Hop latent viroid (HLVd) is the biggest concern for cannabis and hop growers worldwide. Although most HLVd-infected plants remain asymptomatic, research on hops has demonstrated a decrease in both the α-bitter acid and terpene content of hop cones, which affects their economic value. The HLVd-associated “dudding” or “duds” disease of cannabis was first reported in 2019 in California. Since then, the disease has become widespread in cannabis-growing facilities across North America. Although severe yield loss associated with duds disease has been recorded, little scientific information is available to growers in order to contain HLVd. Consequently, this review aims to summarise all of the scientific information available on HLVd so as to be able to understand the effect of HLVd on yield loss, cannabinoid content, terpene profile, disease management and inform crop protection strategies. Cannabis sativa, Cannabis indica, Cannabis ruderalis) in cannabis-growing areas. The causative agent of this disease was found to be hop latent viroid (HLVd). A survey conducted in 2021 by the Dark Heart Nursery Research that involved 200,000 tissue tests concluded that 90% of cannabis--growing facilities in California were contaminated with HLVd. Furthermore, the researchers noted that the HLVd infection could significantly affect the plants’ vigor and yield. It has been estimated that it could cause losses of up to USD 4 billion annually for the cannabis industry (https://www.cannabisbusinesstimes.com/article/cannabis-hop-latent-viroid-infections-dark-heart-nursery-crop-loss/ [accessed on 13 February 2023]). HLVd has been detected throughout the cannabis-growing facilities of North America . Another report indicated that infected crops could suffer anywhere from a 50%–70% loss in tetrahydrocannabinol (THC) content, thus considerably lowering their commercial value (https://stratcann.com/2021/04/01/the-hop-latent-viroids-warning-shot-to-the-canadian-cannabis-industry/ [accessed on 13 February 2023]).Since its initial detection in 2019 in California, the “duds” disease of cannabis has become the most devastating cannabis disease (syn. Hemp) , low molecular weight, plant pathogenic RNA molecule . SpecifiAvsunviroidae or the Pospiviroidae families based on the structure of the mature viroid RNA, the central conserved region (CCR), any self-cleaving activity , the subcellular site of replication in the host plant, the mode of replication and the enzymes involved in that replication ). Like in hops, only a few cultivars of cannabis show HLVd-associated symptoms, implying that both symptom expression and disease severity are cultivar genotype dependent. In 2014, cannabis growers started an on-line thread discussing the “dudding disease” symptoms in cannabis plants (https://www.thcfarmer.com/threads/what-to-do-with-duds.64342/ [accessed on 13 February 2023]). The association of HLVd infection with symptomatic plants was confirmed in 2019 by two independent teams using high-throughput sequencing technology and subsequent bioassays [https://www.plantcelltechnology.com/blog/everything-you-should-know-about-hop-latent-viroid-hplvd/ [accessed on 13 February 2023]). A survey conducted in 2021 revealed that approximately 90% of all cannabis-growing facilities in California tested positive for HLVd, and 30% of the plants in each facility showed symptoms of the viroid’s infection (https://docs.google.com/document/d/1G5Lwz14F5-baVGveMe_0yGnam7z1E0qQZJ8YAcardFw/edit# [accessed on 13 February 2023]). Since its first detection in California, HLVd did not take much time to find its way to British Columbia, Canada. Now, HLVd is prevalent in Canadian cannabis-growing facilities. This clearly illustrates the severity of HLVd disease and its threat to the cannabis industry in North America.HLVd disease in Cannabis plants is loosely described as “duds” or “dudding disease”  and all of the HLVd sequences isolated from cannabis plants were compared. In order to achieve this goal, all of the HLVd sequences isolated from cannabis plants that are available in NCBI were retrieved, and only the full-length sequence (256-nt) was considered for the analysis. Warren et al.  reportedhttp://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi [accessed on 30 November 2022]) and was compared with the secondary structure of the HLVd-type species. The ΔG values for both the Can2 isolate and the HLVd-type species were found to be −95.30. The single nucleotide change (U225A) did not affect the structure of the Can2 isolate as compared to that of the type species. Although a single point mutation at position 225 did not alter the secondary structure, it is interesting to note that this change is located within the lower pathogenicity domain (In order to understand the effect of U225A on HLVd structure, the secondary structure of the HLVd Can2 isolate was predicted using the RNAfold WebServer (y domain . PreviouNicotiana benthamiana plants, both of which are a non-host for the HLVd-type species [The basis of viroid control is the production of viroid-free propagative materials. There are several layers of control measures that are involved. The first and foremost is the prevention provided by testing all of the incoming cannabis plants and cannabis products that could potentially act as a carrier for HLVd. Hence, it is good practice to test the plants and products for potential HLVd contaminants before bringing them into the facility. Once the plants are received, a 30-day quarantine is critical in order to properly evaluate for the presence of plant pathogens in incoming plant material. Given that HLVd infection is asymptomatic in many cannabis cultivars, it is recommended to test any new plant during the third week of quarantine, and certainly before sending the newly received plants into production. Although there is no chemical treatment available for controlling HLVd disease, methods such as meristem tip culture , thermot species . Althoug species .As discussed elsewhere, HLVd primarily spreads via vegetative cuttings, contaminated tools and by other mechanical means within either the hop yards or the indoor cannabis-growing facility. Since there are no control measures available for viroid-associated diseases, HLVd spread can only be prevented by employing a multilayer management system that includes, but is not limited to, the timely identification and removal of infected plants. The absence of visible symptoms associated with HLVd challenges the ability of growers to contain these viroids. Biological indexing is not practical for HLVd due to its limited host range and lack of symptomatology. However, if a susceptible host plant is available, graft inoculation was found to be more effective than rub-inoculation for the infection of hop plants with the hop latent viroid . Due to The removal of viroid-infected plants, and the sanitation of equipment, tools and of the vicinity of the viroid-infected plants are critical to checking the spread of the viroid in both fields and in indoor growth facilities. Heating blades at 160 °C for 10 min was found to be effective, while doing so at 140 °C was not. These measures have seen the virtual elimination of HSVd from the Kirin Brewery yards in Japan . HoweverSolanaceae. HSVd is one of the very important viroids infecting hops. HSVd was first discovered in Japanese hop fields, with typical symptoms, including stunting, leaf curling, small cone formation and a substantial reduction of alpha-acid content, being observed after 3–7 years of infection, [Cocadviroid. Aside from HLVd, at least three other viroid species are known to infect hops. As hops and cannabis belong to the same family, there is a significant risk that these viroids can also adapt and infect cannabis under ideal environmental conditions. Such a scenario is not uncommon for viroids. For instance, PSTVd is known to cause disease in potato and tomato plants, both of which belong to the family fection, . HSVd hafection, . Due to fection, . HSVd wafection, ,45. The fection,  and alsofection, . Howeverfection, . In othefection, . It shouAlthough HLVd infection was first reported in hop plants in 1988, both the scientific community and stakeholders prioritized research on HSVd which caused more severe visible disease symptoms in infected hop plants than did HLVd. However, scientific data on the negative effects of HLVd on both the α-bitter acid and terpene contents in both symptomatic and asymptomatic hop collected over the years have attracted the attention of researchers in recent years. The effect of HLVd on asymptomatic hop plants is supported by recent transcriptomic studies conducted on hop plants co-inoculated with HLVd and CBCVd aimed at understanding the increased aggravation of the CBCVd-induced disease symptoms in the presence of HLVd . AlthougTo date, HLVd-resistant cannabis cultivars are not known. Meristem tissue culture is the only effective control method via which infected plants can be saved. This process is both laborious and expensive. Although tissue-cultured plantlets are viroid-free, it is important to understand that they are not viroid-resistant. Hence, following preventive measures in order to avoid an insurge of HLVd into a given growth environment is crucial in HLVd-associated disease management. However, for sustainability reasons, it is important to find a practical long-term solution by employing strategies such as the control of HLVd infection by breeding HLVd-resistant plants, by cross-protection and by developing RNA interference-mediated resistance in the plants.


