OBJECTIVE To investigate the improvement effects and mechanism of Achyranthes bidentata total saponins （ABS） extract on vascular endothelial dysfunction in spontaneously hypertensive rat （SHR） based on cytochrome P450 4A （CYP4A）/20-hydroxyeicosatetetraenoic acid （20-HETE）/G protein-coupled receptor 75 （GPR75） axis. METHODS Ten Wistar- Kyoto rats were taken as the normal control group. Forty SHR were first stratified by systolic blood pressure and then， within each stratum， randomly assigned using a random-number table to the model group （MOD group）， captopril positive control group （CAP group， 10 mg/kg）， ABS low- and high-dose extract groups （ABS-L group， ABS-H group， 60 and 120 mg/kg）， with 10 rats in each group. Animals in each group were given the corresponding drug or equal volume of pure water by gavage， once a day， for 28 consecutive days. After the last administration， systolic blood pressure of rats was measured. The levels of vasoactive substances， inflammatory factors and oxidative stress indicators in serum were measured. The pathological changes of rat thoracic aorta were observed. The level of reactive oxygen species （ROS） in aortic tissue was analyzed. The expressions of endothelial nitric oxide synthase （eNOS）， CYP4A， GPR75， nuclear factor-κB p65 （NF-κB p65）， phosphorylated NF-κB p65， p22phox， and reduced nicotinamide adenine dinucleotide phosphate oxidase 4（NOX4） in thoracic aorta tissue were detected. RESULTS After 28 d of treatment， compared with MOD group， the systolic blood pressure of rats in the ABS-L and ABS-H groups decreased significantly. The levels of 20-HETE， angiotensin Ⅱ， interleukin-1β， interleukin-6， tumor necrosis factor-α， intercellular cell adhesion molecule-1 and malondialdehyde in serum were significantly reduced （P＜0.05 or P＜0.01）， while the levels of nitric oxide， superoxide dismutase， glutathione peroxidase and catalase were significantly increased （P＜0.05 or P＜0.01）. Intimal damage of thoracic aorta was reduced， and endothelial cell morphology was improved. The expressions of ROS， CYP4A， GPR75， p22phox， NOX4 and the phosphorylation level of NF-κB p65 protein in thoracic aorta were down-regulated or reduced （P＜0.05 or P＜0.01）， while the expression of eNOS was up-regulated （P＜0.05 or P＜0.01）. CONCLUSIONS ABS extract may alleviate the inflammatory response and oxidative stress in SHR effectively by down-regulating the expression of CYP4A， reducing the production of 20-HETE， inhibiting the activation of GPR75， and subsequently suppressing the activation of downstream NF-κB and NOX4， thereby improving hypertension-related vascular endothelial dysfunction.

ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

ObjectiveGastrodia elata has evolved ecological types with shortened rhizome internodes and diversified flower and fruit coloration in response to different altitudes. Studying the genetic mechanisms of different ecotype germplasm is significant for guiding variety breeding in different cultivation areas. MethodsThe bHLH gene family was identified based on the whole-genome datasets of G. elata f. elata and G. elata f. glauca. Subsequently， the gene family members were subject to analysis， including gene structure， chromosomal localization， cis-acting elements， gene synteny， and phylogeny. Combined with transcriptome data and quantitative Real-time PCR， the expression patterns of bHLH genes in the stems of the different G. elata ecotype germplasm were analyzed. Finally， correlation analysis was conducted between gene expression patterns and color to obtain the key bHLH genes regulating the color formation of stem. ResultsA total of 63 bHLH genes were identified in both G elata f. elata and G. elata f. glauca， unevenly distributed across 17 chromosomes and clustered into 16 subfamilies， with significant expansion in some family members. Obvious inversions of bHLH genes on the same chromosome and interchromosomal translocations were detected in the two ecotype germplasm. Among these genes， 12 bHLH genes （such as bHLH62-3 and bHLH74） were associated with the bright yellow color of G elata f. elata stem， while 9 bHLH genes （such as PIL13， UNE12， and bHLH130） were correlated with the red color of G. elata f. glauca stem. Compared to G. elata f. glauca， the bHLH48 expression level was significantly higher in flowers and scale leaves of G elata f. elata， and the bHLH62-3 expression level was significantly higher in all organs of G elata f. elata. ConclusionsFunctional pathway divergence of the bHLH family members has occurred across different chromosomes in G elata f. elata and G. elata f. glauca. Through synergism or antagonism with other genes， 21 bHLH genes participate in the coloration metabolic pathway regulation of stems， flowers， and fruits. Specifically， bHLH62-3 is involved in regulating stem color differentiation in the anthocyanin biosynthesis pathway of G. elata， thus relevant to the color formation of stem. Additionally， GebHLH48 positively regulates flowering-related pathways to promote the early-flowering phenotype of G. elata f. elata. These findings have laid the foundation for analyzing the genetic regulatory mechanisms underlying the color formation of the G. elata stem.

