cells carrying driver mutations in a prospective observational and longitudinal study of 48 patients over more than 5 years. We measured the clonal architecture of early and late hematopoietic progenitors (84 845 measurements) and the global variant allele frequency in mature cells (409 measurements) several times per year. Using mathematical modeling and hierarchical Bayesian inference, we further inferred the dynamics of IFNα-targeted mutated HSCs. Our data support the hypothesis that IFNα targets JAK2V617F HSCs by inducing their exit from quiescence and differentiation into progenitors. Our observations indicate that treatment efficacy is higher in homozygous than heterozygous JAK2V617F HSCs and increases with high IFNα dose in heterozygous JAK2V617F HSCs. We also found that the molecular responses of CALRm HSCs to IFNα were heterogeneous, varying between type 1 and type 2 CALRm, and a high dose of IFNα correlates with worse outcomes. Our work indicates that the long-term molecular efficacy of IFNα implies an HSC exhaustion mechanism and depends on both the driver mutation type and IFNα dose.

BR55 Ultrasound Molecular Imaging of Clear Cell Renal Cell Carcinoma Reflects Tumor Vascular Expression of VEGFR-2 in a Patient-Derived Xenograft Model.

Standard imaging cannot reliably predict the nature of renal tumors. Among malignant renal tumors, clear cell renal cell carcinoma (ccRCC) is the most common histological subtype, in which the vascular endothelial growth factor 2 (VEGFR-2) is highly expressed in the vascular endothelium. BR55, a contrast agent for ultrasound imaging, consists of gas-core lipid microbubbles that specifically target and bind to the extracellular portion of the VEGFR-2. The specific information provided by ultrasound molecular imaging (USMI) using BR55 was compared with the vascular tumor expression of the VEGFR-2 by immunohistochemical (IHC) staining in a preclinical model of ccRCC. Patients' ccRCCs were orthotopically grafted onto Nod-Scid-Gamma (NSG) mice to generate patient-derived xenografts (PdX). Mice were divided into four groups to receive either vehicle or axitinib an amount of 2, 7.5 or 15 mg/kg twice daily. Perfusion parameters and the BR55 ultrasound contrast signal on PdX renal tumors were analyzed at D0, D1, D3, D7 and D11, and compared with IHC staining for the VEGFR-2 and CD34. Significant Pearson correlation coefficients were observed between the area under the curve (AUC) and the CD34 (0.84, p < 10-4), and between the VEGFR-2-specific signal obtained by USMI and IHC (0.72, p < 10-4). USMI with BR55 could provide instant, quantitative information on tumor VEGFR-2 expression to characterize renal masses non-invasively.

The Potential Impact of Plastic Surgery Expertise on Body Contouring Procedure Outcomes.

BACKGROUND: With the increasing demand for body contouring procedures in the United States over the past 2 decades, more surgeons with diverse specialty training are performing these procedures. However, little is known regarding the comparative outcomes of these patients. OBJECTIVES: The purpose of this study was to compare outcomes of body contouring procedures based on the specialty training of the surgeon. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program (2005-2015) were reviewed for all body contouring procedures. Patients were stratified by surgeon training (plastic surgery [PS] vs general surgery [GS]). Descriptive statistics and regression analyses were used to evaluate differences in outcomes. RESULTS: A total of 11,658 patients were included; 9502 PS cases and 2156 GS cases. Most were women (90.4%), aged 40 to 59 (52.7%) and white (79.5%). Compared with PS patients, GS patients were more likely to be obese (61.4% vs 40.6%), smokers (13.6% vs 9.8%), and with ASA classification ≥3 (35.3% vs 18.6%) (all P < 0.001). Abdominal contouring procedures were the most common (76%) cases. Multivariate regression revealed that compared with PS cases, those performed by GS practitioners were associated with increased wound and infectious complications (adjusted odds ratio [aOR], 1.81; 95% confidence interval [CI], 1.44-2.27), reoperation (aOR, 1.85; 95% CI, 1.31-2.62), and predicted mean length of stay (1.12 days; 95% CI, 0.64-1.60 days). CONCLUSIONS: The variable outcomes in body contouring procedures performed by PS compared with GS practitioners may imply procedural-algorithmic differences between the subspecialties, leading to the noted outcome differential.

Comprehensive landscape of immune-checkpoints uncovered in clear cell renal cell carcinoma reveals new and emerging therapeutic targets.

