Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic.

Nuclear fusion is a potential source of energy that could supply the growing needs of the world population for millions of years. Several experimental thermonuclear fusion devices try to understand and control the nuclear fusion process. A very interesting diagnostic called Thomson scattering (TS) is performed in the Spanish fusion device TJ-II. This diagnostic takes images to measure the temperature and density profiles of the plasma, which is heated to very high temperatures to produce fusion plasma. Each image captures spectra of laser light scattered by the plasma under different conditions. Unfortunately, some images are corrupted by noise called stray light that affects the measurement of the profiles. In this work, we propose the use of deep learning models to reduce the stray light that appears in the diagnostic. The proposed approach utilizes a Pix2Pix neural network, which is an image-to-image translation based on a generative adversarial network (GAN). This network learns to translateimages affected by stray light to images without stray light. This allows for the effective removal of the noise that affects the measurements of the TS diagnostic, avoiding the need for manual image processing adjustments. The proposed method shows a better performance, reducing the noise up to 98% inimages, which surpassesprevious works that obtained 85% for the validation dataset.

A Machine Learning Approach to Predict Radiation Effects in Microelectronic Components.

This paper presents an innovative technique, Advanced Predictor of Electrical Parameters, based on machine learning methods to predict the degradation of electronic components under the effects of radiation. The term degradation refers to the way in which electrical parameters of the electronic components vary with the irradiation dose. This method consists of two sequential steps defined as 'recognition of degradation patterns in the database' and 'degradation prediction of new samples without any kind of irradiation'. The technique can be used under two different approaches called 'pure data driven' and 'model based'. In this paper, the use of Advanced Predictor of Electrical Parameters is shown for bipolar transistors, but the methodology is sufficiently general to be applied to any other component.

Application of Information Theory to Computer Vision and Image Processing.

Our perception of the world is the product of the human visual system's complex optical and physical process [...].

Prevalence of lymphedema among Anderson-Fabry disease patients: A report from the Fabry registry.

BACKGROUND: Anderson-Fabry disease (AFD) is a rare X-linked lysosomal storage disease due to a genetic variation in the α-galactosidase A (GLA) gene. As a result, the activity of the α-galactosidase A (AGAL-A) enzyme is reduced or absent, which causes sphingolipid deposition within different body parts. AFD typically manifests with cardiovascular, renal, cerebrovascular, and dermatologic involvement. Lymphedema is caused by sphingolipid deposition within lymphatics. Lymphedema can cause intolerable pain and limit daily activities. Very limited data exist on lymphedema in AFD patients. METHODS: Using data from the Fabry Registry (NCT00196742) with 7671 patients included (44% males and 56% females), we analyzed the prevalence of lymphedema among AFD patients who were ever assessed for lymphedema and studied the age of first reported lymphedema. Additionally, we assessed whether patients received AFD-specific treatment at some point during their clinical course. The data was stratified by gender and phenotype. RESULTS: Our study showed that lymphedema occurred in 16.5% of the Fabry Registry patients who were ever assessed for lymphedema (n = 5487). Male patients when compared to female patient have higher prevalence (21.7% vs 12.7%) and experienced lymphedema at a younger age (median age at first reported lymphedema of 43.7 vs 51.7 years). When compared to other phenotypes, classic phenotype has the highest prevalence of lymphedema with the earliest reported lymphedema. Among those who reported lymphedema, 84.5% received AFD-specific treatment during their clinical course. CONCLUSIONS: Lymphedema is a common manifestation of AFD in both genders, with a tendency to present later in female patients. Recognition of lymphedema can offer an important opportunity for intervention and potential impact on associated morbidity. Additional future studies are needed to characterize the clinical implications of lymphedema in AFD patients and identify additional treatment options for this growing population.

Equivalence Class Formation in Adults with Severe Behavioral Problems.

