Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care. Law and ethics seek to produce a governance framework for the processing of patient data that produces a solution to the issues that arise between the competing desires of individuals in society for privacy and advances in health care. Traditional safeguards to achieve this governance have come from, for example, the anonymization of data or informed consent. These are not adequate safeguards for the new big data and artificial intelligence methodologies in research; it is increasingly difficult to create anonymous data (rather than pseudonymized/coded data) or to maintain it against re-identification . The technology of big data and artificial intelligence, however, itself increasingly offers safeguards to solve the governance problem. In this article we explore how privacy-preserving distributed machine learning from federated databases might assist governance in health care. The article first outlines the basic parameters of the law and ethics issues and then discusses machine learning and deep learning. Thereafter, the results of the review are presented and discussed. The methodology for this research is that distributed machine learning is an evolving field in computing, with 665 articles published between 2001 and 2018; the study is based on a literature search, focuses on the medical applications of distributed machine learning, and provides an up-to-date summary of the field.16. The issue is one of balance: privacy of health data and access to data for research. This issue is likely to become more pronounced with the foreseeable developments in health care, notably in relation to rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, recent developments in law, for example, in the European Union’s General Data Protection Regulation (GDPR), appear to maintain the traditional approach that seems to favor individualism above solidarity. Individualism is strengthened in the new legislation. There is a narrowing of the definition of informed consent in Article 4.11 of the GDPR, with the unclear inclusion of the necessity for broad consent in scientific research included in Recital 33.The challenges in law and ethics in relation to big data and artificial intelligence are well documented and discussedKey ObjectiveReview the contribution of distributed learning to preserve data privacy in health care.Knowledge GeneratedData in health care are greatly protected; therefore, accessing medical data is restricted by law and ethics. This restriction has led to a change in research practice to adapt to new regulations. Distributed learning makes it possible to learn from medical data without these data ever leaving the medical institutions.RelevanceDistributed learning allows learning from medical data while guaranteeing preservation of patient privacy.16aIn relation to the continuing ambiguity of the unclear legal landscape for research using and reusing large datasets and linking between datasets, the GDPR is not clear in the area of re-identification of individuals. For the GDPR, part of the problem is clear—when data have the potential when added to other data to identify an individual, then those data are personal data and subject to regulation. The question is, is this absolute , or is there a reasonableness test? Recital 26 includes such a reasonable test: “To ascertain whether means are reasonably likely to be used to identify the natural person, account should be taken of all objective factors, such as the costs of and the amount of time required for identification, taking into consideration the available technology at the time of the processing and technological developments.”From this overview of legal difficulties, it is clear that there are obstacles to processing data in big data, machine learning, and artificial intelligence methodologies and environments. It must be stressed that the object is not to circumvent the rights of patients or to suggest that privacy should be ignored. The difficulty is that where the law is unclear, there is a tendency toward restrictive readings of the law to avoid liability, and, in the case of the methodologies and applications of data science discussed here, the effect of unclear law and restrictive interpretations of the law will be to block potentially important medical and scientific developments and research. Each of the uncertainties will require regulators to take a position on the best interpretation of the meaning of the law according to the available safeguards. The question for the data science community is, how far can that community itself address concerns about privacy, about re-identification, and about safeguarding autonomy of individuals and their legitimate expectations to dignity in their treatment through the proper treatment of their personal data? How far distributed learning might contribute a suitable safeguard is the question addressed in the remainder of this paper.1 These algorithms are implemented to support decision making in different domains, including health care, manufacturing, education, financial modeling, and marketing.3 In medical disciplines, machine learning has contributed to improving the efficiency of clinical trials and decision-making processes. Some examples of machine learning applications in medicine are the localization of thoracic diseases,4 early diagnosis of Alzheimer disease,5 personalized treatment,6 outcome prediction,8 and automated radiology reports.9Machine learning comes from the possibility to apply algorithms on raw data to acquire knowledge.11There are three main categories of machine learning algorithms. First, in supervised learning, the algorithm generates a function for mapping input variables to output variables. In unsupervised learning, the applied algorithms do not have any outcome variable to estimate, and the algorithms generate a function mapping for the structure of the data. The third type is referred to as reinforcement learning, whereby in the absence of a training dataset the algorithm trains itself by learning from experiences to make increasingly improved decisions. A reinforcement agent decides what action to perform to accomplish a given task.12 as represented in 13 The output of a node is calculated by applying an activation function to the weighted average of this node’s input. As described by Andrew Ng14, “The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data that we can feed in to these algorithms,” meaning that the more data are fed into the model the better the performance. Yet, this continuous improvement of the performance in concordance with the amount of the data are not correct for traditional machine learning algorithms reaching a steady performance level that does not improve with the increase of the amount of the training data.15Deep learning is a subset of machine learning, which, in turn, is a subset of artificial intelligence,16 The PRISMA flow diagram and checklist are slightly modified and presented in Appendix A PubMed search was performed to collect relevant studies concerning the utilization of distributed machine learning in medicine. We used the search strings: “distributed learning,” “distributed machine learning,” and “privacy preserving data mining.” The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adopted to select and compare distributed learning literature.A total of 127 articles were identified in PubMed using the search query: (“distributed learning” OR “distributed machine learning” OR “privacy preserving data mining”). Six papers were screened; a brief summary of each article is presented in 17 Also, before processing with the learning process, researchers must make sure all data have been successfully anonymized and secured by means of hashing algorithms and semantic web techniques, respectively, as can be seen in 19Distributed learning ensures data safety by only sharing mathematical parameters (or metadata) and not the actual data or in any instance data that might enable tracking back the patient information . In other words, distributed algorithms iteratively analyze separate databases and return the same solution as if data were centralized, essentially sharing research questions and answers between databases instead of data.21 However, the sensitivity of the personal data greatly hinders the conventional centralized approach to machine learning, whereby all data are gathered in a single data store. Distributed machine learning resolves legal and ethical privacy concerns by learning without the personal data ever leaving the firewall of the medical centers.22A large quantity of training data is required for machine learning to be applied, especially in outcome modeling, where multiple factors influence learning. Provided there are sufficient and appropriate data, machine learning typically results in accurate and generalizable models.23 and ukCAT24 projects are a proof of distributed learning being successfully implemented into clinical settings to overcome data access restrictions. The purpose of the euroCAT project was to predict patient outcomes  by learning from data stored within clinics without sharing any of the medical data.The euroCAT25 using dedicated hardware 26 reduces the computational time. Second, as the memory of this dedicated hardware is often limited, it is possible to divide the training data into subsets called batches. In this situation, the training process iterates over the batches, only considering the data of one batch at each iteration.27 On top of easing the computing burden, using small batches during training improves the model’s ability to generalize.28Training a deep learning model typically requires thousands to millions of data points and is therefore computationally expensive as well as time consuming. These challenges can be mitigated with different approaches. First, because it is possible to train deep learning models in a parallelized fashion,30 Moreover, distributed deep learning also improves computing performance, as in the case of wireless sensor networks, where centralized learning is inefficient in terms of both communication and energy.32These approaches address computation challenges but do not necessarily preserve data privacy. As for machine learning, deep learning can be distributed to protect patient data.33 who deployed a deep learning model across four medical institutions for image classification purposes using three distinct datasets: retinal fundus, mammography, and ImageNet. The results were compared with the same deep learning model trained on centrally hosted data. The comparison showed that the