ObjectiveTo observe the effects of modified Chaihu Shugansan on the calmodulin-dependent protein kinase Ⅱ（CaMKⅡ）/cAMP-response element binding protein （CREB） signaling pathway in the hippocampus and heart tissue of a rat model with myocardial ischemia and depression and explore the mechanism by which this formula prevents and treats coronary heart disease combined with depression. MethodsThe model of myocardial ischemia combined with depression was established by high-fat diet， intraperitoneal injection of isoproterenol （ISO）， and chronic unpredictable mild stress （CUMS）. A total of 108 SD male rats were randomly divided into normal group， model group， high （23.4 g·kg^-1）， medium （11.7 g·kg^-1）， and low （5.85 g·kg^-1） dose groups of modified Chaihu Shugansan， CaMKⅡ inhibitor （KN93） group， and KN93 + high， medium， and low dose groups of modified Chaihu Shugansan， with 12 rats in each group. From the first day of modeling to the end of modeling， drugs were administered once a day. In the seventh and eighth weeks， the KN93 group and the KN93 + high， medium， and low dose groups of modified Chaihu Shugansan were intraperitoneally injected with KN93 three times weekly. At the end of the eighth week， behavioral tests including sucrose preference， open field， and elevated plus maze were conducted. Electrocardiogram （ECG） lead Ⅱ changes were observed in each group of rats， and hematoxylin-eosin （HE） staining was performed to observe changes in heart tissue. Serum levels of triglycerides （TG）， total cholesterol （TC）， high-density lipoprotein （HDL）， low-density lipoprotein （LDL）， and lactate dehydrogenase （LDH） were measured by using an enzyme-labeled instrument. Creatine kinase （CK） and creatine kinase-MB （CK-MB） were detected by ultraviolet spectrophotometry， while serum monocyte chemoattractant protein-1 （MCP-1） was measured by enzyme-linked immunosorbent assay （ELISA）. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect mRNA expression of CaMKⅡ and CREB in hippocampal and heart tissue， and Western blot was performed to assess protein expression of CaMKⅡ， phosphorylated （p）-CaMKⅡ， CREB， and p-CREB. ResultsCompared to the normal group， the model group showed significant reductions in sucrose preference rate， total activity distance in the open field， number of entries into the center area of the open field， and percentage of entries into the open arms of the elevated plus maze （P<0.01）. The ECG showed ST-segment elevation， and HE staining showed serious degeneration of myocardial fibers， disordered arrangement， and infiltration of a large number of inflammatory cells. In addition， serum TC and LDL levels increased （P<0.01）， and HDL level decreased （P<0.01）. CK， CK-MB， LDH， and MCP-1 levels significantly increased （P<0.05， P<0.01）. The mRNA expression of CaMKⅡ and CREB and the protein expression of p-CaMKⅡ and p-CREB decreased in the hippocampal tissue （P<0.05， P<0.01）， but those increased in the heart tissue （P<0.01）. Compared to the model group， the high， medium， and low dose groups of modified Chaihu Shugansan showed improvements in these abnormalities. The KN93 group had reduced sucrose preference， total activity distance in the open field， number of entries into the center area of the open field， and percentage of entries into the open arms of the elevated plus maze （P<0.01）， as well as decreased serum CK， CK-MB， LDH， and MCP-1 levels （P<0.05， P<0.01）. KN93 also reduced ST-segment elevation， alleviated the degeneration degree of myocardial fibrosis， and lowered inflammatory cell infiltration. The mRNA expression of CaMKⅡ and CREB and the protein expression of p-CaMKⅡ and p-CREB in both the hippocampal and heart tissue were reduced （P<0.05， P<0.01）. The KN93 + high， medium， and low dose groups of modified Chaihu Shugansan showed further improvements in these abnormalities compared to the KN93 group. ConclusionThe modified Chaihu Shugansan exerts antidepressant and myocardial protective effects in rats with myocardial ischemia and depression， possibly related to bidirectional regulation of the CaMKⅡ/CREB signaling pathway， with the high-dose modified Chaihu Shugansan showing the best effects.