Clear cell renal cell carcinoma (ccRCC) constitutes the most common renal cell carcinoma subtype and has long been recognized as an immunogenic cancer. As such, significant attention has been directed toward optimizing immune-checkpoints (IC)-based therapies. Despite proven benefits, a substantial number of patients remain unresponsive to treatment, suggesting that yet unreported, immunosuppressive mechanisms coexist within tumors and their microenvironment. Here, we comprehensively analyzed and ranked forty-four immune-checkpoints expressed in ccRCC on the basis of in-depth analysis of RNAseq data collected from the TCGA database and advanced statistical methods designed to obtain the group of checkpoints that best discriminates tumor from healthy tissues. Immunohistochemistry and flow cytometry confirmed and enlarged the bioinformatics results. In particular, by using the recursive feature elimination method, we show that HLA-G, B7H3, PDL-1 and ILT2 are the most relevant genes that characterize ccRCC. Notably, ILT2 expression was detected for the first time on tumor cells. The levels of other ligand-receptor pairs such as CD70:CD27; 4-1BB:4-1BBL; CD40:CD40L; CD86:CTLA4; MHC-II:Lag3; CD200:CD200R; CD244:CD48 were also found highly expressed in tumors compared to adjacent non-tumor tissues. Collectively, our approach provides a comprehensible classification of forty-four IC expressed in ccRCC, some of which were never reported before to be co-expressed in ccRCC. In addition, the algorithms used allowed identifying the most relevant group that best discriminates tumor from healthy tissues. The data can potentially assist on the choice of valuable immune-therapy targets which hold potential for the development of more effective anti-tumor treatments.

Association of metastatic pattern and molecular status in stage IV non-small cell lung cancer adenocarcinoma.

OBJECTIVES: The aim of our study was to investigate the association between driver oncogene alterations and metastatic patterns on imaging assessment, in a large cohort of metastatic lung adenocarcinoma patients. METHODS: From January 2010 to May 2017, 550 patients with stage IV lung adenocarcinoma with molecular analysis were studied retrospectively including 135 EGFR-mutated, 81 ALK-rearrangement, 47 BRAF-mutated, 141 KRAS-mutated, and 146 negative tumors for these 4 mutations (4N). After review of the complete imaging report by two radiologists (junior and senior) to identify metastatic sites, univariate correlation analyzes were performed. RESULTS: We found differences in metastatic tropism depending on the molecular alteration type when compared with the non-mutated 4N group: in the EGFR group, pleural metastases were more frequent (32% versus 20%; p = 0.021), and adrenal and node metastases less common (6% versus 23%; p < 0.001 and 11% versus 23%; p = 0.011). In the ALK group, there were more brain and lung metastases (respectively 42% versus 29%; p = 0.043 and 37% versus 24%; p = 0.037). In the BRAF group, pleural and pericardial metastases were more common (respectively 47% versus 20%; p < 0.001 and 11% versus 3%; p = 0.04) and bone metastases were rarer (21% versus 42%; p = 0.011). Lymphangitis was more frequent in EGFR, ALK, and BRAF groups (respectively 6%, 7%, and 15% versus 1%); p = 0.016; p = 0.009; and p < 0.001. CONCLUSION: The application of these correlations between molecular status and metastatic tropism in clinical practice may lead to earlier and more accurate identification of patients for targeted therapy. KEY POINTS: • Bone and brain metastasis are the most common organs involved in lung adenocarcinoma but the relative incidence of each metastatic site depends on the molecular alteration. • EGFR-mutated tumors preferentially spread to the pleura and less commonly to adrenals, ALK-rearrangement tumors usually spread to the brain and the lungs, whereas BRAF-mutated tumors are unlikely to spread to bones and have a serous (pericardial ad pleural) tropism. • These correlations could help in the clinical management of patients with metastatic lung adenocarcinoma.

Spread of clonal linezolid-resistant Staphylococcus epidermidis in an intensive care unit associated with linezolid exposure.

The aim of the study was to determine factors associated with spread of linezolid (LNZ)-resistant Staphylococcus epidermidis isolates in a surgical intensive care unit (ICU). A case-control study was conducted in one French adult surgical ICU. From January 2012 to December 2016, patients with at least a single positive LNZ-resistant S. epidermidis blood culture were matched to control with LNZ-susceptible S. epidermidis blood culture in a 1:4 manner. Cases were compared to controls regarding baseline clinical characteristics and LNZ exposure before positive blood culture. Bacterial isolates were genotyped by using pulsed-field gel electrophoresis (PFGE) and MLST. We identified 13 LNZ-resistant S. epidermidis isolates, 1 in 2012, 3 in 2014, 6 in 2015, and 3 in 2016. LNZ use increased steadily from 8 DDDs/100 patient days in 2010 to 19 in 2013 and further decrease by more of 50% in 2015 and 2016. The only independent risk factors associated to LNZ-resistant S. epidermidis isolation were length of stay in ICU before infection (OR 1.45; 95% CI 1.07-1.98), prior exposure to LNZ (OR 109; 95% CI 3.9-3034), and Charlson comorbidities score (OR 3.19; 95% CI 1.11-9.14). PFGE typing showed that all LNZ-resistant isolates were clonal belonging to ST2 and that LNZ-susceptible isolates were highly diverse. We report herein that previous exposure to LNZ substantially increased the risk of occurrence of LNZ resistance in S. epidermidis even in the case of clonal spread of LNZ-resistant isolates. These findings highlight the need for reducing the use of LNZ to preserve its efficacy in the future.