distributed model accuracy is similar to the centrally hosted model.33 In a different study, McClure et al34 developed a distributed deep neural network model to reproduce FreeSurfer brain segmentation. FreeSurfer is an open source tool for preprocessing and analyzing  of human brain magnetic resonance images.35 The results demonstrated performance improvement on the test datasets. Similar to the previous study, a brain tumor segmentation was successfully performed using distributed deep learning across 10 institutions (BraTS distribution).36An example of distributed deep learning in the medical domain is that of Chang et al,37 In the case of sharing the locale gradients,25 it might be possible to retrieve estimations of the original data from these gradients. Training the local models on batches may prevent retrieving all the data from the gradients, as these gradients correspond to single batches rather than all the local data.38 However, setting an optimal batch size needs to be considered25 to assure data safety and the model’s ability to generalize.40In the matter of distributed deep learning, the training weights are combined to train a final model, and the raw data are never exposed.41 preventing leakage of weights during the training, and cryptographic techniques.42 These cryptographic techniques provide a set of multiparty protocols that ensure security of the computations and communication. Once the model is ready, not only can the network participants use it to learn from their data, but this learning should be able to be performed locally and under highly private and secure conditions to protect the model’s output.23Privacy in a distributed learning network addresses three main areas: data privacy, the implemented model’s privacy, and the model’s output privacy. Data privacy is achieved by means of data anonymization and data never leaving the medical institutions. The distributed learning model can be secured by applying differential privacy techniques,The users of a machine/deep learning model are not necessarily the model’s developers. Hence, documentation and the integration of automated data eligibility tests have two important assets:The documentation ensures providing a clear view of what the model is designed for, a technical description of the model, and its use.23 a distributed learning expert installed quality control via data extraction pipelines at every participant point in the network. The pipeline automatically allowed data records fulfilling the model training eligibility criteria to be used in the training. The experts also test the extraction pipeline thoroughly in addition to the machine learning testing. However, there were post-processing compensation methods to correct for the variations caused by using different local protocols.19The eligibility tests are important to ensure that correct input data are extracted and provided before executing the model. In euroCAT,43 This number climbed to 18.1 million cases in 2018.44 This increasing number of cancer incidences45 means that there are undoubtedly sufficient data worldwide to put machine/deep learning to meaningful work. However, as highlighted earlier, this requires access to the data and, as also highlighted earlier, distributed learning enables this in a manner that resolves legal and ethical concerns. Nonetheless, integration of distributed learning into health care is much slower compared with other fields, which raises the question of why this should be. Here, we summarize a set of methodologies to facilitate the adoption of distributed learning and provide future directions.If one examines oncology, for instance, cancer is clearly one of the greatest challenges facing health care. More than 16 million new cancer cases were reported in 2017 alone.39 In this case, the information communication technology preparation for distributed learning requires significant energy, time, and manpower, which can be costly. This same process (data acquisition and preprocessing) needs to be repeated for each participating hospital,48 and subsequently development and adoption of medical data standardization protocols need to be developed for this implementation process.Every hospital has its own storage devices and architecture.49 A description of FAIR principles is represented in 50 Such solutions will address emerging concerns about the nonreproducibility of scientific research, particularly in data science .52 Because workflows are fundamental to research activities, FAIR has broad applicability, which is vital in the context of distributed learning with medical data.One way to enable a virtuous circle network effect is to embrace another community engaged in synergistic activities (joining a distributed learning network is worthwhile if it links to another large network). The Findable, Accessible, Interoperable, Reusable (FAIR) Guiding Principles for data management and stewardship have gained substantial interest, but delivering scientific protocols and workflows that are aligned with these principles is significant.53 by proposing data-sharing procedures and protocols aiming to harmonize regulatory frameworks and research governance.55 Despite the efforts made toward data-sharing globalization, the sociocultural issues surrounding data sharing remain pertinent.56 Large clinical trials also face limitations in the data collection capabilities because of limited data storage capacities and manpower. To retrospectively perform additional analysis, all the participating centers need to be contacted again, which is time consuming and delays research.57Some studies were conducted trying to facilitate and secure data-sharing procedures to encourage related researchers and organizations to publicly share their data and embrace transparency,58 This is, of course, in no small part about ensuring the trust and confidence of patients who display a wide range of sensitivities toward the use of their personal data.Furthermore, medical institutions prefer not to share patient data to ensure privacy protection.59 Change management principles, such as defining a global vision, networking, and continuous communicating, could facilitate the integration of new technologies and bring up the clinical capabilities. However, this process of change management can be complicated, because it requires the involvement of multiple health care centers from different countries and continents. This diversity can trigger a fear of loss , which stems from differences of opinion and regulation,60 and the absence of data standardization, making the processes of data acquisition and preprocessing harder. In addition, the lack of knowledge about the new technology leads to resistance to accept the change and innovation.61 Therefore, it is important to help health care organizations understand the need for distributed learning by explaining the context of the change in terms of traditional ways of learning to distributed learning and a long-term vision of the improvements that it can bring, including time and money savings for both hospitals and patients. This could in turn improve patient lives, in addition to conducting more studies on research databases to consolidate proof of safety and quality of distributed models.The adoption of distributed learning will require a change in organizational management . Provided knowledge and understanding of proper change management concepts, health care providers can implement the latter successfully.63a thyroid cancer,64 heart cardiac events,65 and schizophrenia,66 in addition to the continuous development of tools and algorithms facilitating the adoption of distributed learning, such as the variant learning portal, the alternating direction method of multipliers algorithm,2 as well as the application of FAIR data principles. The cited studies provide a proof that distributed learning can ensure patient data privacy and guarantee that accurate models are built that are the equivalent of centralized models.As can be seen in A shared limitation of the studies presented in 67 Recent advances of information technology, such as blockchain, can be integrated into a distributed learning network.68 Blockchain allows trusted partners to visualize the history of the transactions and actions taken in the distributed network. This integration of blockchain should help in easing the resistance to the new distributed technology among health care workers as it provides both provenance and enforceable governance.An automated monitoring system accessible by the partners or medical centers participating in the distributed learning network can promote transparency, traceability, and trust.69 introduced the concept of a peer-to-peer electronic cash system known as Bitcoin. Blockchain was made famous as the public transaction ledger of this cryptocurrency.70 It ensures security by using cryptography in a decentralized, immutable distributed ledger technology.71 It is easy to manage as it can be made public, whereby any individual can participate, or it can be made private, where all participants are known to each other.72 It is an efficient monitoring system, as records cannot be deleted from the chain. By these means, blockchain exceeds its application as a cryptocurrency to a permanent trustful tracing system. In 2008, Satoshi Nakamoto71 demonstrated how blockchain could be used to contribute in health care: securing patient information and provider identities, managing health supply chains, monetizing clinical research and data (giving patients the choice to share), processing claims, detecting fraud, and managing prescriptions (replace incorrect and outdated data). In addition to the above-mentioned uses of blockchain, it has been also used to maintain security and scalability of clinical data sharing,73 secure medical record sharing,74 prevent drug counterfeiting,75 and secure a patient’s location.76Boulos et al77 Cybersecurity is a key aspect in preserving privacy and ensuring safety and trust among patients and health care systems.78 The continuous development or postmarketing surveillance can be seen as the set of checks and integrations that should occur when a distributed learning network is launched. This practice should make it possible to identify any weak security measures in the network or non-up-to-date features that may require re-implementation.80It is essential that the use of distributed machine/deep learning and blockchain be harmonized with the available security-preserving technologies , which begins at the user levels  and ends with more complex information technology infrastructures (such as data