Fibromyalgia syndrome (FMS) is a complex disorder where widespread musculoskeletal pain is associated with many heterogenous symptoms ranging from affective disturbances to cognitive dysfunction and central fatigue. FMS is currently underdiagnosed and often very poorly responsive to pharmacological treatment. Pathophysiology of the disease remains still obscure even if in the last years fine structural and functional cerebral abnormalities have been identified, principally by neurophysiological and imaging studies delineating disfunctions in pain perception, processing and control systems. On such basis, recently, neurostimulation of brain areas involved in mechanism of pain processing and control  has been explored by means of different approaches and particularly through non-invasive brain stimulation techniques . Here we summarize studies on tES application in FMS. The great majority of reports, based on direct currents  and targeting M1, showed efficacy on pain measures and less on cognitive and affective symptoms, even if several aspects as maintenance of therapeutical effects and optimal stimulation parameters remain to be established. Differently, stimulation of DLPFC, explored in a few studies, was ineffective on pain and showed limited effects on cognitive and affective symptoms. Very recently new tES techniques as high-density tDCS (HD-tDCS), transcranial random noise stimulation (tRNS) and tDCS devices for home-based treatment have been explored in FMS with interesting even if very preliminary results opening interesting perspectives for more effective, well tolerated, cheap and easy therapeutic approaches. Fibromyalgia syndrome (FMS) is a complex disorder where a widespread musculoskeletal pain (without a clear lesion basis) is associated with a great variety of symptoms including affective disturbances, central fatigue, cognitive dysfunction and even a particular skin reactivity to several chemical substances . The manDespite intense research effort, especially in the last years, the pathophysiology of the disease remains to be clarified. However, principally thanks to electrophysiological and brain imaging techniques, some important advance has been obtained concerning the putative brain structural and functional abnormalities underlying the clinical manifestation of FMS. On such basis, great attention was pointed toward the role of central dysfunctional mechanisms in pathophysiology of FMS and the targets for research and potential new treatments moved to brain areas and networks involved in pain processing and control  that can be accessed directly or through connected areas like motor cortex or dorsolateral prefrontal cortex. In this regard, particular interest has been raised by techniques able to perform effective modulation of brain areas through magnetic or electric currents applied to the scalp like transcranial magnetic and electrical stimulation (TMS and tES) . TMS andPrincipal aim of the present paper was to perform a structured review concerning evidence on tES treatment for fibromyalgia with particular regard to transcranial directs stimulation (tDCS) that represents the most studied approach. We present also an overview on new tES techniques drawing perspective for future development. We discuss the results obtained in terms of effectiveness and safety, mentioning also the controversial aspect and raising suggestions for evaluating the real efficacy of the technique and for further therapeutic developments. The analysis of tES studies will be preceded by introductory notes on tES techniques and on pathophysiology of FMS with particular reference to area or network dysfunctions that could represent useful targets for neurostimulation.Transcranial electrical stimulation is a neurophysiological technique able to perform effective, safe, not invasive and painless brain stimulation in humans . tES worTranscranial alternating current stimulation work through alternate, sinusoid currents that change polarity between electrodes at different stimulation frequencies. Currents can also be delivered at not fixed oscillating rate but with randomly changing frequencies across stimulation: transcranial random noise stimulation (tRNS)  Figure . DifferePathophysiological mechanisms underlying the multifaceted clinical picture of fibromyalgia remain largely obscure. Recently, however, the application of new investigational approach able to better explore structural and functional brain changes has been successfully applied to the study of FMS revealing abnormalities that could at least in part account for the complex pain perception and processing dysfunction underlying the manifestations of the disease.Fibromyalgia is considered a chronic pain syndrome characterized by an increased responsivity to painful stimuli. FMS patient have normal ability to perceive sensory stimuli but show reduced thresholds to pain . Such dyAccording to this, electrophysiology and functional magnetic resonance imaging, revealed an abnormally increased reactivity of cortical regions of the pain network involved, at different extent, in pain perception and processing like medial prefrontal and insular areas, sensory and motor cortex and cerebellum, while a decreased activity and connectivity was found in areas and network exerting inhibitory control on nociceptive input like rostral anterior cingulate regions . In lineFunctional differences in cortical excitability emerged also in a study using TMS of motor cortex in FMS patients. The report showed increased motor threshold, thus a lower corticospinal excitability and reduced short lasting intracortical inhibition (SICI) as well as reduced intracortical facilitation (ICF), that could be related to reduced intracortical GABA and glutamate modulating circuits . MoreoveIn agreement with functional changes, also fine structural abnormalities of the gray matter of the same dysfunctional regions emerged from studies using the Voxel Based Morphometry (VBM) technique, that can explore with great detail neuronal density of cortical areas. VBM indeed showed changes in gray matter of prefrontal, insular and cingulate cortex in FMS patients that correlated with intensity of pain, cognitive and affective impairment . Taken tWe searched three data bases: PubMed, Cochrane library and Scopus until August 1, 2018 for articles published in English with the search terms: “fibromyalgia” and “transcranial” in the field “Title or Abstract.” As concerns tDCS we included only randomized controlled trials, where the diagnosis of Fibromyalgia was made according to the criteria of American College of Rheumatology .Risk bias in the selected studies was explored trough the specifically suited Cochrane tool  designedOn such a basis, different degree of risk bias  has been attributed for all bias sources to each of the included studies see . ScoringFourteen tES studies meeting the selection criteria have been included. The majority of them (12) employed the tDCS technique, , while oTwo brain area were principally targeted in these studies: M1 and DLPFC that were stimulated alone or compared each-other in the same trial., M1 alone was stimulated in the majority of reports . DLPFC wStimulation intensities of 1.5 mA was employed in the studies by All but three studies used cephalic montages with anode on left side (M1 or DLPFC) and cathode on contralateral supraorbital area. Bilateral DLPFC stimulation was performed in two studies with anode on left- in the report by Pain and impact on life quality, were the most explored outcomes, while less studies examined also effects on other clinical aspects like affective and cognitive symptoms, sleep disturbances and fatigue. In two studies tDCS was explored in add-on with physical exercise with interesting synergic effects , while iHere we separately describe: (1) tDCS studies, further distinguishing by outcomes: effects on pain and on other symptoms  and by stimulation sites; (2) tDCS studies in add-on with physical exercise and ONS; and (3) studies on other tES variants like HD-tDCS, tRNS and home based tDCS treatment.All studies mentioned employing tES for FMS treatment are summarized in Positive effects on pain and life quality were obtained also in other studies that explored longer stimulation periods to induce more lasting effects. Targeting of DLPFC was generally less effective for treatment of pain in FMS patients. In the three studies comparing M1 and DLPFC, no effects by DLPFC stimulation was reported by The great majority of studies performed anodal stimulation of M1 and/or DLPFC areas all with a cephalic reference. Less studies explored ability of anodal tDCS to ameliorate cognitive and affective symptoms in FMS patients. In the first report by Differently no effects on cognitive/affective symptoms were reported in the stud by In the study by More specific cognitive targets were assessed in a very recent study  that expEffects on fatigue were explored only in one study employing tDCS of bilateral DLPFC (with anode on left side) compared to peripheral occipital nerve stimulation (ONS). Authors showed that targeting of bilateral DLFPC was able to significantly ameliorate fatigue together with pain as compared to ONS that was instead only able to reduce pain .No changes at all by DLPFC stimulation on measures of depression and anxiety were instead found in the study by Another therapeutic strategy explored the potential synergic effects of tDCS in add-on to other therapeutic tools. In the study by It is known that aerobic exercise can affect a large neural circuit inducing neuroendocrine responses  and otheAnother add-on strategy was explored by Besides classical