Objective To prepare Zanthoxylum alkaloid thermosensitive hydrogel, optimize the preparation process and conduct related performance studies. Methods Zanthoxylum alkaloids were obtained by reflux extraction, followed by enrichment and purification using macroporous adsorption resin. Poloxamer 407 and Poloxamer 188 were used as substrates to prepare the thermosensitive hydrogel of Zanthoxylum alkaloids, and the preparation process was optimized by orthogonal design. The quality of the hydrogel was systematically evaluated based on its gelation temperature, gelation time, Fourier transform infrared （FTIR） spectroscopy, scanning electron microscopy （SEM） images, mechanical properties, and in vitro release profile. Results The optimal preparation conditions for the Zanthoxylum alkaloid thermosensitive hydrogel were: 20% （g/ml） poloxamer 407, 2% （g/ml） poloxamer 188 and 100 μg/ml Zanthoxylum alkaloid. The gelation temperature was 32.6℃, and the average gelling time was 143.3 s. The hydrogel appeared as a transparent liquid at room temperature and was transformed into a semi-solid gel state when the temperature exceeded 33℃. Experimental results confirmed the successful preparation of poloxamer 407 and poloxamer 188 thermosensitive hydrogel loaded with Zanthoxylum alkaloids, which exhibited good bio adhesion, self-healing properties, and tensile strength. Conclusion The Zanthoxylum alkaloid thermosensitive hydrogel demonstrated favorable mechanical properties and a sustained-release effect, showing promising potential for further development and application.

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

This paper summarizes Professor WANG Xiuxia's clinical experience in treating hyperprolactinemia using the liver soothing therapy. Professor WANG identifies liver qi stagnation and rebellious chong qi （冲气） as the core pathomechanisms of hyperprolactinemia. Furthermore， liver qi stagnation may transform into fire or lead to pathological changes such as spleen deficiency with phlegm obstruction or kidney deficiency with essence depletion. The treatment strategy centers on soothing the liver， with a modified version of Qinggan Jieyu Decoction （清肝解郁汤） as the base formula. Depending on different syndrome patterns such as liver stagnation transforming into fire， liver stagnation with spleen deficiency， or liver stagnation with kidney deficiency， heat clearing， spleen strengthening， or kidney tonifying herbs are added accordingly. In addition， three paired herb combinations are commonly used for symptom specific treatment， Danggui （Angelica sinensis） with Chuanxiong （Ligusticum chuanxiong）， Zelan （Lycopus lucidus） with Yimucao （Leonurus japonicus） ， and Jiegeng （Platycodon grandiflorus） with Zisu （Perilla frutescens）.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To analyze the effect of panax notoginseng saponins(PNS)on mTORC1-HIF1α signaling pathway,and to explore its effect and mechanisms on the differentiation balance of Th17/Treg cells in CD4+T cells.Methods:Isolate the spleens of C57BL/6 mice,then select CD4+T cells by magnetic beads and cultured in vitro.The optimal concentration of PNS was screened by the CCK-8,and then these cells were divided into control group and PNS treatment group(5,10 and 20 μg/ml),each gives correspond-ing drug treatment after 48 h.Afterwards,flow cytometry was used to detect differentiation of Th17/Treg cells.Real-time quantitative fluorescent PCR was used to detect the expressions of RORγt,Foxp3,mTOR,Raptor,HIF1α mRNA.ELISA was used to detect the levels of IL-17A and IL-10 in the supernatant of cell culture.Western blot was used to detect the expressions and phosphorylation levels of 4EBP1,S6K and HIF1α proteins.Results:5,10,20 μg/ml PNS could significantly inhibit Th17 cells differentiation and promote Treg cells differentiation;5,10,20 μg/ml PNS could significantly reduce the expression of RORγt mRNA,and then reduce the level of IL-17A;20 μg/ml PNS could significantly promote the expression of Foxp3 mRNA and increase the level of IL-10;10,20 μg/ml PNS could significantly decrease the phosphorylation of 4EBP1 and S6K;5,10,20 μg/ml PNS could significantly reduce the expression of HIF1α mRNA and inhibit the expression of HIF1α protein.Conclusion:Certain concentrations of PNS can inhibit the differentiation of Th17 cells in CD4+T cells,and promote the differentiation of Treg cells,which is related with modulating mTORC1-HIF1α signaling pathway.

