Quantifying the excess mortality attributable to antimicrobial-resistant (AMR) bacterial infections is important for assessing the potential benefit of preventive interventions and for prioritization of resources. However, there are few data from low- and middle-income countries.We conducted a 2-year prospective surveillance study to estimate the excess mortality attributable to AMR infections for all types of hospital-acquired infection (HAI), and included bacterial species that were both locally relevant and included in the World Health Organization priority list. Twenty-eight-day mortality was measured. Excess mortality and population attributable fraction (PAF) of mortality caused by AMR infections compared to antimicrobial-susceptible (AMS) infections, adjusted for predefined confounders, were calculated.Acinetobacter baumannii was responsible for the largest number of excess deaths. Among all types of infection, urinary tract infections were associated with the highest number of excess deaths, followed by lower respiratory tract infections and bloodstream infections.We enrolled 2043 patients with HAIs. The crude 28-day mortality of patients with AMR and AMS infections was 35.5% (491/1385) and 23.1% (152/658), respectively. After adjusting for prespecified confounders, the estimated excess mortality attributable to AMR infections was 7.7  percentage points. This suggests that 106  deaths among 1385 patients with AMR infections might have been prevented if all of the AMR infections in this study were AMS infections. The overall PAF was 16.3% . Among the bacteria under evaluation, carbapenem-resistant Estimating and monitoring excess mortality attributable to AMR infections should be included in national action plans to prioritize targets of preventive interventions.NCT03411538. Burden of antimicrobial-resistant (AMR) bacterial infection is high in low- and middle-income countries. Data generated from prospective surveillance on multiple organisms and for every type of hospital-acquired infection could support prioritizing targets of preventive interventions to control the spread of AMR infection. Quantification of the health burden related to antimicrobial resistance (AMR) is essential to understand the extent of the health impact, to improve management, and to allocate resources accordingly , 2. DiffTo bridge the gap of missing quality data on the relative mortality of each type of AMR infections in LMICs, we conducted a prospective surveillance study with an objective to estimate excess mortality and deaths attributable to AMR infections compared to antimicrobial-susceptible (AMS) infections for all types of HAIs in a large public hospital in northeast Thailand.Staphylococcus aureus, Enterococcus spp, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, or Acinetobacter spp. Protocol training and a “dry run” were performed between January and February 2018, before the surveillance study was rolled out between 1 March 2018 and 29 February 2020.We prospectively enrolled patients who were admitted to Sunpasitthiprasong Hospital, a 1201-bed tertiary hospital in Ubon Ratchathani, northeast Thailand. The most commonly used parenteral antibiotics in this hospital were ceftriaxone, carbapenems, and ceftazidime . All warThe definitions of BSI, LRTI, SSI, UTI, and OTH were based on the US Centers for Disease Control and Prevention surveillance guidelines , the InfS aureus (MRSA), ampicillin-resistant Enterococcus faecium (AMPREfm), Enterococcus faecalis (AMPREfc), third-generation cephalosporin-resistant E coli and K pneumoniae (3GCREC and 3GCRKP), and carbapenem-resistant P aeruginosa and Acinetobacter baumannii (CRPA and CRAB) are defined as AMR infections. Infections caused by bacteria that were susceptible to the corresponding antibiotic of interest are defined as AMS infections. Polymicrobial infections are defined as infections with >1 of the bacteria under evaluation. AMPREfm and AMPREfc were chosen rather than vancomycin-resistant E faecium (VREfm)  and Acinetobacter spp that were not A baumannii [n = 13]). In total, 2043 patients were included in the analysis. Of the 2043 patients, 39 patients were lost to follow-up and, among these patients, 28 were discharged against medical advice with a serious medical condition that had not improved and hence were assumed to have died within 28 days of follow-up since the first date of culture-positive specimen collection. Separately, sensitivity analyses were performed where these 39 patients were assumed to have died within 28 days of follow-up and where the patients were assumed to have survived within 28 days of follow-up.Over the 2-year study period, bacterial culture results (both negative and positive culture results) were received from 282 247 clinical samples  collected from 54 281 patients. Of the 282 247 clinical samples, 36%, 13%, 34%, and 17% were blood, urine, respiratory tract specimens, and other clinical samples, respectively. The study team checked the eligibility criteria for a total of 7726 patients who had at least 1 of the bacteria under evaluation isolated from a clinical specimen collected >48 hours after admission . Of the Of the 2043 patients, 1385 (67.8%) had infections with at least 1 of the AMR bacteria under evaluation; they were classified as having AMR infections . Of the A baumannii (n = 401), followed by K pneumoniae (n = 340) and E coli (n = 289), were the most commonly identified bacteria. Three hundred eighty-one (18.7%) patients had polymicrobial infection in this cohort  had monomicrobial infections . Among the patients with monomicrobial infections, s cohort .S aureus, E faecium, and E faecalis were 7.2% (5/69), 96.7% (176/182), and 18.1% (29/160), respectively. The proportion of 3GCREC, 3GCRKP, CRPA, and CRAB among infections with E coli, K pneumoniae, P aeruginosa, and A baumannii were 65.1% (188/289), 78.5% (267/340), 36.7% (81/221), and 83.8% (336/401), respectively. Among infections with E faecium, 6.0% (11/182) were vancomycin resistant, and these 11 isolates were also resistant to ampicillin. None of the E faecalis infections were vancomycin resistant.Of all monomicrobial infections, the proportion of MRSA, AMPREfm, and AMPREfc among infections with Most of the patients had infections involving a single body site (n = 1926/2043 [94.3%]). The most common type of infection was UTI (n = 796), followed by LRTI (n = 618), BSI (n = 289) with no focus of infection, SSI (n = 155), >1 type of infection (n = 117), and OTH (n = 68). Of 117 patients with >1 type of infection, 27 were secondary bacteremia related to UTI , 48 were secondary bacteremia related to LRTI, and 6 were secondary bacteremia related to SSI.Patient characteristics at hospital admission varied by type of infection. Patients with >1 type of infection had, on average, the highest CCI score .Overall, crude 28-day mortality was higher in the AMR infection group (n = 491/1385 [35.5%]) than in the AMS infection group (n = 152/658 [23.1%]). Among patients who survived up to 28 days since the first date of culture-positive specimen collection, the length of hospital stay after the first date of culture-positive specimen collection was longer in the patients with AMR infections  compared to those with AMS infections . Of those who died within 28 days, death occurred earlier in patients with AMR infections  compared to patients with AMS infections . The crude 28-day mortality by causative bacteria and type of infection is shown in The model that included an interaction term between the bacterial species and AMR infections and adjusted for all predefined confounding factors was used to estimate excess mortality attributable to AMR infections. We estimated that if all of the infections were AMS infections, the mortality would have been 26.3% , and if all of the infections were AMR infections, the mortality would have been 34.0% . This represented an absolute difference of 7.7  percentage points of excess mortality attributable to AMR infections. The estimated PAF of mortality caused by AMR infections was 16.3% , which implied that approximately 106 deaths of the 643 observed deaths could be prevented if all individuals in the study cohort had AMS infections. In this study, we estimated overall mortality attributable to AMR infections, and mortalities attributable to AMR infections for each causative bacteria and for each type of HAI in a large public hospital in northeast Thailand. The excess mortality attributable to AMR infections in this study cohort is 7.7 percentage points  and the overall PAF is 16.3% . This is relatively higher than that reported from high-income countries (HICs). Of the AMR bacteria under evaluation, CRAB, AMPREfm, and 3GCRKP had the largest number of excess deaths. Of types of infections under evaluation, UTI, LRTI, and BSI had the largest number of excess deaths. This suggests that the local action plans on AMR would need to focus on those infections and develop interventions that can reduce the burden of AMR related to those infections effectively.Acinetobacter spp infections in the European Union and European Economic Area was 603 966 (based on result table of the article) and the estimated number of deaths attributable to those AMR infections was 26 320. Therefore, with post hoc estimation, excess mortality attributable to those AMR infections is about 4.4% (26 320/603 966). In the GBD study 13], Gree in HICs , 13, 23 ectively . A studyectively . It is pAn important strength of this study is that we prospectively collected detailed patient information allowing us to adjust for important confounders associated with mortality due to AMR infections systematically . We alsoS aureus, Acinetobacter spp) could colonize skin and could be incidental in blood cultures. Third, excess mortality and deaths attributable to AMR infections compared to AMS infections represents the lower bound of burden of AMR infections, and may underestimate potential impacts of future interventions and preventive measures against AMR infections [There are several limitations to this study, as with many studies addressing this important question. First there are important residual confounding factors. For example, antibiotic use before hospital admission is common but often not recorded. Second, some important pathogens (fections , 27. An fections .ofac305_Supplementary_DataClick here for additional data file.