direct current approach, the most till now employed, new application of tES have been recently tried for treatment of FMS. Two recent studies explored the effects of HD-tDCS. Despite the great interest raised about alternate currents and their potential to interact with the ongoing cortical rhythms and function, no study has until now investigated the therapeutic ability of tACS in fibromyalgia. Our group recently explored the approach of tRNS over motor cortex to treat pain and associated symptoms (cognitive and mood dysfunctions) in patients with FMS. We chose this new approach because the motor cortex was a successful target for the majority of anodal tDCS trials in FMS patients and also because tRNS over motor cortex induced greater facilitation of evoked motor potentials with respect to anodal tDCS  showing In all these studies, however, patients had to reach Hospitals or Clinical or Research Centers to underwent tES treatment. Differently, in a very recent study on FMS patients, Transcranial direct currents stimulation was safe and well tolerated in all the studies examined and no serious side effects were reported. The most frequent complaints concerned only itching and tingling sensations that were, however, short-living, vanishing completely in a few minutes after stimulation.All included studies were randomized but randomization criteria were adequately described in 8/14 (57%), leaving a condition of unclear risk bias in the remaining. More concerns are for blinding because strategy for ensure accurate blinding of subjects and assessor are described only in 3/14 (21.5%) and 4/14 (28.6%) respectively, of the studies examined configuring a condition of unclear risk-bias in the remaining. As concerns blinding of subject a critical factor is represented for stimulation intensity, as for currents ≥ 1.5 mA the subject could be able to distinguish between sham and real stimulation. At these intensities indeed, cutaneous sensations could persist across the stimulation period.The bias risk for incomplete outcome data was generally low as the majority of studies (10/14: 71.4%) correctly addressed this issue reporting opportunely about drop-out level. Similarly, a low bias risk has been found in 8/14 (57%) studies as concerns selective reporting. However, 3 studies  have beeAll studies examined were based on small samples (less than 20 subjects per arm) configuring therefore a condition of high-risk bias according to the criteria by Generally, the studies performing prolonged stimulation obtained longer lasting therapeutic effect. Ten instead than five stimulation days was able to maintain benefit on pain at one  and 2 moTo summarize, the analysis of the tES studies on treatment of FMS evaluated in the review, showed that anodal tDCS of motor cortex, that represent the most studied stimulation target, is able to induce significant therapeutic effects on pain measures and/or life quality in FMS patients, as compared to placebo sham tDCS. Less evidence instead is available about efficacy on other symptoms of FMS  also because these and in particular cognitive dysfunction and fatigue have been generally less explored in the tES studies examined and specially in those targeting M1. Indeed, a few M1 studies reported efficacy on affective symptoms and only one, based on a new tES approach (tRNS) showed therapeutic effects on both motor and cognitive/affective manifestations of FMS .The other target area studied, DLPFC, was explored only in a few reports and was less effective. A limited, short lasting efficacy on pain and life quality was showed only in other studies  where stConcerning mechanisms underlying the effects of M1 or DLPFC stimulation we have only sparse, direct evidence (trough imaging studies), about activity or connectivity changes induced by the modulation of the target areas and their correlation with the clinical outcome. The study by Even if M1 stimulation showed significant ability to ameliorate pain and life quality in FMS in the studies examined, several critical aspects emerge, principally by the risk bias analysis, that reduce the strength of the observed effect. The quality of evidence is indeed hampered by the small patient series investigated, the lack of effective and accurate blinding and the consistent methodological heterogeneity across studies. Moreover, a substantial lack of knowledge remains about the ability to maintain the therapeutic effects over time as only a limited evidence is at moment available about long lasting stimulation protocols. Given to this criticism, anodal M1 tDCS didn’t reach an evidence level to be considered a certainly effective procedure for treatment of FMS. A level B recommendation (probably effective) was indeed provided by a recent consensus paper by the European chapter of the International Society of Clinical Neurophysiology  while anEven considering this criticism, tES approach thanks to its safety, ease of use and potential for home-based treatment is worth to be further explored to better define its real therapeutic potential.Therefore, operational strategies are needed to overcome limitations emerged in the available reports and to exploit the potential of new tES based approaches. To this aim it appears of striking importance to:(1)Plan adequately powered, randomized controlled trials (20 or more patients × arm) taking care of accurate randomization and blinding and more homogenous methodology, with stimulation periods and follow-up of at least 2 weeks.(2)Explore, trough appropriately designed RCT studies the ability to maintain long term therapeutic effects through maintenance protocols.(3)Investigate the potential of new tES stimulation methods like HD-tDCS, tRNS, and tACS, and the add-on combination with other non-stimulation approaches and, last but not least, the generation of new devices for home-based treatment.All these lines of action are worth to be followed, better through a consensus between researchers,  to make the final point about the efficacy of tDCS and other tES variants for treatment of fibromyalgia. This, with final aim to obtain the most effective, extensive, and lasting therapeutic effects through the most easy and inexpensive approach for treatment of FMS patients.FB: work conception, data collection, data analysis, manuscript writing, and manuscript revision. MC, MDT, and PS-P: data analysis, manuscript writing, and manuscript revision. GG: data collection, manuscript writing, and manuscript revision. GB, GC, and BF: data analysis and manuscript revision. All authors contributed to the work and approved paper submission.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.


China has a large population; however, medical resources are unevenly distributed and extremely limited, and more medical services are needed. With the development and ever-increasing popularity of mobile internet communication, China has created a mode of mobile health  care to resolve this problem.The aim of this study was (1) to describe the problems associated with China’s medical care practice, (2) explore the need for and the feasibility of internet-based medical care in China, and (3) analyze the functionality of and services offered by internet-based health care platforms for the management of chronic diseases.Data search was performed by searching national websites, the popular search engine Baidu, the App Store, and websites of internet medical care institutions, using search terms like “mobile health,” “Internet health,” “mobile medical,” “Internet medical,” “digital medical,” “digital health,” and “online doctor.” A total of 6 mobile apps and websites with the biggest enrollment targeting doctors and end users with chronic diseases in China were selected.We recognized the limitations of medical and health care providers and unequal distribution of medical resources in China. An mHealth care platform is a novel and efficient way for doctors and patients to follow up and manage chronic diseases. Services offered by these platforms include reservation and payment, medical consultation, medical education assessment, pharmaceutical and medical instruments sales, electronic medical records, and chronic disease management. China’s health policies are now strongly promoting the implementation of mHealth solutions, particularly in response to the increasing burden of chronic diseases and aging in the population.China's internet-based medical and health care mode can benefit the populace by providing people with high-quality medical resources. This can help other countries and regions with high population density and unevenly distributed medical resources manage their health care concerns. China has the largest population and the second largest economy in the world (2018 gross domestic product: 90 trillion Chinese Yuan [CNY]), but medical care resources in China are relatively limited. There are only 2.21 licensed (assistant) physicians for every 1000 people in China. There exists an uneven geographical distribution of medical resources as well. These two problems have led to countless transprovincial patients, resulting in numerous extra economic and time costs.An understanding of the Chinese health care system and mobile phone usage provides a framework for understanding mobile health  apps in China. Acknowledging the increasing pressure exerted by an aging population, behavioral changes, and rapid urbanization , the govWith the rapid development of mobile internet communication and the increasing needs of medical services, a new mode of mHealth care is being implemented in China to effectively overcome some traditional barriers. Mobile phones  and