Distinct from normal differentiated tissues, cancer cells reprogram nutrient uptake and utilization to accommodate their elevated demands for biosynthesis and energy production. A hallmark of these types of reprogramming is the increased utilization of, and dependency on glutamine, a nonessential amino acid, for cancer cell growth and survival. It is well-accepted that glutamine is a versatile biosynthetic substrate in cancer cells beyond its role as a proteinogenic amino acid. In addition, accumulating evidence suggests that glutamine metabolism is regulated by many factors, including tumor origin, oncogene/tumor suppressor status, epigenetic alternations and tumor microenvironment. However, despite the emerging understanding of why cancer cells depend on glutamine for growth and survival, the contribution of glutamine metabolism to tumor progression under physiological conditions is still under investigation, partially because the level of glutamine in the tumor environment is often found low. Since targeting glutamine acquisition and utilization has been proposed to be a new therapeutic strategy in cancer, it is central to understand how tumor cells respond and adapt to glutamine starvation for optimized therapeutic intervention. In this review, we first summarize the diverse usage of glutamine to support cancer cell growth and survival, and then focus our discussion on the influence of other nutrients on cancer cell adaptation to glutamine starvation as well as its implication in cancer therapy.  More than half a century ago, Harry Eagle noticed that the supplementation of glutamine at millimolar levels in tissue-culturing medium could robustly enhance cell growth and proliferation . Since tIn contrast to in vitro tissue culture conditions, where glutamine is supplied at millimolar levels, the level of glutamine in tumor tissues in vivo was found significantly lower when compared to the normal surrounding tissues or plasma ,9,10. ItGlutamine is a NEAA that can be synthesized de novo by using glucose-derived carbon and free ammonium in mammals. Thus, glutamine acquisition through diet is not necessary. Even still, glutamine is one of the most abundant amino acids in human plasma (0.5~0.8 mM), consistent with its versatile usage as a biosynthetic substrate. First, glutamine provides carbon and nitrogen atoms to synthesize nucleotides and other NEAAs. Of note, the carbon and nitrogen atoms can come directly from glutamine or from glutamine-derived metabolites at multiple biosynthetic steps A. For ex+ ratio, which inhibits oxygen consumption and ATP production [Second, glutamine can drive the TCA cycle and ATP production. One reason that cancer cells rely on high levels of exogenous glutamine is because glutamine can be used to fuel the TCA cycle through α-ketoglutarate to allow its further oxidation . It was oduction . Thus, i+ to generate NADPH  B. It wasThemes of glutamine starvation were first brought to attention by nutritionists who noticed that plasma glutamine levels reduce significantly during severe injury . SimilarConsidering the fact that cancer cells increase glutamine consumption to support biosynthesis, the observed reduction of glutamine concentration in the blood stream of cancer patients raises the belief that glutamine limitation may occur during tumor progression. Indeed, glutamine levels in tumor tissues or body fluids around tumor tissues were found lower than normal tissues or plasma ,9,10,40.Of interest, studies from the past five years suggest that accessibility to other nutrients, particularly NEAAs, can profoundly affect tumor cells’ responses to glutamine starvation ,45,46,47Asparagine, a NEAA that can be synthesized de novo from glutamine, was found to be able to suppress glutamine-depletion-induced apoptosis in brain tumor cells . Since aThese two works indicate that most glutamine-dependent biosynthetic activities can still proceed, with the exception of asparagine production, when exogenous glutamine is absent. Indeed, work done in vasculature-forming endothelial cells and Kaposi’s sarcoma-associated herpesvirus (KSHV)-transformed fibroblast cells showed a similar effect of asparagine to support cell survival and proliferation following glutamine depletion ,49. ImpoFurthermore, like glutamine, intracellular asparagine was also found to be able to function as a counter ion for the exchange of extracellular EAAs to sustain mTORC1 activity ,50. SuppIn addition to asparagine, two recent papers have shown that aspartate, another NEAA that can be synthesized de novo from glutamine-derived carbon and nitrogen atoms, plays a critical role in tumor cells’ adaptation to glutamine starvation or the inhibition of glutamine catabolism ,45. TajaArginine was recently found to be able to support tumor cell adaptation to glutamine starvation . It was Unlike asparagine, aspartate and arginine, cystine was found to increase tumor cells’ sensitivity to glutaminase inhibition, when supplemented above physiological levels in tissue-culturing medium . Since cAll the results above mentioned suggest the existence of a comprehensive program to mediate tumor cell adaptation to glutamine limitation. However, the fact that more than one single amino acid can alter tumor cells’ responses to glutamine starvation or its catabolic inhibition creates a challenge for the efficacy of targeting glutamine metabolism in cancer. An obvious question one may have is why tumor cells choose different amino acids to modulate their dependencies on exogenous glutamine. The simplest answer is that the intrinsic property of tumor cells differs. This specificity can be dictated by tumor origin, oncogene/tumor suppressor status and tumor microenvironment. For example, one may predict that tumors with loss of function of p53 may not be able to adapt to glutamine starvation through uptake of aspartate or arginine. Further investigation is needed for the development of complementary strategies that should be combined when targeting glutamine metabolism in cancer.Another way to reconcile the diversity of other amino acids to support tumor cell adaptation to compromised glutamine metabolism is to define the most critical limiting metabolite. The fact that asparagine alone is able to rescue cell proliferation without restoring the TCA cycle intermediates and other glutamine-derived NEAAs following glutamine depletion provides a compelling example . In thisHowever, one may question why tumor cells cannot simply use glutamine-derived carbon and nitrogen to synthesize asparagine through asparagine synthetase (ASNS), an enzyme highly expressed in solid tumor cells , if glutDefining the critical limiting metabolite in each context will not only elucidate the fundamental connection between the biochemistry of glutamine metabolism and its biological functions, but will also provide new metabolic targets that can be inhibited to enhance the efficacy of targeting glutamine metabolism in cancer. However, to define the critical limiting metabolite, several factors need to be considered rigorously. First, the amino acid composition in the tissue-culturing medium defines the types of starvation. In the setting of Dulbecco’s modified Eagle’s medium (DMEM), which does not contain exogenous asparagine and aspartate, glutamine deprivation is anticipated to cause intracellular depletion of at least these three amino acids ,44,46. ISecond, glutamine deprivation and glutaminase inhibition may define different types of critical limiting metabolite. In experimental conditions where exogenous glutamine is depleted or reduced to a low level, one may predict that all glutamine-dependent cellular processes are compromised. However, glutaminase inhibition is anticipated to block glutamine to glutamate conversion and its further utilization in the TCA cycle, NEAA production and nucleotide biosynthesis. In this setting, intracellular glutamine itself and any glutamine-dependent growth/survival signaling are not expected to be limiting. The fact that exogenous aspartate rescues glutaminase inhibition through nucleotide synthesis but not the TCA cycle and other NEAAs suggests that these metabolic activities can proceed, likely by using other carbon sources in the tissue-culturing medium  Figure D. It neeThird, the critical limiting metabolite that is responsible for cell proliferation and survival can be different under glutamine limitation. Since cell proliferation requires biomass accumulation, one may anticipate that when a single amino acid can rescue cell proliferation during glutamine deprivation or glutaminase inhibition, biosynthesis of all the macromolecules must be able to continue B–D. In tUnlike in vitro cell culture experiments where we can precisely control the amount of nutrient in tissue-culturing media, the nutrient accessibility in the tumor environment fluctuates. In general, tumors with poor vascular supply of nutrient have a higher chance to suffer from glutamine starvation as well as to engage adaptive mechanisms to mitigate the stress. Even with efficient vascular supply, the concentration of glutamine in the blood stream and body fluids is several folds lower when compared to most tissue-culturing media. On the other hand, unlike in vitro experiments where tumor cells are often subjected to extreme glutamine deprivation, the stress caused by glutamine limitation in tumor cells in vivo may be compensated to a certain extent by a low rate of continuous supply. To determine tumor cells’ dependency on exogenous glutamine at a nutritional status close to physiological conditions, Tardito et al. (2015) developed a new formula of medium containing nutrient at concentrations close to the human serum. In this medium, glioma cells can grow without exogenous glutamine . Of inteThe ability of glioma cells to acquire glutamine from astrocytes reminds us of the importance of other cell types co-existing in the tumor microenvironment . In modeSince the first description of “glutamine addiction” in cancer cells, the potential to target glutamine metabolism has been extensively studied in pre-clinical models. However, the existence of various adaptive mechanisms to mitigate the stress caused by glutamine limitation plus a complicated tumor microenvironment can create practical challenges. Due to toxicity and the lack of specificity, most chemical inhibitors of glutamine uptake and catabolism are still used as tool compounds . The besAnother difference between glutaminase inhibition and glutamine deprivation is the existence of other glutamine to glutamate converting enzymes that can compensate when glutaminase is inhibited. These include the enzymes in the biosynthesis of asparagine, nucleotides, NAD and glucosamine . All theSince tumor cells can use other amino acids to mitigate the stress caused by glutamine limitation or inhibition of glutamine catabolism, perturbing the acquisition and metabolism of these amino acids should be considered at least as a complementary approach when we target glutamine metabolism in cancer. For example, exogenous asparagine is a limiting nutrient for acute lymphoblastic leukemia (ALL) cells to grow due to their inability to synthesize asparagine de novo . As a reDespite an enormously growing understanding of glutamine metabolism and its diverse biological functions in cancer, challenges remain before this pathway can be broadly targeted to treat cancer patients. First, we need to define specific mechanisms mediating tumor cell adaptation to glutamine limitation. Tumor cells can adapt to glutamine limitation through various mechanisms, reflecting a metabolic plasticity that may be selected during tumor evolution as a means of optimized nutrient utilization. The specific mechanism that a tumor cell chooses is dictated by tumor type or subtype, oncogene/tumor suppressor status, tumor site and stages of tumor development. Defining a specific adaptive mechanism can not only facilitate the decision of targeting glutamine metabolism or not, but also provide insights into new pathways that may need to be inhibited together to maximize the efficacy of targeting glutamine metabolism. Second, since the existence of other nutrients can profoundly affect tumor cell response to glutamine limitation, it is critical to define the precise nutrient status in the tumor environment. Currently, most mass spectrometry (MS)-based quantification of nutrients from primary tumor tissues do not distinguish between nutrient levels inside tumor cells or in the surrounding tumor environment. Furthermore, an efficient method needs to be developed to timely separate tumor cells, stromal cells and immune cells from the same tumor tissue for metabolite measurement. Third, we need to further understand the metabolic interaction between tumor cells and immune cells, such as T cells, when glutamine uptake or catabolism is compromised. It has come to light that many glutamine-dependent cellular functions have gone beyond cancer cells. In T cells, perturbation of glutamine acquisition and catabolism has a profound effect on T cell differentiation and immune response ,69,70. T


Conventional therapies for cancer such as chemotherapy and radiotherapy remain a mainstay in treatment, but in many cases a targeted approach is lacking, and patients can be vulnerable to drug resistance. In recent years, novel concepts have been emerging to improve the traditional therapeutic options in cancers with poor survival outcomes. New therapeutic strategies involving areas like energy metabolism and extracellular vesicles along with advances in immunotherapy and nanotechnology are driving the next generation of cancer treatments. The development of fields such as theranostics in nanomedicine is also opening new doors for targeted drug delivery and nano-imaging. Here we discuss the use of innovative technologies presented at the Irish Association for Cancer Research (IACR) Annual Meeting, highlighting examples of where new approaches may lead to promising new treatment options for a range of cancer types. The transformative impact of technological advances on cancer research featured prominently at the 54th annual meeting of the Irish Association for Cancer Research (IACR). Translational research is advancing its enormous potential at breakneck speed, as mature applied technologies meet with the innovations of multi-disciplinary research groups. High-throughput omics studies  utilize new technologies to generate enormous datasets that are being mined with ever-more specificity and value . The potIn recent years, the development of nanomedicine has shown great promise for both advanced imaging and therapeutic capabilities against cancer. Nanomedicine is a form of nanotechnology applied to the biomedical field, in which engineered nanoparticles (NPs) with dimensions of less than 100 nm are used to treat disease, primarily cancer. With respect to traditional cancer therapies, such smart and highly engineered NPs provide advantages in passively or actively targeting drugs, with high solubility, bioavailability, biocompatibility, and multifunctionality. These NPs can be used in multimodal therapies. Several kinds of smart NPs have thus been developed for targeted drug delivery, as multi-target inhibitors, and as nano-imaging tools. Only recently, the combination of both therapy and diagnosis was proposed to operate using the same nanoparticle, thus introducing the concept of theranostics to the field of nanomedicine field . The corProf. Valentina Cauda and her group (Department of Applied Science and Technology (DISAT) of Politecnico di Torino, Turin, Italy) have reported some recent results on smart nanoparticles that are able to successfully visualize and kill cancer cells in vitro. At first, insights on silica nanoparticles with a highly porous structures and tiny nanopores of around 3–4 nm in diameter were given, i.e., mesoporous silica nanoparticles (MSNs). These MSNs can be easily chemically functionalized  to show Particular attention was given to a novel and multifunctional nanoparticle based on a dense nanocrystalline metal oxide core (zinc oxide) shielded with a lipidic bilayer able to be highly stable in biological media, even for long-term applications, with the intention of rendering it injectable into the blood stream . MoreoveThese results show the promising role of highly engineered and functionalized nanoparticles for cancer treatment and molecular imaging. However, global success in cancer therapies based on such nanomedicine tools can be achieved only with a profound knowledge of tumour biology, markers, and microenvironment.Traditional biology generally attempts to molecularly dissect diseases and study them part by part with the idea that the sum of knowledge of the parts will help explain the disease as a whole. This strategy has only rarely been successful when trying to understand the causes and cures for complex diseases. Therefore, a systems-based approach seems a better strategy to study and understand this complexity. Specifically, this approach takes into account a large number of interrelated variables such as gene expression profile, tumour cellular architecture, and microenvironment , three-dimensional tissue architecture and vascularization (seen through dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)), and metabolic features (seen through magnetic resonance spectroscopy (MRS) or PET), to identify a specific disease phenotype. Prof. Anant Madabhushi  at Case Western Reserve University, Cleveland, OH, USA) presented some of the tools that they have developed to integrate and correlate heterogeneous biological data spanning different spatial and temporal scales, modalities, and functionalities. These tools include computerized feature analysis methods for extracting sub-visual attributes for characterizing disease appearance and behaviour on radiographic (radiomics) and digitized pathology images (pathomics). At the IACR meeting, Prof. Madabhushi presented a number of examples of the application of radiomics and pathomics to predict disease outcome, recurrence, progression, and response to therapy which are reported below.(1) Computational imaging of digital pathology for risk stratification of cancers: Prof. Madabhushi’s group has been developing powerful computerized image-based risk assessment tools for the mining of digital pathology images for outcome prediction and risk stratification of prostate cancers , breast (2) New radiomic approaches for diagnosis and prognosis of disease on radiologic imaging: Prof. Madabhushi’s group has developed a new image-based descriptor called CoLlAGe   which al(3) Fused imaging-molecular predictors of treatment outcome: Prof. Madabhushi presented a new computational image and bioinformatic algorithm  to combiRadiotherapy is a main treatment modality for approximately 50% of cancer patients and second only to surgery in curative ability. However, the biological mechanisms conferring radioresistance in inter- and intra-cell types are poorly understood. The importance of the mitochondria and its links with radiation resistance are topical in the literature. During the last decade, numerous approaches that selectively target cancer cells by virtue of their mitochondrial defects have been shown to exert anti-tumour effects . HypotheProf. O’Sullivan’s group  has a particular focus on understanding the role of the mitochondria and energy metabolism in models of oesophageal cancer (OAC) radiation resistance. In Ireland, incidence of OAC has increased by 48% over the past 15 years and the overall cure rate is less than 20% . ConsequUsing an isogenic model of OAC radioresistance, Prof. O’Sullivan’s group has shown that radioresistance is associated with altered mitochondria structure and size. The levels of random mitochondrial mutations and altered metabolic profiles demonstrated metabolic plasticity, efficiently switching between glycolysis and oxidative phosphorylation energy metabolism pathways accompanied by enhanced clonogenic survival . In vivoThrough a gastrointestinal drug discovery programme, Prof. O’Sullivan’s group has identified and patented a novel radiosensitiser with dual anti-metabolic and anti-angiogenic activity. Using an in vivo zebrafish model and human ex vivo OAC explant models, they have shown this small molecule inhibitor can significantly reduce both metabolic and anti-angiogenic activity in real time and in parallel with increasing radiosensitivity in an isogenic model of radioresistance. Importantly, the action of this novel small molecule is also effective under hypoxic conditions. Prof. O’Sullivan’s group is building further pre-clinical data on the use of this novel radiosensitiser in the neoadjuvant treatment setting for gastrointestinal malignancies. Cellular stress responses are essential for tumour suppression in healthy cells. Stress responses mediated by checkpoints in numerous complex signalling pathways allow the cells time to repair the damage induced by a plethora of potentially dangerous events (including dysregulated proliferation and nutrient deprivation) . If the The endoplasmic reticulum (ER) is a major site for protein folding and quality control. Tumour cells need to overcome powerful ER stress responses to survive. However, even when it does this, the tumour cell can still remain vulnerable to therapeutic opportunities . Dr. EriDr. Chevet has found that the ER stress sensor IRE1alpha (referred to as IRE1) contributes