Recruited macrophages elicit atrial fibrillation.

Atrial fibrillation disrupts contraction of the atria, leading to stroke and heart failure. We deciphered how immune and stromal cells contribute to atrial fibrillation. Single-cell transcriptomes from human atria documented inflammatory monocyte and SPP1+ macrophage expansion in atrial fibrillation. Combining hypertension, obesity, and mitral valve regurgitation (HOMER) in mice elicited enlarged, fibrosed, and fibrillation-prone atria. Single-cell transcriptomes from HOMER mouse atria recapitulated cell composition and transcriptome changes observed in patients. Inhibiting monocyte migration reduced arrhythmia in Ccr2-∕- HOMER mice. Cell-cell interaction analysis identified SPP1 as a pleiotropic signal that promotes atrial fibrillation through cross-talk with local immune and stromal cells. Deleting Spp1 reduced atrial fibrillation in HOMER mice. These results identify SPP1+ macrophages as targets for immunotherapy in atrial fibrillation.

[Intraaortic Ballon-pump: Physiology, Indication, Management]. / Intraaortale Ballonpumpe: Physiologie, Indikationen, Management.

The intraaortic ballon-pump (IABP) is a percutaneous mechanical circulatory support device, which is used in patients either with insufficient cardiac output or in patients with high-risk situation before cardiac intervention, like surgical revascularisation or percutaneous coronary intervention (PCI). Due to electrocardiographic or arterial pressure pulse the IABP augments diastolic coronary perfusion pressure and reduces systolic afterload. Thereby, myocardial oxygen supply-demand ratio is improved and cardiac output is increased. Many national and international cardiology, cardiothoracic and intensive care medicine societies and associations worked together in order to develop evidence-based recommendations and guidelines for the preoperative, intraoperative and postoperative management of the IABP. This manuscript is mainly based on the S3 guideline "Use of intraaortic balloon-pump in cardiac surgery" from the German Society for Thoracic and Cardiovascular Surgery (DGTHG).

Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization.

Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composition. We found that by combining in situ hybridization (ISH) images with cell-type-specific transcriptomes, position-dependent cortical composition in P56 mouse could be estimated in the somatosensory cortex. The method uses ISH images from the Allen Institute for Brain Science. There are two novel aspects of the methodology. First, it is not necessary to select a subset of genes that are particular for a cell type of interest, nor is it necessary to only use ISH images with low variability among samples. Second, the method also compensated for differences in soma size and incompleteness of the transcriptomes. The soma size compensation is particularly important in order to obtain quantitative estimates since relying on bulk expression alone would overestimate the contribution of larger cells. Predicted distributions of broader classes of cell types agreed with literature distributions. The primary result is that there is a high degree of substructure in the distribution of transcriptomic types beyond the resolution of layers. Furthermore, transcriptomic cell types each exhibited characteristic soma size distributions. Results suggest that the method could also be employed to assign transcriptomic cell types to well-aligned image sets in the entire brain.

Mapping of morpho-electric features to molecular identity of cortical inhibitory neurons.

Knowledge of the cell-type-specific composition of the brain is useful in order to understand the role of each cell type as part of the network. Here, we estimated the composition of the whole cortex in terms of well characterized morphological and electrophysiological inhibitory neuron types (me-types). We derived probabilistic me-type densities from an existing atlas of molecularly defined cell-type densities in the mouse cortex. We used a well-established me-type classification from rat somatosensory cortex to populate the cortex. These me-types were well characterized morphologically and electrophysiologically but they lacked molecular marker identity labels. To extrapolate this missing information, we employed an additional dataset from the Allen Institute for Brain Science containing molecular identity as well as morphological and electrophysiological data for mouse cortical neurons. We first built a latent space based on a number of comparable morphological and electrical features common to both data sources. We then identified 19 morpho-electrical clusters that merged neurons from both datasets while being molecularly homogeneous. The resulting clusters best mirror the molecular identity classification solely using available morpho-electrical features. Finally, we stochastically assigned a molecular identity to a me-type neuron based on the latent space cluster it was assigned to. The resulting mapping was used to derive inhibitory me-types densities in the cortex.

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience.

Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.g. neurons and glia) and even multicellular scales of resolution (e.g. cerebral vasculature and minicolumns). Resulting models are exported as triangulated surface meshes and annotated volumes for multiple applications in in silico neuroscience, allowing scalable supercomputer simulations that can unravel intricate cellular structure-function relationships. Ultraliser implements a high-performance and unconditionally robust voxelization engine adapted to create optimized watertight surface meshes and annotated voxel grids from arbitrary non-watertight triangular soups, digitized morphological skeletons or binary volumetric masks. The framework represents a major leap forward in simulation-based neuroscience, making it possible to employ high-resolution 3D structural models for quantification of surface areas and volumes, which are of the utmost importance for cellular and system simulations. The power of Ultraliser is demonstrated with several use cases in which hundreds of models are created for potential application in diverse types of simulations. Ultraliser is publicly released under the GNU GPL3 license on GitHub (BlueBrain/Ultraliser). SIGNIFICANCE: There is crystal clear evidence on the impact of cell shape on its signaling mechanisms. Structural models can therefore be insightful to realize the function; the more realistic the structure can be, the further we get insights into the function. Creating realistic structural models from existing ones is challenging, particularly when needed for detailed subcellular simulations. We present Ultraliser, a neuroscience-dedicated framework capable of building these structural models with realistic and detailed cellular geometries that can be used for simulations.

A method to estimate the cellular composition of the mouse brain from heterogeneous datasets.

The mouse brain contains a rich diversity of inhibitory neuron types that have been characterized by their patterns of gene expression. However, it is still unclear how these cell types are distributed across the mouse brain. We developed a computational method to estimate the densities of different inhibitory neuron types across the mouse brain. Our method allows the unbiased integration of diverse and disparate datasets into one framework to predict inhibitory neuron densities for uncharted brain regions. We constrained our estimates based on previously computed brain-wide neuron densities, gene expression data from in situ hybridization image stacks together with a wide range of values reported in the literature. Using constrained optimization, we derived coherent estimates of cell densities for the different inhibitory neuron types. We estimate that 20.3% of all neurons in the mouse brain are inhibitory. Among all inhibitory neurons, 18% predominantly express parvalbumin (PV), 16% express somatostatin (SST), 3% express vasoactive intestinal peptide (VIP), and the remainder 63% belong to the residual GABAergic population. We find that our density estimations improve as more literature values are integrated. Our pipeline is extensible, allowing new cell types or data to be integrated as they become available. The data, algorithms, software, and results of our pipeline are publicly available and update the Blue Brain Cell Atlas. This work therefore leverages the research community to collectively converge on the numbers of each cell type in each brain region.

Physical Therapist Telehealth Delivery at 1 Year Into COVID-19.

OBJECTIVE: The purpose of this study was to examine telehealth physical therapy utilization 1 year into the COVID-19 pandemic and identify factors that influence physical therapists' delivery of telehealth in an urban academic medical center. METHODS: Electronic medical record data were extracted within the dates of interest (March 22, 2021 to May 15, 2021), the proportion of physical therapy sessions delivered via telehealth were identified, and patient characteristics were compared by telehealth volume (0 vs ≥1 session, 1 vs >1 session). Qualitative data also were collected from physical therapists via semi-structured interviews, and a directed content analysis was conducted, informed by the Capability, Opportunity, Motivation, and Behavior model, to identify factors influencing telehealth delivery. RESULTS: Telehealth was used for 3793 of 8038 (47.2%) physical therapist sessions, and 1028 unique patients had at least 2 physical therapist sessions (without telehealth: 6.6% [n = 68], telehealth once: 39.1% [n = 402], telehealth more than once: 54.3% [n = 558]). Patients without telehealth were older, non-English speaking, had non-commercial insurance, and had at least 1 chronic health condition. Patients with telehealth more than once had a neurologic diagnosis and lived farther from the treating clinic. Capabilities that influenced telehealth delivery were physical therapist clinical skills and knowledge, technical proficiency, telehealth-specific interpersonal skills, and cognitive flexibility. Factors external to physical therapists-including the environment, patient equipment and technology proficiency, physical therapist equipment, clinic factors, and patient and referring provider perspectives-also influenced telehealth delivery. Finally, patient needs and telehealth as a beneficial tool guided physical therapist intention to use telehealth. CONCLUSION: Sustained telehealth utilization outcomes 1 year into the COVID-19 pandemic and an interaction among physical therapist, patient, and environmental factors support the long-term potential of telehealth physical therapy in an urban academic medical center. IMPACT: These findings support the long-term potential of telehealth approaches and can be used to inform telehealth physical therapist training programs and clinical implementation, future research, and health policy.

