ObjectiveTo analyze the pharmacodynamic substance basis of Shangkeling Spray and its potential mechanisms in intervening knee osteoarthritis （KOA） using ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS）， network pharmacology， and molecular docking technology. MethodsUPLC-MS was used to identify the chemical components of Shangkeling Spray. Pharmacokinetic properties were employed to screen potential active ingredients. Network pharmacology methods were utilized to collect potential targets of these ingredients and the pathological gene set of KOA. An "active ingredient-disease" target network was constructed using databases such as STRING. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） functional enrichment analyses were performed using clusterProfiler. Libraries including NumPy were employed to calculate shortest path lengths to identify dominant pharmacodynamic links. Core gene clusters were identified using MCODE， validated through the Gene Expression Omnibus （GEO） database， and molecular docking was performed between key active ingredients and core targets. ResultsA total of 322 and 314 chemical components were identified under positive and negative ion modes， respectively， with 410 components in total after de-duplication， mainly including flavonoids， coumarins， terpenoids， organic acids， and alkaloids. Analysis of the "active ingredient-disease" network identified "development and regeneration"， "cell growth and death"， "immune system"， and "nervous system" as the dominant pharmacodynamic links of Shangkeling Spray in the treatment of KOA. Molecular docking showed that key active ingredients， such as bletillin A， formononetin， morin， oxymatrine， aconitine， gallic acid， curdione， apigenin， naringenin， and oleanolic acid， tightly bound to functional domains of 10 key targets including Jun proteins（JUN）， interleukin-6 （IL-6）， protein kinase B1 （Akt1）， Caspase-3， nuclear transcription factor-κB subunit p65（RELA）， nuclear factor-kappaB1（NF-κB1）， Cyclin D₁， mammalian target of rapamycin（mTOR）， tumor necrosis factor （TNF）， and Fos proto-oncogene protein （FOS）. These interactions synergistically regulated the phosphatidylinositol 3-kinase （PI3K）/Akt/mTOR-related signaling axis and nervous system-related pathways， mediating cartilage repair， reducing inflammation and pain， and improving KOA. ConclusionThis study preliminarily clarifies the pharmacodynamic substance basis of Shangkeling Spray and suggests that its main active ingredients may improve KOA by synergistically regulating the PI3K/Akt/mTOR-related pathways， providing a reference for subsequent exploration of its substance benchmark and mechanism of action.

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 investigate the clinical and pathological significance of the DICER1 mutation in follicular thyroid carcinoma (FTC).Methods:Sixty-eight cases of primary FTC resected between 2009 and 2023 were retrieved from The Affiliated Hospital of Qingdao University, Qingdao, China. Sanger sequencing was performed to identify DICER1 and TERT promoter mutations in all cases. Cases with DICER1 or TERT promoter mutations were subject to additional examination of potential mutations in KRAS, HRAS, and NRAS. The clinical and pathological features of DICER1-mutant FTCs were then analyzed. The relationship between DICER1 mutations and TERT-promoter/RAS mutations was also assessed.Results:DICER1 mutations were detected in 16 of the 68 FTC cases (23.5%), with 11 near E1813 at exon 25, 6 near D1709 at exon 24, and 1 in the splice region of exon 25. Two cases harbored two (distinct) mutations. All patients with DICER1-mutant FTC were female. Compared with patients with DICER1-wild-type FTC, those with DICER1-mutant were much younger, and had a higher proportion of minimally invasive subtype. Nine FTCs with DICER1 mutations were subject to further sequencing on adjacent non-cancerous tissues or lymph node tissues, but no mutations were detected. TERT-promoter or RAS hotspot mutations were not identified in any of the DICER1-mutant cases. However, TERT-promoter mutation was found in 6 DICER1-wild-type cases (8.8%, 6/68), with 3 cases also having RAS hotspot mutations and exhibiting highly aggressive biological behaviors.Conclusion:DICER1 mutations may occur in FTCs and appear mutually exclusive with RAS and TERT-promoter mutations, warranting further study as RAS-like mutations.

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.

Significant advancements have been achieved in both basic and clinical research pertaining to thyroid cancer,leading to substantial updates in clinical diagnostic and therapeutic protocols and guidelines for various thyroid cancer subtypes.The advent and widespread application of next-generation sequencing technology in clinical practice have brought protein kinase fusion-driven thyroid cancer into sharper focus.This particular subtype accounts for over 10％of papillary thyroid carcinomas and frequently exhibits more aggressive histological characteristics and biological behaviors.The identification of kinase fusion-related thyroid cancer and the implementation of optimal detection strate-gies pose notable challenges for pathologists in China.This article provides a comprehensive review of the molecular mechanisms underlying phosphorylation signal transduction driven by kinase fusion,the pathological characteristics and detection methodologies of kinase fusion-related thyroid cancer,as well as the progress in research on related targeted therapies.The aim is to enhance the understanding of kinase fusion-related thyroid cancer,with the aspiration of facili-tating personalized and optimal diagnostic and therapeutic strategies in daily clinical practice,ultimately maximizing pa-tient 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.

