Androgen receptor variant 7 (AR-V7) is an important biomarker to guide treatment options for castration-resistant prostate cancer (CRPC) patients. Its detectability in circulating tumour cells (CTCs) opens non-invasive diagnostic avenues. While detectable at the transcript level, AR-V7 protein detection in CTCs may add additional information and clinical relevance. The aim of this study was to compare commercially available anti-AR-V7 antibodies and establish reliable AR-V7 immunocytostaining applicable to CTCs from prostate cancer (PCa) patients. We compared seven AR-V7 antibodies by western blotting and immmunocytostaining using a set of PCa cell lines with known AR/AR-V7 status. The emerging best antibody was validated for detection of CRPC patient CTCs enriched by negative depletion of leucocytes. The anti-AR-V7 antibody, clone E308L emerged as the best antibody in regard to signal to noise ratio with a specific nuclear signal. Moreover, this antibody detects CRPC CTCs more efficiently compared to an antibody previously shown to detect AR-V7 CTCs. We have determined the best antibody for AR-V7 detection of CTCs, which will open future studies to correlate AR-V7 subcellular localization and potential co-localization with other proteins and cellular structures to patient outcomes. However, ADT resistance inevitably occurs, and disease is then referred to as castrate resistant prostate cancer (CRPC).Aberrant activity of the androgen receptor (AR) is central to prostate cancer development and first line therapy for metastatic prostate cancer is androgen deprivation therapy (ADT) which targets AR signallingAR splice variants has been proposed as a mechanism of ADT resistance4. Expression of the AR splice variant 7 (AR-V7), is correlated with CRPC and is the most frequently identified disease associated variant. AR-V7 is proposed to be ligand independent and constitutively active as a nuclear transcription factor6.The expression of altered AR proteins translated from alternative AR gene including exon 1, 2 and 3 together with a cryptic exon 3E (CE3) results in the AR-V7 transcript  with the most reliable data originating from AR-V7 analysis in CTCs11. Our recent metanalysis emphasises the potential of AR-V7 detection in liquid biopsies as clinical biomarker, as it demonstrates significant correlation with patient survival overall and in context of specific treatment1. The presence of full-length AR (AR-FL) and AR-V7 in CTCs has been investigated at the RNA level in a number of studies and CTC-based AR-V7 was found to correlate with metastatic CRPC and primary resistance to abiraterone and enzalutamide15.Splicing of the 17. This is an important finding as it potentially increases the value of AR-V7 screening as a biomarker in prostate cancer. Additionally, cellular AR-V7 protein analysis may enable future detailed investigations into interactions of AR-V7 with other proteins and nucleic acids to help understanding its CRPC related functions.The AR-V7 protein has 16 distinctive C-terminal amino acids, encoded by an alternate cryptic exon 3 producing a unique AR-V7 C-terminal protein domain, allowing for generation of specific antibodies. To our knowledge seven antibodies are now commercially available designated to specifically detect the AR-V7 protein and have been raised to C-terminal peptides /AR−V7−, DU145AR−/AR−V7− according to their published and in this study validated AR-FL and AR-V7 expression20 and cells were grown in DMEM supplemented with 10% Foetal Bovine Serum (FBS), 2 mM l-Glutamine, 4 nM HEPES or MEM supplemented with 10% FBS, 2 mM l-Glutamine, 4 nM HEPES at 37 °C in 5% CO2. Cell lines were obtained directly or through Australian distributers  of the European Collection of Authenticated Cell Cultures (ECACC) or American Type Culture Collection (ATCC) and tested to be mycoplasma free  and STR authenticated . Cells were seeded at approximately 30–40% confluency and harvested after 72-h culture for immunoblotting and gene expression analysis.22RV1, LNCaP, VCaP, PC3 and DU145 prostate cancer cell lines are here referred to as 22RV1’)2 fragment (1:1000 dilution)  and Alexa fluor 555 Phalloidin .Six rabbit anti-human-AR-V7 antibodies were compared in this study: clone EPR15656 , clone E308L and “polyclonal antibody” , clone SN8 , clone DHH-1 , and clone RM7 , as well as the mouse anti-human-AR-V7 clone AG10008 . The available information of antigens used for the anti-AR-V7 antibody generation is shown in Fig. 6 cells. Quality and quantity of RNA was tested using a fragment analyser . cDNA was synthesised from 1 µg of total RNA per cell line using the SensiFAST cDNA Synthesis Kit . ddPCR to detect AR-V7 and full-length AR (AR-FL) was performed as described previously15. Quality of RNA was confirmed by conducting GAPDH ddPCR as described previously11.In brief, total RNA was extracted with ISOLATE II RNA Mini Kit  from approximately 5 × 106 cultured cells were harvested and lysed in RIPA lysis buffer  containing 1 × protease inhibiters  for 30 min placed on ice, followed by maximum microfuge centrifugation speed  and recovery of supernatant. Protein concentrations were determined using the DC protein assay kit . 30 µg total protein per sample was separated on 4–12% Bis–Tris gels  and transferred to Polyvinyl difluoride (PVDF) membrane . Membranes were incubated with primary antibodies (dilutions see Supp. Table ’)2 fragment (1:1000 dilution) for 1 h at room temperature and again washed three times. Membranes were developed using Western Lightning ™ Plus-ECL Enhanced Luminol Reagent Plus  and Western Lightning ™ Plus-ECL Oxidizing Reagent Plus (LOT 265-13481)  and imaging was performed with an Odyssey imager .Approximately 1 × 10For each cell line approximately 20,000 cells were seeded on sterile, round coverslips in 12-well plates and cultured for 72 h followed by fixation with 3.7% paraformaldehyde for 10 min. In case of PBMC probing cells were cytospun onto superfrost + slides. Cells were permeabilized with 0.2% Triton-X for 10 min and blocked using 10% goat serum in PBS for 30 min. Primary antibodies were diluted in 0.5% FBS in PBS  immunocytostaining was essentially performed as above including anti-AR-V7 (E308L or EPR15656) staining and probing for CD45 to exclude lymphocytes and Hoechst to secure nucleated cellular identity. All patient CTC samples were stained in parallel to a positive control 22RV1 sample. To identify CTCs the definition CD45-, AR-V7 + and Hoechst positive was used, where AR-V7 positivity was determined by the parallel 22RV1 probing. Typically, weak but clear AR-V7 staining in 22RV1 was around an