Stimulus equivalence is a behavioral phenomenon that has been related to complex human behavior (e.g., remembering, cognitive functioning, and symbolic behavior). As a rule, people diagnosed with severe mental disorders (e.g., schizophrenia, bipolar disorder) that exhibit delusional and hallucinatory behavior, and disorganized speech have shown cognitive impairment (e.g., processing speed, reasoning/problem solving). Not enough research has analyzed the stimulus equivalence performance in this population. This study aims to investigate the stimulus equivalence performance in adults diagnosed with severe mental disorders. In particular, this study analyzes the many-to-one (MTO) and one-to-many (OTM) training structures effects, and the simultaneous (SIM) and the simple-to-complex (STC) training and testing protocol effects on equivalence class formation in this population. To achieve it, we analyzed the behavior of 18 participants diagnosed with severe mental disorders in three different conditions (Condition 1 OTM/SIM; 2 MTO/SIM; and 3 MTO/STC). Behavior consistent with stimulus equivalence was found in 11 out of 13 participants who had finished the study (5 participants decided to leave before completing the tasks). STC yielded better results than the SIM protocol. No differences were found between MTO and OTM training structures. Implications and suggestions for further research have been discussed.

Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring.

A novelty signal processing method is proposed for a technical vision system (TVS). During data acquisition of an optoelectrical signal, part of this is random electrical fluctuation of voltages. Information theory (IT) is a well-known field that deals with random processes. A method based on using of the Shannon Entropy for feature extractions of optical patterns is presented. IT is implemented in structural health monitoring (SHM) to augment the accuracy of optoelectronic signal classifiers for a metrology subsystem of the TVS. To enhance the TVS spatial coordinate measurement performance at real operation conditions with electrical and optical noisy environments to estimate structural displacement better and evaluate its health for a better estimation of structural displacement and the evaluation of its health. Five different machine learning (ML) techniques are used in this work to classify optical patterns captured with the TVS. Linear predictive coding (LPC) and Autocorrelation function (ACC) are for extraction of optical patterns. The Shannon entropy segmentation (SH) method extracts relevant information from optical patterns, and the model's performance can be improved. The results reveal that segmentation with Shannon's entropy can achieve over 95.33%. Without Shannon's entropy, the worst accuracy was 33.33%.

Plasma circular RNA hsa_circ_0001445 and coronary artery disease: Performance as a biomarker.

The role of circular RNAs (circRNAs) as biomarkers remains poorly characterized. Here, we investigated the performance of the circRNA hsa_circ_0001445 as a biomarker of coronary artery disease (CAD) in a real-world clinical practice setting. Plasma hsa_circ_0001445 was measured in a study population of 200 consecutive patients with suspected stable CAD who had undergone coronary computed tomographic angiography (CTA). Multivariable logistic models were constructed combining conventional risk factors with established biomarkers and hsa_circ_0001445. Model robustness was internally validated by the bootstrap technique. Biomarker accuracy was evaluated using the C-index. The integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were also calculated. Risk groups were developed via classification tree models. The stability of plasma hsa_circ_0001445 was evaluated under different clinical conditions. hsa_circ_0001445 levels were associated with higher coronary atherosclerosis extent and severity with a 2-fold increase across tertiles (28.4%-50.0%). Levels of hsa_circ_0001445 were proportional to coronary atherosclerotic burden, even after comprehensive adjustment for cardiovascular risk factors, medications, and established biomarkers (fully adjusted OR = 0.432 for hsa_circ_0001445 as a continuous variable and fully adjusted OR = 0.277 for hsa_circ_0001445 as a binary variable). The classification of patients was improved with the incorporation of hsa_circ_0001445 into a base clinical model (CM) composed of conventional cardiovascular risk factors, showing an IDI of 0.047 and NRI of 0.482 for hsa_circ_0001445 as a continuous variable and an IDI of 0.056 and NRI of 0.373 for hsa_circ_0001445 as a binary variable. A trend toward higher discrimination capacity was also observed (C-indexCM  = 0.833, C-indexCM+continuous hsa_circ_0001445  = 0.856 and C-indexCM+binary hsa_circ_0001445  = 0.855). Detailed analysis of stability showed that hsa_circ_0001445 was present in plasma in a remarkably stable form. In vitro, hsa_circ_0001445 was downregulated in extracellular vesicles secreted by human coronary smooth muscle cells upon exposure to atherogenic conditions. In patients with suspected stable CAD referred for coronary CTA, plasma hsa_circ_0001445 improves the identification of coronary artery atherosclerosis.

Circulating microRNAs in suspected stable coronary artery disease: A coronary computed tomography angiography study.

