Search

Patients with cancer-associated thrombosis (CAT) are at high risk of recurrent venous thromboembolism (VTE) and major bleeding complications. Risks vary significantly between individuals based on cancer status, treatment, and other characteristics. To facilitate the evidence-based management of anticoagulant therapy in this patient population, a committee of 11 Canadian clinical experts updated a consensus-based algorithm for the acute and extended treatment of symptomatic and incidental CAT that was developed in 2018. Following a systematic review of the literature, updates to the algorithm were discussed during an online teleconference, and the algorithm was subsequently refined based on feedback from committee members. Clinicians using this treatment algorithm should consider bleeding risk, type of cancer, and drug–drug interactions, as well as patient and clinician preferences, in tailoring anticoagulation for patients with CAT. Anticoagulant therapy should be adapted as the patient’s cancer status and management change over time.

The present paper focuses on the efficiency enhancement of the energy-intensive natural gas (NG) sweetening process in the context of upstream natural gas production. A bi-level heat integration scheme is proposed including direct recycling of available high-temperature waste heat and harnessing the excess low-temperature waste heat in an optimized organic Rankine cycle (ORC) for power production. The energy performance of the whole model was studied under a range of possible reservoir conditions. A particle swarm optimization (PSO) algorithm was adopted to simultaneously optimize the parameters of the heat recovery network as well as the ORC cycle parameters. Finally, in order to account for the impact of perturbations of the heat source and sink, an off-design performance analysis was conducted using real-time data from an industrial plant. The proposed integration methodology was found to be effective across most of the reservoir conditions covered in this study. At optimal integration, a reduction of 40% up to 100% in heating requirements of the amine process was reported, as well as a net electricity production of 30% up to 190% of the electrical demand of the background process. The use of propane (R290) as a working fluid resulted in the highest energy output, whereas higher carbon number fluids allowed a better energy/working pressure trade-off. The off-design analysis allowed for the quantification of the impact of operational fluctuations of the background process on integration performance. Energy savings resulting from direct heat integration were found to range from 68% up to 103% of the expected design value, whereas the ORC net energy output respective to the use of R290, R600a, and R601a was found to range from 60% to 132%, 47% to 142%, and 52% to 135%.

Collective behavior of cells emerges from coordination of cell–cell-interactions and is important to wound healing, embryonic and tumor development. Depending on cell density and cell–cell interactions, a transition from a migratory, fluid-like unjammed state to a more static and solid-like jammed state or vice versa can occur. Here, we analyze collective migration dynamics of astrocytes and glioblastoma cells using live cell imaging. Furthermore, atomic force microscopy, traction force microscopy and spheroid generation assays were used to study cell adhesion, traction and mechanics. Perturbations of traction and adhesion were induced via ROCK or myosin II inhibition. Whereas astrocytes resided within a non-migratory, jammed state, glioblastoma were migratory and unjammed. Furthermore, we demonstrated that a switch from an unjammed to a jammed state was induced upon alteration of the equilibrium between cell–cell-adhesion and tension from adhesion to tension dominated, via inhibition of ROCK or myosin II. Such behavior has implications for understanding the infiltration of the brain by glioblastoma cells and may help to identify new strategies to develop anti-migratory drugs and strategies for glioblastoma-treatment.

D-dimer is a multifaceted biomarker of concomitant activation of coagulation and fibrinolysis, which is routinely used for ruling out pulmonary embolism (PE) and/or deep vein thrombosis (DVT) combined with a clinical pretest probability assessment. The intended use of the tests depends largely on the assay used, and local guidance should be applied. D-dimer testing may suffer from diagnostic errors occurring throughout the pre-analytical, analytical, and post-analytical phases of the testing process. This review aims to provide an overview of D-dimer testing and its value in diagnosing PE and discusses the variables that may impact the quality of its laboratory assessment.

Although rare, pulmonary embolism (PE) remains one of the most common causes of severe maternal morbidity and mortality during pregnancy. Among pregnant women with suspected PE, the prevalence of confirmed disease is far lower than in the general population, reflecting the fear of missing the diagnosis and a low threshold to suspect PE in this setting. Two prospective management outcome trials have recently assessed two different diagnostic algorithms based on the assessment of clinical probability, D-dimer, venous compression ultrasonography of the lower limbs (CUS), and computed tomography pulmonary angiography (CTPA). Both demonstrated the safety of such strategies to exclude PE, with a very low failure rate defined as the rate of subsequent 3-month venous thromboembolism in women left untreated after a negative work-up. These studies were also the first to prospectively demonstrate the safety of negative D-dimer associated with a clinical prediction rule to exclude PE without any chest imaging. Pregnant women are known to be a subgroup at particularly high risk of inappropriate diagnostic management, so the implementation of such validated diagnostic strategies in clinical practice should represent a high priority goal.

