Objective To identify the key factors influencing hospital scientific and technological innovation and their interrelationships,providing references for enhancing the level of hospital scientific and technological inno-vation.Methods Based on The Science and Technology Evaluation Metrics for hospital,DEMATEL method was applied to calculate the centrality,causality,influence,and impactability of the influencing factors.Then,ISM method was used to construct a hierarchical structure of the influencing factors of hospital scientific and techno-logical innovation,thereby revealing the interrelationships and interaction mechanisms among the influencing factors.Results Nine factors,including standards and guidelines,citations of international authoritative guidelines papers,and the transformation of invention patents,were identified as key influencing factors for hospital scientific and tech-nological innovation.Through the ISM method,the influencing factors were classified into three levels:surface fac-tors,middle-level factors,and deep-level factors.Conclusion Hospital scientific and technological innovation is influ-enced by multiple factors.To enhance hospital scientific and technological innovation capabilities,efforts should be made in high-quality scientific and technological output,efficient transformation of scientific and technological achievements,and high clinical value orientation.

Objective To identify the key factors influencing hospital scientific and technological innovation and their interrelationships,providing references for enhancing the level of hospital scientific and technological inno-vation.Methods Based on The Science and Technology Evaluation Metrics for hospital,DEMATEL method was applied to calculate the centrality,causality,influence,and impactability of the influencing factors.Then,ISM method was used to construct a hierarchical structure of the influencing factors of hospital scientific and techno-logical innovation,thereby revealing the interrelationships and interaction mechanisms among the influencing factors.Results Nine factors,including standards and guidelines,citations of international authoritative guidelines papers,and the transformation of invention patents,were identified as key influencing factors for hospital scientific and tech-nological innovation.Through the ISM method,the influencing factors were classified into three levels:surface fac-tors,middle-level factors,and deep-level factors.Conclusion Hospital scientific and technological innovation is influ-enced by multiple factors.To enhance hospital scientific and technological innovation capabilities,efforts should be made in high-quality scientific and technological output,efficient transformation of scientific and technological achievements,and high clinical value orientation.

Objective:To investigate associations of neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) and monocyte-to-high-density lipoprotein cholesterol ratio (MHR) with carotid plaque and its vulnerability in patients with acute ischemic stroke (AIS).Methods:Consecutive patients with first-ever AIS admitted to the Department of Neurology, the First People's Hospital of Lianyungang from January 1, 2022 to January 31, 2023 were enrolled retrospectively. According to the carotid ultrasound findings after admission, they were divided into a non-plaque group and a plaque group. The latter was further divided into a stable plaque group and a vulnerable plaque group. Multivariate logistic regression analysis was used to evaluate the independent risk factors for carotid plaque and its vulnerability. The odds ratio ( OR) and its 95% confidence interval ( CI) were calculated. Results:A total of 369 patients with AIS were enrolled, including 259 males (70.20%), with a median age of 63.00 years (interquartile range, 56.00-70.00 years). There were 20 patients (5.42%) in the non-plaque group and 349 (94.58%) in the plaque group, including 142 (40.69%) in the stable plaque group and 207 (59.31%) in the vulnerable plaque group. Multivariate logistic regression analysis showed that higher NHR ( OR 2.558, 95% CI 1.068-6.126; P=0.035) and higher MHR ( OR 2.312, 95% CI 1.111-4.814; P=0.025) were the independent risk factors for carotid plaques; advanced age ( OR 1.069, 95% CI 1.037-1.103; P<0.001), alcohol consumption ( OR 2.072, 95% CI 1.049-4.092; P=0.036), higher NHR ( OR 1.521, 95% CI 1.146-2.019; P=0.004), and higher MHR ( OR 1.765, 95% CI 1.388-2.244; P<0.001) were the independent risk factors for vulnerable carotid plaques. Conclusion:Higher NHR and MHR are associated with carotid plaque and its vulnerability in patients with AIS.