With the fast development of modern society, environmental pollution and energy crisis have been drawing broad attention. It is highly desirable to develop renewable and clean energy conversion and storage technologies to overcome these issues . ElectroAmong various strategies to modulate the electronic structure of electrocatalysts, defect engineering has recently become a very hot topic and draws much attention to the application of electrocatalytic fields . In rece2 reduction. They summarized various strategies for adjusting and modifying the surface defects of catalysts, including intrinsic defects, heteroatom doping, single-metal-atom sites, vacancies, grain boundaries and lattice defects. (2 through a large-scale and simple ball milling strategy, which can greatly enhance the hydrogen evolution reaction performance.Herein, we collected five valuable contributions focusing on the defect chemistry of different materials and different electrocatalytic reactions. This special Research Topic includes four reviews and one original research. Defect engineering on carbon-based and metal-based single-atom electrocatalysts was highlighted by  along widefects.  successfAs guest editors, we would like to thank all the authors for their valuable contributions to this Research Topic and all the reviewers for their important and thoughtful comments. We hope that this Research Topic will provide useful insights for the development of and help readers understand more about the effects of defect chemistry on different electrocatalytic reactions.


Existing tools to estimate cardiovascular (CV) risk have sub-optimal predictive capacities. In this setting, non-invasive imaging techniques and omics biomarkers could improve risk-prediction models for CV events. This study aimed to identify gene expression patterns in whole blood that could differentiate patients with severe coronary atherosclerosis from subjects with a complete absence of detectable coronary artery disease and to assess associations of gene expression patterns with plaque features in coronary CT angiography (CCTA). Patients undergoing CCTA for suspected coronary artery disease (CAD) were enrolled. Coronary stenosis was quantified and CCTA plaque features were assessed. The whole-blood transcriptome was analyzed with RNA sequencing. We detected highly significant differences in the circulating transcriptome between patients with high-degree coronary stenosis (≥70%) in the CCTA and subjects with an absence of coronary plaque. Notably, regression analysis revealed expression signatures associated with the Leaman score, the segment involved score, the segment stenosis score, and plaque volume with density <150 HU at CCTA. This pilot study shows that patients with significant coronary stenosis are characterized by whole-blood transcriptome profiles that may discriminate them from patients without CAD. Furthermore, our results suggest that whole-blood transcriptional profiles may predict plaque characteristics. Atherosclerotic cardiovascular (CV) disease still represents a major cause of mortality, despite a decline in incidence rates in several countries in Europe. Several risk scores have been proposed for the assessment of individual 10-year CV risk based on traditional risk factors  . CoronarNoninvasive imaging techniques could improve risk-prediction models for CV events. In this setting, coronary CT angiography (CCTA) plays an important role in the identification of individuals with suspected coronary artery disease (CAD) and shows excellent diagnostic accuracy compared to invasive coronary angiography ,4. CCTA In addition, investigation of molecular biomarkers , which are present and altered in the biological fluids and at the level of the injured tissue , may helRecently a few authors have used the RNA sequencing (RNA-Seq) technique to explore the whole-blood transcriptome in search of biomarkers of coronary atherosclerosis and/or related conditions. Preliminary studies have identified gene expression profiles that would distinguish women with acute myocardial infarction (MI) with nonobstructive CAD from those with MI-CAD or controls , and subThrough the identification of biomarkers and non-invasive imaging analysis, it should be possible to monitor the development of atherosclerotic lesions, identify the “vulnerable patient”, and potentially prevent fatal outcomes.This study makes use of CCTA and RNA-Seq to (1) identify whole-blood gene expression patterns that could differentiate patients with severe coronary atherosclerotic pathology from subjects with complete absence of CAD, (2) detect possible pathways and immune cell populations associated with the pathological phenotype through functional inference analysis, and (3) assess the association of gene expression patterns with CCTA-derived plaque features. It should be emphasized that this is a pilot study and proof-of-concept research that is intended to evaluate the experimental validity of an innovative hypothesis in a small number of subjects.The present work made use of the EPIFANIA prospective observational study underway at our Institute, which enrolled a large cohort of consecutive patients undergoing CCTA for suspected CAD, in order to identify molecular and imaging biomarkers of coronary atherosclerosis progression and occurrence of CV events. Inclusion criteria were: age between 18 and 80 years and negative history of previous coronary events  or revascularization (coronary angioplasty or bypass graft). Exclusion criteria were: age <18 and >80 years, pregnancy, history of previous CV or coronary revascularization, indication for immediate revascularization, arrhythmias, congestive heart failure, non-ischemic cardiomyopathies, pacemakers, defibrillators, severe or disabling extra-cardiac disease or infectious disease, immunosuppressive therapy or chemotherapy in the previous year, major surgery in the previous six months, blood perfusions in the previous two months, and failure to obtain the informed consent.Patient enrollment and signing of informed consent, as well as peripheral blood sampling from an antecubital vein, were undertaken upon admission at the Department of Cardiovascular Imaging before CCTA was performed. Medical history and clinical information were collected. Routine hematochemical tests were performed along with the CCTA examination.For this exploratory pilot study, we adopted a strategy that would allow us to search for highly relevant differences and remove possible confounding factors. Therefore, we selected two groups of individuals at the extremes of the distribution of coronary artery stenosis extent , matched for sex, age, and CV risk factors, from those enrolled between October 2016 and November 2018.The study protocol conformed to the principle of the Declaration of Helsinki and was approved by the Ethics Committee of the IRCCS Istituto Europeo di Oncologia and Centro Cardiologico Monzino. All recruited patients signed the written informed consent form and participants agreed to share their de-identified information.2 area within and/or adjacent to the artery lumen, clearly distinguishable from the vessel lumen. High-risk plaque features, such as the arterial remodeling index (RI), spotty calcification (SC), and napkin ring sign (NRS), were evaluated. The RI is defined as the ratio between the lesion plaque area and a reference lumen area. The NRS is a thin, rim-like enhancement (no more than 130 HU) distributed along the outer contour of the vessel and surrounding a fibro-lipidic plaque. SC is any discrete calcification ≤3 mm in length and occupying ≤90° arc when viewed on a short axis  < 5). Thus, the