OBJECTIVES: To explore the diagnostic performance of circulating microRNAs (miRNAs) as biomarkers in patients with suspected stable coronary artery disease (CAD). METHODS: Plasma samples were collected from 237 consecutive patients referred for coronary computed tomography angiography (CCTA). Presence, extension and severity of coronary stenosis were evaluated using the indexes: presence of diameter stenosis ≥ 50%, segment involvement score (SIS), segment stenosis score (SSS) and 3-vessel plaque score. A panel of 10 miRNAs previously associated with CAD was analysed using RT-qPCR. Multivariate analyses were used to analyse the associations between biomarkers and indexes. Discrimination was evaluated using the area under the ROC curve (AUC). Decision trees were generated using chi-squared Automatic Interaction Detector (CHAID) prediction models. RESULTS: After comprehensive adjustment including cardiovascular risk factors, medication use, confounding factors and protein-based biomarkers (hs-TnT and hs-CRP), several circulating miRNAs were inversely associated with coronary atherosclerosis extension (SIS and 3-vessel plaque score) and severity (SSS). In the whole population, circulating miRNAs showed a poor discrimination value for all indexes (AUC = 0.539-0.644) and did not increase the discrimination capacity of a clinical model of coronary stenosis presence, extension and severity based on conventional cardiovascular risk factors. Conversely, the inclusion of circulating miRNAs in decision trees produces models that improve the classification of cases and controls in specific patient subgroups. CONCLUSIONS: This study identifies a group of circulating miRNAs that failed to improve the discrimination capacity of cardiovascular risk factors but that has the potential to define specific subpopulations of patients with suspected stable CAD.

Comparison between Different Types of Sensors Used in the Real Operational Environment Based on Optical Scanning System.

The present paper describes the experimentation in a controlled environment and a real environment using different photosensors, such as infrared light emitting diode (IRLED-as receiver), photodiode, light dependent resistor (LDR), and blue LED for the purpose of selecting those devices, which can be employed in adverse conditions, such as sunlight or artificial sources. The experiments that are described in this paper confirmed that the blue LED and phototransistor could be used as a photosensor of an Optical Scanning System (OSS), because they were less sensitive to sunlight radiation. Moreover, they are appropriate as reference sources that are selected for the experiment (blue LED flashlight and light bulb). The best experimental results that were obtained contained a digital filter that was applied to the output of the photosensor, which reduced the standard deviation for the best case for the phototransistor LED from 100.26 to 0.15. For the best case, using the blue LED, the standard deviation was reduced from 86.08 to 0.11. Using these types of devices the cost of the Optical Scanning System can be reduced and a considerable increase in resolution and accuracy.

Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application.

In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

IgE sensitization to Thaumetopoea pityocampa : diagnostic utility of a setae extract, clinical picture and associated risk factors.

BACKGROUND: Setae from Thaumetopoea pityocampa larvae (the pine processionary moth or PPM) can induce hypersensitivity reactions, but their clinical role in IgE-mediated responses is still subject to discussion. The aim of this study was to evaluate a setae extract for in vivo and in vitro diagnosis in nonhospitalized patients with reactions to PPM. METHODS: Forty-eight adult patients presenting with PPM cutaneous reactions were studied by skin prick test (SPT) and specific IgE using setae and whole larval (WL) extracts. Biological standardized extracts were used for skin tests. RESULTS: A total of 47.9% patients had a positive SPT for PPM (70% to both extracts, 17% only to the WL extract and 13% only to the setae extract). IgE immunoblotting detected several reactive bands in 91% of the SPT-positive cases. In multivariate analysis, male sex, immediate latency (<1 h) and duration of skin symptoms (<24 h) were independent predictors of a positive SPT. CONCLUSIONS: IgE sensitization to PPM was found in 48% of the study patients, which was associated with immediate reactions and evanescent cutaneous lesions. Most of these patients reacted to both WL and setae extracts, but some reacted to only one of them. According to our data, skin and in vitro tests to PPM should be performed using both extracts.

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors.

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices.

Identification of physically demanding work tasks among cleaners: field study using electromyography.

