Using proteomics, we aimed to reveal molecular types of human atherosclerotic lesions and study their associations with histology, imaging, and cardiovascular outcomes.Two hundred nineteen carotid endarterectomy samples were procured from 120 patients. A sequential protein extraction protocol was employed in conjunction with multiplexed, discovery proteomics. To focus on extracellular proteins, parallel reaction monitoring was employed for targeted proteomics. Proteomic signatures were integrated with bulk, single-cell, and spatial RNA-sequencing data, and validated in 200 patients from the Athero-Express Biobank study.This extensive proteomics analysis identified plaque inflammation and calcification signatures, which were inversely correlated and validated using targeted proteomics. The inflammation signature was characterized by the presence of neutrophil-derived proteins, such as S100A8/9  and myeloperoxidase, whereas the calcification signature included fetuin-A, osteopontin, and gamma-carboxylated proteins. The proteomics data also revealed sex differences in atherosclerosis, with large-aggregating proteoglycans versican and aggrecan being more abundant in females and exhibiting an inverse correlation with estradiol levels. The integration of RNA-sequencing data attributed the inflammation signature predominantly to neutrophils and macrophages, and the calcification and sex signatures to smooth muscle cells, except for certain plasma proteins that were not expressed but retained in plaques, such as fetuin-A. Dimensionality reduction and machine learning techniques were applied to identify 4 distinct plaque phenotypes based on proteomics data. A protein signature of 4 key proteins  predicted future cardiovascular mortality with an area under the curve of 75% and 67.5% in the discovery and validation cohort, respectively, surpassing the prognostic performance of imaging and histology.Plaque proteomics redefined clinically relevant patient groups with distinct outcomes, identifying subgroups of male and female patients with elevated risk of future cardiovascular events. Atherosclerotic plaques are currently characterized using imaging and histology.Transcriptomic signatures have been associated with plaque subtypes.This proteomics study doubles the coverage of the plaque proteome in large cohorts.Calcification and inflammatory protein signatures of atherosclerosis were identified and integrated with bulk, single-cell, and spatial transcriptomics.A sex-specific signature, enriched in large-aggregating proteoglycans, contributed to grouping patients into clusters with different outcome trajectories.We generated the largest proteomics data set of fresh-frozen human plaques to date, combining untargeted and targeted proteomics with network reconstruction and clustering techniques to gain molecular insights into protein changes related to clinical characteristics. The proteomic signatures of plaque composition were found to predict long-term, adverse cardiovascular events, improving risk stratification for patients with advanced atherosclerosis. Notably, the proteoglycan content of plaques was inversely associated with serum estradiol levels and contributed to the stratification of predominantly female patients into a subgroup associated with worse cardiovascular outcomes.539In This Issue, see p 540Meet the First Author, see p 1 the binding of cholesterol-containing lipoprotein particles to intimal proteoglycans is the central pathogenic process in early atherogenesis. Once retained, lipoproteins get oxidized, accumulate in foam cells, and provoke a cascade of inflammatory processes that drive the formation of atherosclerosis. In addition to its critical role in the retention of lipoprotein particles, ECM (extracellular matrix) degradation processes are causally linked to the development of vulnerable plaques.2 The ECM, in particular of the fibrous cap, ultimately defines the propensity of plaques to rupture leading to atherothrombotic events. There is a need to further understand how ECM composition differs across plaque phenotypes. The matrisome refers to the ensemble of ECM and ECM-associated proteins.3 Proteomics offers an opportunity to define the protein composition of human atherosclerosis that can advance current assessments by histology and imaging. Till now, mapping initiatives lack information on large blood vessels and atherosclerotic plaques.Despite decades of research, there remain ambiguities on the mechanisms that induce atherosclerotic plaque instability. According to the response-to-retention hypothesis,4 Calcification is supposedly a late feature of advanced atherosclerotic lesions.5 According to ultrasound measurements, atherosclerotic plaques are classified as echolucent, mixed, or echogenic. Echolucent plaques are soft, unstable plaques with a thin fibrous cap, a necrotic lipid core, and inflammation. Echolucent plaques are more prone to rupture and cause clinically overt disease.6 In contrast, echogenic plaques are less likely to cause clinical symptoms due to reduced inflammation and increased ECM content.7According to the modified American Heart Association classification, there are 8 types of atherosclerotic lesions: intimal thickening, fibroatheroma, late fibroatheroma, healed plaque rupture, fibrocalcific plaques, erosions, thin-capped atheroma, and ruptured plaques.8 We developed novel mass spectrometry (MS)–based methods to characterize extracellular proteins.10 We successfully applied these methods to a small cohort of atherosclerotic plaques from patients with symptomatic and asymptomatic carotid stenosis, revealing a molecular signature with known and novel inflammatory biomarkers that could be measured in plasma and predicted future cardiovascular events.11 Combining large-scale tissue proteomics with bioinformatics, we strove to reveal molecular types of human atherosclerotic lesions, study their associations with the current classifications by histology and imaging techniques and relate them to cardiovascular outcomes in 2 independent cohorts.Until now, attempts to molecularly phenotype atherosclerotic plaques have primarily relied on transcriptomics.Summary data and additional analyses that support the findings of this study are available from the corresponding authors on reasonable request. Proteomics and spatial transcriptomics data were deposited at the Proteomics Identifications (PRIDE) and Gene Expression Omnibus (GEO) repositories, respectively.12One hundred twenty patients with carotid artery stenosis undergoing carotid endarterectomy were included in the discovery cohort from Medical University of Vienna, Austria. The validation cohort consisted of 200 patients undergoing carotid endarterectomy from the Athero-Express Biobank study.10 All extracts were labeled using tandem mass tags and analyzed on an Orbitrap Fusion Lumos Tribrid MS for proteomics (Thermo Scientific). A parallel reaction monitoring method was developed on a Q Exactive HF MS (Thermo Scientific). For validation in the Athero-Express Biobank,12 proteins were extracted using 0.5 M NaCl and 4M GuHCl, followed by label-free quantitation by MS in the GuHCl extracts.Proteins were extracted using sequential incubation with 0.5 M sodium chloride (NaCl), 0.1% sodium dodecyl sulfate (SDS), and 4 M guanidine hydrochloride (GuHCl).ELISA measurements were conducted in plasma samples of the discovery cohort using DuoSet ELISA Ancillary Reagent Kit 2 (DY008B).13 The spatial RNA-seq analysis was conducted on sections from 2 asymptomatic, male patients with 80% to 90% stenosis.Publicly available single-cell RNA-sequencing (scRNAseq) data sets of carotid endarterectomies from 12 patients were integrated using the Harmony tool.P values were used to identify dysregulated proteins. P values corrected for multiple testing are provided in the Supplemental Material. For validation in the Athero-Express cohort, correlation, and enrichment analyses, significance was based on Benjamini-Hochberg corrected P values. Proteins belonging to the revealed proteomics signatures were considered potential inputs to classification models to predict the primary end points. An ensemble dimensionality reduction technique was used deploying a multiobjective evolutionary algorithm to identify the optimal feature subset to be used as input to a support vector machines model and optimize the support vector machine’s parameter values. A more detailed methods description is provided in the Supplemental Material.The proteins identified in the GuHCl and NaCl extracts were filtered for ECM and extracellular proteins. The data set was further filtered for missing values. The limma package was used to compare different phenotypes using the Ebayes algorithm correcting for selected covariates. For discovery proteomics nominal Table S1). No statistically significant differences were observed between symptomatic and asymptomatic patients based on ultrasound or histology plaque classification. We validated our findings using carotid plaques from 200 patients from the Athero-Express Biobank.12 Statistical comparisons were corrected for statin use, which was higher in symptomatic patients. With proteomics data collected from 419 carotid endarterectomy specimens, this study surpasses previous carotid plaque proteomics studies19 . Quantitative, multiplexed proteomics with tandem mass tags (TMT) was conducted on 3 extracts resulting in consistent quantification of 1459 predominantly cellular proteins in SDS  and 381 extracellular proteins in NaCl and GuHCl fractions .Our discovery cohort consisted of 219 human carotid endarterectomy specimens from 120 patients  supported the findings on regional differences. Enrichment analysis of proteomics and spatial transcriptomics data revealed 3 positional clusters: cluster 1 representing changes in the periphery, cluster 2 in the core, and cluster 3 corresponding to an intermediate area  differentiated core from periphery of the plaques Figure A. SpatiaFigure S2), while CD14 (monocyte differentiation antigen CD14) and CD45 (PTPRC [receptor-type tyrosine-protein phosphatase C]) showed positive correlations with inflammation-related proteins and an inverse relationship to structural ECM proteins, especially collagens. In contrast, alternative macrophage markers such as C163A (scavenger receptor cystein-rich type 1 protein M130) and MRC1 (mannose receptor 1) did not show an association with the loss of structural ECM proteins.Quantification of cell markers for smooth muscle cells (SMC), macrophages, and platelets revealed strong positive correlations between SMC markers  and structural ECM proteins . By conducting a comparative analysis between changes related to calcification , FRIL, FRIH, CATD (cathepsin D), and MMP9  remained the most upregulated proteins in symptomatic plaques. The upregulation of MMP9 and cathepsins is consistent with the findings of our previous proteomics study.16 The increase in FRIL and FRIH in symptomatic plaques was confirmed at the transcript level using a publicly available bulk RNAseq data set comprising stable and unstable carotid plaque samples . In the plaque matrisome extracts, ferritin levels neither correlated with hemopexin and haptoglobin nor with albumin . In the cellular SDS extracts, ferritin levels moderately