final, cohort was composed of 52 patients .For this pilot study, we selected 54 patients, either with severe coronary stenosis  range between −0.96 and 0.93. Of these, 1507 (51%) were on average more expressed in CAD subjects, while 1442 (49%) were less expressed in CAD subjects  with a log2 FC ranging from −0.63 to 0.75. Of these 138 genes, 74 (54%) showed a higher expression in CAD subjects, while 64 (46%) had a lower expression. The volcano plot in Following alignment, read count, and data filtering, 15,128 expressed genes were identified. Genes expressed in peripheral blood were used to evaluate their ability to discriminate between the two phenotypes under study. We observed 2949 differentially expressed genes at a nominal subjects . Applyinn = 22) are grouped into a single cluster and 76% (n = 19) of noCAD subjects constitute a second well-separated group.Unsupervised hierarchical clustering analysis was performed to evaluate if the genes differentially expressed were able to distinguish CAD from noCAD subjects. The dendrogram  shows a p-value < 0.05). The most significant GO-BP terms are reported in We performed GSEA using the collection of gene sets reported in the Gene Ontology database of biological processes (GO-BP) to determine whether members of a set of genes tend to be better associated with one phenotype than another . Fifty gThe results obtained through the GSEA analysis were graphically displayed in an enrichment network, which highlights the most significant pathways and the relationships between them . The cor+, T-CD8+, and follicular T cells) and B lymphocytes (naive and memory cells). All these cell types play important roles in the adaptive immune system. Conversely, the CAD group was found to be negatively enriched by all cell types associated with the innate immune system, such as granulocytes (both neutrophils and eosinophils), monocytes, and activated and resting mast cells.Through the GSEA, we also carried out a cell enrichment analysis  using a n = 14 vs. noCAD n = 13, matched for sex, age, and cardiovascular risk factors) and repeated the differential gene expression analysis using microarrays. Then, we correlated the mean differences (log2 FC) in hematopoietic cell gene sets observed with RNA-Seq to those detected with microarrays. The analysis revealed highly significant correlations between the two independent detection methods for each hematopoietic gene set  with a peak near the zero value . Notably, regression analysis showed reliable p-value distributions for Leaman score, SIS, SSS, and plaques with density < 150 HU in CCTA  that patients with significant coronary stenosis (≥70%) at first diagnosis are characterized by whole-blood transcriptome profiles that can discriminate them from patients without coronary plaques as detected by CCTA and (b) that blood-based transcriptional signatures may predict specific high-risk CT plaque features. These results were derived from genome-wide transcript profiling in the whole blood of a group of 54 patients—27 with CCTA-documented CAD and 25 without signs of atherosclerotic lesions—matched for sex, age, and CV risk factors. Clustering analysis highlighted that the expression patterns identified could distinguish the two phenotypic groups, and functional enrichment analysis showed specific pathways and cell subpopulations positively or negatively associated with the CAD phenotype.The main findings of the present study suggest  is an informative tool for investigating disease states and identifying markers reflecting genetic predisposition and/or disease activity. Blood is an ideal surrogate tissue for CAD studies ,11 becauRNA sequencing has unveiled new molecular players that could be useful for an in-depth understanding of the pathophysiology of CAD. The molecular and cellular mechanisms underlying coronary atherosclerosis include intracellular lipid accumulation and immune-inflammatory response, which play crucial roles in all the different stages of the atherosclerotic process  ,36. In a+, T-CD8+, and follicular T cells) and B lymphocytes (naive and memory cells), whereas the noCAD group was enriched with cell types associated with the innate immune system, such as granulocytes, monocytes, and mast cells. Although atherosclerosis is considered a chronic inflammatory disorder whose progression is driven by the innate immune response, with the principal involvement of myeloid cells, the hypothesis that both innate and adaptive immunity are involved in the development of this disease has emerged from recent studies [Accordingly, cell-type enrichment analysis revealed that CAD and noCAD groups had a divergent association with the immune response. The CAD phenotype was associated with transcriptional profiles of T lymphocytes , the autoimmune response is prevalent over the innate inflammatory processes. Consistent with this hypothesis, in a recent study  that inv3 only, with boundaries of agreement of 21.8 and −13.9 mm3 and a very high correlation coefficient (0.98) [Finally, this study suggests the existence of peripheral blood gene expression patterns able to predict the degree of some high-risk features in CCTA. Of interest, regression analysis unveiled several genes whose expression was consistently correlated with the Leaman score, SIS, and SSS, as well as plaques with density <150 HU in CCTA. Indeed, the combination of these parameters allows a comprehensive assessment of atherosclerotic burden, as SIS represents the diffuseness of CAD, SSS combines the extent of disease with the degree of stenosis, the Leaman score points out plaque characteristics and lesion proximity, and plaque volume < 150 HU is focused on plaque composition. In contrast, no association was found with the total coronary plaque volume, LAP, NRS, spotty calcification, and RI > 1.4. LAP (expression of the lipidic composition of the plaque), NRS (representing a plaque with a large necrotic core), spotty calcification (associated with intraplaque hemorrhage), and RI > 1.4 (expression of early atherosclerosis) are qualitative plaque features previously associated with increased risk of ACS . Howevert (0.98) .Thus, an association between transcriptional profiles in the whole blood and plaque characteristics could help early identification of the “vulnerable patient” and, therefore, prevent CV events, as previously suggested in the literature .The strength of these results is that they were obtained from whole blood and with an RNA sequencing method, increasing the reliability and robustness of emerging biomarkers for the management and stratification of patients. The small size of the population represents a limitation of this study and it will be necessary to confirm the results on a larger number of patients and validate them with a new independent sample group. In addition, it should be considered that the sequencing analysis of the circulating transcriptome was carried out on all cell populations present in the peripheral blood  and, therefore, it was not possible to associate a specific pathway with a precise cellular subpopulation.The results of this explorative pilot study show differences in the expression of the circulating transcriptome between patients with significant coronary stenosis (≥70%) and subjects with the absence of coronary atherosclerosis in CCTA. These different patterns are functionally associable with two divergent immune-inflammatory profiles. Moreover, the results of regression analysis suggest the hypothesis that a possible association between whole-blood transcriptional profiles and plaque characteristics could help identify the “vulnerable patient” and potentially prevent CV events.