anonymization and user ID encryption).The distributed learning and blockchain technologies presented here show that there are emerging data science solutions that begin to meet the concerns and shortcomings of the law. The problems of re-identification are greatly reduced and managed through the technologies. Clearly, there are conceptual issues of understanding the impact of these technologies on privacy, and the relationship between privacy and confidentiality, but there are significant technical developments for the regulators to consider that could answer a number of their concerns.Currently, a combination of regulations and ethics makes it difficult to share data even for scientific research purposes. The issues relate to the legal basis for processing and anonymization. Specifically, there has been reluctance to move away from informed consent as the legal basis for processing toward processing in the public interest, and there are concerns about the re-identification of individuals where data are de-identified and then shared in aggregated environments. A solution could be to allow researchers to train their machine learning programs without the data ever having to leave the clinics, which in this paper we have established as distributed learning. This safe practice makes it possible to learn from medical data and can be applied across various medical disciplines. A limitation to its application, however, is that medical centers need to be convinced to participate in such practice, and regulators also need to know suitable safeguards have been established. Moreover, as can be seen in


The impacts of the current global food system are already visible in the environment and in the health of the population. The promotion of sustainable diets is key to counter the negative consequences. The healthcare system could be a powerful tool to educate patients by guiding their diets towards sustainability. This study aimed to assess the size and scope of the available literature regarding the promotion of sustainable diets in the healthcare system and to obtain a reliable approximation of the processes and roles related to sustainable diet promotion within healthcare systems. A scoping review where online databases were used to identify English written scientific and grey literature published between 2000–2019 was carried out. The analytical–synthetic approach was used for data charting. Twelve studies were included that were published between 2007–2020. The data highlight education, community and clinical health services, community engagement and policy advocacy, and governance as main action areas along with two transversal aspects, social support, and gender. A systemic approach to the food system is emphasized. Evidence suggests that health professionals have the potential to drive a paradigm shift in food–health environments. Currently, however, their role and potential impact is underestimated within healthcare systems. This review has identified a framework with key areas where processes need to be developed to guarantee sustainable diet promotion in healthcare services. Food is a central and essential component of our lives. The food that we produce, distribute, consume and waste is already having major impacts on environment degradation, climate change and the health of the population ,2,3. FooThe use of pesticides and nitrogen-based fertilizers leads to impacts on plant and insect life, and the resulting biodiversity loss jeopardizes a range of environmental services, including the pollination of many food crops, threatening future yields and costing about 3% of the global Gross Domestic Product (GDP) yearly .In addition to issues related to production, the food system has been globalized over recent decades due to, among other things, the evolution of refrigerated transport, communication technology and reduced trade barriers . ConsequDietary patterns are shifting, with more food of animal origin being consumed globally ,13. If tOn the other hand, food waste is a global issue due to its environmental, economic, and social impacts, requiring a change in political actions . Twenty This excessive consumption has led to epidemic proportions of diet-related non-communicable chronic diseases, where currently seven of the top ten causes of death are linked to diet . Young cThese series of negative externalities of the current food systems, from production to consumption, are not reflected in the cost of food, resulting in the need for the public sector to absorb these expensive costs in healthcare and in ecosystems ,18. MoreAltogether, these data show that current global food systems are unsustainable and non-resilient, and this has been further exacerbated during the Coronavirus Disease (COVID-19) outbreak . Even thThe United Nations Decade of Action on Nutrition 2016–2025) is positioned to use the “opportunity to transform our current food system into one that is sustainable, resilient and provides healthy diets for all”  is posit,27. BothSpecifically, the wider educational role of hospitals is being acknowledged, with attempts to improve the diets of patients even after they leave a care facility . Health Encouraging sustainable diet intake and reducing food waste are part of the main solutions to reduce environmental detriment and to improve the health of the population ,30,31,32Specific scientific literature on the potential role of health professionals in driving the sustainability agenda within the clinical and community settings of healthcare services is relatively scarce. This paper aims to conduct a scoping review in order to obtain a preliminary assessment of the potential size and scope of the available literature regarding the promotion of sustainable diets in the healthcare system, and to obtain a reliable approximation of the current situation of the role of health professionals in sustainable diet promotion within healthcare systems. The results will shed light on gaps and new initiatives that are required to normalize the implementation of sustainable diet promotion in hospitals.To achieve this objective, we first identify the key concepts related to the aims of this study. Next, we describe the methodological plan and protocol developed to carry out the scoping review study and the theoretical framework. With this framework, we present in detail the results obtained and discuss them by systematizing the content on key issues related to the study objective. Finally, we present the conclusions.This type of literature review provides a preliminary assessment of the nature, potential size and scope of the research to date on the promotion of sustainable diets in the healthcare system .The scoping review was conducted following the protocol explained by Arksey and O’Malley, the PRISMA extension for Scoping Reviews and others ,38,39,40The objective of the present review is addressed by the following research questions: what are the identified processes that are related with diet promotion/education in the European health service? How do they relate to sustainability dimensions ?A three-step search strategy was developed to identify relevant studies. Firstly, related keywords  derived The literature review was conducted by two researchers and was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) statement . All typTwo independent researchers entered the data into a data charting form using the program Microsoft Excel. The variables included for a standardized extraction of the data were: author, date published, country, type of document, research design, aim, setting and target population and relevant main findings. Any disagreements were resolved through discussion between the two researchers. Afterwards, the analysis of data was carried out by applying the analytical–synthetic approach. In the analytical process, the data were distributed and analyzed in additional parts, analyzing keywords and the content of the data. Then, in the synthetic process, the information and elements of the previous analysis were logically related to each other and integrated to make a meaningful contribution towards the development of the objective . This leThe initial database search yielded a total of 382 items, as shown in n = 5), research reviews (n = 2) and those associated with position papers, guides, and white papers (n = 5) were included. All of the retrieved articles were from 2007 onwards, with eight of them published in the last two years. Most of the articles came from the United States, Canada and the United Kingdom. Research articles , diets and related concepts is highlighted in four articles, along with a need to develop standard operating procedures for practices that are focused on SFS and sustainable diet promotion ,42,43,48Six out of the twelve articles focus on the need to improve and complete the higher education, training and professional development requirements towards sustainable food systems; this also includes the training of educators that are responsible for teaching the future nutrition professionals ,42,43,47Social support and external leaderships are seen as strategic in order to maintain momentum towards sustainable means, along with a cultural transition from the current narrow view of dietetic practice ,50.The very scarce literature on sustainable diet promotion in healthcare services illustrates that this is an emerging area of work. Studies still focus on defining the different responsibilities and roles that health professionals should carry out in order to leverage sustainable food systems within their practice. Most articles included in this review were published in the last two years and it is striking that the majority of this research has come out of the US and Canada.The responsibilities of promoting sustainable diets within health services seems to still be reliant on the self-efficacy and individual beliefs of health professionals ,50. HencThis limited interest and lack of self-efficacy are linked to two areas of work yet to be developed. Firstly, the lack of knowledge, which leads to a lack of capacity to practice with a sustainable approach; and, in turn, it demonstrates a huge gap in the areas of higher education and CPD ,42,43,47Food system challenges have an inherent socioecological complexity that requires interdisciplinary approaches to problem solve . The intWhile individual choice is a part of the equation, the food environment plays a significant role in how successful any change in individual behavior will be ,55. DietPettinger (2018) stated that to translate complex, multi-disciplinary sustainability evidence and to influence the