correlated with hemoglobin . For a subset of the cohort (n=57), we also had measurements of serum ferritin. Ferritin levels in plaques only weakly correlated with ferritin levels in the circulation . This correlation was of similar strength as the correlation seen for hemoglobin and hematocrit. Thus, the increase in ferritin in plaques of symptomatic patients is not a result of increased contamination by serum proteins.Further correlation analysis was conducted between FRIL and FRIH with other serum proteins . Targeted proteomics results showed good agreement with those of the untargeted discovery approach, with correlations exceeding r=0.6 (P<0.001).To validate the findings from our untargeted proteomic analysis, we conducted targeted proteomics  to quantify 135 matrisome proteins by MS in the discovery cohort . Consistent with the M1 and M2 paradigm, the M1 cluster contains CD14, while the M2 cluster contains C163A and MRC1 (CD206). The strong association of cathepsins with CD14 suggests that cathepsins may be predominantly expressed in M1 macrophages. The neutrophil cluster contained several proteins that constituted the inflammatory signature of symptomatic plaques, including S10A8/A9, MMP9, and TIMP1.11Ranking changes in the inflammation signature in the plaque cores, based on their effect size revealed that CD14 and PERM showed the most pronounced changes Figure A. FurtheNext, we ranked the proteins of the calcification signature and identified OSTP and PROC as among the most upregulated proteins in cores of calcified plaques Figure C. Notabl20 we compared sex-specific protein abundance changes in our study (Table S2). We reconstructed a matrisome network for the plaque cores and used hierarchical clustering to analyze the changes between females and males in all clusters (outer circle) for soluble proteins and the core matrisome, respectively . Conversely, sex differences were inversely associated with changes in symptomatic plaques (inner circle).Aside from calcification and symptomatic status, sex was identified as another important covariate in the proteomics analysis Figure C. Given y Figure A and 4B.Figure S6).Four clusters of soluble matrisome proteins . Out of the 15 protein changes observed in relation to sex, 11 were confirmed, including SODE (superoxide dismutase), HPLN3, and the 2 large-aggregating proteoglycans CSPG2 and PGCA . The expression patterns of different regions in spatial RNAseq were visualized using feature plots . The combination of spatial RNAseq and integrated scRNAseq data analysis  showed distinct clusters of SMCs, with CSPG2 primarily expressed in SMC clusters 7 and 10 , while SMC markers were primarily expressed by SMC cluster 3 . Spatial RNAseq revealed that CD14+ cell–enriched regions were the main source of OSTP expression. scRNAseq detected OSTP expression in a subset of macrophages . Despite being among the most significant changes detected by proteomics , FA9, and FETUA were undetectable by scRNAseq .Three publicly available scRNAseq data sets were integrated and visualized in Unifold Manifold Approximation plots before and after integration . Cluster 4, 5, and 8 were the main clusters of macrophages. Cluster 4 were M2 macrophages  that differed from other macrophage clusters by increased expression of SELENOP (selenoprotein P), macrophage inflammatory protein 1 beta (CCL4L2) and several complement factors. High OSTP (SPP1 gene) expression characterized the macrophages in cluster 5. The CellChat tool was used to further study cell-cell communication patterns. OSTP was predominantly implicated in the outgoing communication of Cluster 5 macrophages via CD44 . The hyaluronan receptor CD44 was increased in symptomatic lesions . Cluster 2 had the strongest association with adverse cardiovascular outcomes  >50.0% in the discovery cohort, unsupervised clustering based on proteomics achieved an AUC of 60.4%  with PERM, which was one of the most upregulated proteins in symptomatic plaques in our targeted proteomics analysis. We trained and tested elastic-net logistic regression models to predict the plaque proteomic clusters by combining the inflammatory plasma biomarkers, with the ultrasound classification of plaques and the available clinical information from the patients. Among the clinical, ultrasound, and plasma biomarkers, all 5 inflammatory plasma biomarkers were selected using the elastic-net approach. Results of 10-fold cross-validation revealed that the logistic regression models were able to predict plaque clusters with 82.29% AUC.To explore whether plasma measurements could identify plaque clusters, we expanded our previously identified inflammation signatureTable S4).Finally, we utilized a multiobjective evolutionary optimization algorithm to identify the minimum plaque signature that performs best as input for a nonlinear classifier (support vector machines). A 4 protein signature, consisting of CNN1, PROC, the collagen-binding protein SERPH1 (serpin H1), and CSPG2, returned an AUC of 75.0% using 10-fold cross-validation. When tested on the Athero-Express Biobank study, the proteomics signature outperformed the histology-based Plaque Vulnerability Index with an AUC of 67.5% compared with 51.0%  and PRG4 (proteoglycan 4) were found to be significantly upregulated both at the transcript and the protein level. In contrast, APOC1 was increased in the proteome but reduced in the transcriptome of calcified plaques, consistent with a potential plasma origin. When exploring changes between symptomatic and asymptomatic plaques using only noncalcified plaques, PROC and FETUA were upregulated in asymptomatic plaques, suggesting a role in the early calcification response or plaque stability.Typically, there is a poor yield of RNA from calcified lesions. Karlöf et al28 In a recent study20 using RNAseq, sex-stratified gene regulatory network analysis identified GAS6 and serpin G1 as potential key SMC driver genes in plaques from females compared with males. In our cohort, GAS6 was also more abundant in plaques of female patients, but serpin G1 was unchanged at the protein level. GAS6, however, was part of the calcification signature, and calcification was more common in carotid plaques from female patients. When further comparisons were restricted to noncalcified plaques, GAS6, and other calcification-related proteins were not different between plaques from male and female patients. Thus, these changes with sex might be explained by a higher susceptibility to calcification in age-matched female patients. When excluding protein changes associated with calcification, only clusters containing the 2 large-aggregating proteoglycans, CSPG2 and PGCA, were found to be sex-related. When calcified plaques were excluded, differences in PGCA and CSPG2 remained as the protein changes most prominently associated with sex along with hyaluronic acid–binding link proteins HPLN1 and HPLN3 (data not shown). These sex-related matrisome changes were validated in the Athero-Express cohort.12Although the pathobiology of atherosclerotic cardiovascular disease is generally similar between both sexes, there are unique aspects specific to women, with the coronary artery calcium scoring, for instance, predicting mortality better in women than in men.29 by controlling tissue hydration and vascular wall viscosity. CSPG2 is known to contribute to lipoprotein retention in atherosclerosis. In murine atherosclerosis, Hartmann et al30 identified a transition state SMC phenotype linked with plaque stability that expressed ECM proteins and PGCA. We have recently uncovered an increase in CSPG2 and PGCA in stented porcine coronary arteries.31 Proteases of the ADAMTS  family partly control the accumulation of large proteoglycans after vascular injury.29 Proteoglycan deposition could represent a missing link between lipoprotein retention and hemodynamics, consistent with the known predilection sites for atherosclerosis. In this study, we report differences in hyaluronan-binding proteoglycans in carotid plaques between males and females. This observation was further supported by an inverse association of the hyaluronan-binding proteoglycan content in plaques to circulating estradiol levels in female patients.CSPG2 and PGCA are expressed in a subset of SMCs in human carotid plaques. Large, aggregating proteoglycans play an important role in regulating the mechanical properties of the vessel wall32 Echolucent plaques are associated with an increased risk for future ischemic cerebrovascular events.33 Carotid plaque area as well as plaque neovascularization detected in carotid ultrasound are also predictive for future cardiovascular events.34 However, this imaging prediction is mainly based on the separation of advanced from early stages of atherosclerosis. In our study, all patients had advanced atherosclerosis. The proteome changes corresponding to echogenic plaques were predominantly fibrillar collagens and few proteins of the calcification signature. Although ultrasound imaging captures the collagen and lipid content of the plaque, it is not able to distinguish between complex lesions with high inflammation and stable fibroatheroma with lipid deposits and low-grade inflammation. Thus, proteomic signatures could complement carotid imaging read-outs to improve prognostication.Carotid duplex ultrasound is commonly used as a first-line diagnostic tool to assess extracranial carotid artery stenosis.35 Although there are some data regarding histological plaque properties and patient outcome in contemporary cohorts, such as the ability of a histology-based Plaque Vulnerability Index to predict outcomes,36 symptomatic carotid stenosis is defined as the occurrence of symptoms ipsilateral to stenosis during the preceding 6 months.32 Given that atherosclerotic plaque morphology is a dynamic process, most plaques undergo several subclinical ruptures and healings at the stage of advanced lesions.37 Therefore, the in-depth molecular characterization offered by proteomics may better capture the evolution of plaques over time. Expanding on the traditional classification of vulnerable and nonvulnerable plaques, male patients with molecular features of vulnerable plaques (cluster 2) had the worst prognosis followed by a subgroup of predominantly female patients with calcified lesions (cluster 3) with large-aggregating proteoglycans driving most of the clustering. Histology captured only a subset of these proteomic changes. Thus, assessment of plaque proteome content even at a single location, together with clinical risk stratification may improve identification of vulnerable patients at risk of future cardiovascular events.Histological evaluation with American Heart Association classification is performed after surgery to assess morphological properties of atherosclerotic plaques.32 This warrants more aggressive treatment of cardiovascular risk factors for secondary prevention in these patients.32 In a previous study, plaque calcification and plaque eccentricity were positively correlated to plaque burden in both peripheral and coronary artery disease.38 We show here that proteomic characteristics of carotid plaques are associated with long-term cardiovascular events. Echolucent plaques with reduced structural ECM proteins had the worst prognosis (cluster 