Modifications to the framework regions eliminate chimeric antigen receptor tonic signaling

Chimeric antigen receptor (CAR) tonic signaling, defined as spontaneous activation and release of proinflammatory cytokines by CAR-T cells, is considered a negative attribute because it leads to impaired antitumor effects. Here, we report that CAR tonic signaling is caused by the intrinsic instability of the mAb single-chain variable fragment (scFv) to promote self-aggregation and signaling via the CD3z chain incorporated into the CAR construct. This phenomenon was detected in a CAR encoding either CD28 or 4-1BB costimulatory endodomains. Instability of the scFv was caused by specific amino acids within the framework regions (FWR) that can be identified by computational modeling. Substitutions of the amino acids causing instability, or humanization of the FWRs, corrected tonic signaling of the CAR, without modifying antigen specificity, and enhanced the antitumor effects of CAR-T cells. Overall, we demonstrated that tonic signaling of CAR-T cells is determined by the molecular instability of the scFv and that computational analyses of the scFv can be implemented to correct the scFv instability in CAR-T cells with either CD28 or 4-1BB costimulation

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Simple Parameters from Complete Blood Count Predict In-Hospital Mortality in COVID-19

The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions

In vitro labelling and detection of mesenchymal stromal cells (MSCs) : a comparative study between Magnetic Resonance Imaging (MRI) of iron-labelled cells and 19F-Magnetic Resonance Spectroscopy (MRS) of fluorine-labelled cells

Background Among the various stem cell populations used for cell therapy, adult mesenchymal stromal cells (MSCs) have emerged as a major new cell technology. These cells must be tracked after transplantation to monitor their migration within the body and quantify their accumulation at the target site. This study assessed whether rat bone marrow MSCs can be labelled with superparamagnetic iron oxide (SPIO) nanoparticles and perfluorocarbon (PFC) nanoemulsion formulations without altering cell viability and compared magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) results from iron-labelled and fluorine-labelled MSCs, respectively. Methods Of MSCs, 2\u2009 7\u2009106 were labelled with Molday ION Rhodamine-B (MIRB) and 2\u2009 7\u2009106 were labelled with Cell Sense. Cell viability was evaluated by trypan blue exclusion method. Labelled MSCs were divided into four samples containing increasing cell numbers (0.125\u2009 7\u2009106, 0.25\u2009 7\u2009106, 0.5\u2009 7\u2009106, 1\u2009 7\u2009106) and scanned on a 7T MRI: for MIRB-labelled cells, phantoms and cells negative control, T1, T2 and T2* maps were acquired; for Cell Sense labelled cells, phantoms and unlabelled cells, a 19F non-localised single-pulse MRS sequence was acquired. Results In total, 86.8% and 83.6% of MIRB-labelled cells and Cell Sense-labelled cells were viable, respectively. MIRB-labelled cells were visible in all samples with different cell numbers; pellets containing 0.5\u2009 7\u2009106 and 1\u2009 7\u2009106 of Cell Sense-labelled cells showed a detectable 19F signal. Conclusions Our data support the use of both types of contrast material (SPIO and PFC) for MSCs labelling, although further efforts should be dedicated to improve the efficiency of PFC labelling

7-T MRI tracking of mesenchymal stromal cells after lung injection in a rat model

Background: Mesenchymal stromal cells (MSCs) are able to migrate and engraft at sites of inflammation, injuries, and tumours, but little is known about their fate after local injection. The purpose of this study is to perform MSC tracking, combining in vivo 7-T magnetic resonance imaging (MRI) and histological assessment, following lung injection in a rat model. Methods: Five lungs were injected with ferumoxide-labelled MSCs and five with perfluorocarbon-labelled MSCs and underwent 7-T MRI. MRI acquisitions were recorded immediately (T0), at 24 h (T24) and/or 48 h (T48) after injection. For each rat, labelled cells were assessed in the main organs by MRI. Target organs were harvested under sterile conditions from rats sacrificed 0, 24, or 48 h after injection and fixed for histological analysis via confocal and structured illumination microscopy. Results: Ferumoxide-labelled MSCs were not detectable in the lungs, whereas they were not visible in the distant sites. Perfluorocarbon-labelled MSCs were seen in 5/5 injected lungs at T0, in 1/2 at T24, and in 1/3 at T48. The fluorine signal in the liver was seen in 3/5 at T0, in 1/2 at T24, and in 2/3 at T48. Post-mortem histology confirmed the presence of MSCs in the injected lung. Conclusions: Ferumoxide-labelled cells were not seen at distant sites; a linear decay of injected perfluorocarbon-labelled MSCs was observed at T0, T24, and T48 in the lung. In more than half of the experiments, perfluorocarbon-labelled MSCs scattering to the liver was observed, with a similar decay over time as observed in the lung

Simple parameters from complete blood count predict in-hospital mortality in covid-19

Introduction. The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods. In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results. At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ2 10.4; p < 0:001), neutrophil-to-lymphocyte (NL) ratio (χ2 7.6; p = 0:006), and platelet count (χ2 5.39; p = 0:02), along with age (χ2 87.6; p < 0:001) and gender (χ2 17.3; p < 0:001), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL ratio > 4:68 was characterized by an odds ratio for in-hospital mortality ðORÞ = 3:40 (2.40-4.82), while the OR for a RDW > 13:7% was 4.09 (2.87-5.83); a platelet count > 166,000/μL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion. Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment