Supervised and unsupervised learning reveal heroin-induced impairments in astrocyte structural plasticity.

Astrocytes regulate synaptic activity across large brain territories via their complex, interconnected morphology. Emerging evidence supports the involvement of astrocytes in shaping relapse to opioid use through morphological rearrangements in the nucleus accumbens (NAc). However, a comprehensive assessment of astrocyte structural diversity within and between NAc subdivisions is lacking because of limitations in existing methodologies to quantify meaningful alterations in astrocyte structure.

Functional vs Structural Cortical Deficit Pattern Biomarkers for Major Depressive Disorder.

Importance: Major depressive disorder (MDD) is a severe mental illness characterized more by functional rather than structural brain abnormalities. The pattern of regional homogeneity (ReHo) deficits in MDD may relate to underlying regional hypoperfusion. Capturing this functional deficit pattern provides a brain pattern-based biomarker for MDD that is linked to the underlying pathophysiology.

βIV spectrin abundancy, cellular distribution and sensitivity to AKT/GSK3 regulation in schizophrenia.

Schizophrenia (SCZ) is a complex psychiatric disorder with unclear biological mechanisms. Spectrins, cytoskeletal proteins linked to neurodevelopmental disorders, are regulated by the AKT/GSK3 pathway, which is implicated in SCZ. However, the impact of SCZ-related dysregulation of this pathway on spectrin expression and distribution remains unexplored.

Exploring the Granularity of the Illnesses-Related Changes in Regional Homogeneity in Major Depressive Disorder using the UKBB Data.

Illness related brain effects of neuropsychiatric disorders are not regionally uniform, with some regions showing large pathological effects while others are relatively spared. Presently, Big Data meta-analytic studies tabulate these effects using structural and/or functional brain atlases that are based on the anatomical boundaries, landmarks and connectivity patterns in healthy brains. These patterns are then translated to individual level predictors using approaches such as Regional Vulnerability Index (RVI), which quantifies the agreement between individual brain patterns and the canonical pattern found in the illness.

SPACe: an open-source, single-cell analysis of Cell Painting data.

Phenotypic profiling by high throughput microscopy, including Cell Painting, has become a leading tool for screening large sets of perturbations in cellular models. To efficiently analyze this big data, available open-source software requires computational resources usually not available to most laboratories. In addition, the cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused.

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 R, with d≪D, using deep neural networks.

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.

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.

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.

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.

Identifying and managing CAR T-cell-mediated toxicities: on behalf of an Italian CAR-T multidisciplinary team.

Introduction: Chimeric antigen receptor (CAR)-T-cell therapy is a new treatment for patients with hematologic malignancies in which other therapies have failed.

Areas Covered: The review provides an overview for recognizing and managing the most acute toxicities related to CAR-T cells.

Expert Opinion: The development of immune-mediated toxicities is a common challenge of CAR-T therapy.

Inhibition of AKT Signaling Alters βIV Spectrin Distribution at the AIS and Increases Neuronal Excitability.

The axon initial segment (AIS) is a highly regulated subcellular domain required for neuronal firing. Changes in the AIS protein composition and distribution are a form of structural plasticity, which powerfully regulates neuronal activity and may underlie several neuropsychiatric and neurodegenerative disorders. Despite its physiological and pathophysiological relevance, the signaling pathways mediating AIS protein distribution are still poorly studied.

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.

Efficacy and Effect of Inhaled Adenosine Treatment in Hospitalized COVID-19 Patients.

Lack of specific antiviral treatment for COVID-19 has resulted in long hospitalizations and high mortality rate. By harnessing the regulatory effects of adenosine on inflammatory mediators, we have instituted a new therapeutic treatment with inhaled adenosine in COVID-19 patients, with the aim of reducing inflammation, the onset of cytokine storm, and therefore to improve prognosis. The use of inhaled adenosine in COVID19 patients has allowed reduction of length of stay, on average 6 days.

Aggregometry and thromboelastography to identify the timing to trach a COVID-19 patient receiving both antiplatelet therapy and enoxaparin.

In COVID-19 patients receiving enoxaparin and antiplatelets therapy, aggregometry and thromboelastography might be considered an adjunctive tool to identify the time to perform procedures at risk of bleeding, such as tracheostomy.

Case Report: Canakinumab for the Treatment of a Patient With COVID-19 Acute Respiratory Distress Syndrome.

Severe cases of COVID-19 present with serious lung inflammation, acute respiratory distress syndrome and multiorgan damage. SARS-CoV-2 infection is associated with high cytokine levels, including interleukin-6 and certain subsets of immune cells, in particular, NK, distinguished according to the cell surface density of CD56. Cytokine levels are inversely correlated with lymphocyte count, therefore cytokine release syndrome may be an impediment to the adaptive immune response against SARS-CoV-2 infection.

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.

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue.

While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the CNS are still poorly understood. This knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics.

Imaging of the Axon Initial Segment.

The axon initial segment (AIS) is the first 20- to 60-μm segment of the axon proximal to the soma of a neuron. This highly specialized subcellular domain is the initiation site of the action potential and contains a high concentration of voltage-gated ion channels held in place by a complex nexus of scaffolding and regulatory proteins that ensure proper electrical activity of the neuron. Studies have shown that dysfunction of many AIS channels and scaffolding proteins occurs in a variety of neuropsychiatric and neurodegenerative diseases, raising the need to develop accurate methods for visualization and quantification of the AIS and its protein content in models of normal and disease conditions.

Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer.

Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons.

Background: Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures.

New Method: In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel.