ABSTRACT
Diabetes is a chronic noncommunicable disease,which is can't be cured,and only can be suppressed by long-term treatment and self-management.The clinical decision support system can simulate the thinking process of diabetes specialists in disease diagnosis,and can provide the regular medical treatment plans and recommend the optimal plans to doctors.Most of the existing clinical decision support systems are based on clinical guidelines,rule-based and case-based reasoning as well as ontology-based systems.The big data technology can acquire and process multiple heterogeneous data,and provide a more scientific personalized treatment plan.In recent years,a variety of big date processing methods have been applied to the clinical diagnosis of diabetes based on decision tree,neural network,fuzzy logic,support vector machine,APRIORI association rules and multidimensional analysis,and timing mining.However,these methods are still in preliminary stage.The framework of diabetes clinical decision support system based on big data technology was analyzed,and the future diagnostic and treatment methods were forecast.
ABSTRACT
Basic Requirements of Teaching Basic Courses of Computer in Universities proposed pro-moting reforms to universities' computer basic education, whose core was to cultivate the students' computational thinking ability, infiltrating the essential thinking method of computational science in content and design of the course. Tianjin Medical University distinguished different levels of students according to a proficiency test, implemented graded teaching, introduced MATLAB in the course of program design, and performed modular teaching according to the students' specialties. All courses implemented small class teaching to strengthen the students' ability to apply computational thinking and computer technology to medical practice and introduced network-based test, in order to provide ideas for the teaching reform of computer basic courses in medical col-leges and universities.
ABSTRACT
Objective Toinvestigatetheentropyoflocalfieldpotentials(LFPs)recordedinratmedialprefrontal cortex during a Y-maze working memory (WM) task, to provide computing support for neural coding mechanism.Methods Sixteen-channel LFPs were recorded from SD rats while they performed a Y-maze WM task.The data came from 4 rats, 20 trials (10 correct trials and 10 incorrect trials) per rat provided by laboratory of neurobiology in medicine,Tianjin Medical University.Original LFPs were preprocessed to remove 50 Hz power line noise and baseline drift.Multi-taper Fourier transform was applied to calculate spatial distributions of LFPs and band pass filter were used to extract characteristic signal.The entroy coding of 16 channel LFPs was as follows: the physiological window was set to be 500 ms, the step length of physiological window was set to be 125 ms, windows were added to LFPs data, and then LFPs entropy of each sliding window was computed and averaged to get the trend of multichannel entropy values duringthe WM task.Results The power of θ band (4-12 Hz) in LFPs increased.The averaged entropy value ofmultichannel θ band LFPs in correct trials was 0.939±-0.020, which were larger than those in the resting state, 0.795±0.031 (P＜0.05).Those during wrong WM task had no significant difference, which didn't encode the WM task.Conclusions The principal frequency band related to WM is the θ band and LFPs entropy encodes the WM effectively.
ABSTRACT
Objective This paper aimed to investigate the collaborative coding of two different modes of neural signals including local field potentials and spiking activity (multi-unit activity) which recorded in medial prefrontal cortex of sprague-dawley (SD) rats in Y maze working memory (WM) task,to provide computing support for neural coding mechanism of WM.Methods 1.Experiment data was multi-channel neural signals (local field potentials (LFPs)-Spikes) recorded from prefrontal cortex of 4 SD rats during WM process and resting state,provided by the lab of Neuro-engineering,Tianjin Medical University.2.LFPs preprocessing included baseline drift removing and power-line noise eliminating.3.Physiology window width 500 ms,step 125 ms were selected and average rate per channel was calculated to turn the discrete point signal spikes to continuous signal.4.LFPs characteristic frequency band was obtained by using short time Fourier transform and signals of the characteristic frequency band were extracted by band pass filter.5.Mutual information (MI) of LFPs-spikes was computed to get the distribution of multi-channel MI values.Results 1.LFPs distribution demonstrated that the power focused at θ band (4-12 Hz)during the WM tasks.2.The average MI value of θ band LFPs and spikes (4 rats,over 10 trials) were 0.49±0.04,0.39±0.03,0.41±0.03,0.48±0.02,respectively,which were significantly larger than those in the resting state (t test,P＜0.05).Conclusions These findings indicate that θ band LFPs represents behavior correlated to WM and its synergy with spiking activity plays an important role in encoding WM task.
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Objective To investigate the effects of propofol anesthesia on cognitive function of aged rats. Methods Ninety-six male aged SD rats (16 months) were collected and given propofol anesthesia via tail vein catheter. At 7, 30, and 90 d after anesthesia, fear conditioning experiment was performed to test long-term memory of the aged rats (12 rats at each time point, total 36 rats). At 1, 2, 3, 4, and 5 d after anesthesia, spontaneous alternation in Y-maze experiment was performed to test spatial working memory of the aged rats (12 rats at each time point, total 60 rats). Results There were no statistical differences in long-term memory at 7, 30, and 90 d after anesthesia between the propofol group and control group (P>0.05). While spatial working memory of aged rats in propofol group was impaired at 1 and 2 d after anesthesia (P0.05). Conclusions These results indicate that clinical dose propofol anesthesia will not induce long-term memory impairment of aged rats, although it impairs spatial working memory of aged rats within 48 h after anesthesia.