SRSF3 Knockdown Inhibits Lipopolysaccharide-Induced Inflammatory Response in Macrophages.

Serine/arginine-rich splicing factor 3 (SRSF3), the smallest member of the SR protein family, serves multiple roles in RNA processing, including splicing, translation, and stability. Recent studies have shown that SRSF3 is implicated in several inflammatory diseases. However, its impact on macrophage inflammation remains unclear. Herein, we determined the expression of SRSF3 in inflammatory macrophages and found that the level of SRSF3 was increased in macrophages within atherosclerotic plaques, as well as in RAW-264.7 macrophages stimulated by lipopolysaccharides. Moreover, the downregulation of SRSF3 suppressed the levels of inflammatory cytokines by deactivating the nuclear factor κB (NFκB) pathway. Furthermore, the alternative splicing of myeloid differentiation protein 2 (MD2), a co-receptor of toll-like receptor 4 (TLR4), is regulated by SRSF3. The depletion of SRSF3 increased the level of the shorter MD2B splicing variants, which contributed to inflammatory inhibition in macrophages. In conclusion, our findings imply that SRSF3 regulates lipopolysaccharide-stimulated inflammation, in part by controlling the alternative splicing of MD2 mRNA in macrophages.

Computational investigation of the inhibitory interaction of IRF3 and SARS-CoV-2 accessory protein ORF3b.

ORF3b is one of the SARS-CoV-2 accessory proteins. Previous experimental study suggested that ORF3b prevents IRF3 translocating to nucleus. However, the biophysical mechanism of ORF3b-IRF3 interaction is elusive. Here, we explored the conformation ensemble of ORF3b using all-atom replica exchange molecular dynamics simulation. Disordered ORF3b has mixed α-helix, ß-turn and loop conformers. The potential ORF3b-IRF3 binding modes were searched by docking representative ORF3b conformers with IRF3, and 50 ORF3b-IRF3 complex poses were screened using molecular dynamics simulations ranging from 500 to 1000 ns. We found that ORF3b binds IRF3 predominantly on its CBP binding and phosphorylated pLxIS motifs, with CBP binding site has the highest binding affinity. The ORF3b-IRF3 binding residues are highly conserved in SARS-CoV-2. Our results provided biophysics insights into ORF3b-IRF3 interaction and explained its interferon antagonism mechanism.

Controllable Silver Release for Efficient Treatment of Drug-Resistant Bacterial-Infected Wounds.

The use of traditional Ag-based antibacterial agents is usually accompanied by uncontrollable silver release, which makes it difficult to find a balance between antibacterial performance and biosafety. Herein, we prepared a core-shell system of ZIF-8-derived amorphous carbon-coated Ag nanoparticles (Ag@C) as an ideal research model to reveal the synergistic effect and structure-activity relationship of the structural transformation of carbon shell and Ag core on the regulation of silver release behavior. It is found that Ag@C prepared at 600 °C (AC6) exhibits the best ion release kinetics due to the combination of relatively simple shell structure and lower crystallinity of the Ag core, thereby exerting stronger antibacterial properties (>99.999 %) at trace doses (20 µg mL-1) compared with most other Ag-based materials. Meanwhile, the carbon shell prevents the metal Ag from being directly exposed to the organism and thus endows AC6 with excellent biocompatibility. In animal experiments, AC6 can effectively promote wound healing by inactivating drug-resistant bacteria while regulating the expression of TNF-α and CD31. This work provides theoretical support for the scientific design and clinical application of controllable ion-releasing antibacterial agents.

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

Cleavage and Noncleavage Chemistry in Reactive Oxygen Species (ROS)-Responsive Materials for Smart Drug Delivery.

The design and development of advanced drug delivery systems targeting reactive oxygen species (ROS) have gained significant interest in recent years for treating various diseases, including cancer, psychiatric diseases, cardiovascular diseases, neurological diseases, metabolic diseases, and chronic inflammations. Integrating specific chemical bonds capable of effectively responding to ROS and triggering drug release into the delivery system is crucial. In this Review, we discuss commonly used conjugation linkers (chemical bonds) and categorize them into two groups: cleavable linkers and noncleavable linkers. Our goal is to clarify their unique drug release mechanisms from a chemical perspective and provide practical organic synthesis approaches for their efficient production. We showcase numerous significant examples to demonstrate their synthesis routes and diverse applications. Ultimately, we strive to present a comprehensive overview of cleavage and noncleavage chemistry, offering insights into the development of smart drug delivery systems that respond to ROS.

