CTFNet: Long-Sequence Time-Series Forecasting Based on Convolution and Time-Frequency Analysis.

Although current time-series forecasting methods have significantly improved the state-of-the-art (SOTA) results for long-sequence time-series forecasting (LSTF), they still have difficulty in capturing and extracting the features and dependencies of long-term sequences and suffer from information utilization bottlenecks and high-computational complexity. To address these issues, a lightweight single-hidden layer feedforward neural network (SLFN) combining convolution mapping and time-frequency decomposition called CTFNet is proposed with three distinctive characteristics. First, time-domain (TD) feature mining-in this article, a method for extracting the long-term correlation of horizontal TD features based on matrix factorization is proposed, which can effectively capture the interdependence among different sample points of a long time series. Second, multitask frequency-domain (FD) feature mining-this can effectively extract different frequency feature information of time-series data from the FD and minimize the loss of data features. Integrating multiscale dilated convolutions, simultaneously focusing on both global and local context feature dependencies at the sequence level, and mining the long-term dependencies of the multiscale frequency information and the spatial dependencies among the different scale frequency information, break the bottleneck of data utilization, and ensure the integrity of feature extraction. Third, highly efficient-the CTFNet model has a short training time and fast inference speed. Our empirical studies with nine benchmark datasets show that compared with state-of-the-art methods, CTFNet can reduce prediction error by 64.7% and 53.7% for multivariate and univariate time series, respectively.

Small RNA Sequencing Analysis of STZ-Injured Pancreas Reveals Novel MicroRNA and Transfer RNA-Derived RNA with Biomarker Potential for Diabetes Mellitus.

MicroRNAs (miRNAs) and transfer RNA-derived small RNAs (tsRNAs) play critical roles in the regulation of different biological processes, but their underlying mechanisms in diabetes mellitus (DM) are still largely unknown. This study aimed to gain a better understanding of the functions of miRNAs and tsRNAs in the pathogenesis of DM. A high-fat diet (HFD) and streptozocin (STZ)-induced DM rat model was established. Pancreatic tissues were obtained for subsequent studies. The miRNA and tsRNA expression profiles in the DM and control groups were obtained by RNA sequencing and validated with quantitative reverse transcription-PCR (qRT-PCR). Subsequently, bioinformatics methods were used to predict target genes and the biological functions of differentially expressed miRNAs and tsRNAs. We identified 17 miRNAs and 28 tsRNAs that were significantly differentiated between the DM and control group. Subsequently, target genes were predicted for these altered miRNAs and tsRNAs, including Nalcn, Lpin2 and E2f3. These target genes were significantly enriched in localization as well as intracellular and protein binding. In addition, the results of KEGG analysis showed that the target genes were significantly enriched in the Wnt signaling pathway, insulin pathway, MAPK signaling pathway and Hippo signaling pathway. This study revealed the expression profiles of miRNAs and tsRNAs in the pancreas of a DM rat model using small RNA-Seq and predicted the target genes and associated pathways using bioinformatics analysis. Our findings provide a novel aspect in understanding the mechanisms of DM and identify potential targets for the diagnosis and treatment of DM.

Corrigendum to: Use of electronic medical records to monitor the safe and effective prescribing of medicinal cannabis: is it feasible?

BACKGROUND: General practitioners are well positioned to contribute to the pharmacovigilance of medical cannabis via the general practice electronic medical record (EMR). The aim of this research is to interrogate de-identified patient data from the Patron primary care data repository for reports of medicinal cannabis to ascertain the feasibility of using EMRs to monitor medicinal cannabis prescribing in Australia. METHODS: EMR rule-based digital phenotyping of 1 164 846 active patients from 109 practices was undertaken to investigate reports of medicinal cannabis use from September 2017 to September 2020. RESULTS: Eighty patients with 170 prescriptions of medicinal cannabis were identified in the Patron repository. Reasons for prescription included anxiety, multiple sclerosis, cancer, nausea, and Crohn's disease. Nine patients showed symptoms of a possible adverse event, including depression, motor vehicle accident, gastrointestinal symptoms, and anxiety. CONCLUSIONS: The recording of medicinal cannabis effects in the patient EMR provides potential for medicinal cannabis monitoring in the community. This is especially feasible if monitoring were to be embedded into general practitioner workflow.

School Bullying Victimization and Associated Factors Among School-Aged Adolescents in China.