Nevertheless, the discard of organs considered too damaged still sustains the imbalance between donor organs supply and demands. Therefore, there is the pressing clinical need for strategies to repair and/or regenerate organs before transplantation, and MP is uniquely positioned to satisfy this need. The systemic administration of mesenchymal stromal cells (MSC) was shown to reduce ischemia-reperfusion injury in pre-clinical organ transplant models but could not be reproduced in clinical transplantation, largely because of inefficient cell delivery. The administration of MSC during MP is one strategy that recently gained much attention as an alternative delivery method to target MSC directly to the donor organ. However, careful reinterpretation of preliminary results reveals that this approach is equally limited by a suboptimal delivery of short-lived MSC to the target organ. In contrast, the use of MSC secretome and/or extracellular vesicles therapy during MP seems to be more efficient in harnessing MSC properties during MP. In this mini review we speculate on the future of the novel niche of  Recent advancements in liver normothermic-MP (NMP), which allows for the preservation of the liver for up to 1 week [The field of organ preservation for transplantation has undergone significant changes due to the increasing use of grafts from high-risk donors. The need for improved preservation of these organs has prompted a progressive shift from static cold storage to dynamic organ preservation strategies, also known as machine perfusion (MP). Dynamic organ preservation strategies have also moved the field at the intersection with regenerative medicine as they provide a platform for repairing and regenerating organs before transplantation . Howevero 1 week , suggestex situ organ repair and regeneration.To this end, several interventions during MP have been proposed, including cell therapy, pharmacological agents, gene modulation and editing, and nanoparticles . Whereasin vitro [Unlike preclinical transplant models of MSC-therapy, clinical studies have failed to show significant benefits from systemic MSC administration on post-transplant IRI , 7. Thisin vitro . To overHowever, the currently available pre-clinical evidence shows that MSC delivery during MP presents shortcomings similar to those of systemic therapy, as summarized in Next to the “device barrier” and low cell retention rates, there are also indications that MSC infused during MP are short-lived, likely due to factors such as mechanical trauma, perfusate toxicity, or phagocytosis by resident monocytes. Pool et al. consistently observed disintegrated MSC in porcine glomeruli colonized by stem cells , whereasDespite the low cell retention rates, there are indications of significant anti-inflammatory , 22, immHence, while a direct comparison is lacking, available pre-clinical evidence indicates that MSC-therapy during MP presents similar shortcomings and may not be more effective than MSC systemic therapy in delivering the cells to the graft. Furthermore, allogeneic MSC-therapy during MP does not eliminate the potential for recipient allo-sensitization to cell donor antigens , 25 or mWhile MSC administration during MP does not have high efficiency in cell delivery, the anti-inflammatory , immunomex situ dynamic organ preservation, cell-free therapy with concentrated stem cell-derived EV has already shown encouraging results (EV are nano-sized particles released by every (stem) cell. As they contain genetic information, growth factors, and signal transduction molecules , they pl results . StudiesHypothetically, cell-free therapy during MP could also avail of the delivery of MSC secretome, which contains both soluble factors and EV. To our knowledge, this therapeutic option has not yet been investigated.ex situ should be investigated in the future.Dynamic organ preservation strategies have entered the clinical arena and are expected to improve the preservation of high-risk organs. MSC-therapy during MP was proposed as an approach to repair high-risk grafts that are deemed too damaged and render them suitable for transplantation , 3. Howeex situ dynamic preservation. In a recent systematic review of preclinical studies, we examined the efficacy of EV-therapy derived from stem cells in mitigating IRI in transplantable organs. Our findings indicate that EV-therapy significantly enhances post-reperfusion outcomes, histology, and function in the heart, lung, liver, and kidney, regardless of the originating stem cell source [MSC-derived cell-free therapy during MP has several advantages and circumvent the shortcomings of MSC delivery during l source . As EV al source –30, it cl source  ensures l source , 31; nevex situ organ repair and regeneration before transplantation.To move toward clinical applications, it is crucial to determine whether the therapeutic effects of EV match those of their parent stem cells. Preliminary studies suggest that MSC and EV-therapy during MP yield similar results , 34. Nonex situ organ repair and regeneration before transplantation.In conclusion, MSC-therapy during MP is burdened by suboptimal delivery of short-lived MSC. However, their therapeutic benefits may be leveraged using a cell-free therapy consisting of concentrated EV and/or MSC secretome administered during MP. This approach resulted in the intracellular delivery of EV during perfusion and yielded therapeutic benefit in non-transplant models. We hypothesize that technology at nanoscale, such as EV, gene editing, and nanoparticles, have the highest likelihood of successfully translating into clinical applications and will shape the future of


Age-related macular degeneration (AMD) is a neurodegenerative disease and a leading cause of irreversible vision loss in the developed world. While not classically described as an inflammatory disease, a growing body of evidence has implicated several components of the innate immune system in the pathophysiology of age-related macular degeneration. In particular, complement activation, microglial involvement, and blood-retinal-barrier disruption have been shown to play key roles in disease progression, and subsequent vision loss. This review discusses the role of the innate immune system in age-related macular degeneration as well as recent developments in single-cell transcriptomics that help advance the understanding and treatment of age-related macular degeneration. We also explore the several potential therapeutic targets for age-related macular degeneration in the context of innate immune activation. Age-related macular degeneration (AMD) is a neurodegenerative disorder characterized by the presence of drusen, extracellular deposits, followed by retinal pigment epithelium (RPE) and photoreceptor loss. AMD is rarely seen before age 55, after which prevalence increases with increasing age . SeveralAMD is a multifactorial disease, with a complex set of risk factors, like age, environment, and genetic susceptibility; the heritability of late stage AMD has been estimated to be up to 71%, higher than most complex age-related diseases . The strModifiable risk factors for AMD include smoking, diet, and physical activity. Smoking has been shown to increase the chance of developing AMD by two to four times, and cessation reduces AMD risk . ConsumpWhile the precise pathogenesis of AMD remains unknown, several lines of evidence implicate drusen, the hallmark lesions of AMD. Several factors, including amyloid, complement, and 7-ketocholesterol, have been isolated from drusen and are hypothesized to contribute to AMD progression . The devIn the retina, glial cells support photoreceptor function by providing physical and metabolic support through a number of mechanisms such as transporting nutrients and clearing debris. The three broad types of glial cells in the retina are microglia, astrocytes and Müller cells . RetinalMethods exploring the underpinnings of AMD are rapidly developing. In recent years, scientists have been using single-cell RNA sequencing (scRNA-seq) to analyze the pathogenesis and etiology of diseases whose origins remain unclear. Due to the genetic complexity of AMD, it has been difficult to precisely identify the cell-types associated with AMD development. In 2019, researchers used scRNA-seq to develop the first single-cell transcriptomic atlas of the human retina, a full record of the transcriptomes of each cell type . Use of the transcriptomic atlas predicts Müller glia and astrocytes have a role in the pathogenesis of AMD .A 2021 study built upon these findings by focusing on the genes transcribed instead of the cells. This study used scRNA-seq to obtain the transcriptomes of 93,000 retinal cells; in addition to using the data to transcriptionally identify differences in retinal neurons, researchers also assessed the cell-type expression levels of each AMD risk gene . ThroughA growing body of research has shown that AMD pathogenesis is associated with neuroimmune interactions; most recently, neovascular AMD pathology was associated with COVID-19 infection severity and morbidity, providing just one example of the potential neuroimmune interactions involved in AMD . The bloMacrophages are a diverse group of cells that serve several functions across tissue repair, immunity, and homeostasis . In the In the murine model, macrophage ablation by CSF1R blockade leads to choroid vascular atrophy in addition to structural and angiogenic RPE dysfunction, suggesting that macrophages residing in the choroid may serve an important role in structural maintenance of the RPE and choroid vasculature . One sigMonocytes, as part of the immune system, can differentiate into macrophages or dendritic cells. Monocyte-derived macrophages, as aforementioned, appear to play a role in AMD, but the primary difference between these two cells is their localization; monocytes circulate in the bloodstream where macrophages infiltrate tissue. Peripheral monocyte counts, in particular, have been shown to be higher in AMD patients versus controls . In inteIn atrophic, or dry, AMD, CCR2+ monocytes have been identified in the subretinal space . In the in vivo depletion of interferon-γ-secreting NK cells lead to reduced choroidal neovascularization (CNV)  cells are a component of the innate immune system, affecting cytokine production and cytotoxicity, with antiviral and antitumoral effects by killing cells that downregulate the MHC Class I self-antigen presentation system . In a geon (CNV) .Neutrophils are a type of leukocyte that drive both acute and chronic inflammation . As withNeutrophils are characterized by their strategy for pathogen eradication, which is known as a neutrophil extracellular trap . NeutropDendritic cells are innate immune cells that activate lymphocytes, in addition to capturing and processing antigens . Early d+ and CD8+ patients differ between AMD and control conditions (+ memory T cells (+ CD28- T cells in the peripheral blood are higher in AMD versus control patients, suggesting a possible function of T cells in AMD (T cells are lymphocytes that can be activated by dendritic cells. Chemokine profiles of CD4nditions . Further T cells . T cells T cells . T-helpe T cells . CD56+ Cs in AMD .+ T cells, C5a stimulates IL-17 production, which may imply a role for C5a in AMD and T lymphocyte activation (CNV lesions in the murine model triggered T cell immune response as well, with an increase in IL-17-producing γδT-cells through C5a . In CD4+tivation . Naturaltivation .in vitro (Possible therapeutic targets include targeting the interferon-beta signaling pathway or the AKT2 signaling pathway . Targetiin vitro . One limin vitro .Progress in understanding the peripheral immune response in AMD has been made and future research on human retinas with AMD using single-cell approaches will further help the field. Furthermore, the development of non-human primate models of AMD or human retinal organoids derived from human pluripotent stem cells from patients with AMD may facilitate the translational development therapeutic interventions for AMD targeting immune dysregulation.


