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.

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.