Due to their robust antioxidant properties, phenolic acids and their analogs are extensively studied for their ability to activate cellular antioxidant pathways, including nuclear factor (erythroid-derived-2)-like 2 (Nrf2)-antioxidant response element (ARE) pathway. Caffeic, ferulic, and gallic acid are well-studied members of phenolic acids. Constant efforts are made to improve the pharmacological effects and bioavailability of phenolic acids by synthesizing their chemical derivatives. This study determines how modifications of the chemical structure of these phenolic acids affect their antioxidant and cytoprotective activities. We have selected six superior antioxidant compounds (12, 16, 26, 35, 42, and 44) of the 48 caffeic acid phenethyl ester (CAPE) analogs based on their ability to scavenge free radicals in vitro using standard antioxidant assays. These compounds exhibited minimal toxicity as indicated by cell cycle and cytochrome C release assays. Among these compounds, 44, the ketone analog of CAPE, exhibited the ability to increase p-Nrf2 (Ser40) levels in 293T cells (p < 0.05). Further, 44, exhibited its antioxidant effect in Drosophila Melanogaster as indicated by an increase in mRNA levels of Nrf2 and GPx (p < 0.05). Finally, the ability of 44 to activate the antioxidant pathway was abolished in the presence of extracellular signal-regulated kinase (ERK) inhibitor in 293T cells. Thus, we identify 44, the ketone analog of CAPE, as a unique antioxidant molecule with the function of ERK-mediated Nrf2 activation.

The effect of probiotics in improving or maintaining oral health in orthodontic patients is understudied. The aim of this study is to evaluate the effect of probiotic administration in addition to tooth brushing on clinical gingival inflammation, plaque formation, subgingival microbiota composition, and salivary biomarkers of inflammation in adolescents with fixed orthodontic appliances. The present study is a 6-month, double-blind, two-arm, placebo-controlled, single-center trial, in which 116 adolescent volunteers aged 12–16 years will be recruited from the patients of the orthodontics clinic of the University Hospital of Lille, France. Subjects who meet the eligibility criteria will be allocated to one of the following groups: (i) control: two placebo lozenges per day for 90 days together with regular oral hygiene, (ii) test: two probiotic lozenges per day for 90 days together with regular oral hygiene. Clinical assessment and biological sample collection will be performed at baseline, 3 and 6 months. In addition, compliance outcomes and adverse events will be monitored.

Reliable laboratory diagnosis of heparin-induced thrombocytopenia (HIT) remains a major clinical concern. Immunoassays are highly sensitive, while confirmatory functional tests (based on heparin-dependent platelet activation) lack standardization. We evaluated the diagnostic performance of a functional flow cytometric assay (FCA) based on the detection of heparin-dependent platelet activation with an anti-p-selectin. A total of 288 patients were included (131 HIT-positive and 157 HIT-negative) with a HIT diagnosis established by expert opinion adjudication (EOA) considering clinical data and local laboratory results. The FCA was centrally performed in a single laboratory on platelet-rich plasma, using a very simple four-color fluorometer. The results were standardized according to the Heparin Platelet Activation (HEPLA) index. The serotonin release assay (SRA) was performed in the four French reference laboratories. Based on the final HIT diagnosis established by EOA, the sensitivity and specificity of the FCA were 88 and 95%, respectively, values very similar to those of the SRA (88 and 97%, respectively). This study showed that the FCA, based on easily implementable technology, may be routinely used as a reliable confirmatory test for HIT diagnosis.

The recently developed rough Bergomi (rBergomi) model is a rough fractional stochastic volatility (RFSV) model which can generate a more realistic term structure of at-the-money volatility skews compared with other RFSV models. However, its non-Markovianity brings mathematical and computational challenges for model calibration and simulation. To overcome these difficulties, we show that the rBergomi model can be well-approximated by the forward-variance Bergomi model with wisely chosen weights and mean-reversion speed parameters (aBergomi), which has the Markovian property. We establish an explicit bound on the L2-error between the respective kernels of these two models, which is explicitly controlled by the number of terms in the aBergomi model. We establish and describe the affine structure of the rBergomi model, and show the convergence of the affine structure of the aBergomi model to the one of the rBergomi model. We demonstrate the efficiency and accuracy of our method by implementing a classical Markovian Monte Carlo simulation scheme for the aBergomi model, which we compare to the hybrid scheme of the rBergomi model.

