The spatial distribution of BUN reference values of Chinese healthy adults: a cross-section study.

The blood urea nitrogen (BUN) is generally regarded as a significant serum marker in estimating renal function. This study aims to explore the geographical distribution of BUN reference values of Chinese healthy adults, and provide a scientific basis for determining BUN reference values of Chinese healthy adults of different regions according to local conditions. A total of 25,568 BUN reference values of healthy adults from 241 Chinese cities were collected in this study, and 17 geographical indices were selected as explanatory variables. The correlation analysis was used to examine the significance between BUN reference value and geographical factors, then five significant indices were extracted to build two predictive models, including principal component analysis (PCA) and support vector regression (SVR) model, then the optimal model was selected by model test to predict BUN reference values of the whole China, finally the distribution map was produced. The results show that BUN reference value of Chinese healthy adult was characteristically associated with latitude, altitude, annual mean temperature, annual mean relative humidity, and annual precipitation. The model test shows, compared with SVR model, the PCA model possesses superior simulative and predictive ability. The distribution map shows that the BUN reference values of Chinese healthy adult are lower in the east and higher in the west. These results indicate that the BUN reference value is significantly affected by geographical environment, and the BUN reference values of different regions could be seen clearly on distribution map.

The Spatial Distribution Characteristics of the Tumor Biomarker Alpha-fetoprotein: A Cross-section Study.

OBJECTIVES: This study aims to explore the spatial characteristics of the alpha-fetoprotein (AFP) reference value in healthy Chinese adults, and its relationship to geographical location. METHODS: A total of 9396 AFP reference values were collected from patients in 96 administrative units. A correlation analysis and support vector machine (SVM) were employed to extract dependent geographical factors and predict the reference values in the entire country, respectively. A geostatistics analysis was developed to reveal the spatial characteristics of the value. RESULTS: Under the long-term influence of geographical environment, AFP reference values show spatial autocorrelation and regional variation. The values are higher in western and northern areas than in eastern and southern areas of China. CONCLUSIONS: The AFP reference values show regional differences, and this difference should be considered in clinical practice.

[Correlation of Heart Rate and Geographical Environmental Factors with Reference Values of Left Ventricular Ejection Fraction in Healthy Adult Males].

Objective To establish a model for obtaining the reference values of left ventricular ejection fraction(LVEF) in Chinese healthy adult males by exploring the relationships of these reference values with heart rate and geographical environment factors. Methods LVEF and heart rate reference values (X1) were collected from 3502 healthy adult males from 2006 to 2016. Correlation analysis and ridge regression were employed to extract dependent geographical environment factors and predict the LVEF reference values. The Kriging interpolation was applied to reveal the spatial distribution of the LVEF reference values. Results LVEF and heart rate (X1) were significantly correlated with five geographical environment factors. LVEF was negatively correlated with heart rate (X1),latitude (X3),and annual range of temperature (X9) and positively correlated with annual mean air temperature (X6),annual mean relative humidity (X7),and annual precipitation amount (X8). The reference values of LVEF had a negative correlation with heart rate. The ridge regression equation of LVEF reference values and geographical environment factors was as follows:Y=68.464-0.0949X3-0.0619X6-0.00128X7+0.00069X8-0.0199X9±3.329. The equation of LVEF reference values with heart rate and geographical environment factors was Y=75.923-0.1035X1-0.0958X3-0.0741X6+0.00094X7+0.00081X8-0.0211X9±3.288. Conclusion The LVEF reference values among Chinese healthy adult males decreased from south to north. They can be determined based on the regression models after the geographical factors of a certain region are obtained. The new model offers a geographic basis for the establishment of LVEF reference values.

Residual and bidirectional LSTM for epileptic seizure detection.

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88-100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold cross-validation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches.

Automatic Detection of Coronary Metallic Stent Struts Based on YOLOv3 and R-FCN.

An artificial stent implantation is one of the most effective ways to treat coronary artery diseases. It is vital in vascular medical imaging, such as intravascular optical coherence tomography (IVOCT), to be able to track the position of stents in blood vessels effectively. We trained two models, the "You Only Look Once" version 3 (YOLOv3) and the Region-based Fully Convolutional Network (R-FCN), to detect metal support struts in IVOCT, respectively. After rotating the original images in the training set for data augmentation, and modifying the scale of the conventional anchor box in both two algorithms to fit the size of the target strut, YOLOv3 and R-FCN achieved precision, recall, and AP all above 95% in 0.4 IoU threshold. And R-FCN performs better than YOLOv3 in all relevant indicators.

Regional variation of alanine aminotransferase serum levels in the People's Republic of China.

The regional variation of the blood concentration of alanine aminotransferase (ALT), a sensitive predictor of liver damage, was studied in the People's Republic of China with reference to its potential association with environmental variables and geographic location. The research results presented are based on 121,977 blood samples from healthy adults in 93 cities in the country using correlation analysis, ridge regression estimation and trend surface analysis that were applied to explore if there was any tendency of spatial variation. A regression formula using a simulation equation under the condition of known local geographic factors was used. Statistical significance was set at P<0.05. A positive correlation between ALT concentration altitude and sunshine hours and a negative correlation between ALT concentration and temperature, humidity and precipitation were found. With respect to geographical location, there was a negative correlation between ALT concentration and longitude. Higher ALT values were found in western China compared to eastern regions, dividing the country into three different regions with respect to serum ALT levels.

[Geographical distribution of the Serum creatinine reference values of healthy adults].

OBJECTIVE: To explore the relationship between serum creatinine (Scr) reference values in healthy adults and geographic factors and provide evidence for establishing Scr reference values in different regions. METHODS: We collected 29 697 Scr reference values from healthy adults measured by 347 medical facilities from 23 provinces, 4 municipalities and 5 autonomous regions. We chose 23 geographical factors and analyzed their correlation with Scr reference values to identify the factors correlated significantly with Scr reference values. According to the Principal component analysis and Ridge regression analysis, two predictive models were constructed and the optimal model was chosen after comparison of the two model's fitting degree of predicted results and measured results. The distribution map of Scr reference values was drawn using the Kriging interpolation method. RESULTS: Seven geographic factors, including latitude, annual sunshine duration, annual average temperature, annual average relative humidity, annual precipitation, annual temperature range and topsoil (silt) cation exchange capacity were found to correlate significantly with Scr reference values. The overall distribution of Scr reference values featured a pattern that the values were high in the south and low in the north, varying consistently with the latitude change. CONCLUSION: The data of the geographic factors in a given region allows the prediction of the Scr values in healthy adults. Analysis of these geographical factors can facilitate the determination of the reference values specific to a region to improve the accuracy for clinical diagnoses.