Exploring microproteins from various model organisms using the mip-mining database.

Microproteins, prevalent across all kingdoms of life, play a crucial role in cell physiology and human health. Although global gene transcription is widely explored and abundantly available, our understanding of microprotein functions using transcriptome data is still limited. To mitigate this problem, we present a database, Mip-mining ( https://weilab.sjtu.edu.cn/mipmining/ ), underpinned by high-quality RNA-sequencing data exclusively aimed at analyzing microprotein functions. The Mip-mining hosts 336 sets of high-quality transcriptome data from 8626 samples and nine representative living organisms, including microorganisms, plants, animals, and humans, in our Mip-mining database. Our database specifically provides a focus on a range of diseases and environmental stress conditions, taking into account chemical, physical, biological, and diseases-related stresses. Comparatively, our platform enables customized analysis by inputting desired data sets with self-determined cutoff values. The practicality of Mip-mining is demonstrated by identifying essential microproteins in different species and revealing the importance of ATP15 in the acetic acid stress tolerance of budding yeast. We believe that Mip-mining will facilitate a greater understanding and application of microproteins in biotechnology. Moreover, it will be beneficial for designing therapeutic strategies under various biological conditions.

Construction of Covalent Organic Frameworks via a Visible-Light-Activated Photocatalytic Multicomponent Reaction.

Multicomponent reactions (MCRs), as a powerful one-pot combinatorial synthesis tool, have been recently applied to the synthesis of covalent organic frameworks (COFs). Compared with the thermally driven MCRs, the photocatalytic MCR-based COF synthesis has not yet been investigated. Herein, we first report the construction of COFs by a photocatalytic multicomponent reaction. Upon visible-light irradiation, a series of COFs with excellent crystallinity, stability, and permanent porosity are successfully synthesized via photoredox-catalyzed multicomponent Petasis reaction under ambient conditions. Additionally, the obtained Cy-N3-COF exhibits excellent photoactivity and recyclability for the visible-light-driven oxidative hydroxylation of arylboronic acids. The concept of photocatalytic multicomponent polymerization not only enriches the methodology for COF synthesis but also opens a new avenue for the construction of COFs that might not be possible with the existing synthetic methods based on thermally driven MCRs.

Irinotecan and vandetanib create synergies for treatment of pancreatic cancer patients with concomitant TP53 and KRAS mutations.

BACKGROUND: The most frequently mutated gene pairs in pancreatic adenocarcinoma (PAAD) are KRAS and TP53, and our goal is to illustrate the multiomics and molecular dynamics landscapes of KRAS/TP53 mutation and also to obtain prospective novel drugs for KRAS- and TP53-mutated PAAD patients. Moreover, we also made an attempt to discover the probable link amid KRAS and TP53 on the basis of the abovementioned multiomics data. METHOD: We utilized TCGA & Cancer Cell Line Encyclopedia data for the analysis of KRAS/TP53 mutation in a multiomics manner. In addition to that, we performed molecular dynamics analysis of KRAS and TP53 to produce mechanistic descriptions of particular mutations and carcinogenesis. RESULT: We discover that there is a significant difference in the genomics, transcriptomics, methylomics, and molecular dynamics pattern of KRAS and TP53 mutation from the matching wild type in PAAD, and the prognosis of pancreatic cancer is directly linked with a particular mutation of KRAS and protein stability. Screened drugs are potentially effective in PAAD patients. CONCLUSIONS: KRAS and TP53 prognosis of PAAD is directly associated with a specific mutation of KRAS. Irinotecan and vandetanib are prospective drugs for PAAD patients with KRASG12Dmutation and TP53 mutation.

DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.

Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based models, especially binary classification models, have been developed to predict whether a drug-target pair interacts or not. However, there is still much room for improvement in the performance of current methods. Multi-label learning can overcome some difficulties caused by single-label learning in order to improve the predictive performance. The key challenge faced by multi-label learning is the exponential-sized output space, and considering label correlations can help to overcome this challenge. In this paper, we facilitate multi-label classification by introducing community detection methods for DTI prediction, named DTI-MLCD. Moreover, we updated the gold standard data set by adding 15,000 more positive DTI samples in comparison to the data set, which has widely been used by most of previously published DTI prediction methods since 2008. The proposed DTI-MLCD is applied to both data sets, demonstrating its superiority over other machine learning methods and several existing methods. The data sets and source code of this study are freely available at https://github.com/a96123155/DTI-MLCD.

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods.