Background The positive rate of work-related musculoskeletal disorders (WMSDs) among coal mine workers remains high, which seriously affects the quality of life of the workers. Objective To estimate the prevalence of WMSDs among coal miners in Shanxi Province and analyze their influencing factors. Methods From May to December 2023, 2315 coal miners from a coal mine enterprise inShanxi Province were selected by convenience sampling, and the self-administered Basic Situation Survey Scale, Musculoskeletal Disorders Questionnaire, Effort-Reward Imbalance Questionnaire, and Pittsburgh Sleep Quality Scale were used to evaluate general demographic characteristics, work organization characteristics, living habits, sleep quality, occupational stress, and occurrence of WMSDs symptoms and the corresponding absences (sickness absence) in past 1 year. The positive rate and absenteeism rate of WMSDs were caculated, and Pearson's chi-square test was used for identify influencing factors of WMSDs, and finally a binary logistic regression method was used to further studying the factors. Results A total of 2577 questionnaires were distributed in this survey, and 2315 valid questionnaires were recovered, with an effective recovery rate of 89.9%. The positive rate of WMSDs symptoms was 48.7% (1127/2315), the absenteeism rate due to WMSDs was 22.6% (523/2315), and the waist (17.5%), neck (11.7%), and knee (9.0%) were the most common areas presenting WMSDs symptoms. The results of logistic regression showed that compared with ≤10 years of service, the risk of WMSDs was higher for those with > 20 years of service (OR=2.167, 95%CI: 1.419, 3.311). Compared with the daily working time of ≤8 h, the risk of WMSDs was higher for those >8 h daily working time (OR=1.463, 95%CI: 1.211, 1.767). A higher risk of WMSDs was reported in workers with moderate labor intensity (OR=1.247, 95%CI: 1.009, 1.542) or heavy labor intensity (OR=1.570, 95%CI: 1.215, 2.027) than those with light labor intensity. The higher the education level (OR _{high school and technical secondary school}=1.866, 95%CI: 1.435, 2.425; OR _{junior college/bachelor degree or above}=1.953, 95%CI: 1.445, 2.639), the higher the risk of WMSDs. The risk of WMSDs was higher in workers reporting smoking (OR=1.225, 95%CI: 1.021, 1.470) or alcohol consumption (OR=1.345, 95%CI: 1.122, 1.612). The risk of WMSDs in coal miners with high occupational stress (OR=1.229, 95%CI:1.030, 1.466) or those with sleep disorders (OR=3.616, 95%CI:2.824, 4.629) was higher than that in workers with low occupational stress or no sleep disorders respectively. Conclusion The positive rate of WMSDs among coal mine personnel in Shanxi Province is high, and education level, length of service, daily working hours, smoking, alcohol consumption, labor intensity, sleep disorders, and occupational stress are influencing factors for WMSDs.

According to the work goals and tasks determined by edition outline of the Chinese Pharmacopoeia 2025 Edition, the Chinese Pharmacopoeia 2025 Edition has been completed. Among them, 52 new pharmaceutical excipients monographs have been added, an increase of 15.5% compared with the 2020 Edition, and the total number has reached 387. This article focuses on the general framework and the main characteristics of the standards of pharmaceutical excipients in the Chinese Pharmacopoeia 2025 Edition, which can contribute to accurately understand and utilize the standards in Chinese Pharmacopoeia.

The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified SmIAA7 which is potentially associated with Salvia miltiorrhiza leaf development through comparatively analyzed the transcriptome data from different pinnate leaves. SmIAA7 was successfully isolated from S. miltiorrhiza using the specific primers. Then subsequent bioinformatic analysis, prokaryotic expression and purification, subcellular localization, and induction expression analysis under auxin and abiotic stress were performed. The full-length of SmIAA7 contained an ORF of 684 bp encoding a protein of 227 amino acid with a molecular weight of 25.3 kD. Conserved domain analysis showed that SmIAA7 contains the conserved Aux_IAA domain (pfam02309). Sequence analysis and phylogenetic tree analysis results indicated SmIAA7 was phylogenetically close to IAA7 and IAA14 from other plants, suggesting SmIAA7 involved in plant growth and development as well as response to environmental stresses. The prokaryotic expression vector pET28a-SmIAA7 was constructed and SmIAA7 recombinant protein was successfully expressed in E. coli Rosetta (DE3) strain. Subcellular localization experiment demonstrated that SmIAA7 localized in the nucleus of plant cells. Real-time fluorescence quantitative PCR results showed that the expression level of SmIAA7 was upregulated in response to auxin. Drought, low temperature, and salt stress significantly increased the transcript level of SmIAA7 gene. This study lays a foundation for further elucidating the role of SmIAA7 in leaf development, signal transduction, and stress defense in S. miltiorrhiza.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Objective To analyze the characteristics of nystagmus during the Dix-Hallpike and Roll tests in patients with benign paroxysmal positional vertigo (BPPV) using three-dimensional videonystagmography (3D-VNG), in order to to optimize diagnostic and therapeutic strategies of BPPV. Methods A retrospective analysis was conducted on 68 patients with posterior semicircular canal (PSC)-BPPV and 26 patients with horizontal semicircular canal (HSC)-BPPV. Nystagmus data obtained from 3D-VNG were reviewed for all patients, with a focus on the eye movement components during the Dix-Hallpike test in PSC-BPPV patients and the Roll test in HSC-BPPV patients. The direction and reversal rates of the vertical, horizontal, and torsional components were recorded and analyzed. Results All PSC-BPPV patients exhibited highly consistent three-dimensional nystagmus characteristics during the Dix-Hallpike test: vertical nystagmus was uniformly upward, torsional nystagmus was predominantly clockwise in left-side BPPV patients (17/23) and counterclockwise in right-side BPPV patients (44/45), while the horizontal component was mostly directed contralaterally (50/68); upon transitioning from the head-hanging to the sit-up position, vertical nystagmus components in all patients reversed, and torsional and horizontal nystagmus components reversed in approximately 50.0% or more patients. Among HSC-BPPV patients, right-side BPPV patients all showed right-beating (geotropic) horizontal nystagmus with predominantly upward vertical component (16/19), while most left-side BPPV patients showed left-beating horizontal nystagmus (6/7) with predominantly downward vertical component (6/7). During head rotation toward the healthy side, most (25/26) HSC-BPPV patients exhibited a reversal in the horizontal nystagmus direction, reduced intensity compared to the affected side, with a reversal in vertical components in 3 patients, and atypical torsional components. Conclusions 3D-VNG could precisely quantitative analyze three-dimensional features of nystagmus in BPPV patients, improve diagnostic accuracy in canal and side localization, particularly in PSC-BPPV patients.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.