33%. 3-, 4-, and 5-fold cross-validation of the prediction accuracy of the support vector machine model is 77.79%~81.94%. Conclusion The pathological grading of IDC can be predicted and evaluated by texture analysis and feature extraction of breast tumors. This method can provide much valuable information for doctors’ clinical diagnosis. With further development, the model demonstrates high potential for practical clinical use. Copyright © 2020 Gaoteng Yuan et al.The purpose of this study is the application of pressure sensors in diagnostics and evaluation of the accuracy diagnostics of lumbar disc herniation at levels L4/L5 and L5/S1 using the aforementioned platform. The motivation behind the idea to apply the pressure measurement platform is the fact that the motor weakness of plantar and dorsal flexia of the feet is one of the absolute indications for the operative treatment of patients with lumbar disc herniation at the indicated levels. In patients, MRI diagnosis of the lumbosacral spine served as the ground truth in the diagnosis of herniation at L4/L5 and L5/S1 levels. The inclusive criteria for the study were the proven muscle weakness based on manual muscle tests performed prior to surgery, after seven days of surgery and after physical therapy. The results obtained with the manual muscular test were compared with the results obtained using our platform. The study included 33 patients who met the inclusion criteria. The results of the measurements indicate that the application of our platform with pressure sensors has the same sensitivity diagnostics as a manual muscle test, when done preoperatively and postoperatively. After physical therapy, pressure sensors show statistically significantly better sensitivity compared to the clinical manual muscle test. The obtained results are encouraging in the sense that the pressure platform can be an additional diagnostic method for lumbar disc herniation detection and can indicate the effectiveness of operative treatment and physical therapy after operation. The main advantage of the system is the cost; the whole system with platform and sensors is not expensive. Copyright © 2020 Miodrag Peulić et al.Electromagnetic source imaging (ESI) techniques have become one of the most common alternatives for understanding cognitive processes in the human brain and for guiding possible therapies for neurological diseases. However, ESI accuracy strongly depends on the forward model capabilities to accurately describe the subject’s head anatomy from the available structural data. Attempting to improve the ESI performance, we enhance the brain structure model within the individual-defined forward problem formulation, combining the head geometry complexity of the modeled tissue compartments and the prior knowledge of the brain tissue morphology. We validate the proposed methodology using 25 subjects, from which a set of magnetic-resonance imaging scans is acquired, extracting the anatomical priors and an electroencephalography signal set needed for validating the ESI scenarios. Obtained results confirm that incorporating patient-specific head models enhances the performed accuracy and improves the localization of focal and deep sources. Copyright © 2020 Yohan Céspedes-Villar et al.Venoarterial extracorporeal life support (VA-ECLS) is used in ICUs (intensive care units) for the most extreme presentations of acute and severe cardiogenic shock, and one of the main issues the clinicians have to deal with is the weaning from VA-ECLS. In this study, a patient-specific model of the cardiovascular system connected to a VA-ECLS is built to improve the understanding of this complex system. Pig experiments are performed to validate the model, and the results are quite promising since the mean difference between experimental data and simulation is smaller than 5% for all the hemodynamic quantities. It is also a major objective of this paper to provide a proof-of-concept analysis that model-based approaches could improve the weaning strategy for VA-ECLS therapy. Copyright © 2020 Simon Habran et al.The type III secretion system (T3SS) is a special protein delivery system in Gram-negative bacteria which delivers T3SS-secreted effectors (T3SEs) to host cells causing pathological changes. Numerous experiments have verified that T3SEs play important roles in many biological activities and in host-pathogen interactions. Accurate identification of T3SEs is therefore essential to help understand the pathogenic mechanism of bacteria; however, many existing biological experimental methods are time-consuming and expensive. New deep-learning methods have recently been successfully applied to T3SE recognition, but improving the recognition accuracy of T3SEs is still a challenge. In this study, we developed a new deep-learning framework, ACNNT3, based on the attention mechanism. We converted 100 residues of the N-terminal of the protein sequence into a fusion feature vector of protein primary structure information (one-hot encoding) and position-specific scoring matrix (PSSM) which are used as the feature input of the network model. We then embedded the attention layer into CNN to learn the characteristic preferences of type III effector proteins, which can accurately classify any protein directly as either T3SEs or non-T3SEs. We found that the introduction of new protein features can improve the recognition accuracy of the model. Our method combines the advantages of CNN and the attention mechanism and is superior in many indicators when compared to other popular methods. Using the common independent dataset, our method is more accurate than the previous method, showing an improvement of 4.1-20.0%. Copyright © 2020 Jie Li et al.Background Reference interval (RI) research is to make it a concise, effective, and practical diagnostic tool. This study aimed to establish sex- and age-specific RI for myocardial enzyme activity in population aged 1- less then 18 years old in Changchun, China. ALKBH5inhibitor2 Methods Healthy subjects (n = 6,322, 1- less then 18 years old) were recruited from communities and schools. Aspartate aminotransferase (AST), lactate dehydrogenase (LDH), creatine kinase (CK), and creatine kinase isoenzyme (CKMB) were measured using an automatic biochemical analyzer. Fisher’s optimal segmentation method was used to partition by including percentiles as impact factors, aiming at minimizing the sum of the squares of the total dispersion into groups as splitting sequence of ordered data. Results AST decreased gradually and was partitioned as 1, 2∼ less then 10 and 10∼ less then 18 years old. LDH presented disparate descending rate among 1∼ less then 4, 4∼ less then 12, and 12∼ less then 18 years old. CK stood quite stable with the same RI in all ages.