2), followed by calcified plaques (cluster 3). Although calcified plaques are traditionally considered stable lesions with low risk of embolization, the incidence of preoperative neurological symptoms was similar in patients with calcified and noncalcified plaques.39 Similarly, recent findings suggest that stable calcified plaques in coronary arteries are associated with high cardiovascular risk.40 In our cohort, calcified plaques were more common in female patients, highlighting potential sex differences also in advanced stages of atherosclerosis. A signature of 4 proteins, as revealed by machine learning predicted the composite cardiovascular end point with high accuracy in both the discovery and validation cohort over a 9 and 3-year follow-up periods, respectively. The signature included the SMC marker CNN1, the gamma-carboxylated protein PROC, the collagen-binding protein SERPH1 and the hyaluronan-binding proteoglycan CSPG2. Although plaque ECM proteins may not be readily detectable in the circulation, inflammatory plasma proteins may aid in classifying patients. The identified plaque clusters could be predicted using inflammatory plasma biomarkers  in combination with clinical characteristics .Atherosclerosis is a systemic disease. Patients with atherosclerosis at multiple vascular beds are at particularly high risk for atherothrombotic events, and each affected territory increases the risk not only of local organ damage but also the total risk of cardiovascular events.41 Coronary plaques, however, are more difficult to attain than fresh-frozen samples. Furthermore, MS has limitations in detecting very low-abundant proteins. Finally, our spatial RNAseq analysis was limited by the small sample size (n=2).Carotid endarterectomies represent advanced atherosclerotic plaques, which differ from early atherosclerosis. Moreover, all patients in the study were white patients. In addition, future comparisons should include plaques from different locations within the arterial bed, which have been associated with distinct genetic susceptibility loci.In the largest proteomics data set of fresh-frozen human plaques to date, we combined untargeted and targeted proteomics with network reconstruction and clustering techniques to provide molecular insights into protein changes related to clinical characteristics. Our findings demonstrate the potential of proteomics to complement histology and imaging modalities for improved phenotyping of stable and unstable lesions. Furthermore, our results suggest that hyaluronan-binding proteoglycans exhibit a sex-specific dysregulation pattern, are inversely associated with serum estradiol, and contribute to risk stratification of patients into clinically relevant groups that are associated with cardiovascular outcomes. Future studies should investigate whether the accumulation of hyaluronan-binding proteoglycans contributes to the accelerated progression of atherosclerosis in postmenopausal women.Spatial transcriptomics was performed at the Core Facilities of the Medical University of Vienna, a member of Vienna Life-Science Instruments (VLSI). We would also like to acknowledge the contribution of Dr Marion Groeger from the Core Facilities of the Medical University of Vienna in Austria. The authors also like to thank all the (former) employees involved in the Athero-Express Biobank Study of the Departments of Surgery of the St. Antonius Hospital Nieuwegein and University Medical Center Utrecht for their continuing work. Lastly, we would like to thank all participants of the Athero-Express Biobank Study; without them, these kinds of studies would not be possible. Additional Information: Coauthor Prof. Marion Groeger, died April 14, 2022.M. Mayr is a British Heart Foundation (BHF) Chair Holder (CH/16/3/32406) with BHF program grant support (RG/F/21/110053). K. Theofilatos and M. Mayr were also supported with a BHF project grant (PG/20/10387). M. Mayr received support from the Leducq Foundation . M. Mayr is also supported by the Vascular Ageing and Clinical Stroke Research center (VASCage). VASCage is a Competence Center for Excellent Technologies (COMET) within the COMET program and funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology, the Federal Ministry of Labor and Economy, and the federal states of Tyrol, Salzburg, and Vienna. COMET is managed by the Austrian Research Promotion Agency (Project number: 898252). J. Barallobre-Barreiro was supported by a BHF Intermediate Fellowship (FS/19/33/34328). This work was supported by the Anniversary Fund of Austrian National Bank grant to Johann Wojta, Austrian National Bank (OeNB) grant number: AP15688ONB. Furthermore, the work was supported by the Association for the Promotion of Research in Arteriosclerosis, Thrombosis, and Vascular Biology. S.W. van der Laan is funded through European Union Horizon 2020 (EU H2020) TO_AITION (grant number: 848146). We are thankful for the support of the Netherlands CardioVascular Research Initiative of the Netherlands Heart Foundation , the European Research Area Network on Cardiovascular Diseases (ERA-CVD) program “druggable-MI-targets” (grant number: 01KL1802), and the Leducq Fondation “PlaqOmics.”S.W. van der Laan has received Roche funding for unrelated work. The other authors report no conflicts.Supplemental MethodsSummary Proteomics and Statistics Results of TMT Proteomics Data on SDS Extract.Summary Results and Statistics of the TMT Proteomics Using the NaCl Extract.Summary Results and Statistics of the TMT Proteomics Using the GuHCl Extract.Differential Expression Results Comparing Clusters of Spatial RNA-Sequencing Data From Carotid PlaquesSummary details of Proteins and Peptides Quantified Using Targeted Proteomics PRM MethodSummary Results and Statistics of the PRM Targeted Proteomics Analysis Using the GuHCl ExtractMajor Resources TableTables S1–S4Figures S1–S10References 51


In multivariate analysis, compared to MSD transplants, the risk of relapse was significantly lower following 8/8MUD transplants , whereas non-relapse mortality was significantly higher following UCB transplants . However, donor type did not determine overall survival, disease-free survival, or graft-versus-host disease (GVHD)-free, relapse-free survival, but chronic GVHD-free, relapse-free survival was better after UCB  and 8/8MUD  compared to MSD transplants. Our study demonstrated that MSDs are not superior to alternative HCT methods, such as 8/8MUD, 7/8MUD, or UCB, in this population.Human leukocyte antigen (HLA)-matched sibling donors (MSDs) are the preferred choice for allogeneic hematopoietic cell transplantation (HCT). However, as myelodysplastic syndrome (MDS) is most frequently diagnosed in the elderly, MSDs are also likely to be of advanced age. It is unclear whether an MSD should be considered the primary choice for allogeneic HCT in elderly patients with MDS. We retrospectively compared survival and other outcomes in 1787 patients with MDS over 50 years of age and receiving allogeneic HCT between 2014 and 2020, using either MSD ( Allogeneic hematopoietic cell transplantation (HCT) represents the only curative treatment for myelodysplastic syndrome (MDS). While MDS is predominantly a disease of the elderly, recent advances in transplantation techniques, such as safer conditioning regimens and increased availability of unrelated donors, potentially expand indications for allogeneic HCT in elderly patients with MDS , 2.For allogeneic HCT in MDS and other hematological disorders, a human leukocyte antigen (HLA)-matched sibling donor (MSD) is preferred. The Center for International Blood and Marrow Transplant Research (CIBMTR) demonstrated that MSD and 8/8 allele-matched unrelated donor (MUD) transplants showed similar overall survival (OS) and disease-free survival (DFS), whereas 7/8 allele-MUD transplants showed inferior OS and DFS for patients with MDS . HoweverOlder donor age is correlated with an increased incidence of graft-versus-host disease (GVHD) and poor survival after unrelated HCT –10. A prData were obtained from the Transplant Registry Unified Management Program of the Japanese Data Center for Hematopoietic Cell Transplantation , 17. A tThe primary objective of this retrospective study was to evaluate whether an MSD resulted in superior survival outcomes in older patients with MDS compared to alternative donor types, including 8/8 allele-MUD, 7/8 allele-MUD, and UCB. The secondary objective was to compare the rates of hematopoietic engraftment, incidences of acute and chronic GVHD, relapse, and NRM among the donor types. The degree of HLA disparity was determined at the antigen level for HLA-A, HLA-B, and HLA-DR for MSD and UCB transplants, whereas it was determined at the allele level for HLA-A, HLA-B, HLA-C and HLA-DR for MUD transplants.9/L for three consecutive days. Platelet engraftment was defined as a platelet count of 50 × 109/L for three consecutive days without platelet transfusion. Acute and chronic GVHD were graded according to standard criteria [The time from HCT to death defined OS, while DFS was defined as the time from HCT to death or recurrence of MDS. GVHD-free, relapse-free survival (GRFS) was defined as the absence of grade III or IV acute GVHD, chronic GVHD requiring systemic therapy, relapse, or death . Chroniccriteria , 21.Performance status (PS) , HCT-speThe baseline patient characteristics were compared using Chi-squared tests for categorical variables and Kruskal-Wallis tests for continuous variables. The correlation between donor and recipient age was tested by the Spearman rank correlation coefficient. The probabilities of OS, DFS, GRFS, and CRFS were estimated using the Kaplan-Meier method, and the groups were compared using the log-rank test. The probabilities of neutrophil and platelet engraftment, acute GVHD and chronic GVHD, relapse, and NRM were calculated using the cumulative incidence method (taking competing risks into account), and the groups were compared using Gray’s test. Death without hematopoietic engraftment or GVHD was a competing event for hematopoietic engraftment or GVHD. Relapse and NRM were considered mutually competing events.p-value of less than 0.05.To estimate hazard ratios (HRs) with a 95% confidence interval (CI), multivariate analysis was performed with a Cox proportional hazard model for OS, DFS, GRFS, and CRFS or a Fine and Gray proportional hazard model for neutrophil and platelet recovery, GVHD, relapse, and NRM. The following factors were included in the multivariate model: donor type (MSD vs. 8/8MUD vs. 7/8MUD vs. UCB), age (<60 years vs. ≥60 years), sex , PS (0-1 vs. 2-4), HCT-CI (0–2 vs. ≥3), karyotype (other than poor vs. poor), disease risk at HCT (low-risk vs. high-risk), conditioning regimen (myeloablative conditioning vs. reduced-intensity conditioning), GVHD prophylaxis (with methotrexate vs. without methotrexate), and the use of antithymocyte globulin (ATG) (no vs. yes). Statistical data analyses were carried out using EZR , a graphical user interface for R 4.2.2  . StatistP < 0.001). The proportion of patients with PS 2–4 was higher in MSD and UCB recipients (P = 0.018). A higher proportion of 8/8 MUD and 7/8 MUD recipients had a diagnosis to HCT interval of more than 12 months (P < 0.001). MSD recipients less frequently received azacytidine prior to HCT (P = 0.024). MDS etiology, karyotype risk, and disease risk at HCT did not differ among the four donor groups. The median donor age was 57 years  for MSDs, 40 years  for 8/8 MUDs, and 40 years  for 7/8 MUDs (P < 0.001). The proportion of female donors was lower among 8/8 MUD and 7/8 MUD recipients (P < 0.001), resulting in a lower proportion of female donors to male recipients in the 8/8 MUD and 7/8 MUD groups (P < 0.001). A higher proportion of UCB recipients received ABO-major/bidirectional mismatched HCT (P < 0.001), reduced-intensity conditioning (P < 0.001), and mycophenolate mofetil-based GVHD prophylaxis (P < 0.001). 