to GBM progression, impacting both tissue invasion and tumour vascularization. IRE1 is an RNase that signals by catalysing the splicing of mRNA encoding the transcription factor X-box binding protein 1 (XBP1), in addition to regulating the stability of certain miRNAs and mRNAs through a process known as regulated IRE1-dependent decay (RIDD). Somatic mutations in the IRE1 gene have been identified in GBM and other forms of cancer. Taking advantage of the specific signalling outputs of the RNase domain of IRE1 engaged by distinct GBM-related mutations, they defined specific expression signatures that were then investigated in the context of human GBM transcriptomes. This approach allowed them to demonstrate the antagonistic roles of XBP1 mRNA splicing and RIDD on tumour outcomes. As part of this response, they demonstrated that the IRE1/XBP1 axis ensures the secretion of pro-inflammatory chemokines by the tumour cells, thereby promoting the recruitment of tumour-associated macrophages, microglial cells and to some extent, neutrophils. This was demonstrated with cell lines, primary GBM lines, and using in vivo GBM orthotopic mouse models and patient tumour samples .This study provides the first demonstration of a dual role of IRE1 downstream signalling in cancer and opens a new therapeutic window to abrogate tumour progression. This study also indicates that ER stress as well as UPR and IRE1 signalling might represent appealing targets for the development of therapeutics that could in turn be used either alone or as adjuvant to the current treatments.Apoptosis is one of the main mechanisms of response to cellular stress which function through clearance of dysfunctional cells that pose a risk to the healthy organism . ApoptosDr. Letai’s group  takes a different approach towards precision medicine, based on the belief that the most important outcome of precision medicine in cancer is to match the right patient to the right drugs. Many studies on precision medicine are exclusively focused on the study of the cancer genome. While genomic approaches have yielded some exciting successes, it must be acknowledged that the vast majority of cancer patients do not derive direct therapeutic benefit from such approaches. Therefore, new avenues must be investigated to improve the current situation .One characteristic that genomic approaches have in common with most  omic approaches is that they are static studies of the cancer cell. In fact, the first step one takes to initiate most of these studies is to kill the cancer cell. The valuable functional information from the genomic studies can be lost when the cancer cell is reduced to a bag of molecules. The emergent properties of the immense complexity of interactions among macromolecules are poorly predicted by initial condition measurements alone. Therefore, Dr. Letai’s group has focused on making targeted perturbations of living, functioning cancer cells. To do this the patient’s cancer cells are put in direct contact with the therapeutics of interest. Following this perturbation, it is essential to measure a component that rapidly captures key information about the ability of the therapeutic that kill the cancer cells. To achieve this, they have focused on profiling BH3-only proteins. BH3 profiling, as shown by IACR member Dr. Triona Ni Chonghaile  , can ideProf. Clotilde Thery  delivered the annual Irish Cancer Society keynote lecture on the molecular and functional diversity of exosomes and other extracellular vesicles in tumour-immune system cross-talk. Cells secrete different types of extracellular vesicles (EVs) into their environment with distinct properties depending on their intracellular site of origin. Exosomes are a subtype of EVs with a mean diameter lower than 150 nm that are formed inside multivesicular compartments of the endocytic pathway. Exosomes secreted by dendritic cells (DCs) have been shown to bear functional major histocompatibility complex (MHC) class I and class II molecules able to activate cognate T lymphocytes and induce anti-tumour immune responses. These findings motivated the use of DC-derived exosomes in cancer clinical trials although with limited clinical effects. Other EVs also bear functional immune molecules and may thus represent alternative immunotherapy tools, but side-by-side comparison of exosomes and other EVs in terms of biochemical and functional properties must be performed to determine their respective therapeutic values.http://evtrack.org) is a valuable resource for EV scientists to ensure the standards of EV isolation and characterisation, while developments in the area can be followed through meetings of the International Society for Extracellular Vesicles (www.isev.org).Prof. Thery described her recent work optimising the isolation of different subtypes of EVs simultaneously released by live human primary DCs to characterise their protein composition and their abilities to activate T lymphocytes. Specific markers to allow isolation and analysis of EVs were an unmet need. Extensive proteomic analysis identified markers used for exosome identification  as general markers of EVs . Within It is clear that innovative technologies are being utilized to inform and alter our understanding and treatment of cancer. While all of the technologies discussed at the IACR face challenges and are still developing, many are already impacting patients. Approved clinical stage nanomedicines are exemplified by liposomal irinotecan for metastatic pancreatic cancer and nab-paclitaxel for breast lung and pancreatic cancer . RadiomiEach plenary session at the 2018 IACR annual meeting featured advanced technology employed directly and indirectly in the research reported. Technology is now essential in the progression of modern cancer treatment, pushing the boundaries of personalized and targeted medicine to improve patient outcomes. Exciting new possibilities will continue to emerge as knowledge, technical expertise, and accessibility converge within multi-disciplinary academic research groups.


Abnormalities in social interaction are a common feature of several psychiatric disorders, aligning with the recent move towards using Research Domain Criteria (RDoC) to describe disorders in terms of observable behaviours rather than using specific diagnoses. Neuroeconomic games are an effective measure of social decision-making that can be adapted for use in neuroimaging, allowing investigation of the biological basis for behaviour. This review summarises findings of neuroeconomic gameplay studies in Axis 1 psychiatric disorders and advocates the use of these games as measures of the RDoC Affiliation and Attachment, Reward Responsiveness, Reward Learning and Reward Valuation constructs. Although research on neuroeconomic gameplay is in its infancy, consistencies have been observed across disorders, particularly in terms of impaired integration of social and cognitive information, avoidance of negative social interactions and reduced reward sensitivity, as well as a reduction in activity in brain regions associated with processing and responding to social information. Psychiatric disorders are prevalent and often debilitating conditions for which the underlying biological causes or contributing factors are largely unknown. Research progress has been limited by a lack of measurable biomarkers to distinguish categories of disorders, relying instead upon self-report criteria. Here, we review how differences in social and cognitive processing measured through neuroeconomic gameplay may offer one useful approach towards the ambitions of the Research Domain Criteria (RDoC) initiative, proposed by the National Institute of Mental Health (USA), which advocates such a biomarker approach . As we wSocial interaction is an intrinsic feature of human behaviour and any disruption to the ability to understand or act upon social information can have a serious impact on everyday life. Making decisions in social situations is a complicated process that requires substantial cognitive capacity as well as awareness of the context of the decision, inference of others’ emotion (theory of mind (ToM)), understanding the motivations for others’ actions and consideration of the potential consequences of decisions for both parties. At a neuronal level, these processes require rapid integration of complex information across a network of brain regions. It is therefore not surprising that social cognition is impaired in many, if not all psychiatric disorders , and is Functional neuroimaging techniques have shown that social information processing involves networks of multiple cortical brain regions, including areas associated with emotion and reward, such as the anterior insula, orbitofrontal cortex (OFC) and rostral anterior cingulate cortex (rACC); the perception and evaluation of social stimuli, including the medial prefrontal cortex (mPFC), temporal poles, superior temporal sulcus (STS), temporoparietal junction (TPJ), paracingulate cortex and precuneus; regulation of reactions to stimuli, including the dorsolateral prefrontal cortex (dlPFC) and dorsal ACC –4. DisruGame Theory paradigms allow social decision-making to be studied experimentally by mimicking real-life social interactions in controlled laboratory settings, providing an intermediate step between social behaviour and the underlying neurobiological mechanisms . These pOne of the simplest games, the Ultimatum Game  Fig. 1a1a evaluaMeta-analyses of functional magnetic resonance imaging (fMRI) studies have shown that when receiving unfair offers, brain areas related to emotion , cognition , reward processing , striatum, posterior cingulate cortex (PCC) and putamen) and action planning (supplementary motor area (SMA)) show increases in the blood oxygen level-dependent (BOLD) response , 12. TheThe Dictator Game  is similAnother well-established neuroeconomic game is the Trust Game , which mA review of the neuroscience underpinning the Trust Game proposed that different brain areas are involved at different phases: a cortical and subcortical network is implicated in decisions about trustworthiness; frontal areas are involved in deciding what to send/return; discovering the outcome activates reward circuitry, evaluation mechanisms and emotion-processing regions . A meta-In the Prisoner’s Dilemma , individThe Public Goods Game is similar to the Prisoner’s Dilemma, but is played in larger groups and it has a public and a personal pot. Earnings in the private pot are as specified, but earnings in the public pot are doubled and split between the participants Fig. . Again, Since neuroeconomic games can provide an ecologically valid measure of social decision-making behaviour and its underlying brain