Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective:To construct a representative index system for evaluating pediatric orthopedic nursing quality, providing a basis for hospital pediatric orthopedic nursing quality assessment and monitoring.Methods:From April to July 2023, using the "structure-process-outcome" three-dimensional quality structure model as the theoretical framework, a literature review was conducted, and an item pool was formulated. Through two rounds of Delphi method expert consultations, the hierarchical analysis method was finally employed to determine the indicators and their weights at each level.Results:The effective recovery rates of the questionnaire of the two rounds of expert consultations were 100% (20/20), the authority coefficients of experts were 0.87 and 0.88, the coefficients of variation were 0.00 to 0.27 and 0.00 to 0.24. The Kendell harmony coefficients of the second and third indicators in the two rounds of inquiry were 0.140, 0.166 and 0.192, 0.161(all P<0.05). The final pediatric orthopedic nursing quality evaluation index system included 3 primary indicators, 21 secondary indicators and 83 tertiary indicators. Among the primary indicators, the weight of process quality was the highest at 0.493 4, followed by outcome quality at 0.310 8, and the lowest was structural quality at 0.195 8. In the secondary indicators, "assessment criteria of limb blood circulation" had the highest weight at 0.099 8. Conclusions:The constructed pediatric orthopedic nursing quality evaluation index system covers key aspects and is more operationally feasible. It provides better guidance for nursing interventions and quality control.

Objective To investigate the sleep disorders and its effects on the changes in quality of life in patients with liver cancer from the hospital admission to 6 months after surgery and to analyse the correlation between the sleep disorder and quality of life.Methods A total of 214 patients who underwent surgery for liver cancer for the first time were included in the study.Demographic questionnaire,Pittsburgh sleep quality index(PSQI),and functional assessment of cancer therapy-hepatobiliary(FACT-Hep)were used for the investigation at admission and at 1,3 and 6 months after surgery.Multiple linear regression was employed to analyse the correlation between the sleep disorders at the admission and its effect on quality of life up to 6 months after surgery.Results Toally 214 patients finished the study at admission and 209 finished the study 1 month after surgery,and 208 finished the stuoly 3 months after surgery,and 205 patients finished the study 6 months after surgery.The scores of both of PSQI at admission and the quality of life at 6 months after surgery varied across the tested time points with a statistically significant difference(both P<0.001).The overall level of sleep disorder in the patients showed a characteristic pattern with initially increasing and then decreasing,and the quality of life presented a characteristic tendency of starting from high to low and then gradually increasing.It showed that the sleep disorder at admission was attributive to the poorer quality of life at 6 months after surgery.The hierarchical regression analysis showed that among the patients at BCLC Stage A,sleep disorder at admission was the influencing factor of the quality of life at 6 months after surgery.Conclusions The sleep disorder and quality of life in the patients who had surgical operations for hepatocellular carcinoma both changed dynamically from admission to the 6 months after surgery.The quality of life was poor in the patients with sleep disorder at admission.Therefore,medical staff should enhance the sleep management at admission,conduct dynamic assessment of the sleep disorder and quality of life of the patients,and then develop continuity nursing measures to improve the quality of life after surgery.

【Objective】 To establish a blood quality monitoring indicator system, in order to continuously improve blood quality and standardized management. 【Methods】 Based on the research of literature and standards, and guided by the key control points of blood collection and supply process, the blood quality monitoring indicator system was developed. Through two rounds of Delphi expert consultation, the indicator content was further revised and improved according to expert opinions after six months of trial implementation. The indicator weight was calculated by questionnaire and analytic hierarchy process. 【Results】 A blood quality monitoring indicator system covering the whole process of blood collection and supply was constructed, including five primary indicators, namely blood donation service, blood component preparation, blood testing, blood supply and quality control, as well as 72 secondary indicators, including definitions, calculation formulas, etc. Two rounds of expert consultation and two rounds of feasibility study meeting were held to revise 17 items and the weight of each indicator was obtained through the analytic hierarchy process. After partial adjustments, a blood quality monitoring indicator system was formed. 【Conclusion】 A blood quality monitoring indicator system covering the whole process of blood collection and supply has been established for the first time, which can effectively evaluate the quality management level of blood banks and coordinate blood quality control activities of blood banks in Shandong like pieces in a chess game, thus improving the standardized management level

