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.

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.

BACKGROUND:Osteoporotic vertebral fractures are the most common complication in patients with osteoporosis.As a new imaging technique,spine magnetic resonance imaging(MRI)is much more sensitive than X-ray film in the diagnosis of osteoporotic vertebral fractures.However,total spine MRI is costly and takes a long time to scan.Therefore,there is no consensus on whether all patients with osteoporotic vertebral fractures need to undergo total spine MRI scan and which patients need to undergo total spine MRI. OBJECTIVE:To analyze the distribution characteristics of vertebral fractures and explore their clinical significance by observing the whole spine MRI data of osteoporotic vertebral fractures patients. METHODS:Data of cases and MRI images of all patients diagnosed with fresh osteoporotic vertebral fractures who visited the Department of Orthopedics,Affiliated Hospital of Southwest Medical University from August 2018 to September 2022 were retrospectively analyzed.903 patients were included in the study based on inclusion and exclusion criteria.General information(age,gender,and body mass index),medical history characteristics(duration of illness,history of trauma surgery,percussion pain area,and pain score)were collected.The characteristics of vertebral fractures were analyzed through whole spine magnetic resonance imaging.Firstly,based on the number of vertebral fractures in patients,they were divided into the single vertebral fracture group(484 cases)and the multi-vertebral fracture group(419 cases),and the differences were analyzed between the two groups.Then,based on whether the farthest interval between the fractured vertebrae was greater than or equal to 5,the multi vertebral fracture group was further divided into two subgroups.Among them,Group A(the farthest interval between the fractured vertebrae was less than 5)contained 306 cases;Group B(with the farthest interval between fractured vertebral bodies greater than 5)included 113 cases.The differences were analyzed between two subgroups. RESULTS AND CONCLUSION:(1)Among 903 patients,419 patients(46.4%)had more than two fractured vertebrae.There were 654 patients(72.4%)with thoracolumbar fractures,and 54 patients(6%)with fractures in the thoracic plus lumbar region and the entire thoracic to lumbar region.In group B,96.5%of patients had multiregional percussion pain.(2)Compared with the patients in the single vertebral fracture group and the multi-vertebral fracture group,there were significant differences in bone mineral density,whether the medical history was greater than or equal to 1 month,the history of low energy injury,and the distribution and number of axial percussion pain areas in the spine during physical examination between the two groups(P<0.05).Age,gender,body mass index,whether there was underlying disease,pain visual analog scale score,whether there was a history of elderly thoracolumbar fracture,and whether there was a history of thoracolumbar surgery,and the number of fractured vertebrae had no statistical significance(P>0.05).(3)There were statistically significant differences between the Groups A and B in bone mineral density,the distribution and quantity of percussion pain area,and the history of low energy injury(P<0.05).There were no significant differences in age,gender,history of old fractures,visual analog scale score,body mass index,whether the medical history was longer than or equal to 1 month,history of underlying diseases,and history of thoracolumbar surgery between the two groups(P>0.05).(4)Patients with multiple low-energy trauma history,history of more than 1 month,multiple percussion pain,and the lower bone mineral density should be alert to the occurrence of multiple vertebral fracture and jump fracture.We recommend the whole spinal MRI for these 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.

ObjectiveTo systematically evaluate the public health governance capacity of 40 cities in Jiangsu, Zhejiang, and Anhui Provinces, providing a scientific evaluation basis for building a "Healthy Yangtze River Delta". MethodsA comprehensive collection of policy documents, public information reports, and research literature related to public health governance capacity in Jiangsu, Zhejiang, and Anhui Provinces was conducted, totaling 6 920 policy documents, 1 720 information reports, and 1 200 literature pieces. Based on the evaluation standards for an appropriate public health system established by the research team, the basic status of public health governance capacity was assessed to identify the strengths and weaknesses of the 40 cities. ResultsIn 2022, the public health governance capacity score for the 40 cities in Jiangsu, Zhejiang, and Anhui Provinces was (562.5±38.0) points. In terms of specific areas, the emergency response field received the highest score of (791.4±49.7) points, while the chronic disease prevention and control field received the lowest score of (368.2±29.6) points. The Jiangsu-Zhejiang-Anhui region has largely achieved the strategic priority of health, gradually improved public health legal regulations, and established a basic organizational framework with a solid foundation for information and data infrastructure. However, challenges still need to be addressed, such as unstable government funding for public health, unclear departmental responsibilities, and barriers to information interoperability. ConclusionThe public health governance capacity of the 40 cities in Jiangsu, Zhejiang, and Anhui Province has been at a moderate level, but disparities have still existed across regions and fields. In the future, while continuing to deepen existing advantages, it is essential to accurately identify the causes of problems, establish a long-term and stable investment mechanism, enhance information connectivity mechanisms, further clarify departmental responsibilities, and promote the achievement of the "Healthy Yangtze River Delta" goal.

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.

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.

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.