sustainability agenda, dietitians and nutritionists need to amplify their visibility, consolidate their skills and become more sustainability literate . There iHowever, there is limited understanding and emphasis on the education and training needs within this emerging area of sustainability practice . MoreoveThe curriculum needs to include new areas of knowledge that belong to the food system but are not exclusive to nutrition, such as environmental sciences, agriculture methodology, food system related policy, food system related strategy design, etc.; the cross-disciplinary approach within the curriculum is essential in order to empower professionals to include sustainability within their practice ,47,48. IPractical training opportunities in sustainable food systems are scarce, and educators do not feel that they are well prepared to face this challenge . InvestiCPD needs to prepare those that are currently active to guide the advocacy of sustainable food systems in the healthcare field ,48. PeerTherefore, meaningful ways to acquire up-to-date knowledge and skills to practice effectively within this emerging multidisciplinary field are needed, as well as evaluation methods for the professional capacity of healthcare workers ,57.Evidence suggests that the scope of practice in promoting sustainable food systems and the prescription of sustainable diets in healthcare services is still limited . As an eNutrition health professionals should focus their community and clinical setting practices in guiding their clients and patients towards making ecologically sustainable food choices that are also healthy. For diet promotion to be feasible, health professionals need to make the sustainable food choice the easy choice for patients by facilitating the connection with local producers, exploring the venues where locally grown produce and animal products are sold directly to consumers and educating patients on seasonal foods and food citizenship (responsible consumption) ,47.In a clinical setting, one of the strongest options for advising and educating patients about sustainable foods is leading by example, with a sustainable food procurement strategy within the organization and menu planning that goes with it ,44,59.Tagtow et al. said that the health benefits of sustainable food systems extend beyond single nutrients to create vibrant and resilient communities  by impacCommunity engagement has also been included as a learning resource for CPD; it gives nutrition professionals the information necessary to advise patients on how to access sustainable food within their communities .To catalyze the social paradigm change, various public policies affecting food systems should be urgently reformed to address climate change and biodiversity loss, to prevent obesity and non-communicable diseases and to make farming viable for the next generation . Within At the same time, at the organization level , nutrition professionals should involve themselves within a sustainable food system strategy development team. They should be involved in the design of the strategic processes to accomplish local and sustainable food consumption within the organization  ,42,47. HThis review identifies two transversal conditions to consider within the role of nutrition professionals advocating for sustainable food systems.Hawkins et al.  advise oThe second transversal concept is the gender approach. The studies included in this review showed that women are prominently represented in nutrition-related professions. The historic split of working roles between genders, where the “feeding” side has always been considered the women’s role, is still present . AlthougDespite the methodological care taken with this research and the reliability of the sources analyzed, a limitation of this scoping review comes from the small sample size of papers . We consider that this has come about because of the novelty of the research question regarding sustainable diet promotion processes in healthcare systems, which confirms the need for the objective of this research and the methodology used and opens up new and important research lines.The healthcare system has a unique opportunity to protect environmental and public health by implementing sustainable food practices in its institutions. Evidence gathered in this review article suggests that nutrition-related health professionals have a huge potential to leverage change; however, currently, their role and impact level is underestimated by healthcare systems in clinical as well as in community settings. Sustainable diet promotion in healthcare services is not yet fully nor equally implemented, as it is more driven by personal beliefs or implications than by actual SOPs that set specific protocols for practice.This review has identified a framework with key areas of work in order to guarantee sustainable diet promotion in healthcare services . Health All of the above require a process approach for transforming the current reality to a desired one by enabling the participation of key stakeholders that come from different realities . The process´ leadership needs to embrace a systemic view of the food system, with a rightful distribution of the value and importance of the stakeholders.The challenges shown in this review also feed into the areas of research and innovation that need to be developed within the healthcare system in order to enable the promotion of sustainable diets within healthcare services.The time is ripe for health professionals to cultivate a systems approach which optimizes the nutrition and health of all eaters while preserving the environment and re-localizing the economy. Also, from a feminist perspective, it is essential to question the leadership model and the established roles and determine time and resources to promote inclusiveness and the mobilization of said roles and privileges.For the change in the social paradigm towards consolidating sustainable food systems, it is essential to define the role of health professionals and the health system as key for the promotion of sustainable diets.


In recent years, there has been a transformation in the value chain of different industrial sectors, like the electricity networks, with the appearance of smart grids. Currently, the underlying knowledge in raw data coming from numerous devices can mark a significant competitive advantage for utilities. It is the case of the Advanced Metering Infrastructure (AMI). Such technology gets user consumption characteristics at levels of detail that were previously not possible. In this context, the terms big data and data analytics become relevant, which are tools that allow using large volumes of information and the generation of valuable knowledge from raw data that can support data-driven decisions for operating on the grid. This paper presents the results of the big data implementation and data analytics techniques in a case study with smart metering data from the city of London. Implemented big data and data analytic techniques to show how to understand user consumption patterns on a broader horizon, the relationships with seasonal variables identify behaviors related to specific events and atypical consumptions. This knowledge helps support decision making about improving demand response programs and, in general, the planning and operation of the Smart Grid. A smart grid is an electrical network that includes digital and emerging technologies to supervise and manage the operating processes for the transport of electricity from generation sources to end-users . The maiSmart grids also promote the integration of new information and communication technologies (ICT) to achieve bidirectional communications and automated control. This combination of traditional and new digital technologies adds intelligence to the network since it increases operational capacity to acquire, communicate, process, analyze, and protect data automatically. Such intelligence allows the operator to have a better situational awareness of the network to enhance control by improving the real-time response to variations that may arise on the grid, which means a contribution towards the interoperability of the grid for the safe and reliable exchange of information . HoweverSince the smart grid incorporates significant processes like generation, transmission, and distribution of electricity, the installation for each of these processes includes a wide variety of equipment like generators, turbines, transformers, switches, current, and potential transformers, among others. The final goal of using ICTs is to measure, monitor, and even control every point in the system. It means all assets can be governed and managed by intelligent devices from generation to consumption, and is optimized based on environmental, social, institutional, or economic restrictions. Thus, the application of ICTs in the smart grid could collect large amounts of data, which also demands the use of big data and data analytics approaches .Experts from McKinsey Global Institute Energy evaluated 150 use cases from several companies resulting in a full study about the impact of data analytics on them. Their reports indicate that the energy sector is one of the nine industries with the most positive potential implications of big data and data analytics. According to that study, utilities potentially have many areas of benefit around incorporating big data and data analytics . For insThe arrival of big data applications for the smart grid brings benefits for both utilities and clients. Big data provides the opportunity to monitor, correct, and better integrate smart grid technologies, extract knowledge from data flowing through the grid, and further generate value and profits for utilities and customers . It can One of the most critical technologies in the deployment of smart grids is the advanced metering infrastructure (AMI). This technology has allowed, in recent years, the installation of a large number of smart energy meters and other measurement terminals on the end-user side . Smart mThis work finds its purpose in the confrontation of two clear challenges in the panorama previously exposed. First, there is the use of big data techniques for the handling and processing of large volumes of raw data from smart meters. These devices generate data at time intervals that previous technologies could not offer. Second, the use of data analytics techniques to process such raw data and transform it into knowledge that adds value to the company or its customers.The case study implemented big data and data analytics techniques with energy consumption information from 