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.

The classic formula Fangji Fulingtang is from ZHANG Zhongjing's Synopsis of the Golden Chamber in the Eastern Han dynasty. It is composed of Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma， with the effects of reinforcing Qi and invigorating spleen， warming Yang and promoting urination. By a review of ancient medical books， this paper summarizes the composition， original plants， processing， dosage， decocting methods， indications and other key information of Fangji Fulingtang， aiming to provide a literature basis for the research， development， and clinical application of preparations based on this formula. Synonyms of Fangji Fulingtang exist in ancient medical books， while the formula composition in the Synopsis of the Golden Chamber is more widespread and far-reaching. In this formula， Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma are the dried root of Stephania tetrandra， the dried root of Astragalus embranaceus var. mongholicus， the dried shoot of Cinnamomum cassia， the dried sclerotium of Poria cocos， and the dried root and rhizome of Glycyrrhiza uralensis， respectively. Fangji Fulingtang is mainly produced into powder， with the dosage and decocting method used in the past dynasties basically following the original formula. Each bag is composed of Stephaniae Tetrandrae Radix 13.80 g， Astragali Radix 13.80 g， Cinnamomi Ramulus 13.80 g， Poria 27.60 g， and Glycyrrhizae Radix et Rhizoma 9.20 g. The raw materials are purified， decocted in water from 1 200 mL to 400 mL， and the decoction should be taken warm， 3 times a day. Fangji Fulingtang was originally designed for treating skin edema， and then it was used to treat impediment in the Qing dynasty. In modern times， it is mostly used to treat musculoskeletal and connective tissue diseases and circulatory system diseases， demonstrating definite effects on various types of edema and heart failure. This paper clarifies the inheritance of Fangji Fulingtang and reveals its key information （attached to the end of this paper）， aiming to provide a theoretical basis for the development of preparations based on this formula.

The classic formula Fangji Fulingtang is from ZHANG Zhongjing's Synopsis of the Golden Chamber in the Eastern Han dynasty. It is composed of Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma， with the effects of reinforcing Qi and invigorating spleen， warming Yang and promoting urination. By a review of ancient medical books， this paper summarizes the composition， original plants， processing， dosage， decocting methods， indications and other key information of Fangji Fulingtang， aiming to provide a literature basis for the research， development， and clinical application of preparations based on this formula. Synonyms of Fangji Fulingtang exist in ancient medical books， while the formula composition in the Synopsis of the Golden Chamber is more widespread and far-reaching. In this formula， Stephaniae Tetrandrae Radix， Astragali Radix， Cinnamomi Ramulus， Poria， and Glycyrrhizae Radix et Rhizoma are the dried root of Stephania tetrandra， the dried root of Astragalus embranaceus var. mongholicus， the dried shoot of Cinnamomum cassia， the dried sclerotium of Poria cocos， and the dried root and rhizome of Glycyrrhiza uralensis， respectively. Fangji Fulingtang is mainly produced into powder， with the dosage and decocting method used in the past dynasties basically following the original formula. Each bag is composed of Stephaniae Tetrandrae Radix 13.80 g， Astragali Radix 13.80 g， Cinnamomi Ramulus 13.80 g， Poria 27.60 g， and Glycyrrhizae Radix et Rhizoma 9.20 g. The raw materials are purified， decocted in water from 1 200 mL to 400 mL， and the decoction should be taken warm， 3 times a day. Fangji Fulingtang was originally designed for treating skin edema， and then it was used to treat impediment in the Qing dynasty. In modern times， it is mostly used to treat musculoskeletal and connective tissue diseases and circulatory system diseases， demonstrating definite effects on various types of edema and heart failure. This paper clarifies the inheritance of Fangji Fulingtang and reveals its key information （attached to the end of this paper）， aiming to provide a theoretical basis for the development of preparations based on this formula.

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 optimize the prescription pre-review system in our hospital and evaluate its application effects. METHODS Aiming at the problems of imperfect rule base and high false positive rate in the early operation of the system， optimization measures were taken， including improving the content of the rule base， adjusting the interception level and prompt mode， refining the working model of prescription review pharmacists， and strengthening clinical communication. A retrospective cohort study was conducted， with prescription data from June to December 2023 （before optimization） as the control group and June to December 2024 （after optimization） as the observation group. Through inter group comparative analysis， the actual effect of optimizing the prescription pre-approval system was evaluated. RESULTS The prescription qualified rate increased from （82.51± 4.04）% before optimization to （90.98±1.55）% after optimization； the false positive rate decreased from （20.87±1.64）% before optimization to （7.41±2.04）% after optimization. The monthly range of prescription qualified rate narrowed from 10.24% to 4.11%， and the coefficient of variation decreased from 4.92% to 1.73%. The monthly range of false positive rate slightly increased from 4.40% to 5.34%， the coefficient of variation rose from 8.32% to 26.18%. CONCLUSIONS Through multi-dimensional optimizations of the prescription pre-review system in our hospital， its prescription review efficiency has been significantly enhanced， the quality of prescriptions has steadily improved， and the accuracy of reviews has notably improved.

