Comment on "Binding Debye-Hückel theory for associative electrolyte solutions" [J. Chem. Phys. 159, 154503 (2023)].

It is argued that the Binding Debye-Hückel (BiDH) model proposed by Naseri Boroujeni et al. [J. Chem. Phys. 159, 154503 (2023)] might not be appropriate for the description of Monte Carlo simulation data obtained for primitive model electrolytes. The first reason is that the original Debye-Hückel (DH) theory is of low accuracy for describing deviations from ideality in concentrated solutions of strong salts. The DH framework is thus a poor basis for building a model including association. The second reason is that the mean-spherical approximation, without assumption of association, apparently predicts Monte Carlo (MC) data for primitive electrolytes better than BiDH. Thus, the BiDH model seems to be simply a way of compensating for the deficiencies of DH theory by assuming association.

Immediate myeloid depot for SARS-CoV-2 in the human lung.

In the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, epithelial populations in the distal lung expressing Angiotensin-converting enzyme 2 (ACE2) are infrequent, and therefore, the model of viral expansion and immune cell engagement remains incompletely understood. Using human lungs to investigate early host-viral pathogenesis, we found that SARS-CoV-2 had a rapid and specific tropism for myeloid populations. Human alveolar macrophages (AMs) reliably expressed ACE2 allowing both spike-ACE2-dependent viral entry and infection. In contrast to Influenza A virus, SARS-CoV-2 infection of AMs was productive, amplifying viral titers. While AMs generated new viruses, the interferon responses to SARS-CoV-2 were muted, hiding the viral dissemination from specific antiviral immune responses. The reliable and veiled viral depot in myeloid cells in the very early phases of SARS-CoV-2 infection of human lungs enables viral expansion in the distal lung and potentially licenses subsequent immune pathologies.

Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping.

Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme by utilizing physics-informed neural networks (PINNs) and a physics-guided nnU-Net-based supervised approach. When evaluated on simulated color Doppler images derived from a patient-specific computational fluid dynamics model and in vivo Doppler acquisitions, both approaches demonstrate comparable reconstruction performance to the original iVFM algorithm. The efficiency of PINNs is boosted through dual-stage optimization and pre-optimized weights. On the other hand, the nnU-Net method excels in generalizability and real-time capabilities. Notably, nnU-Net shows superior robustness on sparse and truncated Doppler data while maintaining independence from explicit boundary conditions. Overall, our results highlight the effectiveness of these methods in reconstructing intraventricular vector blood flow. The study also suggests potential applications of PINNs in ultrafast color Doppler imaging and the incorporation of fluid dynamics equations to derive biomarkers for cardiovascular diseases based on blood flow.

The ETO2 transcriptional cofactor maintains acute leukemia by driving a MYB/EP300-dependent stemness program.

Transcriptional cofactors of the ETO family are recurrent fusion partners in acute leukemia. We characterized the ETO2 regulome by integrating transcriptomic and chromatin binding analyses in human erythroleukemia xenografts and controlled ETO2 depletion models. We demonstrate that beyond its well-established repressive activity, ETO2 directly activates transcription of MYB, among other genes. The ETO2-activated signature is associated with a poorer prognosis in erythroleukemia but also in other acute myeloid and lymphoid leukemia subtypes. Mechanistically, ETO2 colocalizes with EP300 and MYB at enhancers supporting the existence of an ETO2/MYB feedforward transcription activation loop (e.g., on MYB itself). Both small-molecule and PROTAC-mediated inhibition of EP300 acetyltransferases strongly reduced ETO2 protein, chromatin binding, and ETO2-activated transcripts. Taken together, our data show that ETO2 positively enforces a leukemia maintenance program that is mediated in part by the MYB transcription factor and that relies on acetyltransferase cofactors to stabilize ETO2 scaffolding activity.

Prognostic impact of genetic abnormalities in 536 first-line chronic lymphocytic leukaemia patients without 17p deletion treated with chemoimmunotherapy in two prospective trials: Focus on IGHV-mutated subgroups (a FILO study).

