Objective: To explore the causal association between dietary components (carbohydrate, fat, protein, and sugar) and 119 genera of known gut microbiota using Mendelian randomization (MR) methods. Methods: Genome-wide association study (GWAS) data for dietary components were collected from the DietGen, while GWAS data for gut microbiota were collected from the MiBioGen. Single nucleotide polymorphism (SNP) loci associated with the four dietary components were used as instrumental variables, and 119 known gut microbiota genera were used as the outcomes. MR analysis was performed using inverse variance weighted (IVW) method. Heterogeneity was evaluated using Cochran's Q test, horizontal pleiotropy and exclude outliers were tested using MR-Egger regression and MR-PRESSO test. Common genetic pleiotropic genes between dietary components and gut microbiota were identified by MAGMA and PLACO analyses. Results: The MR analysis revealed causal associations between carbohydrates and 4 gut microbiota genera, fats and 14 genera, proteins and 14 genera, and sugars and 11 genera (all P<0.05). The MR-Egger regression analysis showed no horizontal pleiotropy among the selected SNPs, and the MR-PRESSO test did not identify any outliers (all P>0.05). The MAGMA and PLACO analyses revealed that 74.42% (32/43) of the causal associations had pleiotropic genes, with 1 to 10 pleiotropic genes identified. Multiple causal association groups shared the same pleiotropic genes. Conclusion There are potential genetic and causal associations between dietary components and gut microbiota.

Objective To establish an individualized nomogram model and evaluate its efficacy to provide a possible evaluation basis for the prognosis of lower third and abdominal part of oesophageal adenocarcinoma (EAC). Methods Lower third and abdominal part of EAC patients from 2010 to 2015 were chosen from the SEER Research Plus Database (17 Regs, 2022nov sub). The patients were randomly allocated to the training cohort and the internal validation cohort with a ratio of 7∶3 using bootstrap resampling. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) in EAC patients, which would be elected to construct the nomogram prediction model. C-index, calibration curve and receiver operating characteristic (ROC) curve were performed to evaluate its efficacy. Finally, the efficacy to evaluate the OS of EAC patients was compared between the nomogram prediction model and TNM staging system. Results In total, 3945 patients with lower third and abdominal part of EAC were enrolled, including 3475 males and 470 females with a median age of 65 (57-72) years. The 2761 patients were allocated to the training cohort and the remaining 1184 patients to the internal validation cohort. In the training and the internal validation cohorts, the C-index of the nomogram model was 0.705 and 0.713, respectively. Meanwhile, the calibration curve also suggested that the nomogram model had a strong capability of predicting 1-, 3-, and 5-year OS rates of EAC patients. The nomogram also had a higher efficacy than the TNM staging system in predicting 1-, 3-, and 5-year OS rates of EAC patients. Conclusion This nomogram prediction model has a high efficiency for predicting OS in the patients with lower third and abdominal part of EAC, which is higher than that of the current TNM staging system.

Xiaoji Yinzi is one of the classic prescriptions for treating urinary diseases， originated from the Yan's Prescriptions to Aid the Living （Yan Shi Ji Sheng Fang） written by YAN Yonghe in the Song dynasty. Xiaoji Yinzi is composed of Rehmanniae Radix， Cirsii Herba， Talcum， Akebiae Caulis， Typhae Pollen， Nelumbinis Rhizomatis Nodus， Lophatheri Herba， Angelicae Sinensis Radix， Gardeniae Fructus， and Glycyrrhizae Radix et Rhizoma and has the effects of cooling blood and stopping bleeding， draining water and relieving stranguria. The medical experts of later generations have inherited the original prescription recorded in the Yan's Prescriptions to Aid the Living， while dispute has emerged during the inheritance of this prescription. In this study， the method of bibliometrics was employed to review and analyze the ancient documents and modern clinical studies involving Xiaoji Yinzi. The results showed that Xiaoji Yinzi has two dosage forms： powder and decoction. According to the measurement system in the Song Dynasty， the modern doses of hers in Xiaoji Yinzi were transformed. In the prepration of Xiaoji Yinzi powder， 149.2 g of Rehmanniae Radix and 20.65 g each of Cirsii Herba， Talcum， Akebiae Caulis， stir-fried Typhae Pollen， Nelumbinis Rhizomatis Nodus， Lophatheri Herba， wine-processed Angelicae Sinensis Radix， stir-fried Gardeniae Fructus， and stir-fried Glycyrrhizae Radix et Rhizoma are grounded into fine powder with the particle size of 4-10 meshes and a decocted with 450 mL water to reach a volume of 240 mL. After removal of the residue， the decoction was taken warm before meals， 3 times a day （i.e.， 7.77 g Rehmanniae Radix and 0.97 g each of the other herbs each time）. In the preparation of Xiaoji Yinzi decoction， 20.65 g each of the above 10 herbs are used， with stir-fried Typhae Pollen， wine-processed Angelica Sinensis Radix， stir-fired Gardeniae Fructus， stir-fired Glycyrrhizae Radix et Rhizoma， and raw materials of other herbs. Xiaoji Yinzi is specialized in treating hematuresis and blood stranguria due to heat accumulation in lower energizer， which causes injury of the blood collaterals of gallbladder and dysfunction of Qi transformation. In modern clinical practice， Xiaoji Yinzi is specifically used for treating urinary diseases and can be expanded to treat diseases of the cardiovascular system and other systems according to pathogenesis. The comprehensive research on the key information could provide a scientific reference for the future development of Xiaoji Yinzi.