Background With economic development and globalization, shift work has become prevalent across industries. Its relationship with type 2 diabetes mellitus (T2DM) attracts increasing attention. Objective To thoroughly explore the relationship between shift work and T2DM, and analyze the impacts of specific shift patterns on T2DM, so as to provide a basis for formulating reasonable shift schedules. Methods We conducted a 1:2 matched case-control study among adults (20-60 years) who ordered occupational health examinations at the Wuxi No.8 People's Hospital from November to December 2023. The case group comprised 200 T2DM patients, while the controls were 400 age-stratified matched non-diabetic individuals. General demographic characteristics, behavioral habits, medical history, and shift work exposure data (including shift patterns, frequency, and length of service) 5 years prior to diagnosis were collected through standardized questionnaires. Logistic regression adjusted for selected confounders was employed to evaluate the association between shift work and T2DM. Results The logistic regression analysis demonstrated that shift work was associated with an increased risk of T2DM. After adjusting for confounding factors, shift workers had a 3.55 times higher risk of being diagnosed T2DM compared to non-shift workers (OR=3.55, 95%CI: 1.026, 12.263). The risk varied across different shift patterns, and the three-shift two-rotation system showed the highest risk (OR=4.17, 95%CI: 1.921, 9.035), followed by the two-shift system (OR=2.94, 95%CI: 2.016, 4.281) and four-shift three-rotation system (OR=2.66, 95%CI: 1.611, 6.093). Workers with more than 3 monthly shift days had a 2.74-fold increased risk (95%CI: 1.658, 4.512) compared to non-shift workers. Additionally, working more than 8 h daily (OR=1.74, 95%CI: 1.185, 2.562) and having more than 20 years of service (OR=2.51, 95%CI: 1.581, 3.976) were both significantly associated with a higher T2DM risk. The trend tests revealed that each incremental increase in monthly shift days and length of service elevated T2DM risk by 2.61 times (95%CI: 1.813, 3.765) and 1.49 times (95%CI: 1.147, 1.931), respectively (P<0.05). Conclusion Shift work is an independent risk factor for T2DM, with three-shift two-rotation system posing the highest risk. Shift frequency, daily working hours, and length of service are all significant factors affecting the risk of T2DM. These findings support industry-specific shift policy reform and targeted glucose monitoring and health interventions are recommended for workers engaged in high-risk shift patterns (e.g., three-shift two-rotation system, frequent shifts) and those with prolonged shift work history (>20 years).

Objective: To investigate the correlation among α2, 6-sialyltransferase (ST6Gal-Ⅰ), CD36 desialylation, and caveolin-1 (Cav-1) in phorbol ester (PMA)-induced MEG-01 cell model, as well as their potential mechanism in immune thrombocytopenia (ITP). Methods: MEG-01 cells were treated with 10 ng/mL PMA for 48 hours (control group: 0.1% DMSO). Flow cytometry was used to detect cell surface markers: desialylation (CD41 RCA ) and α2, 6-sialylation (CD41 SNA ). Western blot was performed to analyze the protein expressions of ST6Gal-Ⅰ, CD36, and Cav-1. Results: Flow cytometry analysis revealed that, compared with the control group (set as 100%), the proportion of CD41 RCA positive cells in the MEG-01 cells after PMA intervention significantly increased to (127.79±2.01)%, while the proportion of CD41 SNA positive cells significantly decreased to (78.09±1.76)% (both P<0.05). Western blot analysis results showed that, compared with the control group, PMA intervention significantly downregulated the expression of ST6Gal-Ⅰ protein (0.602±0.023 vs 0.768±0.068) and Cav-1 protein (1.012±0.028 vs 1.253±0.068) (both P<0.05), while significantly upregulating the expression of CD36 protein (0.936±0.033 vs 0.694±0.070, P<0.05). Conclusion: PMA can significantly inhibit the expression of ST6Gal-Ⅰ, accompanied by increased desialylation (β-galactose exposure), elevated CD36, and downregulated Cav-1. These changes suggest that the increased exposure of CD36 antigen and the disorder of membrane microenvironment may be involved in the pathological process of ITP, providing a new direction for mechanism research.