Pediatric telerehabilitation has been quickly adopted by clinicians during the pandemic. This precipitated shift in the model of healthcare delivery is significant and compounded by clinicians' training and knowledge needs related to evidence-based practices. This instigated a knowledge translation initiative TelereHUB-CHILD—an online platform designed for clinicians, patients, and families. The aim of this brief report is to describe its development, including the roles of key stakeholders in these processes.n = 24 rehabilitation professionals) and parent-partners (n = 4 parents of children with disabilities) were undertaken. Clinical partners were engaged in five web-activities. These were designed to gather their feedback regarding training and knowledge needs, present preliminary findings of the systematic review and explore their perceived importance and usefulness with respect to different sections of TelereHUB-CHILD, including Tele-treatments, Tele-Assessments, and Resources. Parent-partners were engaged asynchronously to provide feedback on the content and presentation of the Patient/Family Information section.Following a systematic review on telerehabilitation, a series of co-creation activities with clinical (Tele-treatments section provides standardized summaries outlining the effectiveness of the tele-treatment approach and the level of the evidence for each outcome of interest, according to the different diagnosis groups and professional discipline. For patients/family, common questions and answers can be explored in three user-friendly formats, including printable learning briefs, onsite accordions, and animation videos. The Tele-assessments section outlines existing measures by professional discipline. Resources offer preparatory forms for families and clinicians, questionnaires, and other learning material.Clinical partners reported moderate-high usefulness and importance with each section of the tool and the presented features. As per partners' feedback, the TelereHUB-CHILD was co-developed with key stakeholders. It can guide telerehabilitation evidence-based practices, empower patients and families, and pinpoint research and practice gaps. DevelopWith the COVID-19 pandemic, the need for services for this population is predicted to further rise , 12. In 2.2.1.Step 1—Create knowledge and Step 2—Adapt to local context were applied in the development of TelereHUB-CHILD (The development of TelereHUB-CHILD is grounded in an integrated knowledge translation approach (iKT) , a robusUB-CHILD .2.2.Step 1—Create Knowledge, a systematic review on existing tele-interventions and assessments for children/youth with developmental disabilities and their families was conducted  of children (age range 18 months to 21 years old) with various developmental disabilities . Members of this advisory committee were recruited by the author's (TO) research laboratory using a snowball and word-of-mouth recruitment strategies. Parent-partners are regularly engaged in patient-oriented research activities of the laboratory since March 2021.Partnerships were established with two main stakeholder groups, including pediatric rehabilitation professionals and parents of children with disabilities. To target the rehabilitation professionals, clinical managers and clinical research coordinators from three urban pediatric rehabilitation settings providing services to children with developmental disabilities and their families  were engaged to promote and facilitate the logistics of KT activities (described in detail below). Moreover, a parent-advisory committee was formed to include parents  are described in full detail elsewhere (n > 200) child- and parent-related health outcomes. The review comprehensively reported the effectiveness of each tele-approach, the comparison intervention, and the level of the evidence. Moreover, information on the health providers' discipline  was extracted and allowed to build another classification layer into the KT tool. In addition, the review identified several tele-assessments, along with information on its purposes and administration specifics . Overall, these findings served to design a first draft for the KT tool prototype, including major sections , subsections , and other potentially useful attributes .In lsewhere . The finwww.strokengine.ca). StrokEngine provides evidence-based information for clinicians and patients/families on stroke rehabilitation strategies and has been shown to be highly usable and navigable among rehabilitation professionals (n = 15) StrokEngine learning modules .Following the review and feedback received from parent-partners, in addition to the information being displayed in reading format, a printable PDF and a short Animaker® video were also created for each module to accommodate different learning needs. In the video, a parent of a child is asking questions about Telerehab to a healthcare professional who is providing the answers with supporting visual material. Moreover, parent-partners have provided feedback for the question 2.5.3.n = 6) Tele-Assessments modules were designed per professional discipline, including: (1) Audiology; (2) Multidisciplinary team; (3) Neuropsychology/psychology; (4) Physiotherapy; (5) Speech language pathology; and (6) Vision. Within each Tele-Assessment module, submodules  are listed to display the different outcomes that were assessed . As in the section on Tele-Treatments outcomes and Family/patient Information, an accordion was designed to display the different features of the assessment that could be clicked on and explored further . Moderate (in 40% of respondents) to high (60% of respondents) usefulness was reported for resources about confidentiality, ethics, data storage, charting; surveys to improve services; and discussion board where experiences could be shared .Resources section was designed to have two modules: one for patients/family (https://telerehubchild.com/resources-for-patients-and-families/) and one for clinicians (https://telerehubchild.com/resources-for-clinicians/). The patient/family module contains supporting material on how to get ready for Telerehab, including advice for different situations regarding wi-fi and device access, items and space/environment needed, and tips and tricks for comfort and privacy . Downloadable and fillable PDFs are available for patients and family to further prepare for a Telerehab visit (https://telerehubchild.com/wp-content/uploads/2023/02/PIF_Feb19_1-1.pdf), and checklists to ensure readiness depending on wi-fi access at home (https://telerehubchild.com/wp-content/uploads/2023/02/WAH_EN_Feb19_5-5.pdf) or remotely (https://telerehubchild.com/wp-content/uploads/2023/02/RAW_EN_Feb19_7-7-1.pdf).In view of these findings and additional feedback from parent-partners, the Resources section includes information on how to access the settings' and patient's Telerehab readiness, a training video on levels of evidence, and a fillable PDF form to prepare patients and families for a Telerehab visit (https://telerehubchild.com/wp-content/uploads/2023/02/CF_EN_Feb24-1.pdf). This form is discipline specific, where a clinician can indicate the purpose , target , and focus of the visit ; and items required.For clinicians, the 3.Patient/family Information and Resources sections were adjusted as per their feedback and additional modalities of the material were created to suit a set of diverse learning needs .The aim of this brief report was to describe the co-creation process of an iKT tool for pediatric telerehabilitation, TelereHUB-CHILD, and to outline the roles of the key stakeholders in this initiative. In a series of collaborative activities with key stakeholders and end-users, the content of TelereHUB-CHILD and its display were adjusted to reflect partners' feedback. Overall, most prototype's features were moderately to highly rated for importance and usefulness by clinical-partners and were therefore included in the product to reflect these ratings. The materials reviewed by parent-partners for the TelereHUB-CHILD directly addresses the reported need for training in telerehabilitation evidence-based approaches previously reported by pediatric health providers . The crehttps://www.childhooddisability.ca/edit-cp-toolkit/)) ) . This onolkit/)) . OverallGiven that the platform was designed in tight collaboration with end-users, the way in which the content is presented is more likely to be adopted and satisfactory. For instance, in the co-creation webinar, it was made clear by clinical partners that they wish to see all the studied outcomes at once  and then be able to choose the ones of interest to them so that they could expand on it to find out more. Hence, the accordion presentation of the outcomes was designed to reflect that need, where in each accordion line, a user can quickly view the outcome of interest, the effectiveness, comparison interview, and the level of evidence.The present initiative has limitations. Namely, the co-creation process did not include youth with disabilities, which could have been highly appropriate provided that certain tele-interventions targeted adolescents. Moreover, although not all features were rated by all clinical partners present at the co-creation webinar, 70% of the features were rated by more than 50% of participants. Lastly, TelereHUB-CHILD is currently only available in English. However, work is ongoing to have it fully translated into French, as well as adapting resources for patients/family  for the Indigenous community.Resources section. Moreover, to move further in the KTA process, I intend to assess barriers/facilitators to knowledge use (Step 3 of the KTA). In addition, the training material for clinicians is projected to be integrated into a standardized multimodal KT activity that could be delivered to different clinical settings worldwide . The impact of the training could then be investigated  and the platform further enhanced to promote learning and usage (Step 7—Sustain knowledge use). In addition, there is intention to track more in-depth users' activity on the platform  and to integrate a visitor's survey. The survey will allow to determine users' characteristics , their needs, and the impact of the platform . Moreover, yearly updates of TelereHUB-CHILD's content with newly emerged evidence that is rapidly growing worldwide is a priority.Future work with TelereHUB-CHILD is in the planning. To continue integrating partners' feedback, sections on training for clinicians, discussion boards, and information on confidentiality, ethics, data storage, and charting will be included in the In conclusion, TelereHUB-CHILD is an iKT initiative built in collaboration with key stakeholder groups and a thorough review/appraisal/evidence extraction process. TelereHUB-CHILD could be a valuable tool in empowering and guiding clinicians in their evidence-based Telerehab clinical practice, as well as patients and families in their rehabilitation journeys.


