The active components in traditional Chinese medicine （TCM） generally have problems such as poor solubility and low bioavailability， which seriously restrict their clinical efficacy. In recent years， TCM nano-self-assembly based on the non-covalent interactions between molecules has become the research frontier in this field because it can spontaneously form an orderly structure and improve the insoluble components in the body. Moreover， TCM nano-self-assembly has shown unique advantages in the treatment of fibrosis， inflammatory and immune diseases， infectious diseases， and cancer. This article summarizes the research context， structural system， and potential as a pharmacological substance basis of TCM nano-self-assembly by reviewing recent published studies， with focuses on the application of TCM nano-self-assembly in the treatment of diseases from single components to complex systems. In addition， this article summarizes its advantages， mechanism characteristics， and main challenges faced. At present， this field still faces issues such as insufficient mechanism research， a lack of in-depth understanding of the competitive assembly rules of multiple components， difficulty in quality control， unclear in vivo processes and safety， and obstacles to clinical translation. To address these issues， this article proposes a series of solutions， including introducing molecular dynamics simulation and artificial intelligence to predict assembly behavior， using experimental techniques to analyze non-covalent interactions， establishing a comprehensive evaluation system with multiple indicators， using isotope labeling and in vivo imaging techniques to dynamically track in vivo processes， conducting standardized safety evaluation， promoting the filing of hospital preparations and new drug applications， and strengthening early communication between research and regulation. Finally， this article points out the future development directions， aiming to provide reference for subsequent research and clinical translation.

The active components in traditional Chinese medicine （TCM） generally have problems such as poor solubility and low bioavailability， which seriously restrict their clinical efficacy. In recent years， TCM nano-self-assembly based on the non-covalent interactions between molecules has become the research frontier in this field because it can spontaneously form an orderly structure and improve the insoluble components in the body. Moreover， TCM nano-self-assembly has shown unique advantages in the treatment of fibrosis， inflammatory and immune diseases， infectious diseases， and cancer. This article summarizes the research context， structural system， and potential as a pharmacological substance basis of TCM nano-self-assembly by reviewing recent published studies， with focuses on the application of TCM nano-self-assembly in the treatment of diseases from single components to complex systems. In addition， this article summarizes its advantages， mechanism characteristics， and main challenges faced. At present， this field still faces issues such as insufficient mechanism research， a lack of in-depth understanding of the competitive assembly rules of multiple components， difficulty in quality control， unclear in vivo processes and safety， and obstacles to clinical translation. To address these issues， this article proposes a series of solutions， including introducing molecular dynamics simulation and artificial intelligence to predict assembly behavior， using experimental techniques to analyze non-covalent interactions， establishing a comprehensive evaluation system with multiple indicators， using isotope labeling and in vivo imaging techniques to dynamically track in vivo processes， conducting standardized safety evaluation， promoting the filing of hospital preparations and new drug applications， and strengthening early communication between research and regulation. Finally， this article points out the future development directions， aiming to provide reference for subsequent research and clinical translation.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

ObjectiveTo explore the listed varieties and prescription characteristics of Chinese patent medicines for stroke in China， explore the medication rules of Chinese medicine for stroke， and provide guidance for further clinical research and development of Chinese patent medicines. MethodsExcel 2021 and the Ancient and Modern Medical Record Cloud Platform （V2.3.5） were used to systematically mine and analyze the varieties and prescriptions of Chinese patent medicines for stroke in China. ResultsA total of 244 Chinese patent medicines （two for different dosage forms of the same prescription）， 1 736 approval documents for Chinese patent medicines， 792 manufacturers， and 83 varieties of protected Chinese patent medicines were finally included in the database. The top three dosage forms were capsules （75）， pills （53）， and tablets （42）. There were 28 Chinese patent medicines for stroke in the National Essential Drug Catalogue （2018）， 129 in the National Essential Medical Insurance， Industrial Injury Insurance and Maternity Insurance Drug Catalogue （2023）， and 4 in the National Non-prescription Drug Catalogue. Among the 138 prescriptions screened out， Chinese patent medicines mainly treated stroke patients with the syndrome of Qi deficiency and blood stasis. The top three most frequent medicinal herbs were Chuanxiong Rhizoma （63）， Pheretima （47）， and Salviae Miltiorrhizae Radix et Rhizoma （47）. The medicinal herbs used were mainly warm， pungent， with the meridian tropism to the liver meridian. The correlation analysis showed that the herb pair with the highest support was Astragali Radix-Chuanxiong Rhizoma， and that with the highest confidence was Carthami Flos-Chuanxiong Rhizoma. Five herb combinations were identified based on the cluster analysis. ConclusionThe Chinese patent medicines for stroke mainly treat patients with the syndrome of Qi deficiency and blood stasis. The medicinal herbs used in the prescriptions mainly have the functions of activating blood and resolving stasis， extinguishing wind and stopping convulsions. Drug compatibility usually focuses on activating blood and resolving stasis， as well as expelling phlegm and opening orifices. This review of the varieties and prescription characteristics of Chinese patent medicines for stroke helps optimize clinical decision-making， guide drug research and development， promote medical research and scientific progress， and provide more effective support and guarantee for the treatment of stroke patients.