Gastrointestinal (GI) cancers are a leading cause of cancer morbidity and mortality worldwide. Despite advances in treatment, cancer relapse remains a significant challenge, necessitating novel therapeutic strategies. In this study, we engineered nanobody-based chimeric antigen receptor (CAR) natural killer (NK) cells targeting cadherin 17 (CDH17) for the treatment of GI tumors. In addition, to enhance the efficacy of CAR-NK cells, we also incorporated CV1, a CD47-SIRPα axis inhibitor, to evaluate the anti-tumor effect of this combination. We found that CDH17-CAR-NK cells effectively eliminated GI cancers cells in a CDH17-dependent manner. CDH17-CAR-NK cells also exhibit potent in vivo anti-tumor effects in cancer cell-derived xenograft and patient-derived xenograft mouse models. Additionally, the anti-tumor activity of CDH17-CAR-NK cells is synergistically enhanced by CD47-signal regulatory protein α (SIRPα) axis inhibitor CV1, likely through augmented macrophages activation and an increase in M1-phenotype macrophages in the tumor microenvironment. Collectively, our findings suggest that CDH17-targeting CAR-NK cells are a promising strategy for GI cancers. The combination of CDH17-CAR-NK cells with CV1 emerges as a potential combinatorial approach to overcome the limitations of CAR-NK therapy. Further investigations are warranted to speed up the clinical translation of these findings.

Photodynamic immunotherapy is a promising strategy for cancer treatment. However, the dysfunctional tumor vasculature results in tumor hypoxia and the low efficiency of drug delivery, which in turn restricts the anticancer effect of photodynamic immunotherapy. In this study, we designed photosensitive lipid nanoparticles. The synthesized PFBT@Rox Lip nanoparticles could produce type I/II reactive oxygen species (ROS) by electron or energy transfer through PFBT under light irradiation. Moreover, this nanosystem could alleviate tumor hypoxia and promote vascular normalization through Roxadustat. Upon irradiation with white light, the ROS produced by PFBT@Rox Lip nanoparticles in situ dysregulated calcium homeostasis and triggered endoplasmic reticulum stress, which further promoted the release of damage-associated molecular patterns, enhanced antigen presentation, and stimulated an effective adaptive immune response, ultimately priming the tumor microenvironment (TME) together with the hypoxia alleviation and vessel normalization by Roxadustat. Indeed, in vivo results indicated that PFBT@Rox Lip nanoparticles promoted M1 polarization of tumor-associated macrophages, recruited more natural killer cells, and augmented infiltration of T cells, thereby leading to efficient photodynamic immunotherapy and potentiating the anti-primary and metastatic tumor efficacy of PD-1 antibody. Collectively, photodynamic immunotherapy with PFBT@Rox Lip nanoparticles efficiently program TME through the induction of immunogenicity and oxygenation, and effectively suppress tumor growth through immunogenic cell death and enhanced anti-tumor immunity.

Background and aims:Environmental pollutants,particularly organochlorine insecticides like endosulfan(ENDO),are increasingly linked to liver toxicity and related diseases.Despite its widespread historical use,the mechanisms underlying ENDO-induced liver damage remain poorly understood.This study aims to elucidate the cellular and molecular mechanisms of ENDO-induced hepatotoxicity.Methods:C57BL/6 mice were exposed to ENDO for two weeks.Single-cell RNA sequencing(scRNA-seq)was subsequently performed on mouse livers to explore ENDO-induced hepatotoxicity at the single-cell level.Differentially expressed genes(DEGs)across cell types and treatments were identified and then subjected to pathway enrichment to uncover key biological processes affected by ENDO.Transcription factor(TF)regulatory network,pseudotime trajectory,and cellular communication analysis were used to explore the molecular and cellular changes after ENDO exposure.Results:ENDO not only caused direct hepatocyte injury but also activated hepatic stellate cells and lymphocytes,triggering inflammatory responses with upregulation of multiple key chemokines and cytotoxic genes.Additionally,ENDO exposure led to the recruitment and activation of myeloid cells,contributing to the inflammatory milieu.An increase in intercellular communication and changes to the hepatic microenvironment,especially the interaction between activated hepatic stellate cells and CD8+T cells were observed,further implicating these processes in ENDO-induced liver damage.Conclusions:This study provides new insights into the cellular and molecular mechanisms underlying liver injury induced by organochlorine insecticides like ENDO.Key genes and pathways involved in ENDO-associated liver toxicity have been identified at a single-cell resolution.These findings suggest that altered cellular communications and inflammatory responses may play pivotal roles in the pathogenesis of ENDO-induced liver injury.