To introduce the general thinking, guidelines, work objectives and elaboration process of the general technical requirements adopted by volume Ⅳ of the Chinese Pharmacopoeia 2025 Edition, and to summarize and figure out the main characteristics on dosage forms, physico-chemical testing, microbial and biological testing, reference standards and guidelines The newly revised general chapters of pharmacopoeia give full play to the normative and guiding role of the Chinese Pharmacopoeia standard, track the frontier dynamics of international drug regulatory science and the elaboration of monographs, expand the application of state-of-the-art technologies, and steadily promote the harmonization and unification with the ICH guidelines； further enhance the overall capacity of TCM quality control, actively implement the 3 R principles on animal experiments, and practice the concept of environmental-friendly； replace and/or reduce the use of toxic and hazardous reagents, strengthen the requirements of drug safety control This paper aims to provide a full-view perspective for the comprehensive, correct understanding and accurate implementation of general technical requirements included in the Chinese Pharmacopoeia 2025 Edition.

Objective: To analyze the effects of health literacy on overweight and obesity among primary school students and their parents in terms of salt, oil and sugar reduction (referred to as the "three reductions"), so as to provide a theoretical basis for the development of obesity control measures. Methods: From March to April 2024, a total of 1 022 fourthgrade primary school students and 913 parents were surveyed in 24 classes in six counties in Shandong Province using multistage cluster random sampling, and physical measurements of primary school students were conducted. Pearsons correlation analysis and ordered multivariate Logistic regression were used to investigate the associations between health literacy of primary school students and their parents with overweight and obesity among children. Results: The detection rates of overweight and obesity primary school students in Shandong Province were 14.87% and 24.66%, respectively, with significant sex difference in obesity rate (29.46% for boys and 19.76% for girls) (χ2=12.93, P<0.01). In addition to students reducing oil scores, parental reducing salt,reducing oil,reducing sugar, comprehensive health literacy scores and students reducing salt,reducing sugar and comprehensive health literacy scores showed a negative relationship with students overweight and obesity (r=-0.10, -0.08, -0.07, -0.10, -0.04, -0.07, -0.03, P<0.05). The overweight and obesity rates among primary school students with high parental reducing salt,reducing oil,reducing sugar and composite health literacy scores were lower (OR=0.69, 0.69, 0.71, 0.63, P<0.05); and the overweight and obesity rate among students with high parental and low parental and high and low parental health literacy scores were lower (OR=0.68, 0.57, P<0.05). Conclusion Improving health literacy regarding "three reductions" for parents and children, especially parents, can effectively reduce the risk of childhood overweight and obesity.

[摘 要] 目的：探索预后营养指数（PNI）在接受诱导化疗联合免疫（化免）序贯放疗的局部晚期食管鳞状细胞癌（ESCC）中的疗效预测价值及预后影响。方法: 回顾性分析浙江省肿瘤医院2019年5月至2023年8月期间收治的126例行诱导化免序贯放疗的局部晚期ESCC患者的临床资料。绘制受试者工作特征曲线（ROC曲线），确定患者诱导化免前1周内、放疗前1周内、放疗开始后4 ± 1周的PNI最佳临界值并对患者进行分组。采用Kaplan-Meier法绘制生存曲线，并用Log-Rank法比较组间患者的总生存期（OS）及无进展生存期（PFS），采用Cox回归分析探讨诱导化免序贯放疗的局部晚期ESCC患者的预后影响因素。结果: 共纳入126例局部晚期ESCC患者，男性118例，女性8例，中位年龄65岁（44~78岁）。运用ROC曲线确认的患者诱导化免前、放疗前和放疗中PNI最佳临界值为46.2、48.3和37.9。放疗前PNI ≥ 48.3组中位OS、PFS分别为47.3、28.2个月，放疗前PNI < 48.3组中位OS、PFS分别为18.7、15.2个月（P < 0.01，P < 0.05）。放疗中PNI ≥ 37.9组中位OS未达到，中位PFS为25.7个月，放疗中PNI < 37.9组中位OS、PFS分别为17.0、12.5个月（P < 0.01，P < 0.05）。诱导化免后PNI升高组中位OS未达到，中位PFS为28.4个月；PNI降低组中位OS、PFS分别为20.4、16.0个月（P < 0.01，P < 0.05）。多因素分析显示，放疗中PNI[HR = 2.292，95% CI（1.264,4.159），P < 0.05]、诱导化免后PNI变化[HR = 2.120, 95% CI（1.007, 4.463），P < 0.05]为影响OS因素。结论: 放疗中PNI、诱导化免后PNI变化与患者治疗疗效及预后有一定相关性，可作为预测ESCC化免序贯放疗获益的重要指标。

