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.

The phase changes and quantity-quality transfer of 17 batches of Ostreae Concha(Ostrea rivularis) during the raw material-calcined decoction pieces-standard decoction process were analyzed. The content of calcium carbonate(CaCO_3), the main component, was determined by chemical titration, and the extract yield and transfer rate were calculated. The CaCO_3 content in the raw material, calcined decoction pieces, and standard decoction was 94.39%-98.80%, 95.03%-99.22%, and 84.58%-90.47%, respectively. The process of raw material to calcined decoction pieces showed the yield range of 96.85% to 98.55% and the CaCO_3 transfer rate range of 96.92% to 99.27%. The process of calcined decoction pieces to standard decoction showed the extract yield range of 2.86% to 5.48% and the CaCO_3 transfer rate range of 2.59% to 5.13%. The results of X-ray fluorescence(XRF) assay showed that the raw material, calcined decoction pieces, and standard decoction mainly contained Ca, Na, Mg, Si, Br, Cl, Al, Fe, Cr, Mn, and K. The chemometric results showed an increase in the relative content of Cr, Fe, and Si from raw material to calcined decoction pieces and an increase in the relative content of Mg, Al, Br, K, Cl, and Na from calcined decoction pieces to standard decoction. X-ray diffraction(XRD) was employed to establish XRD characteristic patterns of the raw material, calcined decoction pieces, and standard decoction. The XRD results showed that the main phase of all three was calcite, and no transformation of crystalline form or generation of new phase was observed. Fourier transform infrared spectroscopy(FTIR) was employed to establish the FTIR characteristic spectra of the raw material, calcined decoction pieces, and standard decoction. The FTIR results showed that the raw material had internal vibrations of O-H, C-H, C=O, C-O, and CO■ groups. Due to the loss of organic matter components after calcination, no information about the vibrations of C-H, C=O, and C-O groups was observed in the spectra of calcined decoction pieces and standard decoction. In summary, this study elucidated the quantity-quality transfer and phase changes in the raw material-calcined decoction pieces-standard decoction process by determining the CaCO_3 content, calculating the extract yield and transfer rate, and comparing the element changes, FTIR characteristic spectra, and XRD characteristic pattern. The results were reasonable and reliable, laying a foundation for the subsequent process research and quality control of the formula granules of calcined Ostreae Concha(O. rivularis Gould), and providing ideas and methods for the quality control of the whole process of raw material-decoction pieces-standard decoction-formula granules of Ostreae Concha and other testacean traditional Chinese medicine.
Drugs, Chinese Herbal/isolation & purification* ; Calcium Carbonate/analysis* ; Quality Control

Guided by the concept of quality by design(QbD), this study optimizes the calcination and quenching process of calcined Magnetitum and establishes the XRD characteristic spectra of calcined Magnetitum, providing a scientific basis for the formulation of quality standards. Based on the processing methods and quality requirements of Magnetitum in the Chinese Pharmacopoeia, the critical process parameters(CPPs) identified were calcination temperature, calcination time, particle size, laying thickness, and the number of vinegar quenching cycles. The critical quality attributes(CQAs) included Fe mass fraction, Fe~(2+) dissolution, and surface color. The weight coefficients were determined by combining Analytic Hierarchy Process(AHP) and the criteria importance though intercrieria correlation(CRITIC) method, and the calcination process was optimized using orthogonal experimentation. Surface color was selected as a CQA, and based on the principle of color value, the surface color of calcined Magnetitum was objectively quantified. The vinegar quenching process was then optimized to determine the best processing conditions. X-ray diffraction(XRD) was used to establish the characteristic spectra of calcined Magnetitum, and methods such as similarity evaluation, cluster analysis, and orthogonal partial least squares-discriminant analysis(OPLS-DA) were used to evaluate the quality of the spectra. The optimized calcined Magnetitum preparation process was found to be calcination at 750 ℃ for 1 h, with a laying thickness of 4 cm, a particle size of 0.4-0.8 cm, and one vinegar quenching cycle(Magnetitum-vinegar ratio 10∶3), which was stable and feasible. The XRD characteristic spectra analysis method, featuring 9 common peaks as fingerprint information, was established. The average correlation coefficient ranged from 0.839 5-0.988 1, and the average angle cosine ranged from 0.914 4 to 0.995 6, indicating good similarity. Cluster analysis results showed that Magnetitum and calcined Magnetitum could be grouped together, with similar compositions. OPLS-DA discriminant analysis identified three key characteristic peaks, with Fe_2O_3 being the distinguishing component between the two. The final optimized processing method is stable and feasible, and the XRD characteristic spectra of calcined Magnetitum was initially established, providing a reference for subsequent quality control and the formulation of quality standards for calcined Magnetitum.
X-Ray Diffraction/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Particle Size