activity, they might be used as a tool within the RDoC framework to assess abnormal function. Previous reviews of neuroeconomic gameplay in psychiatric disorders mostly describe individual disorders. This review therefore aims to summarise behavioural and neuroimaging findings from neuroeconomic gameplay studies of adults with Axis 1 psychiatric disorders, and to interpret the findings in the context of RDoC domains.The search for publications was conducted through the Medline (Pubmed), Web of Science and Scopus databases in September 2017. The search terms were (neuroeconomic* OR neuroeconomic* OR “economic game*” OR “trust game*” OR “ultimatum game*” OR “prisoner’s dilemma”) AND . The date of the search was restricted to 1960 onwards. Only articles written in English were included, and only journal articles or books were included.The search returned 762 records  compared with controls, but similar acceptance of clearly fair and unfair offers . PatientGeneralised anxiety disorder (GAD) is associated with avoidance of social confrontation. One study has reported that patients with GAD accept more unfair Ultimatum Game offers and rate these as less unequal than both controls and patients with panic disorder . PatientAs the Investor in a Trust Game, patients with generalised social anxiety disorder (GSAD) and controls are both more likely to cooperate with a cooperative Trustee, so there was no difference in behaviour between groups , 51. HowCooperation may be more affected by other symptoms than by anxiety itself: patients with GSAD gave less than controls in a Prisoner’s Dilemma , but givTwo studies on adolescents with anxiety disorders playing the Prisoner’s Dilemma report contradictory results: one found that after their partner cooperated, patients were more cooperative than controls, but there was no difference after defection ; the othPatients with anxiety respond in the opposite way to those with depression in the Ultimatum Game, accepting more unfair offers and reacting less negatively to unfairness. Social decision-making in anxiety is influenced by internal factors such as personal characteristics, and by external influences like cooperation and perceived security, and is associated with abnormalities in prefrontal cortex activity.The use of neuroeconomic games in schizophrenia and psychosis has been summarised in reviews focussed on social functioning  and moraAs the Proposer in Ultimatum Games, patients with schizophrenia offer a greater share of the stack than controls, making more hyperfair and fair offers, and fewer unfair offers –61. SimiAcceptance of offers correlates with delusions and suspiciousness/persecution , and accPatients with schizophrenia also respond less consistently than controls, e.g., rejecting higher offers after accepting lower ones , and shoIn an EEG study, no difference was observed between patients with schizophrenia and controls playing the Proposer, but the amplitude of feedback-related negativity (FRN) in the dlPFC and mPFC was reduced in patients playing the Responder, which may reflect difficulty in interpreting others’ behaviours . When anSeveral studies by the same research group have found that patients with early and chronic psychosis and their first-degree relatives have lower initial levels of trust than controls when playing the Investor in Trust Games –71, suggIn a Prisoner’s Dilemma where money could be lost as well as won, controls behaved less cooperatively, showing loss aversion. Patients with schizophrenia did not show this effect, but those with less severe symptoms behaved more like controls . PatientIn general, patients with schizophrenia and psychotic disorders behave less “strategically”, making higher offers in Ultimatum and Dictator Games –61, acceOne study has reported that children with ASD accept more unfair initial Ultimatum Game offers and reject more fair offers, perhaps because they do not recognise the other’s unfair or generous intent . InteresChildren and adolescents with ADHD did not differ from controls in offers made as the Proposer in the Ultimatum Game, but patients made fewer fair offers in a Dictator Game . Self-reWomen with and recovering from anorexia showed less reciprocity than controls as the Trustee in a Trust Game, and had a diminished precuneus and right angular gyrus BOLD responses to high offers by the Proposer compared with controls . The degIn a Trust Game and a non-social task with a similar format, women with assault-related PTSD were slower to learn the probability of success for decisions, and errors were less likely to affect their future decisions, suggesting that they are less flexible in using their experiences to guide future decision-making . PatientNeuroeconomic games provide a “snapshot” of social functioning that is more ecologically valid than current diagnostic measures, such as interviews or self-report questionnaires. This type of measure aligns with the recent move towards using the RDoC framework to describe mental health in terms of behaviours and functions rather than diagnoses , an apprPerformance in neuroeconomic games differs between individuals with psychiatric disorders and healthy controls in several ways, with some similarities and some differences between diagnoses. Table (2) Increased risk avoidance  and reduced reward sensitivity, which result in reduced profit-seeking. These behaviours are associated with the Affiliation and Attachment, Reward Valuation and Reward Responsivity RDoC constructs. Examples of avoidance of the risk of negative social interactions include increased generosity in depression and schizophrenia; tolerance of unfairness in anxiety, schizophrenia and ASD; reciprocity in depression and bipolar disorder. Examples of reduced reward sensitivity include rejection of possible reward in depression, bipolar disorder, schizophrenia and ASD and a reduced striatal response in depression.There are two areas that show different effects across diagnoses, both of which fall under the Affiliation and Attachment RDoC construct: (1) the emotional reaction to negative interactions is more negative in MDD and bipolar disorder, but less negative in anxiety, and (2) there is mixed evidence for cooperative and altruistic behaviour. There are reduced levels of cooperation in depression: trust in schizophrenia and PTSD; altruism in ADHD; reciprocity in anorexia. However, there are increased levels of generosity in depression and schizophrenia; reciprocity in depression and bipolar disorder; cooperation in schizophrenia.There has not been sufficient neuroimaging research to discriminate which brain regions are dysfunctional during all of the behaviours observed in neuroeconomic games; however, several regions involved in social, emotional and cognitive processing show differences between patients and controls, including the precuneus, caudate, cingulate cortex, insula, TPJ, mPFC and dlPFC. Most of these regions show reduced activity in patients, so they may reflect decreased sensitivity to the information required to play the games, or reduced communication and integration of information across social decision-making networks. Several studies have also reported differences in neural responses but no difference in behavioural performance, so patients could have underlying biological deficits that are obscured by compensatory behavioural strategies.If neuroeconomic games are to be used in identifying deficits in social function in mental ill health, it will be necessary to ensure that more consistent procedures are followed: parameters such as the proportions of the stack available, knowledge about the identity or habits of the opponents, inclusion of a computer partner “control” condition and opportunities for cheating or punishing should be standardised in order to create a profile of the expected results in healthy controls, so deviations from these can be readily identified and attributed to a deficit in a particular function or neural circuit. Indeed, the sometimes-conflicting results reported here may be attributed to these differences as well as to moderate sample sizes, focus on clinical populations with varying degrees of severity and inclusion of nonclinical populations . NeuroecIn addition to potentially being incorporated into the RDoC framework, future prospects for use of neuroeconomic games in psychiatric disorders could include use in neuroimaging studies of structural and functional connectivity of brain networks involved in social processing. Identification of abnormalities in this way could potentially lead to the ability to stratify patients into diagnosis-spanning subgroups.AVPR1A) versus those carrying the shorter repeat [AVPR1A has long been known to be critical for social cognition and behaviour in both lower mammals and humans [4 (DRD4) gene has been associated with fairness in the Ultimatum Game Responder role in a Chinese sample, where individuals with the 4/4 genotype  required a greater threshold for the minimal acceptable offer, but there was no effect on the Proposer [2 gene (DRD2) and the behaviour of the Proposer, where individuals carrying at least one T–T haplotype at rs1800497 and rs2283265 proposed significantly lower amounts than those not carrying this haplotype [DRD2 has long been a candidate gene for schizophrenia and other psychiatric disorders and was recently implicated in the list of genome-wide significant loci in the Psychiatric Genomics Consortium meta-analysis of schizophrenia genome-wide studies [Investigation of genetic factors associated with suboptimal performance on the games could lead to the identification of molecular processes that could elucidate molecular mechanisms and potentially druggable targets. There is some evidence that behaviour in neuroeconomic games is heritable, suggesting a genetic component to social decision-making. In the Trust Game, trust from the Investor shows heritability of 20% in a Swedish sample and 10% in an American sample, and trustworthiness of the Responder shows heritability of 18 and 17% in the two groups . In the r repeat . AVPR1A d humans . A variaProposer . The effProposer . In addiaplotype . DRD2 ha studies . HoweverA few studies have assessed performance on neuroeconomic games before and after an intervention. Depletion of serotonin, which is implicated in social behaviour, increases rejection of unfair Ultimatum Game offers in healthy controls  and reduNeuroeconomic games could therefore be used in future to stratify patients into diagnosis-spanning subgroups based on their social decision-making ability, or to identify targets for behavioural, pharmacological or genetic interventions to alleviate the impact of dysfunctional social decision-making in psychiatric disorders.