7/8 MUD recipients were more likely to receive ATG (P < 0.001).The baseline characteristics of the patients are presented in Table P < 0.001)  7/8 MUD , and UCB  recipients  Fig. . In multP < 0.001) , 7/8 MUD , and UCB  recipients  Fig. . In multP = 0.043)   and UCB  recipients compared with MSD recipients (Table The cumulative incidences of grades II–IV acute GVHD significantly differed among the donor types (43) Fig. , but gra40) Fig. . In multP < 0.001)   and extensive chronic GVHD  were significantly lower in UCB recipients compared with MSD recipients  Fig.  and exte01) Fig.  differedP = 0.248)   Fig. . In mult7) Table .Fig. 2ReP = 0.046)   Fig. . In mult1) Table .P = 0.017) , GRFS (P = 0.385), and CRFS (P = 0.106) was comparable among the donor types  and UCB  recipients compared with MSD recipients  Fig. . Howeverpes Fig. . In multThe causes of death in each donor type are given in Table r = 0.612, P < 0.0001), but not in 8/8 MUD  or 7/8 MUD  transplantations   , but not in MSD or 7/8 MUD transplantations  only in 8/8 MUD transplants P = 0.04 (SuppleP = 0.075)  , except for the cohort of UCB recipients. Univariate analysis showed that younger 8/8 MUD (≤30 years) recipients had the best OS (Fig. This was a nationwide registry-based study that compared the outcomes of 1787 patients with MDS over 50 years of age receiving allogeneic HCT by MSDs, 8/8 MUDs, 7/8 MUDs, or UCB between 2014 and 2020 in Japan. Our study demonstrated that MSDs were the best in terms of neutrophil and platelet engraftments among the four donor types. The relapse rate was significantly lower following 8/8 MUD transplants, whereas NRM was significantly higher following UCB transplants compared to MSD transplants. However, donor type did not determine OS, DFS, or GRFS. These data suggest that an MSD should not always be considered the primary choice for allogeneic HCT in patients with MDS over 50 years of age.Previous studies have investigated whether older MSDs or younger MUDs provide better survival outcomes for elderly patients, but conflicting results have emerged , 29, 30.UCB is a suitable alternative donor source for adult patients who lack an HLA-matched related or unrelated donor. Several prior studies have demonstrated similar survival rates between MSD and UCB transplantations for adult patients with hematologic malignancies , 33–37, Older recipient age has also been associated with a higher incidence of chronic GVHD rather than acute GVHD . The lowOur study had several limitations. First, this was a registry-based retrospective study. Treating physicians or institutions might enact alternative donor selection procedures. In addition, data regarding the genetic profile and maintenance therapy were not available in our registry. Second, the use of post-transplant cyclophosphamide as GVHD prophylaxis has resulted in a drastic expansion of haploidentical-related donor transplantations for MDS in recent years –43. HoweIn summary, our registry-based study demonstrated that MSDs do not lead to superior OS or DFS compared to alternative HCT from 8/8 MUDs, 7/8 MUDs, or UCB for patients with MDS over 50 years of age. While MSDs were the best in terms of neutrophil and platelet engraftments among the four donor types, an MSD should not always be considered the primary choice for allogeneic HCT in this population.Supplementary information


Background: Due to the poor prognosis and rising occurrence, there is a crucial need to improve the diagnosis of Primary Central Nervous System Lymphoma (PCNSL), which is a rare type of non-Hodgkin’s lymphoma. This study utilized targeted metabolomics of cerebrospinal fluid (CSF) to identify biomarker panels for the improved diagnosis or differential diagnosis of primary central nervous system lymphoma (PCNSL).Methods: In this study, a cohort of 68 individuals, including patients with primary central nervous system lymphoma (PCNSL), non-malignant disease controls, and patients with other brain tumors, was recruited. Their cerebrospinal fluid samples were analyzed using the Ultra-high performance liquid chromatography - tandem mass spectrometer (UHPLC-MS/MS) technique for targeted metabolomics analysis. Multivariate statistical analysis and logistic regression modeling were employed to identify biomarkers for both diagnosis (Dx) and differential diagnosis (Diff) purposes. The Dx and Diff models were further validated using a separate cohort of 34 subjects through logistic regression modeling.Results: A targeted analysis of 45 metabolites was conducted using UHPLC-MS/MS on cerebrospinal fluid (CSF) samples from a cohort of 68 individuals, including PCNSL patients, non-malignant disease controls, and patients with other brain tumors. Five metabolic features were identified as biomarkers for PCNSL diagnosis, while nine metabolic features were found to be biomarkers for differential diagnosis. Logistic regression modeling was employed to validate the Dx and Diff models using an independent cohort of 34 subjects. The logistic model demonstrated excellent performance, with an AUC of 0.83 for PCNSL vs. non-malignant disease controls and 0.86 for PCNSL vs. other brain tumor patients.Conclusion: Our study has successfully developed two logistic regression models utilizing metabolic markers in cerebrospinal fluid (CSF) for the diagnosis and differential diagnosis of PCNSL. These models provide valuable insights and hold promise for the future development of a non-invasive and reliable diagnostic tool for PCNSL. Primary central nervous system lymphoma (PCNSL) is a rare and highly malignant type of non-Hodgkin’s lymphoma that predominantly affects the brain, cerebrospinal fluid (CSF), intraocular structures, and spinal cord . It accoIn recent years, metabolomics has gained popularity as a powerful tool in medicine and life sciences. It can identify and measure small molecule metabolites in biological samples, helping to elucidate the underlying pathological mechanisms of diseases by exploring the relationships between metabolites . Amino aAs for the brain tumor, metabolomic profiling of CSF also reveals a great potential for discoveries of novel biomarkers for disease screening and diagnosis. Because CSF directly interacts with the tissue of the central nervous system (CNS) and is readily accessible with less-invasive procedures, it provides an attractive source of useful markers for clinical diagnostics. This was supported by findings from our lab and other groups. Thirty-nine metabolites have been identified with significant changes in the CSF of the malignant gliomas relative to the control samples . Wang etHowever, the investigation of amino acid and acylcarnitine levels in the cerebrospinal fluid (CSF) of PCNSL patients using targeted metabolomics techniques has been limited in existing studies. To explore the potential association between amino acid and acylcarnitine levels in CSF and PCNSL, we employed a comprehensive targeted metabolomic approach. This method allowed us to quantify 20 amino acids and 25 acylcarnitines, aiming to identify distinctive metabolic patterns specific to primary central nervous system lymphoma (PCNSL). Through the application of this advanced assay, we successfully developed and validated biomarker panels capable of accurately discerning PCNSL patients from individuals with non-malignant diseases and other brain tumor patients.CSF samples from both patients and healthy individuals were obtained from the departments of neurosurgery, neurology, infection, and hematology at Huashan Hospital of Fudan University in Shanghai, China. The collection of CSF followed established protocols, with samples being collected before any treatment was administered. The procedure involved lumbar puncture at room temperature, with the initial 10 drops of CSF being discarded to prevent blood contamination. After collection, the CSF samples were centrifuged at 3,000 rpm for 10 min to remove cellular debris. The resulting supernatant was divided into 1 mL aliquots and stored at −80°C until analysis. The study received approval from the Ethics Committee of Huashan Hospital of Fudan University, and all procedures adhered to the principles outlined in the Declaration of Helsinki and Good Clinical Practice guidelines. Informed consent was obtained from all participants prior to their involvement in the study.The Labeled amino acid mix, labeled acylcarnitine mix, and labeled acylcarnitine mix supplement used in the study were obtained from Cambridge Isotope Laboratories Inc. . LC-MS grade methanol, ammonium formate, water and formic acid were obtained from Thermo-Fisher Scientific . HPLC grade Hexane was obtained from Fisher Scientific . The GC grade beta-mercaptoethanol was purchased from Sigma .13C5, 15N-Proline (CNLM-436), isotope labeled Amino Acid Mix (NSK-A), Acylcarnitine Mix (NSK-B), and Acylcarnitine Mix Supplement (NSK-B-G).To prepare the CSF sample for analysis, a 10 μL aliquot of the CSF sample was spiked with 10 μL of an internal standard working solution. The internal standard working solution contained 25 μM of The mixture was then subjected to extraction using 90 μL of extraction buffer (1‰ HCL and 1% beta-mercaptoethanol in methanol) and 200 μL of hexane. After vigorous vortexing for 60 s, the mixture was centrifuged at 12,000 g for 5 min. The hydrophilic amino acids and acylcarnitines were extracted into bottom methanol layer and the lipophilic molecules were extracted into up hexane layer and discarded. Subsequently, 80.00 μL of the lower layer was transferred into an auto-sampler vial for further analysis using UHPLC-MS/MS.Following the CSF sample preparation, a 2 µL extract was injected into the Vanquish UHPLC system . Isocratic elution was performed using a mobile phase A composed of water with 0.5% formic acid and 10 mM ammonium formate, and a mobile phase B consisting of methanol with 0.1% formic acid and 10 mM ammonium formate. The elution was carried out with a constant flow rate of 0.1 mL/min, with 50% mobile phase B. The metabolites were detected using an Altis mass spectrometer  operating in MRM (Multiple Reaction Monitoring) mode. The specific MRM transitions for the targeted amino acids and acylcarnitines can be found in The concentration quantitation of individual targeted amino acdis and acylcarnitines was implemented by multiplying the internal standard concentration with the peak area ratio between the analytes and the corresponding internal standard. The relationship for quantitation is detailed in Five steps of statistical analysis were followed to determine the final panels for diagnosis and differentiation of PCNSL from non-malignant and other brain tumor patients.Feature