Immuno-related cardio-vascular adverse events associated with immuno-oncological treatments: an under-estimated threat for cancer patients.

Immunotherapy represents an emergent and heterogeneous group of anticancer treatments harnessing the human immune-surveillance system, including immune-checkpoint inhibitor monoclonal antibodies (mAbs), Chimeric Antigen Receptor T Cells (CAR-T) therapy, cancer vaccines and lymphocyte activation gene-3 (LAG-3) therapy. While remarkably effective against several malignancies, these therapies, often in combination with other cancer treatments, have showed unforeseen toxicity, including cardiovascular complications. The occurrence of immuno-mediated adverse (irAEs) events has been progressively reported in the last 10 years. These irAEs present an extended range of severity, from self-limiting to life-threatening conditions. Although recent guidelines in CardioOncology have provided important evidence in managing cancer treatments, they often encompass general approaches. However, a specific focus is required due to the particular etiology, unique risk factors, and associated side effects of immunotherapy. This review aims to deepen the understanding of the prevalence and nature of cardiovascular issues in patients undergoing immunotherapy, offering insights into strategies for risk stratification and management.

Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis.

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV-Vis) stellar spectroscopy. The instrument-which is pixel-limited in the entire 300 nm-650 nm spectral range-features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system.

Platelet aggregometry for hip fracture surgery in patients treated with clopidogrel: a pilot study.

Surgery for hip fractures should be performed within 48 h from patient's admission. However, several factors including chronic antiplatelet therapy could delay operation. Among the totality of patients taking clopidogrel, up to 30% are resistant to the drug and have a normal platelets reactivity. We propose thromboelastography (TEG) with an ADP Platelet Mapping assay kit to assess platelet aggregation, a safe tool that could help to avoid surgery delay in those patients treated with antiplatelet therapy. A patient's blood sample was collected for aggregometry. If MA-ADP and platelets aggregation (%) were within normal values, the patient was fit for immediate surgery with neuraxial anesthesia and ultrasound-guided nerve block. If one of the two parameters or both were deranged, a mortality risk assessment was estimated. In the low risk category, the patients waited till normalization of the parameters, whereas in the high-risk group a general anesthesia and peripheral antalgic block was carried out. Nine patients were enrolled. Four of them showed normal aggregometry and surgery was performed within 24 h from admission. Two patients were classified as high mortality risk and surgery was carried out under general anesthesia. Three patients awaited operation till normalization of parameters. No peri or post-operative complications were reported. An aggregometry-guided protocol can safely expedite hip fracture surgery in patients taking clopidogrel. Nonetheless, in presence of a normal platelets function, clinician can opt for a neuraxial instead of general anesthesia reducing the incidence of postoperative delirium and cognitive dysfunction.Trial registration: prospectively registered at clinicaltrials.gov (NCT04642209; date of registration: 23rd November 2020).

Fat mass affects nutritional status of ICU COVID-19 patients.

BACKGROUND: Obesity and steatosis are associated with COVID-19 severe pneumonia. Elevated levels of pro-inflammatory cytokines and reduced immune response are typical of these patients. In particular, adipose tissue is the organ playing the crucial role. So, it is necessary to evaluate fat mass and not simpler body mass index (BMI), because BMI leaves a portion of the obese population unrecognized. The aim is to evaluate the relationship between Percentage of Fat Mass (FM%) and immune-inflammatory response, after 10 days in Intensive Care Unit (ICU). METHODS: Prospective observational study of 22 adult patients, affected by COVID-19 pneumonia and admitted to the ICU and classified in two sets: (10) lean and (12) obese, according to FM% and age (De Lorenzo classification). Patients were analyzed at admission in ICU and at 10th day. RESULTS: Obese have steatosis, impaired hepatic function, compromise immune response and higher inflammation. In addition, they have a reduced prognostic nutritional index (PNI), nutritional survival index for ICU patients. CONCLUSION: This is the first study evaluating FM% in COVID-19 patient. We underlined obese characteristic with likely poorly prognosis and an important misclassification of obesity. A not negligible number of patients with normal BMI could actually have an excess of adipose tissue and therefore have an unfavorable outcome such as an obese. Is fundamental personalized patients nutrition basing on disease phases.

The Nav1.2 channel is regulated by GSK3.