5567 London households. The households participated in the low carbon London project between 2011 and 2014. Approximately 1100 customers underwent a dynamic time of use (dToU) energy price scheme in the 2013 year. Our work consisted of integrating a big data architecture and the use of data analytics techniques for advanced metering infrastructure (AMI) data analysis. The latter stage focused on generating value for the utility or its clients through different tasks: analyzing consumer behavior, energy consumption forecasting, and identifying correlations with exogenous data that may lead to better customer characterization and better grid planning and operation.The following section presents a compilation of several applications developed around big data and data analytics for AMI data. The subsequent chapter presents the case study implemented, describing the data sources, the big data framework, and the data analytics techniques implemented. Lastly, the visualization and access stages account for how advanced information analysis can deliver, beyond a superficial description, relevant knowledge for utilities and their clients.One essential component of the smart grid is the advanced metering infrastructure (AMI) technology since it provides utilities with a considerable amount of new information, which was not available with previous measurement systems. Data coming from AMI Smart Meters offer valuable information that utilities can use to optimize business operations or even customer service. AMI systems also provide a pervasive communication infrastructure for constant monitoring and remote control of the grid components .The extensive use of smart meters allows all the stakeholders involved in the operation of the smart grid to obtain benefits in the execution of their roles, and for the client to actively participate in the electric energy value chain, which generates new markets and possibilities for business in a smart grid . These fWith AMI, it is possible to do continuous monitoring of customer consumption, event occurrence, and power quality, as well as open possibilities for constant interaction between users and utilities. One of the most significant consequences of this type of system is the arrival of a high volume of data that has to be processed. According to data from US Energy Information Administration, the number of AMI devices went from 49.1 million to 150.8 million between 2014 and 2017 in which their significant increase was allocated to residential and commercial buildings ,14.This data growing extents the acknowledge about the consumption patterns of customers improving demand response programs, specifying better tariff schemes, and monitoring other details of the network. The large volume of available information requires dedicated platforms and intensive algorithms for processing.A study by Alahakoon D. presents an overview of the smart metering process, starting from AMI, going through advanced analytics, and then reaching all smart grid stakeholders . The autThe work developed around data analytics applications for data coming from smart meters generally proposes two perspectives: the processing platforms/architectures and the methods and algorithms available to process the data.On the one hand, regarding processing platforms Shyam et al. present Apache Spark as a platform to store and run analytics for applications such as automatic demand response, pricing, and real-time data streaming . Liu et On the other hand, several authors have focused on advanced data analytics approaches for process smart metering data. Jha et al. show several advanced data analytics developed on AMI data implemented in the Puducherry Smart Grid Pilot project. In that project, they make data validation, identify meter tampering and missing information, perform energy audit and accounting, and identify peak demand and consumer profiles . Work frRegarding load profile analysis, the work by Hayn et al. performs a characterization of the consumption profiles of the clients and carry out socio-demographic studies to evaluate the influence of specific technologies and appliances on their consumption patterns . Ramos eOne of the most studied applications of AMI systems is load forecasting. Forecasting is a type of regression aimed to predict the future value of a variable given its past values. Such autoregressive models may or may not consider additional or exogenous variables that share the same time-series .The dynamic electricity market depends on the adequate load forecasting for appropriate demand-side management and planning as well as advanced analysis of external data that influence the behavior of customers and the electricity market. Such actions guarantee efficiency and savings for both utilities and customers.MinT approach [There are several reviews of different linear and nonlinear models for forecasting tasks ,34. Hayeapproach .Other authors have included external data sources for their works. For example, Liu et al. study load forecasting by implementing a Map/Reduce framework. The authors cluster geographical data according to weather conditions . The worAnother crucial aspect of the deployment of AMI is the possibility of active participation of customers in smart grids. For example, Kwac et al. present a methodology for demand response targeting by formulating optimization techniques to solve a stochastic knapsack problem (SKP) based on high-resolution data collection . Mogles AMI data analysis applications do not only focus on identifying demand profiles, customer classification, or forecasting tasks. For example, Gómez Lopez et al. make an overview of AMI data potential applications for power distribution loss reduction . Work frThe privacy of customer data is also a significant challenge faced by developments around AMI data, which are consistently available with precise details about their consumption habits. In this sense, Foreman et al. present a methodology to anonymize customer data with smart meters installed on their properties, while preserving the billing services and automatic connection to the centralized system of a utility .Considering emerging technologies, Bereş et al. present a study of several tools based on cloud computing for data analytics. The work mentions the benefits of considering aspects like security, availability, and reliability. Moreover, they show the possibility to process data in real-time safely . Yan et Next, Although Reference architectures generally serve as a template for developing solutions in an orderly manner in a specific field and may be used for comparison and alignment purposes. The architecture proposed by NIST brings together common elements found in different documented case studies around the world. The reference architecture presented in Five primary roles compose the reference architecture.System Orchestrator: it defines and integrates the required data application activities into an operational vertical system. It provides the overarching requirements about business ownership, governance, data science, and system architecture.Data Provider: it introduces new data or information sources into the big data system, either online or offline. It is also responsible for data persistence (hosting), data scrubbing , metadata (for history and repurposing), policy for others’ access to data, and query without transferring (sometimes).Big Data Framework Provider: supplies a computing infrastructure while protecting the privacy and integrity of data. Some resources or services used by the big data application provider are infrastructure framework , data platform , and processing (software support for applications).Big Data Application Provider: it executes a life cycle to meet security and privacy requirements. It also develops system orchestrator-defined requirements, mechanisms to capture data, preparation, analytics , visualization , and access to the results of the data system.Data Consumer: includes end-users or other systems that use the results of the big data application provider: search and retrieve, download, analyze locally, and reporting and visualization.The data consumer role is in charge of understanding the results. For this, the tasks of visualization and access to the data developed by the application provider are of great importance. On the one hand, visualization allows the results of the analysis carried out to be communicated to an audience to facilitate interpretation and understanding . On the In a utility, there can be different types of data consumers including developer engineers, operational coordinators, decision-makers, and others. Depending on their role in the operation of the utility, each one requires different access to information aggregated or disaggregated at a certain level. An operation coordinator may require detailed operational data about events or clients, while an executive officer may require more comprehensive results on the performance and economy of the utility. Visualization and access to the correct type of information facilitate the communication of results in the way that each individual requires .One of the chapters of the framework presented by NITS documents 51 use cases in industries referring to big data and data analytics applications . From alSo far, we presented an overview showing the importance of AMI deployment within smart grids, given the variety of applications that can potentially generate value for a utility and its customers. A review of some developments focused on the processing of a large volume of AMI data generated by smart meters using big data and data analytics techniques. The model formulated by NIST, described above, stands out as a development framework for big data applications.As motivators for this development, it is necessary to consider the importance of the role of AMI in the smart grid, the availability of a high volume of AMI data, and the growing need for developments focused on analysing this raw data. In this way, the main objective of this work is to implement an exploratory study case that allows us to demonstrate the potential of big data and data analytics techniques applied to AMI data processing.According to the roles depicted by the architecture, for the case described in this paper, there is a big data framework provider in charge of implementing the information value chain. In addition, a data provider includes mainly AMI data coming from smart meters and some exogenous data. The objective is to develop an application acting as a big data application provider that covers the entire process, from collection to final access so