intensity value of 2000 . Consequently, events with intensities below 2000 were considered AR-V7 negative (not CTCs) for patient CTC samples. Images in Fig. To validate AR-V7 CTC detection six prostate cancer patients with advanced CRPC, likely to yield high CTC counts, were recruited. For each patient, 2 × 9 mL peripheral blood was collected into 2 EDTA vacutubes (Greiner Bio-One) and processed within 24 h. 2 × 9 mL blood was used to isolate CTCs using RosetteSep™ CTC enrichment cocktail containing anti-CD36  according to supplier’s instructions. In brief, blood was incubated with antibody cocktail for 10 min and then diluted with 2% FBS in PBS as recommended by manufacturer, transferred to a Sepmate tube containing lymphoprep density gradient medium  and centrifuged at 1200xg for 10 min. The supernatant with cellular layer was recovered and topped up to 50 mL with 2% FBS in PBS and gently mixed. After a 10-min 300xg spin, the supernatant was discarded, and cells were suspended in residual fluid by gentle tapping. Cells were washed once with PBS and spun again , resuspended in 1.5 mL PBS and transferred to a well of a 24-well glass bottom plate  coated with 3.5ug of CellTak  per cm21. CellProfiler segmented cell data for at least 150 cells per sample (nucleus and cytoplasm) based on staining and extracted data on nucleus, cell body and cytoplasm and AR-V7 intensity were saved in excel to transfer to Konstanz Information Miner (Knime)22. The quantitative data from CellProfiler was used in Knime to compare the intensity of AR-V7 detected by different antibodies as well as cellular localization of AR-V7.Image J  was used for RGB stacking and merging of immunostaining images from cell lines before doing quantitative image analysis using CellProfiler  an automated image analysis software to measure biological phenotypes in imagesAll methods were performed in accordance with the relevant guidelines and regulations and the study was approved by the South Western Sydney Local Health District Human Ethics Committee (HREC/13/LPOOL/158). Written informed consent was obtained from all patients participating in the study.All authors agree on the submitted version of the manuscript.For any antibody to be selective for AR-V7, it must recognise a C-terminal peptide epitope corresponding to the 16 amino acid peptide sequence (EKFRVGNCKHLKMTRP) unique to AR-V7, encoded by the cryptic exon 3. Supplier information regarding the exact antigens used for antibody generation is considered imprecise for five of the seven antibodies tested here. Nevertheless, one can deduce that the entire 16 amino acids or most are part of any antigenic peptide used for antibody generation. Three of the antibodies are known or implied to have antigen peptides additionally containing parts of at least the DNA binding domain (DBD) shared by AR-V7 and AR-FL /AR−V7−, DU145AR−/ARV7−, ddPCR also confirmed known AR-FL status for all lines /AR−V7− cells, while additional bands across cell lines evidenced further cross-reactivity for these antibodies. The prominent PC3AR(+/−)/AR−V7− protein band was just below the expected AR-V7 size. AR-V7 detection with the polyclonal antibody proved to be highly nonspecific. , while detecting protein bands corresponding to AR-V7 size in 22RV1AR(+/−)/AR−V7− with undetectable AR-V7 transcript express a slightly truncated form of AR-V7, we wished to rule out AR-V7 identity of this band detected close to 80 kDa. Firstly, we conducted protein Blast searches (blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE = Proteins) of the full AR-V7 specific 16 amino acid sequence as well as the sequence from amino acid 580 and 600 , which identified only the AR-V7 splice variant and for the 580/600 to C-terminus peptide additionally to AR-V7 the partially homologue AR variant 5 (see ref23 for review of AR variants). Additionally, we were able to elute the ~ 62 kDa PC3AR(+/−)/AR−V7− band of interest from a gel to perform mass spectroscopy and the retrieved data confirmed our Blast data with no proteins detected that share homology to AR-V7 or AR-FL in the excised protein band of interest from PC3AR(+/−)/AR−V7− cells. Out of interest the same was done for the 28 kDa band as cross reactivities for that size were detected by several anti-AR-V7 antibodies, no AR(-V7) homology was found. The datasets generated for these analyses are available (https://doi.org/10.26183/7d43-ze13).Although deemed very unlikely that PC3AR+/AR−V7+++ and the AR-V7-negative cell line LNCapAR+/AR−V7−.With Western analysis already pointing towards clear specificity differences between the tested antibodies we excluded two antibodies from further analysis, the polyclonal due to lack of specificity for AR-V7 detection sensitivity and specificity by Western analysis, and the mouse monoclonal AG10008. The latter was excluded due to very strong cross-reactivity in all cell lines with a protein band at ~ 28 kDa compared to specific AR-V7 band intensity and since ultimately, we aim to perform AR-V7 detection by immunocytostaining of CTCs. In our established CTC workflow antibodies of rabbit origin are more easily integrated for technical reasons. Initial immunocytostaining analysis of the five remaining antibodies was performed focusing on the AR-V7-positive cell line, 22RV124. Initial subjective visual analysis clearly favours the E308L and SN8 antibodies that show distinct nuclear AR-V7 staining in 22RV1AR+/AR−V7+++, however SN8 produces what appears to be cross-reactivity with nucleolar structures in the negative control LNCapAR+/AR−V7− cells. EPR15656, DHH-1 and RM7 appear to produce less distinct staining in 22RV1AR+/AR−V7+++ vs LNCapAR+/AR−V7− cells. Analysing images using unbiased digital image analysis essentially confirmed these observations, presented in Fig. AR+/AR−V7+++ cells, and that corresponded to the second lowest nuclear staining intensity in AR-V7 negative LNCapAR+/AR−V7−. Since the staining in LNCapAR+/AR−V7− can be attributed to unspecific antibody binding, E308L is the antibody with the best signal detection to noise ratio for AR-V7 immunocytostaining.Representative immunocytostaining images of all remaining antibodies in the two cell lines used for monochromatic analysis of staining intensity and subcellular localization are shown in Fig. 24, a final validation of both antibodies, E308L and EPR15656, was by detection of CTCs isolated from a small number of CRPC patients (to validate antibody staining this pilot study focussed on patients with advanced CRPC only, see Table The goal of the presented antibody comparison was to find the best suited anti-AR-V7 