A systematic comparison of deep learning methods for Gleason grading and scoring.

Prostate cancer is the second most frequent cancer in men worldwide after lung cancer. Its diagnosis is based on the identification of the Gleason score that evaluates the abnormality of cells in glands through the analysis of the different Gleason patterns within tissue samples. The recent advancements in computational pathology, a domain aiming at developing algorithms to automatically analyze digitized histopathology images, lead to a large variety and availability of datasets and algorithms for Gleason grading and scoring. However, there is no clear consensus on which methods are best suited for each problem in relation to the characteristics of data and labels. This paper provides a systematic comparison on nine datasets with state-of-the-art training approaches for deep neural networks (including fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL and CLAM) applied to Gleason grading and scoring tasks. The nine datasets are collected from pathology institutes and openly accessible repositories. The results show that the best methods for Gleason grading and Gleason scoring tasks are fully supervised learning and CLAM, respectively, guiding researchers to the best practice to adopt depending on the task to solve and the labels that are available.

Natural Vitamin E Supplementation during Pregnancy in Rats Increases RRR-α-Tocopherol Stereoisomer Proportion and Enhances Fetal Antioxidant Capacity, Compared to Synthetic Vitamin E Administration.

INTRODUCTION: Low dietary intake of vitamin E is a global public health issue. RRR-α-tocopherol (RRR-αT) is the only naturally occurring vitamin E stereoisomer, but the equimolecular mixture of all eight stereoisomers, synthetic vitamin E (S-αT), is commonly consumed. The objective of this study was to evaluate bioavailability and antioxidant activity of RRR-αT versus S-αT, in both mother and fetus, after maternal supplementation during pregnancy. METHODS: Female rats (7 weeks of age) received a modified AIN-93G diet supplemented with 75 IU/kg of RRR-αT (NVE, n = 20) or S-αT (SVE, n = 17). At delivery, the levels of αT, stereoisomer distribution, and antioxidant capacity were analyzed in maternal and fetal plasma. RESULTS: NVE administration significantly increased the proportion of RRR-αT stereoisomer in maternal and fetal plasma. The percentage of RRR-αT increased from 32.76% to 88.33% in maternal plasma, and 35.25% to 97.94% in fetal plasma, in the NVE group compared to SVE. Fetal plasma from the NVE group was found to have higher total antioxidant capacity compared to SVE. Lastly, fetal plasma RRR-αT stereoisomer percentage was positively associated with expression levels of scavenger receptor class B type 1 (SR-B1) in the placenta. CONCLUSIONS: Both natural and synthetic sources of vitamin E showed similar bioavailability. Still, NVE supplementation increased the proportion of RRR-αT and promoted higher antioxidant activity in fetal plasma at birth. Placental SR-B1 might be involved in the stereoselective transfer of RRR-αT stereoisomer across the placenta and may improve αT bioactivity in the fetus.

Calcifediol During Pregnancy Improves Maternal and Fetal Availability of Vitamin D Compared to Vitamin D3 in Rats and Modifies Fetal Metabolism.

The fetus depends on the transplacental transfer of vitamin D. Calcifediol (25-OH-D3) is the vitamin D metabolite that crosses the placenta. Previously, oral 25-OH-D3 improved serum 25-OH-D3 compared to vitamin D3 in non-pregnant subjects, although no studies are available in pregnant women. We evaluated the availability of oral 25-OH-D3 compared to vitamin D3 during pregnancy, as well as, their levels in the fetus and effect on metabolism-related proteins. Twenty female rats per group were fed with 25 µg/kg of diet of vitamin D3 (1,000 UI vitamin D/kg diet) or with 25 µg/kg diet of 25-OH-D3. We analyzed 25-OH-D3 levels in maternal and fetal plasma; protein levels of vitamin D receptor (VDR), fatty acid translocase (FAT), and scavenger-receptor class B type-1 (SR-B1) in both maternal liver and placenta; and protein levels of VDR and Glutamate decarboxylase (GAD67) in fetal brain. 25-OH-D3 doubled the concentration of 25-OH-D3 in both maternal and fetal plasma compared to vitamin D3. In addition, maternal liver VDR, FAT, and SR-BI increased significantly in the 25-OH-D3 group, but no changes were found in the placenta. Interestingly, 25-OH-D3 decreased GAD67 expression in the fetal brain and it also tended to decrease VDR (P = 0.086). In conclusion, 25-OH-D3 provided better vitamin D availability for both mother and fetus when administered during pregnancy compared to vitamin D3. No adverse effects on pregnancy outcomes were observed. The effects of 25-OH-D3 on the expression of VDR and GAD67 in fetal brain require further investigation.