BACKGROUND: Phosphorylation plays an essential role in regulating voltage-gated sodium (Na(v)) channels and excitability. Yet, a surprisingly limited number of kinases have been identified as regulators of Na(v) channels. We posited that glycogen synthase kinase 3 (GSK3), a critical kinase found associated with numerous brain disorders, might directly regulate neuronal Na(v) channels. METHODS: We used patch-clamp electrophysiology to record sodium currents from Na(v)1.2 channels stably expressed in HEK-293 cells. mRNA and protein levels were quantified with RT-PCR, Western blot, or confocal microscopy, and in vitro phosphorylation and mass spectrometry to identify phosphorylated residues. RESULTS: We found that exposure of cells to GSK3 inhibitor XIII significantly potentiates the peak current density of Na(v)1.2, a phenotype reproduced by silencing GSK3 with siRNA. Contrarily, overexpression of GSK3ß suppressed Na(v)1.2-encoded currents. Neither mRNA nor total protein expression was changed upon GSK3 inhibition. Cell surface labeling of CD4-chimeric constructs expressing intracellular domains of the Na(v)1.2 channel indicates that cell surface expression of CD4-Na(v)1.2 C-tail was up-regulated upon pharmacological inhibition of GSK3, resulting in an increase of surface puncta at the plasma membrane. Finally, using in vitro phosphorylation in combination with high resolution mass spectrometry, we further demonstrate that GSK3ß phosphorylates T(1966) at the C-terminal tail of Na(v)1.2. CONCLUSION: These findings provide evidence for a new mechanism by which GSK3 modulates Na(v) channel function via its C-terminal tail. GENERAL SIGNIFICANCE: These findings provide fundamental knowledge in understanding signaling dysfunction common in several neuropsychiatric disorders.

Low dimensional approximation and generalization of multivariate functions on smooth manifolds using deep ReLU neural networks.

The expressive power of deep neural networks is manifested by their remarkable ability to approximate multivariate functions in a way that appears to overcome the curse of dimensionality. This ability is exemplified by their success in solving high-dimensional problems where traditional numerical solvers fail due to their limitations in accurately representing high-dimensional structures. To provide a theoretical framework for explaining this phenomenon, we analyze the approximation of Hölder functions defined on a d-dimensional smooth manifold M embedded in RD, with d≪D, using deep neural networks. We prove that the uniform convergence estimates of the approximation and generalization errors by deep neural networks with ReLU activation functions do not depend on the ambient dimension D of the function but only on its lower manifold dimension d, in a precise sense. Our result improves existing results from the literature where approximation and generalization errors were shown to depend weakly on D.

SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data.

Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software ( i.e. , CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe ( S wift P henotypic A nalysis of Ce lls), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover's Distance - EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery.

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Automated detection of GFAP-labeled astrocytes in micrographs using YOLOv5.

Astrocytes, a subtype of glial cells with a complex morphological structure, are active players in many aspects of the physiology of the central nervous system (CNS). However, due to their highly involved interaction with other cells in the CNS, made possible by their morphological complexity, the precise mechanisms regulating astrocyte function within the CNS are still poorly understood. This knowledge gap is also due to the current limitations of existing quantitative image analysis tools that are unable to detect and analyze images of astrocyte with sufficient accuracy and efficiency. To address this need, we introduce a new deep learning framework for the automated detection of GFAP-immunolabeled astrocytes in brightfield or fluorescent micrographs. A major novelty of our approach is the applications of YOLOv5, a sophisticated deep learning platform designed for object detection, that we customized to derive optimized classification models for the task of astrocyte detection. Extensive numerical experiments using multiple image datasets show that our method performs very competitively against both conventional and state-of-the-art methods, including the case of images where astrocytes are very dense. In the spirit of reproducible research, our numerical code and annotated data are released open source and freely available to the scientific community.

A Learning Based Framework for Disease Prediction from Images of Human-Derived Pluripotent Stem Cells of Schizophrenia Patients.

Human induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new available information though, there is still a critical need to establish quantifiable and accessible molecular markers that can be used to reveal the biological causality of the disease. In this paper, we introduce a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of hiPSCs of schizophrenia (SCZ) patients. We show that, by using Support Vector Machines or selected Artificial Neural Networks trained on image-based features associated with somas of hiPSCs derived neurons, we can predict very reliably SCZ and healthy control cells. In addition, our method reveals that [Formula: see text]III tubulin and FGF12, two critical components of the cytoskeleton, are differentially regulated in SCZ and healthy control cells, upon perturbation by GSK3 inhibition.