There has been a significant amount of research on correlates of bullying victimization, but most prior studies are descriptive and do not distinguish between different types of bullying. The current study used a case-control study design to explore factors related to different types of bullying victimization, including physical, relational, verbal, sexual, property, and poly-bullying victimization. This study was conducted in a southern city in China, including 3054 cases who self-reported being victims of school bullying and 3054 controls who reported not being involved in any school bullying in the past 12 months. Each victim case was matched with a control on gender, school, and grade level. Univariate logistic analyses and multivariate conditional logistic regression analyses were used to identify factors associated with being a victim of school bullying. Results suggest physical bullying victimization was only associated with a family-level characteristic (parenting style) while the other four types of bullying victimization (relational, verbal, sexual, and property bullying) and poly-bullying victimization were associated with multiple social domain variables at individual, family, and school levels. Findings from this study provide evidence of factors for different types of bullying victimization and have implications for potential measures to prevent bullying. Measures from multiple social domains, including individual, family and school (e.g., developing healthy behaviors, improving social skills, positive parent-child interactions, building trust between teachers and peers, and forming strong friendships), should be considered in order to effectively prevent adolescent victimization from bullying.

Risk Factors Associated With School Bullying Behaviors: A Chinese Adolescents Case-Control Study, 2019.

This research aimed to identify risk factors including individual characteristics and environment circumstances related to different types of school bullying (physical, relational, verbal, sexual, and possession bullying) among middle school students in China. Cases were the respondents reporting perpetrating bullying behaviors three or more times in the past year. One control was selected for each case from those participants who were not involved in school bullying in the past 12 months. Data were collected between April 2019 and May 2019 in China. After considering potential confounding variables including gender, grade level, and school, multivariable conditional logistic regression analysis was performed based on the univariate logistic analysis including 1,594 adolescents. According to conditional logistic regression analysis, alcohol use and lack of emotional management and control were the significant individual characteristics positively associated with involvement in school bullying. Alcohol use was related to all five types of school bullying perpetration. Poor relationships between family members, father's alcohol use, and parental neglect were strong risk factors for relational bullying. Lack of a sense of safety and absence of trusted people were associated with physical, relational, and verbal bullying perpetration. Results of this study provide evidence about risk factors for school bullying and have implications for potential policies to reduce bullying. Effective policies and programs need to take individual characteristics (social-emotional skills, anger control), family (parent training in conflict resolution, appropriate disciplining), peer and school factors (promoting prosocial networks, zero tolerance for bullying, appropriate disciplining policies against students who bully others, teacher training on building positive teacher-student relationships and positive discipling techniques) into consideration in order to develop effective prevention programs.

Revised road traffic safety law and years of life lost due to traffic deaths in China, 2002-2019.

Legal intervention is a powerful tool to reduce road traffic injuries (RTIs). China amended the Road Traffic Safety Law in 2011, but the impact of amended law on traffic crash deaths is still unknown. In this study, we conducted an interrupted time series analysis and examined years of life lost (YLLs) per 100,000 population as the assessment indicator to evaluate the association of road traffic safety law and traffic crash mortality. Annual YLLs data due to traffic deaths from 2002 to 2019 in China were obtained from the Global Burden of Disease (GBD) 2019. After implementation of the revised law, the average level of total YLLs per 100,000 population due to traffic deaths decreased from 1133.14 to 848.87, and the slope of annual YLLs per 100,000 population decreased by 30.11 (95% CI: 22.46, 37.75), indicating a steeper downward trend. The revised traffic law was associated with YLLs reduction due to traffic deaths for males, females, all age groups, pedestrians, motor vehicle users, and other road users, as well as traffic deaths attributed to alcohol use and tobacco use. These findings suggested that the revised Road Traffic Safety Law improved road safety by decreasing YLLs due to traffic deaths in China. However, the burden of RTIs is still heavy and efforts to further improve traffic laws and the adoption of other interventions are urgently needed.

An Improved Chinese String Comparator for Bloom Filter Based Privacy-Preserving Record Linkage.