One of the post-d-amphetamine scans for each subject was preceded by a single oral dose of TAK-041 . D-amphetamine induced a significant decrease in [11C]PHNO binding potential relative to the non-displaceable component (BPND) in all regions examined (16–28%), consistent with increased synaptic dopamine release. Pre-treatment with TAK-041 significantly attenuated the d-amphetamine-induced reduction in BPND in the a priori defined regions . The reduction in BPND was generally higher after the 40 mg than the 20 mg TAK-041 dose, with the difference between doses reaching statistical significance in the putamen. Our findings suggest that TAK-041 enters the human brain and interacts with GPR139 to affect endogenous dopamine release. [11C]PHNO PET is a practical method to detect the effects of novel drugs on the brain dopaminergic system in healthy volunteers, in the early stages of drug development.The use of positron emission tomography (PET) in early-phase development of novel drugs targeting the central nervous system, is well established for the evaluation of brain penetration and target engagement. However, when novel targets are involved a suitable PET ligand is not always available. We demonstrate an alternative approach that evaluates the attenuation of amphetamine-induced synaptic dopamine release by a novel agonist of the orphan G-protein-coupled receptor GPR139 (TAK-041). GPR139 agonism is a novel candidate mechanism for the treatment of schizophrenia and other disorders associated with social and cognitive dysfunction. Ten healthy volunteers underwent [ Positron emission tomography (PET) studies have become an essential component of early-phase development for novel small-molecule pharmaceuticals targeting the central nervous system (CNS). No other methods allow direct quantification of the relationship between the concentration of the drug in plasma and target engagement in the human brain. These PET studies typically use a radioligand specific for the molecular target in question and relate changes in target occupancy, as measured with PET at several time points subsequent to a single oral dose of the drug under investigation, to drug concentration in the plasma at the time of the PET scans. Such a study helps to confirm brain penetration and central target engagement and, together with proper modelling approaches, provides a rational basis for drug-dosing schedules in future clinical trials. Data derived from such studies can establish the time–exposure–occupancy relationship for a novel compound , 2, enabThis approach depends on the availability of a suitable PET radioligand for the molecular target in question. Such ligands may not exist for truly novel targets. In this manuscript, we describe an alternative approach that can be useful when a specific radioligand for the molecular target is not available. This approach uses a PET radioligand that is sensitive to the concentration of an endogenous neurotransmitter to detect changes in endogenous neurotransmitter levels induced by the novel pharmaceutical.G-protein-coupled receptor 139 (GPR139) is a novel, highly conserved, class A orphan receptor  belonginThe development of a PET ligand suitable for GPR139 occupancy studies has been challenging, with the best available molecular candidates having affinity in the range of 10–50 nM, which may not be sufficient to provide an adequate signal-to-noise ratio for a PET ligand at this target –11. Ther11C]raclopride and [123I]IBZM have been shown to be suitable for the detection of pharmacologically induced dopamine release in healthy non-human primates [11C]PHNO [11C]NPA [11C]raclopride, [123I]IBZM, [18F]fallypride and [11C]FLB-457 that bind to the whole population of D2R, agonists bind primarily to the G-protein-coupled (G-coupled) state of the D2R, analogous to dopamine itself. Owing to this difference, agonist ligands are significantly more sensitive to fluctuations in synaptic dopamine concentration [11C]PHNO and [11C]raclopride demonstrated a 50–100% higher signal for [11C]PHNO in the healthy human brain PHNO , 25 and [11C]NPA . As oppontration , 28. A dan brain .11C]PHNO as a non-invasive method of monitoring synaptic dopamine levels. Conducted as part of a phase 1 development plan, this study is an example of the use of PET imaging as a pharmacodynamic biomarker, reflecting a biological effect of the investigational compound downstream from target engagement.In the present study, we translated the observation of blunted AMPH-induced dopamine release by TAK-041 in microdialysis experiments in rats, into PET studies in humans using PHNO PET scans, one at baseline, the second ~3 h after a single oral dose of d-AMPH (0.5 mg/kg), and the third ~5 h after a single oral dose of TAK-041 (20 mg or 40 mg) and 3 h after an oral dose of d-AMPH (0.5 mg/kg)  scans (including a T1-weighted scan) at screening, performed using a Siemens 3T MRI Verio or Trio magnet . Structural imaging data were acquired in the sagittal plane, using a 3D magnetisation-prepared rapid gradient-echo scan with the following parameters: repetition time = 2300 ms, echo delay time = 2.98 ms, flip angle = 9°, isotropic voxels = 1.0 mm × 1.0 mm × 1.0 mm, number of slices = 160, total scanning time = 5 min 3 s. MRI scans were reviewed by a neuroradiologist to exclude the presence of any clinically relevant brain abnormalities in the study participants. T1 MRI data were used as part of the PET data analysis as described below.11C]PHNO was made as previously described [11C]-propionyl chloride with despropyl-PHNO . The final product was purified by high-performance liquid chromatography (HPLC) and subsequently reformulated through solid- phase extraction in a solution of 10% ethanol in normal saline.1CPHNO waescribed  by the rParticipants were positioned in the PET scanner, after the insertion of a venous cannula in an antecubital or forearm vein. Soft padding and head restraints were used to minimise head movement during data acquisition. All dynamic [11C]PHNO PET scans were performed on Siemens Biograph 6 PET/CT scanners (Hi-Rez [PET/CT1]) or a TruePoint with TrueV . All scans for a particular participant were performed using the same scanner. A low-dose CT scan was performed immediately before each PET study in order to estimate signal attenuation. After an intravenous bolus injection of [11C]PHNO, dynamic emission data were acquired for 90 min in 26 frames of increasing duration. The dynamic images were reconstructed using Fourier rebinning and a 2D-filtered discrete, inverse Fourier transform algorithm with a 5 mm isotropic Gaussian filter on a 128 × 128 matrix with 2.6 zoom giving 2 mm isotropic voxels. Appropriate corrections were applied for attenuation, randoms and scatter.TM software package [http://www.fil.ion.ucl.ac.uk/spm) for image segmentation and registration.All image data were analysed using the MIAKAT package   funEach participant’s structural magnetic resonance (MR) image underwent brain extraction and grey-matter segmentation and was co-registered to a standard reference space . The MN11C]-(+)-PHNO binding potential relative to the non-displaceable component (BPND) after a d-AMPH challenge in these ROIs PHNO PET data PHNO-specific binding after a d-AMPH challenge NDNDReduction in [11C]PHNO-specific binding after administration of TAK-041 post-d-AMPH challenge NDNDReduction in PHNO BPND) were calculated for each ROI for PET2 (d-AMPH-only scan), and ND, (the attenuation of ΔBPND by TAK-041) was determined for each region . There was no relationship between the individual plasma concentration of TAK-041 and the Pre-treatment with TAK-041 attenuated the effect of the d-AMPH challenge in all ROIs examined Fig. . This efND Figs. .Fig. 3TA11C]PHNO BPND, consistent with an increase in synaptic dopamine concentration. As hypothesised, based on microdialysis data from studies in rodents, pre-treatment with TAK-041 reduced the magnitude of post-d-AMPH [11C]PHNO ∆BPND, consistent with a reduced extracellular release of dopamine post d-AMPH administration. The plasma concentration of d-AMPH was similar in PET2  and PET3 (d-AMPH + TAK-041 PET scan); hence, the attenuation of 11C]PHNO BPND returned to baseline levels 5 days after the administration of d-AMPH, in time for the TAK-041 scan day. We feel that this is a safe assumption based on the time-course of the d-AMPH effect measured in rats (using [11C]raclopride but applicable to other radioligands), with BPND returning to baseline levels between 8 and 24 h post-dose raclopride and [123I]IBZM binding were considered to be too small and variable to detect reliable modulation by a novel drug. Agonist radioligands, such as [11C]PHNO, demonstrate a 50–100% greater signal change post-AMPH challenge [Although the principles of PET-based evaluation of dopamine fluctuations in the living human brain are well established , this ishallenge , enablinhallenge , acetylchallenge  and serohallenge  systems.In conclusion, we used a novel application of established PET methodology to provide strong support for the hypothesis that TAK-041 crosses the blood–brain barrier and attenuates dopamine release in healthy human volunteers. GPR139 has been proposed to have a role as a therapeutic target in schizophrenia, addiction and metabolic disorders, and attention-deficit/hyperactivity disorder, as well as Parkinson’s disease and phenylketonuria . The datSupplementary Information - Revised/Clean