The potential prognostic influence of genetic aberrations on chronic lymphocytic leukaemia (CLL) can vary based on various factors, such as the immunoglobulin heavy variable (IGHV) status. We conducted an integrative analysis on genetic abnormalities identified through cytogenetics and targeted next-generation sequencing in 536 CLL patients receiving first-line chemo(immuno)therapies (CIT) as part of two prospective trials. We evaluated the prognostic implications of the main abnormalities, with specific attention to their relative impact according to IGHV status. In the entire cohort, unmutated (UM)-IGHV, complex karyotype, del(11q) and ATM mutations correlated significantly with shorter progression-free survival (PFS). Focusing on the subset of mutated IGHV (M-IGHV) patients, univariate analysis showed that complex karyotype, del(11q), SF3B1 and SAMHD1 mutations were associated with significant lower PFS. The prognostic influence varied based on the patient's IGHV status, as these abnormalities did not affect outcomes in the UM-IGHV subgroup. TP53 mutations had no significant impact on outcomes in the M-IGHV subgroup. Our findings highlight the diverse prognostic influence of genetic aberrations depending on the IGHV status in symptomatic CLL patients receiving first-line CIT. The prognosis of gene mutations and cytogenetic abnormalities needs to be investigated with a compartmentalized methodology, taking into account the IGVH status of patients receiving first-line BTK and/or BCL2 inhibitors.

Non-destructive monitoring of microalgae biofilms.

Biofilm-based cultivation systems are emerging as a promising technology for microalgae production. However, efficient and non-invasive monitoring routines are still lacking. Here, a protocol to monitor microalgae biofilms based on reflectance indices (RIs) is proposed. This framework was developed using a rotating biofilm system for astaxanthin production by cultivating Haematococcus pluvialis on cotton carriers. Biofilm traits such as biomass, astaxanthin, and chlorophyll were characterized under different light and nutrient regimes. Reflectance spectra were collected to identify the spectral bands and the RIs that correlated the most with those biofilm traits. Robust linear models built on more than 170 spectra were selected and validated on an independent dataset. Astaxanthin content could be precisely predicted over a dynamic range from 0 to 4% of dry weight, regardless of the cultivation conditions. This study demonstrates the strength of reflectance spectroscopy as a non-invasive tool to improve the operational efficiency of microalgae biofilm-based technology.

The effect of light intensity on microalgae biofilm structures and physiology under continuous illumination.

The interest by biofilm-based microalgae technologies has increased lately due to productivity improvement, energy consumption reduction and easy harvesting. However, the effect of light, one key factor for system's operation, received less attention than for planktonic cultures. This work assessed the impact of Photon Flux Density (PFD) on Chlorella vulgaris biofilm dynamics (structure, physiology, activity). Microalgae biofilms were cultivated in a flow-cell system with PFD from 100 to 500 [Formula: see text]. In the first stage of biofilm development, uniform cell distribution was observed on the substratum exposed to 100 [Formula: see text] while cell clusters were formed under 500 [Formula: see text]. Though similar specific growth rate in exponential phase (ca. 0.3 [Formula: see text]) was obtained under all light intensities, biofilm cells at 500 [Formula: see text] seem to be ultimately photoinhibited (lower final cell density). Data confirm that Chlorella vulgaris showed a remarkable capability to cope with high light. This was marked for sessile cells at 300 [Formula: see text], which reduce very rapidly (in 2 days) their chlorophyll-a content, most probably to reduce photodamage, while maintaining a high final cell density. Besides cellular physiological adjustments, our data demonstrate that cellular spatial organization is light-dependent.

Exploring the dynamics of astaxanthin production in Haematococcus pluvialis biofilms using a rotating biofilm-based system.

Microalgae biofilm emerged as a solid alternative to conventional suspended cultures which present high operative costs and complex harvesting processes. Among several designs, rotating biofilm-based systems stand out for their scalability, although their primary applications have been in wastewater treatment and aquaculture. In this work, a rotating system was utilized to produce a high-value compound (astaxanthin) using Haematococcus pluvialis biofilms. The effect of nitrogen regime, light intensity, and light history on biofilm traits was assessed to better understand how to efficiently operate the system. Our results show that H. pluvialis biofilms follow the classical growth stages described for bacterial biofilms (from adhesion to maturation) and that a two-stage (green and red stages) allowed to reach astaxanthin productivities of 204 mg m-2 d-1 . The higher light intensity applied during the red stage (400 and 800 µmol m-2 s-1 ) combined with nitrogen depletion stimulated similar astaxanthin productivities. However, by training the biofilms during the green stage, using mild-light intensity (200 µmol m-2 s-1 ), a process known as priming, the final astaxanthin productivity was enhanced by 40% with respect to biofilms pre-exposed to 50 µmol m-2 s-1 . Overall, this study shows the possibility of utilizing rotating microalgae biofilms to produce high-value compounds laying the foundation for further biotechnological applications of these emerging systems.