With the development of information technology, it has become a popular topic to share data from multiple sources without privacy disclosure problems. Privacy-preserving record linkage (PPRL) can link the data that truly matches and does not disclose personal information. In the existing studies, the techniques of PPRL have mostly been studied based on the alphabetic language, which is much different from the Chinese language environment. In this paper, Chinese characters (identification fields in record pairs) are encoded into strings composed of letters and numbers by using the SoundShape code according to their shapes and pronunciations. Then, the SoundShape codes are encrypted by Bloom filter, and the similarity of encrypted fields is calculated by Dice similarity. In this method, the false positive rate of Bloom filter and different proportions of sound code and shape code are considered. Finally, we performed the above methods on the synthetic datasets, and compared the precision, recall, F1-score and computational time with different values of false positive rate and proportion. The results showed that our method for PPRL in Chinese language environment improved the quality of the classification results and outperformed others with a relatively low additional cost of computation.

Effectiveness of implementing the criminal administrative punishment law of drunk driving in China: An interrupted time series analysis, 2004-2017.

In 2011, a more severe drunk driving law was implemented in China, which criminalized driving under the influence of alcohol for the first time and increased penalties for drunk driving. The present study aimed to assess effectiveness of the drunk driving law in China in reducing traffic crashes, injuries, and mortality. Data used in this study was obtained from the Traffic Management Bureau of the Ministry of Public Security of the People's Republic of China. An interrupted time series analysis was conducted to analyze annual data from 2004 to 2017, including the number of road traffic crashes, deaths, and injuries caused by drunk driving in China. The average annual incidences of crashes, mortality, and injuries have decreased after the promulgation of drunk driving law in 2011. In the post-intervention period, the increased slope for crashes, mortality and injury rates were, respectively, -0.140 to -0.006, -0.052 to -0.005 and -0.150 to -0.008, indicating a weaker downward trend of dependent variables. The more stringent drunk driving law is not as effective as expected. Drunk driving is still a severe traffic safety problem to be addressed in China. Both legislation and other prevention programs should be adopted to reduce road traffic injuries caused by drunk driving in China.

Forecasting deaths of road traffic injuries in China using an artificial neural network.

Objectives: This study was conducted to estimate road traffic deaths and to forecast short-term road traffic deaths in China using the Elman recurrent neural network (ERNN) model.Methods: An ERNN model was developed using reported police data of road traffic deaths in China from 2000 to 2017. Different numbers of neurons of the hidden layer were tested and different combinations of subgroup datasets have been used to develop the optimal ERNN model after normalization. The mean absolute error (MAE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE) were measures of the deviation between predicted and observed values. Predicted road traffic deaths from the ERNN model and the seasonal autoregressive integrated moving average (SARIMA) model were compared using the MAPE.Results: By comparing the MAE, RMSE and MAPE of different numbers of hidden neurons and different ERNN models, the ERNN model provided the best result when the input neurons were set to 3 and hidden neurons were set to 10. The best validated neural model (3:10:1) was further applied to make predictions for the latest 12 months of deaths (MAPE = 4.83). The best SARIMA (0, 1, 1) (0, 1, 1)12 model was selected from various candidate models (MAPE = 5.04). The fitted road traffic deaths using the two selected models matched closely with the observed deaths from 2000 to 2016. The ERNN models performed better than the SARIMA model in terms of prediction of 2017 deaths.Conclusions: Our results suggest that the ERNN model could be utilized to model and forecast the short-term trends accurately and to evaluate the impact of traffic safety programs when applied to historical road traffic deaths data. Forecasting traffic crash deaths will provide useful information to measure burden of road traffic injuries in China.

Riding behavior and electric bike traffic crashes: A Chinese case-control study.