Objective:To investigate the immuno-inflammatory regulatory effects of the Qing Gan San Jie Xiao Ying Formula(QGSJXYF)on Hashimoto's thyroiditis(HT)by modulating thyroid cell-derived exosomes to provide experi-mental evidence for its immunomodulatory mechanisms.Methods:Nthy-ori-3-1 thyroid cells were treated with QGSJXYF-medicated serum,with untreated cells serving as controls.Exosomes from both groups were extracted and analyzed using nanoparticle tracking analysis(NTA),transmission electron microscopy(TEM),and Western blot to assess concentration,size distribution,morphology,and the expression of characteristic exosomal markers.An inflammatory model of human T lymphocytes(H9)was established and co-incubated with normal exosomes(EXO-C group)or QGSJXYF-treated exosomes(EXO-T group).The levels of inflammatory cytokines in H9 cells were measured using Western blot(WB)and ELISA.Results:Exosome characterization showed that the particle concentration of Nthy-ori-3-1 cell-derived exosomes in both the control and QGSJXYF groups ranged from 1×109 to 1×1011/mL,with particle diameters between 80～300 nm.The exosomes exhibited a typical spherical or cup-shaped morphology with positive expression of TSG101,CD63,and HSP70.Compared with the inflammation model group and the EXO-C group,the EXO-T group significantly reduced the intracellular expression of IL-17A protein in H9 cells(P<0.05)and suppressed IL-17 and IL-6 levels in the cell supernatant(P<0.01).Conclusion:QGSJXYF may exert its anti-inflammatory and thyroid-protective effects by modulating the functional state of thyroid cell-derived exosomes,regulating the inflamma-tory microenvironment,and inhibiting the expression of inflammatory cytokines associated with Hashimoto's thyroiditis.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Objective To analyze the lung function condition and the prevalence of pulmonary ventilation disorders in the occupational population of Wuhan, and to explore their influencing factors. Methods Physical examination data from the Wuhan Prevention and Treatment Center for Occupational Diseases were used in this study, and finally 9499 people were selected as the study subjects. The linear regression model and logistic regression model were used to analyze the influencing factors of pulmonary ventilation function and pulmonary dysfunction. The restricted cubic spline was used to explore the nonlinear relationship. Results The prevalence of pulmonary ventilation disorders was 1.7%, and the lung function indexes FVC, FEV1, and FEV1/FVC were significantly lower in the population aged >27 years than in the population aged <27 years (P<0.001). The lung function indexes FVC and FEV1 were significantly lower in females than in males (P<0.001). The lung function indexes FVC and FEV1 were significantly lower in underweight occupational groups than in normal-weight groups (P<0.001), and FVC and FEV1 were significantly lower in dust-exposed occupational groups than in groups without dust exposure(P<0.05). Restricted cubic spline plots showed a nonlinear relationship between age and lung function indexes FVC and FEV1 (P_nonlinear< 0.05). Age and BMI were the risk factors for pulmonary ventilation disorders. Conclusion Age, gender, BMI, and dust exposure are risk factors for decreased FVC and FEV1. Age is the risk factor for decreased FEV1/FVC. Age and BMI are the risk factors for pulmonary ventilation disorders.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

This study aims to explore the scientific connotation of sinking Rehmanniae Radix has the best quality and compare the quality between floating and sinking fresh Rehmanniae Radix samples. Ultra-performance liquid chromatography tandem quadrupole electrostatic field Orbitrap high-resolution mass spectrometry(UHPLC-Q-Orbitrap HRMS) was employed to detect the chemical components in floating and sinking fresh Rehmanniae Radix samples. The fingerprint of fresh Rehmanniae Radix was established by high performance liquid chromatography(HPLC), and four index components were determined simultaneously. The cluster analysis, principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) were conducted to compare the quality of floating and sinking fresh Rehmanniae Radix samples. An evaporative light-scattering detector was used to compare the content of five sugars. The extract yield and drying rate were determined, and the quality connotation of sinking Rehmanniae Radix has the best quality was explained by multiple indicators. A total of 41 components were preliminarily identified from fresh Rehmanniae Radix by UHPLC-Q-Orbitrap HRMS, including 7 iridoid glycosides, 9 phenylethanol glycosides, 6 amino acids, 4 sugars, 3 phenolic acids, 5 nucleosides, 3 organic acids, 1 ionone, 1 furan, 1 coumarin, and 1 phenylpropanoid. The results showed that the main chemical components were consistent between floating and sinking fresh Rehmanniae Radix. Nine common peaks were identified in the fingerprints of 15 batches of floating and sinking fresh Rehmanniae Radix samples, and the similarity of fingerprints was greater than 0.9. The cluster analysis, PCA, and OPLS-DA classified floating and sinking fresh Rehmanniae Radix sasmples into two categories, indicating differences in the quality between them. The total content of catalpol, rehmannioside D, ajugol, and verbascoside in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was catalpol. The total content of fructose, glucose, sucrose, raffinose, and stachyose in sinking fresh Rehmanniae Radix samples was higher than that in floating samples of the same batch and specification, and the main differential component was stachyose. The extract yield and drying rate of the sinking samples were higher than those of floating samples. This study preliminarily showed that floating and sinking fresh Rehmanniae Radix samples had the same components but great differences in the content of medicinal substance basis. The total content of four glycosides and five sugars, extract yield, and drying rate of sinking fresh Rehmanniae Radix samples is higher than that of floating samples of the same batch and specification. These findings, to a certain extent, explains the scientificity of sinking Rehmanniae Radix has the best quality recorded in ancient books and provide a reference for the quality control and clinical application of fresh Rehmanniae Radix.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Rehmannia/chemistry* ; Chemometrics ; Mass Spectrometry/methods* ; Quality Control ; Principal Component Analysis ; Plant Extracts