Over the first half of the twentieth century, it has been demonstrated that an important hallmark of cancer cells is the reprogramming of cellular metabolism. The capacity to obtain nutrients from an extremely poor microenvironment and utilize it to both generate biomass and maintain the cellular viability is a major feature of transformed cells. Several papers have proposed new mechanisms by which cancer cells undergo substantial metabolic reprogramming to endure within the hostile microenvironment. However, the metabolic profile adopted by cancer cells with invasive and multidrug-resistance phenotypes is still poorly understood. The research topic “Cancer Metabolism: Current Knowledge and Perspectives” provides exciting articles on the valuable field of cancer metabolism, where one hypothesis and theory, two original researches and five outstanding reviews are presented.Herrero and Gutierrez demonstrated that multiple myeloma cells with ongoing endogenous DNA damage depend on a homologous recombination (HR) pathway, which may be used as therapeutic proposals. They suggested that obstructing HR after the initial step of end resection might be more suitable to promote cell death, since it avoids a compensatory non-homologous end joining repair mechanism. The authors claim that these preclinical observations afford the basis for its clinical assessment.In their original research, Sant'Anna-Silva et al. analyzed the metabolic changes to the metastatic phenotype of human tongue squamous cell carcinoma lineages. By using both metabolomic and fluorescence lifetime imaging microscopy analysis, the authors demonstrated that many pathways linked to lipid metabolism seem to be associated to metastatic phenotype. On the other hand, amino acid metabolism and cell cycle regulation are most correlated to cells with low invasive phenotype.Turgeon et al. addressed evidence that link DNA damage/repair mechanisms with cancer cell metabolism. The authors claim that such connection is progressively apparent, granting opportunities to better understand the metabolic susceptibilities of a considerable fraction of tumors.In their review article, Prakasam et al., summarized recent progress in the understanding of many posttranslation modifications (PTMs) in cancer, in particular the PTMs in the M2 isoform of pyruvate kinase (PKM2). The authors believe that such knowledge will be crucial to evaluate their therapeutic potential for the treatment of different types of cancer.Morrot et al. provided a snapshot of metabolic reprogramming in cancer cells, describing how, even in aerobic conditions, transformed cells opt for glycolysis instead of oxidative phosphorylation (OXPHOS). They discussed how this metabolic reprogramming is able to induce a high-lactate output, then promoting mmunosuppressive events. The authors believe that further studies are needed to better understand the effect of lactate and other “waste” metabolites on cancer progression.Coelho et al. presented an overview of the metabolic reprogramming in thyroid cancer, emphasizing factors that promote enhanced glycolysis in transformed cells. The authors also discussed about promising metabolic targets that might be useful to treat patients with thyroid cancer.Snyder et al. discussed about the metabolic phenotype of cancer stem cells (CSCs), especially taking into account both glycolytic and OXPHOS pathways. Since CSCs present metabolic peculiarities when compared to most of cancer cells in a tumor, the authors believe that such singularities might offer a great potential for developing improved treatments for cancer patients.Vidal et al. addressed the connection between multidrug-resistance phenotype and metabolic reprogramming in cancer cells, taking into consideration the functions mediated by ATP-binding cassette transporters, as well as the numerous non-metabolic roles mediated by enzymes that are part of the glycolytic pathway, with special attention to glyceraldehyde-3-phosphate dehydrogenase.Finally, in their hypothesis and theory article, Taken together, the published papers in this research topic strengthen the concept that cancer cell metabolism is an important field in cancer biology, and further studies in this lively research area might provide important information, which may be useful for treatment, diagnostic and therapeutic purposes.LF, LM-P and LG wrote the paper. All the authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided. Stroke is one of the leading causes of disability worldwide , with a Each of these three aspects present practical challenges. Assigned treatments depend on the assessed early-stage disability . A varieIn addition to thorough standardized assessment scales, progress in patients is observable during the execution of activities of daily living . Nevertheless, task completion not always reflects recovery, as patients often adopt different synergistic patterns to compensate for lost function , and sucMain provision of rehabilitation therapies occurs at hospitals and rehabilitation centers. Evidence of enhanced recovery related to more extensive training has been found , but limWearable sensors used during regular assessments can reduce evaluation times and provide objective, quantifiable data on the patients’ capabilities, complementing the expert yet subjective judgement of healthcare specialists. These recordings are more objective and replicable than regular observations. They have the potential of reducing diagnostic errors affecting the choice for therapies and their eventual readjustment. Additional information  extracted during the execution of multiple tasks can be used to better characterize motor function in patients, allowing for finer stratification into more specific groups, which can then lead to better targeted care . These devices also make it possible to acquire data unobtrusively and continuously, which enables the study of motor function while patients perform daily-life activities. Further, the prospect of remotely acquiring data shows promise in the implementation of independent rehabilitative training outside clinics, allowing patients to work more extensively towards recovery.The objective of this review is to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity, aiming to present a roadmap for translating these technologies from “bench to bedside”. We selected articles based on their reports about tests conducted with actual stroke patients, with the exception of conductive elastomer sensors, on which extensive research exists without tests in patients. In the section “Wearable devices used in stroke patients”, we summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. In the “Discussion” section, we present suggestions concerning data acquisition and processing, as well as opportunities arising in this field, to guide future studies performed by clinicians and engineers alike.Recent availability of ever more compact, robust and power-efficient wearable devices has presented research and development groups in academia and industry with the means of studying and monitoring activities performed by users on a daily basis.Over the past years, multiple research groups have worked towards a reliable, objective and unobtrusive way of studying human movement. From the array of sensors and devices created, a few have gained popularity in time due to their practicality. The next subsections will focus on the wearable devices most frequently used in the study of human motion, with special emphasis on monitoring of upper limbs in stroke patients.2, ±1.9 G). More affordable sensors  [Inertial measurement units (IMUs) are devices combining the acceleration readings from accelerometers and the angular turning rate detection of gyroscopes . Recent ia, USA) ) stream Multiple scales exist for assessing motor function in stroke patients . HoweverIMUs are practical options to assess motor function during the execution of activities of daily life. Lee and colleagues  focused Tracking of the whole body has also been achieved using sensor networks in an attempt to objectively evaluate movement quality in daily-life situations , as wellIMUs allow providing immediate feedback to patients about their performance and posture , 37, as The complexity of tracking and assessing motion depends on how constrained the circumstances for the recordings are. Tracking motion during the execution of daily-life activities is particularly difficult in stroke patients, as their movements are often slower, more segmented and more variable than those of healthy individuals . ProlongRecording over shorter periods, like those during standardized motor function assessment scales, is less vulnerable to these limiting factors, but still susceptible to other issues. Quantifying movements taking place in a single plane  is straightforward, as recordings from either the accelerometer or the gyroscope can be sufficient. In contrast, characterizing complex movements (e.g. flexor synergic movement from the FMA) is more challenging and often requires combining data from both the accelerometer and the gyroscope. Assigning clinically relevant scores (e.g. FMA scores) to performed movements requires characterizing the recorded signals using a variety of features. These features are normally extracted using a sliding-window approach along the acquired signals, and the choice of which features to use depends on the type of movements involved. Common features used in characterization of IMU data are movement intensity, signal amplitude (mean and standard deviation), signal energy and dominant frequency . After eProblems can arise when studying stroke patients, as the acquired inertial signals may not hold enough information due to the very low variation of signals during slow movements. An alternative to selecting features would be to compare waveforms directly by defining a set of signals as templates for unimpaired movements with signals acquired from patients . TechniqSensor noise can cause huge detriment to the outcome of movement classification or assessment. The main source of noise for short-duration recordings is quantization noise , while the aforementioned drift rate plagues longer recordings . WearablTable Surface Electromyography (sEMG) is a technique in which the electrical potential generated whenever muscles contract is measured using electrode pairs placed on the skin over the muscles. The electrodes need to be asymmetrically placed with respect to the neuromuscular plaques in order to capture the electrical potential difference as the depolarization wave travels along the muscle cells’ membranes. Figure Wearable EMG sensors have high potential in the study of stroke patients. Investigation of neural activity as measured through motor-evoked potentials (MEPs) triggered by Transcranial Magnetic Stimulation (TMS)  is simplAfter stroke, patients tend to adopt compensatory strategies to accomplish motor tasks, especially in case of moderate to severe impairment . These cWearable EMG sensors allow providing online feedback during home-based training in a similar way as with IMUs. Instead of tracking gross arm movements, applied force calculated from recordings of muscle activity can serve as a parameter to provide feedback during training. EMG-based biofeedback has been reported to lead to enhanced motor improvements , and DonAfter amplification and preprocessing , these signals can be used to identify patterns of activation related to specific movements or postures. The type of processing applied to recorded signals depends on the application. For example, continuous recordings of muscle activity during the execution of activities of daily living requires epoching the signals, keeping only relevant segments capturing discrete events of interest. It is possible to do this segmentation manually, but automated methods of threshold detection are a much more practical option . After rAs described for IMUs, a sliding-window approach allows extracting significant features for later classification. Classification is generally performed using signal features   chosen bClassification accuracy tends to be high when only a few (five or six) classes (each corresponding to a gesture to be identified) are involved, but accuracy frequently decreases as more gestures are added. Further detriment to classification performance occurs when