Paper substrates are promising for development of cost-effective and efficient point-of-care biosensors, essential for public healthcare and environmental diagnostics in emergency situations. Most paper-based biosensors rely on the natural capillarity of paper to perform qualitative or semi-quantitative colorimetric detections. To achieve quantification and better sensitivity, technologies combining paper-based substrates and electrical detection are being developed. In this work, we demonstrate the potential of electrical measurements by means of a simple, parallel-plate electrode setup towards the detection of whole-cell bacteria captured in nitrocellulose (NC) membranes. Unlike current electrical sensors, which are mostly integrated, this plug and play system has reusable electrodes and enables simple and fast bacterial detection through impedance measurements. The characterized NC membrane was subjected to (i) a biofunctionalization, (ii) different saline solutions modelling real water samples, and (iii) bacterial suspensions of different concentrations. Bacterial detection was achieved in low conductivity buffers through both resistive and capacitive changes in the sensed medium. To capture Bacillus thuringiensis, the model microorganism used in this work, the endolysin cell-wall binding domain (CBD) of Deep-Blue, a bacteriophage targeting this bacterium, was integrated into the membranes as a recognition bio-interface. This experimental proof-of-concept illustrates the electrical detection of 107 colony-forming units (CFU) mL−1 bacteria in low-salinity buffers within 5 min, using a very simple setup. This offers perspectives for affordable pathogen sensors that can easily be reconfigured for different bacteria. Water quality testing is a particularly interesting application since it requires frequent testing, especially in emergency situations.

This paper studies a variation of the continuous-time mean-variance portfolio selection where a tracking-error penalization is added to the mean-variance criterion. The tracking error term penalizes the distance between the allocation controls and a reference portfolio with same wealth and fixed weights. Such consideration is motivated as follows: (i) On the one hand, it is a way to robustify the mean-variance allocation in the case of misspecified parameters, by “fitting" it to a reference portfolio that can be agnostic to market parameters; (ii) On the other hand, it is a procedure to track a benchmark and improve the Sharpe ratio of the resulting portfolio by considering a mean-variance criterion in the objective function. This problem is formulated as a McKean–Vlasov control problem. We provide explicit solutions for the optimal portfolio strategy and asymptotic expansions of the portfolio strategy and efficient frontier for small values of the tracking error parameter. Finally, we compare the Sharpe ratios obtained by the standard mean-variance allocation and the penalized one for four different reference portfolios: equal-weights, minimum-variance, equal risk contributions and shrinking portfolio. This comparison is done on a simulated misspecified model, and on a backtest performed with historical data. Our results show that in most cases, the penalized portfolio outperforms in terms of Sharpe ratio both the standard mean-variance and the reference portfolio.

The authors wish to make the following corrections to this paper [...]

Breast cancer is the second leading cause of death amongst women worldwide. As a result, many have turned their attention to new alternative approaches to treat this disease. Caffeic acid phenylethyl ester (CAPE), a well-known active compound from bee propolis, has been previously identified as a strong antioxidant, anti-inflammatory, antiviral and anticancer molecule. In fact, CAPE is well documented as inducing cell death by inhibiting NFκB and by inducing pro-apoptotic pathways (i.e., p53). With the objective of developing stronger anticancer compounds, we studied 18 recently described CAPE derivatives for their ability to induce apoptosis in breast cancer cell lines. Five of the said compounds, including CAPE, were selected and subsequently characterised for their anticancer mechanism of action. We validated that CAPE is a potent inducer of caspase-dependent apoptosis. Interestingly, some newly synthesized CAPE derivatives also showed greater cell death activity than the lead CAPE structure. Similarly to CAPE, analog compounds elicited p53 activation. Interestingly, one compound in particular, analog 10, induced apoptosis in a p53-mutated cell line. These results suggest that our new CAPE analog compounds may display the capacity to induce breast cancer apoptosis in a p53-dependent and/or independent manner. These CAPE analogs could thus provide new therapeutic approaches for patients with varying genotypic signatures (such as p53 mutations) in a more specific and targeted fashion.

Since most preclinical drug permeability assays across the blood-brain barrier (BBB) are still evaluated in rodents, we compared an in vitro mouse primary endothelial cell model to the mouse b.End3 and the acellular parallel artificial membrane permeability assay (PAMPA) models for drug screening purposes. The mRNA expression of key feature membrane proteins of primary and bEnd.3 mouse brain endothelial cells were compared. Transwell® monolayer models were further characterized in terms of tightness and integrity. The in vitro in vivo correlation (IVIVC) was obtained by the correlation of the in vitro permeability data with log BB values obtained in mice for seven drugs. The mouse primary model showed higher monolayer integrity and levels of mRNA expression of BBB tight junction (TJ) proteins and membrane transporters (MBRT), especially for the efflux transporter Pgp. The IVIVC and drug ranking underlined the superiority of the primary model (r2 = 0.765) when compared to the PAMPA-BBB (r2 = 0.391) and bEnd.3 cell line (r2 = 0.019) models. The primary monolayer mouse model came out as a simple and reliable candidate for the prediction of drug permeability across the BBB. This model encompasses a rapid set-up, a fair reproduction of BBB tissue characteristics, and an accurate drug screening.

This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.