Geniposidic acid(GA), a natural iridoid, exists in the roots, stems, leaves, flowers, bark, fruits, and seeds of medicinal plants of Rubiaceae, Eucommiaceae, and Plantaginaceae. Modern pharmacological studies have revealed that GA has multiple pharmacological activities, including organ-protective, anti-inflammatory, antioxidative, anti-osteoporosis, anti-neurodegenerative, and anti-cardiovascular effects. GA can enhance cell/organism defenses by upregulating key anti-inflammatory and antioxidant cytokines, while downregulating key node proteins in pro-inflammatory signaling pathways such as AhR and TLR4/MyD88, thereby exerting pharmacological effects such as organ protection. Pharmacokinetic investigations have suggested that after oral administration, GA can be distributed in multiple organs(kidney, liver, heart, spleen, lung, etc.). In addition, the pharmacokinetic behavior of GA could be significantly altered under disease conditions, as demonstrated by a marked increase in systematic exposure. This article comprehensively summarizes the reported pharmacological activities and mechanisms and systematically analyzes the pharmacokinetic characteristics and key parameters of GA, with the aim of providing a theoretical basis and scientific reference for the precise clinical application of GA-related Chinese patent medicines, as well as for the investigation and development of innovative drugs based on GA.
Humans ; Drugs, Chinese Herbal/chemistry* ; Animals ; Iridoid Glucosides/chemistry* ; Plants, Medicinal/chemistry* ; Anti-Inflammatory Agents/pharmacology*

OBJECTIVE To comprehensively evaluate the quality of Sanzi powder from different batches based on 12 components quantitative analysis combined with chemical pattern recognition and entropy weight-TOPSIS method. METHODS The contents of 12 components in 15 batches of Sanzi powder （No. S1-S15） were determined by HPLC-MS/MS， such as ethyl gallate， gallic acid， ferulic acid， corilagin， genipin-1-O-β-D-gentiobioside， toosendanin， geniposide， caffeic acid， methyl deacetylated coumarinate， tannic acid， rutin， quercetin. Cluster analysis （CA）， principal component analysis （PCA）， and orthogonal partial least squares-discriminant analysis （OPLS-DA） were conducted on the assay results. Using variable importance projection （VIP） value＞1 and P＜0.05 as the evaluation criteria， the quality differential markers in Sanzi powder were screened. The entropy weight method was used to calculate the weight value， and TOPSIS method was used to rank the quality of 15 batches of Sanzi powder from superior to inferior. RESULTS The contents of the 12 components were 13.494-24.292， 2 069.608-3 188.100， 1.410-3.616， 1 065.030-2 630.584， 1 404.704-1 838.078， 101.640-354.268， 9 193.720-14 777.854， 1.240-5.060， 148.028-5 541.990， 4 261.422-5 607.438， 107.560- 195.512， 2.226-4.192 μg/g， respectively. The results of CA， PCA and OPLS-DA indicated that 15 batches of Sanzi powder could be clustered into two groups. Specifically， batches S3， S7， S10 and S15 were grouped into one category， and remaining batches were grouped into one category. VIP values of geniposide， quercetin， caffeic acid， and methyl deacetylated coumarinate were all greater than 1， with corresponding P-values less than 0.05. The results of the entropy weight-TOPSIS analysis revealed that methyl deacetylate exhibited the smallest information entropy and the highest weight. The relative closeness degrees of samples S3， S7， S10 and S15 ranged from 0.789 to 0.973， while the remaining samples ranged from 0.054 to 0.172. CONCLUSIONS The contents of 12 components in Sanzi powder could be determined accurately by using HPLC-MS/MS technology. Methyl deacetylated coumarinate， geniposide， quercetin and caffeic acid were identified as the quality differential markers. It was found that the overall quality of samples S3， S7， S10 and S15 were superior to that of other batches. Notably， the quality of Gardeniae Fructus decoction pieces emerges as a critical factor in ensuring the consistency of the preparation’s quality.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with few effective treatments currently available. The multifactorial nature of AD, shaped by genetic, environmental, and biological factors, complicates both research and clinical management. Recent advances in artificial intelligence (AI) and multi-omics technologies provide new opportunities to elucidate the molecular mechanisms of AD and identify early biomarkers for diagnosis and prognosis. AI-driven approaches such as machine learning, deep learning, and network-based models have enabled the integration of large-scale genomic, transcriptomic, proteomic, metabolomic, and microbiomic datasets. These efforts have facilitated the discovery of novel molecular signatures and therapeutic targets. Methods including deep belief networks and joint deep semi-non-negative matrix factorization have contributed to improvements in disease classification and patient stratification. However, ongoing challenges remain. These include data heterogeneity, limited interpretability of complex models, a lack of large and diverse datasets, and insufficient clinical validation. The absence of standardized multi-omics data processing methods further restricts progress. This review systematically summarizes recent advances in AI-driven multi-omics research in AD, highlighting achievements in early diagnosis and biomarker discovery while discussing limitations and future directions needed to advance these approaches toward clinical application.