ObjectiveTo investigate the key targets of Jianpi Yiqi prescription （JYP） in the treatment of hepatocellular carcinoma （HCC） based on network pharmacology and explore the effect of JYP on the invasion and proliferation of hepatocellular carcinoma cells via protein tyrosine phosphatase， non-receptor type 1 （PTPN1） by bioinformatics analysis and CRISPR/Cas9. MethodsThe potential targets of JYP in the treatment of HCC were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）， SwissTargetPrediction， GeneCards， NCBI， and CTD. Additionally， the active components of JYP that could interact with PTPN1 were screened out， and then molecular docking between the targets and active components was performed in Autodock 4.0. UALCAN， HPA， and LinkedOmics were used to analyze the expression of PTPN1 in the HCC tissue， and the relationship of PTPN1 expression with the overall survival （OS） of HCC patients was discussed. CRISPR/Cas9 was used to knock down the expression of PTPN1 in HepG2 and SK-hep-1 cells， and the knockdown effect was examined by sequencing， Real-time PCR， and Western blot. HepG2 cells were classified into blank control， low-， medium-， and high-dose JYP （5.25， 10.5， 21 g·kg^-1）， and PTPN1 knockout groups. Real-time PCR and Western blot were employed to determine the mRNA and protein levels， respectively， of PTPN1 in HepG2 cells of each group. The effects of JYP and PTPN1 knockdown on the proliferation， invasion， and apoptosis of HepG2 cells were detected by Cell Counting Kit-8 （CCK-8）， Transwell， and Annexin V-FITC/PI methods， respectively. ResultsJYP had the most active components targeting PTPN1， and 31 of the active components had the binding energy less than -5.0 kcal·mol^-1 in molecular docking. The mRNA and protein levels of PTPN1 in the HCC tissue were higher than those in the normal tissue （P<0.01）. Compared with that in the normal tissue， the mRNA level of PTPN1 in the HCC tissue was up-regulated at the pathological stages Ⅰ-Ⅲ and grades G₁-G₃ （P<0.01）， and it was not significantly up-regulated at the stage Ⅳ or grade G₄. The mRNA level of PTPN1 in the TP53-mutated HCC tissue was higher than that in the TP53-unmutated HCC tissue （P<0.01）. The high mRNA level of PTPN1 was associated with the OS reduction （P<0.01）. After treatment with the JYP-containing serum or knockdown of PTPN1， HepG2 cells demonstrated decreased proliferation and invasion and increased apoptosis （P<0.01）. ConclusionPTPN1 may be one of the core targets of JYP in the treatment of HCC. It is highly expressed in the HCC tissue and cells， which is associated with the poor prognosis of patients. The expression level of PTPN1 is significantly up-regulated in the HCC tissue of the patients with TP53 mutation. However， TP53 mutation or deletion does not affect the expression of PTPN1 in HCC cells. JYP can significantly down-regulate the expression of PTPN1 to inhibit the proliferation and invasion and promote the apoptosis of HCC cells.

The general models for intermediates quality analysis in the production process of Yaobitong capsule were established by near infrared spectroscopy (NIRS) combined with chemometrics, realizing the rapid determination of notoginsenoside R1, ginsenoside Rg1, ginsenoside Re, ginsenoside Rb1, ginsenoside Rd and moisture. The spray-dried fine powder and total mixed granule were selected as research objects. The contents of five saponins were determined by high performance liquid chromatography and the moisture content was determined by drying method. The measured contents were used as reference values. Meanwhile, NIR spectra were collected. After removing abnormal samples by Monte Carlo cross validation (MCCV), Monte Carlo uninformative variables elimination (MC-UVE) and competitive adaptive reweighted sampling (CARS) were used to select feature variables respectively. Based on the feature variables, quantitative models were established by partial least squares regression (PLSR), extreme learning machine (ELM) and ant lion optimization least squares support vector machine (ALO-LSSVM). The results showed that CARS-ALO-LSSVM model had the optimum effect. The correlation coefficients of the six index components were greater than 0.93, and the relative standard errors were controlled within 6%. ALO-LSSVM was more suitable for a large number of samples with rich information, and the prediction effect and stability of the model were significantly improved. The general models with good predicting effect can be used for the rapid quality determination of Yaobitong capsule intermediates.