A new methodology based on sensitivity analysis to simplify the recalibration of functional-structural plant models in new conditions.

Background and Aims: Functional-structural plant models (FSPMs) describe explicitly the interactions between plants and their environment at organ to plant scale. However, the high level of description of the structure or model mechanisms makes this type of model very complex and hard to calibrate. A two-step methodology to facilitate the calibration process is proposed here. Methods: First, a global sensitivity analysis method was applied to the calibration loss function. It provided first-order and total-order sensitivity indexes that allow parameters to be ranked by importance in order to select the most influential ones. Second, the Akaike information criterion (AIC) was used to quantify the model's quality of fit after calibration with different combinations of selected parameters. The model with the lowest AIC gives the best combination of parameters to select. This methodology was validated by calibrating the model on an independent data set (same cultivar, another year) with the parameters selected in the second step. All the parameters were set to their nominal value; only the most influential ones were re-estimated. Key Results: Sensitivity analysis applied to the calibration loss function is a relevant method to underline the most significant parameters in the estimation process. For the studied winter oilseed rape model, 11 out of 26 estimated parameters were selected. Then, the model could be recalibrated for a different data set by re-estimating only three parameters selected with the model selection method. Conclusions: Fitting only a small number of parameters dramatically increases the efficiency of recalibration, increases the robustness of the model and helps identify the principal sources of variation in varying environmental conditions. This innovative method still needs to be more widely validated but already gives interesting avenues to improve the calibration of FSPMs.

Evaluation of clinically applied treatment beams with respect to bunker shielding parameters for a Cyberknife M6.

Compared to a conventional linear accelerator, the Cyberknife (CK) is a unique system with respect to radiation protection shielding and the variety and number of non-coplanar beams are two key components regarding this aspect. In this work, a framework to assess the direction distribution and modulation factor (MF) of clinically applied treatment beams of a CyberKnife M6 is developed. Database filtering options allow studying the influence of different parameters such as collimator types, treatment sites or different bunker sizes. A distribution of monitor units (MU) is generated by projecting treatment beams onto the walls, floor and ceiling of the CyberKnife bunker. This distribution is found to be highly heterogeneous and depending, among other parameters, on the bunker size. For our bunker design, 10%-13% of the MUs are delivered to the right and left wall, each. The floor receives more than 64% of the applied MUs, while the wall behind the patient's head is not hit by primary treatment beams. Between 0% and 5% of the total MUs are delivered to the wall at the patient's feet. This number highly depends on the treatment site, e.g., for extracranial patients no beams hit that wall. Collimator choice was found to have minor influence on the distribution of MUs. On the other hand, the MF depends on the collimator type as well as on the treatment site. The MFs (delivered MU/prescribed dose) for all treatments, all MLC treatments, cranial and extracranial treatments are 8.3, 6.4, 7.7, and 9.9 MU/cGy, respectively. The developed framework allows assessing and monitoring important parameters regarding radiation protection of a CK-M6 using the actually applied treatment beams. Furthermore, it enables evaluating different clinical and constructional situations using the filtering options.

Bayesian parameter estimation for the Wnt pathway: an infinite mixture models approach.

MOTIVATION: Likelihood-free methods, like Approximate Bayesian Computation (ABC), have been extensively used in model-based statistical inference with intractable likelihood functions. When combined with Sequential Monte Carlo (SMC) algorithms they constitute a powerful approach for parameter estimation and model selection of mathematical models of complex biological systems. A crucial step in the ABC-SMC algorithms, significantly affecting their performance, is the propagation of a set of parameter vectors through a sequence of intermediate distributions using Markov kernels. RESULTS: In this article, we employ Dirichlet process mixtures (DPMs) to design optimal transition kernels and we present an ABC-SMC algorithm with DPM kernels. We illustrate the use of the proposed methodology using real data for the canonical Wnt signaling pathway. A multi-compartment model of the pathway is developed and it is compared to an existing model. The results indicate that DPMs are more efficient in the exploration of the parameter space and can significantly improve ABC-SMC performance. In comparison to alternative sampling schemes that are commonly used, the proposed approach can bring potential benefits in the estimation of complex multimodal distributions. The method is used to estimate the parameters and the initial state of two models of the Wnt pathway and it is shown that the multi-compartment model fits better the experimental data. AVAILABILITY AND IMPLEMENTATION: Python scripts for the Dirichlet Process Gaussian Mixture model and the Gibbs sampler are available at https://sites.google.com/site/kkoutroumpas/software CONTACT: konstantinos.koutroumpas@ecp.fr.