that utilities can use that data as the data consumer.The next section describes the case study in detail. The case study presented is exploratory, i.e., the aim was to explore the benefits of different techniques (or at least some) of data analytics techniques applied in an AMI dataset to extract knowledge from raw data. This exploration includes implementing machine learning algorithms for descriptive and predictive analysis. However, a utility can require implementing more sophisticated analytical techniques for a very particular purpose, e.g., the identification of consumption patterns of a customer sector, the financial evaluation of one of its demand response programs, or the identification of losses at different points in the grid.An exploratory case study used smart metering data published by the London Datastore from the Mayor’s Office of London. Data was collected by UK Power Networks as part of the Low Carbon London Program to investigate the impact of different low carbon technologies in the London electricity distribution network . For thiThree sources of data were collected . The gathered data consisted of two parts: meter records and customers’ tariff. The first and most significant part contains around 160 million records from 2011 to 2013. The size of the raw dataset was near 11 GB, which includes the following fields.Unique household identifier,Tariff program of each household (Standard or Dynamic Time of Use),Energy consumption  of each household,Date and time,CACI Acorn group, andCACI Acorn category.Acorn is a geo-demographic segmentation of the UK’s population. This classification segments households into six categories, 18 groups, and 62 types considering social factors and population behavior . Acorn cThe second part contains the detail of the tariff applied to ToU customers throughout each day of 2013. More information is available at the London Datastore Website .External datasets from weather and holidays allowed us to explore the correlation between energy consumption and exogenous variables.Weather data. It came from two datasets for climatic variables like temperature, humidity, pressure, visibility, sunset, and sundown, among others. The first dataset, with daily granularity, includes 30 climatic variables. The second dataset has 1-h granularity, detailing 10 weather variables. Both datasets, collected from the Dark Sky Company API, included data between the years 2011 to 2014 .Holiday’s data. A list of the official UK holidays from 2011 to 2014, was collected from UK Government Digital Service .The Big Data Laboratory of the National Institute of Electricity and Clean Energies (INEEL) played the role of big data framework provider, depicted in Apache HBase, the Hadoop open-source, non-relational, NoSQL, and distributed database .Apache Spark, an analytics engine for large-scale data processing .Apache Zeppelin, a web-based notebook environment that enables interactive data analytics to run different programming languages .The Apache Hadoop processing cluster consists of one master unit and two slaves. Each unit has Xeon E5-2640 processors with 16 cores, 2TB × 7 hard disks, and 128 GB RAM. All of them run Linux OS. The cluster also has big data tools such as:Furthermore, a workstation was networked to the processing units to provide an interface to access the master and slave nodes in the cluster.The application implementation process consists of four main stages: data collection, data preparation, analysis, and visualization. We present the following.The data collection stage implemented a Zeppelin notebook, running a PySpark interpreter to store all data collected into Spark data frames. A Spark data frame is a distributed set of data organized into columns, similar to a relational table. These data frames allow scalability of computation in processing clusters and integration with all big data tooling and infrastructure via Spark .The raw data available in comma-separated values (CSV) files were imported into the processing cluster in eight data frames, as presented in Data preparation is also called the ETL stage from big data lifecycle: extraction (by collecting data), transformation (by curating and preparing data), and loading (by saving data warehouse into HBase). The ETL process begins with raw data, which is the basis for building a data warehouse. The construction of the warehouse included two processes: filtering and data imputation. Filtering to discard incomplete records that cannot be estimated or that are not suitable to be considered in a specific metric . Data imputation to complete missing data, when possible .In addition to data filtering and data imputation, it was also necessary to establish correlations and groupings between the different data sources or analyzing data with different granularity. Therefore, a data warehouse included different data frames, according to their type, granularity, or the kind of analysis to be performed.Since our study was exploratory, we built a data warehouse with different time granularities in addition to the original raw data time-base (30 min): half-hourly, hourly, daily, weekly, and monthly. We also created data frames with information corresponding to the ACORN groups and the individual characteristics of the clients.The initial database contained around 167 million records distributed in 8 data frames. The consolidated data warehouse stored about 676 million records distributed across 29 Spark data frames of different combinations of time granularities and grouping criteria.This redundancy of data is then a consequence of the use of big data tools and facilitates data analytics. Therefore, it is possible to deal with complete datasets instead of a sample of data, as is done in some statistical approaches.Although Spark data frames lie in memory, it is possible to assign some persistence of data frames on the hard disk. Nevertheless, the most appropriate way, especially if persistent access to the data is required, is to save them into the Hadoop File System (HDFS) permanently. For this purpose, the entire data warehouse was migrated to HBase using Apache Phoenix .Once we adequately transformed and stored the necessary data with different granularities, an initial descriptive analysis was necessary before using different algorithms to perform predictive analysis. We performed a descriptive visual analysis by connecting the data stored in the processing cluster to Tableau visualization tools .For this task, we depicted a graphic description of data with Tableau to understand the usage of energy by different user categories and time granularities.For example, Affluent, for all households classified as acorn affluent achieversComfortable, grouping the households from rising prosperity, comfortable communities, and financially stretched categoriesAdversity for households belonging to the urban adversity acorn category.The case study considered five Acorn categories: affluent achievers, rising prosperity, comfortable communities, financially stretched, and urban adversity. However, rising prosperity, comfortable communities, and financially stretched groups presented reasonably similar trends. Therefore, for this study, these categories were grouped as a comfortable category. The new classification, referred in this paper as grouped categories, correspond to:We analyzed consumption patterns for each grouped category at different time horizons: hourly, daily, and monthly. We also made a differentiation between consumption on workdays and holidays. For each category and time horizon, we estimated average, maximum, and minimum consumption and compared users with Std and ToU tariffs.In addition to the analysis of the consumption patterns of users and categories, we included information about exogenous variables such as weather and seasons. We analyzed the effect of variables such as temperature, daylight hours, and seasons of the year on the consumption habits of each grouped category.The main goal of predictive analytics is to use current and historical information to find future patterns or characteristics not explicit in the available information . In thisClustering is a machine learning task aimed to group sets of objects with similar characteristics . In thisTherefore, we now have users segmented considering two different criteria. On one hand, the criteria includes their geo-demographic characteristics and, on the other hand, the criteria involves their consumption patterns. For example, users from the adversity category (geo-demographic based) intuitively should match those users assigned by our clustering algorithm to the low consumption category (energy consumption-based). However, this expectation is not mandatory, e.g., adversity users with high energy consumptions. The possibility of having two user segmentation criteria that may or may not coincide is useful when identifying possible atypical behaviors. The evidence of possible atypical behaviors might be one of the advantages of having complementary grouping criteria.We used K-means as a clustering algorithm. K-means proposes that a set of objects has as many centroids as groups/categories require. Each object is assigned to the group with the closest centroid to the object’s coordinates. Next, Equation (1) describes the objective function of the K-means algorithm.The selected value of There were two possible options considered in the algorithm implementation: the Spark MLlib library  and the The main input arguments for the k-means clustering algorithm in TSlearn are the number of clusters  model, expressed as follows.The estimation function  forests .On the one hand, artificial neural networks (ANN) are assemblies of single neurons that acquire knowledge from experience with historical data. The assemblies establish their learning parameters to predict a future value with the least possible error, which is similar to the learning process of a real neuronal system. On the other hand, decision trees are trees whose ramifications represent possible decisions made according to the characteristics of each record in the available data. In the end, after the decisions are taken based on the available characteristics, each record is assigned a final value or a class label depending on the machine learning application .Due to their implementation simplicity, decision trees have evolved into more robust versions as random forests, which are ensembles of decision trees. A random forest operation proposes that several weak learners join to form a robust learner to increase predictive power and robustness for more extensive and more complicated datasets. These