Evaluation of the neuroprotective efficacy of the gramine derivative ITH12657 against NMDA-induced excitotoxicity in the rat retina

PurposeThe aim of this study was to investigate, the neuroprotective effects of a new Gramine derivative named: ITH12657, in a model of retinal excitotoxicity induced by intravitreal injection of NMDA.MethodsAdult Sprague Dawley rats received an intravitreal injection of 100 mM NMDA in their left eye and were treated daily with subcutaneous injections of ITH12657 or vehicle. The best dose–response, therapeutic window study, and optimal treatment duration of ITH12657 were studied. Based on the best survival of Brn3a + RGCs obtained from the above-mentioned studies, the protective effects of ITH12657 were studied in vivo (retinal thickness and full-field Electroretinography), and ex vivo by quantifying the surviving population of Brn3a + RGCs, αRGCs and their subtypes α-ONsRGCs, α-ONtRGCs, and α-OFFRGCs.ResultsAdministration of 10 mg/kg ITH12657, starting 12 h before NMDA injection and dispensed for 3 days, resulted in the best significant protection of Brn3a + RGCs against NMDA-induced excitotoxicity. In vivo, ITH12657-treated rats showed significant preservation of retinal thickness and functional protection against NMDA-induced retinal excitotoxicity. Ex vivo results showed that ITH12657 afforded a significant protection against NMDA-induced excitotoxicity for the populations of Brn3a + RGC, αRGC, and αONs-RGC, but not for the population of αOFF-RGC, while the population of α-ONtRGC was fully resistant to NMDA-induced excitotoxicity.ConclusionSubcutaneous administration of ITH12657 at 10 mg/kg, initiated 12 h before NMDA-induced retinal injury and continued for 3 days, resulted in the best protection of Brn3a + RGCs, αRGC, and αONs-RGC against excitotoxicity-induced RGC death. The population of αOFF-RGCs was extremely sensitive while α-ONtRGCs were fully resistant to NMDA-induced excitotoxicity

Advanced therapy medicinal products for eye diseases: Goals and challenges

Producción CientíficaThe concept of advanced therapy medicinal products (ATMPs) encompasses novel kinds of medicines for human use that are based on genes, cells or tissues. These intend to offer not only regeneration, but complete functional recovery of diseased tissues and organs using different strategies. Gene therapy, cell therapy and tissue engineering are the main areas in which promising advanced therapies are emerging. The eye is a very complex organ whose main structures, the cornea and the retina, play a pivotal role in maintaining normal vision, as severe alterations in these tissues can lead to blindness. Ocular tissues are starting to benefit from ATMPs by fighting against the enormous complexity and devastating potential of many ocular diseases. However, developments arising from this field of work face important challenges related to vectors to deliver drugs and genetic material to target tissues, suitable biomaterials to prepare cell scaffolds and cell stemness, among others—not to mention the complicated legislation around ATMPs, the complexity in production and quality control and the absence of standardized protocols. The purpose of this Special Issue is to serve as an overview of the current progress in the application of cell and gene therapies, as well as tissue engineering to restore functionality in diseased ocular structures, and the challenges they deal with in order to get to patients.Ministerio de Economía y Competitividad e Instituto de Salud Carlos III - FEDER (FIS PI20/0317 e ICI21-00010)Junta de Andalucía - Consejería de Salud y Familias (PI-0086-2020)Junta de Andalucía - Consejería de Transformación Económica, Industria, Conocimiento y Universidades- FEDER (B-CTS-504-UGR20