tablet computers have become the most popular and widespread types of mobile devices used. Mobile apps allow patients to access information, assessments, and treatments in a timely manner, and improve the health of those living with chronic diseases . TherefoThe aim of this study was (1) to describe the problems associated with China’s medical care practice, (2) explore the need for and the feasibility of internet-based medical care in China, and (3) analyze the functionality of and services offered by internet-based health care platforms for the management of chronic diseases.Data were collected by searching national websites, the popular search engine Baidu, and websites of internet-based medical care institutions , using search terms like “mobile health,” “Internet health,” “mobile medical,” “Internet medical,” “digital medical,” “digital health,” and “doctor,” until June 2018. In addition, we used Baidu and the App Store to identify medical care platforms and mHealth apps. We found more than 200 medical care platforms and mHealth apps.We chose the most frequently used mobile medical care platforms, such as Doctor 7LK, Doctor Xingren, Micro-doctor, Doctor Hao, Doctor Chunyu, and Doctor Shu, to analyze the number of registered doctors. Overall, there were 81,604 hospital-based specialists and 400,344 patients involved in these platforms. Subsequently, we compared registered doctors in several provinces on the Doctor 7LK platform, the permanent resident population of these areas, and the distribution of patients and doctors on the Doctor 7LK platform. Data management and analysis were performed using Microsoft Excel 2017. Nominal and ordinal data were presented by using frequencies, percentages, and bar charts.China has limited health care resources and an overwhelming population, but the growth of primary care medical and health practitioners is relatively slow . From 19The geographical distribution of medical resources is imbalanced in China. Among the top 100 comprehensive hospitals [In 2018, the coverage rate of mobile broadband network had reached 57%, which is 12 times that of 2002 . By the There are more than 100,000 registered doctors on multiple digital medical platforms -17. For The unbalanced distribution of medical resource leads to the exploration and explosion of internet-based medical practice. Although the number of registered doctors in the southeastern area is higher, many doctors are more accepting of an internet-based medical practice in the relatively poor western regions . Gansu pWithin the departments of chronic diseases, the largest number of registered users (patients and doctors) came from the cardiovascular division, which is related to the high incidence of cardiovascular diseases in the nation . In termWe provided an overview of China’s current internet usage and mHealth care mode. The unbalanced distribution of medical resource leads to the exploration and explosion of internet-based medical practice. Internet-based mHealth care platforms could enable people to overcome geographical health care barriers and help reduce the uneven distribution of medical resources. A large number of venture capital funds have flowed into internet-based health care platforms, and the number of users of medical apps has increased gradually each year. Patients in well-developed southeastern China have a higher recognition of internet medical behaviors. Within the departments of chronic diseases, the largest number of registered users (patients and doctors) came from the cardiovascular division, which is related to the high incidence of cardiovascular diseases in the nation.By 2016, the total population of mainland China had reached 1.38 billion, but there were only 2.21 licensed (assistant) physicians per 1000 people and unlicensed doctors , among tWhy do Chinese patients need the mHealth care platform? First, primary health care providers in China are usually inadequately trained and sometimes uncertified. According to the China Health and Family Planning Statistical Yearbook, only 24% of doctors had a regular license in village clinics in 2015 . The conThe good news is that mobile internet communication is booming in China. Statistical data from the National Bureau of Statistics of the People’s Republic of China showed that the broadband network covered 94.5% of the total villages and 78% of impoverished villages by the end of 2015  and imprAccording to the different stages of medical interventions, operation modes of digital medical services can be divided into different types based on the analysis of these mHealth apps. These include (1) reservation and payment: patients can make appointments with doctors through the platform and pay the consultation fee through the platform; (2) medical consultation and assessment: registered patients can consult doctors on the platform, and the registered doctors can answer the patient’s questions. To ensure the standardization of diagnosis and treatment, these consultations usually involve common medical questions, and the doctors cannot provide diagnostic opinions or prescriptions; (3) medical education: these websites or apps provide patients with disease-related popular science and education articles, such as daily life considerations, self-monitoring, self-management, and emergency response to certain acute diseases; (4) pharmaceutical and medical instrument sales: patients can purchase over-the-counter medications or purchase prescription medicine after verifying a valid prescription on these websites; (5) electronic medical records: the users of such website or apps are usually doctors. It facilitates doctors to make medical electronic records; (6) chronic disease management: such websites or apps usually have doctor and patient versions. These types of platforms usually have multiple functions. For example, doctors can send text messages to patients to remind them about their follow-up visits, and the platforms would automatically make an appointment for a follow-up visit after receiving a reply from the patients. They could also send related medical education articles to the patients under the patients’ request. Doctors can write their patients’ medical records on the platform. Patients can purchase prescription medicine on these websites according to the doctor’s prescription.Other countries already use mobile phones to assess and treat certain diseases -31. WithChronic disease management platforms aim to facilitate subsequent follow-ups for patients with chronic diseases. It enables remote medical treatment, which otherwise relies on the offline primary medical and health care institutions, clinical laboratories, and medical image centers. The platforms facilitate setting up a medical closed loop of care for doctors, patients, and medicine, thus helping doctors to build up private clinics to better manage patients with chronic diseases. The proportion of doctors and patients associated with chronic diseases is very high; the top 5 departments of registered users are related to chronic diseases. This correlates to the nature of the disease and the need for follow-ups. Using internet-based health care platforms is an innovative and effective way to help doctors follow up and manage their patients with chronic diseases in China.Nevertheless, in terms of the distribution of registered users, patients in the southeast have a higher recognition of these novel internet-based medical advances. One of the reasons is that it is a relatively economically developed region and the gross regional domestic product is related to higher medical input of the government. Moreover, people in this area are highly educated and they take their health very seriously. However, statistical bias may be caused by the differences among several internet-based medical companies in various areas. The widespread existence of internet-based health care platform has weakened the uneven distribution of medical resources. Many doctors are more likely to accept internet-based medical practices, especially in poor areas as the internet can make it easier for doctors to connect with patients who are geographically distant. Both doctors’ and patients’ needs are fundamental for the survival and development of these platforms.internet plus medical and health care, and allowed medical institutions to provide online services and carry out internet-based medical services to benefit more people with high-quality medical resources and improve the overall health of residents. The government will also expand telemedicine coverage to all medical federations, support high-speed broadband networks, and explore the sharing of prescription and drug retail information in medical institutions, thus improving the internet plus medical care system [internet plus medical and health care, it has the opportunity to build an integrated, cooperative internet-based medical and health care system and gain valuable experiences through the whole process. How China‘s internet-based health care will evolve under existing policies and socioeconomic environment is worthy of attention.China’s health care system needs to be considerably strengthened to manage both the rising burden of chronic diseases and increasing health-related expenditures . On Aprie system . As ChinChina‘s internet health care mode can provide possible solutions to address the lack and uneven distribution of experienced health care providers in some countries or regions. Although it is still being explored, we highlight the important role of the mHealth care mode in China, which could also be helpful for other developing countries facing similar challenges.