Effects of ß-Hydroxy ß-Methylbutyrate Supplementation on Working Memory and Hippocampal Long-Term Potentiation in Rodents.

ß-hydroxy ß-methylbutyrate (HMB), a metabolite of the essential amino acid leucine, has been shown to preserve muscle mass and strength during aging. The signaling mechanism by which HMB elicits its favorable effects on protein metabolism in skeletal muscle is also preserved in the brain. However, there are only a few studies, all at relatively high doses, addressing the effect of HMB supplementation on cognition. This study evaluated the effects of different doses of HMB on the potentiation of hippocampal synapses following the experimental induction of long-term potentiation (LTP) in the hippocampus of behaving rats, as well as on working memory test (delayed matching-to-position, DMTP) in mice. HMB doses in rats were 225 (low), 450 (medium), and 900 (high) mg/kg body weight/day and were double in mice. Rats who received medium or high HMB doses improved LTP, suggesting that HMB administration enhances mechanisms related to neuronal plasticity. In the DMTP test, mice that received any of the tested doses of HMB performed better than the control group in the overall test with particularities depending on the dose and the task phase.

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging.

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.

An Event-Based Digital Time Difference Encoder Model Implementation for Neuromorphic Systems.

Neuromorphic systems are a viable alternative to conventional systems for real-time tasks with constrained resources. Their low power consumption, compact hardware realization, and low-latency response characteristics are the key ingredients of such systems. Furthermore, the event-based signal processing approach can be exploited for reducing the computational load and avoiding data loss due to its inherently sparse representation of sensed data and adaptive sampling time. In event-based systems, the information is commonly coded by the number of spikes within a specific temporal window. However, the temporal information of event-based signals can be difficult to extract when using rate coding. In this work, we present a novel digital implementation of the model, called time difference encoder (TDE), for temporal encoding on event-based signals, which translates the time difference between two consecutive input events into a burst of output events. The number of output events along with the time between them encodes the temporal information. The proposed model has been implemented as a digital circuit with a configurable time constant, allowing it to be used in a wide range of sensing tasks that require the encoding of the time difference between events, such as optical flow-based obstacle avoidance, sound source localization, and gas source localization. This proposed bioinspired model offers an alternative to the Jeffress model for the interaural time difference estimation, which is validated in this work with a sound source lateralization proof-of-concept system. The model was simulated and implemented on a field-programmable gate array (FPGA), requiring 122 slice registers of hardware resources and less than 1 mW of power consumption.

A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders.

The change from face-to-face work to teleworking caused by the pandemic has induced multiple workers to spend more time than usual in front of a computer; in addition, the sudden installation of workstations in homes means that not all of them meet the necessary characteristics for the worker to be able to position himself/herself comfortably with the correct posture in front of their computer. Furthermore, from the point of view of the medical personnel in charge of occupational risk prevention, an automated tool able to quantify the degree of incorrectness of a postural habit in a worker is needed. For this purpose, in this work, a system based on the postural detection of the worker is designed, implemented and tested, using a specialized hardware system that processes video in real time through convolutional neural networks. This system is capable of detecting the posture of the neck, shoulders and arms, providing recommendations to the worker in order to prevent possible health problems, due to poor posture. The results of the proposed system show that this video processing can be carried out in real time (up to 25 processed frames/sec) with a low power consumption (less than 10 watts) using specialized hardware, obtaining an accuracy of over 80% in terms of the pattern detected.

Beneficial effects of dietary supplementation with green tea catechins and cocoa flavanols on aging-related regressive changes in the mouse neuromuscular system.