The impact of light/dark regimes on structure and physiology of Chlorella vulgaris biofilms.

Introduction: Biofilm-based microalgae production technologies offer enormous potential for improving sustainability and productivity. However, the light pattern induced by these technologies is a key concern for optimization. Methods: In this work, the effects of light/dark cycles on architecture, growth, and physiology of Chlorella vulgaris biofilms were assessed in a millifluidic flow-cell with different time cycles (15 s to 3 min) keeping the average light constant at 100 µmol·m-2·s-1. Results and discussion: Results showed that photoinhibition can be mitigated by applying a light fraction of 1/3 and a cycle time of 15 s. By contrast, when the cycle time is extended to 90 s and 3 min, photoinhibition is high and photoefficiency dramatically decreases. To cope with light stress, cells acclimate and organize themselves differently in space. A high peak light (500 µmol·m-2·s-1) triggers a stress, reducing cell division and inducing clusters in the biofilm. This work provides guidelines for optimizing rotating microalgae production systems in biofilms and assesses the minimum rotating frequency required to maintain the net growth rate close to that of continuous light of the same average intensity, mitigating photo-inhibition. The overall gain in productivity is then provided by the total surface of the biofilm turning in the illuminated surface area.

On analytical theories for conductivity and self-diffusion in concentrated electrolytes.

Describing analytically the transport properties of electrolytes, such as their conductivity or the self-diffusion of the ions, has been a central challenge of chemical physics for almost a century. In recent years, this question has regained some interest in light of Stochastic Density Field Theory (SDFT) - an analytical framework that allows the approximate determination of density correlations in fluctuating systems. In spite of the success of this theory to describe dilute electrolytes, its extension to concentrated solutions raises a number of technical difficulties, and requires simplified descriptions of the short-range repulsion between the ions. In this article, we discuss recent approximations that were proposed to compute the conductivity of electrolytes, in particular truncations of Coulomb interactions at short distances. We extend them to another observable (the self-diffusion coefficient of the ions) and compare them to earlier analytical approaches, such as the mean spherical approximation and mode-coupling theory. We show how the treatment of hydrodynamic effects in SDFT can be improved, that the choice of the modified Coulomb interactions significantly affects the determination of the properties of the electrolytes, and that comparison with other theories provides a guide to extend SDFT approaches in this context.

Ultrafast Cardiac Imaging Using Deep Learning for Speckle-Tracking Echocardiography.

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this article, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle-tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e., high-quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on nonsimulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography.

High-efficiency non-ablative UV laser nano-scale processing of fused silica by stable filamentation.

Over the last decades, three-dimensional micro-manufacturing of fused silica via near-infrared ultrafast laser exposure combined with an etching step has become an established technique for producing complex three-dimensional components. Here, we explore the effect of ultraviolet exposure on process efficiency. Specifically, we demonstrate that shorter wavelengths not only enable enhanced resolution but also yield higher etching selectivity, with an order of magnitude lower pulse energy and significantly higher repetition rates than current practice. This result is obtained using an exposure regime where the laser beam alternates between regimes of self-focusing and defocusing in a stable manner, forming a localized filament. Using this principle, we demonstrate the fabrication of self-organized nano-channels with diameters as small as 120 nm after etching, reaching extreme aspect ratios, exceeding 1500.

Phase Unwrapping of Color Doppler Echocardiography Using Deep Learning.

Color Doppler echocardiography is a widely used noninvasive imaging modality that provides real-time information about intracardiac blood flow. In an apical long-axis view of the left ventricle, color Doppler is subject to phase wrapping, or aliasing, especially during cardiac filling and ejection. When setting up quantitative methods based on color Doppler, it is necessary to correct this wrapping artifact. We developed an unfolded primal-dual network (PDNet) to unwrap (dealias) color Doppler echocardiographic images and compared its effectiveness against two state-of-the-art segmentation approaches based on nnU-Net and transformer models. We trained and evaluated the performance of each method on an in-house dataset and found that the nnU-Net-based method provided the best dealiased results, followed by the primal-dual approach and the transformer-based technique. Noteworthy, the PDNet, which had significantly fewer trainable parameters, performed competitively with respect to the other two methods, demonstrating the high potential of deep unfolding methods. Our results suggest that deep learning (DL)-based methods can effectively remove aliasing artifacts in color Doppler echocardiographic images, outperforming DeAN, a state-of-the-art semiautomatic technique. Overall, our results show that DL-based methods have the potential to effectively preprocess color Doppler images for downstream quantitative analysis.