This review provides an overview of the properties of cyclotides and their potential for developing novel peptide-based therapeutics. The selective disruption of protein–protein interactions remains challenging, as the interacting surfaces are relatively large and flat. However, highly constrained polypeptide-based molecular frameworks with cell-permeability properties, such as the cyclotide scaffold, have shown great promise for targeting those biomolecular interactions. The use of molecular techniques, such as epitope grafting and molecular evolution employing the cyclotide scaffold, has shown to be highly effective for selecting bioactive cyclotides. Disruption of pharmacologically relevant protein–protein interactions (PPIs) remains a challenging task ,2,3. ThiGenerally, we can consider two major structural types of therapeutic agents, small molecules and protein-based compounds, known as biologicals. Small molecules are small in molecular size (≤100 atoms) and usually show good pharmacological properties, such as cell permeability and stability. However, small molecules only provide a modest overall surface area available for interacting with the protein target. This has made quite challenging the identification of small molecules able to efficiently disrupt PPIs ,7.The use of polypeptide-based molecules, on the other hand, has provided efficient therapeutic tools to modulate PPIs with high specificity and selectivity . For exaThe use of highly constrained polypeptides and able to cross membranes has recently received special attention for developing de novo stable polypeptide-based therapeutics ,12,19. AThese unusual properties have made the cyclotide scaffold an ideal molecular framework for producing novel peptide-based diagnostic and therapeutic agents by using molecular engineering and/or evolution strategies. This article provides an overview of their most relevant properties as well as their potential to be used as a molecular framework for the development of peptide-based therapeutic agents.Cyclotides consist of a backbone-cyclized polypeptide with six Cys residues that form a Cys-knotted structure. Naturally-occurring cyclotides are medium-sized polypeptides containing from 27 to 37 amino acids , althougIII-CysVI running through the ladder arrangement formed by disulfides CysI-CysIV and CysII-CysV  to prepare the tea extract used as a remedy ,,57,56,57The chemical synthesis of cyclotides can be readily achieved using standard SPPS methods . LinearChemically-produced cyclotide linear precursors have been also chemoenzymatically cyclized using purified AEP-like ligases ,58,63. TMxe GyrA intein designed to produce C-terminal a-thioester peptides that were cyclized in vitro using standard native chemical ligation conditions -DOTA-labeled version of a CXCR4-binding MCoTI-based cyclotide was employed to detect tumors containing tumors cells with high-expression levels in mice in combination with positron emission tomography-computed tomography (PET-CT) [The cyclotides MCoTI-I/II from the trypsin inhibitory subfamily are the most used for producing novel cyclotides with new biological activities, which can be achieved by employing molecular grafting techniques see . For exa(PET-CT) .Ki ≈ 0.1 nM), which displayed 100,000-fold selectivity over related KLKs, was also reported by modifying loops 1 and 6 of cyclotide MCoTI-II [Cyclotides from the trypsin inhibitor subfamily have also been successfully used to develop inhibitors for specific proteases. Many human diseases involve the dysregulation of specific proteases and therefore are well-recognized drug targets ,116. ForMCoTI-II .The cyclotide MCoTI-II was modified by using molecular grafting on loops 1 and 6 to novel cyclotides able to inhibit the BCR-Abl kinase . HoweverSaccharomyces cerevisiae [MCoTI-grafted cyclotides were recently used to inhibit α-synuclein-induced cytotoxicity when expressed in baker’s yeast revisiae . The α-srevisiae .P. aeruginosa, S. aureus, K. pneumoniae, and E. coli), including 20 clinical isolates for the human pathogens P. aeruginosa and S. aureus. The median minimal inhibitory concentration (MIC) 50% (MIC50) and MIC 90% (MIC90) values for several clinical strains of P. aeruginosa (n = 20) were 1.5 μM and 3.1 μM, respectively; while for clinical isolates of S. aureus (n = 20), the MIC50 and MIC90 were 6.25 and 12.5 μM, respectively [P. aeruginosa peritonitis [P. aeruginosa-induced peritonitis animal model, thereby providing a promising lead compound for the design of novel antibiotics.A recent study reported the first design and synthesis of a novel MCoTI-based cyclotide with broad-spectrum antimicrobial activity in vitro against different ESKAPE pathogens  . CTLA-4 S. cerevisiae was readily selected by phenotypic screening from a library of cells transformed with a mixture of plasmids encoding active and inactive cyclotides in a ratio of 1 to 50,000 [A fully folded cyclotide-based genetically encoded library was recently used for phenotypic screening in eukaryotic cells . In thiso 50,000 . A similo 50,000 . These rMore recently, the use of mRNA-display technologies has been successfully employed for the selection of potent cyclotide-based b-FXIIa inhibitors , althougHigh-throughput screening of chemically-produced cyclotide libraries of cyclotides has been also made possible due to the accessibility of efficient methods for the chemical synthesis of cyclotides .The use of a ‘tea-bag’ approach in combination with a high-efficiency “one-pot” cyclization-folding protocol allowed the rapid generation of a small amino acid scanning library based on a CXCR4 cyclotide antagonist . Key to Ki ≈ 150 nM) and anthrax lethal factor protease (Ki ≈ 40 nM) using the θ-defensin RTD-1 as a molecular scaffold [A similar approach was also recently used for developing inhibitors of the TNF-α converting enzyme (TACE)  are the potential immunogenicity and oral bioavailability. However, it is anticipated that more studies on the pharmacological properties of these exciting new micro-proteins may be available very soon.


The models suggested by our study would benefit the cost–minimization of CPAM mass production, where one could find optimal conditions for synthesizing different molecular weights of CPAM more quickly than via the traditional approach.Cationic polyacrylamide (CPAM) emulsifier is widely applied in the wastewater treatment industry, mining industry, paper industry, cosmetic chemistry, etc. However, optimization of input parameters in the synthesis of CPAM by using the traditional approach  would require a long time and a high cost of input materials. Onsite mass production of CPAM requires fast optimization of input parameters  to minimize the production cost of specific–molecular–weight CPAM. Therefore, in this study, we synthesized CPAM using reverse emulsion copolymerization, and proposed response surface models for predicting the average molecular weight and reaction yield based on those input parameters. This study offers a time–saving tool for onsite mass production of specific–molecular–weight CPAM. Based on our response surface models, we obtained the optimal conditions for the synthesis of CPAM emulsions, which yielded medium–molecular–weight polymers and high conversion, with a reaction temperature of 60–62 °C, stirring speed of 2500–2600 rpm, and reaction time of 7 h. Quadratic models showed a good fit for predicting molecular weight (Adj.R Industries produce wastewater, including various components such as suspended solids, organic and inorganic particles, dissolved ions, etc. Sewage treatment is essential for environmental safety and sustainable development. One of the most significant industrial procedures for wastewater treatment is flocculation, which is the process of aggregation of particles caused by chemical coagulants. Flocculation is extensively used due to its ease of use, high efficiency, and cost savings . Among pThere are many studies on CPAM synthesis technology, such as the free radical polymerization method, grafting method, and polymer modification, in which modified polymers often have sizeable molecular weight ,21,22,23Additionally, due to the short storage time and high demand for CPAM, there is a need for fast optimization of synthesis conditions for onsite mass production of CPAM. Companies and factories could minimize production costs by reducing the time required to obtain optimized conditions for producing a specific–molecular–weight CPAM. Response surface methodology (RSM) allows the solution of multivariable equations and evaluation of the relative significance of several relevant factors even in the presence of complex interactions ,29,30. R1H–NMR) and Fourier–transform infrared (FTIR) spectroscopy were used to confirm the structure of the polymers. Gel permeation chromatography (GPC) and a viscosity meter were used to confirm the molecular weight and molecular weight distribution of the desired CPAM. Dynamic light scattering (DLS) was used to confirm the particle size distribution of CPAM.This study aims to develop a time–saving tool for onsite mass production of specific–molecular–weight CPAM. For that purpose, we synthesized CPAM using reverse emulsion copolymerization, and proposed response surface models containing three parameters  to predict CPAM’s molecular weight and conversion efficiency. Hydrogen magnetic resonance spectroscopy (2 (≥99%), ethanol (≥99.5%), KI (≥99%), HgCl2 (≥99.5%), Na2S2O3.5H2O (≥99%), and soluble starch (≥99%) were supplied by Xylong, and other chemicals  were used without further purification.Industrial monomer acrylamide  was purchased from Jinjinle Chemical Co., Ltd. . Methyl acrylacyl oxyethyl trimethyl ammonium chloride (DMC) was bought from Wuxi Xinyu Chemical Co., Ltd.  as an aqueous solution (74.68%). Isopar L (Exxon), vegetable oil (EFKO Russian), Span 80 (99.5%), and Tween 85 (99.5%) were purchased from Beijing Chemical Reagent Company . 