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.

ObjectiveTo explore the mechanism of Jiebiao Qingli decoction （JQD） in treating pneumonia caused by influenza A virus （IAV） infection. MethodsA total of 132 Balb/c mice were randomly assigned into normal control （NC）， model control （IAV）， oseltamivir （OSV， 37.5 mg·kg^-1）， and high-， medium-， low-dose JQD （H-， M-， and L-JQD： 6.05， 3.02， and 1.51 g·kg^-1， respectively） groups. The NC group was treated with normal saline nasal drops， and the other groups were intranasally inoculated with A/Brisbane/02/2018 （H1N1） ［pdm09-like virus （H1N1）］ for the modeling of IAV infection. Two hours post-modeling， the NC and IAV groups were administrated with normal saline by gavage， while other groups received corresponding drugs for 7 d. The body mass， survival status， and deaths of mice were recorded daily during the administration of the drugs. On days 3 and 7， the lung index was measured for mice in each group. Pathological changes in the lung tissue were observed via hematoxylin-eosin staining. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was conducted to measure the viral load （IAV-M） and the mRNA levels of Toll-like receptor 7 （TLR7）， p38 mitogen-activated protein kinase （p38 MAPK）， and nuclear factor-kappa B （NF-κB） in the lung tissue. Western blot was employed to measure the protein levels of p38 MAPK and NF-κB. Enzyme-linked immunosorbent assay was used to quantify serum levels of interleukin-2 （IL-2）， interleukin-6 （IL-6）， and tumor necrosis factor-alpha （TNF-α）. ResultsCompared with the NC group， the IAV group showed reduced survival quality and survival days （P<0.01）， lung congestion， inflammatory cell infiltration， elevated lung index （P<0.01）， increased viral load （P<0.01）， upregulated TLR7， p38 MAPK， and NF-κB levels （P<0.05， P<0.01）， decreased IL-2 level （P<0.01）， and elevated IL-6 and TNF-α levels （P<0.01）. Compared with the IAV group， H-JQD prolonged survival days （P<0.05）. All JQD groups alleviated pathological changes in the lung tissue and reduced the lung index （P<0.01）. M-JQD and H-JQD decreased the viral load （P<0.01）. H-JQD downregulated the mRNA levels of TLR7， p38 MAPK， and NF-κB （P<0.05， P<0.01） and the protein levels of p38 MAPK and NF-κB （P<0.01）， increased the serum IL-2 level （P<0.01）， and lowered the IL-6 and TNF-α levels （P<0.05， P<0.01）. M-JQD downregulated the mRNA level of NF-κB （P<0.01） and the protein level of p38 MAPK （P<0.05）， elevated the IL-2 level （P<0.01）， and lowered the TNF-α level （P<0.01）. ConclusionM- and H-JQD can prevent and control IAV infection-induced pneumonia dose-dependently by inhibiting the TLR7/MAPK/NF-κB signaling pathway， increasing IL-2， and reducing excessive secretion of IL-6 and TNF-α.