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Ruptured Tubo-Ovarian Pregnancy Presenting at 15 Weeks' Gestation.

Ectopic pregnancies develop outside of the uterus and lead to significant maternal morbidity and mortality if they rupture. As the primary diagnostic tool for these presentations, ultrasound has a growing list of signs and measurements that help distinguish between intrauterine and ectopic pregnancies, the latter being exceedingly rare once a woman has entered her second trimester. The present case reports a series of Emergency Department visits by a woman carrying a second-trimester pregnancy-deemed intrauterine on transabdominal ultrasound due to gestational age and location-who developed massive hemoperitoneum and was diagnosed with a ruptured 15-week tubo-ovarian pregnancy on laparotomy. The discussion describes the sonographic findings that could have helped make the proper diagnosis, most notably mantle distance-the thickness of the myometrium surrounding the gestational sac-which would have correctly identified this pregnancy as ectopic.

Representing stimulus information in an energy metabolism pathway.

We explored a computational model of astrocytic energy metabolism and demonstrated the theoretical plausibility that this type of pathway might be capable of coding information about stimuli in addition to its known functions in cellular energy and carbon budgets. Simulation results indicate that glycogenolytic glycolysis triggered by activation of adrenergic receptors can capture the intensity and duration features of a neuromodulator waveform and can respond in a dose-dependent manner, including non-linear state changes that are analogous to action potentials. We show how this metabolic pathway can translate information about external stimuli to production profiles of energy-carrying molecules such as lactate with a precision beyond simple signal transduction or non-linear amplification. The results suggest the operation of a metabolic state-machine from the spatially discontiguous yet interdependent metabolite elements. Such metabolic pathways might be well-positioned to code an additional level of salient information about a cell's environmental demands to impact its function. Our hypothesis has implications for the computational power and energy efficiency of the brain.

A Modular Workflow for Model Building, Analysis, and Parameter Estimation in Systems Biology and Neuroscience.

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model.

Participation of rural patients in clinical trials at a multisite academic medical center.

OBJECTIVE: Clinical trials, which are mainly conducted in urban medical centers, may be less accessible to rural residents. Our aims were to assess participation and the factors associated with participation of rural residents in clinical trials. METHODS: Using geocoding, the residential address of participants enrolled into clinical trials at Mayo Clinic locations in Arizona, Florida, and the Midwest between January 1, 2016, and December 31, 2017, was categorized as urban or rural. The distance travelled by participants and trial characteristics was compared between urban and rural participants. Ordinal logistic regression analyses were used to evaluate whether study location and risks were associated with rural participation in trials. RESULTS: Among 292 trials, including 136 (47%) cancer trials, there were 2313 participants. Of these, 731 (32%) were rural participants, which is greater than the rural population in these 9 states (19%, P < 0.001). Compared to urban participants, rural participants were older (65 ± 12 years vs 64 ± 12 years, P = 0.004) and travelled further to the medical center (103 ± 104 vs 68 ± 88 miles, P < 0.001). The proportion of urban and rural participants who were remunerated was comparable. In the multivariable analysis, the proportion of rural participants was lower (P < 0.001) in Arizona (10%) and Florida (18%) than the Midwest (38%) but not significantly associated with the study-related risks. CONCLUSIONS: Approximately one in three clinical trial participants were rural residents versus one in five in the population. Rural residents travelled further to access clinical trials. The study-associated risks were not associated with the distribution of rural and urban participants in trials.

A Standardized Brain Molecular Atlas: A Resource for Systems Modeling and Simulation.

Accurate molecular concentrations are essential for reliable analyses of biochemical networks and the creation of predictive models for molecular and systems biology, yet protein and metabolite concentrations used in such models are often poorly constrained or irreproducible. Challenges of using data from different sources include conflicts in nomenclature and units, as well as discrepancies in experimental procedures, data processing and implementation of the model. To obtain a consistent estimate of protein and metabolite levels, we integrated and normalized data from a large variety of sources to calculate Adjusted Molecular Concentrations. We found a high degree of reproducibility and consistency of many molecular species across brain regions and cell types, consistent with tight homeostatic regulation. We demonstrated the value of this normalization with differential protein expression analyses related to neurodegenerative diseases, brain regions and cell types. We also used the results in proof-of-concept simulations of brain energy metabolism. The standardized Brain Molecular Atlas overcomes the obstacles of missing or inconsistent data to support systems biology research and is provided as a resource for biomolecular modeling.