Neuropeptides acting as signaling molecules in the nervous system of various animals play crucial roles in a wide range of physiological functions and hormone regulation behaviors. Neuropeptides offer many opportunities for the discovery of new drugs and targets for the treatment of neurological diseases. In recent years, there have been several data-driven computational predictors of various types of bioactive peptides, but the relevant work about neuropeptides is little at present. In this work, we developed an interpretable stacking model, named NeuroPpred-Fuse, for the prediction of neuropeptides through fusing a variety of sequence-derived features and feature selection methods. Specifically, we used six types of sequence-derived features to encode the peptide sequences and then combined them. In the first layer, we ensembled three base classifiers and four feature selection algorithms, which select non-redundant important features complementarily. In the second layer, the output of the first layer was merged and fed into logistic regression (LR) classifier to train the model. Moreover, we analyzed the selected features and explained the feasibility of the selected features. Experimental results show that our model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming the state-of-the-art models. In addition, we exhibited the distribution of selected features by these tree models and compared the results on the training set to that on the test set. These results fully showed that our model has a certain generalization ability. Therefore, we expect that our model would provide important advances in the discovery of neuropeptides as new drugs for the treatment of neurological diseases.

Photocatalytic Radical Cascade Dehalogenation/Carbo-cyclization/Sulfonylation Leading to Indole- and Benzofuran-Based Benzylic Sulfones.

This study presents a convenient approach to the synthesis of indole- and benzofuran-based benzylic sulfones using unactivated alkynes containing aryl iodides and sodium sulfinates under visible light irradiation. The procedure involves a sequential series of dehalogenation, carbo-cyclization, and radical sulfonylation. Plausible insights into the reaction mechanism are derived from control experiments, leading to the proposal of a radical cascade reaction pathway.

Strengthening Fe(II)/Fe(III) Dynamic Cycling by Surface Sulfation to Achieve Efficient Electrochemical Uranium Extraction at Ultralow Cell Voltage.

Electrochemically mediated Fe(II)/Fe(III) redox-coupled uranium extraction can efficiently reduce the cell voltage of electrochemical uranium extraction (EUE). How to regulate the surface structure to enhance the uranium acyl ion adsorption capacity and strengthen the Fe(II)/Fe(III) redox cycle process is crucial for EUE. In this work, we developed surface sulfated nanoreduced iron (S-NRI) for EUE and exhibited improved properties for EUE at an ultralow cell voltage (-0.1 V). Compared with a nanoreduced iron (NRI) adsorbent, S-NRI displayed faster electrochemical extraction kinetics properties and higher extraction efficiency and capacity for uranium. In a more complex seawater electrolyte containing uranyl ion concentration ranging from 1 to 20 ppm, the removal efficiency could reach almost ∼100% after EUE for 24 h. At a higher 50 ppm uranium acyl ion concentration in a seawater electrolyte, S-NRI exhibited higher extraction capacity (755.03 mg/g), which is better than 528.53 mg/g of NRI at a cell voltage of -0.1 V. Outstanding EUE property could be attributed to the fact that sulfate species (M-SO42-) on the S-NRI surface not only enhanced selective adsorption of uranyl ions but also strengthened the Fe(II)/Fe(III) redox cycle, which accelerated electron transfer between Fe(II) and U(VI), promoted the regeneration of Fe(II) active sites, and finally enhanced the EUE property.

Selective Anchoring by Surface Sulfur Species Coupled with Rapid Interface Electron Transfer for Ultrahigh Capacity Extraction of Uranium from Seawater.

Improving the adsorption selectivity, enhancing the extraction capacity, and ensuring the structural stability of the adsorbent are the key to realize the high efficiency recovery of uranium. In this work, we utilized the strong Lewis acid-base interaction between S2- and U(VI)O22+ coupling rapid electron transfer at the MnS/U(VI)O22+ solid-liquid interface to achieve excellent selectivity, high adsorption capacity, and rapid extraction of uranium. The as-synthesized MnS adsorbent exhibited an ultrahigh uranium extraction capacity (2457.05 mg g-1) and a rapid rate constant (K = 9.11 × 10-4 g h-1 mg-1) in seawater with 100.7 ppm of UO2(NO3)2 electrolyte. The kinetic simulation reveals that this adsorption process is a chemical adsorption process and conforms to a pseudo-second-order kinetic model, indicating electron transfer at the MnS/U(VI)O22+ solid-liquid interface. The relevant (quasi) in situ spectroscopic characterization and theoretical calculation results further revealed that the outstanding uranium extraction property of MnS could be attributed to the highly selective UO22+ adsorption of MnS with lower adsorption energy as a result of the strong interaction between S2- and UO22+ and the rapid mass transfer and interface electron transfer from S2- and low-valent Mn(II) to U(VI)O22+.

