Lesser mealworm (A. diaperinus) protein as a replacement for dairy proteins in the production of O/W emulsions: Droplet coalescence studies using microfluidics under controlled conditions.

Dairy proteins are commonly used to stabilize oil-in-water (O/W) emulsions, which can be replaced by other sustainable sources of proteins, such as insects. This study investigated the potential of lesser mealworm protein concentrate (LMPC) as a sustainable alternative to whey protein isolate (WPI) in stabilizing oil-in-water (O/W) emulsions using microfluidics. The frequency of coalescence (Fcoal) was calculated using images of emulsion droplets obtained near the inlet and outlet of the coalescence channel. The stability of O/W emulsions, produced using sunflower oil (SFO) or hexadecane and stabilized with varying concentrations of LMPC and WPI (0.02% to 0.0005% w/v), was compared under controlled conditions. The dispersed phase fraction (5.3%-14.3% v/v), protein adsorption time onto oil droplets (0.0398-0.158 s), and pH (pH = 3 and pH = 7) were also studied. Fcoal was greatest (0.42 s-1) when the protein concentration was lowest (0.0005%), the oil percentage was highest (14.3%), the adsorption period was shortest (0.0398 s), and the pH was 3. Droplet diameters did not vary significantly, with values between 55 and 118 µm, across protein concentrations or adsorption periods, but a rise in oil fraction resulted in a substantial increase in droplet diameters. Increases in protein content, adsorption duration, and oil percentage all resulted in increased stability (reduction of Fcoal). While LMPC and WPI showed similar results in microfluidic experiments and other test conditions, further research is needed to verify LMPC's efficacy as a replacement for WPI in food emulsification. Nonetheless, the findings suggest that LMPC has potential as a substitute for WPI in this application.

Experimental Study of the Deposition of Magnetic Particles on the Walls of Microchannels.

This study analyzes experimentally the deposition of magnetic beads on the walls of a square microchannel by the action of a nearby cubical magnet. The deposition has been studied for different magnetic bead sizes, flow rates, magnetic conditions and with solutions of magnetic and non-magnetic particles. Images of the time evolution of the deposition under the different conditions have been analyzed to determine the spatial distribution of the accumulation and the growth rate of the depositions. It has been found that the way in which the magnetic beads are deposited on the walls of the microchannel depends strongly on their size and the magnetic configuration. The accumulation of the major part of particles is on the wall closest to the magnet and, depending on the size of the particles, near the magnet leading and trailing edges or near the center of the magnet. The experiments with magnetic and non-magnetic particles revealed the screening effect of the non-magnetic particles on the deposition. In this case, the non-magnetic particles displace the deposition toward the region near the center of the magnet and near the trailing edge.

Direct numerical simulation of turbulent dispersion of evaporative aerosol clouds produced by an intense expiratory event.

Airborne particles are a major route for transmission of COVID-19 and many other infectious diseases. When a person talks, sings, coughs, or sneezes, nasal and throat secretions are spewed into the air. After a short initial fragmentation stage, the expelled material is mostly composed of spherical particles of different sizes. While the dynamics of the largest droplets are dominated by gravitational effects, the smaller aerosol particles, mostly transported by means of hydrodynamic drag, form clouds that can remain afloat for long times. In subsaturated air environments, the dependence of pathogen-laden particle dispersion on their size is complicated due to evaporation of the aqueous fraction. Particle dynamics can significantly change when ambient conditions favor rapid evaporation rates that result in a transition from buoyancy-to-drag dominated dispersion regimes. To investigate the effect of particle size and evaporation on pathogen-laden cloud evolution, a direct numerical simulation of a mild cough was coupled with an evaporative Lagrangian particle advection model. The results suggest that while the dispersion of cough particles in the tails of the size distribution are unlikely to be disrupted by evaporative effects, preferential aerosol diameters (30-40 µm) may exhibit significant increases in the residence time and horizontal range under typical ambient conditions. Using estimations of the viral concentration in the spewed fluid and the number of ejected particles in a typical respiratory event, we obtained a map of viral load per volume of air at the end of the cough and the number of virus copies per inhalation in the emitter vicinity.

Direct numerical simulation of the turbulent flow generated during a violent expiratory event.

A main route for SARS-CoV-2 (severe acute respiratory syndrome coronavirus) transmission involves airborne droplets and aerosols generated when a person talks, coughs, or sneezes. The residence time and spatial extent of these virus-laden aerosols are mainly controlled by their size and the ability of the background flow to disperse them. Therefore, a better understanding of the role played by the flow driven by respiratory events is key in estimating the ability of pathogen-laden particles to spread the infection. Here, we numerically investigate the hydrodynamics produced by a violent expiratory event resembling a mild cough. Coughs can be split into an initial jet stage during which air is expelled through mouth and a dissipative phase over which turbulence intensity decays as the puff penetrates the environment. Time-varying exhaled velocity and buoyancy due to temperature differences between the cough and the ambient air affect the overall flow dynamics. The direct numerical simulation (DNS) of an idealized isolated cough is used to characterize the jet/puff dynamics using the trajectory of the leading turbulent vortex ring and extract its topology by fitting an ellipsoid to the exhaled fluid contour. The three-dimensional structure of the simulated cough shows that the assumption of a spheroidal puff front fails to capture the observed ellipsoidal shape. Numerical results suggest that, although analytical models provide reasonable estimates of the distance traveled by the puff, trajectory predictions exhibit larger deviations from the DNS. The fully resolved hydrodynamics presented here can be used to inform new analytical models, leading to improved prediction of cough-induced pathogen-laden aerosol dispersion.

A Machine Learning decision-making tool for extubation in Intensive Care Unit patients.

