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Percutaneous coronary intervention (PCI) has become a vital treatment approach for coronary artery disease, but the clinical data of PCI cannot be directly utilized due to its unstructured characteristics. The existing clinical named entity recognition (CNER) has been used to identify specific entities such as body parts, drugs, and diseases, but its specific potential in PCI clinical texts remains largely unexplored. How to effectively use CNER to deeply mine the information in the existing PCI clinical records is worth studying. In this paper, a total of 24 267 corpora are collected from the Cardiovascular Disease Treatment Center of the People's Hospital of Liaoning Province in China. We select three types of clinical record texts of fine-grained PCI surgical information, from which 5.8% of representative surgical records of PCI patients are selected as datasets for labeling. To fully utilize global information and multi-level semantic features, we design a novel character-level vector embedding method and further propose a new hybrid model based on it. Based on the classic Bidirectional Long Short-Term Memory Network (BiLSTM), the model further integrates Convolutional Neural Networks (CNNs) and Bidirectional Encoder Representations from Transformers (BERTs) for feature extraction and representation, and finally uses Conditional Random Field (CRF) for decoding and predicting label sequences. This hybrid model is referred to as BCC-BiLSTM in this paper. In order to verify the performance of the proposed hybrid model for extracting PCI surgical information, we simultaneously compare both representative traditional and intelligent methods. Under the same circumstances, compared with other intelligent methods, the BCC-BiLSTM proposed in this paper reduces the word vector dimension by 15%, and the F1 score reaches 86.2% in named entity recognition of PCI clinical texts, which is 26.4% higher than that of HMM. The improvement is 1.2% higher than BiLSTM + CRF and 0.7% higher than the most popular BERT + BiLSTM + CRF. Compared with the representative models, the hybrid model has better performance and can achieve optimal results faster in the model training process, so it has good clinical application prospects. [ABSTRACT FROM AUTHOR]

Machine learning provides increasingly reliable assistance for medical experts in diagnosing coronary heart disease. This study proposes a deep learning hybrid model based coronary heart disease (CAD) prediction method, which can significantly improve the prediction accuracy compared to traditional solutions. This research scheme is based on the data of 7291 patients and proposes a hybrid model, which uses two different deep neural network models and a recurrent neural network model as the main model for training. The prediction results based on the main model training use a k-nearest neighbor model for secondary training so as to improve the accuracy of coronary heart disease prediction. The comparison between the model prediction results and the clinical diagnostic results shows that the prediction model has a prediction accuracy rate of 82.8%, a prediction precision rate of 87.08%, a prediction recall rate of 88.57%, a prediction F1-score of 87.82%, and an area under the curve value of 0.8 in the test set. Compared to single model machine learning predictions, the hybrid model has a significantly improved accuracy and has effectively solved the problem of overfitting. A deep learning based CAD prediction hybrid model that combines multiple weak models into a strong model can fully explore the complex inter-relationships between various features under limited feature values and sample size, improve the evaluation indicators of the prediction model, and provide effective auxiliary support for CAD diagnosis. [ABSTRACT FROM AUTHOR]

Computed tomography angiography (CTA) has become the main imaging technique for cardiovascular diseases. Before performing the transcatheter aortic valve intervention operation, segmenting images of the aortic sinus and nearby cardiovascular tissue from enhanced images of the human heart is essential for auxiliary diagnosis and guiding doctors to make treatment plans. This paper proposes a nnU-Net (no-new-Net) framework based on deep learning (DL) methods to segment the aorta and the heart tissue near the aortic valve in cardiac CTA images, and verifies its accuracy and effectiveness. A total of 130 sets of cardiac CTA image data (88 training sets, 22 validation sets, and 20 test sets) of different subjects have been used for the study. The advantage of the nnU-Net model is that it can automatically perform preprocessing and data augmentation according to the input image data, can dynamically adjust the network structure and parameter configuration, and has a high model generalization ability. Experimental results show that the DL method based on nnU-Net can accurately and effectively complete the segmentation task of cardiac aorta and cardiac tissue near the root on the cardiac CTA dataset, and achieves an average Dice similarity coefficient of 0.9698 ± 0.0081. The actual inference segmentation effect basically meets the preoperative needs of the clinic. Using the DL method based on the nnU-Net model solves the problems of low accuracy in threshold segmentation, bad segmentation of organs with fuzzy edges, and poor adaptability to different patients' cardiac CTA images. nnU-Net will become an excellent DL technology in cardiac CTA image segmentation tasks. [ABSTRACT FROM AUTHOR]

