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Purpose: Growth hormone (GH) has neuroprotective effects that have not been evaluated in the mammalian visual system. This study tested the hypothesis that GH administration can promote retinal neuroprotection in an optic nerve crush (ONC) model in male rats., Methods: The ON was compressed for 10 seconds, and bovine GH was injected concomitantly to injury for 14 days (0.5 µg/g every 12 hours). At 24 hours and 14 days after ONC, we evaluated the effects of GH upon several markers by quantitative PCR (qPCR), Western blot, and immunohistochemistry; the ON integrity was assessed using CTB Alexa 488 anterograde tracer, and retinal function was tested by full-field electroretinogram., Results: GH partially prevented the ONC-induced death of retinal ganglion cells (RGCs), as well as the increase in gliosis marker GFAP at 14 days. Most of the ONC-induced changes in mRNA retinal levels of several neurotrophic, survival, synaptogenic, gliosis, and excitotoxicity markers were prevented by GH, both at 24 hours and 14 days, and treatment also stimulated the expression of antiapoptotic proteins Bcl-2 and Bcl-xL at 24 hours. Additionally, GH partially maintained the ON integrity and active anterograde transport, as well as retinal function by avoiding the reduced amplitude and slowing of the A- and B-waves and oscillatory potentials associated with the ONC at 14 days., Conclusions: GH has neuroprotective effects in the ONC model in male rats, it promoted RGC survival, gliosis reduction, and axonal transport increase, likely through the regulation of genes involved in neuroprotection, survival, and synaptogenesis. Furthermore, GH prevented functional impairment, indicating its potential as a therapeutic option for retinal neurodegenerative diseases.

Basal electroretinogram (ERG) oscillations have shown predictive value for modifiable risk factors for type 2 diabetes. However, their origin remains unknown. Here, we seek to establish the pharmacological profile of the low delta-like (δ1) wave in the mouse because it shows light sensitivity in the form of a decreased peak frequency upon photopic exposure. Applying neuropharmacological drugs by intravitreal injection, we eliminated the δ1 wave using lidocaine or by blocking all chemical and electrical synapses. The δ1 wave was insensitive to the blockade of photoreceptor input, but was accelerated when all inhibitory or ionotropic inhibitory receptors in the retina were antagonized. The sole blockade of GABA A , GABA B , GABA C , and glycine receptors also accelerated the δ1 wave. In contrast, the gap junction blockade slowed the δ1 wave. Both GABA A receptors and gap junctions contribute to the light sensitivity of the δ1 wave. We further found that the day light-activated neuromodulators dopamine and nitric oxide donors mimicked the effect of photopic exposure on the δ1 wave. All drug effects were validated through light flash-evoked ERG responses. Our data indicate that the low δ-like intrinsic wave detected by the non-photic ERG arises from an inner retinal circuit regulated by inhibitory neurotransmission and nitric oxide/dopamine-sensitive gap junction-mediated communication., Competing Interests: Declaration of Competing interest All authors declare having no financial/personal interest or belief that could affect their objectivity in this study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Diabetic Macular Edema (DME) is the most common sight-threatening complication of type 2 diabetes. Optical Coherence Tomography (OCT) is the most useful imaging technique to diagnose, follow up, and evaluate treatments for DME. However, OCT exam and devices are expensive and unavailable in all clinics in low- and middle-income countries. Our primary goal was therefore to develop an alternative method to OCT for DME diagnosis by introducing spectral information derived from spontaneous electroretinogram (ERG) signals as a single input or combined with fundus that is much more widespread. Baseline ERGs were recorded in 233 patients and transformed into scalograms and spectrograms via Wavelet and Fourier transforms, respectively. Using transfer learning, distinct Convolutional Neural Networks (CNN) were trained as classifiers for DME using OCT, scalogram, spectrogram, and eye fundus images. Input data were randomly split into training and test sets with a proportion of 80 %-20 %, respectively. The top performers for each input type were selected, OpticNet-71 for OCT, DenseNet-201 for eye fundus, and non-evoked ERG-derived scalograms, to generate a combined model by assigning different weights for each of the selected models. Model validation was performed using a dataset alien to the training phase of the models. None of the models powered by mock ERG-derived input performed well. In contrast, hybrid models showed better results, in particular, the model powered by eye fundus combined with mock ERG-derived information with a 91 % AUC and 86 % F1-score, and the model powered by OCT and mock ERG-derived scalogram images with a 93 % AUC and 89 % F1-score. These data show that the spontaneous ERG-derived input adds predictive value to the fundus- and OCT-based models to diagnose DME, except for the sensitivity of the OCT model which remains the same. The inclusion of mock ERG signals, which have recently been shown to take only 5 min to record in daylight conditions, therefore represents a potential improvement over existing OCT-based models, as well as a reliable and cost-effective alternative when combined with the fundus, especially in underserved areas, to predict DME., Competing Interests: Declaration of competing interest The authors of this study declare no conflict of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities., Competing Interests: JC is employee of RBI and founder of the Luxi Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. MAG-G was coordinator of the research topic collection “Women in neuroscience of Bioelectronic Medicine”, and SCT, MET and SVC were editors of the same research topic collection. This had no impact on the peer review process and the final decision., (Copyright © 2024 González-González, Conde, Latorre, Thébault, Pratelli, Spitzer, Verkhratsky, Tremblay, Akcora, Hernández-Reynoso, Ecker, Coates, Vincent and Ma.)