antibody to probe and analyse AR-V7 in CTCs. With E308L emerging as the favourite candidate anti-AR-V7 antibody while EPR15656 was previously published to be used for AR-V7 immunocytostaining in CTCsAR+/AR−V7+++ cells, clone SN8, caused however clearly noticeable nuclear staining in AR-V7 negative LNCapAR+/AR−V7− cells, interestingly of nucleolar appearance. This means use of this antibody for CTC detection likely would cause false positive AR-V7 detection in the cellular compartment that has been linked to AR-V7 activity24, affecting potential for “true” biomarker detection.Here we compared various antibodies to establish specific detection of AR-V7 by immunocytostaining with ultimate focus to detect AR-V7 positive CTCs from prostate cancer patient bloods. Initially PCa cell lines with known and here confirmed status of AR-V7 expression Fig. B provide24. This prompted us to do a direct comparison of CTC detection in parallel patient blood samples with our favourite E308L as well as the ERP15656 antibody. For proof-of-concept, we focused on a small highly advanced CRPC patient cohort (Table 25. Interestingly the E308L did not only consistently detect more CTCs in the small cohort of 6 patients (Table 24. Nevertheless, evaluation in larger patient cohorts is needed to clarify if AR-V7 detection in CTCs by immunocytostaining is better suited to predict patient outcome than detection by mRNA, or if indeed a combination of both methods may have benefit. A clear benefit of detecting the AR-V7 protein rather than only mRNA in CTCs is that it opens opportunities to evaluate cell by cell heterogeneity and how AR-V7 expression and sub-cellular localization is related to that of other proteins, which may not only add to our understanding of AR-V7 function but reveal ways of therapeutically targeting it in the future.AR-V7 detection in CTCs by immunocytostaining has been reported previously using the ERP15656 antibodyHere we evaluated the commercially available antibodies against AR-V7 for utility in immunocytostaining of cell lines with known AR-V7 status and CRPC patient CTCs. The clone E308L emerged as the favoured antibody considering sensitivity and specificity as shown by immunoblotting and signal to noise ratio in immunocytostaining. With the growing attraction of liquid biopsies in diagnostic settings, identification of the best antibody to detect AR-V7 in CTCs may help to develop a standardised approach for AR-V7 screening in patient CTCs.Supplementary Figure 1.Supplementary Figure 2.Supplementary Information 1.


ITGB3, GPIBA, TBXA2R, ITGA2, PLA2G7, HMOX1, PTGS1, PTGS2, ADRA2A, ABCB1 and PEAR1 genes and the intergenic 9p21.3 region were determined using low-density biochips. We found an association of rs1330344 in the PTGS1 gene with AR and AA-induced platelet aggregation. Rs4311994 in ADRA2A gene also affected AA-induced aggregation, and rs4523 in the TBXA2R gene and rs12041331 in the PEAR1 gene influenced ADP-induced aggregation. Furthermore, the effect of rs1062535 in the ITGA2 gene on NIHSS dynamics during 10 days of treatment was found. The best machine learning (ML) model for AR based on clinical and genetic factors was characterized by AUC = 0.665 and F1-score = 0.628. In conclusion, the association study showed that PTGS1, ADRA2A, TBXA2R and PEAR1 polymorphisms may affect laboratory AR. However, the ML model demonstrated the predominant influence of clinical features.Aspirin resistance (AR) is a pressing problem in current ischemic stroke care. Although the role of genetic variations is widely considered, the data still remain controversial. Our aim was to investigate the contribution of genetic features to laboratory AR measured through platelet aggregation with arachidonic acid (AA) and adenosine diphosphate (ADP) in ischemic stroke patients. A total of 461 patients were enrolled. Platelet aggregation was measured via light transmission aggregometry. Eighteen single-nucleotide polymorphisms (SNPs) in  Aspirin is a key drug widely used for ischemic stroke patients as antiplatelet therapy to prevent recurrent ischemic events . This dr®, PFA-100®, Multiplate®, Plateletworks® and others are widely used for monitoring platelet response to antiplatelet agents including aspirin [The response to aspirin varies between individuals, and up to 57% of patients show the so-called aspirin resistance (AR) . AR is c aspirin ,12,13. H aspirin ,15,16,17AR seems to be a complex phenomenon with a number of factors potentially contributing to it, but its causes and mechanisms are still unclear . One of PTGS1 and PTGS2) were found to influence AR [TBXA2R gene, encoding the specific TXA2 receptor, were associated with the effect of aspirin in a number of studies [HMOX1 [PLA2G7 [ADRA2A [ITGB3 [GPIBA [ITGA2 [PEAR1 [9p21.3, associated with CVD and ischemic stroke, was also connected with AR [ABCB1 encoding P-glycoprotein was shown to protect against AR [A number of genetic markers have already been studied to assess their possible contribution to AR ,20. Firsuence AR ,26,27,28 studies ,30,31. Ts [HMOX1 , PLA2G7 A [ITGB3 ,32, GPIB3 [GPIBA , ITGA2 [A [ITGA2  and PEAR2 [PEAR1 ,36,37 on with AR . P-glyco with AR  and mighainst AR . TherefoOne problem lies in the inconsistency of the results from genetic studies. This may be explained by the differences in the diagnoses , ethnic groups, platelet function tests, sample sizes, etc. . There iAnother problem is the multiplicity of influencing factors that determine the ultimate success or failure of aspirin therapy. The clinical features of the disease, comorbidities, co-medications and non-modifiable risk factors such as age should be taken into account . MoreoveThe purpose of this study was to investigate genetic features associated with laboratory AR in a cohort of patients with ischemic stroke taking aspirin as antiplatelet therapy to be used in pharmacogenetic testing. We have developed a biochip assay to identify 18 SNPs previously described as markers affecting AR. To establish the connection between the patients’ clinical data, genotype and laboratory response to aspirin treatment, we applied the multiple ML approaches.The study included 461 Caucasian patients with primary ischemic stroke treated in the Stroke Center of City Clinical Hospital No.1 named N.I. Pirogov. The inclusion criterion was a verified ischemic stroke. Exclusion criteria comprised hemorrhagic transformation, cancer and severe liver disease, as well as other diseases and conditions affecting the parameters of platelet hemostasis. The pathogenetic variant of stroke was established according to the TOAST criteria  based onThe