Recent studies have found that cardiac stem cells (CSCs) are present in the adult heart. CSCs play an important role in maintaining the balance of the number of myocardial cells. The purpose of this study was to examine characteristics of human CSCs and their correlation with clinical characteristics of patients. We collected heart auricles of 105 patients (age range, 1–78 years; mean, 55.6 ± 17.0 years) undergoing cardiac surgery to obtain CSCs. We assayed the percentage of c-kit positive (c-kit+) CSCs with flow cytometry. Plasma Nɛ-(carboxymethyl)lysine (CML) concentrations were measured by enzyme-linked immunosorbent assay. The percentage of c-kit+ CSCs was 4.96 ± 3.12% (0.98–17.17%), and this was significantly negatively correlated with age, the presence of diabetes mellitus (DM) and coronary heart disease (CHD) (r values were −0.797 [P < 0.01], −0.500 [P < 0.01] and −0.250 [P = 0.011], respectively). The percentage of c-kit+ CSCs was significantly negatively correlated with CML concentrations (r = −0.859, P < 0.01). The percentage of c-kit+ CSCs decreases with ageing and is further decreased in patients with DM and/or CHD. Furthermore, plasma CML concentrations may have potential as an indicator of the number of c-kit+ CSCs. [ABSTRACT FROM AUTHOR]

Background: Nε-carboxymethyl-lysine (CML) is a major advanced glycation end-product (AGEs) widely found in foods. The aim of our study was to evaluate how exogenous CML-peptide is dynamically absorbed from the gastrointestinal tract and eliminated by renal tubular secretion using microPET imaging. Methods: The present study consisted of three investigations. In study I, we synthesized the imaging tracer 18F-CML by reacting N-succinimidyl 4-18F-fluorobenzoate (18F-SFB) with CML. In study II, the biological activity of 18F-CML was evaluated in RAW264.7 cells and HepG2 cells. In study III, the biodistribution and elimination of AGEs in ICR mice were studied in vivo following tail vein injection and intragastric administration of 18F-CML. Result: The formation of 18F-CML was confirmed by comparing its retention time with the corresponding reference compound 19F-CML. The radiochemical purity (RCP) of 18F-CML was >95%, and it showed a stable character in vitro and in vivo. Uptake of 18F-CML by RAW264.7 cells and HepG2 cells could be inhibited by unmodified CML. 18F-CML was quickly distributed via the blood, and it was rapidly excreted through the kidneys 20 min after tail vein injection. However, 18F-CML was only slightly absorbed following intragastric administration. After administration of 18F-CML via a stomach tube, the radioactivity was completely localized in the stomach for the first 15 min. At 150 min post intragastric administration, intense accumulation of radioactivity in the intestines was still observed. Conclusions: PET technology is a powerful tool for the in vivo analysis of the gastrointestinal absorption of orally administered drugs. 18F-CML is hardly absorbed by the gastrointestinal tract. It is rapidly distributed and eliminated from blood following intravenous administration. Thus, it may not be harmful to healthy bodies. Our study showed the feasibility of noninvasively imaging 18F-labeled AGEs and was the first to describe CML-peptide gastrointestinal absorption by means of PET. [ABSTRACT FROM AUTHOR]

• The appropriate structure parameters of the device are put forward. • The instantaneous impact force is decomposed into time-varying and fluctuating part. • The influences of different factors on the impact force are analyzed. • The generation mechanism of self-excited aspiration pulsed jet device is discussed. In this paper, based on the traditional low-frequency self-excited oscillation pulsed jet device, a self-excited aspiration pulsed jet device is designed. For the structural parameters, including the aspiration capacity, impact characteristics, and pulse generation mechanism, the jet impingement is analyzed experimentally under the aspiration and non-aspiration conditions. It was found that, compared to the non-aspiration condition, the maximum aspiration capacity, the increase of instantaneous impact force, and the obvious pulse period, which is between 1 and 2 s, can be obtained under appropriate structural device parameters. The db10 orthogonal wavelet and the empirical mode decomposition methods can decompose instantaneous impact force into time-varying and fluctuating impact forces. However, under different confining pressures, each method has its own advantages in extracting the time-varying impact force. In addition, after aspiration, the confining pressure and target distance have little effect on the pulse frequency of the time-varying impact force, while higher confining pressure and larger target distance result in smaller pulse amplitude. Under different confining pressures and target distances, after aspiration, the time-average impact force at and near the target center increases, while far away from the target center it decreases. Moreover, after aspiration, the fluctuation amplitude and intensity of the fluctuating impact force increase, and the confining pressure has little effect on the pulse frequency. However, higher confining pressure results in smaller fluctuation amplitude, intensity, and energy spectral density. After aspiration, subsonic and supersonic water flows are formed inside the device. In addition, it was found that there is a relationship between the aspiration capacity, the time-variation pressure of the collision body measuring point, and the impact force. Based on this relation, the pulse generation mechanism of the device is revealed. [ABSTRACT FROM AUTHOR]

