The pharmaceutical industry faces challenges in quality digitization for complex multi-stage processes, especially in small-sample systems. Here, an intelligent quality prediction and diagnostic (IQPD) framework was developed and applied to Tong Ren Tang's Niuhuang Qingxin Pills, utilizing four years of data collected from four production units, covering the entire process from raw materials to finished products. In this framework, a novel path-enhanced double ensemble quality prediction model (PeDGAT) is proposed, which combines a graph attention network and path information to encode inter-unit long-range and sequential dependencies. Additionally, the double ensemble strategy enhances model stability in small samples. Compared to global traditional models, PeDGAT achieves state-of-the-art results, with an average improvement of 13.18% and 87.67% in prediction accuracy and stability on three indicators. Additionally, a more in-depth diagnostic model leveraging grey correlation analysis and expert knowledge reduces reliance on large samples, offering a panoramic view of attribute relationships across units and improving process transparency. Finally, the IQPD framework integrates into a Human-Cyber-Physical system, enabling faster decision-making and real-time quality adjustments for Tong Ren Tang's Niuhuang Qingxin Pills, a product with annual sales exceeding 100 million CNY. This facilitates the transition from experience-driven to data-driven manufacturing.

Objective To investigate the active ingredients of Zizyphi Spinosae Semen(ZSS)in relieving benzodiazepine(BDZ)dependence and its molecular mechanism based on the integrated Traditional Chinese Medicine and chemical drugs idea of"enhancing effect and reducing toxicity"via the approach of network pharmacology and the technique of molecular dynamics simulation.Methods First,literature search was undertaken to find the main components of ZSS.Then,the major effective constituents of ZSS in relieving BDZ dependence and its target of action were explored on the basis of network pharmacology and molecular docking.Finally,the relationship between core components of ZSS and key proteins was further verified through the technique of molecular dynamics simulation.Results After literature search,a total of 24 chemical components in ZSS were found to act on 731 targets.Through establishing the network of"ingredients-targets-pathways",topology analysis was performed to obtain nine core components such as linoleic acid,palmitic acid,oleic acid,tetradecanoic acid,spinosin,oleanolic acid and jujuboside A,as well as five key targets including AR,PTGS2,PPARG,RXRA and CYP19A1.Bioinformation enrichment analysis was made to obtain critical pathways such as calcium signaling pathway,cAMP signaling pathway,IL-17 signaling pathway and TNF signaling pathway.The results of molecular docking revealed that there was a good combination between core components of ZSS and key targets,and it was mainly dominated by hydrogen bonding.Furthermore,the molecular dynamics simulation experiments indicated that the combinations between jujuboside A and RXRA,oleanolic acid and RXRA,spinosin and PPARG were stable,and these three active ingredients played an important role in improving BDZ dependence.Conclusion The active components in ZSS may exert multi-target and multi-pathway intervention effects on BDZ dependence by means of processes such as immunoregulation,anti-anxiety,and anti-insomnia.

【Objective】 To explore the effect of Zhikepipa Mixture on the treatment of COVID-19 through network pharmacology analysis and molecular docking technology. 【Methods】 First, we performed the network pharmacology method to screen active compounds and targets so as to explore the mixture’s potential mechanisms in the treatment of COVID-19. In line with ADME screening index, like oral bioavailability (OB) ≥30% or drug likeness index (DL) ≥0.18, the active compounds against COVID-19 related targets were selected to construct the 'herb-compound-target’ network. Mechanism prediction of Zhikepipa mixture in the treatment of COVID-19 was analyzed by the interaction of the target sites, the bioinformatic annotation, and the metabolic pathway. Then, we used a molecular docking model to evaluate the binding ability between active compounds and 2019-nCoV (SARS-CoV-2) 3-chymotrypsin-like cysteine protease (3CL^pro) receptor-binding domain (PBD ID 6LU7), which was involved in mediating viral replication and transcription functions. 【Results】 The'herb-compound-target’ network showed 34 key compounds and 30 disease targets after overlapping with disease targets. The network topology analysis showed that those selected compounds with higher degree would produce marked anti-inflammatory effects by regulating 30 targets like PTGS1, IL1B, IL6, IL10, CXCL8 and JUN. AGE-RAGE signaling pathway, IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, and MAPK signaling pathway were involved in regulating hepatitis B and diabetic complications. In addition, Folium eriobotryae and Radix stemonae played important roles in the network. The molecular docking results showed that nine compounds were identified with higher docking score rank against 2019-nCoV 3CL^pro protease, and most of them were attributed to flavonoids. 【Conclusion】 Zhikepipa Mixture could exhibit both anti-inflammatory and anti-virus actions through multi-component, multi-target, and multi-pathway.

Objective To investigate the serum amyloid A (SAA) and interleukin-18(IL-18)concentration in the pathogenesis of type 2 diabetes mellitus (T2DM) and its macrovascular complications, and study the relation between them. Methods ELISA was used to assay serum SAA and IL-18 levels in 65 T2DM patients (including 31 cases with macrovascular complications) and 30 healthy controls. Results Serum SAA and IL-18 levels [(3.09±0.96)mg/L, (98.8±36.4)ng/L]were significantly elevated in patients with T2DM as compared with those in control subjects [(1.06±0.45)mg/L, (58.9±15.6)ng/L](P<0.05). There was significant difference of SAA and IL-18 levels between T2DM patients with [(6.34±1.52) mg/L,(141.2±48.3)ng/L]and without macrovascukar complications [(2.65±0.39)mg/L, (80.2±20.1)ng/L](P < 0.05).Univariate linear regression analysis showed significant positive correlations between serum IL-18 with SAA (r =0.615, P<0.05), SAA, IL-18 and fasting blood glucose (FBG) had mutual positive correlations (r=0.312, 0.428, P< 0.05, respectively). Conclusions In patients with T2DM, serum SAA and IL-18 concentration is greater than in non-diabetic subjects. SAA and IL-18 play important roles in the initiation and development of T2DM. The study suggests that SAA and IL-18 might be an important independent risk factor.

Objective To study the effects of Tiopronin(MPO)combine Penicillamine(PCA)on the copper metabolism and function of liver in rats model with hepatolenticular degeneration.Methods To copy hepatolenticular degeneration model rats with copper overloaded diet.Divide the models into groups HLD control,MPO,PCA and MPO+PCA,and interfere with the propotional drugs.To investigate the concentration changes of the lalanine aminotransferase(ALT),spartate amino transferase(AST),total protein(TP),albumin(ALB),liver copper and 24 h urine copper in each group.Results The concentrations of liver copper and 24 h urine copper in HLD control group and each group with drugs intervention were significantly higher than those in normal control group(all P