study was approved by the local ethics committee of the Pirogov Russian National Research Medical University (protocol no. 181 dated 28 January 2019). All participants provided a written informed consent. The study adhered to the World Medical Association Declaration of Helsinki. With a 95% confidence level, a standard deviation of 0.5 and a confidence interval (margin of error) of ±5%, the sample size was estimated to be 391 patients.g for 7 min. Then, 2.5 mL of the supernatant containing platelet-rich plasma was carefully taken for analysis in the aggregometer. Platelet aggregation was measured by LTA using the laser analyzer of platelet aggregation ALAT-2  based on the method of Born and O’Brien.Blood samples from the vein of the non-paretic limb were collected in the morning of the third day of aspirin intake. The region of the cubital fossa was usually selected as the venipuncture area. A tourniquet was applied to the middle third of the shoulder, while the pulse was taken on the nearest radial artery. After that, the patient clenched the hand into a fist and unclenched it several times. The skin in the venipuncture area was stretched, fixing the vein. Next, the skin was pierced next to the vein; the needle was moved 1.5 cm deep into the subcutaneous fat, and the vein was punctured. A total of 9 mL of blood was collected in the 14 mL plastic test tubes “Greiner” with 1 mL of 3.8% trisubstituted sodium citrate using 21 G × 1 1/2”/0.8 × 40 mm needles. The blood in the tube was mixed immediately. Stabilized blood was stored at room temperature for no more than 30 min prior to centrifugation. The samples were centrifuged at 200× To identify a group of patients with AR, we relied on the criteria proposed by Gum et al. . AR was Genomic DNA was extracted from the blood collected into the EDTA-containing tubes using the QIamp DNA Mini kit  according to the manufacturer’s instructions. DNA was isolated from 200 μL of the whole blood. The procedure included cell lysis, sorption on the silica gel membrane of the column, washing and elution (in 100 µL of elution buffer). The DNA concentration was measured using the NanoDrop 1000 spectrophotometer . DNA samples were subjected to further analysis if DNA concentration was at least 10 ng/µL and its 260/280 ratio was in the range of 1.75 to 1.95.ITGB3, GPIBA, TBXA2R, ITGA2, PLA2G7, HMOX1, PTGS1, PTGS2, ADRA2A, ABCB1, PEAR1) and one intergenic region (9p21.3) were selected ) [p-value was below 0.05. The boxplots display the median, two hinges which correspond to the first and third quartiles and two whiskers. The upper and lower whiskers extend from the hinges to the largest value no further than 1.5×IQR from the corresponding hinge (where IQR is the inter-quartile range). Points beyond the whiskers indicate the outliers.The online service SNPStats (l 2022))  was usedTo build a predictive machine learning model, several approaches have been tested using the following Python 3.8 libraries: sklearn.linear_model.LogisticRegression, sklearn.svm.SVC, sklearn.ensemble.RandomForestClassifier , XGBoostThe baseline clinical characteristics of patients are shown in p < 0.001). AR patients had a more severe stroke: the mean NHISS score at admission was 12.35 and 10.45 in the AR and AS group, respectively (p < 0.001). Moreover, atrial fibrillation was more frequent in the AR group (41.74%) compared to the AS group (29.58%) (p = 0.0088).The AS and AR groups differed in some clinical parameters . AR patiITGA2 gene as well as rs1051931 and rs7756935 in the PLA2G7 gene were in strong linkage disequilibrium , and only one of them in each pair was included in the analysis.A total of 461 samples were genotyped for selected SNPs . GenotypPTGS1 was significantly higher in the AR group than in the AS group .Allele and genotype frequencies for sixteen SNPs in the AS and AR groups are listed in The association of genotypes with the response to aspirin was investigated using the SNPStats online service. We included age, AF and the NHISS score at admission as covariates in the analysis, since they showed a different distribution between AR and AS groups . The resPTGS1 gene was more frequent in the AR group than in the AS group . Data are shown in The CC genotype of rs1330344 in the PTGS1 rs1330344, mean AA-induced aggregation was 40.5% higher in the CC genotype compared to the TT + CT genotypes (p = 0.038). For rs4311994 in the ADRA2A gene, mean AA-induced aggregation was 72.7% higher in patients with the TT genotype compared to the CC + CT genotypes (p = 0.043). Data are shown in We compared the association of different genotypes with AA- and ADP-induced aggregation. For TBXA2R rs4523 compared to the CC genotype (p = 0.043). For rs12041331 in the PEAR1 gene, mean ADP-induced aggregation was 59.5% lower in AA homozygotes compared to the GG + GA genotypes (p = 0.017). Data are shown in Mean ADP-induced aggregation was 9.2% higher in the TT + CT genotypes of PTGS1 gene was more frequent in the AR group than in the AS group , the difference is not statistically significant.In total, 296 patients had noncardioembolic ischemic stroke, with 127 (43%) and 169 (57%) patients being assigned to the AR and AS groups, respectively. We analyzed the frequency of different genotypes for sixteen SNPs in the AR and AS groups. Although the CC genotype of rs1330344 in the PTGS1 rs1330344 had 55.4% higher mean AA-induced aggregation compared to the TT + CT genotypes (p = 0.026). Mean ADP-induced aggregation was 14.8% higher in the TT + CT genotypes of rs4523 TBXA2R than in the CC genotypes (p = 0.031) and 11.6% lower in the AA + GA genotypes of ITGA2 rs1062535 comparing to GG homozygotes (p = 0.051).The CC homozygotes of p-values are given before the correction for multiple comparisons; after the Bonferroni correction, all p-values were >0.05.Note that We evaluated the clinical outcomes of patients with noncardioembolic ischemic stroke within the first 10 days after admission and their association with aspirin resistance. Five patients died and were excluded from the analysis. The NIHSS score on day 10 was compared with the admission NIHSS score. We found no statistically significant association of the NIHSS score dynamics analyzed with the groups of AR and AS patients.ITGA2 gene had worse dynamics in the NIHSS score compared to the GG genotype .Furthermore, the NIHSS score dynamics was evaluated in patients with different genotypes adjusted by age and the NIHSS score at admission. The AA + GA genotypes of rs1062535 in the To investigate the contribution of clinical and genetic features to AR, we created ML models. The overall best