ObjectiveTo investigate the association of the serum levels of Golgi protein 73 （GP73）， hypoxia-inducible factor-1α （HIF-1α）， and matrix metalloproteinase-9 （MMP-9） before and after transcatheter arterial chemoembolization （TACE） with the treatment outcome of patients with hepatitis B virus （HBV）-related hepatocellular carcinoma （HCC）. MethodsA total of 135 patients with HBV-related HCC who received TACE for the first time in The First Affiliated Hospital of Xinjiang Medical University from January 2023 to October 2025 were enrolled as subjects， and the serum levels of GP73， HIF-1α， and MMP-9 were measured before TACE and at 1 month after TACE. According to the treatment outcome after TACE， the patients were divided into remission group and non-remission group. The two groups were compared in terms of general information and the serum levels of GP73， HIF-1α， and MMP-9 before and after treatment. The Logistic regression analysis was used to investigate the influencing factors for the efficacy of TACE， and the receiver operating characteristic （ROC） curve was used to assess the value of the serum levels of GP73， HIF-1α， and MMP-9 before treatment used alone or in combination in predicting the efficacy of TACE. The independent-samples t test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. ResultsAmong the 135 patients after treatment， 78 （57.78%） were enrolled in the remission group and 57 （42.22%） were enrolled in the non-remission group. Before treatment， the remission group had significantly lower serum levels of GP73， HIF-1α， and MMP-9 than the non-remission group （all P<0.05）； after treatment， both groups had reductions in the serum levels of GP73， HIF-1α， and MMP-9， and the remission group had significantly lower levels than the non-remission group （all P<0.05）. The binary Logistic regression analysis showed that increases in the serum levels of GP73， HIF-1α， and MMP-9 before treatment were independent influencing factors for the efficacy of TACE （all P<0.05）. The ROC curve analysis showed that the serum levels of GP73， HIF-1α， and MMP-9 before treatment used alone had an area under the ROC curve （AUC） of 0.799， 0.835， and 0.777， respectively， in predicting the efficacy of TACE， among which HIF-1α showed the highest predictive value， and the combination of these three indicators had an AUC of 0.950， a Youden index of 0.787， an optimal cut-off value of 0.39， a sensitivity of 87.72%， and a specificity of 91.03%. ConclusionTACE can reduce the serum levels of GP73， HIF-1α， and MMP-9 in patients with HBV-related HCC， and patients with a good outcome tend to have greater reductions. The high serum levels of GP73， HIF-1α， and MMP-9 before treatment are independent risk factors for poor short-term efficacy of TACE， and the combination of these three indicators has a relatively high value in predicting the efficacy of TACE.

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.

Lipid turbidity disease is a metabolic disease featuring lipid metabolism disorders caused by many factors such as social environment， diet， and lifestyle， which is closely related to many diseases in modern medicine， such as hyperlipidemia， obesity， fatty liver， atherosclerosis， metabolic syndrome， and cardiovascular and cerebrovascular diseases， with a wide range of influence and far-reaching harm. According to the Huangdi Neijing， lipid turbidity disease reflects the pathological change of the body's physiologic grease. Grease is the thick part of body fluids， which has the function of nourishing， and it is the initial state and source of important substances in the human body such as brain， marrow， essence， and blood. Once the grease of the human body is abnormal， it can lead to lipid turbidity disease. The Huangdi Neijing also points out the physiological relationship between the transportation and transformation of body fluids and the rise and fall of the spleen and stomach， which can deduce the pathological relationship between the occurrence of lipid turbidity disease and the abnormal rise and fall of the spleen and stomach functions. Lipid turbidity disease is caused by overconsumption of fatty and sweet foods or insufficient spleen and stomach endowments， leading to disorders of the function of promoting clear and reducing turbidity in the spleen and stomach. This leads to the transformation of thick grease in body fluids into lipid turbidity， which accumulates in the body's meridians， blood vessels， skin pores， and organs， forming various forms of metabolic diseases. The research team believed that the pathological basis of lipid turbidity disease was the abnormal rise and fall of the spleen and stomach and the obstruction of the transfer of grease. According to the different locations where lipid turbidity stays， it was divided into four common pathogenesis types： ''inability to distinguish between the clear and turbid， turbid stagnation in the Ying blood''， ''spleen not rising clear， turbid accumulation in the vessels''， ''spleen dysfunction， lipid retention in the pores''， ''spleen failure to transportation and transformation， and grease accumulation in the liver''. According to the pathogenesis， it could be divided into four common syndromes， namely， turbid stagnation in the Ying blood， turbid accumulation in the vessels， lipid retention in the pores， and grease accumulation in the liver， and the corresponding prescriptions were given for syndrome differentiation and treatment， so as to guide clinical differentiation and treatment of the lipid turbidity disease.

Artificial intelligence (AI) has emerged as a transformative technology in accelerating drug discovery and development within natural medicines research. Natural medicines, characterized by their complex chemical compositions and multifaceted pharmacological mechanisms, demonstrate widespread application in treating diverse diseases. However, research and development face significant challenges, including component complexity, extraction difficulties, and efficacy validation. AI technology, particularly through deep learning (DL) and machine learning (ML) approaches, enables efficient analysis of extensive datasets, facilitating drug screening, component analysis, and pharmacological mechanism elucidation. The implementation of AI technology demonstrates considerable potential in virtual screening, compound optimization, and synthetic pathway design, thereby enhancing natural medicines' bioavailability and safety profiles. Nevertheless, current applications encounter limitations regarding data quality, model interpretability, and ethical considerations. As AI technologies continue to evolve, natural medicines research and development will achieve greater efficiency and precision, advancing both personalized medicine and contemporary drug development approaches.
Biological Products/pharmacology* ; Artificial Intelligence ; Humans ; Drug Discovery/methods* ; Machine Learning ; Deep Learning

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.