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Methods: Seventy-five patients with atlas fracture were included from January 2015 to December 2020. Based on the anatomy of the fracture line, atlas fractures were divided into three types. Each type was divided into two subtypes according to the fracture displacement. Unweighted Cohen kappa coefficients were applied to evaluate the reliability and reproducibility. Results: According to the new classification, 17 cases of type A1, 12 of type A2, seven of type B1, 13 of type B2, 12 of type C1, and 14 of type C2 were identified. The K-values of the interobserver and intraobserver reliability were 0.846 and 0.912, respectively, for the new classification. The K-values of interobserver reliability for types A, B, and C were 0.843, 0.799, and 0.898, respectively. The K-values of intraobserver reliability for types A, B, and C were 0.888, 0.910, and 0.935, respectively. The mean K-values of the interobserver and intraobserver reliability for subtypes were 0.687 and 0.829, respectively. Conclusions The new classification of atlas fractures can cover nearly all atlas fractures. This system is the first to evaluate the severity of fractures based on the C1 articular facet and fracture displacement and strengthen the anatomy ring of the atlas. It is concise, easy to remember, reliable, and reproducible.

Background Animal farming may affect the structure and diversity of gut microbiota of farm workers, but it needs more studies to provide solid evidence. Objective To analyze the diversity characteristics of gut microbiota in dairy farm workers, dairy cows, and the control population (non-animal contact occupational group), and to assess the impact of dairy farming on the gut microbiota of workers. Methods The 16S rRNA full-length amplicon sequencing technology was used to sequence 60 fecal samples from dairy farm workers, 89 from dairy cows, and 50 from the general population. The gut microbiota structure characteristics, including operational taxonomic units (OTUs), alpha diversity, beta diversity, and the composition of species at the phylum, family, and genus levels were analyzed. The differences in gut microbiota among the three groups of samples were compared to explore the impact of occupational exposure on the gut microbiota structure of dairy farm workers. Results A total of 50478 OTUs were identified in the gut microbiota of farm workers, dairy cows, and the general population, with 23613, 42723, and 16592 OTUs, respectively. The number of shared OTUs between the dairy cows and the general population was 12674 (27.17%), between the general population and the farm workers was 10099 (33.54%), and between the dairy cows and the farm workers was 17690 (36.36%). Compared with the general population, the farm workers and dairy cows showed more shared gut microbiota (P<0.01). The abundance of Firmicutes in the gut of the farm workers was higher than that of the general population (P<0.01), while Actinobacteriota and Proteobacteria were the opposite (P<0.01). The alpha diversity of gut microbiota in the farm workers was different from that of the general population. The ACE index of the farm workers was slightly lower than that of the general population and much lower than that of dairy cows (P<0.05), while their Shannon index was higher than that of the general population but lower than that of dairy cows (P<0.01). The linear discriminant analysis effect size (LefSe) showed that the abundances of Lachnospiraceae and Ruminococcaceae in the gut microbiota of the farm workers were significantly higher than those of the general population, while Streptococcaceae, Lactobacillaceae, Bifidobacteriaceae, Enterobacteriaceae, and Erysipelatoclostridiaceae were lower than those of the general population. Conclusion The dairy farm workers shares a certain proportion of common bacterial communities with dairy cows. Dairy farming operations can affect the structure and diversity of the gut microbiota of workers by increasing the abundances of Lachnospiraceae and Ruminococcaceae, while decreasing the abundances of Streptococcaceae, Lactobacillaceae, Bifidobacteriaceae, Enterobacteriaceae, and Erysipelatoclostridiaceae. The study results provide reference data for further exploring the cross-host transmission of pathogens related to occupational exposure in dairy farming.