The humanities have been increasingly incorporated into medical school curricula in order to promote clinical skills and professional formation. To understand its current use, we reviewed the literature on visual arts training in medical education, including relevant qualitative and quantitative data. Common themes that emerged from this review included a focus on preclinical students; instruction promoting observation, diagnostic skills, empathy, team building, communication skills, resilience, and cultural sensitivity. Successful partnerships have involved local art museums, with sessions led primarily by art educators employing validated pedagogy such as Visual Thinking Strategies or Artful Thinking. There is evidence that structured visual arts curricula can facilitate the development of clinical observational skills, although these studies are limited in that they have been single-institution reports, short term, involved small numbers of students and often lacked controls. There is a paucity of rigorous published data demonstrating that medial student art education training promotes empathy, team building, communication skills, wellness and resilience, or cultural sensitivity. Given these concerns, recommendations are offered for fostering more robust, evidence-based approaches for using visual arts instruction in the training of medical students. Over the last three decades, the humanities have been increasingly incorporated into medical school curricula . This isThe majority of initiatives employing humanities in student curricula have focused on training in the visual arts, with published literature reporting improvements in skills of observation, empathy, and resilience among medical students ,14. ThesWe searched the literature in PubMed and Google Scholar databases for all publications about humanities and/or visual arts programs at accredited medical institutions. We then narrowed our search to medical institutions in the USA. From this search, we identified 11 institutions with detailed, publicly available curricula and seven institutions with published analyses, which we included in our review. Through analysis of the 11 programs’ syllabi, we designated five distinct ‘domains of focus,’ which allowed us to compare across institutions and systematically characterize the current scope of visual art instruction in medical education.According to an updated report from ‘The Art of Examination: Art Museum and Medical School Partnerships Forum’ from May 2017 , there aFive domains of focus were identified within these visual arts programs: (1) observation and diagnosis; (2) empathy; (3) team building/communication; (4) wellness/resilience; and (5) cultural sensitivity .10.1080/While most schools’ programs incorporated some aspect of all five domains, the specific content and pedagogy were varied . ApproacTwo approaches, Visual Thinking Strategies (VTS) –20 and AVTS –19 was oArtful Thinking is a similar framework that has been utilized in medical school art programs . It was Partnerships between the medical school and a local art museum have been central to almost all of these programs . This alAlthough subjective feedback is typically collected from participants in medical student art education courses/programs, there are few published detailed and/or rigorous reports on the educational impact of this training. Of these reports, studies from six institutions are summarized below.This course was offered to first year medical students starting their doctor–patient encounter course, and was named the ‘Yale Center for British Art (YCBA) Project’. From 1998 to 1999, 90 students were randomized into a control group (30 students), YCBA group (30 students), and lecture group (30 students). In the following year, 86 students were randomized to the YCBA group (51 students) or control group (35 students) after the lecture group showed no benefit. The control group went to clinical sessions led by a preceptor who taught the history and physical exam; the lecture group attended radiology lectures with imaging related to anatomy lab; and the YCBA group engaged in facilitated art observation .The students received a pre- and post-test, in which they were given an image of patient and 3 min to write a detailed description, which were then blinded and scored for visual diagnostic elements. The results demonstrated a significant improvement in the post-test scores of the YCBA group for both years .This qualitative study was unique in that it looked at the delayed effect of its course on the clinical phase of medical education. The 8-month elective course was initially offered to 19 second year medical students, who were then asked to participate during their third and fourth years after they had completed 9 or more months of clinical rotations. They were given eight questions and asked to provide subjective journal responses with specific examples, including ‘the most memorable experience, influence on and understanding biases in the doctor-patient relationship, influence on second year of medical school, usefulness during clinical years of medical school, unique skills or insights, and influence on experiencing art-related activities.’ Analysis of the responses demonstrated that (1) visual arts alongside an integrated clinical component was necessary for improving observational skills, (2) description and interpretation are not systematically taught in other areas of the medical curriculum, and (3) that medical students felt that time for reflection was critical to their education and personal growth .‘Training the Eye: Improving the Art of Physical Diagnosis’ is a multidisciplinary course that aims to improve visual literacy skills by practicing ‘unbiased inspection and accurate reporting.’  It consiThe study was a prospective, partially randomized pre- vs. post-course evaluation. There were 24 students in the intervention group, and 34 in the control group. The pre- and post-tests were distinct 1-h visual skills examinations in which students were asked to describe three patients with various visual diagnoses and two works of fine art.The results demonstrated a consistent increase in post-test scores in students who took the course as compared to the control group. The study also found a possible graded effect of class attendance on post-test scores, which may advocate for continued practice throughout medical training. Additionally, qualitative analysis showed a significant improvement in five response categories of the intervention group which were determined to have clinical correlates: observations; interpretation supported by evidence; speculative thinking; pertinent negatives; and use of fine arts concepts .Robert Wood Johnson Medical School conducted a non-randomized, non-controlled qualitative study on one 3-h long session during a required week ‘Introduction to the Clerkship Experience,’ held for all third year medical students prior to clerkships. There was no museum partnership; instead, students were guided by a fourth year medical student through a large group VTS-based discussion on eight representational and non-representational fine art pieces.Students were assessed with a pre- and post-test, during which they were asked to list observations and give a detailed description of two photographs of patients with visible medical conditions. While the study found no significant difference in the number of observations between pre- and post-tests, analysis of the descriptions revealed significant improvement in four clinically relevant themes: use of subjective terminology; scope of interpretations; speculative thinking; and use of visual analogies. The authors concluded that the session is an easily generalizable intervention with an appreciable impact on clinical observational skills.This randomized, single-masked control trial enrolled 36 first year medical students at the Perelman School of Medicine and randomized them 1:1 into either the control or intervention group. The intervention group received free museum passes and attended art observation sessions led by art educators at the Philadelphia Museum of Art, while the control group only received free passes. The sessions were designed using the ‘Artful Thinking’ approach; the curriculum was based entirely on art observation without any formal clinical or lecture component.The analysis consisted of a computerized pre- and post-test in which students were asked to describe art images, retinal photographs, and clinical photographs involving ocular surface/peri-ocular disease. These descriptions were then scored by graders who were masked to both group (control or intervention) and to time (pre/post). Clinical educators scored the retinal and clinical photographs based on a predetermined a-priori rubric, while the museum educators scored the art images based on how often students described themes of ‘Observing, Interpreting, Evaluating, Associating, Problem-Finding, Comparing, Flexible Thinking, and Evidence.’ The results demonstrated significantly increased scores between the pre- and post-tests in students in the intervention group, and a decrease in scores in the control group. This increase in scores occurred without any formal medical clinical correlation component, to which the authors concluded that art training in isolation can help teach medical students to become better clinical observers. Finally, the study also analyzed emotional recognition test scores, which did show a trend toward improvement in the intervention group.