Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
随着人口的老龄化，越来越多的有症状或无症状的冠心病患者需接受非心脏外科手术。接受非心脏外科手术而死亡的患者大约有50%是由于心脏并发症所致［1］。围手术期发生的心脏并发症大约5%～10%为心肌梗死，主要发生于术后头3天，其病死率很高，可达32%～69%［2，3］。术后发生心肌梗死或不稳定型心绞痛的患者发生心血管问题的几率增加20倍［4］。因此，如何评估非心脏外科手术患者的心脏危险性，如何预防围手术期心脏并发症的发生，已成为外科医生十分关注的一个问题。
ObjectiveTo explore the selection problem of independent variables and stepwise regression method for multiple logistic regression analysis. MethodsAccording to the data of the case-control investigation for coronary heart disease, age (X1), hypertension history (X2), hypertension family history (X3), smoking (X4), hyperlipidemia history (X5), animal fat intake (X6), weight index (X7), type A personality (X8), and coronary heart disease (CHD, Y) were analyzed by SPSS 18.0 software. The multiple logistic regression analysis was done and the differences of risk factors were compared among 6 kinds stepwise regression variable selection method. ResultsThe univariate analysis showed that no difference was found between CHD group and non-CHD group in age distribution (P=0.116). But the multivariate logistic regression analysis showed that, comparing to population over 65 years old, age was a protective factor on the low age groups (OR< 45=0.100, 0.000 to 0.484, P=0.020; OR45-54=0.051, 0.003 to 0.975, P=0.048). If the age was defined as categorical variable, the risk factors for coronary heart disease were animal fat intake (X6), type A personality (X8), hypertension history (X5) and age (X1), respectively (P < 0.05). If the age was defined as a continuous variable, the effect of age (X1) was not statistically significant (P=0.053). The common risk factors were intake of animal fat (X6) and type a personality (X8) by six kinds method of stepwise variable selection. In addition, the risk factor also included hyperlipidemia history (X5) (forward-condition, forward-LR, forward-wald), hypertension family history (X3), age (X1) (backward-condition, backward-LR) and hypertension history (X2) (backward-wald). ConclusionStepwise regression method should be used to analyze all the variables, including no statistically significant independent variables in univariate analysis. If the categorical variable is regarded as continuous variables, some information may be lost, and even the risk factors may be missed. When the risk factors are not the same by several stepwise regression variable selection method, it should be combined with clinical and epidemiological significance, as well as biological mechanisms and other professional knowledge.
Systematic reviews (SRs) serve as a core methodology in evidence-based medicine (EBM), providing critical evidence for clinical practice and health decision-making. However, the manual screening of titles and abstracts in SRs is labor-intensive and time-consuming, becoming a major bottleneck in research efficiency. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), have introduced new opportunities and transformations in this field. This article provided an overview of the current status of intelligent screening for titles and abstracts in systematic reviews, with a focus on the application and effectiveness of LLMs. It aims to provide recommendations for users and developers, facilitating the better integration of automation algorithms into the SR process.
As the largest ecosystem of human body, intestinal microorganisms participate in the synthesis and metabolism of uric acid. Developing and utilizing intestinal bacteria to degrade uric acid might provide new ideas for the treatment of hyperuricemia. The fecal samples of people with low uric acid were inoculated into uric acid selective medium with the concentration of 1.5 mmol/L for preliminary screening, and the initially screened strains that may have degradation ability were domesticated by concentration gradient method, and the strains with high uric acid degradation rate were identified by 16S rRNA sequencing method. A strain of high-efficiency uric acid degrading bacteria was screened and domesticated from the feces of people with low uric acid. The degradation rate of uric acid could reach 50.2%. It was identified as Escherichia coli. The isolation and domestication of high efficient uric acid degrading strains can not only provide scientific basis for the study of the mechanism of intestinal microbial degradation of uric acid, but also reserve biological strains for the treatment of hyperuricemia and gout in the future.
Place cell with location tuning characteristics play an important role in brain spatial cognition and navigation, but there is relatively little research on place cell screening and its influencing factors. Taking pigeons as model animals, the screening process of pigeon place cell was given by using the spike signal in pigeon hippocampus under free activity. The effects of grid number and filter kernel size on the place field of place cells during the screening process were analyzed. The results from the real and simulation data showed that the proposed place cell screening method presented in this study could effectively screen out place cell, and the research found that the size of place field was basically inversely proportional to the number of grids divided, and was basically proportional to the size of Gaussian filter kernel in the overall trend. This result will not only help to determine the appropriate parameters in the place cell screening process, but also promote the research on the neural mechanism of spatial cognition and navigation of birds such as pigeons.
Objective To developapatient-reported outcomes scale of chronic obstructive pulmonary disease used for Chinese, thus offering tools for clinical efficacy assessment. Methods According to the development standard of International Patient-Reported Outcomes, the item pool was established and the preliminary scale was prepared. Then, 100 patients with chronic obstructive pulmonary disease and 50 healthy subjects were face-to-face interviewed with preliminary scale by well-trained investigators.Those copies were collected, surveys were analyzed and items were selected with 5 methods including measure of discrete tendency method, factor analysis, correlation coefficient method, Cronbach’s alpha coefficient method and item response Theory. Finally, the final scale was gained. Results The eventual scale contains 4 areas(physiological dimain, psychological dimension, social dimension, treatment), 11 dimensions(specific symptoms, general symptoms, individual, anxiety, depression, disease cognization, disease influence on social pctivity, social support, compliance, drug adverse reaction, satisfactory), and 52 items. Conclusion The ultimate scale coincides with the theoretical framework and reflects the connotation of the quality of life of patients with chronic obstructive pulmonary disease.
ObjectiveThis study aimed to revise the perioperative recovery scale for integrative medicine (PRSIM) based on item response theory (IRT). MethodsUnder the guidance of IRT, a total of 349 patient data collected during the development of the original version of PRSIM at Guangdong Provincial Hospital of Chinese Medicine were used. Principal component analysis was performed using SPSS 18.0 software to test the unidimensionality. The R language was utilized for parameter estimation, including discrimination coefficient, difficulty parameters and information content, as well as drawing item characteristic curves to assess item quality and estimate item functioning differences. A comprehensive screening process was carried out by combining expert consultations, patient evaluations, and discussions within a core group. ResultsThe degree of discrimination of all items ranged from −0.535 to 2.195. The difficulty coefficient ranged from −10.343 to 5.461, and the average information content of all items ranged from 0.043 to 1.075. Based on the criteria for parameter selection, nine items were retained. The results of expert consultations indicated the removal of 5 items and the modification of 7 items. After discussion within the core group, a final decision was made to remove 5 items. ConclusionBased on a synthesis of IRT and expert consultation feedback, and following discussions within the core group, a revised version comprising 15 items is retained and modified from the original 20 items.