estimators have become widely used for predicting energy consumption ,37.For this case study, the estimation function . We designed Tableau dashboards  to preseThe first two workbooks correspond to the results of descriptive analytics. They include a set of dashboards that display electricity consumption by customers with different time granularities  and a section to visualize the difference in consumption between workdays and holidays. Dashboards also include visualization of consumption and payments differentiated by tariff (Std or ToU).The last three books condense the results of predictive analytics, which result from implementing the forecasting and clustering algorithms. The dashboards with the forecasting results allow seeing information disaggregated by customers and categories. The clustering dashboards present a comparison between the groups assigned to the TSlearn categories and the original grouped categories.Throughout Below, we detail the results obtained in the implementation of the data analytics stages.This section presents the results of the data analytics tasks that we developed: descriptive analytics and predictive analytics that included clustering and forecasting algorithms.While the customers with the standard tariff do not modify their consumption in these hours, customers with ToU tariff reduces the consumption sharply for the same period. Reduction of energy consumption through client awareness is one of the objectives of a demand response program.There are also some other exciting findings in households’ consumption patterns. For example, a utility may find it useful to detail the consumption difference between a working day and a holiday, as observed in Regarding weather variables, there were two of them with an evident relation with households’ consumption, temperature, and light hours per day. For this task, we used the Silhouette score to measure the quality of the clustering exercise. This value varies from −1 to 1. A Silhouette score = 1 means that the clusters are well-differentiated or segmented and that elements inside each group are similar or closer to each other. The standard deviation of the Silhouette scores in the 50 experiments was 0.0049, which indicates algorithm repeatability.The results of the clustering algorithm are presented below in As previously stated in However, as we said before, the idea behind proposing a new type of customer segmentation is not to dismiss their initial classification, but to find value in the information provided by both groupings. For instance, Other clients that may be subject to verification are those that, belonging to an affluent category, present low electricity consumption. These types of correlations can help to recognize, for example, commercial or industrial activities (with high demand for electricity) installed in residential sectors, imbalances in the nodes of the distribution network, or non-technical losses.2 score, mean absolute error, root mean square error, and mean square error) obtained in the forecasting of energy consumption for each case including the average error measure obtained for all 5509 m and each category.In general, forecasting for the grouped categories has good performance indicators, unlike forecasting for individual meters. In that case, the performance was lower, considering that not all meters had the same amount of records available for the construction of their model.This paper presents the results of the application of big data and data analytics concepts in a case study with AMI data, taken from the smart energy meters in the city of London. The work developed shows the transformation of raw data into knowledge that allows rapid identification of trends, average and individual patterns, unusual events, and, in general, provides support for data-driven decisions aimed at the best planning/operation of the distribution system.Data processing on a distributed platform allowed the consolidation of a data warehouse with more than 600 million records including aggregate information in different temporal granularities and categorical grouping. In addition, it includes external variables that are highly related to the patterns of consumption for about 5,500 households.Descriptive analysis showed how households that participated in a dynamic time of use (ToU) tariff program had lower electricity consumption, which indicates the positive impact that the implementation of a demand response program can have when supported by the installation of Advanced Metering Infrastructure (AMI).There is a high relation between variables like temperature and light hours with users’ electricity consumption. In addition, it is important to notice how these changes influence specific consumption habits. For example, the consumption between workdays is, in general, lower than during holidays. However, this behavior is different in the summer months, which may be related to seasonal changes in temperature.Given that the number of elements to be grouped was relatively small (5509 households) and we knew the number of clusters to group users (we aimed to get three groups), K-means was a fit candidate as a clustering algorithm. However, the potential application of this clustering task can grow up to hundreds of millions of users ,14. In tThe application of clustering techniques  allowed proposing a new household segmentation different from the one assigned initially . When compared, these two types of categories allowed identification of users with atypical patterns. It might represent industrial or commercial activities in residential sectors, technical or non-technical losses or even help planning better pricing schemes for demand response programs.The computational resources used for data processing (a distributed data processing cluster) facilitated the implementation of the case study from the role of a big data framework provider. For example, this includes consolidation into a distributed framework of a data warehouse. This consolidation, which involved managing millions of records, was a simple but time-consuming task. Future work might include a test benchmark to evaluate the performance of this type of application, depending on the computational infrastructure detected.The forecasting task presents one of the main challenges in the analysis of AMI data. In this case study, the implemented algorithm presented significantly low error indicators, even in the case of individual meters. The algorithms implemented in this case study are not mandatory. Nevertheless, the results show the potential of combining an autoregressive algorithm (NARX) with an ensemble of learning entities (Random Forest) and libraries specialized in time series data (fireTS) as part of the data life cycle for this type of application.For future work, we propose a more sophisticated development of the stages of visualization and access. As mentioned in Furthermore, the inclusion of a sentimental analysis of information might be useful. That is the correlation of electricity consumption with events that can show trends in social networks: international impact news, world-order events such as Olympic Games, FIFA World Cups, and others.


Radiation-induced brachial plexus neuropathy (RIBPN) is a rare and delayed non-traumatic injury to the brachial plexus, which occurs following radiation therapy to the chest wall, neck, and/or axilla in previously treated patients with cancer. The incidence of RIBPN is more common in patients treated for carcinoma of the breast and Hodgkin lymphoma. With the improvement in radiation techniques, the incidence of injury to the brachial plexus following radiotherapy has dramatically reduced. The currently reported incidence is 1.2% in women irradiated for breast cancer. The progression of symptoms is gradual in about two-thirds of cases; the patients may initially present with paresthesia followed by pain, and later progress to motor weakness in the affected limb. We present the case of a 68-year-old female patient with breast cancer submitted to surgery, chemotherapy, and radiotherapy in the year 2000. Eighteen years later, she developed symptoms and signs compatible with RIBPN and was successfully submitted to omentoplasty for pain control. Omentoplasty is an alternative treatment for RIBPN refractory to conservative treatment, which seems to be effective in improving neuropathic pain. However, postoperative worsening of the motor strength is a real possibility, and all candidates for this type of surgery must be informed about the risk of this complication. Radiation-induced brachial plexus neuropathy (RIBPN) is a delayed non-traumatic injury to the brachial plexus. It is a rare condition that occurs following radiation therapy to the chest wall, neck, and/or axilla in patients previously treated for cancer.,The occurrence of RIBPN is higher in patients treated for carcinoma of the breast and for Hodgkin lymphoma. With the improvement in radiation techniques, the incidence of injury to the brachial plexus following radiotherapy has dramatically reduced, and the current incidence in women irradiated for breast cancer is 1.2%.,RIBPN symptoms can occur from 6 months to 20 years after radiotherapy . The most frequent symptoms are numbness, paresthesia, dysesthesia, lymphedema, and motor weakness. Neurogenic pain is present almost in all cases—the intensity of which varies from mild to incapacitating.The progression of symptoms is gradual in about two-thirds of cases. Patients may initially present with paresthesia followed by pain, and later develop motor weakness in the affected upper limb. The pain often subsides when the motor weakness becomes more severe.For the quantification of normal neurologic tissue damage after radiotherapy, there are several scores. One of the most often used is the LENT–SOMA score: late effects of normal tissues (LENT)–subjective, objective, management, and analytic (SOMA). This score is useful for the stratification and clinical/ surgical management of these patients.For RIBPN, the LENT–SOMA score has 4 grades and respective approaches. Grade 1: mild sensory deficits—no treatment required; grade 2: moderate sensory deficits, tolerable pain, mild arm weakness—conservative management indicated; grade 3: continuous paresthesia with incomplete paresis—surgical or conservative management may be opted; grade 4: complete paresis, excruciating pain—surgical management required.A 68-year-old female was diagnosed with breast cancer in the year 2000 and was submitted to a right side quadrantectomy and axillary emptying, chemotherapy, and radiotherapy. The disease was