Repositioning of experimentally validated anti-breast cancer peptides to target FAK-PAX complex to halt the breast cancer progression: a biomolecular simulation approach.

FAK (focal adhesin kinase), a tyrosine kinase, plays an imperative role in cell-cell communication, particularly in cell signaling systems. It is a multi-functional signaling protein, which integrates and transduces signals into cancer cells through growth factor receptors or integrin and its interaction with Paxillin (PAX). The molecular processes by which FAK promotes the development and progression of cancer have progressively established the possible relationship between FAK-PAX complex in many types of cancer. The interaction of FAX and PAX is very important in breast cancer and thus acts as an essential biomarker for drugs, vaccines or peptide inhibitor designing. In this regard, computational approaches, particularly peptide designing to target the binding interface of the interacting partners, would greatly assist the design of peptide inhibitors against various cancer. Accordingly, in this present study, we screened 236 experimentally validated anti-breast cancer peptides using computational drugs repositioning approach to design peptides targeting the FAK-PAX complex. Using protein-peptide docking the binding site for the HP1 was confirmed and a total of 236 anti-breast cancer peptides were screened. Among the 236, only 12 peptides reported a docking score better than the control. From these 12, Magainin with the docking score - 103.8 ± 10.3 kcal/mol, NRC-07 with the docking score - 100.8 ± 16.5 kcal/mol, and Indolicidin with the docking score - 101.7 ± 3.9 kcal/mol, peptides potentially inhibit the FAX-PAX binding. Calculation of protein's motion and FEL revealed the binding and inhibitory behavior. Moreover, binding free energy (MM/GBSA) confirmed that Magainin exhibited the total binding energy - 53.28 kcal/mol, NRC-07 possessed the TBE - 44.16 kcal/mol, and Indolicidin reported the TBE of - 40.48 kcal/mol, thus explaining the inhibitory potential of these peptides. In conclusion, these peptides exhibit strong inhibitory potential and could abrogate the FAK-PAX complex in in vitro models and thus may relieve the burden of breast cancer.

Data Mining Suggests That CXCL14 Gene Silencing in Colon Cancer Is Due to Promoter Methylation.

CXCL14 is one of the most evolutionarily conserved members of the chemokine family and is constitutionally expressed in multiple organs, suggesting that it is involved in the homeostasis maintenance of the system. CXCL14 is highly expressed in colon epithelial cells and shows obvious gene silencing in clinical colon cancer samples, suggesting that its silencing is related to the immune escape of cancer cells. In this paper, we analyzed the expression profiles of multiple human clinical colon cancer datasets and mouse colon cancer models to reveal the variation trend of CXCL14 expression during colitis, colon polyps, primary colon cancer, and liver metastases. The relationship between CXCL14 gene silencing and promoter hypermethylation was revealed through the colorectal carcinoma methylation database. The results suggest that CXCL14 is a tumor suppressor gene in colorectal carcinoma which is activated first and then silenced during the process of tumor occurrence and deterioration. Promoter hypermethylation is the main cause of CXCL14 silencing. The methylation level of CXCL14 is correlated with the anatomic site of tumor occurrence, positively correlated with patient age, and associated with prognosis. Reversing the hypermethylation of CXCL14 may be an epigenetic therapy for colon cancer.

[Establishment of a quantitative method for GC analysis of polyoxyethylene (35) castor oil in microemulsion extracts].

With the continuous exploration of microemulsions as solvents for traditional Chinese medicine extraction, polyoxyethy-lene(35) castor oil(CrEL), a commonly used surfactant, is being utilized by researchers. However, the problem of detecting residues of this surfactant in microemulsion extracts has greatly hampered the further development of microemulsion solvents. Based on the chemical structures of the components in CrEL and the content determination method of castor oil in the 2020 edition of the Chinese Pharmacopoeia(Vol. Ⅳ), this study employed gas chromatography(GC) and single-factor experiments to optimize the preparation method of methyl ricinoleate from CrEL. The conversion coefficient between the two was validated, and the optimal sample preparation method was used to process microemulsion extracts of Zexie Decoction from three batches. The content of methyl ricinoleate generated was determined, and the content of CrEL in the microemulsion extracts of Zexie Decoction was calculated using the above conversion coefficient. The results showed that the optimal preparation method for CrEL was determined. Specifically, 10 mL of 1 mol·L~(-1) KOH-methanol solution was heated at 60 ℃ for 15 min in a water bath. Subsequently, 10 mL of boron trifluoride etherate-methanol(1∶3) solution was heated at 60 ℃ for 15 min in a water bath, followed by extraction with n-hexane twice. CrEL could stably produce 20.84% methyl ricinoleate. According to this conversion coefficient, the average mass concentration of CrEL in the three batches of Zexie Decoction microemulsion extracts was 11.94 mg·mL~(-1), which was not significantly different from the CrEL mass concentration of 11.57 mg·mL~(-1) during microemulsion formulation, indicating that the established content determination method of this study was highly accurate, sensitive, and repeatable. It can be used for subsequent research on microemulsion extracts of Zexie Decoction and provide a reference for quality control of other drug formulations containing CrEL.