Arsenic compounds are widely used for the therapeutic intervention of multiple diseases.Ancient pharmacologists discovered the medicinal utility of these highly toxic substances,and modern phar-macologists have further recognized the specific active ingredients in human diseases.In particular,Arsenic trioxide(ATO),as a main component,has therapeutic effects on various tumors(including leukemia,hepatocellular carcinoma,lung cancer,etc.).However,its toxicity limits its efficacy,and con-trolling the toxicity has been an important issue.Interestingly,recent evidence has pointed out the pivotal roles of arsenic compounds in phase separation and membraneless organelles formation,which may determine their toxicity and therapeutic efficacy.Here,we summarize the arsenic compounds-regulating phase separation and membraneless organelles formation.We further hypothesize their potential involvement in the therapy and toxicity of arsenic compounds,highlighting potential mecha-nisms underlying the clinical application of arsenic compounds.

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults and is poorly controlled. Previous studies have shown that both macrophages and angiogenesis play significant roles in GBM progression, and co-targeting of CSF1R and VEGFR is likely to be an effective strategy for GBM treatment. Therefore, this study developed a novel and selective inhibitor of CSF1R and VEGFR, SYHA1813, possessing potent antitumor activity against GBM. SYHA1813 inhibited VEGFR and CSF1R kinase activities with high potency and selectivity and thus blocked the cell viability of HUVECs and macrophages and exhibited anti-angiogenetic effects both in vitro and in vivo. SYHA1813 also displayed potent in vivo antitumor activity against GBM in immune-competent and immune-deficient mouse models, including temozolomide (TMZ) insensitive tumors. Notably, SYHA1813 could penetrate the blood-brain barrier (BBB) and prolong the survival time of mice bearing intracranial GBM xenografts. Moreover, SYHA1813 treatment resulted in a synergistic antitumor efficacy in combination with the PD-1 antibody. As a clinical proof of concept, SYHA1813 achieved confirmed responses in patients with recurrent GBM in an ongoing first-in-human phase I trial. The data of this study support the rationale for an ongoing phase I clinical study (ChiCTR2100045380).

Myeloid-derived suppressor cells (MDSCs) play an important immunosuppressive role in the tumor microenvironment. Tumor cells can regulate the immunosuppressive function of MDSCs in the tumor microenvironment through exosomes, thereby affecting the development of tumors. Tumor-derived exosomes (TEXs) promote the development of MDSCs and improve their immunosuppressive function in the tumor microenvironment mainly by participating in the processes such as intercellular information exchange and information transmission. Moreover, the miRNAs in TEXs will also be transferred to recipient cells to inhibit the immunosuppressive function of MDSCs by inducing the negative regulation of target genes. This review summarized the progress in the mechanism of TEXs on MDSCs.

Purpose: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease, and Th17 cells are key factors in the pathogenesis of human inflammatory conditions, such as RA. Catalpol (CAT), a component in Rehmanniae Radix (RR), has been found to regulate human immunity. However, the effects of CAT on Th17 cell differentiation and improvement of RA are not clear. Materials and Methods: Collagen-induced arthritis (CIA) mice were constructed to detect the effects of CAT on arthritis and Th17 cells. The effect of CAT on Th17 differentiation was evaluated with let-7g-5p transfection experiments. Flow cytometry was used to detect the proportion of Th17 cells after CAT treatment. Levels of interleukin-17 and RORγt were assessed by qRT-PCR and enzyme-linked immunosorbent assay. The expression of signal transducer and activator of transcription 3 (STAT3) was determined by qRT-PCR and Western blot. Results: We found that the proportion of Th17 cells was negatively associated with let-7g-5p expression in CIA mice. In in vitro experiments, CAT suppressed traditional differentiation of Th17 cells. Simultaneously, CAT significantly decreased Tregs-to-Th17 cells transdifferentiation. Our results demonstrated that CAT inhibited Tregs-to-Th17 cells transdifferentiation by up-regulating let-7g-5p and that the suppressive effect of CAT on traditional differentiation of Th17 cells is not related with let-7-5p. Conclusion Our data indicate that CAT may be a potential modulator of Tregs-to-Th17 cells transdifferentiation by up-regulating let-7g-5p to reduce the expression of STAT3. These results provide new directions for research into RA treatment.