Clinical and echocardiographic characteristics and outcomes in congestive heart failure at the Hospital of The State University of Haiti.

BACKGROUND: This study aimed to evaluate the clinical and epidemiologic profile of congestive heart failure at the principal free-care hospital in Haiti. Cardiovascular disease represents the most prevalent cause of admissions to the medical service of the University Hospital of the State of Haiti. No previous study has examined the demographics of congestive heart failure in urban Haiti. METHODS: Two hundred forty-seven patients presented to the inpatient service between May 2011 and May 2013. Evaluation included history and physical, CBC, renal/metabolic profile, serum glucose, anti-HIV antibody, ECG, chest radiograph and echocardiogram. Treatment included angiotensin converting enzyme inhibitors, furosemide and spironolactone, carvedilol, digoxin and anticoagulation. RESULTS: Women (62.4%) outnumbered men; patients were relatively young (mean age 50.1) and from the lowest socio-economic levels of the population. Nearly all (98.8%) presented with NYHA III-IV status, with correspondingly high mortality (23.3%). Echocardiography showed 73% dilated cardiomyopathy; 83% showed moderate to severe LV systolic dysfunction (mean EF 36.5 +/- 15%) and 17% preserved LV systolic function. The three principal etiologies were dilated cardiomyopathy (29%) hypertensive cardiomyopathy (27%) and peripartum cardiomyopathy (20%). Ischemic cardiomyopathy was rare (3.4%). At 27 months follow-up, 76.7% of the patients were alive and well. Among those who died, mean survival time was 113 days. Readmission carried a poor prognosis. CONCLUSIONS: This congestive heart failure study from Haiti shows an unusually high proportion of young women, primarily due to peripartum cardiomyopathy. Ischemic cardiomyopathy is rare, as in Africa. Further study is warranted to address the particular problem of the high frequency of peripartum cardiomyopathy in this population.

Turgidity-dependent petiole flexibility enables efficient water use by a tree subjected to water stress.

Water stress is a major cause of tree mortality. In response to drought, leaves wilt due to an increase in petiole flexibility. We present an analytical model coupling petiole mechanics, thermal balance, and xylem hydraulics to investigate the role of petiole flexibility in protecting a tree from water stress. Our model suggests that turgidity-dependent petiole flexibility can significantly attenuate the minimal xylem pressure and thus reduce the risk of cavitation. Moreover, we show that petiole flexibility increases water use efficiency by trees under water stress.

Pazopanib as third line therapy for metastatic renal cell carcinoma: clinical efficacy and temporal analysis of cytokine profile.

PURPOSE: Pazopanib has been assessed primarily in cytokine refractory or treatment naïve patients with metastatic renal cell carcinoma. Outcomes have been associated with a specific immunological profile. However, pazopanib activity in the third line setting and temporal changes in the immunological profile during therapy are poorly understood. MATERIALS AND METHODS: Study eligibility was limited to patients with 2 prior lines of therapy, including at least 1 vascular endothelial growth factor directed therapy, as well as ECOG performance status 0 to 2 and clear cell histology. Patients received pazopanib 800 mg daily. A Simon minmax 2-stage design was used with 80% power to determine an encouraging 23% overall response rate (10% type I error). Immunological profiles were assessed monthly on a Luminex® platform using the Human Cytokine 30-Plex Cytokine Immunoassay (Invitrogen™). RESULTS: A total of 28 patients with a median age of 63 years (range 45 to 86) were enrolled in study. Of the patients 12 (43%) had a confirmed complete (1) or partial (11) response. In the cohort median progression-free survival was 16.5 months (95% CI 14.7-not reached). The most common grade 3/4 toxicities were hypertension (46% of cases) and proteinuria (14%). At 6 and 12 months responders had lower levels of HGF, VEGF, IL-6 and 8, and soluble IL-2R (each p <0.05). Nonresponders also showed increased numbers of myeloid-derived suppressor cells at each interval. Phenotypic and functional studies confirmed that the myeloid-derived suppressor cells were granulocytic. CONCLUSIONS: Progression-free survival and the overall response rate associated with third line pazopanib were encouraging. Immunological profile differences between responders and nonresponders suggest that the mechanism of pazopanib resistance is at least partly related to the generation of systemic tumor immune tolerance.