Electrochemical-Mediated Regenerable FeII Active Sites for Efficient Uranium Extraction at Ultra-Low Cell Voltage.

Nano-reduced iron (NRI) is a promising uranium adsorbent due to its strong reducibility and good selectivity, but it still faces the challenges of slow kinetics, limited and non-renewable active sites. In this work, we realized high efficiency uranium extraction under ultra-low cell voltage (-0.1 V) in seawater with 20 ppm UO2 (NO3 )2 solution by coupling electrochemical mediated FeII /FeIII redox and uranium extraction. The adsorption capacity and extraction efficiency of NRI after electrochemical uranium extraction (EUE) could reach 452 mg/g and 99.1 %, respectively. Combined with quasi-operando/operando characterization technologies, we clarified the mechanism of EUE and revealed that continuously regenerating FeII active sites by electroreduction could significantly enhance the property of EUE. This work here provides a new electrochemical mediated and low energy consumption uranium extraction strategy which also provides a reference for other metal resource recovery.

The novel use of lauromacrogol: A respective study of ultrasound-guided sclerosant injection for cesarean scar pregnancy.

AIM: To introduce the novel use of lauromacrogol for cesarean scar pregnancy (CSP), and to compare the clinical efficacy and safety of curettage combined with ultrasound-guided sclerosant injection (USI) and curettage following uterine artery embolization (UAE) in the treatment of CSP. METHODS: CSP patients undergoing curettage combined with USI (n = 72) from December 2014 to May 2020 were compared to patient with curettage following UAE (n = 72).The basic clinical findings and clinical outcomes were reviewed between the two groups. RESULTS: For USI group, 69 patients underwent successful treatment (95.8% success rate), while the number of cured patients for the UAE group was 70 (97.2% success rate). Differences between USI group and UAE group in intraoperative blood loss (10.0 [10.0-20.0] vs. 10.0 [10.0-20.0] mL) and time for serum ß human chorionic gonadotropin (ß-hCG) to reduce to normal (28.0 [21.0-40.0] vs. 28.0 [21.0-35.0] days) were not statistically significant. The hospital stay for USI group was significantly shorter than that for UAE group (4.0 [4.0-6.0] vs. 6.0 [5.0-7.0] days, respectively). Statistically significant decreases were noted in hospitalization expenses and adverse events in USI group, compared to UAE group. There was no difference in live birth rate between the two groups with fertility intentions during the follow-up. CONCLUSION: For treatment of CSP, curettage combined with USI yielded clinical results comparable to those of curettage following UAE. Curettage combined with USI was associated with lower hospitalization expenses, shorter hospital stay and less complications, and it merited an effective and safe treatment for CSP.

[Optimization of extraction process of Congrong Shujing Granules based on AHP-CRITIC analysis].

The index weight coefficients were determined by comparing the analytic hierarchy process(AHP), the criteria importance through inter-criteria correlation(CRITIC), and the AHP-CRITIC mixed weighting method. The comprehensive scores of index components(echinacoside, salvianolic acid B, paeoniflorin, and ointment yield) of each group in the orthogonal test were compared to optimize the extraction process of Congrong Shujing Granules. The results showed that the AHP-CRITIC mixed weighting method scientifically optimized the extraction process. To be specific, the decoction pieces should be added with the 6-fold amount of water and extracted twice, 1 h each time. After three verification tests, the average mass fractions of echinacoside, salvianolic acid B, and paeoniflorin were 0.72, 9.34, and 5.92 mg·g~(-1), respectively, and the average ointment yield was 47.18%. As verified by the AHP-CRITIC mixed weighting method and the orthogonal test, the optimized extraction process of Congrong Shujing Granules was stable and feasible and could be applied to industrial production.