Besides skeletal muscle wasting, sarcopenia entails morphological and molecular changes in distinct components of the neuromuscular system, including spinal cord motoneurons (MNs) and neuromuscular junctions (NMJs); moreover, noticeable microgliosis has also been observed around aged MNs. Here we examined the impact of two flavonoid-enriched diets containing either green tea extract (GTE) catechins or cocoa flavanols on age-associated regressive changes in the neuromuscular system of C57BL/6J mice. Compared to control mice, GTE- and cocoa-supplementation significantly improved the survival rate of mice, reduced the proportion of fibers with lipofuscin aggregates and central nuclei, and increased the density of satellite cells in skeletal muscles. Additionally, both supplements significantly augmented the number of innervated NMJs and their degree of maturity compared to controls. GTE, but not cocoa, prominently increased the density of VAChT and VGluT2 afferent synapses on MNs, which were lost in control aged spinal cords; conversely, cocoa, but not GTE, significantly augmented the proportion of VGluT1 afferent synapses on aged MNs. Moreover, GTE, but not cocoa, reduced aging-associated microgliosis and increased the proportion of neuroprotective microglial phenotypes. Our data indicate that certain plant flavonoids may be beneficial in the nutritional management of age-related deterioration of the neuromuscular system.

Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification.

Monitoring animals' behavior living in wild or semi-wild environments is a very interesting subject for biologists who work with them. The difficulty and cost of implanting electronic devices in this kind of animals suggest that these devices must be robust and have low power consumption to increase their battery life as much as possible. Designing a custom smart device that can detect multiple animal behaviors and that meets the mentioned restrictions presents a major challenge that is addressed in this work. We propose an edge-computing solution, which embeds an ANN in a microcontroller that collects data from an IMU sensor to detect three different horse gaits. All the computation is performed in the microcontroller to reduce the amount of data transmitted via wireless radio, since sending information is one of the most power-consuming tasks in this type of devices. Multiples ANNs were implemented and deployed in different microcontroller architectures in order to find the best balance between energy consumption and computing performance. The results show that the embedded networks obtain up to 97.96% ± 1.42% accuracy, achieving an energy efficiency of 450 Mops/s/watt.

Performance Evaluation of Deep Learning-Based Prostate Cancer Screening Methods in Histopathological Images: Measuring the Impact of the Model's Complexity on Its Processing Speed.

Prostate cancer (PCa) is the second most frequently diagnosed cancer among men worldwide, with almost 1.3 million new cases and 360,000 deaths in 2018. As it has been estimated, its mortality will double by 2040, mostly in countries with limited resources. These numbers suggest that recent trends in deep learning-based computer-aided diagnosis could play an important role, serving as screening methods for PCa detection. These algorithms have already been used with histopathological images in many works, in which authors tend to focus on achieving high accuracy results for classifying between malignant and normal cases. These results are commonly obtained by training very deep and complex convolutional neural networks, which require high computing power and resources not only in this process, but also in the inference step. As the number of cases rises in regions with limited resources, reducing prediction time becomes more important. In this work, we measured the performance of current state-of-the-art models for PCa detection with a novel benchmark and compared the results with PROMETEO, a custom architecture that we proposed. The results of the comprehensive comparison show that using dedicated models for specific applications could be of great importance in the future.

ED-BioRob: A Neuromorphic Robotic Arm With FPGA-Based Infrastructure for Bio-Inspired Spiking Motor Controllers.