2,2′–Azobis(2–methylpropionamidine) dihydrochloride  and azobisisobutyronitril  were purchased from Tokyo Chemical Industry Co., Ltd. IPA (99%), ICPAM was prepared by reverse emulsion copolymerization via the free radical mechanism, with AM:DMC ratio of 1:4 . The proNitrogen gas was continuously aerated during the reaction. UV lamp and a photoinitiator were used to start phase 1 of reaction, and then after phase 2 of the reaction, with stirring and slowly adding the redox initiator system, the factors affecting the polymerization reaction—stirring speed, reaction time, and temperature—were investigated according to the experimental matrix table. An inverse emulsion cationic polymer was formed at the end of the process following the synthesis reaction shown in The W/O emulsion product was converted to O/W by using NP–10. Then, the O/W emulsions were put into a 100 mL beaker, and isopropyl alcohol was slowly added until the solution was clear. The precipitation was filtered and dried at 45 °C to constant mass for molecular mass determination using a vacuum oven cabinet, and “filtrate X” was used for titration and conversion.The overall monomer conversion was determined by the residual content of the participating monomers (AM and DMC), using the HIP method. HIP solution ; V0 is the volume of Na2S2O3 consumed for the blank sample (mL); V is the volume of Na2S2O3 consumed to titrate the residual monomers in the sample at time i (mL); and i (mL). Measurements for calculating the conversion were performed in triplicate. The titration method was used to provide an experimental confirmation of conversion so that we could use these experimental data for later development of models regarding conversion.The formula for calculating the conversion is as follows:η is the characteristic viscosity of the polymer, M is the molecular weight of the polymer chain, and K and α are constants depending on the nature of the polymer and the solvent [Dissolved CPAM was dried in 200 g of distilled water. The viscosity of the polymers was measured by using an Ubbelohde viscometer; the molecular weight of the polymers was estimated from viscosity by using the Mark–Houwink–Sakurada equation: solvent .The molecular weights and molecular weight distribution of CPAM were confirmed by gel permeation chromatography (GPC) .The particle size distribution of cationic polyacrylamide nanoparticles was determined by dynamic light scattering (DLS) using the Horiba SZ–100 nanoparticle size measuring device.1H–NMR spectra were recorded in D2O media at room temperature using a Bruker Avance Neo 600 MHz spectrometer.FTIR spectra were obtained using an IRAffinity–1S Fourier–transform infrared spectrometer . Experimental data were processed using the statistical software Design–Expert 11.1 . The application of experimental design as a powerful statistical tool allowed us to reduce the process variability, combined with the requirement of fewer resources ; meanwhile, response surface methodology (RSM) allowed us to solve multivariable equations and evaluate the relative significance of several influential factors even in the presence of complex interactions .−1 and 1661 cm−1 were assigned to stretching vibration of –NH2 and C=O, respectively, in the amide groups [−1 was for –CH3 and –CH2– [−1 was for –CH2– flexural vibrations in –CH2–N+ [−1 was attributed to the stretching vibration of C–O from the ester base. The 965 cm−1 characteristic adsorption peak was for quaternary ammonium groups. The infrared spectroscopy indicated that the two monomers, AM and DMC, were copolymerized.The main infrared absorption bands of the CPAM and the assignments are shown in e groups . The asynd –CH2– . The ads –CH2–N+ . The pea1H–NMR spectra of CPAM. The chemical shift of CPAM at about δH = 0.8545 ppm was ascribed to the protons of –CH3– (Ha). The asymmetric peaks of CPAM at δH = 1.615 ppm and δH = 2.118 ppm were attributed to the protons of the backbone methylene and methine groups –CH2– (Hb) and –CH– (Hc), respectively. The sharp peak of CPAM at δH = 3.184 ppm was assigned to the protons of –N+(CH3)3 (Hd). A peak at δH = 4.027 ppm was assigned to He of the O=C–O–CH2+. The sharp peaks at δH = 4.69 ppm were assigned to the proton of –N+CH2– (Hf). Lastly, the chemical shift at about δH = 5.283 ppm was ascribed to the protons of O=C–NH2 (Hg). Analysis of the 1H–NMR spectral data gave comparable results with the provided data [ded data .n) and the weight–average molecular weight (Mw) of CPAM were about 8,518,300 g/mol and 19,035,000 g/mol, respectively. The molecular mass distribution was expressed as a polydispersity index (PDI). (Mw/Mn) = 2.23.The results of the particle size distribution of the cationic polyacrylamide nanoparticles showed that the average diameter of the polymer particles was 32.2 nm, while the particle size distribution was from 25 to 200 nm . The ave1H–NMR, FTIR, GPC, and DLS confirmed that we successfully synthesized CPAM with the desired molecular weight by using our proposed response surface models.Characterization by p-Values of the models < 0.05; (2) adequate precision was used to orient the design space greater than 4.0; (3) the lack–of–fit value reflecting the discreteness of the data was not statistically significant; and (4) the R2 value was greater than 0.8. The quadratic model in this study has a model F-Value of 137.81, implying that the model is significant. The model p-Value less than 0.0001 indicates that the model terms are significant. In this case, A, B, C, AB, AC, BC, A2, B2, and C2 are significant model terms. The lack–of–fit p-Value of 0.6754 implies that the lack of fit is not statistically significant [The results of the actual trial synthesis are shown in nificant .The equation describing the dependence of molecular weight on factors such as stirring speed, temperature, and reaction time is a quadratic equation, as follows:2 and adequate precision values were computed to ensure a satisfactory fit of the data (2 score for the present model was 0.996, indicating the best fit for the data. Its value also ranged from 0 to 1. The value of R2 adjusted for the current model was 0.9888, which also indicates higher accuracy. Adequate precision measures the signal–to–noise ratio. A ratio greater than 4 is desirable [Statistical significance is a necessary but insufficient requirement for ensuring the data’s accuracy. Rthe data . The R2 esirable ; a ratioIn addition, several other factors were used to evaluate whether or not the model was fully compatible with the experimental results, based on the predicted and actual value plots and graphs of the residuals versus runs models. The data in p-Value < 0.0001, AP = 94.6566, LOF = 0.1381, and R2 = 0.9993 indicating a suitable model. The data in As shown in For given values of each element, the equation in terms of real factors may be used to create predictions about the response, as follows:The molecular weight and conversion of the polymer affected by the difference in the independent variables is visualized through a three–dimensional image of the reaction surface plot . The ploThe change in stirring speed caused a significant change in the molecular mass of the polymer produced, as seen in Based on the response surface methodology in Similarly, in For conversion efficiency, the effects of stirring speed, temperature, and reaction time are shown in From the response surface models, we found that the optimal parameters for the reaction were a stirring speed of about 2400–2600 rpm, reaction temperature of 60–62 °C, and reaction time of about 6.5–7.5 h.To check the significance of the regression coefficients and the compatibility of the regression equations, the experiments at the center were performed as shown in From the verified experiments combined with the models, the optimal conditions for the cationic inverse emulsion synthesis to achieve high average molecular weight and high conversion are as follows: stirring speed of about 2500–2600 rpm, reaction temperature of 60–62 °C, and reaction time of 7 h.Depending on the needs of the customers, CPAM with different molecular weights is required for mass production. Our response surface models can help to find optimal conditions of temperature, stirring speed, and reaction time for the synthesis of desired–molecular–weight CPAM. CPAM with different molecular weights might have different costs of production. For example, wastewater with a high concentration of organic matter would require high–molecular–weight CPAM. In that case, a combination of temperature, reaction time, and stirring speed is required to synthesize high–molecular–weight CPAM. Our models are able to provide that set of parameters more quickly than the traditional approach. As shown in this study, we applied our models to obtain CPAM with an average molecular weight of about 8,518,300 g/mol. On the other hand, wastewater with a low concentration of organic compounds would require low–molecular–weight CPAM. We could apply the models used in this study to find the optimal conditions to produce low–molecular–weight CPAM. This would save time and costs for mass production.Previous response surface models were used by Kim to study the pretreatment of paper wastewater with derivatives of polyacrylamide, as the flocculant in the coagulation–flocculation process . QuadratAlthough our models might help in finding the optimal conditions for CPAM synthesis, the models might have limitations in the case of changes in the monomers’ composition, or if a different molecular weight of CPAM is required. In that case, new models developed using response surface methodology would be required.In this study, we were successful in using response surface models to study and synthesize CPAM by reverse emulsion copolymerization. Based on our developed models, we found that the optimal synthesis conditions for 95.948% conversion of CPAM with a molecular weight of 7.639.106 Da were 2500–2600 rpm for stirring rate, 60–62 °C for reaction temperature, and 7 h for reaction time. The response surface model gave predicted values of molecular weight and conversion that matched the experimental values provided by the Ubbelohde viscometer and HIP methods. The models in our study offer a time–saving tool for onsite mass production of specific–molecular–weight CPAM.