controlled, and no recurrence or intercurrence occurred from 2000 until 2018.In 2018 the patient started to complain of tingling in the right hand, progressive hypoesthesia in the right upper limb. Six months later, progressive loss of strength in the right upper limb began, which was associated with neuropathic pain in the upper limb (mainly in hand), and lymphedema.The symptoms became progressively worse and were refractory to the prescribed medications  and physiotherapy.In 2019 the patient was referred to the Peripheral Nerve Surgery Unit of the Neurosurgery of the University of São Paulo Medical School, with a neuropathic pain visual analog scale (VAS) of 10/10 and exuberant lymphedema in all upper limb. She complained of tingling in the lateral palmar region, the dorsal region of the hand, and the lateral areas of the upper arm and forearm.On neurologic examination, the motor strength was graded 4 for arm abduction; 3 for arm adduction, elbow extension, and intrinsic muscles of the hand; and 0 for elbow flexion.With these symptoms and neurological examination, the patient was classified as LENT–SOMA grade 4.This clinical picture raised the diagnostic hypothesis of RIBPN, and surgical treatment was proposed. The expectation of results and the risk of complications were extensively discussed with the patient.The patient was submitted to a supraclavicular and infraclavicular surgical exploration of the brachial plexus. The main intra-operative findings were (i) fibrotic tissue adhered to all elements of the supra clavicular brachial plexus A, and iB. Under Post-operatively, the patient was submitted to lymphatic drainage and physiotherapy and was discharged on the third postoperative day. At the 6-month follow-up, she was still experiencing tingling in the lateral palmar region, the dorsal region of the hand, and the lateral areas of the upper arm and forearm. The lymphedema improved, and the neuropathic pain achieved (VAS) 0/10, motivating a progressive pain killers’ withdrawal. The patient’s motor strength was equivalent to her preoperative strength in arm adduction (grade 3) and elbow flexion (grade 0); however, it had become worse (grade 2) in arm abduction, elbow extension, and the intrinsic muscles of the hand. With these symptoms and neurological examination, the patient was classified as LENT–SOMA grade 2.Owing to the disabling effect of the previous neuropathic pain, the surgical treatment was deemed satisfactory, despite the worsening of the motor strength.,The peripheral nerves are considered somewhat radioresistant due to their protected position, low metabolism, and low reproductive capabilities.Chemotherapy was also reported to increase the incidence of RBP significantly. It is believed that, in this setting, the lesion happens by a direct mechanism of chemo in the vessels, and due to a reduction in the metabolism of a tissue that has an inherent low metabolic rate.RIBPN is a rare but devastating complication that can disable the patient.In our case, the patient presented a typical clinical course of RIBPN with neuropathic pain as the most disabling symptom. The total or partial pain control was the focus of the treatment.Surgical exploration is fully justified in grades 3 and 4 of the LENT–SOMA score.,,,The rationale for surgical exploration in patients with RIBPN is the mechanism of lesion – direct lesion to (i) the nerve, (ii) surrounding soft tissues with fibrosis and (iii) vascular lesion at the vasa nervorum. These three injury components promote the neuropathic pain due to compression of brachial plexus, sensory and motor deficits by neuropathy, which is worsened by hypoxia – vascular lesion.In an attempt to manage the physiopathological mechanisms, the surgical exploration of the plexus, neurolysis with a free graft (muscle or omentum), or pedicled omentoplasty has shown positive results.In 1983, NarakasAfter Narakas experience, a few case reports or small case series were performed. However, we based our therapeutic option for our patient in the Brunelli and BrunelliIn 2019, Warade et al.We believe that omentoplasty is the best choice for surgical treatment of RIBPN based on Brunelli and Brunelli,Despite the absence of strong evidence, we support our decision in some previous case series that have suggested that it is the best alternative for refractory pain relief, although the results for motor strength recovery are poor.There is a paucity of published reports related to omentoplasty; we believe that the publication of this case can help doctors dealing with this type of problem. This case could assure them (and their patients) that despite the fact that in most cases there is no improvement in—and even some worsening of—the motor deficit (as in our case), post-operatively the effect on the neuropathic pain is usually good, with a decrease in its intensity or even with complete control.The expected surgical outcome was widely discussed with the patient, and all ethical aspects were respected for the case publication.Omentoplasty is an alternative treatment of RIBPN refractory to conservative treatment. It is a safe and effective treatment to improve neuropathic pain; however, worsening of motor strength is a real possibility, and the patient must be alerted to this before the surgery occurs.


Differential expression analyses provide powerful tools for the identification of genes playing a role in disease pathogenesis. Yet, such approaches are usually restricted by the high variation in expression profiles when primary specimens are analyzed. It is conceivable that with the assessment of the degree of coordination in gene expression as opposed to the magnitude of differential expression, we may obtain hints underscoring different biological and pathological states. Here we have analyzed a publicly available dataset related to frailty, a syndrome characterized by reduced responsiveness to stressors and exhibiting increased prevalence in the elderly. We evaluated the transcriptome that loses its coordination between the frailty and control groups and assessed the biological functions that are acquired in the former group. Among the top genes exhibiting the lowest correlation, at the whole transcriptome level, between the control and frailty groups were TSIX, BEST1 and ADAMTSL4. Processes related to immune response and regulation of cellular metabolism and the metabolism of macromolecules emerged in the frailty group. The proposed strategy confirms and extends earlier findings regarding the pathogenesis of frailty and provides a paradigm on how the diversity in expression profiles of primary specimens could be leveraged for target discovery. Frailty is a clinical syndrome that is characterized by reduced responsiveness to stressors due to physiological decline in multiple organs and is associated with poor health outcomes including falls, incident disability, hospitalization, and mortality –4. FrailDespite that this condition is being recognized as a distinct clinical entity, our understanding of its pathogenetic mechanism remains limited. Comprehensive molecular studies at the whole transcriptome level, were only recently initiated underscoring the role of a proinflammatory response in the development of this condition , 9. DespConventionally, gene expression analyses aim to identify differentially expressed genes in predefined experimental groups. In such analyses, the magnitude of over- or under-expression is considered indicative for the impact of the corresponding genes in the pathology of interest. Such strategies are frequently limited by the variation in expression between specimens which is particularly relevant when genetically diverse specimens are analyzed , 11. To To apply this strategy to frailty we have reanalyzed publicly available data extracted from a comprehensive study that was performed in individuals that developed this syndrome .By arbitrarily selecting at least 70 reads as the cut-off in the NOR group we identified 178 highly expressed transcripts. This limit was set for the convenience of the calculations and in theory could be increased indefinitely, provided that appropriate tools for computational analysis are developed. For the same reason specimens were assigned to only 2 groups, the NOR and the FRA groups, however additional sub-groups could be utilized, if a higher number of samples were available.Initially, we asked how the expression among these 178 highly expressed genes is correlated between the NOR and FRA groups. To that end we calculated the correlation coefficient R (Pearson’s) for all pairwise comparisons between these 178 highly expressed genes, generating a heatmap illustrating the correlation in their expression. As shown in ectopia lentis, an eye disorder [Subsequently, we estimated how the whole transcriptome is correlated with these 178 genes and compared how this correlation changes during frailty. To that end, a composite correlation (Pc) was calculated for each gene which corresponds to the correlation of the R values this gene has, with the whole transcriptome between the NOR and FRA groups. Then, we ranked these genes according to Pc . Therefodisorder .In order to better understand the relevance of loss of coordination in TSIX, BEST1 and ADAMTSL4 we ranked the transcriptome according to its coordination with these 3 genes, Then, by using R=0.5 as a cut-off, we subjected the corresponding transcriptome to GO analysis , 15. ThiThese findings confirm and extend previous findings on the role of immune system in the pathogenesis of frailty and also identify the significance of metabolic deregulation or reprogramming in the development of this syndrome. In addition, they provide novel gene targets that may play a role in the development of this condition. It is conceivable that refinement of the proposed strategy, by including larger datasets and deeper and more expanded roster of genes to initiate the coregulation assessment, will be applicable to various conditions and be leveraged - as opposed to be restricted - by the high variation, when genetically diverse specimens are analyzed.Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study of the National Institute on Aging Intramural Research Program (NIA IRP), National Institutes of Health. In our analysis we assigned the specimens in 2 groups, with (FRA) or without (NOR) frailty, consistently with the classification of the original study [Data used were retrieved from GEO (Accession number: GSE129534). Specimens’ characteristics are described in detail in the original study [The experimental outline we applied is shown in Supplementary Table 1Supplementary Table 2