Compared to classic robotics, biological nervous systems respond to stimuli in a fast and efficient way regarding the body motor actions. Decision making, once the sensory information arrives to the brain, is in the order of ms, while the whole process from sensing to movement requires tens of ms. Classic robotic systems usually require complex computational abilities. Key differences between biological systems and robotic machines lie in the way information is coded and transmitted. A neuron is the "basic" element that constitutes biological nervous systems. Neurons communicate in an event-driven way through small currents or ionic pulses (spikes). When neurons are arranged in networks, they allow not only for the processing of sensory information, but also for the actuation over the muscles in the same spiking manner. This paper presents the application of a classic motor control model (proportional-integral-derivative) developed with the biological spike processing principle, including the motor actuation with time enlarged spikes instead of the classic pulse-width-modulation. This closed-loop control model, called spike-based PID controller (sPID), was improved and adapted for a dual FPGA-based system to control the four joints of a bioinspired light robot (BioRob X5), called event-driven BioRob (ED-BioRob). The use of spiking signals allowed the system to achieve a current consumption bellow 1A for the entire 4 DoF working at the same time. Furthermore, the robot joints commands can be received from a population of silicon-neurons running on the Dynap-SE platform. Thus, our proposal aims to bridge the gap between a general purpose processing analog neuromorphic hardware and the spiking actuation of a robotic platform.

Enzymatic assays for the assessment of toxic effects of halogenated organic contaminants in water and food. A review.

Halogenated organic compounds are a particular group of contaminants consisting of a large number of substances, and of great concern due to their persistence in the environment, potential for bioaccumulation and toxicity. Some of these compounds have been classified as persistent organic pollutants (POPs) under The Stockholm Convention and many toxicity assessments have been conducted on them previously. In this work we provide an overview of enzymatic assays used in these studies to establish toxic effects and dose-response relationships. Studies in vivo and in vitro have been considered with a particular emphasis on the impact of halogenated compounds on the activity of relevant enzymes to the humans and the environment. Most information available in the literature focuses on chlorinated compounds, but brominated and fluorinated molecules are also the target of increasing numbers of studies. The enzymes identified can be classified as enzymes: i) the activities of which are affected by the presence of halogenated organic compounds, and ii) those involved in their metabolisation/detoxification resulting in increased activities. In both cases the halogen substituent seems to have an important role in the effects observed. Finally, the use of these enzymes in biosensing tools for monitoring of halogenated compounds is described.

Motoneuron deafferentation and gliosis occur in association with neuromuscular regressive changes during ageing in mice.

BACKGROUND: The cellular mechanisms underlying the age-associated loss of muscle mass and function (sarcopenia) are poorly understood, hampering the development of effective treatment strategies. Here, we performed a detailed characterization of age-related pathophysiological changes in the mouse neuromuscular system. METHODS: Young, adult, middle-aged, and old (1, 4, 14, and 24-30 months old, respectively) C57BL/6J mice were used. Motor behavioural and electrophysiological tests and histological and immunocytochemical procedures were carried out to simultaneously analyse structural, molecular, and functional age-related changes in distinct cellular components of the neuromuscular system. RESULTS: Ageing was not accompanied by a significant loss of spinal motoneurons (MNs), although a proportion (~15%) of them in old mice exhibited an abnormally dark appearance. Dark MNs were also observed in adult (~9%) and young (~4%) animals, suggesting that during ageing, some MNs undergo early deleterious changes, which may not lead to MN death. Old MNs were depleted of cholinergic and glutamatergic inputs (~40% and ~45%, respectively, P < 0.01), suggestive of age-associated alterations in MN excitability. Prominent microgliosis and astrogliosis [~93% (P < 0.001) and ~100% (P < 0.0001) increase vs. adults, respectively] were found in old spinal cords, with increased density of pro-inflammatory M1 microglia and A1 astroglia (25-fold and 4-fold increase, respectively, P < 0.0001). Ageing resulted in significant reductions in the nerve conduction velocity and the compound muscle action potential amplitude (~30%, P < 0.05, vs. adults) in old distal plantar muscles. Compared with adult muscles, old muscles exhibited significantly higher numbers of both denervated and polyinnervated neuromuscular junctions, changes in fibre type composition, higher proportion of fibres showing central nuclei and lipofuscin aggregates, depletion of satellite cells, and augmented expression of different molecules related to development, plasticity, and maintenance of neuromuscular junctions, including calcitonin gene-related peptide, growth associated protein 43, agrin, fibroblast growth factor binding protein 1, and transforming growth factor-ß1. Overall, these alterations occurred at varying degrees in all the muscles analysed, with no correlation between the age-related changes observed and myofiber type composition or muscle topography. CONCLUSIONS: Our data provide a global view of age-associated neuromuscular changes in a mouse model of ageing and help to advance understanding of contributing pathways leading to development of sarcopenia.

