Atmospheric pollen concentrations and chronic obstructive pulmonary disease (COPD) patients visits in Beijing: time series analysis using a generalized additive model.

To investigate the correlation between the daily visits of chronic obstructive pulmonary disease (COPD) patients in hospital clinic and pollen concentrations in Beijing. We collected daily visits of COPD patients of Beijing Shijitan Hospital from April 1st, 2019 to September 30th, 2019. The relationship between pollen concentrations and COPD patient number was analyzed with meteorological factors, time trend, day of the week effect and holiday effect being controlled by the generalized additive model of time series analysis. R4.1.2 software was applied to generate Spearman correlation coefficient, specific and incremental cumulative effect curves of relative risks as well as the response and three-dimensional diagrams for the exposure lag effect prediction. The fitting models were used to predict the lag relative risk and 95% confidence intervals for specific and incremental cumulative effects of specific pollen concentrations. The number of COPD patients was positively correlated with pollen concentration. When pollen concentration increased by 10 grains/1000 mm2, the peak value of the specific cumulative effect appeared on day0, with the effect gone on day4 and a lag time of 4 days observed, whereas the incremental cumulative effect's peak value was shown on day17, and the effect disappeared on day18, with a lag time of 18 days. The results showed that pollen concentration was not only positively correlated with the number of COPD patients, but also had a bimodal lag effect on COPD visits in the hospital at Beijing.

Establishment and Validation of a Machine Learning-Based Prediction Model for Termination of Pregnancy via Cesarean Section.

Objective: This study aimed to investigate the risk factors of cesarean section and establish a prediction model for cesarean section based on the characteristics of pregnant women. Methods: The clinical characteristics of 2552 singleton pregnant women who delivered a live baby between January 2020 and December 2021 were retrospectively reviewed. They were divided into vaginal delivery group (n = 1850) and cesarean section group (n = 702). These subjects were divided into training set (2020.1-2021.6) and validation set (2021.7-2021.12). In the training set, univariate analysis, Lasso regression, and Boruta were used to screen independent risk factors for cesarean section. Four models, including Logistic Regression (LR), K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), and Random forest (RF), were established in the training set using K-fold cross validation, hyperparameter optimization, and random oversampling techniques. The best model was screened, and Sort graph of feature variables, univariate partial dependency profile, and Break Down profile were delineated. In the validation set, the confusion matrix parameters were calculated, and receiver operating characteristic curve (ROC), precision recall curve (PRC), calibration curve, and clinical decision curve analysis (DCA) were delineated. Results: The risk factors of cesarean section included age and height of women, weight at delivery, weight gain, para, assisted reproduction, abnormal blood glucose during pregnancy, pregnancy hypertension, scarred uterus, premature rupture of membrane (PROM), placenta previa, fetal malposition, thrombocytopenia, floating fetal head, and labor analgesia. RF had the best performance among the four models, and the accuracy of confusion matrix parameters was 0.8956357. The Matthews correlation coefficient (MCC) was 0.753012. The area under ROC (AUC-ROC) was 0.9790787, and the area under PRC (AUC-PRC) was 0.957888. Conclusion: RF prediction model for caesarean section has high discrimination performance, accuracy and consistency, and outstanding generalization ability.

Effect of atmospheric pollen concentration on daily visits of allergic rhinitis in Beijing: a distributed lag nonlinear model analysis.

To investigate the influence and lag effect of atmospheric pollen concentration on daily visits of patients with allergic rhinitis (AR), we collected the AR data during the pollen seasons from 2018 to 2019 from the outpatient and emergency department of Beijing Shijitan Hospital. The distributed lag non-linear model (DLNM) was used to analyze the correlation and the lag effect between pollen concentration and the incidence of AR. R4.1.2 was used to generate the Spearman correlation coefficients and plot the lag response curves of relative risk specific and incremental cumulative effects. In 2018 and 2019, the number of AR visits was moderately positively correlated with pollen concentration. The peak value of the overall specific cumulative effect for every 10 grains/1000 mm2 increase in atmospheric pollen concentration occurred on day 0 (2018, 2019), and the lag disappearance time was day 6 (2018) and day 7 (2019), and the specific cumulative effect duration was respectively 6 days (2018) and 7 days (2019), with the curve showing a downward trend with time increase. In 2018, the peak value of the overall incremental cumulative effect was on day 7, the lag disappearance time was day 13, and the duration of the incremental cumulative effect was 13 days, forming a curve pattern of rising first and then falling. In 2019, the peak value time of the overall incremental cumulative effect was on day 8, and the curve went down afterwards until it showed the trend of ascending again after day26.

Air pollution and coronary heart disease-related hospital visits in Beijing, China: time-series analysis using a generalized additive model.