However, effective extraction of clinical knowledge from EHR data has been hindered by the sparse and noisy information. We present Graph ATtention-Embedded Topic Model (GAT-ETM), an end-to-end taxonomy-knowledge-graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on topic quality, drug imputation, and disease diagnosis prediction. GAT-ETM demonstrated superior performance over the alternative methods on all tasks. Moreover, GAT-ETM learned clinically meaningful graph-informed embedding of the EHR codes and discovered interpretable and accurate patient representations for patient stratification and drug recommendations. GAT-ETM code is available at  Modern hospitals routinely generate standardized EHR observations such as the International Classification of Diseases (ICD) for diagnoses, Drug Identification Number (DIN) for prescription, and Anatomical Therapeutic Chemical (ATC) for drug ingredients. Specifically, ICD is a widely used health care classification system to classify diseases, symptoms, signs, abnormal findings, social circumstances, complaints and external causes of injury or disease. A DIN (Canada) or NDC (USA) code uniquely identifies all approved pharmaceutical products sold in dosage forms in Canada. Anatomical Therapeutic Chemical (ATC) is a medicine classification system maintained by the World Health Organization (WHO). Each ATC code is specific to an active drug ingredient, which is indicative of the patient’s health state. The rich patient EHR information enables computational phenotyping5. A topic model infers a set of categorical distributions over the vocabulary, called latent topics, and represents each document by a topic mixture. In their applications to EHRs, we treat each patient’s clinical history as a document and each EHR observation (e.g. ICD code) as a word within its document. Our goal then is to learn clinically meaningful phenotype topics and disease mixture memberships of patients. Recently, several topic models were developed to effectively infer latent topics from EHR data9. However, these methods usually perform poorly in the modeling of rare administrative codes due to insufficient observations of them, which results in under-representation of these codes among the inferred topic distributions.Extracting meaningful medical concepts by modeling the joint distribution of the EHR data is challenging due to its large feature space. Among diverse machine learning approaches, topic models provide an efficient way to exploit sparse and discrete data. They were originally developed to identify word occurrence patterns in text corpus10 to compute the embeddings of EHR codes from a taxonomy graph of the relations between and within the disease-code and drug-code modalities via the multi-head attention mechanism. The resulting EHR code embeddings are then used to infer a set of coherent multimodal topics from the patient-level EHR data via the Embedded Topic Model (ETM)11. Learning of the embeddings of the EHR codes and the latent topics are performed simultaneously in an end-to-end fashion without supervision. We evaluated GAT-ETM on a large-scale EHR dataset consisting of administrative records for 1.2 million patients from Quebec, Canada. GAT-ETM demonstrated high-quality topic inference and accurate drug imputation.In this paper, we present a neural topic model called Graph-ATtention Embedded Topic Model (GAT-ETM). To capture higher-level medical concepts, GAT-ETM uses a graph attention network (GAT)14 or machine learning techniques21. MixEHR6 extended latent Dirichlet Allocation (LDA)5 to multimodal topic inference to account for the heterogeneous nature of EHR data distributions. However, MixEHR is unable to make use of knowledge graphs. In order to achieve better performance in modelling noisy and sparse EHR data, several recent methods are able to utilize medical knowledge graphs. For instance, GRAM22 and KAME23 employed attention mechanism to incorporate medical knowledge into clinical modelling. GRAM considers taxonomic hierarchy as a knowledge prior and generates representation of medical concepts for a predictive task. KAME only used the medical knowledge related to the last visit in a recurrent neural network (RNN). RETAIN25 is a two-level attention model that detects influential past visits and crucial clinical variables within those visits. DG-RNN26 employed an attention module and uses long short-term memory (LSTM) to models sequential medical events. To handle various healthcare tasks, TAdaNet27 a meta-learning model makes use of a domain-knowledge graph to provide task-specific customization. These recent models are mostly focused on supervised learning tasks, and therefore their learning algorithms require labelled data. A recent model called Graph Embedded Topic Model (GETM)28 leveraged a knowledge graph by combining node2vec29 with embedded topic model (ETM)30 in a pipeline approach. GETM is an unsupervised model that directly learns the distribution of the EHR data using the node2vec embedding. However, because the graph embedding is learned separately from the EHR modeling task, it may not always help in learning the EHR data.Recently, many automatic EHR-based phenotyping algorithms were developed using rule-basedGAT-ETM is an end-to-end neural topic framework, which simultaneously learns the medical code embedding from a medical knowledge graph of diseases (ICD-9 code) and drugs (ATC code) and the topic embedding from EHR data;To extract meaningful and interpretable disease topics, we use a linear decoder to reconstruct EHR data such that the linear projections can directly map to individual latent topics; and,To maximize information flow among the EHR nodes on the graph, we proposed a graph augmentation strategy by connecting nodes with their ancestry nodes along the taxonomy; we combine the two knowledge graphs (of ICD-9 and ATC) via known disease-drug links (i.e. drug treatments for diseases), which allows information sharing between the two data types during the training.In contrast to the existing works, our contributions are 3-fold: D, K, p, nth code of type p is via a K-dimensional probabilistic mixture membership over K disease topics that sum to 1. For the kth topic, L is the dimension of latent embedding space. The knowledge graph (KG)-informed embedding of medical codes of type We denote the number of patients, the number of topics, the size of ICD vocabulary, and the size of ATC vocabulary as mentclass2pt{minimGAT-ETM assumes the following generative process Fig. a:Draw topic mixture membership For each EHR code where Cat stand for logistic-normal and categorical distribution, respectively. The kth topic distribution kth topic v of type t and k. Inner product, as a similarity metric, indicates the relevance between codes and the topic.For each patient 31. We optimize the network parameters The marginal log-likelihood of the EHR corpus is:p:To infer https://icdlist.com/icd-9/index) augmented by linking each pair of ancestral nodes and child nodes, (2) ATC hierarchy (https://www.whocc.no/atc_ddd_index/) augmented by linking each pair of descendants and ancestors, and (3) ICD-ATC relations (http://hulab.rxnfinder.org/mia/). We extracted these relations from their corresponding websites and constructed an undirected knowledge graph We leverage an ICD-ATC knowledge graph to learn code embedding mentclass2pt{minimThe resulting knowledge graph is sparsely connected because of the tree-structure of both ICD and ATC taxonomy. To further improve the information flow, we augmented the knowledge graph by connecting each node to all of its ancestral nodes Fig. b.10 , which was originally created for monitoring population health from multiple distributed sources35. These include acute myocardial infarction (AMI), asthma, congestive heart failure (CHF), COPD, diabetes, hypertension, ischemic heart disease (IHD), epilepsy, and schizophrenia. Additionally, we constructed gold-standard labels for ADHD, HIV, and Autism based on the disease definitions described in37, and Autism Spectrum Disorder Surveillance