The data input process for most chest pain centers is not intelligent, requiring a lot of staff to manually input patient information. This leads to problems such as long processing times, high potential for errors, an inability to access patient data in a timely manner and an increasing workload. To address the challenge, an Internet of Things (IoT)-driven chest pain center is designed, which crosses the sensing layer, network layer and application layer. The system enables the construction of intelligent chest pain management through a pre-hospital app, Ultra-Wideband (UWB) positioning, and in-hospital treatment. The pre-hospital app is provided to emergency medical services (EMS) centers, which allows them to record patient information in advance and keep it synchronized with the hospital's database, reducing the time needed for treatment. UWB positioning obtains the patient's hospital information through the zero-dimensional base station and the corresponding calculation engine, and in-hospital treatment involves automatic acquisition of patient information through web and mobile applications. The system also introduces the Bidirectional Long Short-Term Memory (BiLSTM)-Conditional Random Field (CRF)-based algorithm to train electronic medical record information for chest pain patients, extracting the patient's chest pain clinical symptoms. The resulting data are saved in the chest pain patient database and uploaded to the national chest pain center. The system has been used in Liaoning Provincial People's Hospital, and its subsequent assistance to doctors and nurses in collaborative treatment, data feedback and analysis is of great significance.

Background: The purpose of this study was to develop a coronary artery disease (CAD) prediction model that optimally estimates the pre-test probability of CAD for patients suspected of CAD., Methods and Results: This retrospective, multi-centre study included 7360 consecutive patients (4678 men, 57.87±11.42 years old; 2682 women, 61.60±9.58 years old) who underwent coronary angiography for evaluation of CAD. A prediction model was fitted for diagnosis of CAD with the help of eight significant risk factors including sex, age, smoking status, diabetes, hypertension, dyslipidaemia, serum creatinine and angina. All potential predictors were significantly associated with the presence of CAD. The prevalence of CAD was significantly higher in men than in women. The clinical model gives a relatively accurate prediction of CAD with an area under the curve (AUC) of 0.74 (95% CI, 0.88-0.96; P<0.001). Addition of angina to the prediction model improves the predictive precision of the model. The optimal cut-off for predicting CAD in this model was 0.79 with a sensitivity of 0.658 and a specificity of 0.709., Conclusion: A prediction model including age, sex, and cardiovascular risk factors allow for an accurate estimation of the pre-test probability of coronary artery disease in Chinese populations. This algorithm may be useful in making decisions relating to the diagnosis of CAD., (Copyright © 2017. Published by Elsevier B.V.)

Background:  Stem cells transplanted to the ischemic myocardium usually encounter massive cell death within a few days after transplantation, and hypoxic preconditioning (HPC) is currently used as a strategy to prepare stem cells for increased survival and engraftment in the heart. The purpose of this study is to determine whether Pim-1 kinase mediates any beneficial effects of HPC for human cardiac progenitor cells (CPCs)., Methods and Results:  Human CPCs were isolated from an adult heart auricle and were purified by magnetic-activated cell sorting using c-kit magnetic beads; they were hypoxic preconditioned for 6h. Both Pim-1 and p-Akt were determined. CPCs were assigned to one of the following groups: (1) control (without HPC); (2) HPC; or (3) HPC+I (Pim-1 inhibitor). HPC can promote the survival of CPCs. HPC enhances the expression of Pim-1 kinase in a time-dependent manner, which causes a reduction of proapoptotic elements (cytochrome c and cleaved caspase-3) and the preservation/modulation of important components of the mitochondria (Bcl-2, Bcl-XL and p-Bad), and attenuates mitochondrial damages. All of these protective effects were blocked by a Pim-1 inhibitor., Conclusions:  Pim-1 plays a pivotal role in the protective effect of HPC for CPCs, and the promotion of the expression of Pim-1 in CPCs can as serve part of molecular therapeutic interventional strategies in the treatment of cardiomyopathy damage by blunting CPC death.