BACKGROUND AND OBJECTIVE: To increase the success rate of invasive mechanical ventilation weaning in critically ill patients using Machine Learning models capable of accurately predicting the outcome of programmed extubations. METHODS: The study population was adult patients admitted to the Intensive Care Unit. Target events were programmed extubations, both successful and failed. The working dataset is assembled by combining heterogeneous data including time series from Clinical Information Systems, patient demographics, medical records and respiratory event logs. Three classification learners have been compared: Logistic Discriminant Analysis, Gradient Boosting Method and Support Vector Machines. Standard methodologies have been used for preprocessing, hyperparameter tuning and resampling. RESULTS: The Support Vector Machine classifier is found to correctly predict the outcome of an extubation with a 94.6% accuracy. Contrary to current decision-making criteria for extubation based on Spontaneous Breathing Trials, the classifier predictors only require monitor data, medical entry records and patient demographics. CONCLUSIONS: Machine Learning-based tools have been found to accurately predict the extubation outcome in critical patients with invasive mechanical ventilation. The use of this important predictive capability to assess the extubation decision could potentially reduce the rate of extubation failure, currently at 9%. With about 40% of critically ill patients eventually receiving invasive mechanical ventilation during their stay and given the serious potential complications associated to reintubation, the excellent predictive ability of the model presented here suggests that Machine Learning techniques could significantly improve the clinical outcomes of critical patients.

Lipids, biomarkers, and subclinical atherosclerosis in treatment-naive HIV patients starting or not starting antiretroviral therapy: Comparison with a healthy control group in a 2-year prospective study.

OBJECTIVE: To assess the effect of HIV infection and combined antiretroviral therapy (c-ART) on various proatherogenic biomarkers and lipids and to investigate their relationship with subclinical atherosclerosis in a cohort of treatment-naive HIV-infected patients. METHODS: We performed a prospective, comparative, multicenter study of 2 groups of treatment-naive HIV-infected patients (group A, CD4>500 cells/µL, not starting c-ART; and group B, CD4<500 cells/µL, starting c-ART at baseline) and a healthy control group. Laboratory analyses and carotid ultrasound were performed at baseline and at months 12 and 24. The parameters measured were low-density lipoprotein (LDL) particle phenotype, lipoprotein-associated phospholipase A2 (Lp-PLA2), interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), sCD14, sCD163, monocyte chemoattractant protein-1(MCP-1), and asymmetric dimethylarginine (ADMA). A linear mixed model based on patient clusters was used to assess differences in biomarkers between the study groups and over time. RESULTS: The study population comprised 62 HIV-infected patients (group A, n = 31; group B, n = 31) and 22 controls. Age was 37 (30-43) years, and 81% were men. At baseline, the HIV-infected patients had a worse LDL particle phenotype and higher plasma concentration of sCD14, sCD163, hs-CRP, and LDL-Lp-PLA2 than the controls. At month 12, there was an increase in total cholesterol (p = 0.002), HDL-c (p = 0.003), and Apo A-I (p = 0.049) and a decrease in sCD14 (p = <0.001) and sCD163 (p<0.001), although only in group B. LDL particle size increased in group B at month 24 (p = 0.038). No changes were observed in group A or in the healthy controls. Common carotid intima-media thickness increased in HIV-infected patients at month 24 (Group A p = 0.053; group B p = 0.048). Plasma levels of sCD14, sCD163, and hs-CRP correlated with lipid values. CONCLUSIONS: In treatment-naive HIV-infected patients, initiation of c-ART was associated with an improvement in LDL particle phenotype and inflammatory/immune biomarkers, reaching values similar to those of the controls. HIV infection was associated with progression of carotid intima-media thickness.

Functionalised alginate flow seeding microparticles for use in Particle Image Velocimetry (PIV).

Alginate microparticles as flow seeding fulfil all the requirements that are recommended for the velocity measurements in Particle Image Velocimetry (PIV). These spherical microparticles offer the advantage of being environmentally friendly, having excellent seeding properties and they can be produced via a very simple process. In the present study, the performances of alginate microparticles functionalised with a fluorescent dye, Rhodamine B (RhB), for PIV have been studied. The efficacy of fluorescence is appreciated in a number of PIV applications since it can boost the signal-to-noise ratio. Alginate microparticles functionalised with RhB have high emission efficiency, desirable match with fluid density and controlled size. The study of the particles behaviour in strong acid and basic solutions and ammonia is also included. This type of particles can be used for measurements with PIV and Planar Laser Induced Fluorescence (PLIF) simultaneously, including acid-base reactions.

A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber.

Thrombus formation is a multiscale phenomenon triggered by platelet deposition over a protrombotic surface (eg. a ruptured atherosclerotic plaque). Despite the medical urgency for computational tools that aid in the early diagnosis of thrombotic events, the integration of computational models of thrombus formation at different scales requires a comprehensive understanding of the role and limitation of each modelling approach. We propose three different modelling approaches to predict platelet deposition. Specifically, we consider measurements of platelet deposition under blood flow conditions in a perfusion chamber for different time periods (3, 5, 10, 20 and 30 minutes) at shear rates of 212 s(-1), 1390 s(-1) and 1690 s(-1). Our modelling approaches are: i) a model based on the mass-transfer boundary layer theory; ii) a machine-learning approach; and iii) a phenomenological model. The results indicate that the three approaches on average have median errors of 21%, 20.7% and 14.2%, respectively. Our study demonstrates the feasibility of using an empirical data set as a proxy for a real-patient scenario in which practitioners have accumulated data on a given number of patients and want to obtain a diagnosis for a new patient about whom they only have the current observation of a certain number of variables.