5G-PPP Technology Board:AI and ML – Enablers for Beyond 5G Networks

This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from 5G PPP projects that research, implement and validate 5G and B5G network systems. The white paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for 5G and B5G networks. A family of neural networks is presented which are, generally speaking, non-linear statistical data modelling and decision-making tools. They are typically used to model complex relationships between input and output parameters of a system or to find patterns in data. Feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks belong to this family. Reinforcement learning is concerned about how intelligent agents must take actions in order to maximize a collective reward, e.g., to improve a property of the system. Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data. Hybrid solutions are presented such as combined analytical and machine learning modelling as well as expert knowledge aided machine learning. Finally, other specific methods are presented, such as generative adversarial networks and unsupervised learning and clustering. In the sequel the white paper elaborates on use case and optimisation problems that are being tackled with AI/ML, partitioned in three major areas namely, i) Network Planning, ii) Network Diagnostics/Insights, and iii) Network Optimisation and Control. In Network Planning, attention is given to AI/ML assisted approaches to guide planning solutions. As B5G networks become increasingly complex and multi-dimensional, parallel layers of connectivity are considered a trend towards disaggregated deployments in which a base station is distributed over a set of separate physical network elements which ends up in the growing number of services and network slices that need to be operated. This climbing complexity renders traditional approaches in network planning obsolete and calls for their replacement with automated methods that can use AI/ML to guide planning decisions. In this respect two solutions are discussed, first the network element placement problem is introduced which aims at improvements in the identification of optimum constellation of base stations each located to provide best network performance taking into account various parameters, e.g. coverage, user equipment (UE) density and mobility patterns (estimates), required hardware and cabling, and overall cost. The second problem considered in this regard is the dimensioning considerations for C-RAN clusters, in which employing ML-based algorithms to provide optimal allocation of baseband unit (BBU) functions (to the appropriate servers hosted by the central unit (CU)) to provide the expected gains is addressed. In Network Diagnostics, attention is given to the tools that can autonomously inspect the network state and trigger alarms when necessary. The contributions are divided into network characteristics forecasts solutions, precise user localizations methods, and security incident identification and forecast. The application of AI/ML methods in high-resolution synthesising and efficient forecasting of mobile traffic; QoE inference and QoS improvement by forecasting techniques; service level agreement (SLA) prediction in multi-tenant environments; and complex event recognition and forecasting are among network characteristics forecasts methods discussed. On high-precision user localization, AI-assisted sensor fusion and line-of-sight (LoS)/non-line-of-sight (NLoS) discrimination, and 5G localization based on soft information and sequential autoencoding are introduced. And finally, on forecasting security incidents, after a short introduction on modern attacks in mobile networks, ML-based network traffic inspection and real-time detection of distributed denial-of-service (DDoS) attacks are briefly examined. In regard to the Network Optimisation and Control, attention is given to the different network segments, including radio access, transport/fronthaul (FH)/backhaul (BH), virtualisation infrastructure, end-to-end 5G PPP Technology Board AI/ML for Networks 3 (E2E) network slicing, security, and application functions. Among application of AI/ML in radio access, the slicing in multi-tenant networks, radio resource provisioning and traffic steering, user association, demand-driven power allocation, joint MAC scheduling (across several gNBs), and propagation channel estimation and modelling are discussed. Moreover, these solutions are categorised (based on the application time-scale) into real-time, near-real-time, and non-real-time groups. On transport and FH/BH networks, AI/ML algorithms on triggering path computations, traffic management (using programmable switches), dynamic load balancing, efficient per-flow scheduling, and optimal FH/BH functional splitting are introduced. Moreover, federated learning across MEC and NFV orchestrators, resource allocation for service function chaining, and dynamic resource allocation in NFV infrastructure are among introduced AI/ML applications for virtualisation infrastructure. In the context of E2E slicing, several applications such as automated E2E service assurance, resource reservation (proactively in E2E slice) and resource allocation (jointly with slice-based demand prediction), slice isolation, and slice optimisation are presented. In regard to the network security, the application of AI/ML techniques in responding to the attack incidents are discussed for two cases, i.e. in moving target defence for network slice protection, and in self-protection against app-layer DDoS attacks. And finally, on the AI/ML applications in optimisation of application functions, the dash prefetching optimization and Q-learning applications in federated scenarios are presented.The white paper continues with the discussions on the application of AI/ML in the 5G and B5G network architectures. In this context the AI/ML based solutions pertaining to autonomous slice management, control and orchestration, cross-layer optimisation framework, anomaly detection, and management analytics, as well as aspects in AI/ML-as-a-service in network management and orchestration, and enablement of ML for the verticals' domain are presented. This is followed by topics on management of ML models and functions, namely the ML model lifecycle management, e.g., training, monitoring, evaluation, configuration and interface management of ML models. Furthermore, the white paper investigates the standardisation activities on the enablement of AI/ML in networks, including the definition of network data analytics function (NDAF) by 3GPP, the definition of an architecture that helps address challenges in network automation and optimization using AI and the categories of use cases where AI may benefit network operation and management by ETSI ENI, and finally the O-RAN definition of non-real-time and near-real-time RAN controllers to support ML-based management and intelligent RAN optimisation. Additionally, the white paper identifies the challenges in view of privacy and trust in AI/ML-based networks and potential solutions by introducing privacy preserving mechanisms and the zero-trust management approach are introduced. The availability of reliable data-sets as a crucial prerequisite to efficiency of AI/ML algorithms is discussed and the white paper concludes with a brief overview of AI/ML-based KPI validation and system troubleshooting. In summary the findings of this white paper conclude with the identification of several areas (research and development work) for further attention in order to enhance future network return-on-investment (ROI): (a) building standardized interfaces to access relevant and actionable data, (b) exploring ways of using AI to optimize customer experience, (c) running early trials with new customer segments to identify AI opportunities, (d) examining use of AI and automation for network operations, including planning and optimization, (e) ensuring early adoption of new solutions for AI and automation to facilitate introduction of new use cases, and (f) establish/launch an open repository for network data-sets that can be used for training and benchmarking algorithms by all