Objective To analyze the characteristics of adverse drug reactions （ADR） reported in Sixth People’s Hospital South Campus, Shanghai Jiaotong University from 2021 to 2023, to provide reference for promoting rational clinical drug use. Methods ADR data reported in our hospital were collected retrospectively, including patients’ basic information, drugs causing adverse reactions, types of adverse reactions and outcomes. Descriptive analysis methods were used to summarize and analyze the data. Results A total of 979 cases of ADR were reported in our hospital from 2021 to 2023. The highest proportion of patients with ADR occurred in the age range of 31 to 50, and more male patients （63.5%）. The top five drugs involved with adverse reactions were antibiotics （48.8%）, Chinese medicine injections（19.2%）, vitamins（7.5%）, Chinese traditional medicine（7.2%）, equine tetanus immunoglobulin（6.3%）. Among antibiotics, cefuroxime, ceftazidime and cefotiam were the majority. The organs/systems involved in all ADR were mainly skin and accessories damage （55.4%）. The clinical manifestations were rash, itching, and maculopapular rash. Conclusion From 2021 to 2023, the most common drugs causing adverse drug reactions in our hospital were mainly antibacterial drugs, and the rational clinical use of antibacterial drugs still needs to be concerned.

Fibrosis, the pathological scarring of vital organs, is a severe and often irreversible condition that leads to progressive organ dysfunction. It is particularly pronounced in organs like the liver, kidneys, lungs, and heart. Despite its clinical significance, the full understanding of its etiology and complex pathogenesis remains incomplete, posing substantial challenges to diagnosing, treating, and preventing the progression of fibrosis. Among the various molecular players involved, platelet-derived growth factor-C (PDGF-C) has emerged as a crucial factor in fibrotic diseases, contributing to the pathological transformation of tissues in several key organs. PDGF-C is a member of the PDGFs family of growth factors and is synthesized and secreted by various cell types, including fibroblasts, smooth muscle cells, and endothelial cells. It acts through both autocrine and paracrine mechanisms, exerting its biological effects by binding to and activating the PDGF receptors (PDGFRs), specifically PDGFRα and PDGFRβ. This binding triggers multiple intracellular signaling pathways, such as JAK/STAT, PI3K/AKT and Ras-MAPK pathways. which are integral to the regulation of cell proliferation, survival, migration, and fibrosis. Notably, PDGF-C has been shown to promote the proliferation and migration of fibroblasts, key effector cells in the fibrotic process, thus accelerating the accumulation of extracellular matrix components and the formation of fibrotic tissue. Numerous studies have documented an upregulation of PDGF-C expression in various fibrotic diseases, suggesting its significant role in the initiation and progression of fibrosis. For instance, in liver fibrosis, PDGF-C stimulates hepatic stellate cell activation, contributing to the excessive deposition of collagen and other extracellular matrix proteins. Similarly, in pulmonary fibrosis, PDGF-C enhances the migration of fibroblasts into the damaged areas of lungs, thereby worsening the pathological process. Such findings highlight the pivotal role of PDGF-C in fibrotic diseases and underscore its potential as a therapeutic target for these conditions. Given its central role in the pathogenesis of fibrosis, PDGF-C has become an attractive target for therapeutic intervention. Several studies have focused on developing inhibitors that block the PDGF-C/PDGFR signaling pathway. These inhibitors aim to reduce fibroblast activation, prevent the excessive accumulation of extracellular matrix components, and halt the progression of fibrosis. Preclinical studies have demonstrated the efficacy of such inhibitors in animal models of liver, kidney, and lung fibrosis, with promising results in reducing fibrotic lesions and improving organ function. Furthermore, several clinical inhibitors, such as Olaratumab and Seralutinib, are ongoing to assess the safety and efficacy of these inhibitors in human patients, offering hope for novel therapeutic options in the treatment of fibrotic diseases. In conclusion, PDGF-C plays a critical role in the development and progression of fibrosis in vital organs. Its ability to regulate fibroblast activity and influence key signaling pathways makes it a promising target for therapeutic strategies aiming at combating fibrosis. Ongoing research into the regulation of PDGF-C expression and the development of PDGF-C/PDGFR inhibitors holds the potential to offer new insights and approaches for the diagnosis, treatment, and prevention of fibrotic diseases. Ultimately, these efforts may lead to the development of more effective and targeted therapies that can mitigate the impact of fibrosis and improve patient outcomes.

