Search

Polymerization reactions often leave unreacted monomers in the polymer. These unreacted monomers can emit vapors at ambient temperatures that cause unpleasant odors, pose both environmental and safety risks, and negatively impacts polymer properties. Therefore, a purification step is necessary after polymerization to remove these residual monomers. High temperature devolatilization is the currently applied technique of removing these monomers, however this treatment can also negatively impact the properties of the polymer. In this study the possibility of using supercritical CO2 (scCO2) as an extraction medium of these residual monomers, which are considered as Volatile Organic Compounds (VOCs), has been investigated experimentally. Polymer samples of either PS or PMMA with 4–7 wt% of residual monomers were extracted continuously with scCO2 at temperatures ranging from 50 to 90 °C and pressures of 100–400 bar. The measured extraction efficiencies, based on 1H NMR analysis, were 99+% and was reached between 4 and 16 h depending on the experimental conditions. In general, higher temperatures and higher pressures lead to enhanced extraction rates and thus higher efficiencies at shorter process times. The experimental results were successfully modelled with an empirical extraction simulation model as originally proposed by Sovová.

Abstract Space situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains.

Oncolytic viruses (OVs) are characterised by their preference for infecting and replicating in tumour cells either naturally or after genetic modification, resulting in oncolysis. Furthermore, OVs can elicit both local and systemic anticancer immune responses while specifically infecting and lysing tumour cells. These characteristics render them a promising therapeutic approach for paediatric brain tumours (PBTs). PBTs are frequently marked by a cold tumour immune microenvironment (TIME), which suppresses immunotherapies. Recent preclinical and clinical studies have demonstrated the capability of OVs to induce a proinflammatory immune response, thereby modifying the TIME. In-depth insights into the effect of OVs on different cell types in the TIME may therefore provide a compelling basis for using OVs in combination with other immunotherapy modalities. However, certain limitations persist in our understanding of oncolytic viruses’ ability to regulate the TIME to enhance anti-tumour activity. These limitations primarily stem from the translational limitations of model systems, the difficulties associated with tracking reliable markers of efficacy throughout the course of treatment and the role of pre-existing viral immunity. In this review, we describe the different alterations observed in the TIME in PBTs due to OV treatment, combination therapies of OVs with different immunotherapies and the hurdles limiting the development of effective OV therapies while suggesting future directions based on existing evidence.

Background Immunocompromised patients are at increased risk of SARS-CoV-2 infections. Patients undergoing chimeric antigen receptor (CAR) T-cell therapy for relapsed/refractory B-cell malignancies are uniquely immunosuppressed due to CAR T-mediated B-cell aplasia (BCA). While SARS-CoV-2 mortality rates of 33%–40% are reported in adult CAR T-cell recipients, outcomes in pediatric and young adult CAR T-cell recipients are limited.Methods We created an international retrospective registry of CAR T recipients aged 0–30 years infected with SARS-CoV-2 within 2 months prior to or any time after CAR T infusion. SARS-CoV-2-associated illness was graded as asymptomatic, mild, moderate, or severe COVID-19, or multisystem inflammatory syndrome in children (MIS-C). To assess for risk factors associated with significant SARS-CoV-2 infections (infections requiring hospital admission for respiratory distress or supplemental oxygen), univariate and multivariable regression analyses were performed.Results Nine centers contributed 78 infections in 75 patients. Of 70 SARS-CoV-2 infections occurring after CAR T infusion, 13 (18.6%) were classified as asymptomatic, 37 (52.9%) mild, 11 (15.7%) moderate, and 6 (8.6%) severe COVID-19. Three (4.3%) were classified as MIS-C. BCA was not significantly associated with infection severity. Prior to the emergence of the Omicron variant, of 47 infections, 19 (40.4%) resulted in hospital admission and 7 (14.9%) required intensive care, while after the emergence of the Omicron variant, of 23 infections, only 1 (4.3%) required admission and the remaining 22 (95.7%) had asymptomatic or mild COVID-19. Death occurred in 3 of 70 (4.3%); each death involved coinfection or life-threatening condition. In a multivariable model, factors associated with significant SARS-CoV-2 infection included having two or more comorbidities (OR 7.73, CI 1.05 to 74.8, p=0.048) and age ≥18 years (OR 9.51, CI 1.90 to 82.2, p=0.014). In the eight patients infected with SARS-CoV-2 before CAR T, half of these patients had their CAR T infusion delayed by 15–30 days.Conclusions In a large international cohort of pediatric and young adult CAR-T recipients, SARS-CoV-2 infections resulted in frequent hospital and intensive care unit admissions and were associated with mortality in 4.3%. Patients with two or more comorbidities or aged ≥18 years were more likely to experience significant illness. Suspected Omicron infections were associated with milder disease.