Objective: The present case-control study sought to explore at-risk riding behaviors associated with e-bike related traffic crashes among e-bike riders in China.Methods: Cases were recruited from residents aged 16 years and over in communities which stated "selected e-bikes as travel tools and experienced traffic crashes in the last year". Two controls for each case were randomly selected from a population of e-bike riders who had not experienced a traffic crash in the past year. The cases and controls were matched by gender, age (within 5 years) and school education level. Data were collected using questionnaires and face-to-face interviews from July 2015 to September 2015 in China. After conducting univariate logistic analysis on study variables, a conditional logistic regression model based on the 1:2 matched case-control study design was developed.Results: Multiple-factor conditional logistic regression analysis of e-bike related traffic crashes showed that running red lights (always vs. never, AOR = 3.094, 95% CI, 1.077-8.891, P < .05), riding after drinking (yes vs. no, AOR = 1.578, 95% CI, 1.102-2.259, P < .05), carrying adults while riding (always vs. never, AOR = 2.140, 95% CI, 1.273-3.595, P < .05), turning without signaling (sometimes vs. never, AOR = 1.446, 95% CI, 1.805-1.928, P < .05), riding in the motor vehicle lane (always vs. never, AOR = 2.413, 95% CI, 1.576-3.695, P < .01), prior crash history (yes vs. no, AOR = 1.670, 95% CI, 1.257-2.220, P < .05), and type of e-bikes (scooter-style e-bikes vs. bicycle-style e-bikes, AOR = 1.471, 95% CI, 1.068-2.026, P < .05) were identified as possible risk factors for e-bike traffic crashes.Conclusion: The findings of this research provide evidence about specific risky behaviors related to road traffic crashes involving e-bikes and indicated that behavioral intervention and education need to be strengthened to reduce dangerous riding behaviors. These results will be helpful for design of e-bike road risk prevention programs.

Increased Peripheral CD137 Expression in a Mouse Model of Permanent Focal Cerebral Ischemia.

Various studies demonstrate that CD137 (TNFRSF9, 4-1BB) promotes atherosclerosis and vascular inflammation in experimental models via interactions with the CD137 ligand (CD137L). However, the exact role of CD137 in ischemic stroke remains unclear. In this study, we analyzed dynamic changes of peripheral CD137 expression on T cells in a mouse model of cerebral ischemia-middle cerebral artery occlusion (MCAO), as well as alternation of neurological function, infarct size and cerebral inflammatory status after inhibition of the CD137/CD137L pathway using an anti-CD137L monoclonal antibody. MCAO mice showed elevated surface expression of CD137 on T cells in both peripheral blood and lymphoid tissues during early cerebral ischemia. Remarkably, blockade of the CD137/CD137L pathway reduced the post-ischemic brain damage. Our findings indicate that enhanced CD137 costimulation occurs in early cerebral ischemia and promotes T cell activation, which in turn upregulates inflammatory immune response and possibly exerting deleterious effects on cerebral ischemia.

Macrophagic CD146 promotes foam cell formation and retention during atherosclerosis.

The persistence of cholesterol-engorged macrophages (foam cells) in the artery wall fuels the development of atherosclerosis. However, the mechanism that regulates the formation of macrophage foam cells and impedes their emigration out of inflamed plaques is still elusive. Here, we report that adhesion receptor CD146 controls the formation of macrophage foam cells and their retention within the plaque during atherosclerosis exacerbation. CD146 is expressed on the macrophages in human and mouse atheroma and can be upregulated by oxidized low-density lipoprotein (oxLDL). CD146 triggers macrophage activation by driving the internalization of scavenger receptor CD36 during lipid uptake. In response to oxLDL, macrophages show reduced migratory capacity toward chemokines CCL19 and CCL21; this capacity can be restored by blocking CD146. Genetic deletion of macrophagic CD146 or targeting of CD146 with an antibody result in much less complex plaques in high-fat diet-fed ApoE-/- mice by causing lipid-loaded macrophages to leave plaques. Collectively, our findings identify CD146 as a novel retention signal that traps macrophages within the artery wall, and a promising therapeutic target in atherosclerosis treatment.

[A correlation study of adhesion molecule CD146 and the vulnerability of carotid atherosclerotic plaque].

OBJECTIVE: To investigate the correlation between the expression of adhesion molecule CD146 and the vulnerability of carotid atherosclerotic plaque. METHODS: The plaque samples were collected from 40 patients who underwent the carotid endarterectomy and were divided into the stable plaque group and the instable plaque group by ultrasound imaging. Five carotid artery samples were taken from the healthy donors as the control. Immunohistochemistry was applied to test the CD146 expression in all samples. RESULTS: Higher expression of CD146 was observed in the atherosclerotic plaques than in the healthy control. Moreover, statistical difference was found in the expression of CD146 in the plaques between the instable plaque group and the stable plaque group (0.31 ± 0.19 vs 0.17 ± 0.07, P < 0.05). The expression of CD146 was positively correlated with the necrotic area (r = 0.471 8, P = 0.019 9) and the matrix metalloproteinase (MMP)-9 expression in the plaques (r = 0.535 6, P = 0.000 9). CONCLUSION: The CD146 expression is correlated with the vulnerability of carotid atherosclerotic plaque.