Based on previous versions of SEPPA 3.0, we present SEPPA-mAb for the above purpose with high accuracy and low false positive rate (FPR), suitable for both experimental and modelled structures. In practice, SEPPA-mAb appended a fingerprints-based patch model to SEPPA 3.0, considering the structural and physic-chemical complementarity between a possible epitope patch and the complementarity-determining region of mAb and trained on 860 representative antigen-antibody complexes. On independent testing of 193 antigen-antibody pairs, SEPPA-mAb achieved an accuracy of 0.873 with an FPR of 0.097 in classifying epitope and non-epitope residues under the default threshold, while docking-based methods gave the best AUC of 0.691, and the top epitope prediction tool gave AUC of 0.730 with balanced accuracy of 0.635. A study on 36 independent HIV glycoproteins displayed a high accuracy of 0.918 and a low FPR of 0.058. Further testing illustrated outstanding robustness on new antigens and modelled antibodies. Being the first online tool predicting mAb-specific epitopes, SEPPA-mAb may help to discover new epitopes and design better mAbs for therapeutic and diagnostic purposes. SEPPA-mAb can be accessed at   The rec protein , yet the protein . Though  protein , CBTOPE  protein , Discoto protein , BepiPre protein ,8 and SE protein , they usMeanwhile, docking-based strategies were also employed by treating the antigen-antibody interaction as a general protein-protein interaction ,11. Typiet al. proposed a pipeline based on AlphaFold to integrate antibody and antigen structural modelling with rigid docking to predict antibody-specific epitopes  . SurfaceThe construction of SEPPA-mAb includes three steps: (i) calculating the antigenicity score of SEPPA 3.0, (ii) constructing the patch model and obtaining the patch model score at the residue level and (iii) consolidating the patch model score with the antigenicity score based on the threshold.In step two, the design of the patch model includes four sub-steps: (i) deriving a group of surface patches for antigen, (ii) generating a series of descriptors for each surface patch of antigen and the CDR patch of the corresponding antibody, then patch complementarity (PC) score was calculated by XGBoost classifier, (iii) mapping the patch scores to each surface residue and then calculating the raw residue score by considering all the patches that contain the target residue, (iv) obtaining the final patch model score on the residue level through the calibration process. Detailed information on each step is described in For any query of an antigen-antibody structure pair, SEPPA-mAb predicts which residues on the antigen surface can bind to the CDR of the antibody through the following steps.Design of Patch Model);Step1: Generate a spatial surface patch for each surface residue on the target antigen (see Design of Patch Model);Step2: Generate the structure fingerprints for the surface patch of the antigen and CDRs patch of the antibody, and then the PC score was predicted for each surface patch and CDR patch pair ;;3);Step5: Consolidate the patch model scores and antigenicity scores predicted by SEPPA 3.0 to obtain the final list of mAb-specific epitope residues, when both scores are over the thresholds.For the input antigen, SEPPA-mAb will automatically generalize the spatial patch for each surface residue on antigen protein and be paired with the CDR patch of the corresponding antibody. Then, the patch model will generate fingerprints, and calculate the patch model score on the residue level.see Supplementary Method 1).During fingerprint generation, the patch model introduced a cylinder model describing the structural layout and physic-chemical properties for each patch based on the defaulted pixel. Eight properties are considered to generate the 200-bit fingerprints for the antigen side and antibody side, separately. After being trained on 860 antigen-antibody pairs through XGBoost, the patch model can predict the PC score for each antigen patch according to the CDR of cognate antibody  value and balanced accuracy (BA) are adopted as illustrated in SEPPA 3.0 . For intThe performance of the patch model is tested on 193 antigens and compared with available tools online including both well-known epitope prediction methods and docking-based methods via AUC and Balanced Accuracy (BA) value. As Figure see ). Since docking approaches output multiple complex structures, the AUC values of top N ranking results are calculated based on combining residues from N regions. When the results are checked between experimental and modelled antibody structures by ABodyBuilder  if both scores are above their default cutoffs, otherwise NO (−1 for non-epitope) or NOT AVAILABLE . In this way, the prediction accuracy on the test dataset containing 193 antigens is 0.790 for the patch model and 0.776 for SEPPA 3.0, with an FPR of 0.196 for the patch model and 0.206 for SEPPA 3.0, respectively. After integration, SEPPA-mAb significantly pushes the accuracy to 0.873 and reduced FPR to 0.097. We also examined the well-known HIV glycoproteins in the test dataset containing 193 antigens, as they represent the largest family of pathogenic antigens. Patch model alone achieves the averaged AUC value of 0.835 on the 36 antigens of gp120 , higher To better illustrate the benefits to integrate the patch model with SEPPA 3.0, the reference epitope in hemagglutinin (HA) antigen targeted by antibody C05  was shown in Figure The pandemic of COVID-19 in 2020 provided an opportunity to test model performance on completely new antigens never seen by SEPPA-mAb. Here, 31 pairs of spike antigens and their cognate Abs were tested with detailed PDB ID listed in http://www.badd-cao.net/seppa-mab) accepts input files of antigen-antibody pair in the below format: (i) existing PDB IDs with chain name, and (ii) local files in PDB format. Similar to SEPPA 3.0  submission information, including model parameters, sequence of antigen, and sequence of antibody, as well as the predicted epitope information, including the residue positions of the input antigen. Predicted epitope residues are presented in red capital letters and the non-surface amino acids are presented in lowercase letters Figure ; (ii) epPredicting the Ab-specific epitopes for protein antigens is highly desired but no tool is available online. In this study, we developed SEPPA-mAb for this purpose, based on the previous antigenicity prediction server of SEPPA 3.0, appended by a new patch model calculating the physic-chemical complementarity between antigen-antibody interaction surface. Compared with current state-of-the-art methods of traditional epitope prediction tools and docking-based algorithms, the patch model alone shows outstanding performance on both Ab-specific interface prediction and robustness on varied structure variation. More importantly, the integrated tool of SEPPA-mAb can efficiently increase the accuracy and reduce the FPR, with the best ability to tolerate structure variation of computer modelling.et al. divided the antigen surface residues into multiple patches to generate 471 features including 237 for antigen patch and 234 for antibody paratope , the layers of local micro-environmental variations can be fully considered for a surface residue under the neighborhood influence of both surface and internal residues. Secondly, different from the previous patch conception where all residues in the patch are equally treated, we give patch residues weighted scaling according to their distance to the patch center. In this way, the residual layout and subsequent physic-chemical properties can be well captured from both sides of the interaction interface describing the complex nature of antigen-antibody interactions. Thirdly, all calculation is made on the residue level, instead of the detailed atom coordinates in docking methods. This coarse-grained description enables rapid surface scanning patch-pairing, also tolerating structural variation caused by computer modelling. Finally, a calibration process is elaborated to further reduce FPR by considering the neighboring influence. In summary, SEPPA-mAb consolidated the results from pan-antigenic sites predicted by SEPPA 3.0, and mAb CDR-complementary surface predicted by patch model, enhancing the prediction performance from baseline  to a level with high accuracy of 0.873 and low FPR of 0.097.The performance of SEPPA-mAb mainly benefits from both the successful prediction of potential antibody-complimentary regions from the patch model, and the pan-antigenic sites predicted by SEPPA 3.0. Importantly, our patch model is designed with several novelty aspects. The first is the patch-based structural and physicochemical fingerprints derived from the cylinder model. Via cylinder (Be noted that, the current model aims to recommend the best epitope positions in antigen surface being recognized by its cognate antibodies. Any input antibody is regarded as interacting with input antigen by expectation. Mechanistically, SEPPA-mAb employed more information from antigens rather than antibodies, leading to its insensitivity to antibodies. In fact, it is more sensitive to antigen mutation and structural variation. As SEPPA-mAb conducts the calculation based on the structure files, incomplete structures may reduce their performance. Further, SEPPA-mAb considered the influence of glycosylation through SEPPA 3.0. Other forms of post-translational modifications such as phosphorylation, ubiquitination, methylation, and so on, have not been considered in the current version. Also, single-chain antibodies are not applicable for now. In the future, with the rapid accumulation of structures generated through experiments and AI technologies, as well as the development of deep learning algorithms, improved versions can be expected for antibody-specific epitope prediction, which may better assist antibody design in therapeutic and diagnostic purposes.The data underlying this article are available in the article and in its online supplementary material. Accession number PDB ID: 6D0U.gkad427_Supplemental_FileClick here for additional data file.


Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate singletype of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy.The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI.  The secondary uses of EHRs give rise to research in a wide range of varieties, especially machine learning (ML)-based digital health solutions for improving the delivery of care8. However, in practice, the benefits of data-driven research are limited to healthcare organizations (HCOs) who possess the data10. Due to concerns about patient privacy, HCO stakeholders are reluctant to share patient data13. Access to clinical data is often restricted, or can be prohibitively expensive to obtain, meaning that ML in biomedical research lags behind other areas in AI.The past decade has witnessed ground-breaking advancements been made in computational health, owing to the explosion of medical data, such as electronic health records (EHRs)synthetic yet realistic data15. By avoiding “one-to-one” mapping to the genuine data compared with data anonymization, synthetic data offers a solution to circumvent the issue of privacy, while the correlations in the original data distributions are preserved for downstream AI applications. There have been successes in the literature using synthetic data to improve AI models where otherwise not possible due to limited availability of resources18. For example, large-scale data sharing programs have been demanded for advancing studies related to COVID-19, such as in National COVID Cohort Collaborative (N3C)19, and Clinical Practice Research Datalink (CPRD) database in the UK20.To accelerate the progress of developing AI methods in medicine, one promising alternative is for the data holder to create 21 and their variants offer efficacious means to generate EHRs for a wide range of clinical applications24. Over the past years, EHR synthesizers have evolved from generating static patient information to producing longitudinal EHR timeseries27. As longitudinal EHRs contain patient trajectories for describing the underlying health condition, synthesizing such EHR timeseries, therefore, enables new clinical applications related to the status of disease progression28, such as dynamic forecasting of risks, predicting the onset of diseases, and survival analysis based on the time-to-event data. However, existing studies focus on synthesizing the longitudinal EHRs of a single data type29, whereas the clinical decision-making in real practice includes a variety of information sources in the form of mixed-type timeseries. For example, patient physiological signals and laboratory test results are collected in the EHR as continuous-valued timeseries, while the medication and diagnostic information are recorded as discretized-valued data as binary indicators or categorical ICD codes. Information provided in these mixed-type longitudinal EHRs offer opportunities for more precise and complex clinical analysis. Furthermore, the predictive power and robustness of the ML models can be boosted by utilizing longitudinal EHR timeseries with various types/sources.Recent advances in generative adversarial networks (GANs)30, its success is typically limited to generating real-valued, continuous data while facing obstacles for directly generating sequences of discrete tokens, such as ICD codes, that also commonly appear in EHRs. Previous methods32 circumvent this problem by learning representations from the original data which further enables backpropagation in discrete settings, but there is still a lack of a generative approach for joint modeling of the mixed-type timeseries with heterogeneous nature. Second, although mixed-type clinical timeseries differ in syntax and distributions, they are highly correlated and inform one another of the underlying health of an individual35. It is therefore important to capture the temporal correlations between them when generating the synthetic EHR data. For example, the medications (documented in the form of discrete data) prescribed to patients are based on measurements of patients’ physiological status . Concurrently, the efficacy of the medical treatments, affect the patient’s physiological condition directly. It is therefore critical to accurately capture the temporal correlation between the mixed-type patient trajectories simultaneously to improve clinical decision support.Existing GANs are limited in simulating mixed-type EHRs due to two reasons. Firstly, it is intrinsically difficult to model the underlying joint distribution of mixed data type timeseries using a single unified framework. Since GANs require the network architectures of the generator and discriminator to be fully differentiabledual variational autoencoder . Then, to capture the correlated temporal dynamics of the mixed-type data, a sequentially coupled generator that is built upon a coupled recurrent network (CRN) is employed. In addition, a conditional version of our model—EHR-M-GANcond—is also implemented, which is capable of synthesizing condition-specific EHR patient data, such as those result in ICU mortality or hospital readmission. The code of our proposed work is publicly available on GitHub.A GAN model entitled EHR-M-GAN is proposed for simultaneously generating mixed-type multivariate EHR timeseries with high fidelity, and overcoming the challenges when extending GANs into the mixed-type data settings see Fig. . First, 36, eICU37, and HiRID38 from a total of 141,488 patients. Standardized preprocessing pipelines are applied for the three ICU datasets to provide generalizable machine learning benchmarks. The code for the end-to-end preprocessing pipelines is also available on GitHub.Evaluations are performed based on three publicly available ICU datasets: MIMIC-IIIOur EHR-M-GAN outperforms the state-of-the-art benchmarks on a diverse spectrum of evaluation metrics. When compared to real EHR data, both qualitative and quantitative metrics are used to assess the representativeness of the mixed-type data and their inter-dependencies. We further demonstrate the advantages offered by EHR-M-GAN in augmenting clinical timeseries for downstream tasks under various clinical scenarios.39. We then further evaluate the performance of EHR-M-GAN under the framework of differential privacy for its application in downstream task40.In the evaluation of privacy risks, we perform an empirical analysis on EHR-M-GAN based on membership inference attackTo address the aforementioned limitations, for the first time, we propose a GAN framework for simultaneously synthesizing mixed-type longitudinal EHR data (denoted as EHR-M-GAN thereafter). Specifically, we focus on generating timeseries in the critical care setting, where the intensive care units (ICU) patients are continuously and closely monitored see Fig. . Patient41. In this work, a systematic evaluation framework is adopted to assess the quality of synthetic patient EHRs with respect to its fidelity, correlation, utility, and privacy (see Table Maximum mean discrepancy and Dimension-wise probability), comparing the feature-level statistics between the real and synthetic data (Patient trajectories), and assessing the indistinguishability of the synthetic data to the true data . Secondly, we evaluate to which extent our model can reconstruct the interdependency between different features (Pearson pairwise correlations), and the temporal dynamics in the patient trajectories (Autocorrelation function), by using a set of qualitative and quantitative metrics. Thirdly, we introduce data augmentation by incorporating synthesized EHR timeseries under various settings, and quantitatively assess the improvement provided by EHR-M-GAN in the Downstream tasks for medical intervention prediction in the ICU . Lastly, we measure the attribute of patient privacy-preserving of EHR-M-GAN under Membership inference attack. We also evaluate the performance of the same downstream tasks under Differential privacy guarantees , we therefore handle these multivariate timeseries as matrices and use the kernel function to calculate the Frobenius norm  is used. MMD can assess whether two sets of samples are from the same distributions, and in our case, one from the true data N and M denoting the corresponding sample sizes), the estimation of MMD can be defined as:Finally, given samples entclass1pt{minimacond—further boosts the performance of the model by leveraging the information of the condition-specific inputs. Furthermore, as shown in the ablation study, EHR-M-GAN and EHR-M-GANcond produce smaller MMD values when compared to their variants. Using MIMIC-III as an example, compared with the basic model GANVAE, by integrating the shared latent space learning using dual-VAE under multiple loss constraints, the performance of GANSL significantly improves . By further building the sequentially coupled generator and exploiting the information within mixed-type data, the MMD of EHR-M-GAN shows a nearly 24% improvement over GANVAE. When synthesizing mixed-type timeseries using the unified network, the performance of GANUnified for generating continuous-valued timeseries lags behind the proposed EHR-M-GAN. It therefore can be inferred that, compared with EHR-M-GAN which extracts useful hierarchical representations for each data type using tailored encoding layers, it is quite challenging for GANUnified to learn marginal distributions from raw mixed-type timeseries with a unified architecture.As indicated in Table p ∈  is calculated for the discrete-valued timeseries, and is visualized through scatterplot. As a sanity check, it investigates if the probability of the medical intervention being active at the given timestamps is matched between the real data (x-axis) and synthetic data (y-axis). The correlation coefficients (CCs) and root-mean-square errors (RMSEs) are also adopted43 based on the Bernoulli success probabilities to quantitatively measure the distribution divergence between real and synthetic data.To evaluate the representativeness of the synthetic discrete-valued timeseries, the dimension-wise probability test is employed. To test the probability distributions between the real and synthetic binary features, the Bernoulli success probability cond. The close-to-real probability distributions that appear along the diagonal line indicate the remarkable similarity between the real data and the synthetic data provided by our models. The quantified CC and RMSE also correspond with the visualization results, which are close to the highest mark . Similar to the results in MMD, the dimensional-wise distributions are better captured when modules such as dual-VAE and sequentially coupled generator are introduced in EHR-M-GAN. GANUnified suffers from mode collapse (the generator fails to produce outputs with sufficient diversity), and therefore shows poor performance compared with other variants when synthesizing discrete-valued timeseries. As the mixed-type features are treated as unimodal input without differentiating their heterogeneous nature, no marginal representations are explicitly learned.As shown in Fig. dual-VAE.Among all state-of-the-art benchmark models, DualAAE shows the best result but is slightly sub-optimal when compared to EHR-M-GAN. In contrast, both skewed distribution and low performance scores are observed in medGAN, as it lacks the ability to capture the temporal correlations within timeseries. SynTEG shows improved performance over medGAN, as it is capable of synthesizing discrete-valued features in EHRs with timestamps. The non-GAN generative method PrivBayes also shows good performance among all the benchmark synthesizers when modeling the underlying probability distribution of the discrete-valued EHR timeseries. On the other hand, despite the well-known performance of SeqGAN in natural language generation, it is not quite applicable in synthesizing sequential clinical EHRs. The result of EHR-M-GAN shows its superiority in explicitly capturing each dimension of the discrete-valued sequences. This indicates that the proposed EHR-M-GAN mitigates the challenge of generating discrete-valued features in traditional GANs by learning the shared latent representations using Oxygen Saturation, Systolic Blood Pressure, Respiratory Rate, Heart Rate, Temperature, as well as two medical intervention features—Mechanical Ventilation and Vasopressor, are considered and compared as an exemplar in Fig. Oxygen Saturation gradually