performance was achieved after utilizing CatBoost algorithm, high-performance open-source library for gradient boosting on decision trees. The parameters of the model that showed best performance in CV are listed in the The ML models did not have enough predictive power if they were based only on genetic features. To overcome this limitation, we included anthropometric and clinical data in the model.After training several models in a five-fold cross-validation setting, we compared the output metrics in order to choose the classification method with the best performance. As expected, the gradient boosting on the decision tree algorithm, CatBoost, outperformed logistic regression, the support vector machine and random forest classifiers since it was designed to leverage the information gained from categorical features. The average values of classification metrics were as follows: AUC = 0.665, F1-score = 0.628, specificity = 0.773, sensitivity = 0.60, precision = 0.63. The ML model is in the To assess the impact of each feature on the model performance and identify the most important factors, we conducted the Shapley additive explanations analysis , which aIn the present study, we used a biochip-based assay to analyze 18 SNPs in patients with acute ischemic stroke and variable response to aspirin treatment. The SNPs were selected based on the literature data. All of them are involved in platelet activation and aggregation, and their contribution to aspirin resistance is discussed in numerous studies ,35,36,37The aspirin resistance was associated with the following clinical parameters: age, the NIHSS score at admission and atrial fibrillation . Aging iPTGS1 (rs1330344), ADRA2A (rs4311994), TBXA2R (rs4523) and PEAR1 (rs12041331).Among 16 SNPs studied, four genetic variants showed a significant association with aspirin non-sensitivity in the whole cohort: PTGS1 gene encoding COX-1 were associated with AR and a higher level of AA-induced aggregation. A similar observation was made in the study by Li et.al. [The C allele and CC genotype rs1330344) of the i et.al. . The CC i et.al. ,56. Howei et.al. ,30,57,58344 of thi et.al. . HoweverADRA2A gene; its effect may arise from the regulation of gene expression or linkage disequilibrium with the other variants. In our study, the minor allele T of the ADRA2A gene (rs4311994) was associated with a higher level of AA-induced aggregation. The ADRA2A gene encodes the alpha-2A-adrenergic receptor involved in epinephrine-induced platelet aggregation and shear-dependent platelet function. This allele was associated with increased platelet reactivity to aspirin in the population with type 2 diabetes mellitus. [Another polymorphism that demonstrated an association with AA-induced aggregation in our study, rs4311994, is also located in the regulatory region downstream (63 kb) of the 3’ end of the ellitus. . Howeverellitus. .PTGS1 and ADRA2A genes, associated with AR and/or high AA-induced aggregation in our study, correlated with a reduced risk of complications from the gastrointestinal tract when taking aspirin in other studies [Notably, the alleles of the  studies ,62,63. TTBXA2R rs4523 (T924C) affected ADP-induced aggregation: the aggregation was higher in the TT and CT genotypes than in the CC genotype. In other studies, the TT homozygotes also showed increased platelet reactivity [TBXA2R gene showed no association with AR in our study.The activity ,65. It iactivity ,65. The PEAR1 gene being lower in the AA homozygote as compared to the GG and GA genotypes. These data are consistent with some other studies [PEAR1 gene encodes the type 1 membrane protein expressed in platelets and endothelial cells. Its phosphorylation appears to promote platelet aggregation [PEAR1 expression [PEAR1 gene may not be specific to the aspirin action. The AA genotype of PEAR1 rs12041331 was shown to be associated with an increased response to ticagrelor in healthy people [The ADP-induced aggregation was affected by intronic rs12041331 in the  studies ,37,66,67regation ,69. The pression . Accordipression , the majy people . Howevery people .PTGS1 rs1330344 showed a significant association with AA-induced aggregation. Thus, PTGS1 rs1330344 might be considered as the strongest predictor of laboratory AR among the analyzed SNPs, both in the whole cohort of ischemic stroke and noncardioembolic patients. The second genetic marker associated with laboratory AR in both cohorts was rs4523 in TBXA2R gene. The T allele acted as a risk factor for increased ADP-induced aggregation during aspirin treatment.In patients with noncardioembolic stroke, the polymorphism ITGA2 rs1062535 was revealed in noncardioembolic patients. The ITGA2 gene encodes the alpha chain of the platelet collagen receptor integrin α2β1 , which promotes an initial interaction between platelets and collagen with further platelet activation and aggregation. The A allele of rs1062535 was suggested to stimulate the protein expression and increase affinity to collagen, which in turn facilitated platelet reactivity. The A allele of ITGA2 rs1062535 was significantly associated with reduced post-operative bleeding after cardiac surgery [An ambiguous association for  surgery . In our  surgery . This suThus, the role of genetic factors underlying the inter-individual differences in aspirin action is of immense interest, but further research is required to understand how genetic data can be efficiently applied to personalized therapy. Different approaches, such as general multifactor dimensionality reduction (GMDR), were employed to study the potential contribution of multiple genetic factors along with the single-locus analysis .We applied the ML method to predict the risk of AR development using clinical and genetic factors. This is the first attempt to bring in the ML approach to the analysis of genetics of AR. We obtained an AUC = 0.665 for our best model . On the As there is an alternative to aspirin for secondary stroke prevention, such as dual antiplatelet therapy or ticagrelor , identifThe current study has several limitations. First, when choosing genetic polymorphisms, we relied on the published studies focusing on certain candidate genetic markers. Searching for more relevant genetic markers using such high-throughput methods of genetic analysis as next-generation sequencing may prove promising. Moreover, given the complex nature of aspirin resistance, the polygenic risk score may be introduced for identifying patients with a high risk of aspirin treatment failure. The second limitation is related to the size of the studied population. It seems to be large