While chronic forearm pain is a common and debilitating condition among cleaners, the most physically demanding tasks remain unidentified. The present field study examines forearm muscle activity during 9 common cleaning tasks in a real working environment. Seven healthy cleaners participated in this study (age: 35.17 ± 9.62 yr; height: 168.17 ± 8.06 cm; weight: 77.14 ± 13.78 kg; experience: 5.60 ± 3.29 yr). Surface wireless electromyography (EMG) was recorded from 2 muscles on both sides of the upper limb, flexor carpi ulnaris (FCU), and extensor carpi radialis (ECR), and normalized to maximal voluntary isometric contractions (MVIC). Top-3 demanding high-force tasks (90th percentile EMG) were the rough floor, dirty rough floor, and office floor mopping for the FCU, and mopping high walls, ceiling mopping, and baize cleaning for the ECR. Top-3 static work tasks (10th percentile EMG) were mopping low walls, ceiling mopping, and dirty rough floors mopping for the FCU and mopping of high walls, low walls, and ceiling for the ECR. The study identified the forearm muscles' most physically demanding work tasks during cleaning tasks. The development of better working tools is recommended to avoid high-force overload as well as prolonged static overload of these muscles in cleaners.

Analyses of relational coherence and rule following: Consistent liars are preferred over occasional truth tellers.

The current study explored the influence of different levels of speaker coherence on rule following and speaker preference. In Experiment 1, rules provided by three different speakers were either 100% accurate, 0% accurate, or 50% accurate/inaccurate. Experiment 2 was similar to Experiment 1 except that the speaker's coherence was adjusted to 80% accurate, 20% accurate, and 50% accurate/inaccurate, respectively. Overall, participants tended to follow coherent speaker rules and avoid following incoherent speaker rules during training and testing phases. The results also indicated that following and not following rules provided by speakers may be generalizable to novel stimuli and maintained in the absence of differential reinforcement (i.e., in experimental test phases). Additionally, in a preference test, participants tended to prefer coherent over incoherent and partially coherent speakers. Furthermore, participants tended to prefer the relatively more incoherent speaker (i.e., 0% or 20% accurate) over the 50% accurate coherent speaker in both experiments. Finally, a comparison of the results of both experiments indicated that different levels of relational coherence affected the variability of rule-following and speaker preference behaviors. These findings are discussed in the context of the complexities that appear to be involved in rule-following behaviors and speaker preference.

Comparative electrocardiographic analysis of midventricular and typical takotsubo syndrome.

Introduction: Takotsubo syndrome (TTS) encompasses distinct variants, with midventricular (MV) as the most common atypical subtype. While electrocardiogram (ECG) abnormalities are well documented in typical TTS, they are less explored in MV-TTS. Methods: A retrospective case-control study was conducted where ECGs were reviewed at three time points from symptom onset (within the first 12 h, at 48 h, and at 5-7 days) and compared between patients with typical TTS (n = 33) and those with MV-TTS (n = 27), as classified by ventriculography. Results: 12-h ECG findings revealed that typical TTS featured ST-segment elevation through anterior leads V3-V6, with maximal deviation in V3 (0.98 ± 0.99 mm) and V4 (0.91 ± 0.91 mm), whereas MV-TTS featured ST-segment depression in inferior leads (-0.24 ± 0.57 mm in II, -0.30 ± 0.52 mm in III, and -0.32 ± 0.47 mm in aVF) and in precordial leads V4-V6. In 48-h ECG findings, the most significant change was T wave inversion, which was more widespread and deeper in typical TTS, with the most pronounced negative T wave depths, exceeding 3 mm, observed in leads V3-V5; in contrast, in MV-TTS, T wave inversion was evident in fewer leads and showed less depth, with the most pronounced negative T waves reaching 1 mm at most in leads I, aVL, and V2. While the QTc interval was prolonged in both groups at 48 h, this prolongation was more pronounced in typical TTS than in MV-TTS (523 ± 52 ms vs. 487 ± 66 ms; p = 0.029). In ECGs at 5-7 days, results essentially returned to baseline. Conclusion: Patients with MV-TTS exhibited a distinctive pattern of ECG abnormalities, marked by ST-segment depression in inferolateral leads, less profound and less extensive T wave inversion that mostly affected leads I, aVL and V2, and attenuated QT interval prolongation compared to typical TTS.

Myxomas and myxoid liposarcomas of the extremities: Our preliminary findings in conventional, perfusion, and diffusion magnetic resonance.