Objective:To build and optimize the academic performance evaluation system and process of medical colleges, and to provide reference and support for the construction of "double first-class" medical colleges.Methods:Based on CIPP model (context, evaluation, input evaluation, process evaluation and product evaluation), this paper studied the evaluation method of "double first-class" university academic performance analysis, constructed the evaluation model of medical colleges academic performance from four elements of reflecting the background, input, process and results of academic research activities, established the evaluation process, and selected 10 universities for empirical research.Results:The comprehensive weight was obtained by entropy weight method and expert scoring method. The relative proximity between each evaluation object and the optimal scheme was calculated by technique for order preference by similarity to an ideal solution (TOPSIS) method, and the comprehensive calculation value of academic performance level of each university was obtained.Conclusion:The construction of academic performance evaluation system of universities based on CIPP model and entropy weight TOPSIS method is an effective discussion and expanding research on academic evaluation of colleges, and can provide references for the analysis of academic performance with medical characteristics.

ObjectiveTo explore the mechanism of brain plasticity after lower limb amputation, and the electroencephalogram (EEG) energy changes during motor imagery. MethodsFrom April to October, 2021, 21 lower limb amputees and 18 healthy subjects were selected to perform motor imagery task, while the 256-EEG were recorded synchronously. The power spectrum of EEG at C3 and C4 electrodes in α (8~13 Hz) and β (13~30 Hz) frequency bands were compared. ResultsThe power of C4 electrode in β frequency band was stronger in the amputees than in the healthy subjects as imagery of movements of left and right legs, and left foot. ConclusionEEG power increases in the amputees as motor imagery, which may relate to phantom limb pain and brain plasticity after amputation.

Objective To investigate the changes of functional brain connectivity over multiple frequency bands in resting-state electroencephalography (EEG) for lower limb amputation patients. Methods Resting-state EEG was collected from 18 lower limb amputees and 22 healthy controls from November, 2020 to June, 2021. Functional connectivity matrix was constructed with phase-locked values (PLV), and compared between groups. Results The functional connectivity was weaker in the amputees than in the controls on α band (t = 3.433, P = 0.001) and β band (t = 3.806, P = 0.001), and there was no significant difference on δ band (t = 1.429, P = 0.161) and θ band (t = 1.211, P = 0.233). Conclusion EEG functional connectivity weakens in lower limb amputees in on the α and β band, which results in neuroplasticity of multiple brain regions, not only for limb-respond cortex, but also for frontal, temporal and occipital cortices.

Objective:To comprehensively evaluate the scientific and technological innovation capability and efficiency of hospitals and provide references for effectively promoting their high-quality development in scientific innovation.Methods:The indexes of 69 tertiary public general hospitals were collected in 2018. The science and technology innovation capability of these hospitals was evaluated using science and technology evaluation metrics(STEM), while their science and technology innovation efficiency was evaluated using the super-efficiency data envelopment analysis model. Then the matching relation of the two dimensions was analyzed, with outstanding hospitals identified using the skyline algorithm.Results:The STEM and science/technology innovation efficiency of 69 tertiary public hospitals averaged 29.61 and 0.960 respectively. Based on STEM and efficiency, the 69 hospitals were divided into four types: the type of high capability and efficiency comprising 9 hospitals(13.04%), that of high capability and low efficiency comprising 16 hospitals(23.19%), that of low capability and high efficiency comprising 16 hospitals(23.19%) and that of low capability and efficiency comprising 28 hospitals(40.58%). Five hospitals(7.24%)were selected as outstanding examples of science and technology innovation.Conclusions:This study constructed a set of scientific and effective methods for comprehensively evaluating scientific and technological innovation capability and efficiency, for classifying the current state of science and technology innovation of a hospital and identifying outstanding ones. The assessment results could enable the hospitals to make targeted management measures.