To investigate correlations between environmental and meteorological factors and frequency of presentation for coronary heart disease (CHD) in Beijing. Daily measurements of levels of six atmospheric pollutants were made, data relating to meteorological conditions collected, and CHD-related outpatient visits recorded from January 2015 to December 2019 in Beijing. A time-series analysis was made, using a generalized additive model with Poisson distribution, and R 3.6.3 software was used to estimate relationships among levels of atmospheric pollutants, ambient temperature, and visits occasioned by CHD. Results were controlled for time-dependent trend, other weather variables, day of the week, and holiday effects. Lag-response curves were plotted for specific and incremental cumulative effects of relative risk (RR). The aim was to correlate meteorological-environmental factors and the daily number of CHD-related hospital visits and to quantify the degree of correlation to identify any pathological associations. Response diagrams and three-dimensional diagrams of predicted exposure lag effects were constructed in order to evaluate relationships among the parameters of air pollution, temperature, and daily CHD visits. The fitted model was employed to predict the lag RR and 95% confidence interval (95% CI) for specific and incremental cumulative effects of random air pollutants at random concentrations. This model may then be used to predict effects on the outcome variable at any concentration of any defined pollutant, giving flexibility for public health purposes. The overall lag-response RR curves for the specific cumulative effects of the pollutants, particulate matter (PM)2.5, PM10, SO2, CO, and NO2, were statistically significant and for PM2.5, PM10, CO, and NO2, the overall lag-response RR curves for the incremental cumulative effect were statistically significant. When PM2.5, PM10, SO2, CO, and NO2 concentrations were above threshold values and the temperature was below 45 °F (reference value 70 °F), the number of CHD-related hospital visits increased with a time lag effect. The outpatient volume of CHD was predicted by the model to guide the flexible distribution of medical resources. Elevated PM2.5, PM10, SO2, CO, and NO2 concentrations in the atmosphere combined and low ambient temperature increased the risk of CHD with a time lag effect.

Muscle-related parameters-based machine learning model for predicting postinduction hypotension in patients undergoing colorectal tumor resection surgery.

Objectives: This study used machine learning algorithms to identify important variables and predict postinduction hypotension (PIH) in patients undergoing colorectal tumor resection surgery. Methods: Data from 318 patients who underwent colorectal tumor resection under general anesthesia were analyzed. The training and test sets are divided based on the timeline. The Boruta algorithm was used to screen relevant basic characteristic variables and establish a model for the training set. Four models, regression tree, K-nearest neighbor, neural network, and random forest (RF), were built using repeated cross-validation and hyperparameter optimization. The best model was selected, and a sorting chart of the feature variables, a univariate partial dependency profile, and a breakdown profile were drawn. R2, mean absolute error (MAE), mean squared error (MSE), and root MSE (RMSE) were used to plot regression fitting curves for the training and test sets. Results: The basic feature variables associated with the Boruta screening were age, sex, body mass index, L3 skeletal muscle index, and HUAC. In the optimal RF model, R2 was 0.7708 and 0.7591, MAE was 0.0483 and 0.0408, MSE was 0.0038 and 0.0028, and RMSE was 0.0623 and 0.0534 for the training and test sets, respectively. Conclusion: A high-performance algorithm was established and validated to demonstrate the degree of change in blood pressure after induction to control important characteristic variables and reduce PIH occurrence.

Random forest algorithm for predicting postoperative delirium in older patients.

Objective: In this study, we were aimed to identify important variables via machine learning algorithms and predict postoperative delirium (POD) occurrence in older patients. Methods: This study was to make the secondary analysis of data from a randomized controlled trial. The Boruta function was used to screen relevant basic characteristic variables. Four models including Logistic Regression (LR), K-Nearest Neighbor (KNN), the Classification and Regression Tree (CART), and Random Forest (RF) were established from the data set using repeated cross validation, hyper-parameter optimization, and Smote technique (Synthetic minority over-sampling technique, Smote), with the calculation of confusion matrix parameters and the plotting of Receiver operating characteristic curve (ROC), Precision recall curve (PRC), and partial dependence graph for further analysis and evaluation. Results: The basic characteristic variables resulting from Boruta screening included grouping, preoperative Mini-Mental State Examination(MMSE), CHARLSON score, preoperative HCT, preoperative serum creatinine, intraoperative bleeding volume, intraoperative urine volume, anesthesia duration, operation duration, postoperative morphine dosage, intensive care unit (ICU) duration, tracheal intubation duration, and 7-day postoperative rest and move pain score (median and max; VAS-Rest-M, VAS-Move-M, VAS-Rest-Max, and VAS-Move-Max). And Random Forest (RF) showed the best performance in the testing set among the 4 models with Accuracy: 0.9878; Matthews correlation coefficient (MCC): 0.8763; Area under ROC curve (AUC-ROC): 1.0; Area under the PRC Curve (AUC-PRC): 1.0. Conclusion: A high-performance algorithm was established and verified in this study demonstrating the degree of POD risk changes in perioperative elderly patients. And the major risk factors for the development of POD were CREA and VAS-Move-Max.

A time-series analysis on generalized additive model for atmospheric pollen concentration and the number of visits of allergic conjunctivitis, Beijing, China.