Background: The differentiation between myxomas and myxoid liposarcomas (MLPS) often is a serious challenge for the radiologists. Magnetic resonance imaging (MRI) is the most useful imaging technique in characterization of the soft tissue tumors (STT). Purpose: To evaluate in a sample of myxomas and MLPS of the extremities, what morphological findings in conventional MRI allow us to differentiate these two types of myxoid tumors, in addition to analyzing the validity of the apparent diffusion coefficient (ADC) values of diffusion-weighted MRI (DW-MRI). Material and Methods: Magnetic resonance imaging studies in myxomas and MLPS of extremities searched in our PACS between 2015 and 2019. All studies had conventional MRI with T1, T2, and PD SPAIR sequences, while DW-MRI with ADC mapping and perfusion MRI with a T1 sequence repeated for 4 minutes after contrast injection were additional sequences only in some explorations. Two radiologists evaluated independently the MRI studies by examining the qualitative parameters. Apparent diffusion coefficient values were calculated using two methods-ADC global and ADC solid, and Receiver Operating Characteristic (ROC) curves were applied for analysis. Results: The features were consistent with MLPS: size greater than 10 cm, heterogeneous signal on T1, and nodular enhancement, while the common findings for myxomas were a homogenously hypointense signal on T1 and diffuse peritumoral enhancement. The solid and global ADC values were higher in myxomas. We observed that the solid ADC value less than 2.06 x 10-3mm2 x s would support the diagnosis of MLPS against myxoma. Conclusion: Overall, MRI with its different modalities improved the diagnostic accuracy when differentiating myxomas from MLPS of extremities.

Same day discharge after structural heart disease interventions in the era of the coronavirus-19 pandemic and beyond.

With recent advancements in imaging modalities and techniques and increased recognition of the long-term impact of several structural heart disease interventions, the number of procedures has significantly increased. With the increase in procedures, also comes an increase in cost. In view of this, efficient and cost-effective methods to facilitate and manage structural heart disease interventions are a necessity. Same-day discharge (SDD) after invasive cardiac procedures improves resource utilization and patient satisfaction. SDD in appropriately selected patients has become the standard of care for some invasive cardiac procedures such as percutaneous coronary interventions. This is not the case for the majority of structural heart procedures. With the coronavirus disease 2019 pandemic, safely reducing the duration of time spent within the hospital to prevent unnecessary exposure to pathogens has become a priority. In light of this, it is prudent to assess the feasibility of SDD in several structural heart procedures. In this review we highlight the feasibility of SDD in a carefully selected population, by reviewing and summarizing studies on SDD among patients undergoing left atrial appendage occlusion, patent foramen ovale/atrial septal defect closure, Mitra-clip, and trans-catheter aortic valve replacement procedures.

Verbal interaction pattern analysis in clinical psychology.

Recent developments in pattern analysis research have made this methodology suitable for the study of the processes that are set in motion in psychological interventions. Outcome research, based on the comparison between clinical results from treatment and control groups, has leveraged our empirical knowledge about the efficacy of psychological interventions. However, these methods of research are not precise enough for the analysis of these processes. On the contrary, pattern analysis could be a powerful tool to study moment-to-moment interactions typical of psychological interventions. This is methodology is relevant because clinical psychology is experiencing a paradigm shift from a protocol for syndrome perspective to a principle-based and person-centered intervention. This evidence-based, theory-grounded, and process-oriented paradigm of clinical intervention needs new research methods to thrive (i.e., pattern analysis). The analysis of the therapeutic relationship built into the verbal interaction between the clinician and the client is one of the cornerstones of this new era of research. So, the purpose of this article is three-fold: (1) to discuss the role of the verbal interaction pattern analysis in the clinical context to the development of the principle-based clinical psychology, (2) to analyze the patterns of verbal interaction in a clinical case, and (3) to compare the results using two different methods. To reach these purposes, using the observational methodology, we have coded the verbal interaction of 16 clinical sessions with a person diagnosed with a borderline personality disorder. We have analyzed the data using sequential analysis (GSEQ) and pattern recognition algorithms (i.e., T-Pattern detection). We have been able to detect typical patterns during different phases of psychological intervention (i.e., evaluation, explanation, treatment, and consolidation). Finally, the conceptual, methodological, and empirical implications of this study will be discussed within the realms of pattern analysis research and principle-based clinical psychology.