increases towards the ICU endpoints in the real data, and is closely reflected in the synthetic timeseries. Furthermore, EHR-M-GANcond shows superior performance as it can generate correct trajectories with specific patient conditions  is implemented with a single-layered Bi-directional Long Short-Term Memory (Bi-LSTM) model, with its parameters randomly initialized (as opposed to critic built upon representations from the trained generative model28). The critic trained from the supervised learning task can be used to characterize the temporal correlations across the patient EHR timeseries.For both continuous-valued and discrete-valued data, the discriminative score is measured as the accuracy of a discriminator trained post-hoc to separate real from generated samples. Synthetic data are generated with the same amount of the hold-out test set from the original data, and are labeled as cond can produce synthetic data that are less distinguishable from real data than the benchmarked models. Especially for EHR-M-GANcond, it achieves the optimal discriminative scores consistently against other benchmarks for both continuous-valued and discrete-valued timeseries. For discrete-valued data generation, EHR-M-GAN-generated samples achieve the discriminative score of 0.813 on the MIMIC-III dataset, which has a 4% statistically significant improvement over the best performing benchmark (p < 0.05). The overall discriminative scores produced by PrivBayes on three ICU databases are comparable with the GAN models such as SynTEG and DualAAE. For continuous-valued timeseries generation, the discriminative score of TimeGAN on HiRID dataset outperforms the other models as well as EHR-M-GAN, though not statistically significant (p = 0.4374). By leveraging the additional information from the conditional inputs, EHR-M-GANcond can provide significantly better result than TimeGAN (p < 0.05).As indicated from the results in Table dual-VAE  have shown remarkable success compared with GANVAE, which generates the latent embeddings based on separate VAEs. The sequentially coupled generator further improves the model by capturing the dynamics between mixed data types. Further compared with GANUnified which models the mixed-type data in a unified network, our proposed model enables effective learning for the marginal distributions from each data type. In addition, as shown in Supplementary Table dual-VAE module alone . We also adopted correlation accuracy (CorAcc)44, which quantifies the similarity of two heatmaps within the range of 0 to 1. We discretize the correlation coefficients into 6 correlation levels: strong negative ), middle negative ), low negative ), no correlation ), low positive ), middle positive ), and strong positive ). Then, CorAcc can be calculated as the percentage of pairs where the real and synthetic data is assigned to the same correlation level.In this section, we first employ Pearson pairwise correlation (PPC), which ranges from −1 to 1, to evaluate how closely the synthetic data can model the correlations between continuous-valued and discrete-valued timeseries. Timestamps of the patient trajectories are extracted with every 3 h interval in a total of 24 h ICU stay, to explore the temporal dependencies within different variables. To further quantitatively measure the difference between heatmaps generated from real and synthetic samples, we calculate the mean value of the absolute difference between the two PCC matrices  are calculated to show how EHR-M-GAN can capture the temporal correlations among the timeseries. ACF measures the relationship between the timeseries and its lagged version. Supplementary Figs. Then, autocorrelation functions (ACF)Heart Rate, Oxygen Saturation, and Systolic Blood Pressure, the positive ACF coefficients rapidly decrease within the period of first few hours, followed by the growing trends of negative temporal correlation. The lag with the lowest correlation coefficient is identified at approximately 4 hours. Specifically, global peaks appear roughly at the 12-hour ticks of Temperature for both real and synthetic data on three critical databases. Meanwhile, the negative correlation strengthens as the time lag increase for Mechanical ventilation in the original timeseries. Since these behaviors can be reproduced by EHR-M-GAN, therefore they demonstrate that our model can effectively capture the temporal characteristics in the original timeseries.Similar patterns are presented between the ACF calculated for real data and their synthetic counterparts, while the quantitative statistics also correspond with the observation. Moreover, overlapping confidence intervals indicate that the synthetic data is able to consistently capture the underlying temporal distributions within the real timeseries. For variables such as clinical intervention prediction tasks are implemented to evaluate the potential of EHR-M-GAN and EHR-M-GANcond in synthesizing high-fidelity synthetic data to further boost the performance of ML classifiers. In line with prior work48, we establish LSTM-based classifiers to predict the status of mechanical ventilation and vasopressors using continuous-valued multivariate physiological signals as the predictors. A fixed duration of 12 h is used for both observation window and prediction window  with Train on Synthetic, Test on Real (TSTR), to show whether the performance of a classifier trained on synthetic data from EHR-M-GAN or EHR-M-GANcond can be generalized to real data. In addition to the proposed models, synthetic data produced by the baseline models are also used to train the downstream classifiers for comparison. Other than a measurement of data utility where the downstream task is to predict discrete-valued medical intervention (described as outcomes in this scenario) using continuous-valued physiological features (denoted as predictors), TSTR can also be used to assess data synthesizers’ ability to capture the interdependencies between the mixed-type features. (ii) Data augmentation approach: As data augmentation is employed as a means of circumventing the issue caused by the under-resourced EHR data, here we explore whether synthetic data can be used to improve the existing ML algorithms through data augmentation. Therefore, Train on Synthetic and Real, Test on Real (TSRTR) is compared with TRTR to measure the improvement of the classifier’s performance when trained on the augmented data49. The augmentation ratio α or β is applied on sub-train data B, in two different scenarios of TSRTR, respectively. Details are explained as follows , which further is combined with limited real data , to train the downstream classifiers . Then by implementing EHR-M-GAN or EHR-M-GANcond in TSRTR, we investigate the proportion of the real data ratioβ) required to maintain the same performance as in TRTR based on the entire synthetic dataset B . Our experiment results have demonstrated that the proposed models can be used for data augmentation to overcome the issue of data scarcity and subsequently improve the classifiers’ performance.The results in Table TRTR. For EHR-M-GANcond, the ratio needed for real data to maintain the comparable predictive power is further reduced to 25%, which equivalently indicates a 75% reduction of sample size required in real data collection. Overall, results presented in Table cond can robustly maintain the level of prediction performance, therefore alleviating the necessity for acquiring clinical data at scale.On the other hand, as shown in Table Patient privacy is a major concern with regards to sharing electronic health records in any means. Generative models can overcome the explicit one-to-one mapping towards the underlying original data in contrast to data anonymization. However, GAN could potentially raise privacy concerns of information leakage if they simply “memorize” the training data, or synthesize samples nearly identical to the real samples (often due to mode collapse). In that case, sensitive medical information  belonging to a specific patient used in training GANs can be retrieved during the generative stage, thus posing challenges for preserving privacy in downstream applications.membership inference attacks, also known as presence disclosure51. The threat model is implemented under the membership inference for GANs in the black-box settings50. The attacker is assumed to possess complete knowledge of all the patient records set P, where a subset from P further is used to train GANs. During the experiment, the number of samples in the subset for training EHR-M-GAN are varied to investigate the impact of the availability of training data on the success of the attacker  identical to the real samples, it would be straightforward for the adversary to identify samples that are used as training data. Determined by whether the attacker can correctly infer a given record is in or not in GAN’s training, the accuracy and recall can be calculated.In this section, we first quantify the vulnerability of EHR-M-GAN to adversary’s see Fig. a. By obs52.As shown in Fig. 53, which is a rigorous mathematical definition of privacy, has emerged to be the prevailing notion in the context of statistically analyzing data privacy. The -differential privacy is guaranteed for model M, if given any pair of adjacent datasets D and D or S is the subset of any possible outcomes of the generative process. By perturbing the underlying data distribution, DP bounds the maximum variations of the algorithm when any single individual is included or excluded from the dataset. In practice, recent works on developing differentially private deep learning models have benefited from differential private stochastic gradient descent (DP-SGD) algorithm. DP-SGD operates DP by gradient clipping and noise adding during SGD, thereby ensuring that the impact of single record in the training dataset on algorithm parameters is limited within DP’s extend. In this section, -differential privacy is implemented in EHR-M-GAN using TensorFlow Privacy. We then perform the same downstream tasks on medical intervention prediction using synthetic data generated from DP-guaranteed EHR-M-GAN, and compare its performance with TSTR TSTR performance of EHR-M-GAN under differential privacy guarantee with varying budgets ϵ (δ fixed at ≤0.001). The value ϵ determines how strict the privacy is, where the smaller value indicates a stronger privacy restriction. As suggested in Fig. ϵ increases). We observe that the AUROC of DP-bounded EHR-M-GAN can maintain at an acceptable level even under strict privacy setting. For example, the AUROC for predicting the treatment of Vasopressor can maintain at 0.714 (AUROC = 0.725 under TRTR) even when the ϵ decrease to 4, which is an empirically reasonable value for implementing DP in practice54. Future work that focuses on privacy-preserving GAN under DP-guarantee is expected, where the fidelity of the synthetic data can be restored without compromising its privacy.Figure cond, demonstrate consistent improvements against the state-of-the-art benchmark GANs in synthesizing timeseries data with high-fidelity. This indicates that the distributional characteristics of the EHR timeseries can be well-preserved in the synthetic data provided by EHR-M-GAN, therefore ensuring its usability during clinical data sharing. Second, as opposed to previous models which were confined to synthesizing only one specific type of data, EHR-M-GAN can produce mixed-type timeseries and successfully capture the temporal dynamics and correlation between features. By accurately reconstructing the interdependencies and complex clinical relationships between features, downstream studies such as association analysis and outcome prediction can be supported. Notably, the proposed models also outperform the GAN variants that allow mixed-type inputs in the ablation study, indicating that the components in EHR-M-GAN are effective in synthesizing mixed-type timeseries with high fidelity, while successfully reconstructing the interdependencies between them. Then, during downstream task evaluation, given the prediction of medical interventions in fast-paced critical care environments as an exemplar, the results demonstrate the broad applicability of our model in developing ML algorithm-based decision support tools by data augmentation. Lastly, the generative capability of the proposed model avoids the “one-to-one” mapping to the