generation-Features used for univariant analysis included two sets, 45 individual amino acids and acylcarnitine concentrations and 1,642 ratios. Individual concentrations were quantified based on the above quantification method and ratios were calculated using all different combinations with either 2 individual analyte’s concentrations. Complete features were listed in Univariant analysis-Univariate analysis was performed between PCNSL and non-malignant controls and between PCNSL and other brain tumor patients. Fold change  and an AUC ROC (Area Under the Receiver Operating Characteristic Curve) of each feature were calculated for following feature selection.Feature selection-Significant differential features between groups were selected based on an AUC ROC greater than 0.8 and a |log2 fold change| greater than 0.26. A reverse ablation analysis using logistic coefficients was performed for significant differential features to select a minimum feature panel with highest classification performance (highest AUCROC).Multivariate modeling-Four multivariate modeling algorithms, Logistic Regression (LR), Lasso, Elastic Net (EN) and K-Nearest Neighbors (KNN), were compared to evaluate the performance of classifying PCNSL patients from other groups.Validation- In this study, a cohort of 33 PCNSL patients, 33 other brain tumor patients, and 36 non-malignant disease control participants were divided into training (n = 68) and validation (n = 34) sets. Model developed from training sets was tested in the validation set and classification model AUCROC was evaluated. Homemade R script was utilized for feature selection and model fitting processes in this study. We utilized the logistic regression implementation available in the ‘glm’ package, Elastic Net (EN) model was implemented using the ‘glmnet’ package and the KNN model was implemented using the ‘caret’ package. Specifically, hyperparameter tuning for EN and KNN model was conducted using the entire dataset. The performance of model (AUCROC) was calculated using ‘pROC’ package.The overall study workflow was depicted in In this study, a total of 102 individuals were collected, consisting of 33 PCNSL patients, 36 non-malignant disease controls, and 33 other brain tumor patients. Baseline demographic and clinical data, including sex, age, CSF nucleated cells, CSF protein, CSF tumor cells, CSF IL-10, CSF IL-6, and brain parenchyma lesion, were recorded and summarized in In order to investigate the potential link between CSF levels of amino acids and acylcarnitines and PCNSL, we conducted a comprehensive targeted metabolomic analysis using LC-MS/MS. We quantified a total of 18 amino acids and 26 acylcarnitines in the CSF samples obtained from PCNSL patients, controls with non-malignant diseases, and other brain tumor patients. To enhance the scope of our analysis, we expanded the number of features of interest by not only considering the direct concentration measurements of the 45 metabolites, but also incorporating the ratios of any two metabolite combinations as new features. This resulted in a total of 1,682 features. As depicted in To determine the most effective combination of features for classification, we conducted a reverse ablation analysis using the logistic coefficient of each feature. Based on this analysis, we selected the top 20 features with the highest coefficients and evaluated the logistic model performance. We found that the minimal feature panel consisting of the top 5 important features yielded the highest classification performance in the discovery set. These top 5 features were C14-Carnitine/Tyrosine, C8-Carnitine/Tyrosine, C14-Carnitine/C4-Carnitine, C5-Carnitine/Histidine, and C12-Carnitine/Tyrosine, as illustrated in We conducted diagnostic model testing using 5 ratios in 4 different models, and the logistic model demonstrated the best performance for PCNSL diagnosis in the discovery set, as depicted in The analysis pipeline and criteria for differentially diagnosing PCNSL from other brain tumor cancers were similar to those used for diagnosing PCNSL from non-malignant diseases. In our univariate analysis, as illustrated in The four models used in the PCNSL diagnostic analysis were also applied to evaluate the performance of the nine metabolite features in the differential diagnosis of PCNSL from other brain tumor cancers. Consistent with the PCNSL diagnostic analysis, the logistic model exhibited the best performance on the discovery set, as shown in Over the past decade, there has been a significant amount of metabolomics research aimed at identifying reliable biomarkers for screening and diagnosing PCNSL using nuclear magnetic resonance and liquid chromatography-mass spectrometry techniques, resulting in extensive findings. Several types of human cancers, such as thyroid cancer , lung caversus non-malignant disease control comparison, the potential biomarkers included C14 Carnitine/Tyrosine, C8-Carnitine/Tyrosine, C14-Carnitine/C4-Carnitine, C5-Carnitine/Histidine, and C12-Carnitine/Tyrosine. For the differential diagnosis of PCNSL, the potential biomarkers comprised C10-Carnitine, C18:1-Carnitine, C6-Carnitine/Valine, C6-Carnitine/Tyrosine, C10 Carnitine/Valine, C10-Carnitine/Tyrosine, C14:1OH Carnitine/Pyroglutamic acid, C16:1-Carnitine/Pyroglutamic acid, and C18:2-Carnitine/Pyroglutamic acid. Detailed information and violin plots of these selected biomarkers are shown in In our study, we identified specific metabolites and metabolite ratios that have the potential to serve as biomarkers for distinguishing primary central nervous system lymphoma (PCNSL) patients from non-malignant disease controls, as well as for the differential diagnosis of PCNSL from other brain tumor cancers. For the PCNSL Amino acids, being the essential constituents of proteins, play vital roles in human metabolism, cell proliferation, gene expression, and inflammatory response. The proliferation of tumor cells relies heavily on the availability of amino acids for nitrogen supply, supporting protein and nucleotide biosynthesis . This prβ-oxidation of fatty acids. Alterations in their levels can indicate disruptions in fatty acid oxidation, glycolysis, and branched-chain amino acid metabolism, providing valuable insights into cancer development and progression (Acylcarnitines, which are acylated derivatives of carnitine, play a crucial role in mitochondrial and peroxisomal transport for gression . The targression . Similargression . In the gression , while Hgression . Additiogression . Ni et agression . Moreovegression . In anotgression . These fThere are several limitations to our study that should be considered. Firstly, the sample size was relatively small, and larger studies are needed to confirm and generalize our findings. Additionally, we did not take into account other potential factors that could influence amino acid and acylcarnitine metabolism, such as the stage of cancer progression. Further research is necessary to explore these factors and their impact on the observed metabolic changes.


In the field of the muscle–computer interface, the most challenging task is extracting patterns from complex surface electromyography (sEMG) signals to improve the performance of myoelectric pattern recognition. To address this problem, a two-stage architecture, consisting of Gramian angular field (GAF)-based 2D representation and convolutional neural network (CNN)-based classification (GAF-CNN), is proposed. To explore discriminant channel features from sEMG signals, sEMG-GAF transformation is proposed for time sequence signal representation and feature modeling, in which the instantaneous values of multichannel sEMG signals are encoded in image form. A deep CNN model is introduced to extract high-level semantic features lying in image-form-based time sequence signals concerning instantaneous values for image classification. An insight analysis explains the rationale behind the advantages of the proposed method. Extensive experiments are conducted on benchmark publicly available sEMG datasets, i.e., NinaPro and CagpMyo, whose experimental results validate that the proposed GAF-CNN method is comparable to the state-of-the-art methods, as reported by previous work incorporating CNN models. Electromyographic signals (EMGs), which provide representations of electrical potential fields, are crucial biomedical signals produced by membrane depolarization of the muscle fibers. The well-established surface electromyography (sEMG) is a kind of EMG signal acquired from the skin surface . TraditiThe identification of sEMG signals with regard to various motion patterns is the most important task for MCI. Meanwhile, conventional machine learning techniques have shown promising performance in a variety of applications . HoweverTo address the problem caused by using sEMG data as the direct input for CNN learning ,19, we pA two-stage architecture consisting of a 2D presentation stage and a CNN-based classification stage is proposed for myoelectric pattern recognition, where the raw sEMG signal is converted to a suitable 2D representation for better CNN feature extraction.A 2D presentation method employing GAF is proposed to encode instantaneous multichannel sEMG signals in image form, and the rationale of the proposed method is given.Evaluations on four benchmark sEMG datasets show that GAF-CNN has outstanding classification performance compared with the state-of-the-art CNN-based myoelectric pattern recognition methods.Gramian angular field (GAF) encodes one-dimensional data into a two-dimensional symmetrical matrix with the matrix elements defined in polar coordinates . ExistinThe remainder of this paper is organized as follows. Recent studies show the promise of machine learning in feature extraction, which has frequently been used in myoelectric pattern recognition for human movement identification . ConventIn the past decade, the DL technique has undergone significant development. The deep network can intrinsically extract effective features, enabling competitive performance on challenging datasets ,29. WithTo further explore the correlation between specific gestures and the related sEMG signals of muscular activities, Wei et al. proposed a multi-stream CNN architecture, which divides the input data into small-size images and extracts effective features by convolutional layers before further processing with fully connected layers . ConsideRecently, novel DL frameworks have been designed for myoelectric pattern recognition. Demir et al.  proposedWe propose a GAF-CNN method that formulates the sEMG-based gesture recognition as the 2D data representation and CNN-based classification problems. The pipeline includes two stages: sEMG-GAF image encoding and CNN-based classification. In the first stage, the multi-channel sEMG values at instant sampling moments are encoded in image form based on GAF features. Hence, the value of each point in the sEMG-GAF image indicates the correlation between signals from two sEMG sensors. In the classification stage, a deep CNN model is adopted to classify the sEMG-GAF images. After the sEMG signals have been divided into small segments with an overlapped time window, building suitable network inputs is essential and is highly dependent on the preferred DL architecture. The