‘Observation and Uncertainty in Art and Medicine’ was designed to improve tolerance of uncertainty and reflective capacity through art observation. It represents a unique collaborative effort between Weill Cornell and Columbia University and enrolls six students per year from both institutions.The published analysis involves 4 years of course administration with a total of 47 student participants. The researchers employed a mixed-methods analysis involving (1) quantitative analysis of three pre- and post-course surveys aimed at studying the course objectives ; Tolerance for Ambiguity (TFA) scale; and Best Intentions Questionnaire (BIQ) for awareness of personal bias) and (2) qualitative analysis of focus group transcripts and written evaluations.Results demonstrated a statistically significant improvement in GRAS scores after completion of the course, indicating improvement in student reflection. There was also a small improvement in TFA and BIQ scores, although these did not show statistical significance. The authors identified four major themes of improvement from the qualitative evaluations, including ‘enhancement of observational skills, awareness of the subjectivity and uncertainty of perception, exploration of multiple points of view, and recognition of the course as a place for restoration and connection to classmates.’From this review, common course themes for medical student visual arts education  and 2 inThe studies described above provide reasonable evidence that structured visual arts curricula can facilitate the development of clinical observational skills. In contrast, although anecdotal experience and trends in some reports suggest a benefit, there is a paucity of rigorous published data demonstrating that medial student art education training promotes empathy, team building, communication skills, wellness/resilience, or cultural sensitivity. In regard to the studies that have demonstrated an educational impact, they have all involved single institutions, with small cohorts lacking controls, and thus the generalizability of their findings is unknown. Further, these studies have not objectively accessed the durability of improvements in art observation or clinical skills and have not sorted out the ‘dose’ required for an effect. Additionally, there are unresolved issues about pedagogy. Further, although earlier studies suggested a direct benefit of integrated clinical components ,25 in thMoving beyond assessments of student satisfaction to more rigorous, prospective assessments of course offeringsPre- and post-course surveys and assessments, using validated measures, that explicitly assess the five areas potentially impacted by visual arts trainingInclusion of control groups and the use of randomization to remove confounding effects of instruction in other parts of the curriculumProspective evaluations of the amount of instruction  required for effects and the durability of those effectsProviding course offerings that target post-clerkship students (with appropriate assessments)Collaborative partnerships between medical schools  to jointly develop and implement visual arts education offeringsGiven the concerns noted above, it is clear there is a need for more robust, evidence-based approaches for using visual arts instruction in the training of medical students. We therefore recommend the following:The Art of Examination: Art Museum and Medical School Partnership Forum [We believe the implementation of these recommendations, building on recent efforts such as that of ip Forum ,16, will


The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems. To create a complete profile of any individual, to perform predictive and inferential analytics, and to investigate the mechanisms behind any biological event, the biomedical researcher has at his disposal several different sources of data: medical records, imaging data , claims, sequencing data , genotypes, sensor data .The 21st century is the age of data and knowledge explosion in biomedicine. Several key events, such as the completion of the Human Genome Project and the advent of next-generation sequencing technologies,5 UniProt6) and ontologies  are widely-used and popular resources in biomedicine, and contain knowledge pertaining to molecules  and their characteristics, biological pathways, animal models and phenotypes, organs, symptoms, diseases, and adverse reactions.10 As of January 2019, there are more than 750 ontologies and terminologies in BioPortal,11 the world’s most comprehensive repository of biomedical ontologies. MEDLINE,12 the largest repository of scientific articles in biomedicine and the primary component of the PubMed search engine,13 currently contains more than 25 million citations and thousands more are added each day.There is also a rapid increase in the number of structured, machine-processable knowledge artifacts, as well as an increase in unstructured knowledge sources in the form of publications in biomedicine. Knowledge bases  to retrieve relevant data pertaining to their unique requirements or to retrieve answers to queries, such as “What are the medications prescribed to melanoma patients that have a V600E mutation in their BRAF gene?”.Despite the open availability of many important databases and knowledge bases, biomedical researchers still face severe logistical and technical difficulties when integrating, analyzing and visualizing heterogeneous data and knowledge from these diverse and isolated sources. These tasks pose a steep learning curve for most biomedical researchers. Researchers need to be aware of the sources where the data and knowledge relevant to their research exist. Depending on the availability and the accessibility, biomedical researchers need exhaustive computational resources and extensive programming skills to query and explore the data and knowledge sources. The heterogeneity across these sources, in terms of formats, syntaxes, notations and schemas, severely stymies the systematic consumption of data and knowledge stored in these sources. The biomedical researcher ends up learning multiple systems, configurations and access requirements, significantly increasing the complexity and time of scientific research. In most cases, the researcher just hops across web portals and search engines  cloud to enable Web-scale semantic processing and to develop applications that integrate data and knowledge from multiple heterogeneous sources in different biomedical domains. We will provide our perspective on the challenges associated with querying and consuming data and knowledge from multiple LSLOD sources in an integrated fashion, which are faced by most biomedical researchers. Finally, we will provide a few technical solutions that address these challenges and that can assist software engineers and biomedical researchers to develop the next generation of intelligent infrastructures to power advanced machine learning methods.16 The World Wide Web Consortium (W3C) has recommended and standardized a set of Semantic Web languages and technologies that aim toward accomplishing specific tasks for the creation of this Web of data and knowledge. We present a brief technical overview on Uniform Resource Identifiers (URIs), the Resource Description Framework (RDF)17 and Linked Data principles18 for representing and linking data on the Web as graphs in Box 19 (RDFS) and the Web Ontology Language20 (OWL) for defining Web-based vocabularies and ontologies in Box 21 to query multiple diverse RDF graphs in Box The Linked Open Data (LOD) cloud has emerged from the vision of a Web of data that co-exists with the current Web of documents.sortium WC has recrug entity . In the current state of art, biomedical data and knowledge exist on the Web in fragmented and isolated sources  that may or may not provide programmatic access to users. Consider that two imaginary isolated sources, a drug-related knowledge base (Source 1) and a biological pathway or disease-related knowledge base (Source 3) exist on the Web. The facts Gleeveclioma may exist in Source 1 and Source 3, respectively. Such facts are not always necessarily represented as directed edges—for example, these facts may be represented using cell values in a database table. Similarly, other arbitrary databases  that may be relevant for the researcher also exist on the Web.Using a hypothetical scenario from biomedicine, we will provide an intuitive explanation on what it means for data and knowledge to be linked and queried on the Web Fig. . Supposeleevec drug entities present in different sources). In an ideal sense, the boundaries between different sources will vanish and a Web of Data composed of interlinked entities will manifest. A human user or a computational agent can explore this linked Web of Data by just navigating the different URIs (similar to how a user navigates on the World Wide Web using the URLs of web pages), and generate novel hypotheses  that have molecular weight <1000 g/mol and target Protein entities involved in the Signal Transduction process. A visual representation of the SPARQL query is depicted in Fig. 5 and the Kyoto Encyclopedia of Genes and Genomes (KEGG)22 are present as RDF graphs on the LOD cloud . An “intelligent” SPARQL query federation architecture determines to query the DrugBank RDF graph for half-life information, and the KEGG RDF graph for knowledge on drug–protein interactions and biological pathways  and  as query results for three variables: (i) Drug ID/Label, (ii) Protein ID/Label, and (iii) Half-life of the drug .36 A few of these biomedical initiatives that use Semantic Web technologies are listed in Table 37), or even incorporate SPARQL functionality in their Web portals . From the perspective of a biomedical researcher, Semantic Web technologies and the LSLOD cloud may have potential advantages for Web-scale computation, seamless integration of big biomedical data and knowledge, and structured querying and reasoning over multiple heterogeneous sources simultaneously .The challenges stemming from the integration of disparate, heterogeneous biomedical data and knowledge sources on the Web have led biomedical publishers to be some of the earliest adopters of Semantic Web technologies and Linked Data principles.Box 1 Resource Description Framework (RDF)17 Each entity  or a class of entities  is considered to be a “thing” or a “resource”, that is represented using a Uniform Resource Identifier (URI). An example of an HTTP URI is http://bio2rdf.org/drugbank:DB00619, where http://bio2rdf.org/drugbank: is the URI namespace and DB00619 is the identifier of the drug Gleevec.RDF is a simple, standard triple-based model for data interchange and representation on the Web.leevec, has-target, PDGFR] will be a valid triple in the RDF Source 1, where each component is represented using an URI .Using RDF, the relations will be expressed as  triples. Each component of this triple  is represented using an URI. Hence, RDF extends the linking structure of the Web by using the URIs to represent relations between two resources. This facilitates integration and discovery of relevant data and knowledge even if the schemas and syntaxes of the underlying data sources differ. RDF allows structured and semi-structured