original data, and enables the collaborative uses and sharing of EHRs by creating realistic novel samples. The assessment of privacy risks further demonstrates the synthetic data provided by EHR-M-GAN can preserve the sensitive information in patient records while maintaining an acceptable level of data utility.In this study, we propose a generative adversarial network entitled EHR-M-GAN, aiming at mitigating the challenge of synthesizing longitudinal EHR with mixed data types. A comprehensive list of evaluation metrics is introduced for the systematic assessment, in terms of the fidelity, correlation, utility, and privacy of the synthesis model. First, both EHR-M-GAN and its conditional version, EHR-M-GAN56. Second, the experimental results indicate that the quality of the synthetic EHR data can be improved by the integration of mixed-type information, in contrast to the benchmarks that utilize single-type data for learning. This also enables us to mimic how information is presented in clinical practices. Furthermore, we can generate condition/outcome-specific patient trajectories along with corresponding interventions, to facilitate clinical prediction and decision-making. Third, though facing the privacy-utility tradeoffs, the synthetic EHRs data provided by the proposed model leads to negligible privacy risks under the membership inference attacks. This paves the way for a series of applications in clinical research, including but not limited to, enabling the development of ML models by accessing the synthetic data, overcoming the paucity of medical data and improving the robustness of ML algorithms through data augmentation.The results in our study have several notable implications with respect to the synthesis of EHR data. First, as the proposed model can be used to provide synthetic longitudinal EHRs for various data types while preserving their underlying correlations, it is now feasible to use the synthesized data to improve the performance of ML models for downstream applications such as the prediction of next intervention, or understanding the disease dynamics and patient phenotyping, based on both the continuous and discrete components of EHR timeseries27, and continuous-valued measurements. These mixed-type EHR timeseries capture a patient’s health status and better align with clinical decision-making process than those using the single-type data alone. Therefore, developing GANs targeting mixed-type EHRs generation have the potential to pave the way for complex deep-learning systems that are capable of integrating information from various sources. However, it is worth noting that the validation of our proposed model is based on critical care settings with limited feature dimensions, can only serve as a proof of concept. When extending the proposed model to other clinical settings, such as synthesizing ICD codes with hundreds or thousands of feature dimensions27, the scalability and utility of our proposed model when dealing with the enlarged, sparse feature space needs further investigation.Due to the heterogeneous nature of EHR data, besides the ICU setting in our empirical evaluation, there are needs for synthesizing mixed-type EHR timeseries in various clinical scenarios. For example, patients’ encounters in hospitals are documented as structured EHRs recorded in the temporal order. Each visit is typically associated with the corresponding medical events presented in the form of discrete-valued ICD codes57. Although these preprocessing steps are commonly used in clinical research under the critical care settings46, the proposed model cannot model the irregular time intervals between signals nor missing values within the timeseries. However, dealing with irregularity of the timestamps when synthesizing clinical events in EHRs could be useful for predicting outcomes that are time-aware in the downstream tasks27. Modeling such time intervals could be non-trivial as the determinative perspectives sometimes go beyond the scope of inferring patients physiological status such as resource allocations within hospitals. Also, synthesizing timeseries while incorporating the missing values could also be beneficial in the real-world application scenarios. As ML models are sometimes sensitive to the data missingness, imputing the incomplete data in EHRs using generative approaches could improve the performance of ML models, and has become an area of active research58. Furthermore, as evaluations are performed based on clinical timeseries with a fixed length, no comparisons are made between the model’s scalability when dealing with timeseries with varying lengths. Recent studies have found the quality of the synthetic longitudinal data degenerates over time, also called as the “drift problem”28. Such problems when dealing with long sequences should be recognized and mitigated with techniques such as conditional fuzzing and regularization methods28, in both the generation and evaluation steps.There are limitations in the current work. First, data curation strategies on clinical timeseries, including truncating, smoothing and imputation, are applied before the EHR timeseries are used for the training of generative models. As during the data preprocessing, we first extract the timeseries with a fixed duration , and then hourly aggregate patients’ physiological and intervention signals based on their mean statistics, followed by completing the missing value in the timeseries through the “Simple Imputation” approach59. In this study, a comprehensive evaluation list is provided with regards to the fidelity, correlation, utility and privacy of the synthesis models. It is also worth noting that evaluation metrics should be properly chosen and implemented based on the purpose of the task, otherwise may lead to biased results. For example, recent findings28 have reported that the traditional implementation of the discriminative score which trains the critic using the randomly initialized parameters, though widely used29, may lead to unreliable results. Improvement has been made to this evaluation metric for a more robust assessment, where the parameters of the trained generative model can be used for the critic’s initialization.The evaluation of GANs is still a challenging task. Recent findings have suggested that systematical assessment for EHR synthesizers is critical before their applications in different use cases61. Ultimately, by constructing the “synthetic twin” of patients using GANs, the synthesis tool can become more generalizable for precision medicine and support the clinical decision making in delivering personalized healthcare.Finally, the conditional aspect of our model is currently limited as it can not generate patient-specific EHRs conditioning on information at a more granular level. Even though the proposed conditional GANs can synthesize a subgroup of patients with target outcomes or statuses that clinicians specify, it is still limited in incorporating personalized information during the conditional generation. Future work for developing GANs in healthcare data can be extended to patient-level EHRs generation, such as synthesizing counterfactual information of a target patient for treatment effect estimationcond can generate realistic longitudinal EHR timeseries with mixed data types. By providing synthetic EHR data better mimicking the nature of clinical decision-making, the proposed model can therefore enable faster development in AI-driven clinical tools with increased robustness and adaptability. In addition to the improved performance against the existing state-of-the-art benchmark models, augmentation provided by synthetic data during training boosts the predictive performance in downstream clinical tasks. EHR-M-GAN can help eliminate the barriers to data acquisition for healthcare studies, therefore overcoming the challenges posed by the paucity of medical data available for research use. Despite the novelty of this study in filling the research gap for synthesizing longitudinal EHRs in mixed-type settings, we acknowledge that there is still a gap between the real EHRs data and its synthetic counterparts produced by current generative methods. Therefore developing advanced EHR synthesizers especially in mixed-type settings still requires active research in the future study.Synthetic data provides an alternative to sharing real patient data while preserving patient privacy. Results in our study demonstrate that the proposed EHR-M-GAN and EHR-M-GANMIMIC-III 36—a freely accessible database that comprises EHR data associated with approximately 60,000 ICU admitted patients and 312 million observations to Beth Israel Deaconess Medical Center.eICU (eICU Collaborative Research Database)37—a multi-center critical care database containing data for over 200,000 admissions and 827 million observations to ICUs from 208 hospitals located throughout the United States.HiRID (High time-resolution ICU dataset)38—a high-resolution ICU dataset relating to more than 3 billion observations from almost 34,000 ICU patient admissions, monitored at the Department of Intensive Care Medicine, Bern University Hospital, Switzerland.The following three publicly accessible ICU datasets with de-identified EHR data are used for evaluating the performance of EHR-M-GAN in generating the longitudinal data:24 h prior to patients’ ICU endpoints (discharge or death) are extracted for the three critical care databases. Data are preprocessed following an open-source framework—MIMIC-Extract46, where the patients’ physiological and intervention signals are hourly aggregated for denser representations. Details on data curation, including the cohort selection criteria, full list of features, and imputation method, are explained in Supplementary Note All these critical care databases include vital sign measurements, laboratory tests, treatment information, survival records, and other routinely collected data from hospital EHR systems. From these clinical observations, we featurize the patient trajectories as the combination of continuous-valued physiological timeseries  and discrete-valued medical intervention timeseries (such as the usage of therapeutic devices or intravenous medications). Temporal trajectories i ∈ {1, 2, . . . N}. Here we consider the i-th instance tuple J∣-dimensional continuous-valued timeseries, such as physiological signals from real-time bedside monitors. And K∣-dimensional discrete-valued timeseries, such as life-support interventions with the categorical value indicate its status (presence or absence).The longitudinal patient EHR dataset is denoted as 30. Specifically, as GANs require generators and discriminators to be both fully differentiable, generating discrete-valued timeseries using traditional GANs architectures would raise problems during backpropagation as no direct gradient can be provided32. Therefore, it is intrinsically difficult to model the underlying joint distribution of mixed data type timeseries using a single unified framework. Second, as the mixed-type timeseries are correlated , it is therefore important to model the interdependencies among heterogeneous types of timeseries.There are two main challenges when synthesizing mixed-type EHR timeseries. First, GANs have serious limitations on the type of data they can modelFirst, to jointly model the distribution of continuous-valued and discrete-valued timeseries using GANs, we build the generative model based on the latent space encoded by VAE networks. Instead of directly synthesizing discrete-valued timeseries that deactivate the backpropagation in GANs, the generator first synthesizes latent representations that allow the direct gradient in the network, therefore satisfying the prerequisite for GANs architecture to be fully differentiable. The synthetic latent representations for both types of data can be further decoded into raw timeseries using the decoders in VAEs.dual-VAE module for pretraining step and sequentially coupled generator module for generation step. The dual-VAE incorporates multiple loss constraints, which were previously adopted in domains such as self-supervised learning (SSL), timeseries representation learning, and domain adaptation, to extract useful hierarchical representations from heterogeneous but correlated data types. The sequentially coupled generator module replaces the traditional LSTM cell with the bilateral LSTM (BLSTM) cell we