in Quebec report38, respectively. Therefore, we obtained in total 12 phenotypes where we have rule-based labels to evaluate the classification accuracy of a given model as detailed in “For disease classification tasks, we obtained gold-standard labels for 9 chronic diseases using the corresponding rules defined by the Chronic Disease Surveillance Division of the Public Health Agency of CanadaWe conducted a document completion task and calculated log-likelihood as the metric of predictive capacity. We split the PopHR dataset into 60% training, 30% validation, and 10% test. We randomly divided each test EHR document into halves. We used the first half to predict the expected topic mixture of the test patient . In both training and test datasets, patients with less than 5 ATC codes were filtered out.The drug-wise accuracy measures the imputation accuracy at of different observed frequency. Specifically, we sorted and binned the ATC codes into five frequency quantiles, where 0–20% contains the rarest ATC codes and 80–100% contains the most frequently observed ATC codes. We then computed the recall on each ATC code and took the average (weighted by frequency) of the codes in each bin. We then computed the top-30 precision  at each quantile for each method.6 is a generative multimodal topic model. We considered it as a baseline because it was developed to deal with EHR data of high sparsity, bias, and heterogeneity but uses strong mean-field assumption to perform varaitional inference of the latent topic distributions.MixEHR30 is a topic model that introduces feature embedding of words and topics. We considered it as a baseline because it has a similar generative process as GAT-ETM but does not utilize knowledge graph.ETM28 is an embedded topic model that leverages ICD and ATC medical taxonomy hierarchies by initializing word embedding as the output of node2vec. Note that GETM obtains code embedding on only ICD and ATC taxonomy hierarchies respectively. It neither connects ICD and ATC taxonomy together nor does it conduct augmentation. We considered GETM as the baseline because it harnesses external medical knowledge graphs although not in an end-to-end manner.GETMBased on empirical study, we set the number of topics K as 100 for the baseline models and our model and the number of embedding dimensions as 256 for embedding-based methods . The number of layers of inference networks  was set to be 3 for ETM and GETM to fairly compared with ours.We compared the performance of GAT-ETM  with the following baseline approaches:Frequency-based model: we counted the occurrence of all ATC codes in the training data, and then imputed the most frequent codes for the test patients.K nearest neighbors: for each patient in the test set, we found K nearest neighbors according to its frequency vector For drug imputation, we also evaluated two traditional approaches:Initialization of code embedding: when this procedure is discarded, we randomly initialized the embedding for GAT rather than pre-trained them by node2vec.Augmentation of knowledge graph: when this procedure is discarded, we did not connect each node with all of its ancestors.Graph attention networks: when this module is discarded, we fixed the code embedding that is generated by node2vec. In other word, it is equivalent to GETM with the augmented, merged knowledge graph.An ablation study was conducted to evaluate the three key features of GAT-ETM: As shown in Table Table We further evaluated each method by phenotype classification on 12 rule-based phenotypes “” sectionWe next evaluated the model performance in terms of drug imputation accuracy “” sectionN05AX08, N05AH03, N03AG01) based on the observed ICD codes 297.0, 297.1, 298.8, 307.9 and their parent codes. The distances from an imputed ATC codes to an observed ICD codes are 3 because it requires traversing through other related ICD codes in order to reach to the observed ICD codes. Specifically, the minimal paths of the first two nodes is {N05AX08, N05AH03} 295295-299297.0. The minimal path of N03AG01 is N03AG01346A03AX307.9.We then conducted a case study to further ascertain our drug imputation results. For each patient, we measured the distance of each imputed ATC code from the observed ICD codes in the original knowledge graph. We collapsed the last classification level of ICD and ATC for easier analysis, while preserving sufficient granularity. As an example, Fig. Following the above principle, Fig. To qualitatively assess the disease comorbidity implicated in each topic, we examined 5 randomly chosen topics in terms of their top 5 ICD and ATC codes Fig. . These 5We then visualized the code embedding of both ICD and ATC using t-distributed Stochastic Neighbourhood Embedding (t-SNE) Fig. . As a prIn this study, we present an end-to-end graph-embedded topic model that: (1) learns interpretable topic and code embeddings in the same embedding space; (2) is able to handle multimodal data; and, (3) leverages a medical knowledge graph to improve performance quantitatively and qualitatively. We compared the performance of GAT-ETM against several existing methods on the EHR reconstruction task, automated phenotyping task and drug imputation task. GAT-ETM consistently outperformed the alternative methods in these tasks. These results showcase the benefits of our end-to-end learning framework. Additionally, we show that integrating knowledge graphs of multiple views  brings complementary information to characterize the same phenotypes. Moreover, our graph augmentation strategy improves the information flow through the taxonomy graphs. Qualitative analysis further illustrated that GAT-ETM learned coherent phenotype topics and meaningful latent embedding of the EHR codes.In future work, we will explore four promising directions. First, we will leverage large and comprehensive biomedical knowledge graphs with richer relations that comprise not only ICD codes and ATC codes but also other codes such as gene ontology terms available from Universal Medical Language System (UMLS) and elsewhere. Additionally, we will extend GAT to multi-relational graphs to account for heterogeneous graphs. For example, a drug may treat or induce a disease, which should be considered as different types of relations. Furthermore, in this work, for drug graph we use ATC code that merely has drug classification hierarchy information. We plan to incorporate drug-drug interactions (DDI) in future multi-relational graph-based approaches so that we can impute drugs without adverse effects.41 make use of expert-curated phenotype concepts such as PheCodes and Clinical Classification Software (CCS)42 to guide disease topic inference. In the future, we will incorporate the guided mechanism as anchor topic nodes in graph embedding learning to generate identifiable and presumably more interpretable topics.Second, topic identifiability is a challenge in completely unsupervised topic modeling. Guided topic models43. GAT-ETM utilizes a GAT network, where each node computes attention weights over its neighbors and then controls information flow through the attention weights. This provides venues to look into the blackbox of the deep learning framework to understand the disease connections. We will find effective ways to dissect the attention weights among EHR codes in order to predict their comorbidity associations.Third, attention mechanism enables us to track the contribution of input features44 that accounts for the evolution of patients’ health status over time. There are several ways to track patients’ health status. One is to regard longitudinal visits as document series with timestamps. Based on this, we can infer disease progression and train predictive models. We will also need to consider irregular visits in outpatient data when modelling longitudinal EHR. Another approach is to group visits by fixed partitions, e.g. age. Such approach can model progression of age-dependent diseases such as hypertension.Lastly, we will harness longitudinal EHR data. We will extend our model to a dynamic topic model