【Objective】 To objectively evaluate the quality control level of blood testing process in blood banks through quantitative monitoring and trend analysis, and to promote the homogenization level and standardized management of blood testing laboratories in blood banks. 【Methods】 A quality monitoring indicator system covering the whole process of blood collection and supply, including blood donation service, blood component preparation, blood testing, blood supply and quality control was established. The questionnaire Quality Monitoring Indicators for Blood Collection and Supply Process with clear definition of indicators and calculation formulas was distributed to 17 blood banks in Shandong province. Quality monitoring indicators of each blood bank from January to December 2022 were collected, and 31 indicators in terms of blood testing were analyzed using SPSS25.0 software. 【Results】 The proportion of unqualified serological tests in 17 blood bank laboratories was 55.84% for ALT, 13.63% for HBsAg, 5.08% for anti HCV, 5.62% for anti HIV, 18.18% for anti TP, and 1.65% for other factors (mainly sample quality). The detection unqualified rate and median were (1.23±0.57)% and 1.11%, respectively. The ALT unqualified rate and median were (0.74±0.53)% and 0.60%, respectively. The detection unqualified rate was positively correlated with ALT unqualified rate (r=0.974, P<0.05). The unqualified rate of HBsAg, anti HCV, anti HIV and anti TP was (0.15±0.09)%, (0.05±0.04)%, (0.06±0.03)% and (0.20±0.05)% respectively. The average unqualified rate, average hemolysis rate, average insufficient volume rate and the abnormal hematocrit rate of samples in 17 blood bank laboratories was 0.21‰, 0.08‰, 0.01‰ and 0.02‰ respectively. There were differences in the retest concordance rates of four HBsAg, anti HCV and anti HIV reagents, and three anti TP reagents among 17 blood bank laboratories (P<0.05). The usage rate of ELISA reagents was (114.56±3.30)%, the outage rate of ELISA was (10.23±7.05) ‰, and the out of range rate of ELISA was (0.90±1.17) ‰. There was no correlation between the out of range rate, outrage rate and usage rate (all P>0.05), while the outrage rate was positively correlated with the usage rate (r=0.592, P<0.05). A total of 443 HBV DNA positive samples were detected in all blood banks, with an unqualified rate of 3.78/10 000; 15 HCV RNA positive samples were detected, with an unqualified rate of 0.13/10 000; 5 HIV RNA positive samples were detected, with an unqualified rate of 0.04/10 000. The unqualified rate of NAT was (0.72±0.04)‰, the single NAT reaction rate [(0.39±0.02)‰] was positively correlated with the single HBV DNA reaction rate [ (0.36±0.02) ‰] (r=0.886, P<0.05). There was a difference in the discriminated reactive rate by individual NAT among three blood bank laboratories (C, F, H) (P<0.05). The median resolution rate of 17 blood station laboratories by minipool test was 36.36%, the median rate of invalid batch of NAT was 0.67%, and the median rate of invalid result of NAT was 0.07‰. The consistency rate of ELISA dual reagent detection results was (99.63±0.24)%, and the median length of equipment failure was 14 days. The error rate of blood type testing in blood collection department was 0.14‰. 【Conclusion】 The quality monitoring indicator system for blood testing process in Shandong can monitor potential risks before, during and after the experiment, and has good applicability, feasibility, and effectiveness, and can facilitate the continuous improvement of laboratory quality control level. The application of blood testing quality monitoring indicators will promote the homogenization and standardization of blood quality management in Shandong, and lay the foundation for future comprehensive evaluations of blood banks.

【Objective】 To establish an effective quality monitoring indicator system for blood quality control in blood banks, in order to analyze the quality control indicators for blood collection and supply, and evaluate blood quality control process, thus promoting continuous improvement and standardizing management of blood quality control in blood banks. 【Methods】 A quality monitoring indicator system covering the whole process of blood collection and supply, including blood donation services, component preparation, blood testing, blood supply and quality control was established. The Questionnaire of Quality Monitoring Indicators for Blood Collection and Supply Process was distributed to 17 blood banks in Shandong, which clarified the definition and calculation formula of indicators. The quality monitoring indicator data from January to December 2022 in each blood bank were collected, and 20 quality control indicators data were analyzed by SPSS25.0 software. 【Results】 The average pass rate of key equipment monitoring, environment monitoring, key material monitoring, and blood testing item monitoring of 17 blood banks were 99.47%, 99.51%, 99.95% and 98.99%, respectively. Significant difference was noticed in the pass rate of environment monitoring among blood banks of varied scales(P<0.05), and the Pearson correlation coefficient (r) between the total number of blood quality testing items and the total amount of blood component preparation was 0.645 (P<0.05). The average discarding rates of blood testing or non-blood testing were 1.14% and 3.36% respectively, showing significant difference among blood banks of varied scales (P<0.05). The average discarding rate of lipemic blood was 3.07%, which had a positive correlation with the discarding rate of non testing (r=0.981 3, P<0.05). There was a statistically significant difference in the discarding rate of lipemic blood between blood banks with lipemic blood control measures and those without (P<0.05). The average discarding rate of abnormal color, non-standard volume, blood bag damage, hemolysis, blood protein precipitation and blood clotting were 0.20%, 0.14%, 0.06%, 0.06%, 0.02% and 0.02% respectively, showing statistically significant differences among large, medium and small blood banks(P<0.05).The average discarding rates of expired blood, other factors, confidential unit exclusion and unqualified samples were 0.02%, 0.05%, 0.003% and 0.004%, respectively. The discarding rate of blood with air bubbles was 0.015%, while that of blood with foreign body and unqualified label were 0. 【Conclusion】 The quality control indicator system of blood banks in Shandong can monitor weak points in process management, with good applicability, feasibility, and effectiveness. It is conducive to evaluate different blood banks, continuously improve the quality control level of blood collection and supply, promote the homogenization and standardization of blood quality management, and lay the foundation for comprehensive evaluation of blood banks in Shandong.