The objective of this study is to investigate the correlation between atmospheric pollen concentration and daily visits for allergic conjunctivitis. Daily counts of outpatient visits for allergic conjunctivitis, atmospheric pollen concentration, and meteorological data during pollen season of 2018 and 2019 were collected from Beijing Shijitan Hospital, China. A time-series analysis on generalized additive model with Poisson distribution was used to estimate the relationship between pollen concentration and visits for allergic conjunctivitis, after controlling for the time trend, weather variables, day of the week, and holiday effect. The RStudio was used to generate Spearman correlation coefficients and then to plot the lag-response curves for specific and incremental cumulative effects of relative risk (RR). There was a moderate positive correlation between pollen concentration and visits for allergic conjunctivitis, and Spearman's correlation coefficient was 0.521 in 2018 and 0.515 in 2019 (P<0.01). The specific cumulative effect peak associated with per 10 grains/kmm2 increases of atmospheric pollen concentration was within 0 day, and the lag time was 8 days(2018, 2019). The incremental cumulative effect peak associated with per 10 grains/kmm2 increases of atmospheric pollen concentration occurred on lag day 10 (2018) and lag day 8 (2019), and the lag time was 14 days (2018) and 20 days (2019). Elevated concentrations of pollen increase the risk of allergic conjunctivitis with a time lag effect.

Atorvastatin attenuates surgery-induced BBB disruption and cognitive impairment partly by suppressing NF-κB pathway and NLRP3 inflammasome activation in aged mice.

In clinic, perioperative neurocognitive disorder is becoming a common complication of surgery in old patients. Neuroinflammation and blood-brain barrier (BBB) disruption are important contributors for cognitive impairment. Atorvastatin, as a strong HMG-CoA reductase inhibitor, has been widely used in clinic. However, it remains unclear whether atorvastatin could prevent anesthesia and surgery-induced BBB disruption and cognitive injury by its anti-inflammatory property. In this study, aged C57BL/6J mice were used to address this question. Initially, the mice were subject to atorvastatin treatment for 7 days (10 mg/kg). After a simple laparotomy under 1.5% isoflurane anesthesia, Morris water maze was performed to assess spatial learning and memory. Western blot analysis, immunohistochemistry, and enzyme-linked immunosorbent assay were used to examine the inflammatory response, BBB integrity, and cell apoptosis. Terminal-deoxynucleotidyl transferase mediated nick end labeling assay was used to assess cell apoptosis. The fluorescein sodium and transmission electron microscopy were used to detect the permeability and structure of BBB. The results showed that anesthesia and surgery significantly injured hippocampal-dependent learning and memory, which was ameliorated by atorvastatin. Atorvastatin could also reverse the surgery-induced increase of systemic and hippocampal cytokines, including IL-1ß, TNF-α, and IL-6, accompanied by inhibiting the nuclear factor kappa-B (NF-κB) pathway and Nucleotide-Binding Oligomerization Domain, or Leucine Rich Repeat and Pyrin Domain Containing 3 (NLRP3) inflammasome activation, as well as hippocampal neuronal apoptosis. In addition, surgery triggered an increase of BBB permeability, paralleled by a decrease of the ZO-1, occludin, and Claudin 5 proteins in the hippocampus. However, atorvastatin treatment could protect the BBB integrity from the impact of surgery, by up-regulating the expressions of ZO-1, occludin, and Claudin 5. These findings suggest that atorvastatin exhibits neuroprotective effects on cognition in aged mice undergoing surgery.

Atorvastatin Attenuates Isoflurane-Induced Activation of ROS-p38MAPK/ATF2 Pathway, Neuronal Degeneration, and Cognitive Impairment of the Aged Mice.

Isoflurane, a widely used volatile anesthetic, induces neuronal apoptosis and memory impairments in various animal models. However, the potential mechanisms and effective pharmacologic agents are still not fully understood. The p38MAPK/ATF-2 pathway has been proved to regulate neuronal cell survival and inflammation. Besides, atorvastatin, a 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor, exerts neuroprotective effects. Thus, this study aimed to explore the influence of atorvastatin on isoflurane-induced neurodegeneration and underlying mechanisms. Aged C57BL/6 mice (20 months old) were exposed to isoflurane (1.5%) anesthesia for 6 h. Atorvastatin (5, 10, or 20 mg/kg body weight) was administered to the mice for 7 days. Atorvastatin attenuated the isoflurane-induced generation of ROS and apoptosis. Western blotting revealed a decrease in cleaved caspase-9 and caspase-3 expression in line with ROS levels. Furthermore, atorvastatin ameliorated the isoflurane-induced activation of p38MAPK/ATF-2 signaling. In a cellular study, we proved that isoflurane could induce oxidative stress and inflammation by activating the p38MAPK/ATF-2 pathway in BV-2 microglia cells. In addition, SB203580, a selected p38MAPK inhibitor, inhibited the isoflurane-induced inflammation, oxidative stress, and apoptosis. The results implied that p38MAPK/ATF-2 was a potential target for the treatment of postoperative cognitive dysfunction.