dealing with highly impaired stroke patients, as their muscle signals tend to be less pronounced . ElectroThere are several factors affecting the EMG signal. Repeated recordings performed on the same test subjects during several days has been reported to decrease hand-gesture classification in close to 30%, compared to results obtained from repeated measurements taking place during the same day . This miOther sources of EMG noise affecting the performance of used classifiers include cable motion artifacts, power-line noise, thermal noise from the sensor’s electronic components, electrochemical noise from the interface between the electrodes and the skin and mechanical disturbances . CurrentAn accurate way of measuring the angular displacement around joints is by means of potentiometers and encoders. Potentiometers are devices containing a conductive disc with a certain resistance and two contact points on top. The distance between these contact points can vary, which results in more or less resistive material between the contact points. As resistance varies in an approximately linear way with changes in arc length, it is possible to map a direct relationship between resistance and angular displacement. This means that aligning the knob to the rotation axis of a joint allows a good estimation of its angular position. Encoders are optical sensors containing a slitted disc. A LED (light-emitting diode) shines against the disc, which allows light to pass through the slits but blocks it otherwise. Presence and absence of light, detected by a photosensitive component, is encoded into ones and zeroes and is used to determine angular displacement. Potentiometers are analog sensors with “infinite” resolution, whereas encoders can have resolutions as high as 1 million counts per revolution . Figure Encoders and potentiometers can be used in clinical environments to measure ROM in patients. Researchers at Peter S. Lum’s lab , 81 builChen and Lum  focused Chen and colleagues  studied The advantage of not needing to apply machine learning algorithms notwithstanding, the need of a parallel structure  or embedding them in a glove restricts the range of applications these sensors may have for stroke patients. Donning and doffing equipment might be challenging for patients with low dexterity or high spasticity .Conductive Elastomer (CE) sensors are flexible components with varying piezo-resistivity. Piezo-resistivity changes due to deformations suffered by a textile substrate deposited with conductive particles (e.g. silver nanoparticles). When placed along a moving body part, such as fingers, it is possible to map the sensor readout related to a particular deformation of joint angles. Figure Yu and colleagues used flexible sensors in combination with IMUs to assess motor function , and obtMovement tracking using deformable sensors embedded into clothes would allow monitoring patients as they perform activities of daily living. For example, Tognetti et al.  embeddedPrange-Lasonder and colleagues  implemenFlexible sensors embedded into clothing constitute an attractive option for unobtrusively tracking movements in stroke patients during motor assessments, execution of daily living activities, and rehabilitative training. At present, their use in clinical environments and in-home settings is difficult due to practical issues related to donning, doffing and washing the garments. Furthermore, some sensors require a large amount of wiring , which rStroke is a frequent disorder that often results in long-lasting loss of motor functions. After stroke, the rehabilitative process relies on three main elements: 1. Diagnosis, in which clinicians use standardized scales to estimate maximum recovery for every patient  and assiConventional motor assessment is vulnerable to biases derived from measurement errors  and ceilA promising option to assess stroke patients objectively resides in the use of wearable technology. As high-end sensors become more accessible, more reliable and less obtrusive, the chance of acquiring relevant data during patients’ training or daily routines gets easier. A variety of wearable sensors e.g. , 62, 98), 9862, 9In the present paper, we seek to compare different setups with the intention of finding the most promising candidates for different applications. There are four main wearable sensors used in the study of stroke: IMUs, EMG, potentiometers/encoders and flexible sensors. IMUs allow measuring changes in acceleration, inclination and orientation unobtrusively. Wireless, energy-efficient  transmisEMG wearable sensors have also been used for diagnosis , 62 and Potentiometers and encoders are robust to noise and require little processing of signals, as the output from these sensors can be mapped directly to angular displacement . The range of applications in stroke for these sensors is limited to measuring ROM of limbs, and requires mounting them on a parallel structure, such as an orthosis, limiting the degrees of freedom of measured movements. Still, their potential in extensive home-based training is clear . The neeAs IMU and EMG data cannot be mapped directly into the movements and actions that generated them, acquired signals must be processed differently. Depending on the objective  data can either be classified using different forms of statistical processing, such as common methods applied in machine learning , or compThe choice for the best setup depends on the intended application. The best candidate to study movement quality while remaining unobtrusive and easy to deploy is likely IMUs. Data from IMUs provide enough information to characterize movement execution (e.g. ), detectAs mentioned earlier, the analyses pipeline depends heavily on the object of study . There are multiple features to characterize EMG and IMU signals for later classification (e.g. into classes related to motor function), and the choice depends on the property of interest. For example, muscle force is well- represented using the RMS of the EMG signal, whereas movement quality can be better observed by calculating jerk  from IMU data. Alternatively, comparing waveforms directly requires either normalizing the length of the time series or somehow matching them to account for different signal durations, such as with DTW.For classification problems, it might be better to have many features and then trim them down by means of PCA or other relevance determination algorithms (e.g. RRelief). This is a necessary step, as dataset sizes are often quite small, and keeping too many features might result in models not generalizing to new data (overfitting). The choice for the model depends on the application and on its final objective. Several studies discussed in Table The way in which models are fine-tuned and validated is an important aspect too. Several studies shown in Table In general, the more data is available to train models, the better. The most effective algorithms used across domains, such as neural networks, are only useful if used on large amounts of data. For this reason, initiatives like the “NinaPro” database  should bAn EMG + IMU device that had been gaining momentum in multiple scientific domains was the “Myo” armband  . This deEasily deployed, inexpensive IMU devices are available off-the-shelf. Mbientlab , for exaAs for the acquisition of EMG data, we were not able to find a low-cost solution providing quality data while remaining simple to use. Systems built by companies like Noraxon , Delsys®Presenting a similar design to that of the “Myo” armband, Yang and colleagues  built a A different approach trying to enhance EMG systems with near-infrared spectroscopy (NIRS) was taken initially by Herrmann and Buchenrieder  in an atInteresting possibilities exist in the realm of printable  and temThe processing steps and the implementation challenges described before may appear daunting when thinking about integrating these sensors into clinical practice. The importance of discussing these challenges lies in the joint effort towards democratizing these technologies such that their advantages might be widespread, accessible to all, their performance and reliability ensured. To achieve this goal further research is necessary, and research can greatly benefit from knowledge acquired in the clinic.There is a variety of readily available systems dedicating wearable sensors to rehabilitation. For example, the “ArmeoSenso” system   uses IMUHughes and colleagues  reportedThe ICF is an important and well-established tool in clinical neuro-rehabilitation and seeks to provide a framework based on two models of disability, one coming from individual factors and another from social factors . This biEscorpizo and colleagues  proposedThe ICF seeks to provide comparable/replicable statistics of disability as a whole. The ICF’s performance and capacity qualifiers describe activities of daily living in natural environments and execution of specific tasks, respectively, which correspond to the “Diagnostics” and the “Evaluation of Recovery/Adaptation” dimensions described before for each sensor type. The “Extended Training” dimension addresses some social factors like degree of independence and integration to society by allowing patients to continue recovering after leaving the rehabilitation facility.Baets and colleagues  reviewedThe European Union spends €45 billion on treating stroke patients every year, with 44% of these costs spent on direct health care, 22% related to productivity losses and 35% on informal care of patients . Care afA study in Switzerland revealed that 37% of direct health care costs after stroke correspond to rehabilitation at the clinic . Using sResults from meta-analyses have shown that early planned and coordinated hospital discharge combined with home-based rehabilitation yields better results, and home-based rehabilitation was found to be superior to center-based, as measured by the Bartel Index 6 months after stroke . HealthcEvidence of enhanced recovery related to more extensive training has been found in stroke patients , but higUnsupervised, home-based rehabilitative training has the potential to largely improve outcome of rehabilitation in patients , 137. HoThere are many aspects to look into for home-based rehabilitative training and its corresponding assessments and measurements. First, training must be thoroughly and carefully explained to patients and, when applicable, to their caregivers. An option is to provide center/lab-based training for a short amount of time and then instruct patients to train at home . FurtherAnother important aspect to consider is data logging. One option is to keep all data on the devices and extract it once the participants give the devices back at the end of their study contribution . NeverthEven though home-based training offers beneficial possibilities in terms of high-intensity training, other aspects, such as motivation derived from human interaction  might beStroke rehabilitation is an iterative process involving impairment assessment, recovery prognosis, therapy definition, rehabilitative training and monitoring of functional changes. Conventional assessments of motor function face limitations due to several factors, resulting in biased predictions of recovery, which prevent an adequate assignment of treatment for patients. Furthermore, limited resources at rehabilitation centers and clinics prevent patients from receiving intensive treatment and extensive attention, frequently reducing the degree up to which they recover. Wearable sensors show promise resolving at least some of these problems. Regular assessments complemented with this technology can reduce bias in measurements and estimations, as well as reduce assessment time for therapists. Short-term rehabilitative training, offered during the first 6 months after stroke, could be prolonged by offering home-based therapies, designed and monitored remotely by therapists, allowing patients to train in a familiar environment. Among the wide array of sensors available, inertial measurement units (IMUs) and electromyography (EMG) offer the best balance between unobtrusiveness, robustness, ease of use and data quality. An optimal solution comprising both sensor types is still lacking in the market, but the collection of studies presented in this review indicate that this might be the most promising way to go.