data to be mixed, exposed, and shared across different applications. If the isolated sources in Fig. leevec URI (http://bio2rdf.org/drugbank:DB00619) using any Web browser  will provide additional information on the entity Gleevec retrieved from other relevant sources .To ‘dereference’ a URI means to convert a relative URI reference to an absolute form by attempting to obtain a representation of the resource that it identifies. Dereferencing any URI enables the user to discover additional data and knowledge on the LOD cloud related to the representative entity. For example, dereferencing the GLinkedData principles: To ensure the quality of the data and knowledge sources published using RDF, the W3C has established the following four principles for publishing Linked Data:18 [noitemsep]Use URIs as names for entities.Use HTTP URIs so that people can look up those entities using a Web browser.Provide useful information when someone looks up a URI .Include RDF statements that link an entity to other URIs so that users can discover related information regarding that entity (reuse and linking).Box 2 RDF Schema (RDFS) and Web Ontology Language (OWL)19 or OWL.20 RDFS and OWL enable publishers to define structured Web-based vocabularies and ontologies that enable richer integration and interoperability of data among descriptive communities. Such an independent representation facilitates the evolution and modularization of the schemas separately from the data.RDF is essentially only a triple-based, schema-less modeling language. The schema of an RDF dataset is represented using secondary specifications such as RDFSleevec, type, Anti-neoplastic Drug]), and classification triples of the types subClassOf  and subPropertyOf . All entities within a class share similar characteristics, such as attributes and relations. RDFS also provides annotation properties that can aid publishers to include human-readable annotations for different entity and property URIs  or as its mesylate salt, imatinib mesilate (INN).”)RDFS facilitates the modeling and inclusion of instantiation triples  and property restrictions . OWL documents, known as ontologies, can also be published on the LOD cloud and may refer to or to be referred  from other OWL ontologies and Linked Data resources. Knowledge expressed in RDFS vocabularies and OWL ontologies can be exploited by computer programs, called reasoners, to verify the consistency of that knowledge  or to make implicit knowledge explicit and to generate novel inferences .OWL extends the capabilities of RDFS and facilitates the inclusion of advanced class expressions, often composed of logical operators  when using these URIs to represent classes and properties in their RDFS vocabularies and OWL ontologies .It should be noted that all class and property mentions in these above examples are essentially URIs. Data and knowledge publishers are expected to adhere to the standard best practices 21The Linked Open Data (LOD) cloud consists of different data and knowledge sources, published as directed graphs using the RDF triple-based model and linked with each other  through reuse of different URIs, with schemas described using the RDFS and OWL languages. The SPARQL graph query language can facilitate users to query multiple diverse RDF graphs, as well as the RDFS vocabularies and OWL ontologies, exposed through SPARQL endpoints in the LOD cloud.rug entities with exactly one target), (ii) filtering of literals , and (iii) constraining queries by source RDF graph . In some cases, SPARQL can be combined with an ontology reasoner for semantic query expansion–for example, the query ‘Retrieve Drug entities that target Protein entities involved in Signal Transduction’ will retrieve drug entities related to Apoptotic Signaling Pathway and Necroptotic Signaling Pathway, since both these classes will be children classes of Signal Transduction. Multiple triple patterns can be combined to create basic graph patterns. SPARQL graph pattern matching is defined in terms of combining the results from matching basic graph patterns with RDF graphs. SPARQL enables users to query RDF graphs using required and optional graph patterns along with their conjunctions and disjunctions.Each SPARQL query is composed of triple patterns. A triple pattern is essentially similar to an RDF triple, but has a variable node  in at least one of the subject, predicate or the object components of the triple. For example,  triple pattern will retrieve all drugs that target the protein entity PDGFR. SPARQL also supports (i) extensible value testing  for efficient source selection, query execution, and structured reasoning.108Ideally, using the SPARQL graph query language any user can query multiple RDF graphs simultaneously on the LOD cloud. This approach is often called ‘SPARQL query federation’ or ‘distributed SPARQL query processing’.Scalability: Easily deal with volume, variety and velocity of underlying sources.Flexibility: Easily incorporate multiple remote sources during query processing and execution.Exhaustivity: Easily retrieve all available and relevant knowledge related to a specific entity.Mutability: No update mechanisms are required to handle modifications in remote sources.Minimal technicality and redundancy: No requirements of downloading, transforming and storing content locally, no additional copies of the remote sources, minimal requirements of programming skills for most users, sharing of queries between projects that integrate similar sources.Few benefits of such a Web-based SPARQL query federation approach over conventional approaches for data and knowledge integration  are enumerated below: [noitemsep]Using examples from three main research domains: (1) pharmacology, (2) cancer biology, and (3) epidemics, we will provide our perspective on how Semantic Web technologies and the LSLOD cloud can tackle several challenges for big biomedical data and knowledge integration, endowed due to the: (i) the volume, velocity, variety, and veracity of biomedical data and knowledge, (ii) the heterogeneity across different sources, (iii) and the requirements of exhaustive biomedical entity reconciliation, and enable the discovery novel associations, often serendipitously, in the context of available knowledge.38 Researchers are now looking for novel uses of drugs existing in the market, often called drug repurposing, to mitigate these costs.39 Federal regulators monitor the occurrence of adverse drug reactions (ADR) after the public release of a particular drug, often called pharmacovigilance or drug safety. ADRs are not always be detected during the clinical trials, and may also manifest due to drug–drug interactions in patients.40 ADRs are the 4th leading cause of death exceeded only by diabetes, AIDS, and pneumonia.41 The ever-rising cost of drug-related morbidity and mortality in the United States was estimated to be US$177.4 billion in 2000.42Currently, it costs US$2.87 billion (in 2013 dollars) for the discovery of a novel drug by a bio-pharma company, and there is bound to be exponential increase in these costs.rug entity  or need to pose queries, an example of which was introduced earlier using Fig. For biomedical research pertaining to drug discovery, drug repurposing, and drug safety, biomedical researchers often need an aggregated summary on available data and knowledge for a specific DPharmacological Concept Triple Store) data warehouse exposes integrated content, harvested from several legacy databases and structured using a common vocabulary with normalized entity identifiers, through user-friendly software interfaces to accelerate drug discovery research.43 Himmelstein et al.44 manually integrated content from 29 different sources using a common data model to create a systems pharmacology network ‘HetioNet’ composed of different biological entities. Similarly, Li et al.45 generated a causal systems pharmacology network ‘CauseNet’ by manually integrating four sources: DrugBank,5 PharmGKB,46 KEGG,22 and the Comparative Toxicogenomics Database (CTD).47Biomedical researchers have often used conventional methods to address the problem of integrating data and knowledge from multiple pharmacological sources. The Open PHACTS (Open 48 Bio2RDF27). Whereas, there has been research in ‘downloading’ the pharmacological RDF graphs from multiple LSLOD sources and integrating the content ‘locally’, these research methods do not perform Web-scale semantic processing and integration. Noor et al.49 constructed a mechanism-based DDI knowledge warehouse by integrating LSLOD content at the pharmacokinetic, pharmacodynamic, and pathway interaction level, and used an inference engine to generate mechanistic explanations for DDIs. ReDrugS50 uses a data warehousing approach to integrate Bio2RDF Linked Data sources, and the integrated content is analyzed using a probabilistic graphical model to predict drug repurposing candidates for melanoma.A few of the foremost biomedical projects on the LSLOD cloud were related to publishing pharmacological data and knowledge on the Web , along with other disadvantages  over Bio2RDF sources, and used the network in signal detection algorithms to detect pharmacovigilance associations from the US FDA Adverse Event Reporting database (FAERS) with explanations on underlying biological mechanisms.The above methods often entail redundancy of technical efforts , proteins and drugs data , ‘-omics’ data , and patients’ clinical data, environmental and nutritional data, etc.51 emphasized the need for developing novel approaches that investigate somatic mutations  in cancer genomes collectively, in conjunction with knowledge in gene sets  or biological pathways and interaction networks . Most of these sources are already available on the LSLOD cloud for querying. Apart from outperforming single-gene tests , such systems biology approaches can enhance our understanding of somatic mutations, decipher disease mechanisms and also aid in repurposing existing drugs for treatment toward different cancer types . Moreover, knowledge that is readily available along with the genomics and clinical data of a patient, can aid the physician to make better clinical decisions .Ding et al.8 a popular reference terminology, to represent cancer data across different research centers, Common Terminology Criteria for Adverse Events58 to capture adverse events