Background: Impairment of coronary flow reserve (CFR) has been generally demonstrated in diabetic patients and animals with microvascular complications but without obvious obstructive coronary atherosclerosis. There have been few studies investigating CFR in cases of relatively well-controlled therapy. The purpose of this study is to evaluate the effect of treatment with a Sphingosine-1-phosphate (S1P) receptor potent agonist, FTY720, on early diabetic rats in terms of CFR. METHODS AND RESULTS: Male Sprague-Dawley (SD) rats were divided into 3 groups: (1) streptozotocin-uninjected rats (control rats); (2) streptozotocin-injected hyperglycemic rats (diabetic group); and (3) FTY720-fed and streptozotocin-injected hyperglycemic rats. FTY720 (1.25 mg/kg per day orally) was administrated for 9 weeks in SD rats (from 6 weeks old to 15 weeks old). CFR was evaluated by (13)NH3-positron emission tomography. No obvious pathological changes of macrovascular atherosclerosis were observed in each group. Diabetic rats had impaired CFR compared with the control group (1.39±0.26 vs. 1.94±0.24, P<0.05). Treatment with FTY720 for 9 weeks attenuated the heart histological changes and improved CFR in 32% of diabetic rats (1.84±0.36 vs. 1.39±0.26, P<0.05)., Conclusions: In summary, long-term therapy with the Sphingosine-1-phosphate receptor agonist, FTY720, improved CFR by attenuating the heart histological changes, and it might have a beneficial effect on coronary microvascular function in diabetic rats.

Heart disease is one of the most important causes of death in developed countries. N(ε)-carboxymethyllysine (CML) is a major advanced glycation end product formed by combined reactions of non-enzymatic glycation and oxidation (glycoxidation), and it represents a general marker of oxidative stress. CML has been suggested to be involved in the pathogenesis of heart disease. Plasma CML is elevated in aging, atherosclerosis and/or diabetes. In this study, we measured cardiac CML levels to elucidate its role in the pathogenesis of heart disease. Cardiac tissues were collected from 105 patients (55.6 ± 17.0 years old: age range, 1-78 years) undergoing cardiac surgery. The diseases comprised coronary heart disease (CHD), CHD associated with diabetes mellitus (DM), valvular heart disease and congenital heart disease. The concentration of CML in cardiac tissues of each group was 4.31 ± 0.66, 5.29 ± 0.59, 2.74 ± 1.05 and 1.75 ± 1.16 μg/g, respectively. ELISA was used for measuring cardiac and plasma CML concentrations. Multiple linear regression analysis showed a significant positive correlation of CML concentrations with age (r = 0.803, p < 0.001), DM (r = 0.567, p < 0.001) and CHD (r = 0.523 p < 0.001). R(2) was 0.872 (p < 0.001); the three independent variables could explain 87.2% variation of CML concentrations. Cardiac CML concentrations exhibited a significant positive correlation with plasma CML (r = 0.983, p < 0.001). Our data indicate that cardiac CML concentrations increase with age, DM and/or CHD, and exhibit a positive correlation with plasma CML concentrations.

Background: N(ε)-carboxymethyl-lysine (CML) is a major advanced glycation end-product (AGEs) widely found in foods. The aim of our study was to evaluate how exogenous CML-peptide is dynamically absorbed from the gastrointestinal tract and eliminated by renal tubular secretion using microPET imaging., Methods: The present study consisted of three investigations. In study I, we synthesized the imaging tracer (18)F-CML by reacting N-succinimidyl 4-(18)F-fluorobenzoate ((18)F-SFB) with CML. In study II, the biological activity of (18)F-CML was evaluated in RAW264.7 cells and HepG2 cells. In study III, the biodistribution and elimination of AGEs in ICR mice were studied in vivo following tail vein injection and intragastric administration of (18)F-CML., Result: The formation of (18)F-CML was confirmed by comparing its retention time with the corresponding reference compound (19)F-CML. The radiochemical purity (RCP) of (18)F-CML was >95%, and it showed a stable character in vitro and in vivo. Uptake of (18)F-CML by RAW264.7 cells and HepG2 cells could be inhibited by unmodified CML. (18)F-CML was quickly distributed via the blood, and it was rapidly excreted through the kidneys 20 min after tail vein injection. However, (18)F-CML was only slightly absorbed following intragastric administration. After administration of (18)F-CML via a stomach tube, the radioactivity was completely localized in the stomach for the first 15 min. At 150 min post intragastric administration, intense accumulation of radioactivity in the intestines was still observed., Conclusions: PET technology is a powerful tool for the in vivo analysis of the gastrointestinal absorption of orally administered drugs. (18)F-CML is hardly absorbed by the gastrointestinal tract. It is rapidly distributed and eliminated from blood following intravenous administration. Thus, it may not be harmful to healthy bodies. Our study showed the feasibility of noninvasively imaging (18)F-labeled AGEs and was the first to describe CML-peptide gastrointestinal absorption by means of PET.