"His lack of a mask ruined everything." Restaurant customer satisfaction during the COVID-19 outbreak: An analysis of Yelp review texts and star-ratings.

The aim of the study was to provide practical advice to restaurant managers for improving star ratings as well as information for researchers on how the pandemic has impacted established determinants of satisfaction. The study examined criteria used by restaurant customers in assigning star-ratings on Yelp during the COVID-19 pandemic using keyword analysis and Multiple Correspondence Analysis. In evaluating restaurants, the reviewers focused on service, overall experience, and food quality. Service was discussed in relation to the pandemic and included safety of the dine-in experience, contrasted with take-out options and compliance with COVID-19 guidelines. These criteria applied differently with lower-star reviews focusing on safety, social distancing, and mask policies. Higher-star reviews focused on take-out/delivery services, high-quality food, and an overall positive experience. The study provides valuable contributions to our understanding of how the COVID-19 pandemic will impact the restaurant sector in a post-pandemic world.

Increased Utilization of Virtual Visits and Electronic Approaches in Clinical Research During the COVID-19 Pandemic and Thereafter.

OBJECTIVES: To assess the impact of the COVID-19 pandemic on clinical research and the use of electronic approaches to mitigate this impact. METHODS: We compared the utilization of electronic consenting, remote visits, and remote monitoring by study monitors in all research studies conducted at Mayo Clinic sites (Arizona, Florida, and Minnesota) before and during the COVID-19 pandemic (ie, between May 1, 2019 and December 31, 2020). Participants are consented through a participant-tracking system linked to the electronic health record. RESULTS: Between May 2019, and December 2020, there were 130,800 new consents across every modality (electronic and paper) to participate in a non-trial (107,176 [82%]) or a clinical trial (23,624 [18%]). New consents declined from 5741 in February 2020 to 913 in April 2020 but increased to 11,864 in November 2020. The mean (standard deviation [SD]) proportion of electronic consent increased from 22 (2%) before to 45 (20%) during the pandemic (P=.001). Mean (SD) remote electronic consenting increased from 0.3 (0.5%) to 29 (21%) (P<.001). The mean (SD) number of patients with virtual visits increased from 3.5 (2.4%) to 172 (135%) (P=.003) per month between pre-COVID (July 2019 to February 2020) and post-COVID (March to December 2020) periods. Virtual visits used telemedicine (68%) or video (32%). Requests for remote monitor access to complete visits increased from 44 (17%) per month between May 2019 and February 2020 to 111 (74%) per month between March and December 2020 (P=.10). CONCLUSION: After a sharp early decline, the enrollment of new participants and ongoing study visits recovered during the COVID-19 pandemic. This recovery was accompanied by the increased use of electronic tools.

Digital Reconstruction of the Neuro-Glia-Vascular Architecture.

Astrocytes connect the vasculature to neurons mediating the supply of nutrients and biochemicals. They are involved in a growing number of physiological and pathophysiological processes that result from biophysical, physiological, and molecular interactions in this neuro-glia-vascular ensemble (NGV). The lack of a detailed cytoarchitecture severely restricts the understanding of how they support brain function. To address this problem, we used data from multiple sources to create a data-driven digital reconstruction of the NGV at micrometer anatomical resolution. We reconstructed 0.2 mm3 of the rat somatosensory cortex with 16 000 morphologically detailed neurons, 2500 protoplasmic astrocytes, and its microvasculature. The consistency of the reconstruction with a wide array of experimental measurements allows novel predictions of the NGV organization, allowing the anatomical reconstruction of overlapping astrocytic microdomains and the quantification of endfeet connecting each astrocyte to the vasculature, as well as the extent to which they cover the latter. Structural analysis showed that astrocytes optimize their positions to provide uniform vascular coverage for trophic support and signaling. However, this optimal organization rapidly declines as their density increases. The NGV digital reconstruction is a resource that will enable a better understanding of the anatomical principles and geometric constraints, which govern how astrocytes support brain function.

A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19.

SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.

Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas.