Lung Models to Evaluate Silver Nanoparticles' Toxicity and Their Impact on Human Health.

Nanomaterials (NMs) solve specific problems with remarkable results in several industrial and scientific areas. Among NMs, silver nanoparticles (AgNPs) have been extensively employed as drug carriers, medical diagnostics, energy harvesting devices, sensors, lubricants, and bioremediation. Notably, they have shown excellent antimicrobial, anticancer, and antiviral properties in the biomedical field. The literature analysis shows a selective cytotoxic effect on cancer cells compared to healthy cells, making its potential application in cancer treatment evident, increasing the need to study the potential risk of their use to environmental and human health. A large battery of toxicity models, both in vitro and in vivo, have been established to predict the harmful effects of incorporating AgNPs in these numerous areas or those produced due to involuntary exposure. However, these models often report contradictory results due to their lack of standardization, generating controversy and slowing the advances in nanotoxicology research, fundamentally by generalizing the biological response produced by the AgNP formulations. This review summarizes the last ten years' reports concerning AgNPs' toxicity in cellular respiratory system models (e.g., mono-culture models, co-cultures, 3D cultures, ex vivo and in vivo). In turn, more complex cellular models represent in a better way the physical and chemical barriers of the body; however, results should be used carefully so as not to be misleading. The main objective of this work is to highlight current models with the highest physiological relevance, identifying the opportunity areas of lung nanotoxicology and contributing to the establishment and strengthening of specific regulations regarding health and the environment.

Prevalence and Risk for Bundle Branch Block, Atrioventricular Block and Pacemaker Implantation in Spondyloarthritis. A Systematic Review of the Literature.

Objective: To evaluate the evidence regarding the prevalence and risk of bundle branch block (BBB), atrioventricular block (AVB) and pacemaker implantation (PMI) in patients with spondyloarthritis compared to a control group without spondyloarthritis. Methods: A systematic review of the literature was performed using Pubmed (Medline), EMBASE (Elsevier) and Cochrane Library (Wiley) databases until December 2021. The prevalence and risk for AVB, BBB and PMI were analyzed. Cohort, case control and cross-sectional studies in patients ≥18 years meeting the classification criteria for spondyloarthritis were included. The Odds ratio (OR), risk ratio (RR), or Hazard ratio (HR) and prevalence difference were considered as outcomes. Data was synthesized in a previously defined extraction form which included a risk of bias assessment using the Newcastle-Ottawa Scale. Results: In total, eight out of 374 studies were included. None of the studies provided results regarding the risk of low grade AVB and BBB in SpA patients. Only indirect results comparing prevalences from low to medium quality studies were found. According to population based registries, the sex and age adjusted HR of AVB was 2.3 (95% CI 1.6-3.3) in ankylosing spondylitis, 2.9 (95% CI 1.8-4.7) in undifferentiated spondyloarthritis and 1.5 (95% CI 1.1 a 1.9) in psoriatic arthritis. The absolute risk for AVB was 0.4% (moderate to high; 95% CI 0.34%-0.69%) for AS, 0.33% (moderate to high; 95% CI 0.21%-0.53%) for uSpA and 0.34% (moderate to high; 95% CI 0.26%-0.45%) for PsA.The RR for PMI in AS patients was 1.3 (95% CI 1.16-1.46) for groups aged between 65 and 69 years, 1.33 (95% CI 1.22-1.44) for 70-75 years, 1.24 (95% CI 1.55-1.33) for 75-79 years and 1.11 (95% CI 1.06-1.17) for groups older than 80 years. The absolute risk for PMI in AS patients was 0.7% (moderate to high risk; 95% CI 0.6-0.8%) for groups aged between 65-69, 1.44% (high risk; 95% CI 1.33-1.6%) for 70-75 years, 2.09% (high risk; 95% CI 2.0-2.2%) for 75-79 years and 4.15% (high risk; 95% CI 4.0-4.3%) for groups older than 80 years. Conclusions: Very few cases of low grade AVB and BBB were observed in observational studies. No study evaluated association measures for low grade AVB and BBB but the differences of prevalence were similar in SpA and control groups even though studies lacked the power to detect statistical differences. According to population based registries there was an approximately two fold-increased risk of high grade AVB in SpA patients. RR for PMI was higher in younger age groups.