Hospitals worldwide are facing an increasing incidence of hard-to-treat infections. Limiting infections and providing patients with optimal drug regimens require timely strain identification as well as virulence and drug-resistance profiling. Additionally, prophylactic interventions based on the identification of environmental sources of recurrent infections  and reconstruction of transmission chains  could help to reduce the incidence of nosocomial infections. WGS could hold the key to solving these issues. However, uptake in the clinic has been slow. Some major scientific and logistical challenges need to be solved before WGS fulfils its potential in clinical microbial diagnostics. In this review we identify major bottlenecks that need to be resolved for WGS to routinely inform clinical intervention and discuss possible solutions. In principle, WGS can provide highly relevant information for clinical microbiology in near-real-time, from phenotype testing to tracking outbreaks.However, despite this promise, the uptake of WGS in the clinic has been limited to date, and future implementation is likely to be a slow process.The increasing information provided by WGS can cause conflict with traditional microbiological concepts and typing schemes.Decreasing raw sequencing costs have not translated into decreasing total costs for bacterial genomes, which have stabilised.Existing research pipelines are not suitable for the clinic, and bespoke clinical pipelines should be developed. All of these have obvious clinical relevance and provide case studies where WGS could, in principle, provide additional information and even replace the knowledge obtained through standard clinical microbiology techniques. This review reiterates the potential of WGS for clinical microbiology, but also its current limitations, and suggests possible solutions to some of the main bottlenecks to routine implementation. In particular, we argue that applying existing WGS pipelines developed for fundamental research is unlikely to produce the fast and robust tools required, and that new dedicated approaches are needed for WGS in the clinic.Thanks to progress in high-throughput sequencing technologies over the last two decades, generating microbial genomes is now considered neither particularly challenging nor expensive. As a result, WGS) see  has beenmultilocus sequence typing (MLST) and can be used when standard techniques such as pulsed-field gel electrophoresis (PFGE), variable-number tandem repeat (VNTR) profiling, and MALDI-TOF are unable to accurately distinguish lineages accessory genome on the chromosome and/or plasmid carriage.At the most basic level, WGS can be used to characterize a clinical isolate, informing on the likely species and/or subtype and allowing phylogenetic placement of a given sequence relative to an existing set of isolates. WGS-based strain identification gives a far superior resolution compared to genetic marker-based approaches such as ad hoc taxonomical nomenclature, such as biochemical serovars for Salmonella enterica or mycobacterial interspersed repetitive units (MIRUs) for Mycobacterium tuberculosis. While the rich information contained in WGS should in principle allow superseding traditional taxonomic classifications horizontal gene transfer (HGT), such as M. tuberculosis, it is possible to devise a ‘natural’ robust phylogenetically based classification virulence factors or AMR profile may all produce incompatible categories  regimes. Prediction of AMR from sequence data has also received considerable attention for bacterial pathogens but has not led to comparable success at this stage.SCCmec cassette is a reliable predictor for broad-spectrum beta-lactam resistance in Staphylococcus aureus, with strains carrying this element referred to as methicillin-resistant S. aureus (MRSA). In principle, WGS offers the possibility to predict the full resistance profile to multiple drugs (the ‘resistome’). Possibly the first real attempt to predict the resistome from WGS data was a study by Holden et al. in 2013, showing that, for a large dataset of S. aureus ST22 isolates, 98.8% of all phenotypic resistances could be explained by at least one previously documented AMR element or mutation in the sequence data Resistance against single drugs can be relatively straightforward to predict in some instances. For example, the presence of the https://github.com/tseemann/abricate), together with those also taking into account point mutations in chromosome-borne genes such as Arg-Annot Since then, several tools have been developed for the prediction of resistance profiles from WGS. These include those designed for prediction of resistance phenotype from acquired AMR genes, including ResFinder M. tuberculosis and S. aureus, which are characterised by essentially no, or very limited, accessory genomes, respectively. Other successful examples include streptococcal pathogens, where WGS-based predictions and measured phenotypic resistance show good agreement even in large and diverse samples of isolates Escherichia coli, where only 6% of genes are found in every single strain It has been proposed that WGS-based phenotyping might, in some instances, be equally, if not more, accurate than traditional phenotyping S. pneumoniae, can be predicted through, for example, penicillin-binding protein (PBP) typing and machine-learning-based approaches The transition to WGS might appear relatively straightforward if viewed as merely replacing PCR panels which are already used when traditional phenotyping can be cumbersome and unreliable. However, to put the problem in context, there are over 2000 described β-lactamase gene sequences responsible for multiresistance to β-lactam antibiotics such as penicillins, cephalosporins, and carbapenems At this stage, many of the AMR reference databases are not well integrated or curated and have no minimum clinical standard. They often have varying predictive ranges and biases and produce fairly inaccessible output files with little guidance on how to interpret or utilise this information for clinical intervention. Perhaps because of these limitations, although of obvious benefit as part of a diagnostics platform, both awareness and uptake in the clinic has been limited.Additionally, with some notable exceptions, such as the pneumococci Klebsiella pneumoniae, Kleborate K. pneumoniae various imperfect proxies for virulence are used. These include capsule type, hypermucovisity, biofilm and siderophore production, or survival in serum-killing assays. While all of these traits are quantifiable and reproducible, and could thus in principle be predicted using WGS, it remains unclear how well they correlate with virulence in the patient. Given that virulence is one of the most commonly studied phenotypes, yet lacks a clear definition, the general problem of predicting bacterial phenotype from genotype may be substantially more complex than the special case of AMR, which is itself far from solved for all clinically relevant species.The majority of efforts to predict phenotypes from bacterial genomes have been on AMR profiling. Yet, some tools have also been developed for multispecies virulence profiling: the Virulence Factors Database (VFDB) K. pneumoniaeAcinetobacter baumanniiNeisseria gonorrhoeatransmission chains can also be used to identify sources of recurrent infections , and do not necessarily rely on all isolates within the outbreak having been sequenced, allowing for partial sampling and analyses of ongoing outbreaks Beyond phenotype prediction for individual isolates, WGS has allowed reconstructing outbreaks within hospitals and the community across a diversity of taxa ranging from carbapenem-resistant However, WGS-informed outbreak tracking is usually performed only retrospectively. Typically, the publication dates of academic literature relating to outbreak reconstruction lag greatly, often in the order of at least 5 years since the initial identification of an outbreak Some of this time-lag probably relates to the difficulty of transmission-chain reconstruction at actionable time-scales. This can be relatively straightforward for viruses with high mutation rates, small genomes, and fast and constant transmission times, such as Ebola One of the key promises of WGS is in molecular surveillance and real-time tracking of infectious disease. This relies on transparent and standardized data sharing of the millions of genomes sequenced each year, together with accompanying metadata on isolation host, date of sampling, and geographic location. With enough data, surveillance initiatives have the potential to identify the likely geographic origin of emerging pathogens and AMR genes, group seemingly unrelated cases into outbreaks, and clearly identify when sequences are divergent from other circulating strains. In a hospital setting, surveillance can help to detect transmission within the hospital and inflow from the community, optimize antimicrobial stewardship, and inform treatment decisions; at national and global scales, it can highlight worldwide emerging trends for which collated evidence can direct both retrospective but also anticipatory policy decisions.gisaid.org) and online data exploration and phylodynamics available through web tools such as NextFlu http://nextstrain.org), which also allows examination of other significant viruses including Zika, Ebola, and avian influenza. Another popular tool for the sharing of data and visualization of phylogenetic trees and their accompanying meta-data is Microreact (microreact.org) www.wgsa.net). WGSA allows the upload of genome assemblies through a drag-and-drop web browser, allowing for a quick characterization of species, MLST type, resistance profile, and phylogenetic placement in the context of the existing species database based on core genes. At the time of writing WGSA comprises 20 649 genomes predominantly from S. aureus, N. gonorrhoeae, and Salmonella enterica serovar Typhi, together with Ebola and Zika viruses, all with some associated metadata.Amongst the most successful global surveillance initiatives and analytical frameworks are those relating specifically to the spread of viruses. Influenza surveillance is arguably the most developed, with large sequencing repositories such as the GISAID database , such data are near useless if the accompanying metadata (see above) are not also released, as remains the case far too often. Additionally, as more genomes are routinely sequenced in clinical settings as part of standard procedures, ensuring that the culture of sharing sequence data persists beyond academic research will become increasingly important.www.genome.gov/sequencingcostsdata). This has led to claims that a draft bacterial genome can currently cost less than 1 USD to generate For WGS to be routinely adopted in clinical microbiology, it needs to be cost-effective. It is commonly accepted that sequencing costs are plummeting with the National Human Genome Research Institute (NHGRI) estimating the cost per raw megabase (Mb) of DNA sequence to 0.12 USD . Traditional culture methods require long turnaround time, with most bacterial cultures taking 1-5 days, fungal cultures 7-30 days, and mycobacterial cultures up to 14-60 days. In this scenario, WGS is used as an adjunct technology