Association of Maternal Gestational Vitamin D Supplementation with Respiratory Health of Young Children.

This study aimed to evaluate the association between maternal gestational Vitamin D3 supplementation and early respiratory health in offspring. This was a population-based record-linkage study which used data from the French National Health Database System. Maternal Vitamin D3 supplementation consisted of a single high oral dose of cholecalciferol, (100,000 IU) from the seventh month of pregnancy, according to national guidelines. In total, 125,756 term-born singleton children were included, of which 37% had respiratory illness defined as hospital admission due to respiratory causes or inhalation treatment up to 24 months of age. Infants prenatally exposed to maternal Vitamin D3 supplementation (n = 54,596) were more likely to have a longer gestational age (GA) at birth (GA 36-38 weeks, 22% vs. 20%, p < 0.001 in exposed vs. non-exposed infants, respectively). After adjusting for the main risk factors (maternal age, socioeconomic level, mode of delivery, obstetrical and neonatal pathology, birth weight appropriateness, sex, and birth season), the risk of RD was found to be 3% lower than their counterparts (aOR [IC 95%], 0.97 [0.95-0.99], p = 0.01). In conclusion, this study provides evidence for the association between maternal gestational Vitamin D3 supplementation and improved early respiratory outcomes in young children.

Quality Indexes of the ECG Signal Transmitted Using Optical Wireless Link.

This work relates to the quality of the electrocardiogram (ECG) signal of an elderly person, transmitted using optical wireless links. The studied system uses infrared signals between an optical transmitter located on the person's wrist and optical receivers placed on the ceiling. As the elderly person moves inside a room, the optical channel is time-varying, affecting the received ECG signal. To assess the ECG quality, we use specific signal quality indexes (SQIs), allowing the evaluation of the spectral and statistical characteristics of the signal. Our main contribution is studying how the SQIs behave according to the optical transmission performance and the studied context in order to determine the conditions required to obtain excellent quality indexes. The approach is based on the simulation of the whole chain, from the raw ECG to the extraction process after transmission until the evaluation of SQIs. This technique was developed considering optical channel modeling, including the mobility of the elderly. The obtained results show the potential of optical wireless communication technologies for reliable ECG monitoring in such a context. It has been observed that excellent ECG quality can be obtained with a minimum SNR of 11 dB for on-off keying modulation.

Precision Medicine Approach Based on Molecular Alterations for Patients with Relapsed or Refractory Multiple Myeloma: Results from the MM-EP1 Study.

BACKGROUND: Despite that cytogenetic and molecular analysis of tumor cells can rapidly identify recurring molecular abnormalities, no personalized therapy is currently available in the setting of relapsed/refractory multiple myeloma (r/r MM). METHODS: MM-EP1 is a retrospective study aimed at comparing a personalized molecular-oriented (MO) versus a non-molecular-oriented (no-MO) approach in r/r MM. Actionable molecular targets and their associated therapies were the BRAF V600E mutation and BRAF inhibitors; t(11;14)(q13;q32) and BCL2 inhibitors; and t(4;14)(p16;q32) with FGFR3 fusion/rearrangements and FGFR3 inhibitors. RESULTS: One hundred three highly pretreated r/r MM patients with a median age of 67 years (range 44-85) were included. Seventeen (17%) patients were treated using an MO approach with BRAF inhibitors (vemurafenib or dabrafenib, n = 6), BCL2 inhibitor (venetoclax, n = 9), or FGFR3 inhibitor (erdafitinib, n = 2). Eighty-six (86%) patients received non-MO therapies. Overall response rate was 65% in MO patients versus 58% in the non-MO group (p = 0.053). Median PFS and OS were 9 and 6 months (HR = 0.96; CI95 = 0.51-1.78; p = 0.88) and 26 and 28 months (HR = 0.98; CI95 = 0.46-2.12; p = 0.98), respectively, in MO and no-MO patients. CONCLUSION: Despite the low number of patients treated with an MO approach, this study highlights the strengths and weakness of a molecular-targeted approach for the treatment of multiple myeloma. Widespread biomolecular techniques and improvement of precision medicine treatment algorithms could improve selection for precision medicine in myeloma.

Relationship between large and small for gestational age and hospital readmission after postpartum discharge: a population-based, data-linkage study.