enough compared with other studies in the field ,32,33,34PTGS1, ADRA2A, TBXA2R and PEAR1 genes, and laboratory indicators of platelet activity in ischemic stroke patients taking aspirin for secondary stroke prevention. The ML model of AR in the studied cohort of patients showed the prevailing contribution of clinical parameters. However, we assume that the genetic factors are a promising predictor of aspirin resistance. The ML approach revealed the prospective future directions of predicting the risk of AR development. Further replication studies including more homogeneous groups of patients, the implementation of high-throughput genotyping technologies and development of risk-predictive models based both on clinical and genetic features may be considered as key steps towards better understanding aspirin resistance in patients with an ischemic stroke.Early detection of aspirin resistance in ischemic stroke patients is important for timely prescription of other antiaggregant drugs when possible. Therefore, searching for predictive markers of aspirin treatment failure is of great importance. In our study, we revealed the association between clinical parameters , as well as SNPs in the


Augmented reality (AR) and brain-computer interface (BCI) are promising technologies that have a tremendous potential to revolutionize health care. While there has been a growing interest in these technologies for medical applications in the recent years, the combined use of AR and BCI remains a fairly unexplored area that offers significant opportunities for improving health care professional education and clinical practice. This paper describes a recent study to explore the integration of AR and BCI technologies for health care applications.The described effort aims to advance an understanding of how AR and BCI technologies can effectively work together to transform modern health care practice by providing new mechanisms to improve patient and provider learning, communication, and shared decision-making.The study methods included an environmental scan of AR and BCI technologies currently used in health care, a use case analysis for a combined AR-BCI capability, and development of an integrated AR-BCI prototype solution for health care applications.The study resulted in a novel interface technology solution that enables interoperability between consumer-grade wearable AR and BCI devices and provides the users with an ability to control digital objects in augmented reality using neural commands. The article discusses this novel solution within the context of practical digital health use cases developed during the course of the study where the combined AR and BCI technologies are anticipated to produce the most impact.As one of the pioneering efforts in the area of AR and BCI integration, the study presents a practical implementation pathway for AR-BCI integration and provides directions for future research and innovation in this area. Augmented reality (AR) and brain-computer interface (BCI) technologies are among the most promising technologies to date offering to revolutionize human-computer interaction in health care and health professional education. AR provides a mixed user experience where virtual and real elements seamlessly coexist to allow the user to see the real world supplemented by virtual objects and data. Most modern AR implementations represent a fusion of computer-generated imagery and real environment using a head-mounted display (HMD) or goggles typically used in gaming, maintenance training, rehabilitation, and surgical performance support. An HMD allows the users to maintain a clear line-of-sight alignment with real elements in the actual environment, which can be beneficial in any data-intensive setting . This isThe BCI represents a communication pathway between the brain and a computer device using a variety of biosensors that gather and interpret the signals from body and mind to enable neural controls over computer functions. The BCI has been used in a wide variety of applications, including rehabilitation, robotics, entertainment, and virtual reality -6. No loWhile there has been a growing interest in the AR and BCI technologies in the recent years, the combined use of these technologies remains a relatively uncharted territory both in research and practice. The last decade has brought significant advancements in the area of AR and BCI technologies; however, both of them still exist in a relative isolation from each other. While the idea of bringing these two technologies together has prevailed among futurists, technology enthusiasts, and government research silos for quite some time, it still remains a fairly unexplored area, which had been associated with the realm of science fiction requiring paradigm shifts in digital innovation dynamics .As the researchers and consumers are starting to recognize the benefits for combining BCI and AR fields, this interest continues to fuel the innovation around improved technology interaction and visualization capabilities. There is a growing body of literature suggesting the potential to revolutionize health care through the use of emerging AR and BCI technologies, with a few intriguing examples starting to demonstrate the applicability of these emerging technologies in a variety of health care contexts; for example, surgery, ophthalmology, elderly care, sensory system rehabilitation, and others ,11-13. DA combination of AR and BCI technologies can offer an enhanced user experience both for patients and health care professionals, particularly from procedure-intensive specialties, by allowing them to interact with a mix of real and virtual objects, contextual elements, and each other, while using the BCI as an additional communication vehicle, besides the spoken word and hand gesture traditionally used in AR. For instance, through interactive 3D visualizations, an AR component can be used to help a health professional explain a disease or a medical procedure to a patient during a clinical encounter or provide visual cues to a physician during a complex procedure, while a BCI component can simultaneously use biosensors, such as an electroencephalogram (EEG), to enhance the range of options for performing clinical tasks through a combination of verbal, tactile, and neural triggers as well as provide new information about the user’s mental state.This paper presents the results of a recent effort aimed to advance an understanding of how AR and BCI technologies can work together to transform modern health professional education and clinical practice by providing practical mechanisms to