Abstract Corneal thickness (pachymetry) maps can be used to monitor restoration of corneal endothelial function, for example after Descemet’s membrane endothelial keratoplasty (DMEK). Automated delineation of the corneal interfaces in anterior segment optical coherence tomography (AS-OCT) can be challenging for corneas that are irregularly shaped due to pathology, or as a consequence of surgery, leading to incorrect thickness measurements. In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans. Three different deep learning strategies were developed based on 960 B-scans from 50 patients. On an independent test set of 320 B-scans, corneal thickness could be measured with an error of 13.98 to 15.50 μm for the central 9 mm range, which is less than 3% of the average corneal thickness. The accurate thickness measurements were used to construct detailed pachymetry maps. Moreover, follow-up scans could be registered based on anatomical landmarks to obtain differential pachymetry maps. These maps may enable a more comprehensive understanding of the restoration of the endothelial function after DMEK, where thickness often varies throughout different regions of the cornea, and subsequently contribute to a standardized postoperative regime.

The prognosis of pediatric central nervous system (CNS) malignancies remains dismal due to limited treatment options, resulting in high mortality rates and long-term morbidities. Immunotherapies, including checkpoint inhibition, cancer vaccines, engineered T cell therapies, and oncolytic viruses, have promising results in some hematological and solid malignancies, and are being investigated in clinical trials for various high-grade CNS malignancies. However, the role of the tumor immune microenvironment (TIME) in CNS malignancies is mostly unknown for pediatric cases. In order to successfully implement immunotherapies and to eventually predict which patients would benefit from such treatments, in-depth characterization of the TIME at diagnosis and throughout treatment is essential. In this review, we provide an overview of techniques for immune profiling of CNS malignancies, and detail how they can be utilized for different tissue types and studies. These techniques include immunohistochemistry and flow cytometry for quantifying and phenotyping the infiltrating immune cells, bulk and single-cell transcriptomics for describing the implicated immunological pathways, as well as functional assays. Finally, we aim to describe the potential benefits of evaluating other compartments of the immune system implicated by cancer therapies, such as cerebrospinal fluid and blood, and how such liquid biopsies are informative when designing immune monitoring studies. Understanding and uniformly evaluating the TIME and immune landscape of pediatric CNS malignancies will be essential to eventually integrate immunotherapy into clinical practice.

Chimeric antigen receptor T-cell therapy (CAR-T) targeting CD19 has been associated with remarkable responses in paediatric patients and adolescents and young adults (AYA) with relapsed/refractory (R/R) B-cell precursor acute lymphoblastic leukaemia (BCP-ALL). Tisagenlecleucel, the first approved CD19 CAR-T, has become a viable treatment option for paediatric patients and AYAs with BCP-ALL relapsing repeatedly or after haematopoietic stem cell transplantation (HSCT). Based on the chimeric antigen receptor molecular design and the presence of a 4-1BB costimulatory domain, tisagenlecleucel can persist for a long time and thereby provide sustained leukaemia control. “Real-world” experience with tisagenlecleucel confirms the safety and efficacy profile observed in the pivotal registration trial. Recent guidelines for the recognition, management and prevention of the two most common adverse events related to CAR-T — cytokine release syndrome and immune-cell–associated neurotoxicity syndrome — have helped to further decrease treatment toxicity. Consequently, the questions of how and for whom CD19 CAR-T could substitute HSCT in BCP-ALL are inevitable. Currently, 40–50% of R/R BCP-ALL patients relapse post CD19 CAR-T with either CD19− or CD19+ disease, and consolidative HSCT has been proposed to avoid disease recurrence. Contrarily, CD19 CAR-T is currently being investigated in the upfront treatment of high-risk BCP-ALL with an aim to avoid allogeneic HSCT and associated treatment-related morbidity, mortality and late effects. To improve survival and decrease long-term side effects in children with BCP-ALL, it is important to define parameters predicting the success or failure of CAR-T, allowing the careful selection of candidates in need of HSCT consolidation. In this review, we describe the current clinical evidence on CAR-T in BCP-ALL and discuss factors associated with response to or failure of this therapy: product specifications, patient- and disease-related factors and the impact of additional therapies given before (e.g., blinatumomab and inotuzumab ozogamicin) or after infusion (e.g., CAR-T re-infusion and/or checkpoint inhibition). We discuss where to position CAR-T in the treatment of BCP-ALL and present considerations for the design of supportive trials for the different phases of disease. Finally, we elaborate on clinical settings in which CAR-T might indeed replace HSCT.