CNN-based method traditionally requires an M × N gray image or an M × N × 3 RGB image as input. To this end, the sEMG signals must be reshaped as 2D or 3D matrix form. Intuitively, reshaping the sEMG signals as an image could be a solution, where a pixel of the image can be regarded as one instantaneous value from the sEMG sensor electrode . This woWe present an effective approach to encode sEMG signals as images via the GAF method . Unlike We rescaled In this way, the rescaled sEMG signals p and q are the channel indices. In this way, the sEMG-GAF image at moment After transforming the rescaled sEMG signal to polar coordinates, the angular increment can be calculated by the trigonometric sum between each point to identify the channel correlation of different values in sensor channels. Therefore, we define the inner product as To fulfill the data preprocessing requirement at the training and classification stages, the sEMG signals are formed into segments by a sliding time window. One segment of sEMG signals DCNN can effectively extract crucial spatial features from the data and has shown exemplary performance in several computer vision and pattern recognition competitions . It is aC × C × L, where C is the number of sEMG sensor channels and L is the length of the sliding time window. The kernel size and stride applied on the input layer are resized to adapt to the following convolutional layers. For example, the size of the input is 10 × 10 × 20 under a sliding time window with a length of 200 ms from a dataset with 10 channels and a sampling rate of 100 Hz. Therefore, the convolutional kernel size will be set to 3 × 3 with a zero-padding size of 1 × 1, and the stride should be set to 1. In addition, a dropout strategy with a probability of 0.5 on the two fully connected layers is adopted to prevent overfitting. With AlexNet, we employ batch normalization instead of using max-pooling layers after the convolution operation on each convolutional layer to optimize the network. Batch normalization can enhance the model’s generalization ability to reduce the dependence on parameter initialization, accelerate network training, and reduce the computational cost.To design a classification network for the sEMG-GAF image, an AlexNet architecture is introduced to the proposed framework to construct an appropriate DL model for extracting crucial features from sEMG-GAF images. As shown in We discuss the rationale of the GAF-CNN method. It is well known that CNNs can not only preserve the neighborhood relations and spatial locality of an input image in latent higher-level feature representations but also can scale well to realistic-sized high-dimensional images in terms of computational complexity, since the number of free parameters that describe their shared weights does not depend on the input dimensionality . AlthougWe propose a GAF-CNN method to formulate sEMG-based gesture recognition as a multichannel sEMG image CNN-based classification problem. The GAF method is introduced for the 2D representation of multichannel sEMG signals. Unlike conventional GAF-based applications that encode time-series signals as images, we encode the multichannel sEMG signals on the channel dimension for two reasons. On one hand, the raw sEMG signals in each channel are nonstationary, nonlinear, stochastic, and unpredictable, making the correlations of time stamps during the process of raw sEMG signals not unsuitable for CNN-based gesture recognition. Our preliminary experimental results show that the performance of the CNN model is extremely poor and even cannot converge in most cases while applying the GAF method to convert sEMG signals on time series. On the other hand, the activation levels of different muscles will maintain relatively stable values while keeping the same hand gesture. Patterns of multichannel sEMG signals between instant values in different sensor channels should be easier to extract compared to the case in time series. Encoding multichannel signals as images can make the feature space more expressive by representing relationships between the values in different sensor channels rather than treating each channel independently. In addition, we consider sEMG-GAF image construction from a mathematical rationale. As described in To summarize, GAF-CNN employs the GAF method to exploit a 2D representation of multichannel sEMG signals for CNN-based classification. Compared with existing work ,19,31 thNinaPro DB1  consistsNinaPro DB2 30], i.e.L, where the amount of the sensor channel is 10 and the L is the length of the sliding window of 10 ms, 100 ms, and 200 ms, corresponding to a single frame, 10 frames, and 20 frames of sEMG-GAF images, respectively. Since the GAF-CNN method can handle the input size of sEMG signals with different sensor channel amounts and various sliding windows as presented in Based on the experimental results, the classification accuracy was calculated for each dataset asThe proposed GAF-CNN method was implemented based on the Keras 1.18.5 platform, written in Python and capable of running on top of TensorFlow. Experiments were conducted on a computer equipped with one Nvidia GPU GTX 3090 with 64 GB RAM, and an Intel CPU i9 9900 k. The proposed GAF-CNN method was analyzed with respect to the performance using sliding window and voting strategies. For the low-density dataset, we tried both sliding windows and voting strategies. For the high-density datasets, we employed a voting strategy for recognition accuracy calculation as in the existing works ,31, sincThe CNN model was initialized with randomized weights and trained using stochastic gradient descent (SGD) with a data batch size of 512 for all experiments. For fine-tuning the CNN model, the initial learning rate was assigned as 0.05 with a decay rate of 0.5. The initial learning rate was chosen large enough to accelerate the learning process in the beginning. The epoch number was set to 60 for dataset NinaPro DB1, and 30 for other datasets. We evaluated the proposed GAF-CNN method on four publicly available datasets. The classification accuracies on a single frame of an instantaneous sEMG image were 71.0% on NinaPro DB1, 77.8% on NinaPro DB2, 45.0% on CSL-HDEMG, and 90.2% on CapgMyo DB-a, respectively. We found that data augmentation could significantly improve classification accuracies, achieving 71.2% on NinaPro DB1, 79.8% on NinaPro DB2, 46.3% on CSL-HDEMG, and 93.3% on CapgMyo Db-a, respectively, as well as an average improvement of 1.7%.Based on the instantaneous classification results, the recognition accuracies could be improved with a majority voting strategy. To evaluate the performance of the proposed GAF-CNN method, we compared the experimental results with the state of the art as reported by previous work incorporating CNN models, which include a benchmark classifier with a handcrafted feature set, single-stream CNN by Atzori et al. , multi-sEvaluations were also performed on the sparse sEMG datasets. The proposed GAF-CNN achieved classification accuracies of 83.9% over the window size of 100 ms and 85.5% over the window size of 200 ms, respectively, on NinaPro DB1. For NinaPro DB2, GAF-CNN achieved the classification accuracies of 79.0% and 80.2%, respectively. The experimental results show that the GAF-CNN method outperformed the CNN-based methods, including single-stream CNN .Regarding the evaluations performed on the high-density sEMG datasets, GAF-CNN showed comparable performances to all compared methods. Classification accuracies of 95.0% and 96.1% were achieved on CSL-HDEMG over window sizes of 150 ms and 300 ms, respectively. For CapgMyo DB-a, corresponding classification accuracies of 99.7% and 99.8% were achieved.We also compared the proposed method with a 3D CNN-based method  on CSL-HWe present a two-stage CNN-based architecture to effectively extract patterns from complex sEMG signals to improve the accuracy of myoelectric pattern recognition. Multichannel sEMG signals are encoded to sEMG-GAF image series by the GAF method so as to map the relationship between hand movements and active forearm muscle regions. Thus, the correlation patterns between individual muscles of specified hand movements could be thoroughly learned by the defined CNN model. To evaluate the effectiveness of the proposed method, we conducted extensive experiments on sparse and high-density sEMG datasets under sliding time windows and majority voting time windows, which showed that GAF-CNN achieves great performance on classification accuracy. Evaluation results on four publicly available sEMG datasets show that GAF-CNN outperforms the state-of-the-art CNN-based myoelectric pattern recognition methods.In the future, we will improve the GAF-CNN-based myoelectric pattern recognition method in two ways: (1) A concise and practical deep CNN model for training and classification can be employed. We believe the classification accuracy can be improved by advanced DL techniques, such as deep transfer learning. (2) A CNN-based model can be employed to focus on the processing of sEMG data, which are temporal signals. Novel temporal models, such as R-CNN and LSTM , can be


Strong evidence supports the benefits of exercise for healthy ageing, including reduced risk of neurodegenerative diseases. Recent studies suggested interorgan crosstalk as a key element of systemic adaptive response, however, the role of specific molecules in mediating exercise effects on the human brain are not fully understood. In the present study, we explored the exercise-related regulation of Growth Differentiation Factor 11 (GDF11) in cerebrospinal fluid (CSF) and blood.2max, cycle spiroergometry) and cognitive functions  were evaluated.The samples of serum, plasma and CSF were obtained before and 60min after acute exercise (90min run) from twenty healthy young individuals. Additional serum and plasma samples were collected immediately after run. GDF11 protein content (immunoblotting), body composition , physical fitness  as well as with the exercise-induced change in GDF11 levels in CSF . Indirect measure of blood-brain barrier permeability  tended to positively correlate with CSF/serum GDF11 ratio (p=0.060). CSF levels of GDF11 correlated positively with cognitive functions, including working memory, both before and after run (p<0.05).Running decreased GDF11 protein content in CSF (-20.6%. p=0.046), while GDF11 in plasma and serum were not regulated. Two GDF11-specific antibodies of different origin were used to corroborate this result. Individuals with higher physical fitness displayed greater exercise-induced decrease of GDF11 in CSF than those with lower physical fitness (p=0.025). VORunning-induced down-regulation of the GDF11 protein in the cerebrospinal fluid of healthy young individuals indicates the potential role of GDF11 in the exercise-induced cross-talk between periphery and the brain. ImportaExercise is a powerful physiological stimulus with a potential to prevent and even treat various age-related chronic diseases, enhance resilience against stress and reduce the burden of „diseasome of physical inactivity” . CirculaThe study protocol was described in detail elsewhere . BrieflyDay 1 (run) consisted of a 90 min monitored  run at the intensity of 75-80% maximal heart rate (HRmax), which was preceded by