propose, where the “communication” of the two types of information are introduced into the networks. Therefore, the temporal dynamics between the mixed-type data can be preserved during the iteration.Even though the aforementioned network architectures enable the joint modeling of mixed-type data distribution, it still lacks the capability of capturing the inter-dependencies in heterogeneous data. In order to address the second issue, we devised dual-VAE framework for learning the shared latent space representations; (2) an RNN-based sequentially coupled generator and its corresponding sequence discriminators.As illustrated above, EHR-M-GAN can be factorized into two key components see Fig. : (1) a dpretrain stage, both continuous-valued and discrete-valued temporal trajectories are first jointly mapped into a shared latent space using the dual-VAE component (Step 1). Then, the sequentially coupled generator in EHR-M-GAN produces the synthetic latent representations (Step 2), which further can be recovered into features in the observational space by the pretrained decoders in the dual-VAE (Step 3). Finally, the adversarial loss is provided based on discriminative results and backpropagated to update the network (Step 4). The following sections discuss them in turn.As shown in Fig. same patient indexed with i, both same latent space S∣ denotes its spatial dimension. For the sake of simplicity, the subscripts i are omitted throughout most of the paper. To achieve this, we propose to use a dual-VAE framework, which exploits two VAE networks to encode both continuous and discrete multivariate timeseries into dense representations within One premise of successfully training EHR-M-GAN to generate reversible latent codes is to meet the assumption that for the dual-VAE framework for learning the shared latent representations. We start with training two encoders, i.e., Supplementary Fig. After passing data from 63 within specific layers of both the encoders pairs and the decoders pairs . The goal of contrastive loss is to ensure that patients with similar trajectories stay close to each other in the latent space (population-wise). And semantic loss used in EHR-M-GANcond encourages patients with the same conditional labels  to share similar latent representations. Intuitions and descriptions behind the objectives are discussed in turn.In the following subsections, we define multiple loss constraints for the optimization of DKL is the Kullback–Leibler divergence. The first term in Eq. . VAE holds the assumption of spherical Gaussian prior for the distribution of latent embeddings, where features can then be reconstructed by sampling from that space. The re-parameterization tricks enable differentiable stochastic sampling and network optimization. For encoder and decoder in the dual-VAE for domain same patient are assumed to capture the shared context. Therefore, embedding vectors same patient i, are supposed to be positioned closely in the shared latent space 65. In order to guarantee sufficient bound for representation learning, we incorporate contrastive loss as another distance metric to capture the inter-correlations among the population.On the flip side, pairwise reconstruction error  measured by 66, which aims to capture intrinsic patterns from input data without human annotations. In this study, we instantiate the contrastive loss via NT-Xent, which is proposed by Chen et al. in their work SimCLR67. The core of contrastive learning is to encourage networks to attract positive pairs closer and repulse negative pairs apart in the latent space. In this study, we adapt the corresponding auxiliary tasks for calculating contrastive loss to the scenario of learning representations from mixed-type timeseries. The objective of the task is to determine whether a set of representations transformed from the observational space belong to the same patient. And this leads to the corresponding positive pairs (true) and negative pairs .Contrastive learning is a concept that has recently been popularized in self-supervised learning (SSL)N records, we can obtain N pairs of latent representations from the encoders in dual-VAE. For patient indexed with i, dual-VAE, here we use d and i can be referred as N − 1) samples are regarded as negative pairs. Then the contrastive loss for a positive pair τ > 0 denotes a temperature hyperparameter. n ≠ m. And both data types. The final contrastive loss is computed across the total number of For patient data of cond, semantic loss is imposed to better align patients with same labels (conditions) into the same latent space clusters. For example, if the label of severe clinical deterioration in the ICU is given for conditional data generation, the corresponding synthetic continuous-valued timeseries  is supposed to be strongly associated with the discrete-valued timeseries  under the same label. For both domains, additional linear classifiers are trained to classify the latent embeddings based on their corresponding conditions in the observational space. We implement logistic regression as the linear classifiers and calculate the cross entropy as the semantic losses for both domains. For zd and the conditional information vector y:yj is the ground truth label for class j in condition vector y.In EHR-M-GANdual-VAE for learning the shared latent space representation, the total objective function for cond, the total loss becomes:β0, β1, β2, and β3 are scalar loss weights used to balance the loss terms.In summary, to train the dual-VAE network, we perform the corresponding ablation study using MIMIC-III dataset as an example. The results can be found in Supplementary Note dual-VAE network contribute to the improvement of EHR-M-GAN’s performance when generating mixed-type timeseries data.To validate the effectiveness of multiple losses and the weight-sharing constraint in the proposed sequentially coupled generator for generating latent representations for mixed-type timeseries, which is built based on the network architecture of coupled recurrent network (CRN). Specifically, a CRN exploits bilateral long short-term memory (BLSTM) cells as its recurrent layer to preserve the temporal dependencies between the continuous and discrete-valued sequences. The network architecture of bilateral-LSTM we proposed can extract and transmit the correlations between the mixed-type timeseries, as opposed to vanilla-LSTM which has only one recursive connection. In the following section, we first discuss the structure of BLSTM in detail as its essential recurrent layer of CRN, and then build the sequentially coupled generator based on CRN.We propose the d at time step t, respectively. An additional set of weights for introducing hidden states representations As the traditional LSTM only considers temporal dynamics from single-type timeseries, therefore is incapable to extract and transmit temporal correlation from heterogeneous features. Therefore, we propose the bilateral-LSTM cell with two network connections to characterize the correlations between two types of data. Given As indicated by Eq. , the proinput layers, the recurrent layers, and the fully connected layers. First, the random noise vectors input layers. Then the recurrent layersfrec, which are built based on two streams of BLSTM, one for each data type, are used to recursively iterate hidden states from both branches. Finally, the fully connected layersdual-VAE. At time step t, CRN can be formulated as:The architecture of CRN consists of three layers: the In summary, heterogeneous timeseries that exhibits mutual influence on each other are integrated into CRN to model their interdependencies. By exploiting the BLSTM cell as its recurrent layer, two streams of the inputs in the generator are encouraged to “communicate” with each other. CRN is therefore capable of exploiting the interplay between mixed-type data that correlates over time.pretraining of dual-VAE: First, a dual-VAE network which consists of a pair of encoders  labels from dual-VAE, the additional semantic loss is also incorporated during the optimization for the shared latent space as in Eq. GAN25, and TimeGAN29. For discrete-valued timeseries generation, classic medGAN32, seqGAN31, and two recently proposed work—SynTEG27 and DualAAE26 are used for comparison. Apart from these GAN-based models, we also incorporate PrivBayes70 to synthesize discrete-valued timeseries, which falls in the class of non-GAN generative approaches using a Bayesian framework17. As the original paper of PrivBayes focuses on data anonymization using differential privacy, we therefore implemented its ‘Non-Private’ version for a fair comparison with other baselines (see Section 4.1 Non-Private Methods in ref. 70). For medGAN and PrivBayes, we feed the flattened temporal sequence as the input since the models cannot produce timeseries data.We compare the performance of EHR-M-GAN with eight state-of-the-art generative methods in literature. However, as these benchmarks typically face challenges when modeling mixed-type EHR timeseries and can only synthesize single-type EHRs, we draw the comparison between EHR-M-GAN and the benchmark models using the corresponding partial component of our synthetic results, i.e., either the continuous-valued part or the discrete-valued part. For continuous-valued timeseries generation, benchmark GAN models include C-RNN-GANablation study to investigate whether our introduced novel components in the proposed model have advantages over its variants that also model mixed-type EHRs. First, we compare EHR-M-GAN with a variant that jointly models the mixed-type data using a single unified VAE network (denoted as GANUnified). Second, we test the variant that encodes the mixed-type inputs separately with two independent VAE networks (denoted as GANVAE). Then, we assess the effectiveness of the proposed sequentially coupled generator component in our model by implementing GANSL. Lastly, as the dual-VAE module alone can also be used for generating EHR timeseries, it serves as a non-GAN-based benchmark in the ablation study  is implemented with LSTMs, which are the same as EHR-M-GAN.GANVAE: It is composed of a pair of VAE networks and GANs (one for each type of inputs). The continuous-valued timeseries and discrete-valued timeseries from the same patients are separately fed into the corresponding paths in GANVAE, and then run in parallel. The synthetic outputs of each data type are then combined as the final results. It maintains the basic structure of EHR-M-GAN but lacks the latent space sharing with dual-VAE and the sequentially coupled generator in the original EHR-M-GAN.GANSL: In addition to GANVAE, it learns the shared latent space representations through dual-VAE by adding the corresponding loss functions in EHR-M-GAN, including ELBO loss, Matching loss and Contrastive loss. This model lacks the sequentially coupled generator.GANSL, it incorporates the sequentially coupled generator for the learning the correlated temporal dynamics in timeseries of different data types. This is the proposed full model.EHR-M-GAN: In addition to GANcond: This version is implemented on the basis of conditional GAN68, where the conditional inputs are fed into EHR-M-GAN to generate patients under specific labels.EHR-M-GANWe further perform the cond, auxiliary information from the patient status is used as conditional input. These conditional inputs are selected since synthesizing EHR information of patient subgroups with potential outcomes would be valuable for clinicians in their decision-making process. Other conditional labels (such as patient demographics in the categorized format) can also be used in the proposed conditional synthesizer for other research purposes. For MIMIC-III dataset, the classes are (1) ICU mortality: patient died within the ICU; (2) Hospital mortality: patient discharged alive from the ICU, and died within the hospital; (3) 30-day readmission: patient discharged alive from the hospital, and readmitted to the hospital within 30 days; (4) No 30-day readmission: patient discharged alive from the hospital, and had no readmission record to the hospital within 30 days. For eICU and HiRID datasets, the corresponding labels are also extracted based on the availability of the patient outcomes (see Table For training EHR-M-GANFurther information on research design is available in the Supplementary InformationReporting Summary