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.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

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.

Objective:To investigate the curative effect of red-blue light combined with intense pulsed light(IPL)in treating papular pustular rosacea,and its effect on quality of life.Methods:A total of 76 patients with papular pustular rosacea who admitted to PLA rocket force characteristic medical center and Qinghai provincial traffic hospital from August 2019 to July 2023 were retrospectively selected.According to the different treatment methods,all patients were divided into observation group and control group,with 38 patients in each group.The control group was treated with oral minocycline hydrochloride,while the observation group adopted respectively 87C type red-blue light instrument and M22 type photon therapy device to implement red-blue and IPL therapy on the basis of treatment of control group.The clinical efficacy,clinician's erythema assessment(CEA)scale,investigator's global assessment(IGA)scale,dermatology life quality index(DLQI)and the incidence of adverse reaction were compared between the two groups.Results:The overall effective rate of the observation group was 97.37%,which was significantly higher than 84.21%of the control group,and the difference was statistically significant(x2=3.934,P<0.05).The scores of IGA,CEA and DLQI in two groups after treatment were all lower than those before treatment,and the differences of them were all significant(t=3.820,6.725,7.937,P<0.05).There were no statistically significant differences in the incidence of adverse reactions included dizziness,dry skin,worsening itching and pain and pigmentation between the two groups after treatment(P>0.05).Conclusion:Red-blue light combined with IPL can significant enhance the clinically curative effect in treating papular pustular rosacea,which can significantly reduce the symptom of skin lesions of persistent with erythema,and improve quality of life of patients.It has favorable safety.

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.

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.

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.