cognitive testing (computerized cognitive tests CogState & Memtrax). Blood was collected before, immediately after and 60 min after the 90 min lasting outdoor run. Cerebrospinal fluid (CSF) was collected 60min after the run by atraumatic lumbar puncture. During the run, each participant was provided with a drinking water (~3 dcl) to minimize effects of hemoconcentration on protein levels.Day 2 (baseline/rest) included collection of fasting blood, anthropometric and metabolic  measurements and an atraumatic lumbar puncture to collect CSF.2max by cycle spiroergometry) was performed on a separate day, one week after the Day 1.The assessment of cardiorespiratory fitness  and waist circumference measured at the mid-point between the lower border of the rib cage and the iliac crest. Habitual physical activity profile was evaluated by Baecke questionnaire , resting energy expenditure (REE) and metabolic substrate preference  by indirect calorimetry , blood pressure and heart rate by  and cardiorespiratory fitness by cycle spiroergometry  and cognitive functions by computerized cognitive tests (CogState and Memtrax). Cognitive testing was performed both before and after 90 min run. Body Mass Index (BMI) was calculated . To avoid blood contamination, the first 3 to 5 droplets of clear CSF have not been collected. The samples were immediately centrifuged at 400xg for 10 min at 4°C to remove the contaminating cells, aliquoted, placed on dry ice and stored at −80°C within 1h of collection. Paired CSF samples from both experimental days were obtained from 18 individuals, samples from additional 2 volunteers were taken only on Day 1 (after run). Post-puncture headache was reported in three out of thirty-eight individual punctures (7.9%), with a spontaneous resolution within 1–5 days.Cerebrospinal fluid (5–8 ml) was taken by an atraumatic lumbar puncture technique in the lateral decubitus position using , albumin  and insulin . HOMA-IR was calculated according to formula fasting insulin (mU/l) × fasting glucose (mmol/l)/22.5.Cerebrospinal fluid, plasma and/or serum  were mixed with SDS and DTT containing loading dye, incubated , separated on the 10%-SDS-PAGE and electroblotted to PVDF membrane . After blocking , membranes were incubated overnight with primary antibodies: (i) GDF11 N-terminus specific ,  was performed, with α error probability set to 0.05 and power (1-β error probability) to 0.95. Total sample size of 11 would be needed to determine relevant exercise-induced difference in this parameter. Pearson linear correlation analysis model was used. Values are presented as mean ± SEM and the statistical significance was set at p<0.05. Changes induced by 90 min run were calculated as a fold change (ratio between post-run and pre-run levels).Endurance exercise effect was assessed by either Student’s paired t-test (two-time points) or multiple comparison testing with ANOVA and Tukey post-hoc test (three time-points) using JMP . The characteristics of the study population is presented in 9/l, 0 min post exercise 267 ± 31 109/l, 60 min post-exercise 209 ± 58 109/l, p=0.036), which might reflect hemoconcentration present immediately after run. Interestingly, 90 min run increased lactate in CSF of all volunteers (baseline (resting)) 1.2 ± 0.1 mmol/l, 60 min post exercise 1.9 ± 0.4 mmol/l, p<0.001). Total serum creatinkinase increased immediately after run and compared to baseline remained elevated for another hour (baseline (resting)) 3.2 ± 2.4 umol/l, 0 min post exercise 4.5 ± 3.1 umol/l, 60 min post exercise 4.3 ± 2.9 umol/l, p=0.005).Serum lactate increased immediately after the run and near normalization was observed one hour post-run as compared to baseline (baseline (resting)) 1.1 ± 0.6 mmol/l, 0 min post exercise 3.3 ± 2.3 mmol/l, 60 min post exercise 1.6 ± 1.0 mmol/l; p=0.001). Similar pattern was observed for changes in serum albumin  45.1 ± 3.1 g/l, immediately post-exercise 49.1 ± 2.9 g/l, 60 min post-exercise 45.8 ± 2.7 g/l; p=0.001) and serum thrombocyte count (baseline (resting) 219 ± 43 10An acute bout of exercise led to the reduction of GDF11 levels in CSF, which was confirmed in paired CSF samples with two GDF11-specific antibodies for C and N terminus, respectively  or serum . Plasma and serum GDF11 levels did not differ (p=0.261), and not surprisingly there was a positive correlation between GDF11 in plasma and serum , but not between serum or plasma and CSF levels of GDF11 (data not shown).Neither serum nor plasma GDF11 levels changed immediately after or 60 min after the run (The running-induced changes of GDF11 protein in CSF tended to negatively correlate with changes in its plasma (but not serum) levels .Blood-brain barrier permeability, as indirectly assessed by the ratio of albumin in CSF and serum, was decreased after the 90 min run by 27.1%  as well as in plasma , but positively with GDF11/GDF8 in CSF .2max=47.2 mlO2/kgBW/min, cut off median of VO2max=42.2 mlO2/kgBW/min), individuals with lower physical fitness  displayed greater exercise-induced decrease in CSF levels of GDF11 . However, there was no fitness-related difference in GDF11 protein content in CSF neither at baseline (resting) state nor after run. The link between physical fitness and exercise-induced regulation of GDF11 in CSF was supported also by a positive correlation between running-induced change in GDF11 levels in CSF and VO2max   Figure 3Individuals with higher physical fitness decreased, whereas less fit volunteers rather increased GDF11 serum levels measured 60 min after 90 min run compared to baseline . This observation was further supported by a trend towards negative correlation between exercise-induced change in serum GDF11 and self-reported habitual sport activity . Moreover, baseline GDF11 serum levels positively correlated with physical fitness  Figure 3The pattern of associations between GDF11 levels, clinical & biochemical parameters and their running-induced changes is depicted in the heat map Figure 3Briefly, there were several associations between GDF11 levels in CSF and cognitive functions assessed by sensitive computerized tests. GDF11 levels in CSF were positively associated with psychomotor performance and attention, visual learning, as well as learning and working memory (p<0.05). Visual memory was also associated with CSF levels of GDF11/GDF8 . On the other hand, running-induced change in serum GDF11/GDF8 levels correlated positively with reaction time in two computerized tests , and negatively with learning and working memory .Running-induced change in serum GDF11 was positively associated with body fat (p=0.022) and negatively with muscle mass (p=0.011). Furthermore, it correlated positively with serum insulin immediately after the run (p<0.01) as well as with running-induced change in serum insulin (p=0.024). Running-induced change in GDF11 in CSF correlated negatively with change in serum insulin 60 min after run (p=0.015). Lower serum lactate before run (baseline) was associated with higher GDF11 levels in serum after an acute exercise bout (p<0.05), as well as with a greater running-induced decrease in GDF11 (p=0.01) and GDF11/GDF8 in CSF (p=0.05). Similarly, lower baseline serum creatinkinase was linked with a greater running-induced change in serum GDF11 (p=0.045). Adiponectin, adipokine with a neuroprotective potential which we showed to be regulated by acute intensive exercise , was posAn acute bout of intensive endurance exercise reduced GDF11 levels in CSF of young healthy physically active individuals. Levels of GDF11 in CSF and serum and/or their running-induced changes were associated with body composition, specific metabolic parameters, aerobic physical fitness and cognitive functions. Previous reports suggested neuroprotective effects of GDF11 in the brain  as well Exercise-mediated decrease of GDF11 in the brain, with no change in its blood levels, was previously reported in mice (3-month exercise intervention) . In humaInitial experiments with heterochronic parabiosis proposed GDF11 as an anti-ageing molecule with an ability to foster rejuvenation –6, howevBoth caloric restriction and physical activity are known to preserve or enhance cognitive functions and reduce risk of neurodegenerative disorders in humans , 28, howWe observed physiological changes in serum levels of glucose, insulin and lactate, expected in the population of healthy, young, trained individuals without known metabolic disorder in response to the exercise stimulus . Interestingly, lactate was elevated in CSF of all volunteers one hour after the run (~58%), in contrast to serum lactate which returned to baseline values after an original rapid increase. Thus, dynamic changes of lactate as a marker of exercise exhaustion in serum did not correspond to its changes in CSF. Similarly, there was a distinct exercise-induced dynamics of GDF11 in CSF compared to no change of GDF11 in serum/plasma, the pattern comparable to that of adiponectin, which we have reported previously . These oExercise has been shown as an effective strategy to counteract age-related cognitive decline, but molecular mechanisms underlying neuroprotective effects on human brain remain poorly understood . To elucThe levels of physical fitness was suggested as an important determinant of tissue-specific GDF11 expression, with higher expression found in skeletal muscle, but not in hippocampus, in sedentary old compared to young mice . In our In this work, we confirm in a bigger cohort our previous finding  that bloThe limitations of this report mostly originate from the nature of the study design and include smaller sample size as well as a relative heterogeneity of the study population. Taking into consideration anti-ageing properties of GDF11, it would be intriguing to apply this study design to individuals within different age groups as well as in those already diagnosed with an early phase neurodegenerative disease. The nature of the experimental design and potential health risks imposed by lumbar puncture hampered the possibility to explore dynamic changes of GDF11 levels in CSF (e.g. immediately after or 24 hours after the 90-min run). Further studies exploring the regulation of GDF11 by different exercise modalities and using animal models are warranted. Finally, mechanistic explanation of reported running-induced changes in CSF levels of GDF11 remains only speculative and further research is needed to fully elucidate the putative role of exercise in neuroprotective effects of GDF11 in human brain.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of the University Hospital Bratislava. The patients/participants provided their written informed consent to participate in this study.MS performed the clinical study, analyzed and interpreted the data, wrote and revised the manuscript. KM performed immunoblotting, analyzed the data, contributed to the manuscript preparation and editing. MN, NA, performed immunoblotting. IS, ZK, PM, PV obtained CSF samples. JU co-designed and performed the study, contributed to data analysis, interpretation, writing and revision of the manuscript. BU developed the idea, designed, coordinated and performed the study, analyzed & interpreted data and wrote & revised the manuscript. All authors contributed to the article and approved the submitted version.