Purpose: Colorectal cancer (CRC) often spreads to the liver, necessitating surgical treatment for CRC liver metastasis (CRLM). Iron-deficiency anemia is common in CRC patients and is associated with fatigue and weakness. This study investigated the effects of iron-deficiency anemia on the outcomes of surgical resection of CRLM. Methods: This population-based, retrospective study evaluated data from adults ≥20 years old with CRLM who underwent hepatic resection. All patient data were extracted from the 2005–2018 US National (Nationwide) Inpatient Sample (NIS) database. The outcome measures were in-hospital outcomes including 30-day mortality, unfavorable discharge, and prolonged length of hospital stay (LOS), and short-term complications such as bleeding and infection. Associations between iron-deficiency anemia and outcomes were determined using logistic regression analysis. Results: Data from 7,749 patients (representing 37,923 persons in the United States after weighting) were analyzed. Multivariable analysis revealed that iron-deficiency anemia was significantly associated with an increased risk of prolonged LOS (adjusted odds ratio [aOR], 2.76; 95% confidence interval [CI], 2.30–3.30), unfavorable discharge (aOR, 2.42; 95% CI, 1.83–3.19), bleeding (aOR, 5.05; 95% CI, 2.92–8.74), sepsis (aOR, 1.60; 95% CI, 1.04–2.46), pneumonia (aOR, 2.54; 95% CI, 1.72–3.74), and acute kidney injury (aOR, 1.71; 95% CI, 1.24–2.35). Subgroup analyses revealed consistent associations between iron-deficiency anemia and prolonged LOS across age, sex, and obesity status categories. Conclusion In patients undergoing hepatic resection for CRLM, iron-deficiency anemia is an independent risk factor for prolonged LOS, unfavorable discharge, and several critical postoperative complications. These findings underscore the need for proactive anemia management to optimize surgical outcomes.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective: To understand the dynamic temporal evolution pathways of Internet addiction among university students and to identify the core driving nodes, so as to provide theoretical evidences for the precise implementation of targeted interventions. Methods: Using a convenient cluster sampling method, a total of 1 066 full time freshmen and sophomores were recruited from three universities in Guizhou, Jiangxi, and Guangdong Provinces for a follow up survey (T1:January-March 2024; T2:January-March 2025). The Revised Chen Internet Addiction Scale (CIAS-R) was employed to assess the status of Internet addiction among university students, and cross sectional as well as cross lagged panel network models were constructed to analyze Internet addiction and its multidimensional influencing factors. Results: The T1 network comprised 19 nodes and 114 non zero edges, while the T2 network comprised 19 nodes and 126 non zero edges. Cross sectional network analysis revealed the strongest association between "insufficient sleep" and "daytime fatigue"; the core nodes were "first thought upon waking for going online" and "feeling low after disconnection" (characteristics of psychological dependence) at T1, while the core nodes shifted to "impaired health" and "excitement when online" (characteristics of functional impairment and addictive psychodynamic features) at T2. Cross lagged network analysis further indicated that "reduced leisure" directly predicted "sleep compression", and a bidirectional relationship was observed between "needing more time to achieve satisfaction" and "academic decline". Conclusions Internet addiction among university students exhibits dynamic evolutionary characteristics. Stage specific targeted interventions focusing on core driving nodes are needed, integrating behavioral regulation and academic support to break the vicious cycle and enhancing the ability to cope with real life demands.

OBJECTIVE To investigate the potential mechanism of epigallocatechin gallate （EGCG） enhancing the sensitivity of hepatocellular carcinoma （HCC） cells to lenvatinib based on the phosphoinositide 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway. METHODS Five human HCC cell lines （HepG2， Huh-7， SMMC-7721， SNU-368 and SNU-739） were used to evaluate the effects of lenvatinib alone and in combination with EGCG on survival rates， clone number， proliferation rate， invasion number and the expressions of mRNAs and proteins related to the PI3K/Akt signaling pathway. The PI3K activator insulin-like growth factor-1 （IGF-1） was introduced to investigate the effect of activating the PI3K/Akt signaling pathway on the sensitization effect of EGCG. RESULTS Compared with the control group， lenvatinib （10 μmol/L） and different concentrations of EGCG+ lenvatinib （1， 5 and 10 μg/mL EGCG+10 μmol/L lenvatinib） significantly reduced the survival rates and clone numbers of all five HCC cell lines in a dose-dependent manner （P＜0.05）. Lenvatinib （10 μmol/L） and EGCG+lenvatinib （10 μg/mL EGCG+10 μmol/L lenvatinib） also markedly inhibited the proliferation rate and invasion numbers of these cells， and decreased the mRNA expressions of PI3K， Akt， mammalian target of rapamycin （mTOR）， P70S6K and 4EBP， and the phosphorylation levels of PI3K and Akt， as well as the protein expressions of mTOR and B cell lymphoma-2 （Bcl-2） in HepG2 cells or all five HCC cells； conversely， the mRNA and protein expressions of phosphatase and tensin homologue deleted on chromosome 10（PTEN）， and the protein expressions of caspase-3 and cleaved caspase-3 were significantly upregulated， with more pronounced effects observed in the EGCG+lenvatinib group than in the lenvatinib group （P＜0.05）. Compared with the lenvatinib group and the EGCG+lenvatinib group， the clone number， proliferation rate and invasion number of HepG2 cells in the EGCG+lenvatinib+IGF-1 group （10 μg/mL EGCG+10 μmol/L lenvatinib+50 ng/mL IGF-1） were significantly increased （P＜0.05）. CONCLUSIONS EGCG can enhance the sensitivity of HCC cells to lenvatinib， and its underlying mechanism may be related to the inhibition of the activation of PI3K/Akt signaling pathway activation.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.