Recently, there has been a renewed interest in low-field MRI. Contrast agents (CA) in MRI have magnetic behavior dependent on magnetic field strength. Therefore, the optimal contrast agent for low-field MRI might be different from what is used at higher fields. Ultra-small superparamagnetic iron-oxides (USPIOs), commonly used as negative CA, might also be used for generating positive contrast in low-field MRI. The purpose of this study was to determine whether an USPIO or a gadolinium based contrast agent is more appropriate at low field strengths. Relaxivity values of ferumoxytol (USPIO) and gadoterate (gadolinium based) were used in this research to simulate normalized signal intensity (SI) curves within a concentration range of 0-15 mM. Simulations were experimentally validated on a 0.25T MRI scanner. Simulations and experiments were performed using spin echo (SE), spoiled gradient echo (SGE), and balanced steady-state free precession (bSSFP) sequences. Maximum achievable SIs were assessed for both CAs in a range of concentrations on all sequences. Simulations at 0.25T showed a peak in SIs at low concentrations ferumoxytol versus a wide top at higher concentrations for gadoterate in SE and SGE. Experiments agreed well with the simulations in SE and SGE, but less in the bSSFP sequence due to overestimated relaxivities in simulations. At low magnetic field strengths, ferumoxytol generates similar signal enhancement at lower concentrations than gadoterate.

The development of cleaner, more environmentally friendly processes in polymerization technology is crucial due to the prevalent use of volatile organic solvents (VOCs), which are harmful and toxic. Future regulations are likely to limit or ban VOCs. This review explores the use of supercritical solvents, specifically supercritical CO2 (scCO2), in polymerization processes. The study focuses on reversible addition‐fragmentation chain‐transfer (RAFT) induced homo‐polymerization of various monomers using specific chain transfer agents (CTAs) in scCO2. RAFT polymerization, a reversible deactivation radical polymerization (RDRP) polymerization, relies heavily on the choice of CTA, which significantly influences the dispersity and molar mass of the resulting polymers. Stabilizers are also crucial in controlling product specifications for polymerizations in supercritical CO2, except for fluor‐based polymers, although they must be removed and preferably recycled to ensure product purity and sustainability. The review notes that achieving high molar mass through RAFT polymerization in scCO2 is challenging due to solubility limits, which lead to polymer precipitation. Despite this, RAFT polymerization in scCO2 shows promise for sustainable, circular production of low molar mass polymers, although these cannot yet be fully considered green products. [ABSTRACT FROM AUTHOR]

Oncolytic viruses (OVs) are characterised by their preference for infecting and replicating in tumour cells either naturally or after genetic modification, resulting in oncolysis. Furthermore, OVs can elicit both local and systemic anticancer immune responses while specifically infecting and lysing tumour cells. These characteristics render them a promising therapeutic approach for paediatric brain tumours (PBTs). PBTs are frequently marked by a cold tumour immune microenvironment (TIME), which suppresses immunotherapies. Recent preclinical and clinical studies have demonstrated the capability of OVs to induce a proinflammatory immune response, thereby modifying the TIME. In-depth insights into the effect of OVs on different cell types in the TIME may therefore provide a compelling basis for using OVs in combination with other immunotherapy modalities. However, certain limitations persist in our understanding of oncolytic viruses' ability to regulate the TIME to enhance anti-tumour activity. These limitations primarily stem from the translational limitations of model systems, the difficulties associated with tracking reliable markers of efficacy throughout the course of treatment and the role of pre-existing viral immunity. In this review, we describe the different alterations observed in the TIME in PBTs due to OV treatment, combination therapies of OVs with different immunotherapies and the hurdles limiting the development of effective OV therapies while suggesting future directions based on existing evidence. [ABSTRACT FROM AUTHOR]

Space situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few‐shot domain adaptation technique, leveraging thousands of rapidly simulated low‐fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real‐case scenario, fine‐tuning a deep learning‐based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue‐to‐digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains. [ABSTRACT FROM AUTHOR]