Background and objectiveTesticular torsion (TT) is a surgical emergency, affecting approximately one in every 4000 males under the age of 25 years annually in the United States. In this study, we aimed to determine the outcomes of emergency scrotal surgical exploration of cases that raised suspicion of TT presenting to Salmaniya Medical Complex (SMC), Bahrain's largest secondary and tertiary care center.MethodsThis was a retrospective cohort study. The data were collected from the hospital's electronic medical record software (I-SEHA). The data included patient age, Doppler ultrasound (DUS) findings prior to any surgical procedure, type of surgical procedure, and the surgical findings of that procedure.ResultsOf the 198 patients who underwent scrotal exploration, 141 had presented with signs and symptoms suggestive of TT. The mean age of the patients was 22.3 ±9.3 years. Doppler imaging was performed preoperatively on 135 out of 141 (95.7%) patients. After scrotal exploration, 91.4% of the patients were found to have TT. The proportion of patients with salvageable testis was 78.7%.ConclusionsThe study found that surgical exploration remains the definitive method in the management of acute scrotum in TT patients. Our findings are in line with those from other similar studies and meta-analyses. Testicular torsion (TT) is a surgical emergency, and it affects approximately one in every 4000 males under the age of 25 annually in the United States . PatientThe diagnosis of TT is mainly based on clinical suspicion; however, other diagnostic modalities such as Doppler Ultrasound (DUS) can also be used to assist in the diagnosis. DUS findings such as decreased or absent blood flow are suggestive of TT . HoweverPatients with TT require urgent surgical intervention to restore blood flow to the ischemic tissue. The duration between the onset of symptoms and detorsion, as well as the degree of cord twisting, are the two most important determinants of the early salvage rate of the testis . There iAccording to the European Association of Urology (EAU), manual detorsion of the testis should be performed initially, as it is associated with higher surgical salvage rates ,11. HoweIn this study, our objective was to find the outcomes of emergency scrotal exploration in patients who presented with acute scrotal pain that raised suspicion of TT at Salmaniya Medical Complex (SMC), Bahrain's largest secondary and tertiary care center. We also assessed the efficacy of the preoperative utilization of DUS to aid in the diagnosis.This retrospective cohort study was conducted at SMC Bahrain. Ethical approval was obtained from the Research and Research Ethics Committee for Government Hospitals on May 9, 2023. Data were collected using the hospital's electronic medical record software (I-SEHA). The sample initially included 223 patients booked in the system for bilateral scrotal exploration by the Urology Department between November 2018 and April 2023. All duplicate bookings and all patients who had undergone scrotal exploration for reasons other than TT, such as epididymo-orchitis, testicular abscess, scrotal trauma, and Fournier's gangrene, were excluded from the study. Details of the above-mentioned excluded patients are presented in Table Following data collection, all data were analyzed using IBM SPSS Statistics for Mac, Version 27.0 .Of the 198 patients who underwent scrotal exploration, 141 had presented with signs and symptoms suggestive of TT  patients who presented with signs and symptoms suggestive of TT. DUS findings were suggestive of TT in 132 (93%) patients. DUS sensitivity was 87.2%, while the specificity was 50%, the positive predictive value (PPV) was 95.6%, and the negative predictive value (NPV) was 31.25%. Table Of the 141 patients who underwent scrotal exploration due to the suspicion of TT, 12 (8.5%) were found not to have TT after surgical exploration  patients were found to have unsalvageable testis and underwent orchiectomy. The average age of those patients was 24.96 ±11.6 years. Out of those patients, 15 (50%) were found to have testis twisted at 360 degrees or more.None of the patients who underwent orchidopexy needed further exploration in the subsequent follow-up at our institution and nor did they have any recurrence of torsion.If we compare the number of cases based on the date of presentation, we notice that most patients (67.4%) presented between November and April compared to the period between May and October Figure . These mDUS is used as a diagnostic aid in patients presenting with acute scrotal pain, and its use is recommended by EAU . HoweverIn our study, 12 patients who underwent scrotal exploration were found not to have TT. It is unclear if those patients had torsion-detorsion or presented with signs and symptoms mimicking TT. Looking at the surgical findings of those 12 patients, seven had hydrocele with or without varicocele, two had inguinal hernias, one had edematous testis, and two had normal testis. Hydrocele and varicocele can present with scrotal plain, thus mimicking symptoms of TT . TorsionOur study had 30 patients who were found to have unsalvageable testis and underwent orchiectomy Table . A reasoOur study had a salvage surgery rate of 78.7% for TT. This is higher than the salvage rate of 58.4% and 70.9% found in a multi-institutional study by Chun et al. . This isThe strengths of this study included the readily available Doppler scrotal ultrasound findings with 24-hour in-house coverage by senior radiologists that promptly ensured that all patients were being worked up without delay before surgical exploration. However, all cases with a high index of clinical suspicion of TT were surgically explored regardless of the findings of the scrotal DUS. Another advantage of this study is the clear documentation of the relevant aspects mentioned in this study in the medical electronic software system since 2018. Moreover, urologists and radiologists were involved early and simultaneously upon the presentation of acute scrotal pain cases with high clinical suspicion of TT.This study has a few limitations. Primarily, the surgical notes were handwritten before 2018, and computer documentation using I-SEHA was optional. This made getting detailed notes of the surgical findings more difficult. There needs to be more emphasis on documentation of the duration of the presentation time to the time of surgical management. An audit comparing the duration of time from the patient's presentation to ER presentation, that from ER to the DUS, and then to the operation theater would be helpful in assessing the hospital's quality of care.This study sought to determine the outcomes of scrotal exploration in patients who presented to the Emergency Department of the SMC with acute scrotal pain that raised suspicion of TT. We also discussed the methods we adopt to manage these patients in our hospital setting. Our findings are comparable with those of other similar studies and meta-analyses. DUS that does not lead to a delay in surgical exploration is a useful adjunct to the management of these patients, and surgical exploration remains the definitive method to manage TT.


This paper hosts the first meaningful dialogue between two important epistemic movements for criminology: zemiology and decolonisation. I identify that zemiology has a disciplinary blindness to colonialism and explain this using Gurminder K. Bhambra’s scholarship—and cognate scholarship—as a frame. Three cases—Pemberton’s Harmful Societies, Grenfell, and Border Zemiology—are selected for their critical importance within zemiology. They are used to argue that zemiology works within a standard narrative of modernity characterised by capitalist nation-states, which does not recognise the colonial foundations of both of these. Capitalist modernity is, however, a colonial formation. Recognising this allows for a better understanding for a wide range of harms. I then discuss future directions for decolonial zemiology, advocating not for expansion of repertoire, but canonical revision so that colonialism is afforded space as an explanatory frame and zemiology can better explain social harm on a global level. Bhatt : 398 refColonialism and Social Theory, Bhambra and Holmwood ask the question: ‘What does it mean to ‘decolonise’ a curriculum in which colonialism is not recognised?’ Their response is that ‘Paradoxically, if our book is to be understood as an attempt at ‘decolonisation’, it is one that has had to proceed by putting colonialism into the picture. In doing so we aim to create a different way of seeing sociology’. colonial and indigenous struggles , and to some extent Europe. This could be considered contentious, not least in that decolonization is, for some, about Relatedly, the canonical zemiology referred to above obscures analyses of social harm that do not specifically operate overtly under the umbrella of zemiology. This includes much scholarship produced in the Global South or within Indigenous communities that discuss and seek to undo harms, which do not work within European zemiological frames. Reconfiguring the zemiological canon so that it includes such scholarship, though it is a highly worthy cause, is a different project to what I aim to achieve here. As a reminder, following Bhambra et al.  and BhamWhereas other studies typically analyse one harmful phenomenon and situate these in relation to capitalist structure to explain its causes, Pemberton  undertakPemberton colonial, whilst analytically locating the gendered harms of migration within patriarchy and capitalism. Though Canning briefly notes that landscape of contemporary British border control is ‘neocolonial’ Canning : 41, ovewhy is it the case that border control is deliberately designed in this way, to be degrading, controlling and violent, stripping those who are processed of ‘their autonomy and humanity’ . Race evidently matters here, as Soliman suggests, though without an account of the structure productive of race, the aetiology of border harms remains absent. The Mediterranean Sea is not only ‘the EU’s southern border’ colonial is elided within zemiology. In all cases, however, engaging with such scholarship provides further explanation of various harms between different contexts, and works towards a fruitful decolonization within the field. With the above as precedent, this section engages with Canning and Tombs’s  statementrade in conflict minerals, tourism, racism and global inequalities. Alongside but separate to these neocolonial harms colonial as an explanation for social harm has largely been ignored within this area of study, which works within a standard social scientific account of modernity. This takes the nation-state and capitalism as the basic frames for analysis, ignorant to the colonial foundations of both. Recognising that the foundations of the present are colonial, however, and that colonial relations are variously renewed in the twenty-first century provides a greater understanding of harm, currently unaccounted for in this area of study.Overall, then, positing that future directions for zemiology could include neocolonial harms There are some efforts (e.g. Agozino Harmful Societies (between nations on a global level are obscured. The harms in contemporary Britain, though, formed through a neoliberal harm regime are necessarily indebted to colonial economies. Similarly, the harms endured in former colonies are related to their colonial past.In addressing an entire area of study, this article has necessarily had to work with specific examples. The first of these, which is discussed as a landmark study within this field (Canning and Tombs ocieties . Whilst In the case of Grenfell, it was identified that the zemiological aetiology of the fire was neoliberal capitalism. However, as Canning and Tombs have recently noted, the majority of people affected by this fire were not white, and, as this paper argues, this is not coincidental, nor an epiphenomenon of neoliberal capitalism. Rather, Grenfell was a racialised form, indebted to a postcolonial geography of the city, premised upon a moral economy of racialised personhood. Such discussion cannot be ignored in explaining Grenfell, though, within major zemiological accounts this has been elided.The third case considered was border zemiology. Nation-state borders as they exist today are a legacy of colonialism, and the policing and protection of borders reproduces coloniality. This is thoroughly rehearsed within migration studies, though zemiological analyses are all but blind to their formation in this way. That harms are administered at European borders is a matter of racial hierarchy, one produced through a colonial conceptualisation of the subject. For a discipline wishing to address harms at the border, it is entirely necessary to engage with such theorisation. This paper provides an entry point to do so, by considering how Yar’s framework of recognition can be applied to contemporary migration issues, whilst recognising the colonial undergirding to harms in this context.Though there have been recent calls for zemiology to decolonize, this call has been made in such a way that leaves the kernel of the research programme intact. This should not remain the case. There have been endeavours in other disciplines to address such core ideas in a decolonial way, which have renewed analysis and promoted the development of new knowledge. Within sociology, for instance, modernity—its foundational area of enquiry—has been excavated (e.g. Bhambra and Holmwood Through a meaningful decolonization, zemiology would be armed with means to recognise harms beyond those caused by capitalism, to meaningfully work towards post-imperial justice, as is Tombs’s  unrealisRelatedly, the purpose of this paper was not to work towards redefining harm or expanding its conceptual remit through a redrawing of canonical lines to include scholarship from the Global South which theorises harm. This, however, does deserve some comment here. Cunneen and Tauri : 377 notGacaca—a form of justice in Rwanda, which focuses reconciliation and social harmony, and focuses on collective responsibility for harm, rather than being limited to a focus on the individual (Tamale epistemic harm of colonialism involves the imposition of a ways of life—for instance, schemas of thought, perception, action, time—by settler-colonists (Tamale One initial example here is l Tamale . This fol Tamale  as the ‘l Tamale : 157. Acl Tamale : 432. Ins Tamale . Canoniss Tamale  and addrThe philosophers have only interpreted the world in various ways; but what matters is to change it. The very way in which thinking and acting are posited as oppositional, or separate categories, however, is problematic. Thinking is action, and this paper contributes to changing the world through providing a new lens through which harm should be addressed, one that responds to calls from within zemiology (Canning and Tombs As with many critical endeavours, zemiologists (e.g. Tombs ‘The point is to change the way we do theory … the eleventh thesis was not just a crude call to do away with theory and focus on the ‘real task’ of changing the world, although it has been unfortunately interpreted in that fashion by generations of Marxists Nigam : xiii.Theory is something that is done, and it can and does change the world, in terms of how others see the world and act in it. Decolonization is an ongoing process Gopal , and wha