Event-Based Gesture Recognition through a Hierarchy of Time-Surfaces for FPGA.

Neuromorphic vision sensors detect changes in luminosity taking inspiration from mammalian retina and providing a stream of events with high temporal resolution, also known as Dynamic Vision Sensors (DVS). This continuous stream of events can be used to extract spatio-temporal patterns from a scene. A time-surface represents a spatio-temporal context for a given spatial radius around an incoming event from a sensor at a specific time history. Time-surfaces can be organized in a hierarchical way to extract features from input events using the Hierarchy Of Time-Surfaces algorithm, hereinafter HOTS. HOTS can be organized in consecutive layers to extract combination of features in a similar way as some deep-learning algorithms do. This work introduces a novel FPGA architecture for accelerating HOTS network. This architecture is mainly based on block-RAM memory and the non-restoring square root algorithm, requiring basic components and enabling it for low-power low-latency embedded applications. The presented architecture has been tested on a Zynq 7100 platform at 100 MHz. The results show that the latencies are in the range of 1 µ s to 6.7 µ s, requiring a maximum dynamic power consumption of 77 mW. This system was tested with a gesture recognition dataset, obtaining an accuracy loss for 16-bit precision of only 1.2% with respect to the original software HOTS.

Impact of selenium co-administration on methylmercury exposed eleutheroembryos and adult zebrafish (Danio rerio): Changes in bioaccumulation and gene expression.

Mercury still represents one of the most hazardous threats for the aquatic ecosystem due to its high toxicity, and the fact that it can be easily incorporated into the food chain by accumulation in fish as MeHg. On the other hand, selenium is a micronutrient that is part of different antioxidant enzymes that regulate the cellular redox state, and whose complex interaction with Hg has been extensively studied from a toxicological point of view. In order to evaluate the protective effect of Se(IV) co-administration against MeHg accumulation and toxicity, we have selected an in-vivo model at two developmental stages: zebrafish eleutheroembryos and adult fish. Embryos were exposed during 48 h to MeHg (5 or 25 µg/l) and a concentration of Se (IV) representing a molar ratio close to one (2.5 or 12.5 µg/l), while adult zebrafish were exposed during 72 h to either 25 µg/l of MeHg alone or co-exposed with 12.5 µg/l of Se (IV). A significant decrease in MeHg bioaccumulation factor was observed in eleutheroembryos co-exposed to Se(IV). A time-dependent accumulation of MeHg was observed in all the analyzed organs and tissues of adult fish, which was significantly reduced in the muscular tissue and the intestine by Se(IV) co-administration. However, such protection against MeHg bioaccumulation was not maintained in the brain and liver. The data derived from the gene expression analysis also demonstrated the protective effect of Se(IV) against MeHg-induced oxidative stress and the activation of different defense mechanisms by Se(IV) co-administration.

NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps.

Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many state-of-the-art (SOA) visual processing tasks. Even though graphical processing units are most often used in training and deploying CNNs, their power efficiency is less than 10 GOp/s/W for single-frame runtime inference. We propose a flexible and efficient CNN accelerator architecture called NullHop that implements SOA CNNs useful for low-power and low-latency application scenarios. NullHop exploits the sparsity of neuron activations in CNNs to accelerate the computation and reduce memory requirements. The flexible architecture allows high utilization of available computing resources across kernel sizes ranging from 1×1 to 7×7 . NullHop can process up to 128 input and 128 output feature maps per layer in a single pass. We implemented the proposed architecture on a Xilinx Zynq field-programmable gate array (FPGA) platform and presented the results showing how our implementation reduces external memory transfers and compute time in five different CNNs ranging from small ones up to the widely known large VGG16 and VGG19 CNNs. Postsynthesis simulations using Mentor Modelsim in a 28-nm process with a clock frequency of 500 MHz show that the VGG19 network achieves over 450 GOp/s. By exploiting sparsity, NullHop achieves an efficiency of 368%, maintains over 98% utilization of the multiply-accumulate units, and achieves a power efficiency of over 3 TOp/s/W in a core area of 6.3 mm2. As further proof of NullHop's usability, we interfaced its FPGA implementation with a neuromorphic event camera for real-time interactive demonstrations.

Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA.

Deep Learning algorithms have become state-of-the-art methods for multiple fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks (CNN) stand out. This kind of network is expensive in terms of computational resources due to the large number of operations required to process a frame. In recent years, several frame-based chip solutions to deploy CNN for real time have been developed. Despite the good results in power and accuracy given by these solutions, the number of operations is still high, due the complexity of the current network models. However, it is possible to reduce the number of operations using different computer vision techniques other than frame-based, e.g., neuromorphic event-based techniques. There exist several neuromorphic vision sensors whose pixels detect changes in luminosity. Inspired in the leaky integrate-and-fire (LIF) neuron, we propose in this manuscript an event-based field-programmable gate array (FPGA) multiconvolution system. Its main novelty is the combination of a memory arbiter for efficient memory access to allow row-by-row kernel processing. This system is able to convolve 64 filters across multiple kernel sizes, from 1 × 1 to 7 × 7, with latencies of 1.3  µs and 9.01  µs, respectively, generating a continuous flow of output events. The proposed architecture will easily fit spike-based CNNs.

Sialic Acid and Sialylated Oligosaccharide Supplementation during Lactation Improves Learning and Memory in Rats.

Sialic acids (Sia) are postulated to improve cognitive abilities. This study evaluated Sia effects on rat behavior when administered in a free form as N-acetylneuraminic acid (Neu5Ac) or conjugated as 6'-sialyllactose (6'-SL). Rat milk contains Sia, which peaks at Postnatal Day 9 and drops to a minimum by Day 15. To bypass this Sia peak, a cohort of foster mothers was used to raise the experimental pups. A group of pups received a daily oral supplementation of Neu5Ac to mimic the amount naturally present in rat milk, and another group received the same molar amount of Sia as 6'-SL. The control group received water. After weaning, rats were submitted to behavioral evaluation. One year later, behavior was re-evaluated, and in vivo long-term potentiation (LTP) was performed. Brain samples were collected and analyzed at both ages. Adult rats who received Sia performed significantly better in the behavioral assessment and showed an enhanced LTP compared to controls. Within Sia groups, 6'-SL rats showed better scores in some cognitive outcomes compared to Neu5Ac rats. At weaning, an effect on polysialylated-neural cell adhesion molecule (PSA-NCAM) levels in the frontal cortex was only observed in 6'-SL fed rats. Providing Sia during lactation, especially as 6'-SL, improves memory and LTP in adult rats.

Overview on the safety of commercial products containing metallic nanoparticles in the food sector / Seguridad de los productos comerciales que contienen nanopartículas metálicas en el sector alimentario

The objective of this work was to highlight the importance of testing in a realistic way, metallic nanoparticles-based products, used in the food sector in order to provide representative data for a realistic and accurate risk assessment. To this end we give a brief overview of the current use and applications of metallic nanoparticles in the food industry, how the toxicological evaluations of nanoparticles in food should be performed and how nanoparticles-based products are regulated worldwide. This brief overview work provides a picture of the latest issues on the safety evaluation of metallic nanoparticles in the food sector and identifies also some major points that are important to take into consideration for future studies

El objetivo de este trabajo fue resaltar la importancia de analizar de forma realista los productos que contienen nanopartículas metálicas utilizados en el sector alimentario a fin de proporcionar datos representativos para una evaluación de riesgos realista y precisa. Con este fin, presentamos una breve descripción del uso y de las aplicaciones actuales de las nanopartículas metálicas en la industria alimentaria, cómo deben realizarse sus evaluaciones toxicológicas en los alimentos y cómo están regulados los productos de nanopartículas a nivel internacional. Este breve trabajo general proporciona una visión de los problemas más actuales sobre la evaluación de la seguridad de las nanopartículas metálicas en el sector alimentario e identifica también algunos puntos importantes a tener en cuenta para futuros estudios