Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k‐nearest neighbor, naïve Bayes, and multilayer perceptron) were used to further verify the predictive ability of the model. Forty‐one (12.9%) women experienced adverse maternal events, and 93 (29.2%) neonates experienced adverse neonatal events. Seven high‐risk factors were discovered in the maternal model, including New York Heart Association class, Eisenmenger syndrome, pulmonary hypertension, left ventricular ejection fraction, sinus tachycardia, arterial blood oxygen saturation, and pregnancy duration. The machine learning–based algorithms showed that the maternal model had an accuracy of 0.76 to 0.86 (area under the receiver operating characteristic curve=0.74–0.87) in the development cohort, and 0.72 to 0.86 (area under the receiver operating characteristic curve=0.68–0.80) in the validation cohort. Three high‐risk factors were discovered in the neonatal model, including Eisenmenger syndrome, preeclampsia, and arterial blood oxygen saturation. The machine learning–based algorithms showed that the neonatal model had an accuracy of 0.75 to 0.80 (area under the receiver operating characteristic curve=0.71–0.77) in the development cohort, and 0.72 to 0.79 (area under the receiver operating characteristic curve=0.69–0.76) in the validation cohort. Conclusions Two prenatal risk assessment models for both adverse maternal and neonatal events were established, which might assist clinicians in tailoring precise management and therapy in pregnant women with congenital heart disease.
Some 2D graphical representations of DNA sequences have been reported by several authors, which give visual characterizations of DNA sequences. In this paper, we present a new 2D graphical representation of DNA sequences without degeneracy. Furthermore, we propose two methods for the visualization and analysis of long DNA sequences. Keywords: DNA, 2DD-curve, graphical representation, leading eigenvalue
In this paper, we propose a novel deep reinforcement learning (DRL) system for the autonomous navigation of mobile robots that consists of three modules: map navigation, multi-view perception and multi-branch control. Our DRL system takes as the input a routed map provided by a global planner and three RGB images captured by a multi-camera setup to gather global and local information, respectively. In particular, we present a multi-view perception module based on an attention mechanism to filter out redundant information caused by multi-camera sensing. We also replace raw RGB images with low-dimensional representations via a specifically designed network, which benefits a more robust sim2real transfer learning. Extensive experiments in both simulated and real-world scenarios demonstrate that our system outperforms state-of-the-art approaches.
Objective
To investigate the clinical effects of acupuncture and moxibustion on promoting early motor dysfunction in patients with acute ischemic stroke.
Methods
A total of 130 acute ischemic stroke patients with early motor dysfunction were selected as the study subjects. The patients were divided into observation group and control group by random number table, with 65 cases in each group. The observation group received acupuncture rehabilitation. The control group was given traditional acupuncture and moxibustion. Fugl-Meyer motor function assessment score (FMA) and clinical neurological deficit score and evaluation criteria were compared. The improvement of limb motor function was compared between the two treatment methods.
Results
Compared with the degree of neurological deficit, the scores of improvement at two weeks and four weeks after treatment in the observation group were all better than those in the control group. The differences between the two groups were significant (P<0.05). FMA in the observation group and the control group were significantly improved compared with that before treatment, the difference was significant (P<0.05); the observation group improved better than the control group, the difference was significant (t=2.0152, P<0.05).
Conclusions
Acupuncture and Moxibustion can promote limb function and restore neurological deficits in patients with acute ischemic stroke associated with early motor dysfunction, which is more satisfactory than traditional acupuncture and rehabilitation therapy.
Key words:
Acupuncture rehabilitation generalization; Stroke; Early; Motor dysfunction
Pelvic landmark detection is a significant pre-task to measure the clinical measurement in pelvic abnormality analysis. Accurate pelvic landmark detection could provide reliable clinical parameter measurement results, which are helpful for doctors to diagnose and treat pelvic diseases. However, the multi-scale characteristics, temporal diversity, and pathological abnormalities of different pelvic X-rays bring enormous challenges to the landmark detection task. In order to retain strong robustness in irregular pelvic X-rays, we propose a novel, flexible two-stage framework. In the initial stage, a single neural network is employed to estimate the locations of every landmark simultaneously, enabling the identification of potential landmark regions. Then, the receptive field of candidate region proposals is expanded by 4 times through the receptive field amplification module. In the second stage, the landmark detection module fuses semantically rich features at different scales through a multi-scale semantic fusion module. So that the framework can fully learn the strongly relevant semantic information around the landmark at high resolution. We collected a data set of 430 pelvic X-rays, including a large number of irregular pelvic X-rays, to evaluate our framework. The experimental results demonstrate that our framework achieves a state-of-the-art detection mean radial error of 3.724 ± 4.247 mm. The experimental results show that the proposed method can help doctors quickly and accurately find the characteristic points of the pelvis and could be applied to clinical diagnosis.
Abstract The large and heavy‐duty hexapod robot has strong motion stability and load capacity, which promises to have a wide range of application prospects in rescue and disaster relief. Multi‐mode gait and static stability during walking make the hexapod robot adapt to more diverse terrains, while little research has been conducted on the motion control methods of heavy‐duty hexapod robots in complex environments. A novel heuristic whole‐body motion control framework for the heavy‐duty hexapod robot to traverse complex terrain is presented. By splitting the legged locomotion into a single task, the whole‐body motion could be planned in a reasonable time. The terrain adaptation strategy is designed to improve the complex terrain passability. Ground reaction forces are then optimised based on single rigid‐body dynamics with heuristics. This framework utilised simple but powerful heuristics to approximate complex dynamics and allows for a single set of parameters for all task conditions. Simulation results demonstrate the robustness and adaptability of the proposed framework.
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