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Performing two tasks simultaneously involves the coordination of their processing. This task coordination is particularly required in dual‐task situations with varying task orders. When task order switches between subsequent trials, task order coordination leads to task order switch costs in comparison with order repetitions. However, it is open, whether task order coordination is exclusively controlled by the relation of the task orders of the current and the previous trials, or whether additional conditions such as task order before the previous trial leads to a behavioral and neural adjustment of task order coordination. To answer this question, we reanalyzed the data of two previously published experiments with order‐cued dual‐task paradigms. We did so with regard to whether task order switch costs and the EEG component order‐switch positivity in the current dual‐task trial would be modulated by order switches vs. repetitions in the previous trial (Trial N‐1). In Experiment 1, we found a modulation of the task order switch costs in RTs and response reversals; these costs were reduced after an order switch compared with order repetitions in Trial N‐1. In Experiment 2, there were no effects on the task order switch costs in the behavioral data. Nonetheless, we found the order‐switch positivity to be strongly modulated by the order transition of the previous trial in both experiments. The order‐switch positivity was substantially reduced if the previous trial was an order switch (compared to an order repetition) by itself. This implies that order coordination of dual tasks is adjusted in a gradual way depending on trial's history. The present study investigates the neurophysiological effects of task‐order coordination and its meta‐control adjustment in dual‐task situations. The EEG component order‐switch positivity was modulated by task‐order coordination and by coordination adjustment. This implies that order coordination of dual tasks is adjusted in a gradual way depending on the trial's history. [ABSTRACT FROM AUTHOR]

The magneto-optic surface plasmon resonance in transverse geometry of glass/Au/IrMn/Co/Au/dielectric exchange biased thin film systems has been studied as a function of the Au-cap layer thickness for the dielectric air and water. The results show that it is possible to achieve either an optimum steepness of the reflectivity as compared to a glass/Au/dielectric layer system or to maximize the magneto-optic activity. As the Au-cap layer thickness is increased, the magneto-optic activity is reduced whereas the steepness of the reflectivity in the range of the resonance is raised. The width of the reflectivity curves and the magneto-optic signals is broadened for thin Au-capping layers. [ABSTRACT FROM AUTHOR]

Background: Quantification of [68 Ga]-labeled PSMA PET predicts response in patients with prostate cancer (PC) who undergo PSMA-targeted radioligand therapy (RLT). Given the increasing use [18F]-labeled radiotracers, we aimed to determine whether the uptake derived from [18F]PSMA-1007 PET can also identify responders and to assess its prognostic value relative to established clinical parameters. Methods: We retrospectively analyzed 103 patients with metastatic, castration-resistant PC who were treated with [177Lu]Lu-PSMA I&T. We calculated SUVmean, SUVmax, PSMA-avid tumor volume (TV), and total lesion PSMA (defined as PSMA-TV*SUVmean) on pre-therapeutic [18F]PSMA-1007 PET. Laboratory values for hemoglobin, C-reactive protein (CRP), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), and alkaline phosphatase (AP) were also collected prior to RLT. We performed univariable Cox regression followed by multivariable and Kaplan–Meier analyses with overall survival (OS) serving as endpoint. Last, we also computed a risk factor (RF) model including all items reaching significance on multivariable analysis to determine whether an increasing number of RFs can improve risk stratification. Results: A total of 48 patients died and median OS was 16 months. On univariable Cox regression, SUVmean, CRP, LDH, hemoglobin, and the presence of liver metastases were significantly associated with OS. On multivariable Cox regression, the following significant prognostic factors for OS were identified: SUVmean (per unit, HR, 0.91; P = 0.04), the presence of liver metastases (HR, 2.37; P = 0.03), CRP (per mg/dl, HR, 1.13; P = 0.003), and hemoglobin (per g/dl, HR, 0.76; P < 0.01). Kaplan–Meier analysis showed significant separation between patients with a SUVmean below or above a median SUVmean of 9.4 (9 vs 19 months, HR 0.57; P = 0.03). Of note, patients with only one RF (median OS not reached) showed longest survival compared to patients with two (11 months; HR 2.43 95% CI 1.07–5.49, P = 0.02) or more than two RFs (7 months; HR 3.37 95% CI 1.62–7.03, P < 0.001). Conclusion: A lower SUVmean derived from [18F]PSMA-1007, higher CRP, lower hemoglobin, and the presence of liver metastases are associated with reduced OS in patients undergoing RLT. An early RF model also demonstrated that an increasing number of those factors is linked to worse outcome, thereby emphasizing the importance of clinical and imaging parameters for adequate risk stratification. [ABSTRACT FROM AUTHOR]