ObjectiveTo explore the mechanism of Jiebiao Qingli decoction （JQD） in treating pneumonia caused by influenza A virus （IAV） infection. MethodsA total of 132 Balb/c mice were randomly assigned into normal control （NC）， model control （IAV）， oseltamivir （OSV， 37.5 mg·kg^-1）， and high-， medium-， low-dose JQD （H-， M-， and L-JQD： 6.05， 3.02， and 1.51 g·kg^-1， respectively） groups. The NC group was treated with normal saline nasal drops， and the other groups were intranasally inoculated with A/Brisbane/02/2018 （H1N1） ［pdm09-like virus （H1N1）］ for the modeling of IAV infection. Two hours post-modeling， the NC and IAV groups were administrated with normal saline by gavage， while other groups received corresponding drugs for 7 d. The body mass， survival status， and deaths of mice were recorded daily during the administration of the drugs. On days 3 and 7， the lung index was measured for mice in each group. Pathological changes in the lung tissue were observed via hematoxylin-eosin staining. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was conducted to measure the viral load （IAV-M） and the mRNA levels of Toll-like receptor 7 （TLR7）， p38 mitogen-activated protein kinase （p38 MAPK）， and nuclear factor-kappa B （NF-κB） in the lung tissue. Western blot was employed to measure the protein levels of p38 MAPK and NF-κB. Enzyme-linked immunosorbent assay was used to quantify serum levels of interleukin-2 （IL-2）， interleukin-6 （IL-6）， and tumor necrosis factor-alpha （TNF-α）. ResultsCompared with the NC group， the IAV group showed reduced survival quality and survival days （P<0.01）， lung congestion， inflammatory cell infiltration， elevated lung index （P<0.01）， increased viral load （P<0.01）， upregulated TLR7， p38 MAPK， and NF-κB levels （P<0.05， P<0.01）， decreased IL-2 level （P<0.01）， and elevated IL-6 and TNF-α levels （P<0.01）. Compared with the IAV group， H-JQD prolonged survival days （P<0.05）. All JQD groups alleviated pathological changes in the lung tissue and reduced the lung index （P<0.01）. M-JQD and H-JQD decreased the viral load （P<0.01）. H-JQD downregulated the mRNA levels of TLR7， p38 MAPK， and NF-κB （P<0.05， P<0.01） and the protein levels of p38 MAPK and NF-κB （P<0.01）， increased the serum IL-2 level （P<0.01）， and lowered the IL-6 and TNF-α levels （P<0.05， P<0.01）. M-JQD downregulated the mRNA level of NF-κB （P<0.01） and the protein level of p38 MAPK （P<0.05）， elevated the IL-2 level （P<0.01）， and lowered the TNF-α level （P<0.01）. ConclusionM- and H-JQD can prevent and control IAV infection-induced pneumonia dose-dependently by inhibiting the TLR7/MAPK/NF-κB signaling pathway， increasing IL-2， and reducing excessive secretion of IL-6 and TNF-α.

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.

This study selected 6 529 plant-based Chinese materia medica(PCMM) from Chinese Materia Medica as research subjects and applied a random permutation test to explore the overall correlation characteristics between nature and flavor, as well as the correlation characteristics after distinguishing different medicinal parts and harvest seasons. The results showed that the overall correlation characteristics between nature and flavor in PCMM were significantly associated in the following pairs: cold and bitter, cool and bitter, cool and astringent, cool and light, neutral and sweet, neutral and astringent, neutral and light, neutral and sour, hot and pungent, and warm and pungent. When analyzing the data by distinguishing medicinal parts and/or harvest seasons, new correlation patterns emerged, characterized by the disappearance of some significant correlations and the emergence of new ones. When analyzing by medicinal parts alone, significant correlations were found in the following cases: cold and light in leaves, cold and salty in barks, cool and sweet in fruits and seeds, neutral and pungent in whole herbs, neutral and salty in stems, and warm and salty in flowers. However, no significant correlations were found between cool and bitter in stems and other types of herbs, cool and astringent in fruits, seeds, flowers, and other types of herbs, cool and light in leaves, fruits, seeds, barks, flowers and other types of herbs, neutral and sweet in barks, neutral and astringent in whole herbs and stems, neutral and light in leaves, fruits, seeds, and flowers, neutral and sour in whole herbs, stems, barks, flowers, and other types of herbs, and hot and pungent in whole herbs, stems, flowers, and other types of herbs. When analyzing by harvest season alone, significant correlations were found in the following cases: cold and salty, and cool and sour in herbs harvested in winter, and neutral and salty in herbs harvested year-round. However, no significant correlation was found between cool and light in herbs harvested in winter. When considering both medicinal parts and harvest seasons, compared to the independent influence of medicinal parts, 14 new significant correlations emerged(e.g., the correlation between cool and bitter in stems harvested in spring), while 53 previously significant correlations disappeared(e.g., the correlation between cool and bitter in barks harvested in summer). Compared to the independent influence of harvest seasons, 11 new significant correlations appeared(e.g., the correlation between cold and light in barks harvested in autumn), while 50 previously significant correlations disappeared(e.g., the correlation between hot and pungent in leaves harvested in winter). This study is the first to reveal the influence of medicinal parts and harvest seasons on the correlation between nature and flavor in PCMM, which highlights that these two factors can interact and jointly affect nature-flavor correlations. Further research is needed to explore the underlying mechanisms. This study provides a deeper understanding of the inherent scientific connotations of herbal properties and offers a theoretical foundation for the cultivation and harvesting of PCMM.
Seasons ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal/chemistry* ; Taste

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.

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.