Adhesion molecule CD146 and its soluble form correlate well with carotid atherosclerosis and plaque instability.

AIMS: Intraplaque neovascularization and foam cell infiltration contribute to the development of unstable plaque, leading to thromboembolism and stroke. Cell adhesion molecules (CAMs) have been reported to be involved in the progression of atherosclerosis and plaque vulnerability. The aim of this study was to assess the association of adhesion molecule CD146 with carotid plaque instability. METHODS: We collected forty atherosclerotic plaques from 40 patients undergoing carotid endarterectomy. The clinical information of each patient was obtained, and the plaque morphology and characteristics were examined by the ultrasound. The CD146 expressions of the plaques were graded by using semiquantitative scales. The serum level of soluble form of CD146 was detected by enzyme-linked immunosorbent assay (ELISA). RESULTS: CD146 expression was mainly on the intraplaque blood vessels and infiltrated macrophages. The CD146 expression was strongly correlated with the matrix metalloproteinase-9(MMP-9)expressions (P < 0.001) in the plaques. Soluble CD146 (sCD146) was also elevated in patients with atherosclerotic plaques. There was significant correlation between the increased CD146 expression and sCD146 level (P = 0.0057). sCD146 correlated well with serum MMP-9 (P < 0.0044), IL-6 (P = 0.0044) and high sensitivity C-reactive protein (hsCRP) (P = 0.005). CONCLUSIONS: Adhesion molecules CD146 and its soluble form strongly correlated with the development of inflammation of atherosclerosis and plaque instability. CD146 may be a promising biomarker for monitoring the development and instability of atherosclerotic plaque in patients with carotid diseases.

[Association between serum soluble CD146 level and plaque vulnerability in carotid atherosclerosis].

OBJECTIVE: To explore the relationship between circulating level of soluble CD146 (sCD146) and plaque vulnerability or inflammatory factors in patients with carotid atherosclerosis (CAS). METHODS: Forty CAS patients with carotid stenosis ( ≥ 70%) were enrolled and divided into 2 groups of stable and unstable plaque by ultrasonic imaging. And another 40 healthy subjects were enrolled for control group. Double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) was employed to measure the serum levels of sCD146 and matrix metalloproteinase-9 (MMP-9), analyze the correlation of sCD146 with MMP-9 and high sensitivity CRP (hs-CRP), and evaluate whether sCD146 correlates with plaque vulnerability. RESULTS: Soluble CD146 level was elevated in CAS patients versus healthy donors [(212 ± 43) vs (173 ± 36) ng/ml, P < 0.001]. And sCD146 level significantly increased in CAS patients with unstable plaques than those with stable plaque [(218 ± 28) vs (176 ± 25) ng/ml, P < 0.001]. And sCD146 was correlated with high-sensitivity C-reactive protein (hsCRP, r = 0.370 9, P = 0.018 5), a well-known marker for CAS inflammation. Also it was an independent risk factor for plaque vulnerability (OR = 1.16, 95%CI:1.020-1.310, P = 0.019 2). And its level was not correlated with the risk factors of CAS, such as age, homocysteine, triglyceride, total cholesterol (TC), low density lipoprotein-cholesterol (LDL-C) or high density lipoprotein-cholesterol (HDL-C) (P > 0.05). But there was a good correlation with the serum level of MMP-9 in CAS patients (r = 0.677 2, P < 0.001). CONCLUSION: The concentration of soluble CD146 is positively correlated with hsCRP and MMP-9 in CAS patients. And inflammation and neovascularization may interact with each other during atherosclerotic process. The serum level of sCD146 is correlated independently with plaque vulnerability.

Systematic study of cilostazol on secondary stroke prevention: a meta-analysis.