This study aims to determine the association of small for gestational age (SGA) and large for gestational age (LGA) at birth with hospital readmission after postpartum discharge for up to 28 days of delivery. This is a population-based, data-linkage study using the French National Uniform Hospital Discharge Database. "Healthy" singleton term infants born between January 1st, 2017, and November 30th, 2018, in the French South region were included. SGA and LGA were defined as birth weight < 10th and > 90th percentiles, respectively, according to sex and gestational age. A multivariable regression analysis was performed. Among 67,359 included infants, 2441 (3.6%) were readmitted, and 61% of them were hospitalized within 14 days postpartum. Hospitalized infants were more likely to be LGA at birth (10.3% vs. 8.6% in non-hospitalized infants, p < 0.01); the proportion of SGA infants did not differ between both groups. Compared to appropriate birth weight for GA (AGA) infants, LGA infants were more often hospitalized for infectious diseases (57.7% vs. 51.3%, p = 0.05). After regression analysis, LGA infants had a 20% higher odds of being hospitalized than those born AGA (aOR (95%CI) = 1.21 (1.06-1.39)), while aOR (95%CI) for SGA was 1.11 (0.96-1.28). CONCLUSION:  In contrast to SGA, LGA was associated with hospital readmission during the first month of life. Follow-up protocols that include LGA should be evaluated. WHAT IS KNOWN: • Newborns are at high risk of hospital readmission during the postpartum period. • However, the influence of appropriateness for gestational age at birth, i.e. being born small for gestational age (SGA) or large for gestational age (LGA), has been little evaluated. WHAT IS NEW: • In contrast to SGA born infants, we found that infants born LGA were at high risk of hospital admission and the main cause was infectious diseases. • This population should be considered at risk of early adverse outcomes and should require attentive medical follow-up after postpartum discharge.

Theoretical growth rate of microalgae under high/low-flashing light.

Dynamic light regimes strongly impact microalgal photosynthesis efficiency. Finding the optimal way to supply light is then a tricky problem, especially when the growth rate is inhibited by overexposition to light and, at the same time, there is a lack of light in the deepest part of the culture. In this paper, we use the Han model to study the theoretical microalgal growth rate by applying periodically two different light intensities. Two approaches are considered depending on the period of the light pattern. For a large light period, we demonstrate that the average photosynthetic rate can be improved under some conditions. Moreover, we can also enhance the growth rate at steady state as given by the PI-curve. Although, these conditions change through the depth of a bioreactor. This theoretical improvement in the range of 10-15% is due to a recovery of photoinhibited cells during the high irradiance phase. We give a minimal value of the duty cycle for which the optimal irradiance is perceived by the algae culture under flashing light regime.

Sequence analyses of relapsed or refractory diffuse large B-cell lymphomas unravel three genetic subgroups of patients and the GNA13 mutant as poor prognostic biomarker, results of LNH-EP1 study.

Advances in molecular profiling of newly diagnosed diffuse large B-cell lymphoma (DLBCL) have recently refine genetic subgroups. Genetic subgroups remain undetermined at the time of relapse or refractory (RR) disease. This study aims to decipher genetic subgroups and search for prognostic molecular biomarkers in patients with RR-DLBCL. From 2015 to 2021, targeted next-generation sequencing analyses of germline-matched tumor samples and fresh tissue from RR-DLBCL patients were performed. Unsupervised clustering of somatic mutations was performed and correlations with patient outcome were sought. A number of 120 patients with RR-DLBCL were included in LNH-EP1 study and a molecular tumor landscape was successfully analyzed in 87% of patients (104/120 tumor samples). The median age was 67.5 years (range 27.4-87.4), median number of previous treatments was 2 (range 1-9). The most frequently mutated genes were TP53 (n = 53 mutations; 42% of samples), CREBBP (n = 39; 32%), BCL2 (n = 86; 31%), KMT2D (n = 39; 28%) and PIM1 (n = 54; 22%). Unsupervised clustering separated three genetic subgroups entitled BST (enriched in BCL2, SOCS1, and TNFRSF14 mutations); TKS (enriched in TP53, KMT2D, and STAT6 mutations); and PCM (enriched in PIM1, CD79B, and MYD88 mutations). Median overall survival (OS) was 11.0 (95% confidence interval [CI]: 8.1-12.6) months. OS was not significantly different between the three genetic subgroups. GNA13 mutant was significantly associated with an increased risk of death (hazard ratio: 6.6 [95% CI: 2.1-20.6]; p = .0011) and shorter OS (p = .0340). At the time of relapse or refractory disease, three genetic subgroups of DLBCL patients were delineated, which could help advance precision molecular medicine programs.