support the established principles of patient-centered care  as they The study methodology included an environmental scan and analysis of modern AR and BCI technologies, a use case analysis of practical applications for combined use of AR-BCI technologies in health care, and the development of a proof-of-concept AR-BCI technology integration prototype situated within the modeled use case scenarios, as summarized in the following paragraphs.The environmental scan  and analTo explore the potential for the combined use of AR and BCI technologies in health care, a use case analysis technique  was usedMedical or health professional educationPatient educationPatient-provider communicationShared decision-makingClinical performance supportInterprofessional teamworkResults section.These use cases provided the basis for modeling the simulation scenarios used to demonstrate and validate the AR-BCI proof-of-concept technology, which is described in the The prototype development effort involved a proof-of-concept integration of AR-BCI technologies with the intent of demonstrating the potential of the combined technologies within the context of the practical use cases and serve as a test bed for future use case scenarios and implementation. The prototype development effort focused on the integration of commercially available consumer AR and BCI devices to minimize the common barriers associated with the use of specialized technologies, which frequently stand in the way of technology implementation and adoption.The proof-of-concept AR-BCI integration was performed using Microsoft HoloLens as the AR technology component and NeuroSky Mind Wave 2 as the BCI component. While neither of the two devices were designed to work together “out of the box” in a plug-and-play fashion, they do come equipped with a software development kit and application programming interface components, which make integration with other technology platforms and devices possible in principle. It is important to note, however, that coupling these devices involved a technology development and programming effort to create a software interface to enable the communication between them. A WebSocket relay server was implemented as an intermediary component between HoloLens and NeuroSky Mobile Wave 2 because both devices support the internet connection over an HTTPS internet protocol. The WebSocket protocol was selected on the basis of its effective real-time performance as a relay messenger, which, in this case, listened for the messages from the BCI device (NeuroSky) and relayed them to the connected AR device (HoloLens). As part of the prototype validation efforts, this technology integration method was also successfully coupled with other consumer-grade BCI or neurosensing devices, such as MUSE, and is currently being extended to other AR devices, such as Magic Leap and MERGE.The principal outcome of this exploratory assessment and prototype development is that the new technology interface that resulted from it enables the coupling and communication between these devices—a capability that previously did not exist. Specifically, the study resulted in a proof-of-concept integration of mainstream consumer AR and BCI technologies and development of a novel integrated technology platform to demonstrate their potential within the context of medical education and clinical performance support use cases and serve as a test bed, based on which future developments can be performed and evaluated.Augmented Reality and Neurosensing Interaction Environment (ARNIE),” the resultant solution represents a flexible AR-BCI interface platform and a technology test bed that is specifically intended for consumer-grade devices and is technology brand–agnostic. This solution is designed to enable an enhanced learning experience for health professionals in training and enhanced clinical experience for patients and physicians. The ARNIE platform currently enables thought-controlled manipulation of multiple virtual and data objects in AR similar to a simple computer “mouse click.” Named “The ARNIE system enables the 3D models visualizing a human heart and a gastrointestinal tract to be controlled by a neurosensing BCI component using a unique communication protocol developed to enable coupling of AR-BCI components. As pictured in Besides the education and training use cases, the potential of the integrated AR-BCI solution was explored within the context of clinical performance support modeled in a health care environment. Thus, The clinical applications of the integrated solution can also augment inteprofessional teamwork by allowing members of the health care team to control devices and data in the clinical environment via neural triggers, which would help maintain situational awareness and promote shared mental models and shared decision-making as illustrated in Shared mental models are shared cognitive structures that enable members of an interprofessional team to function collaboratively through implicit coordination while performing clinical tasks that require coordination, cooperation, and mutual support. Shared mental models represent a key prerequisite for shared decision-making in inteprofessional teamwork.The medical education, clinical performance support, and interprofessional teamwork use case scenarios described above were modeled within the academic technology research and clinical skills simulation environment and provided the context for demonstrating the potential of the integrated AR-BCI prototype solution. The medical education use case scenario illustrated in After the initial testing by the technology experts, the prototype has been demonstrated to a diverse mix of 2500 potential user representatives, including students, educators, and health care and technology professionals, approximately 600 of whom volunteered to further examine and perform hands-on testing of the integrated AR-BCI solution within the simulation laboratory. The testing volunteers were instructed to don both the HoloLens and NeuroSky Mind Wave 2 devices simultaneously and concentrate their attention on specific 3D objects within the AR environment; for example, “the heart” in order to activate the learning content associated with these objects via neural triggers. All of the volunteers have been able to go through the use case scenarios using the two devices without any problems, which indicates that the technical targets of the integrated AR-BCI system and its individual components have been met under basic operating conditions within the testing environment. The volunteers’ reactions and feedback regarding the