observed in cancer therapy clinical trials, NanoParticle Ontology59 to characterize nano-materials used in cancer diagnosis and therapy, Radiation Oncology Ontology60 to map radiation data across clinical databases).Semantic Web technologies and LSLOD resources are ideal for enabling Web-scale data integration for cancer research. Biomedical researchers have indeed utilized the OWL knowledge representation language to develop several cancer-related ontologies ,61 developed a visual query system ReVeaLD  that used a query federation architecture for querying 80+ LSLOD sources relevant to cancer research. ReVeaLD enabled cancer researchers to formulate SPARQL queries  and showcased the use of a query federation architecture to query Linked TCGA in conjunction with other sources on the LSLOD cloud. The Linked TCGA project and the associated multi-faceted visualization perspectives are described in more detail in Box Kamdar et al.While cancer research and pharmacological research typically deal with data and knowledge of great volume and variety, research pertaining to infectious diseases and epidemics is often characterized by data and knowledge of great velocity. Web-scale semantic processing can benefit several aspects of informatics approaches that analyze social media streams for monitoring epidemics . Apart from social media streams, there is relevant data and knowledge from other research sources pertaining to infectious diseases: sequencing of microbial genomes and proteomes, experimental assays to identify ligands that target select proteins for therapy, publications that document these experiments, etc.67 generated a mashup of time-course microarray gene expression results with protein–protein interaction data from Bio2RDF sources to understand the infection of human macrophages with human immunodeficiency virus 1 (HIV-1). The 2013–2016 Ebola Virus epidemic had a cumulative death rate of 41% and 24,000 reported cases (as of 20 March 2015). Kamdar et al.68 developed a linked mashup Ebola-KB that integrates publicly-available knowledge, pertaining to the Ebola Virus Disease, from several Linked Data sources such as Gene Ontology,7 MEDLINE,12 Protein Data Bank,69 etc. Questions, such as “Retrieve knowledge from KEGG and DrugBank on small molecule ligands which bind to EBOV protein Polymerase”, can be answered by executing SPARQL queries against the Ebola-KB linked mashup in conjunction with the LSLOD cloud , demographic and geographic databases , patient symptoms terminologies , disease ontologies , to determine opioid prescription and usage patterns and predict whether a patient or a user demonstrates opioid abuse behavior.Semantic Web technologies and LSLOD sources can also aid biomedical researchers to gain insights into drug-related epidemics in the United States. To understand the genetic basis of nicotine dependence, Sahoo et al.Box 4 Web-scale Linked Cancer DataCancer systems biology approaches often rely on ‘-omics’ and clinical datasets of cancer patients, few of which are also available publicly. For example, the Cancer Genome Atlas (TCGA) publishes the genomics  and clinical data of individuals, categorized under different cancer types.29 raw TCGA data for 27 different cancer types is preprocessed, converted, and published as Linked Data in order to facilitate the querying and live integration of these cancer datasets via remote SPARQL query processing. Linked TCGA data is also linked with content from several existing LSLOD sources that contain relevant knowledge on biological pathways , proteins , and diseases . Biomedical publication abstracts from the PubMed MEDLINE13 repository are processed through a natural language processing pipeline and named entities  are annotated using concepts from the LSLOD cloud. Hence, unstructured publications are made available for querying along with structured cancer ‘-omics’ and clinical data, as well as knowledge from public knowledge bases.Semantic Web developers have published publicly-available cancer genomics datasets as Linked Data to enable the development of analytical pipelines for automated analyses. Developing such analytical pipelines over genomics data often involves redundant, non-trivial, and difficult tasks for most biomedical researchers, such as downloading and preprocessing large data archives, feature extraction and linkage to existing biological knowledge. Under the Linked Cancer Genome Atlas (Linked TCGA) project,104 perspective  or a gene  using a Network Explorer visualization perspective, which features a highly dense, force-directed network linking the different tumor typologies, genes, publications, and MESH topics. TCGA genomic datasets  of the cancer patients can be visualized against the human genome, and the GenomeSnip and the Network Explorer perspectives can be used to further filter and explore the data interactively on the Web .The Linked TCGA project also provides several different visualization perspectives so that biomedical users can visualize and explore integrated content from Linked TCGA and several other sources on the LSLOD cloud without formulating extensive federated SPARQL queries. For example, the GenomeSnipThe Linked TCGA project demonstrates the true utility of Semantic Web technologies and Life Sciences Linked Open Data for Web-scale semantic processing and data integration. Content (structured and unstructured) from several data and knowledge sources is integrated and made available for the biomedical researcher to interactively explore, as well as to use the integrated content for analysis in machine learning methods.Significant resources have been invested in publishing biomedical data and knowledge on the Web to create the LSLOD cloud. However, to the best of our knowledge, outside of a few research groups (including ours), there are not many biomedical applications that demonstrate Web-scale Semantic Processing and Data Integration by consuming LSLOD in a decentralized fashion . As seen through the examples in the previous section, there are significant benefits of using query federation and graph-based methods over conventional methods for tackling the integration bottlenecks in different research areas of biomedicine. However, decentralized biomedical applications that actually exist  have generally seen minimal adoption by the broader biomedical research community.11 These resources comprise a major portion of the LSLOD cloud. However, biomedical ontologies are typically used in “closed” systems or centralized applications , and they are not queried over the Web in most cases. For example, the Gene Ontology (GO),7 arguably the biomedical ontology with the highest impact in the community, is widely used for Gene Set Enrichment Analysis (GSEA) applications. However, these applications often rely on locally-downloaded versions of Gene Ontology.The biomedical community has indeed been using the Web Ontology Language (OWL) for the development of large biomedical vocabularies and ontologies for a long time.68 However, most biomedical researchers do not retrieve fruitful results (or any useful results in most cases) when they query against the LSLOD sources in the wild. We have identified few of the most important challenges faced while using content directly from the LSLOD cloud in biomedical applications.Publishers often mention the “potential” of Linked Data to solve data integration challenges, but only showcase use cases where the LSLOD sources are queried in a controlled environment.78 In addition, it is relatively easy that online complex queries  may incur in timeouts, given the limited allocated resources of public SPARQL endpoints.The accessibility and availability of LSLOD sources are two of the major reasons why data and knowledge within the LSLOD cloud cannot be queried and consumed by biomedical researchers. According to the statistics and metadata descriptions at lod-cloud.net, the 2017 version of the Linked Open Data (LOD) cloud had 1281 sources, out of which only 50% (646 sources) had a functional Linked Data access point .If the LSLOD sources do not have high availability then the research and development of Semantic Web query federation methods and tools in biomedicine is severely impacted.http://sparql.bioontology.org/) is not frequently updated. Similarly, updated Bio2RDF RDF dumps, available only via a Javascript-based page (http://download.bio2rdf.org/#/), cannot be easily crawled by machines. Academia-based Linked Data resources , which are maintained by only one research group and are often reliant on the funding sources, cease to exist once the funding periods end. Several valuable LSLOD sources  are no longer available.The liveliness and ‘freshness’ of LSLOD sources depends heavily on the continued support and interest of their maintainers (who are often from academia). Once the maintainers leave the project, often, the SPARQL endpoints are not updated anymore and may stop being available. Sustainable access on the Web with regular updates, in compliance with the Linked Data principles, has simply not been a priority for various data and knowledge providers. For example, the highly-available SPARQL endpoint of BioPortal  using the following different UniProt URI representations:Publishers reuse inconsistently  URIs used to represent different biomedical entities. For example, Kamdarhttp://purl.obolibrary.org/obo/UniProt:, (ii) http://bio2rdf.org/uniprot:, (iii) http://purl.uniprot.org/uniprot/, and (iv) http://identifiers.org/uniprot/.