Periodontitis is one of the most prevalent diseases worldwide. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease. Since automated bone loss detection has many benefits, our goal was to develop a multi-object detection algorithm based on artificial intelligence that would be able to detect and quantify radiographic bone loss using standard two-dimensional radiographic images in the maxillary posterior region. This study was conducted by combining three recent online databases and validating the results using an external validation dataset from our organization. There were 1414 images for training and testing and 341 for external validation in the final dataset. We applied a Keypoint RCNN with a ResNet-50-FPN backbone network for both boundary box and keypoint detection. The intersection over union (IoU) and the object keypoint similarity (OKS) were used for model evaluation. The evaluation of the boundary box metrics showed a moderate overlapping with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all five folds were 0.694 and 0.611, respectively. Mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Despite only using a small and heterogeneous set of images for training, our results indicate that the algorithm is able to learn the objects of interest, although without sufficient accuracy due to the limited number of images and a large amount of information available in panoramic radiographs. Considering the widespread availability of panoramic radiographs as well as the increasing use of online databases, the presented model can be further improved in the future to facilitate its implementation in clinics. [ABSTRACT FROM AUTHOR]

A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results. [ABSTRACT FROM AUTHOR]

Background: Oro-antral communication (OAC) is a common complication following the extraction of upper molar teeth. The Archer and the Root Sinus (RS) systems can be used to classify impacted teeth in panoramic radiographs. The Archer classes B-D and the Root Sinus classes III, IV have been associated with an increased risk of OAC following tooth extraction in the upper molar region. In our previous study, we found that panoramic radiographs are not reliable for predicting OAC. This study aimed to (1) determine the feasibility of automating the classification (Archer/RS classes) of impacted teeth from panoramic radiographs, (2) determine the distribution of OAC stratified by classification system classes for the purposes of decision tree construction, and (3) determine the feasibility of automating the prediction of OAC utilizing the mentioned classification systems. Methods: We utilized multiple supervised pre-trained machine learning models (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), one custom-made convolutional neural network (CNN) model, and a Bag of Visual Words (BoVW) technique to evaluate the performance to predict the clinical classification systems RS and Archer from panoramic radiographs (Aim 1). We then used Chi-square Automatic Interaction Detectors (CHAID) to determine the distribution of OAC stratified by the Archer/RS classes to introduce a decision tree for simple use in clinics (Aim 2). Lastly, we tested the ability of a multilayer perceptron artificial neural network (MLP) and a radial basis function neural network (RBNN) to predict OAC based on the high-risk classes RS III, IV, and Archer B-D (Aim 3). Results: We achieved accuracies of up to 0.771 for EfficientNet and MobileNetV2 when examining the Archer classification. For the AUC, we obtained values of up to 0.902 for our custom-made CNN. In comparison, the detection of the RS classification achieved accuracies of up to 0.792 for the BoVW and an AUC of up to 0.716 for our custom-made CNN. Overall, the Archer classification was detected more reliably than the RS classification when considering all algorithms. CHAID predicted 77.4% correctness for the Archer classification and 81.4% for the RS classification. MLP (AUC: 0.590) and RBNN (AUC: 0.590) for the Archer classification as well as MLP 0.638) and RBNN (0.630) for the RS classification did not show sufficient predictive capability for OAC. Conclusions: The results reveal that impacted teeth can be classified using panoramic radiographs (best AUC: 0.902), and the classification systems can be stratified according to their relationship to OAC (81.4% correct for RS classification). However, the Archer and RS classes did not achieve satisfactory AUCs for predicting OAC (best AUC: 0.638). Additional research is needed to validate the results externally and to develop a reliable risk stratification tool based on the present findings. [ABSTRACT FROM AUTHOR]

Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen's kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100% for InceptionV3) but low sensitivity (highest sensitivity 42.86% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1–4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74% to 95.02%, whereas sensitivity ranged from 14.14% to 59.60%. Cohen's kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen's kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14% to 95.24%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction. [ABSTRACT FROM AUTHOR]

Presenter's first name and last name Riccardo Cau Presenter's city and country Cagliari, Italy Presenter's email address riccardocau00@gmail.com 0028 Sensitivity and specificity of the measurement of epicardial adipose tissue thickness as a pr... Pawel Gac, Piotr Macek, Malgorzata Poreba, Rafal Poreba B Keywords b CT/CT-Angiography, epicardial adipose tissue thickness, CAD-RADS B Topic b Imaging Technique B Purpose/Objectives b The aim of the study was to assess the usefulness of measuring epicardial adipose tissue thickness as a predictor of the severity of coronary artery disease (CAD). B Methods and Materials b Out of a few dozen COVID-19 patients who underwent cardiac MRI at our institution, two male patients (Patient 1 26-year-old, Patient 2 40-years-old) showed matching signs and symptoms and were identified as potential MIS-A patients. Presenter's first name and last name Vitaliano Buffa Presenter's city and country Roma Italy Presenter's email address vitalianobuffa@libero.it 0072 Role of Gemstone Spectral Imaging in Evaluation of Coronary Artery Disease: A Primer for Card... Smily Sharma, VENKATA SUBBAIH ARUNACHALAM, Rakesh Chauhan B Keywords b CT/CT-Angiography, Dual Energy CT, Plaque Imaging, Myocardial Iodine Quantification B Topic b Imaging Technique B Purpose/Objectives b To study role of Gemstone Spectral Imaging (GSI) in evaluation of Coronary Artery Disease. Cardiovascular magnetic resonance imaging features and prognostic value in immune checkpoint... Farah Cadour, Jennifer Cautela, Stanislas Rapacchi, Arthur Varoquaux, Paul Habert, ARNAUD, Al... B Keywords b myocarditis, cardio-oncology, cardiac magnetic resonance, immune checkpoint inhibitor B Topic b Area of Interest B Purpose/Objectives b ICI-M is an emerging and severe complication of cancer treatment, for which diagnosis and risk stratification are challenging. [Extracted from the article]

Cold-inducible RNA-binding protein (CIRP) is an intracellular RNA-chaperone and extracellular promoter of inflammation, which is increasingly expressed and released under conditions of hypoxia and cold stress. The functional relevance of CIRP for angiogenesis and regeneration of ischemic muscle tissue has never been investigated and is the topic of the present study. We investigated the role of CIRP employing CIRP deficient mice along with a hindlimb model of ischemia-induced angiogenesis. 1 and 7 days after femoral artery ligation or sham operation, gastrocnemius muscles of CIRP-deficient and wildtype mice were isolated and processed for (immuno-) histological analyses. CIRP deficient mice showed decreased ischemic tissue damage as evidenced by Hematoxylin and Eosin staining, whereas angiogenesis was enhanced as demonstrated by increased capillary/muscle fiber ratio and number of proliferating endothelial (CD31+/BrdU+) cells on day 7 after surgery. Moreover, CIRP deficiency resulted in a reduction of total leukocyte count (CD45+), neutrophils (myeloperoxidase, MPO+), neutrophil extracellular traps (NETs) (MPO+/CitH3+), and inflammatory M1-like polarized macrophages (CD68+/MRC1-), whereas the number of tissue regenerating M2-like polarized macrophages (CD68+/MRC1-) was increased in ischemic tissue samples. In summary, we show that the absence of CIRP ameliorates angiogenesis and regeneration of ischemic muscle tissue, most likely by influencing macrophage polarization in direction to regenerative M2-like macrophages. [ABSTRACT FROM AUTHOR]