Targeted electric signal use for disease diagnostics and treatment is emerging as a healthcare game-changer. Besides arrhythmias, treatment-resistant epilepsy and chronic pain, blindness, and perhaps soon vision loss, could be among the pathologies that benefit from bioelectronic medicine. The electroretinogram (ERG) technique has long demonstrated its role in diagnosing eye diseases and early stages of neurodegenerative diseases. Conspicuously, ERG applications are all based on light-induced responses. However, spontaneous, intrinsic activity also originates in retinal cells. It is a hallmark of degenerated retinas and its alterations accompany obesity and diabetes. To the extent that variables extracted from the resting activity of the retina measured by ERG allow the predictive diagnosis of risk factors for type 2 diabetes. Here, we provided a comparison of the baseline characteristics of intrinsic oscillatory activity recorded by ERGs in mice, rats, and humans, as well as in several rat strains, and explore whether zebrafish exhibit comparable activity. Their pattern was altered in neurodegenerative models including the cuprizone-induced demyelination model in mice as well as in the Royal College of Surgeons (RCS -/- ) rats. We also discuss how the study of their properties may pave the way for future research directions and treatment approaches for retinopathies, among others., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Rodríguez-Arzate, Noguez-Imm, Reyes-Ortega, Rodríguez-Ortiz, García-Peña, Ordaz, Vélez-Uriza, Cisneros-Mejorado, Arellano, Pérez, Hernández-Zimbrón, Dégardin, Simonutti, Picaud and Thébault.)

Motivation: Next-generation sequencing (NGS) technologies are decisive for discovering disease-causing variants, although their cost limits their utility in a clinical setting. A cost-mitigating alternative is an extremely low coverage whole-genome sequencing (XLC-WGS). We investigated its use to identify causal variants within a multi-generational pedigree of individuals with retinitis pigmentosa (RP). Causing progressive vision loss, RP is a group of genetically heterogeneous eye disorders with approximately 60 known causal genes., Results: We performed XLC-WGS in seventeen members of this pedigree, including three individuals with a confirmed diagnosis of RP. Sequencing data were processed using Illumina's DRAGEN pipeline and filtered using Illumina's genotype quality score metric (GQX). The resulting variants were analyzed using Expert Variant Interpreter (eVai) from enGenome as a prioritization tool. A nonsense known mutation (c.1625C > G; p.Ser542*) in exon 4 of the RP1 gene emerged as the most likely causal variant. We identified two homozygous carriers of this variant among the three sequenced RP cases and three heterozygous individuals with sufficient coverage of the RP1 locus. Our data show the utility of combining pedigree information with XLC-WGS as a cost-effective approach to identify disease-causing variants., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Given the ever-increasing prevalence of type 2 diabetes and obesity, the pressure on global healthcare is expected to be colossal, especially in terms of blindness. Electroretinogram (ERG) has long been perceived as a first-use technique for diagnosing eye diseases, and some studies suggested its use for preventable risk factors of type 2 diabetes and thereby diabetic retinopathy (DR). Here, we show that in a non-evoked mode, ERG signals contain spontaneous oscillations that predict disease cases in rodent models of obesity and in people with overweight, obesity, and metabolic syndrome but not yet diabetes, using one single random forest-based model. Classification performance was both internally and externally validated, and correlation analysis showed that the spontaneous oscillations of the non-evoked ERG are altered before oscillatory potentials, which are the current gold-standard for early DR. Principal component and discriminant analysis suggested that the slow frequency (0.4-0.7 Hz) components are the main discriminators for our predictive model. In addition, we established that the optimal conditions to record these informative signals, are 5-minute duration recordings under daylight conditions, using any ERG sensors, including ones working with portative, non-mydriatic devices. Our study provides an early warning system with promising applications for prevention, monitoring and even the development of new therapies against type 2 diabetes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Noguez Imm et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The transient receptor potential vanilloid 4 (TRPV4) ion channel is present in different tissues including those of the airways. This channel is activated in response to stimuli such as changes in temperature, hypoosmotic conditions, mechanical stress, and chemicals from plants, lipids, and others. TRPV4's overactivity and/or dysfunction has been associated with several diseases, such as skeletal dysplasias, neuromuscular disorders, and lung pathologies such as asthma and cardiogenic lung edema and COVID-19-related respiratory malfunction. TRPV4 antagonists and blockers have been described; nonetheless, the mechanisms involved in achieving inhibition of the channel remain scarce, and the search for safe use of these molecules in humans continues. Here, we show that the widely used bronchodilator salbutamol and other ligands of β-adrenergic receptors inhibit TRPV4's activation. We also demonstrate that inhibition of TRPV4 by salbutamol is achieved through interaction with two residues located in the outer region of the pore and that salbutamol leads to channel closing, consistent with an allosteric mechanism. Our study provides molecular insights into the mechanisms that regulate the activity of this physiopathologically important ion channel., (© 2022 Benítez-Angeles et al.)