Simulation-based medical education is becoming increasingly common. In this paper, the status and goal of SBME development is analyzed after a brief introduction of SBME. Secondly, the essentiality and possibility of bringing SBME to a situated paradigm are clarified, because there are rich implications for situated cognition as the theory foundation of SBME. As a main discussion point, eight practical situated designing principles for SBME in theoretical and practical contexts are then expounded. Finally, a specific attitude toward the relationship between theory and practice for the SBME teachers is also elucidated.
Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
OBJECTIVE: To investigate the role of direct and indirect recognition in pig-to-man xenotransplantation. METHODS: Taken the peripheral blood lymphocytes (PBLC) from three Neijiang pigs and two humans as stimulators and respondors, the one-way mixed lymphatic reactions (MLR) of xenograft were carried out, and allo- and self-PBLC as control. RESULTS: Among the three patterns of MLR, syngeneic was MLR the lowest in proliferation, the allogenic MLR was the highest, and the xenogenic MLR was medium. The PBLCs from humans and pigs were matched on HLA-A, B, DR and DQ by means of modified Terasaki assay. The match on pigs was failure because of the pre-existing natural xenogenic antibody in the testing serum. CONCLUSION: The results suggest that the degree of MHC matching still affect the rejection in xenotransplantation, but the present serum assay of MHC matching is not fit for pig.
Objective To review targeted muscle reinnervation (TMR) surgery for the construction of intelligent prosthetic human-machine interface, thus providing a new clinical intervention paradigm for the functional reconstruction of residual limbs in amputees. MethodsExtensively consulted relevant literature domestically and abroad and systematically expounded the surgical requirements of intelligent prosthetics, TMR operation plan, target population, prognosis, as well as the development and future of TMR. Results TMR facilitates intuitive control of intelligent prostheses in amputees by reconstructing the “brain-spinal cord-peripheral nerve-skeletal muscle” neurotransmission pathway and increasing the surface electromyographic signals required for pattern recognition. TMR surgery for different purposes is suitable for different target populations. Conclusion TMR surgery has been certified abroad as a transformative technology for improving prosthetic manipulation, and is expected to become a new clinical paradigm for 2 million amputees in China.
The acoustic detection method based on machine learning and signal processing is an important method of pathological voice detection and the extraction of voice features is one of the most important. Currently, the features widely used have disadvantage of dependence on the fundamental frequency extraction, being easily affected by noise and high computational complexity. In view of these shortcomings, a new method of pathological voice detection based on multi-band analysis and chaotic analysis is proposed. The gammatone filter bank was used to simulate the human ear auditory characteristics to analyze different frequency bands and obtain the signals in different frequency bands. According to the characteristics that turbulence noise caused by chaos in voice will worsen the spectrum convergence, we applied short time Fourier transform to each frequency band of the voice signal, then the feature gammatone short time spectral self-similarity (GSTS) was extracted, and the chaos degree of each band signal was analyzed to distinguish normal and pathological voice. The experimental results showed that combined with traditional machine learning methods, GSTS reached the accuracy of 99.50% in the pathological voice database of Massachusetts Eye and Ear Infirmary (MEEI) and had an improvement of 3.46% compared with the best existing features. Also, the time of the extraction of GSTS was far less than that of traditional nonlinear features. These results show that GSTS has higher extraction efficiency and better recognition effect than the existing features.
Objective To explore the effect of preoperative hypothyroidism on postoperative cognition dysfunction (POCD) in elderly patients after on-pump cardiac surgery. Methods Patients who were no younger than 50 years and scheduled to have on-pump cardiac surgeries were selected in West China Hospital from March 2016 to December 2017. Based on hormone levels, patients were divided into two groups: a hypo group (hypothyroidism group, thyroid stimulating hormone (TSH) >4.2 mU/L or free triiodothyronine 3 (FT3) <3.60 pmol/L or FT4 <12.0 pmol/L) and an eu group (euthyroidism group, normal TSH, FT3 and FT4). The mini-mental state examination (MMSE) test and a battery of neuropsychological tests were used by a fixed researcher to assess cognitive function on 1 day before operation and 7 days after operation. Primer outcome was the incidence of POCD. Secondary outcomes were the incidence of cognitive degradation, scores or time cost in every aspect of cognitive function. Results No matter cognitive function was assessed by MMSE or a battery of neuropsychological tests, the incidence of POCD in the hypo group was higher than that of the eu group. The statistical significance existed when using MMSE (55.56% vs. 26.67%, P=0.014) but was absent when using a battery of neuropsychological tests (55.56% vs. 44.44%, P=0.361). The incidence of cognitive deterioration in the hypo group was higher than that in the eu group in verbal fluency test (48.15% vs. 20.00%, P=0.012). The cognitive deterioration incidence between the hypo group and the eu group was not statistically different in the other aspects of cognitive function. There was no statistical difference about scores or time cost between the hypo group and the eu group in all the aspects of cognitive function before surgery. After surgery, the scores between the hypo group and the eu group was statistically different in verbal fluency test (26.26±6.55 vs. 30.23±8.00, P=0.023) while was not statistically significant in other aspects of cognitive function. Conclusion The incidence of POCD is high in the elderly patients complicated with hypothyroidism after on-pump cardiac surgery and words reserve, fluency, and classification of cognitive function are significantly impacted by hypothyroidism over than other domains, which indicates hypothyroidism may have close relationship with POCD in this kind of patients.
Gait recognition is a new technology in biometric recognition and medical treatment which has advantages such as long-distance and non-invasiveness. Depending on the differences between different people's walking postures, we can recognize individuals by characteristics extracted from the images of walking movement. A complete gait recognition process usually includes gait sequence acquisition, gait detection, feature extracting and recognition. In this paper, the commonly used methods of these four processes are introduced, and feature extraction methods are classified from different multi-angle views. And then the new algorithm of multi-view emerged in recent years is highlighted. In addition, this paper summarizes the existing difficulties of gait recognition, and looks into the future development trends of it.
In order to improve the motion fluency and coordination of lower extremity exoskeleton robots and wearers, a pace recognition method of exoskeleton wearer is proposed base on inertial sensors. Firstly, the triaxial acceleration and triaxial angular velocity signals at the thigh and calf were collected by inertial sensors. Then the signal segment of 0.5 seconds before the current time was extracted by the time window method. And the Fourier transform coefficients in the frequency domain signal were used as eigenvalues. Then the support vector machine (SVM) and hidden Markov model (HMM) were combined as a classification model, which was trained and tested for pace recognition. Finally, the pace change rule and the human-machine interaction force were combined in this model and the current pace was predicted by the model. The experimental results showed that the pace intention of the lower extremity exoskeleton wearer could be effectively identified by the method proposed in this article. And the recognition rate of the seven pace patterns could reach 92.14%. It provides a new way for the smooth control of the exoskeleton.
Immune-mediated necrotizing myopathy (IMNM) is a rare type of autoimmune inflammatory myopathy, which can be divided into anti-signal recognition particle antibody positive IMNM, anti-3-hydroxy-3-methylglutaryl coenzyme A reductase antibody positive IMNM and serum antibody negative IMNM according to different autoantibodies. The prognosis of IMNM is worse than that of most other types of myositis. At present, there are differences in the studies of prognostic factors of IMNM at home and abroad, and there is a lack of large-scale clinical studies. This article will review the prognostic factors of IMNM.
Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.