The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task encumbered by the diversity of staining techniques employed, and by deformations and artifacts in the soft tissue. Recently, deep learning models have garnered particular interest as a viable alternative to traditional intensity-based algorithms for image registration. In this work, we propose a supervised learning model for general multimodal 2D registration tasks, trained with a perceptual similarity loss on a dataset labeled by a human expert and augmented by synthetic local deformations. We demonstrate the results of our approach on the Allen Mouse Brain Atlas (AMBA), comprising whole brain Nissl and gene expression stains. We show that our framework and design of the loss function result in accurate and smooth predictions. Our model is able to generalize to unseen gene expressions and coronal sections, outperforming traditional intensity-based approaches in aligning complex brain structures.

Metaball skinning of synthetic astroglial morphologies into realistic mesh models for visual analytics and in silico simulations.

MOTIVATION: Astrocytes, the most abundant glial cells in the mammalian brain, have an instrumental role in developing neuronal circuits. They contribute to the physical structuring of the brain, modulating synaptic activity and maintaining the blood-brain barrier in addition to other significant aspects that impact brain function. Biophysically, detailed astrocytic models are key to unraveling their functional mechanisms via molecular simulations at microscopic scales. Detailed, and complete, biological reconstructions of astrocytic cells are sparse. Nonetheless, data-driven digital reconstruction of astroglial morphologies that are statistically identical to biological counterparts are becoming available. We use those synthetic morphologies to generate astrocytic meshes with realistic geometries, making it possible to perform these simulations. RESULTS: We present an unconditionally robust method capable of reconstructing high fidelity polygonal meshes of astroglial cells from algorithmically-synthesized morphologies. Our method uses implicit surfaces, or metaballs, to skin the different structural components of astrocytes and then blend them in a seamless fashion. We also provide an end-to-end pipeline to produce optimized two- and three-dimensional meshes for visual analytics and simulations, respectively. The performance of our pipeline has been assessed with a group of 5000 astroglial morphologies and the geometric metrics of the resulting meshes are evaluated. The usability of the meshes is then demonstrated with different use cases. AVAILABILITY AND IMPLEMENTATION: Our metaball skinning algorithm is implemented in Blender 2.82 relying on its Python API (Application Programming Interface). To make it accessible to computational biologists and neuroscientists, the implementation has been integrated into NeuroMorphoVis, an open source and domain specific package that is primarily designed for neuronal morphology visualization and meshing. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Panton-Valentine Leucocidin of Staphylococcus aureus Induces Oxidative Stress and Neurotransmitter Imbalance in a Retinal Explant Model.

Purpose: Endophthalmitis models have reported the virulent role of Panton-Valentine leucocidin (PVL) secreted by Staphylococcus aureus on the retina. PVL targets retinal ganglion cells (RGCs), expressing PVL membrane receptor C5aR. Interactions between PVL and retinal cells lead to glial activation, retinal inflammation, and apoptosis. In this study, we explored oxidative stress and retinal neurotransmitters in a rabbit retinal explant model incubated with PVL. Methods: Reactive oxygen species (ROS) production in RGCs has been assessed with fluorescent probes and immunohistochemistry. Nuclear magnetic resonance (NMR) spectroscopy quantified retinal concentrations of antioxidant molecules and neurotransmitters, and concentrations of neurotransmitters released in the culture medium. Quantifying the expression of some pro-inflammatory and anti-inflammatory factors was performed using RT-qPCR. Results: PVL induced a mitochondrial ROS production in RGCs after four hours' incubation with the toxin. Enzymatic sources of ROS, involving nicotinamide adenine dinucleotide phosphate-oxidase and xanthine oxidase, were also activated after four hours in PVL-treated retinal explants. Retinal antioxidants defenses, that is, glutathione, ascorbate and taurine, decreased after two hours' incubation with PVL. Glutamate retinal concentrations and glutamate release in the culture medium remained unaltered in PVL-treated retinas. GABA, glycine, and acetylcholine (Ach) retinal concentrations decreased after PVL treatment. Glycine release in the culture medium decreased, whereas Ach release increased after PVL treatment. Expression of proinflammatory and anti-inflammatory cytokines remained unchanged in PVL-treated explants. Conclusions: PVL activates oxidative pathways and alters neurotransmitter retinal concentrations and release, supporting the hypothesis that PVL could induce a neurogenic inflammation in the retina.