(i) 35 . However, while the compounds in both these RDF graphs are mapped to terms in the ChEBI ontology,82 the URIs are different. Hence, the researcher cannot navigate or query the two RDF graphs in an integrated fashion . This issue, called “intent for reuse” , is manifested across many biomedical ontologies, as documented by Kamdar et al.81This issue creates a significant burden for biomedical application developers, who use the LSLOD cloud for Web-scale data integration but will be unaware of all these URI representations across different datasets. For example, as shown in Fig. 82). In many cases, instead of using common vocabularies or ontologies  to represent the classes and properties in their RDF graph schemas, data publishers use their own custom vocabularies to generate RDF data.In Fig. rugrotein in different sources . Moreover, completely different graph patterns may be used to capture these relations. For example, in Fig. leevec and PDGFR), provenance information , or even lists of resources.17 Dealing with such blank nodes during Web-scale query federation and integration is inherently difficult. To explain simplistically, in Fig. leevec is located one hop away while navigating DrugBank RDF graph, whereas the protein target is located two hops away while navigating the KEGG RDF graph.As shown in Fig. 83 is generally much less across biomedical ontologies and Linked Data sources in the LSLOD cloud. Kamdar et al.81 found that while significant term overlap exists across biomedical ontologies in the BioPortal repository, most ontologies reuse less than 5% of their terms and ontology developers just use completely different representations . This result is shown in Fig. 80“Actual and Correct Reuse” as advocated by the Linked Data principles and by various ontology engineering methodologiesThere is a steep learning curve to understand and use Linked Data and Semantic Web technologies for biomedical researchers, who will use the content for scientific research and discovery. The architectural and structural issues with the LSLOD cloud, discussed in the preceding sections, make it more difficult for biomedical researchers to use the LSLOD cloud for Web-scale data integration.40). However, he/she has to formulate a complex SPARQL query with >20 triple patterns for this task , since unique relations may exist in each source  as shown in Fig. ask Fig. . It is ‘84It is probably naive to expect that, for their data and knowledge integration needs, most biomedical researchers will formulate sophisticated SPARQL queries over heterogeneous LSLOD sources that have limited availability, without minimal automated support. There is a dire need for HCI-inspired applications and visualizations over the LSLOD cloud to make it easy for biomedical researchers to query and explore LSLOD content , where all data is transformed under a common schema and using a uniform set of entity notations. There are other significant advantages of data warehousing over query federation even in a Linked Open Data scenario—data cleaning, privacy, trust, data preservation, and to a certain extent, indexing and querying.85 Data warehouses indeed require a lot of centralization and maintenance, and need to be updated when the underlying content changes. Data warehousing approaches require significant resources and can only be implemented as part of a consortium or by companies. However, the issues of network latency, the availability and accessibility of SPARQL endpoints, as well as the quality of remote data sources, can easily be remedied through a data warehousing approach.For most biomedical projects that use Semantic Web technologies for reasoning and inference, the most common solution to the semantic heterogeneity problem is to use a data warehousing approach  RDF graphs uploaded as HDT89 (Header-Description-Triples), a highly compressed and queryable RDF format, as well as (ii) Triple Pattern Fragments endpoints90 as the standard access method for LSLOD sources, significantly reduces both infrastructural and maintenance needs. Improving the availability of public SPARQL endpoints is also an area of active research As a part of a solution path to one of the main handicaps for further adoption, monitoring frameworks, such as SPARQLES7 and the Unified Medical Language System (UMLS).24 In our opinion, the success of the Gene Ontology is, in part, due to the following main factors: (1) A dedicated and a very active development team with continuous funding has maintained it over several years; (2) A strong community of domain users from different areas has been actively built around it, and their requirements serve as the main impetus the development process; (3) The ontology itself has an exemplary documentation on its usage in applications targeted to domain users, and on the processes for building and maintaining it; (4) A principled approach was used for developing the ontology; (5) Automated pipelines are used to check and ensure the quality of the ontology. This is also true for UMLS-based semantic resources . Public SPARQL endpoints of DBPedia77 and Wikidata76 have started registering ≈99% uptime, as monitored using the SPARQLES framework. The providers of other LSLOD resources can definitely learn from these projects.There are two main LSLOD sources that are exemptions related to the Web-based availability and “freshness” of biomedical semantic resources: the Gene Ontology (GO)94  for observational drug safety, DisQover95—a commercial platform for semantic search in life sciences, and even the Open PHACTS data warehouse,43 extract and transform content uniformly using a common data model and entity notations, and publish the transformed content as Linked Data interfaces. If such approaches can be combined with methods to detect changes and evolution in LSLOD resources , then we can have sustainable Web-scale data integration solutions.Hybrid approaches  have been successful in the pharmacological research community for heterogeneous data integration. OHDSI collaborative97 and Kamdar,80 can automatically provide fine-grained quality metrics to mitigate availability and semantic heterogeneity problems. In addition to these initiatives, the LSLOD cloud itself needs to be a “live” environment and providers who do not provide minimal availability  or desired quality should be notified. Conversely, LSLOD consumers should be notified of important changes in a dataset. Decentralized protocols using the same technologies, such as Linked Data Notifications,98 can serve as communication vehicles for such synchronization.Novel frameworks, such as Debattista et al.99), validation , integration of legacy systems , and consumption , can assist towards addressing usability issues and maintaining documentation standards.Emergent industry-strength solutions for Linked Data-related tasks, such as extraction and mapping  or using a visualization interface by a domain expert, then such methods can also be sustainable for Web-scale data integration using Linked Open Data without the shackles of centralization using data warehousing methods.Using such mapping rules and a mappings-based query federation method, the SPARQL query shown in Fig. 61), applications that generate multi-faceted visualizations  for biomedical researchers to explore the integrated data, abstracting SPARQL and RDF entirely, or more studies that analyze user interactions with LSLOD sources  are definitely required for increasing the adoption of LSLOD and Semantic Web technologies in the biomedical research community.Finally, more applications that enable biomedical researchers to formulate SPARQL queries using visual interactions  and the ‘Data on the Web Best Practices’,106 both fostering the creation of a self-sustainable ecosystem. Additionally, common-sense good practices suggest to provide: (1) better metadata descriptions of the datasets; (2) better documentation and provision of sample queries for usage of the datasets; (3) better support for enabling reuse of existing vocabularies; and (4) better support for the use of developer-friendly formats , with a toolchain maintained by an active and a broader community.Most of the aforementioned issues can be further ameliorated by following standard best practices, such as the recent FAIR data principlesFinally, while redundancy of technical efforts is not ideal, several research groups may often wish to keep their data, searches, and inferences private. While biomedical data and knowledge sources form the largest portion on the Linked Open Data cloud, a lot of biomedical data is, and will always remain, in closed systems . Hence, we envision that centralized and decentralized approaches will always have to co-exist and complement each other in the biomedical ecosystem to tackle complex problems. Ideally, researchers can avail the benefits of Linked Open Data for Web-scale integration of public data and knowledge, slices of which can then guide advanced searches and inferences over the private data and knowledge stored in their centralized data warehouse.The biomedical data landscape is fragmented with several isolated data and knowledge sources existing on the Web. These biomedical sources may use varying formats, schemas, syntaxes, entity notations, and modes of access, which increase the logistical and technical challenges related to data and knowledge integration for most biomedical researchers. While there is hope that the next generation of artificial intelligence methods can augment human intelligence for achieving better clinical outcomes for patients on a personalized level, for increasing our understanding of living organisms, and for enhancing the quality of biomedical research, we lack scalable, intelligent infrastructures that can generate integrated content for use in these methods. This eventually leads to minimal scalability, minimal flexibility, minimal reproducibility, and increased redundancy of data integration efforts across different research groups that may simultaneously be working on similar biomedical problems.In this paper, we have put forth our perspective on how Semantic Web technologies and the Life Sciences Linked Open Data (LSLOD) can enable the development of such scalable intelligent infrastructures for Web-scale semantic processing and data integration in biomedicine. We have showcased a real-world example pertaining to querying, retrieval, and integration, of data and knowledge from diverse biomedical sources. We have also discussed the main challenges: (i) accessibility and availability, (ii) semantic heterogeneity, and (iii) usability and learnability, which hinder the use and consumption of content from the LSLOD cloud. We present a few technical solutions from the Semantic Web community that hope to convince biomedical researchers, that while these challenges provide a bleak outlook on the future of the LSLOD cloud, there is indeed light at the end of the tunnel. In an ideal state of the LSLOD cloud, the opportunities for data and knowledge integration in pharmacology, cancer research, infectious diseases, and several other biomedical domains, will eventually be realized in biomedicine, leading to better clinical outcomes and enhancing the quality of biomedical research.