primarily to provide information on the presence of AMR and virulence genes, which is particularly useful for mechanisms that are difficult to determine phenotypically (e.g. carbapenem resistance). This use of WGS, whilst solving some of the current clinical problems, does not speed up the diagnosis of infection; it is more the case that new technology is replacing some of the more cumbersome laboratory techniques whilst providing additional information.Box 1WGS in the strict sense usually refers to sequencing the genome of a single organism, and it is common to distinguish between the sample  and the isolate (an organism that has been cultured and isolated from that sample). WGS methods traditionally sequence a cultured isolate to reduce contamination from other organisms, or sometimes rely on enrichment strategies targeting sequences from a specific organism In contrast, metagenomic approaches sequence samples in an untargeted way. This approach is particularly relevant for clinical scenarios where the pathogen of interest cannot be predicted and/or is fastidious . Example applications of clinical metagenomics include: when the disease causing agent is unexpected Metagenomic data are more complex to analyze than single species WGS data and tend to rely on sophisticated computational tools, such as the Desman software allowing inference of strain-level variation in a metagenomic sample et al. used MinION metagenomic profiling to identify pathogen-specific AMR genes present in a faecal sample from a critically ill infant all within 5 h of taking the initial sample However, the differences between these methods should not obscure their fundamental similarities. Obtaining single-species genomes from culture is one end of a continuum of methods that stretches all the way to full-blown metagenomics of a sample. In principle, all methods produce the same kind of data: strings of bases. Furthermore, in all cases what is clinically relevant represents only a small fraction of these data. Integrating sequencing data from different methods into a single diagnostics pipeline is therefore an attractive prospect to quickly identify the genomic needles in the metagenomic haystack in a species-agnostic manner. For example, the presence of a particular antibiotic-resistance gene in sequencing data may recommend against the use of that antibiotic; whether the gene is present in data from a single-species isolate or from metagenomes is irrelevant. As an example, Leggett Alt-text: Box 1WGS is more appealing as a microbiological fast diagnostics solution when combined with procedures that circumvent (or shorten) the traditional culture step. This can be achieved through direct sampling of clinical material  or by usRelative to the time required for culture and downstream analysis of the data, variation in the speed of different sequencing technologies is relatively modest. There is considerable enthusiasm for the Oxford Nanopore Technology (ONT) which outputs data in real time, although the ONT requires a comparable amount of time to the popular Illumina Miseq sequencer to generate the same volume of sequence data. Sequencing on the MiSeq sequencer takes between 13 to 56 hours, but as run time correlates with sequence output and read length, researchers tend to systematically favour runs of longer duration.In the context of this review, genetic material from the human patient present in clinical samples represents contamination, a major obstacle to obtaining a high yield of microbial DNA. Protocols exist to deplete human DNA prior to sequencing http://www.ridom.com/seqsphere/), most laboratories use a collection of open-source tools to perform particular subanalyses. Typically, these tools are then woven together into a patchwork of software (a ‘pipeline’). The idea of a pipeline is to allow within-laboratory standardized analysis of batches of isolates with relatively little manual bioinformatics work. Such pipelines can be highly customizable for a wide range of questions. There are also some communal efforts at streamlining workflows across laboratories. As an example, Galaxy  is a framework that allows nonbioinformaticians to use a wide array of bioinformatics tools through a web interface.A major problem in the analysis of WGS data is that there are currently very few (if any) accepted gold standards. The fundamental steps of WGS analyses in microbial genomics tend to be similar across applications and usually consist of the following steps: sequence data quality control; identification/confirmation of the sequenced biological material; characterization of the sequenced isolate (including typing efforts as well as characterization of virulence factors and putative AMR elements/mutations); epidemiologic analysis; and finally, storage of the results . HoweverOne major limitation to rapidly attaining useful information in a clinical setting is that analysis pipelines for microbial genomics have generally been developed for fundamental research or public health epidemiology k-mers in the raw sequence data, which is orders of magnitude faster. Accurate identification of an isolate can be made rapidly with MinHash-based k-mer matching methods such as Mash The road to stringent, exhaustive analysis of WGS data is long and paved with good intentions. In order to move towards real-time interpretable results for clinics it will be necessary to take certain shortcuts. The focus should be on rapid, automated analysis and clear, unambiguous results. Some steps in the pipeline can simply be omitted for clinical purposes. As an example, genome assembly might appear to be a bottleneck for real-time WGS diagnostics, but is probably rarely required; sufficient characterization of an isolate can be made by analysis of the In Any bespoke pipeline for clinical diagnostics would need to be linked with regularly updated multispecies databases containing information about the latest developments in typing schemes, as well as clinically important factors such as AMR determinants. Results would have to be continuously validated, and international accreditation standards met at regular intervals. At a national level, accreditation bodies  may lack the expertise required. In our experience, many promising databases have collapsed after funding expired or the responsible postdoc left for another job. If WGS is ever to make it into the clinic it will be necessary to secure indefinite funding of both infrastructure and personnel for such databases.The lack of uptake of WGS-based diagnostics may also be in part due to an understandable desire to maintain the ‘status quo’ in a busy hospital environment with already established treatment and intervention systems. Additionally, and perhaps significantly, it also highlights the difficulty to communicate the potential benefits of WGS to the day-to-day life of a clinic. The main proponents of WGS tend to be based in the public health/research environment and are rarely actively involved in clinical decision-making. This in itself can present something of a language barrier, challenging meaningful dialogue over how adoption of new approaches can lead to quantifiable improvements in existing systems. Further, the physical planning, implementation and integration of WGS diagnostics may be unlikely to succeed without carefully planned introduction and continued training of its user base. This is of course challenged by the already resource-limited infrastructure of many clinical settings.Despite its immense promise and some early successes, it is difficult to predict if and when WGS will completely supersede current standards in clinical microbiology. There are several major bottlenecks to its implementation as a routine approach to diagnose and characterise microbial infections (see Outstanding Questions). These include, among others: the current costs of WGS, which remain far from negligible despite a common belief that sequencing costs have plummeted; a lack of training in, and possible cultural resistance to, bioinformatics among clinical microbiologists; a lack of the necessary computational infrastructure in most hospitals; the inadequacy of existing reference microbial genomics databases necessary for reliable AMR and virulence profiling; and the difficulty of setting up effective, standardized, and accredited bioinformatics protocols.Focusing in the near future on WGS applications that fulfil unmet diagnostic needs and demonstrate clear benefits to patients and healthcare professionals will help to drive the cultural changes required for the transition to WGS in clinical microbiology. However, irrespective of how this transition occurs and how complete it is, it is likely to feel highly disruptive for many clinical microbiologists. There is also a genuine risk that precious knowledge in basic microbiology will be lost after the transition to WGS, particularly if investment prioritises new technology at the expense of older expertise. More positively, irrespective of the future implementation of WGS in clinical microbiology, we should not forget that the availability of extensive genomic data has been instrumental in the development of a multitude of routine non-WGS typing schemes.Outstanding QuestionsCan WGS be used to develop robust classification schemes that account for the genetic diversity of organisms with open genomes?Which clinically relevant phenotypes can be reliably predicted using WGS, and for which organisms?How can phylogenetic analyses of outbreaks be speeded up to meaningfully contribute to infection control at actionable time scales?How can publicly available databases be reliably maintained to the required clinical accreditation standards over long time periods?Will the true cost of generating a bacterial genome remain stable as the sequencing market consolidates in fewer hands?How can clinical metagenomic data be managed safely in line with the ethical considerations applicable to identifiable human DNA?How can unwieldy bioinformatics pipelines developed with academic research in mind be adapted for a clinical setting?Can current expertise in traditional clinical microbiology be maintained in the transition to WGS?Efforts to develop WGS-based microbial diagnostics have unsurprisingly focused on high-resource settings. Though, we can see an opportunity for low-/medium-income countries to get up to speed with the latest WGS-based developments in real-time clinical diagnostics, rather than adopting classical microbiological phenotyping which might eventually be largely phased out in high-income countries. One precedent for the successful adoption of a technology without transitions through its acknowledged historical predecessors is the widespread use of mobile phones in Africa. This has greatly increased communication and allowed access to e-banking, despite the fact that many people previously had no traditional bank account and only limited access to landlines. Most hospitals in the developing world do not currently benefit from a clinical microbiology laboratory. The installation of a molecular laboratory based around a standard sequencer, such as a benchtop Miseq, might constitute an ideal investment, as it is neither far more expensive nor more complex than setting up a standard clinical microbiology laboratory.