Objective:To examine the concordance between bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA) in determining body composition of children aged between 3 and 6.Methods:A total of 230 children aged 3-6 from a kindergarten in Jinnan, Tianjin were enrolled in this study from November 16 th to December 8 th, 2017.The soft lean mass (SLM) and the body fat mass (BFM) of the children were measured by using BIA and DXA.The children were divided into several groups by sex, age and body mass index (BMI). The consistency of the body composition results between BIA and DXA in each group was analyzed. Results:The SLM and BFM assessed by BIA were significantly correlated with those measured by DXA ( R2=0.951, 0.947, all P<0.001). The Bland-Altman plots suggested that the SLM measured by BIA was 0.70 kg(95% CI: -1.78-0.38) higher in that by DXA, and the BFM measured by BIA was 1.36 kg (95% CI: 0.19-2.52) lower in that by DXA.A similar trend could be seen in children stratified by age, sex and BMI.The SLM of boys and girls measured by BIA was 0.57 kg (95% CI: -1.63-0.49) and 0.84 kg (95% CI: -1.88-0.20) higher than those by DXA, respectively.The BFM of boys and girls measured by BIA was 1.23 kg (95% CI: 0.11-2.36) and 1.49 kg (95% CI: 0.34-2.64) lower than that by DXA, respectively.The SLM measured by BIA was 0.67 kg (95% CI: -1.74-0.41), 0.76 kg (95% CI: -1.65-0.13), 0.69 kg (95% CI: -1.85-0.47) and 0.67 kg (95% CI: -1.75-0.41) higher than those by DXA in the 3 to 6-year-old groups, respectively.The BFM measured BIA was 1.09 kg (95% CI: -0.12-2.30), 1.44 kg (95% CI: 0.60-2.28), 1.39 kg (95% CI: 0.15-2.64)and 1.43 kg(95% CI: 0.38-2.48)lower than that by DXA in the 3 to 6-year-old groups, respectively.Moreover, the smallest difference of the SLM and BFM were observed between BIA and DXA in the obese child.The SLM deviation between BIA and DXA was estimated within ± 0.39 kg and the BFM deviation was within ± 0.93 kg. Conclusions:There is high consistency between BIA and DXA in the assessment of the SLM and BFM of children aged from 3 to 6.

Objective:To understand the current status and main models of transformation of medical patent in the World-class universities in China, carry out research on the transformation of scientific and technological achievements based on the perspective of patents.Methods:The registration data of assigned medical patent of 42 universities were collected to analyze the overall status of transformation, the cycle and the transfer flow of patent transfer. The main fields and themes involved in the transfer of patented technology are shown by cluster analysis methods. The prominent transfer mode of medical patents in the universities was discovered by analyzing the transferees of assigned patents.Results:Among 42 universities, medical patents transferred within ten years account for approximately 6% of authorized patents, and the average transfer cycle is 28 months, and transferees are business-oriented. The transferred patents cover more topics including hydantoin derivative, fucoidan sulfate, human papilloma virus, brain-computer interface and minimally invasive surgery. 88% of patents are based on the traditional direct patent technology transfer model. In addition, it also includes transfer modes such as transfer through the asset operating company, the spin-offs, the third-party intermediary, the institute, and the technology transfer center.Conclusions:The overall task of building a World-class university is heavier and the demonstration effect is more obvious. However, the status quo is that the proportion of transfer patents of the World-class university is still low, the transfer cycle is long, and the mode of transfer of scientific and technological achievements is relatively single and should be more innovative. The World-class universities should improve the quality of their own scientific and technological achievements, improve the results transformation mechanism, and constantly explore the mode of achievement transformation in a more tailored way.

Objective To evaluate the scientific research efficiency of tertiary hospitals and analyze the influencing factors, providing reference for the decision-making of scientific research managers in China. Methods Evaluation indicators were collected from 100 tertiary hospitals in China, and their research efficiency was evaluated using Bootstrap data envelopment analysis (DEA) method. The influencing factors were analyzed using ordinary least square ( OLS) regression model. Results The averaged scientific research efficiency of these hospitals using Bootstrap DEA method was 0. 5224, lower than that using conventional DEA method (0. 0676), yet with a great variation (from 0. 1103 to 0. 8790) among them. Linear regression analysis showed that factors such as R&D input and output, and hospital types has statistical significance on the saentific research efficiency (P < 0. 05). Conclusions These hospitals are inefficient in scientific research with sizable room for improvement. Conventional DEA method should be used with caution, as deviations should be corrected with Bootstrap DEA method. The research development level and hospital types of their province can influence the research efficiency the hospitals significantly.