overall AR-BCI integration concept and the experience using the prototype have been overwhelmingly positive, ranging from comparisons with science fiction coming to real life to sincere expressions of awe from being introduced to a novel capability that holds tremendous potential for health care applications.This study has successfully proved the concept of integrating commercially available consumer AR and BCI technologies. The results of the described effort present new advancements in the areas of cognitive and computer sciences by providing new capabilities for (1) human-machine interfacing, (2) advanced technology interoperability and Internet of Things networking, (3) multimodal data analytics, and (4) smart and mobile learning technologies in health care. These new capabilities have been realized within the ARNIE technology interface solution that enables interoperability between consumer-grade AR and BCI devices demonstrated within the context of the practical use cases for health professional education and clinical performance support. The proof-of-concept demonstration scenarios involved participation of a broad community of potential end users, including physicians, allied health professionals, medical students, technologists, scientists and researchers, health care administrators who helped validate the integrated AR-BCI technology capability and provided early feedback regarding the prototype, which has been consistently optimistic and supportive in terms of its perceived usability and utility, and also encouraging in terms of its broader testing and implementation in real-life settings.The resultant new technology solution offers a research test bed and enabler for advancing knowledge and understanding about human-computer interaction in health care by creating an opportunity to connect people , systems, and data in health care environments through a combination of AR and BCI. This test bed represents a foundation for moving toward plug-and-play integrated AR-BCI technology interoperability, which currently represents a significant barrier to adoption of these new technologies for health care applications.The study represents an exploratory and developmental technology integration and use case modeling effort, which, thus far, has been successfully demonstrated and implemented in a simulation-based education and research setting, but not in an actual health care delivery environment. The focus of the effort was on technology development and proof-of-concept demonstration of the integrated AR-BCI capability within the realistic use case scenarios. The volunteer participant interaction with the platform was not systematic, and their feedback is considered informal.The following three major differentiators of the described study from the current state of the science in this area can be distinguished:The world of human-machine interfaces is rapidly transitioning from legacy physical instrumentation to a world driven by gesture, spoken word, and now neural command, which is likely to become far more precise than gesture or spoken word in the future. The results of this study demonstrate an expanded range of AR controls, which includes a neurosensing capability that allows the users to control digital objects in an AR environment using the power of their mind.A growing body of research suggests the great potential to revolutionize human-computer interaction through the use of emerging AR and BCI technologies, with several early examples starting to demonstrate the applicability of these emerging technologies in a variety of health care contexts, including prosthetic interfaces , sensoryThe described effort represents a new step toward bringing the AR-BCI technologies together in a device-agnostic way via a novel interface solution that enables communication between consumer-grade wearable AR and BCI devices and provides the user an ability to control digital objects in AR using neural commands. Envisioned to promote effective communication and shared decision-making between health care providers and patients, this new interface represents an extensible and device-agnostic test bed for evaluating future development efforts in this area. Intended to support a wide range of AR and BCI devices, the technology-agnostic integration approach will help promote the adoption of integrated AR-BCI technologies in health care.Next steps in this direction would include the development of an expanded set of integrated AR-BCI capabilities and include further testing and implementation of this platform within the health professional education curriculum of the University of Central Florida (UCF) Academic Health Sciences Center. Further research efforts need to advance an understanding about how AR and BCI technologies can be used to (1) facilitate or enhance shared understanding between human agents in health care, (2) support teaching and learning of complex biomedical and medical information, (3) enable strategies to capture mental states and promote metacognition and comprehension in medical trainees and patient populations, and (4) support the identification of neural signatures of complex cognitive, metacognitive, and affective processes during clinical training and performance.There are many opportunities for broader impacts from the application of integrated AR-BCI solutions in health care, including patient and provider education, physician clinical performance support, interprofessional teamwork, rehabilitation, wellness, telemedicine, and research, as summarized in In the sick room, ten cents' worth of human understanding equals ten dollars' worth of medical science,” still holds true today, particularly as patient-centered care remains one of the fundamental aims of the US health care system. Whether it is about communication between providers and patients or between members of an interprofessional team, the development of shared understanding and mental models is an important prerequisite and enabler for shared decision-making.This described study offers a foundation for future efforts to accelerate the integration of AR and BCI technologies to connect people, data, and systems to enable transformation in health and medicine. As our society continues to become more diverse and global, every effort should be made toward the development of shared understanding in health care . The resTechnology-based innovations can serve as a game-changer for supporting patient and provider education, communication, and shared decision-making, which would improve care and engagement of patients and ultimately population health . Through