BACKGROUND: To study the efficacy and safety of cilostazol on ischemic stroke prevention and treatment, systematic reviews of related clinical randomized controlled trials were analyzed. METHODS: We searched the main databases for eligible trials including literature from January 1966 to November 2012 in MEDLINE, reports from 1980 to November 2012 in EMBASE, and all the studies published in EBSCO, Springer, Ovid, and Cochrane library citations. We also searched for keywords, including cilostazol and aspirin. RewMan 5.0 software was used to conduct the meta-analysis. RESULTS: Our search yielded five eligible trials. The effects of cilostazol and aspirin on ischemic stroke prevention and treatment were almost equal (combined odds ratio (OR) 0.78, 95% confidence interval (CI) (0.59, 1.04)). Additionally, both magnetic resonance angiography (MRA) and transcranial Doppler (TCD) examination showed that cilostazol could significantly decrease the incidence of intracranial artery stenosis exacerbation (MRA: combined OR 0.22, 95% CI (0.07, 0.68); TCD: combined OR 0.17, 95% CI (0.05, 0.51)). In terms of adverse reactions, there were slightly fewer incidences of major bleeding with cilostazol than with aspirin (combined OR 0.38, 95% CI (0.24, 0.60)), and there was no difference in the number of heart palpitations between cilostazol and aspirin. However, the incidence of gastrointestinal disorders, dizziness, and headaches caused by cilostazol was greater. CONCLUSIONS: Cilostazol might be a more effective and safer alternative to aspirin for patients with ischemic stroke. Further studies are required to confirm whether cilostazol is a suitable therapeutic option for secondary stroke prevention in larger cohorts of patients with ischemic stroke.

Low HDL-C level is associated with the development of intracranial artery stenosis: analysis from the Chinese IntraCranial AtheroSclerosis (CICAS) study.

BACKGROUND: Intracranial atherosclerotic stenosis (ICAS) is an important cause of ischemic stroke worldwide. The role of high-density lipoprotein cholesterol (HDL-C) or low-density lipoprotein cholesterol (LDL-C) in the development of ICAS remains to be elucidated. In the current study, we investigated the relationship of HDL-C level and the risk of developing ICAS in Chinese patients with acute ischemic stroke. METHODS: From October 2007 to June 2009, a total of 1,984 consecutive ischemic stroke patients were evaluated for the presence of symptomatic ICAS by magnetic resonance angiography (MRA). Patients were classified into two groups: intracranial steno-occlusion (ICAS group, n = 888) and non-intracranial stenosis (NICAS group, n = 1096). Serum lipid profiles were analyzed and compared between the ICAS and NICAS group. RESULTS: Significantly more patients in ICAS group had low HDL-C level (51.6%) than in the NICAS group (42.9%, P<0.001). The observed association remained significant after adjustment for conventional risk factors [(adjusted OR 1.36; 95%CI (1.13-1.63)]. Such predictive value of low level HDL-C persisted even when LDL-C was at very low level(<1.8 mmol/L). Patients in the lowest serum HDL-C quartile (<0.96 mmol/L) had the highest risk of developing ICAS [adjusted OR 1.52; 95%CI (1.17-1.98)] compared to patients in the highest serum HDL-C quartile (≥ 1.32 mmol/L) after adjustments for the covariates. CONCLUSIONS: Low HDL-C level is strongly associated with the development of ICAS. There was an inverse relationship between the level of HDL-C and the risk of developing ICAS.

[Changes of cognitive function after open heart surgery with cardiopulmonary bypass: study of 50 cases].

OBJECTIVE: To investigate the effects of different factors associated with open heart surgery under cardiopulmonary bypass (CPB) on neurocognitive function. METHODS: The cognitive capacity of 50 patients undergoing open heart surgery under CPB were studied on the day before operation and the 14th day postoperatively with Basic Cognitive Capacity Test battery, including 7 subtest: Digit Discrimination, Mental Arithmetic, Chinese Character Rotation, Digit Working Memory, Dual-word recognition, Tri-digit Recognition, and Meaningless Figure Recognition. The correlation between the postoperative cognitive decline and the factors associated with CPB was analyzed. RESULTS: The total score of the Basic Cognitive Capacity Test 14 days postoperatively in 31 out of the 50 patients (62%) was 50 +/- 13, significantly lower than the preoperative score (54 +/- 10, P < 0.01), the decline being especially marked in Digit Discrimination, Digit Working Memory, and Dual-word Recognition (all P < 0.01). The postoperative decrease in cognitive function was correlated with age (P < 0.05), CPB time (P < 0.01), rewarming rate (P < 0.01), and the highest PaCO(2) (P < 0.05). CONCLUSION: The incidence of cognitive dysfunction is higher in patients undergoing open heart surgery with CPB during the early postoperative period. Shortening CPB time, slowing rewarming, and appropriately increasing the level of PaCO2 will help to degrade the possibility of cognitive dysfunction after open heart surgery.