ObjectiveTo evaluate the public health governance capacity in Jiangsu Province and provide an optimized pathway for the construction of a “strong, rich, beautiful, and high-quality” new Jiangsu. MethodsA total of 806 policy documents, 658 public information reports, and 148 research literatures related to public health governance capacity in Jiangsu Province from January 1995 to December 2023 were collected. The status of current public health goverance was assessed based on the evaluation criteria suitable for public health systems, and the strengths and the weaknesses of the system were identified. ResultsThe public health governance capability of Jiangsu Province was scored at 738.3 points, ranking 3rd nationally. Maternal health care and emergency response capacities achieved leading positions nationwide, both ranking 2nd. Jiangsu had exhibited a standardized guidance in the strategic level, a well-established management mechanism, an extensive coverage in information collection, and a scientifically established health targets setting. However, bottlenecks remained, including an unclear division of responsibilities across organizational departments, an insufficient public-health workforce, the absence of a stable growth mechanism for government funding investment, and difficulties in promptly identifying public needs. ConclusionJiangsu’s public-health system demonstrates leading nationally, yet several components remain underdeveloped. Future efforts should consolidate advantages while addressing weaknesses, further diversify content and forms, establish a stable funding increase mechanism, and clarify departmental functions, thereby providing solid health support for realizing the developmental goals of a “strong, rich, beautiful and high-quality” new Jiangsu.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

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: Based on spinopelvic parameters and biomechanical principles, the pedicle-facet joint (PFJ) morphological characteristics of isthmic and degenerative spondylolisthesis were analyzed, and the mechanism of their onset and progression was discussed. Methods: This retrospective cross-sectional study included 194 patients with L5 spondylolysis or L5–S1 low-grade isthmic spondylolisthesis (IS group), 172 patients with L4–5 degenerative spondylolisthesis (DS group), and 366 patients with nonlumbar spondylolysis (NL group). The spinopelvic parameters and PFJ morphological parameters of the patients were measured, the differences in these parameters among and within the 3 groups were compared, and the correlations were analyzed. Results: Sacral slope (SS) and lumbar lordosis (LL) were the highest in the IS group, the second highest in the DS group, and the lowest in the NL group. Among the 3 groups, the L4 facet joint angle (FJA) was the largest in the IS group, the second largest in the NL group, and the smallest in the DS group. The L4 pedicle-facet joint angle (PFA) was the largest in the DS group, the second largest in the IS group, and the smallest in the NL group. Pearson correlation analysis showed that within each group, SS and LL were negatively correlated with FJA and positively correlated with PFA. Conclusion This study found a correlation between the PFJ morphological characteristics of patients with lumbar spondylolisthesis and spinopelvic parameters, suggesting that the morphological characteristics of PFJs may be caused by varying stresses under different spinopelvic morphologies.

Objective: Based on spinopelvic parameters and biomechanical principles, the pedicle-facet joint (PFJ) morphological characteristics of isthmic and degenerative spondylolisthesis were analyzed, and the mechanism of their onset and progression was discussed. Methods: This retrospective cross-sectional study included 194 patients with L5 spondylolysis or L5–S1 low-grade isthmic spondylolisthesis (IS group), 172 patients with L4–5 degenerative spondylolisthesis (DS group), and 366 patients with nonlumbar spondylolysis (NL group). The spinopelvic parameters and PFJ morphological parameters of the patients were measured, the differences in these parameters among and within the 3 groups were compared, and the correlations were analyzed. Results: Sacral slope (SS) and lumbar lordosis (LL) were the highest in the IS group, the second highest in the DS group, and the lowest in the NL group. Among the 3 groups, the L4 facet joint angle (FJA) was the largest in the IS group, the second largest in the NL group, and the smallest in the DS group. The L4 pedicle-facet joint angle (PFA) was the largest in the DS group, the second largest in the IS group, and the smallest in the NL group. Pearson correlation analysis showed that within each group, SS and LL were negatively correlated with FJA and positively correlated with PFA. Conclusion This study found a correlation between the PFJ morphological characteristics of patients with lumbar spondylolisthesis and spinopelvic parameters, suggesting that the morphological characteristics of PFJs may be caused by varying stresses under different spinopelvic morphologies.

A nodule in the right middle lobe of the lung was treated by a combination of cone-beam CT,three-dimensional registration for fusion imaging,and electromagnetic navigation bronchoscopy-guided thermal ablation.The procedure lasted for 90 min,with no significant bleeding observed under the bronchoscope.The total radiation dose during the operation was 384 mGy.The patient recovered well postoperatively,with only a small amount of blood in the sputum and no pneumothorax or other complications.A follow-up chest CT on the first day post operation showed that the ablation area completely covered the lesion,and the patient was discharged successfully.
Humans ; Bronchoscopy/methods* ; Catheter Ablation/methods* ; Cone-Beam Computed Tomography ; Electromagnetic Phenomena ; Imaging, Three-Dimensional ; Lung Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed

Objective:To screen out the differentially expressed genes(DEGs)in multiple primary lung cancers(MPLCs)using bioinformatics methods,and to analyze their biological functions and their influence in the prognosis of lung adenocarcinoma.Methods:Single-cell transcriptome sequencing data(GSE200972)was downloaded from the Gene Expression Omnibus(GEO)database.After preliminary data processing with R software,the Seurat R package was used for data processing,cell clustering,and annotation.The clusterProfiler R package was used for Gene Set Enrichment Analysis(GSEA).The STRING database and Cytoscape software were employed to construct the protein-protein interaction(PPI)network and to screen out the key genes(Hub genes).The gene expression levels in the lung adenocarcinoma database were analyzed using Gene Expression Profiling Interactive Analysis(GEPIA)database.Real-time fluorescence quantitative PCR(RT-qPCR)method was used to detect the gene expression in tumor tissue of A549 xenograft mice and lung tissue of normal mice.Kaplan-Meier Plotter was used for prognosis analysis.Results:Seven cell types were identified from cell clustering analysis,which were epithelial cells,endothelial cells,fibroblasts,T cells and natural killer(NK)cells,B cells,myeloid cells,and mast cells.A total of 14 605 DEGs were screened out between tumor epithelial cells and normal epithelial cells.The GSEA results revealed four activated pathways in tumor samples[myelocytomatosis oncogene(MYC)pathway,P53 pathway,oxidative phosphorylation pathway and glycolysis pathway]and one inhibited pathway[tumor necrosis factor-α(TNF-α)and nuclear factor kappa B(NF-κB)pathway].The Hub genes identified from PPI network included CXC motif chemokine ligand 8(CXCL8),glyceraldehyde-3-phosphate dehydrogenase(GAPDH),CXC motif chemokine receptor 4(CXCR4),kirsten rat sarcoma viral proto-oncogene(KRAS),CXC motif chemokine ligand 1(CXCL1),C-C motif chemokine ligand 2(CCL2),mucin 1(MUC1),and secreted phosphoprotein 1(SPP1).The GEPIA database analysis and animal experiments showed that the expression levels of SPP1 mRNA in non-small cell lung cancer tissue were increased compared with normal lung tissue(P＜0.01).The Kaplan-Meier survival analysis indicated that the patients with high expression level of SPP1 had shorter overall survival(OS)than those with low expression level(P＜0.01).Conclusion:There are activation of oncogene-related pathways and activation of tumor suppressor pathway antagonizing tumor progression in MPLCs.Moreover,elevated expression of SPP1 in non-small cell lung cancer may indicate a relatively poor prognosis.

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 distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.