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.

Objective To examine the impact of climate, land use and their interaction effect on the population density of Oncomelania hupensis. Methods O. hupensis snail surveillance data in Yunnan Province from 2013 to 2022 were obtained from the National Information System for Parasitic Diseases Prevention and Control of Chinese Information System for Disease Control and Prevention, and the density of living snails was calculated in each county-level administrative district. Climate data were obtained from the China Meteorological Forcing Dataset version 2.0, and land use data were captured from the China Land Cover Dataset. The raster climate data and land use data were clipped to the administrative border of Yunnan Province in the R package version 4.5, and the climate data were resampled to a spatial resolution of 30 m × 30 m consistent with the land use data using bilinear interpolation. Annual meteorological indicators were extracted from each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022, including average temperature, maximum temperature, minimum temperature, temperature difference, average precipitation rate, maximum precipitation rate, minimum precipitation rate, downward shortwave radiation flux, downward longwave radiation flux, air pressure, wind speed, specific humidity and relative humidity. Annual land use structure and landscape pattern index in each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022 were extracted and calculated, including the proportion of areas occupied by each type of land use, patch density, largest patch index, landscape separation index, landscape fragmentation index, landscape segmentation index, contagion index, patch cohesion index, edge density, and shape index. Land use entropy index was introduced to represent the degree of habitat fragmentation and ecological niche breadth. The density of living snails and environmental factors were subjected to rank correlation analysis, and variables with no statistically significant correlation (P>0.1) were excluded. Then, a correlation coefficient matrix was constructed, and |r_s| of 0.85 and greater was defined as the threshold of high collinearity. For highly correlated variables in each group, indicators with a more biological explanatory power or a better statistical efficiency were retained. A generalized additive model (GAM) was constructed with O. hupensis snail density as a response variable and screened environmental variables as independent variables, and the model performance was evaluated using the variance explained. Results The area occupied by O. hupensis snail habitats was 1 056 to 1 680 hm² in Yunnan Province from 2013 to 2022, appearing an overall tendency towards a decline; however, the density of living snails appeared a tendency towards a slight rise. Based on the correlation coefficient matrix, 14 core explanatory variables were retained, including farmland, water bodies, bare land, impervious surface, landscape separation index, landscape fragmentation index, edge density, shape index, land use entropy index, downward longwave radiation flux, downward shortwave radiation flux, maximum temperature, minimum precipitation rate and specific humidity. Two types of GAM were constructed, including the main-effects model without interaction terms and the interaction model with interaction terms. The corrected Akaike information criterion (AICc) of the optimal main-effects model was −285.223, and the core variables included bare land, impervious surface, landscape separation index, edge density, shape index, land use entropy index and downward shortwave radiation flux. Following introduction of second-order interaction terms, the AICc value of the optimal interaction model was −345.526, and the significant interaction combinations included: farmland × impervious surface, farmland × landscape separation index, farmland × shape index, farmland × land use entropy index, impervious surface × landscape separation index, impervious surface × edge density, and shape index × downward shortwave radiation flux. The proportion of the main-effects model explaining the variation of O. hupensis snail density was 39.8%, and the explanatory power of GAM increased to 79.0% following introduction of interaction terms. Conclusion Landscape separation index, edge density, shape index and other landscape pattern indicators pose significant impacts on the intensity and direction of changes in O. hupensis snail density in Yunnan Province.

This study identified 8 members including NjBIN2 of the GSK3 family in Nardostachys jatamansi by bioinformatics analysis. Moreover, the phylogenetic tree revealed that the GKS3 family members of N. jatamansi had a close relationship with those of Arabidopsis. RT-qPCR results showed that NjBIN2 presented a tissue-specific expression pattern with the highest expression in roots, suggesting that NjBIN2 played a role in root growth and development. In addition, the application of epibrassinolide or the brassinosteroid(BR) synthesis inhibitor(brassinazole) altered the expression pattern of NjBIN2 and influenced the photomorphogenesis(cotyledon opening) and root development of N. jatamansi, which provided direct evidence about the functions of NjBIN2. In conclusion, this study highlights the roles of BIN2 in regulating the growth and development of N. jatamansi by analyzing the expression pattern and biological function of NjBIN2. It not only enriches the understanding about the regulatory mechanism of the growth and development of N. jatamansi but also provides a theoretical basis and potential gene targets for molecular breeding of N. jatamansi with improved quality in the future.
Brassinosteroids/metabolism* ; Steroids, Heterocyclic/metabolism* ; Gene Expression Regulation, Plant/drug effects* ; Plant Proteins/metabolism* ; Phylogeny ; Nardostachys/metabolism* ; Plant Growth Regulators/pharmacology* ; Plant Roots/drug effects*

The chemical constituents were systematically separated from the roots of Berberis polyantha by various chromatographic methods, including silica gel column chromatography, HP20 column chromatography, polyamide column chromatography, reversed-phase C_(18) column chromatography, and preparative high-performance liquid chromatography. The structures of the compounds were identified by physicochemical properties and spectroscopic techniques(1D NMR, 2D NMR, UV, MS, and CD). Four phenylpropanoids were isolated from the methanol extract of the roots of B. polyantha, and they were identified as(2R)-1-(4-hydroxy-3,5-dimethoxyphenyl)-1-propanone-O-β-D-glucopyranoside(1), methyl 4-hydroxy-3,5-dimethoxybenzoate(2),(+)-syringaresinol(3), and syringaresinol-4-O-β-D-glucopyranoside(4). Compound 1 was a new compound, and other compounds were isolated from this plant for the first time. The anti-inflammatory activity of these compounds was evaluated based on the release of nitric oxide(NO) in the culture of lipopolysaccharide(LPS)-induced RAW264.7 macrophages. At a concentration of 10 μmol·L~(-1), all the four compounds inhibited the LPS-induced release of NO in RAW264.7 cells, demonstrating potential anti-inflammatory properties.
Plant Roots/chemistry* ; Animals ; Mice ; Berberis/chemistry* ; RAW 264.7 Cells ; Macrophages/immunology* ; Drugs, Chinese Herbal/isolation & purification* ; Nitric Oxide/metabolism* ; Molecular Structure ; Anti-Inflammatory Agents/isolation & purification*

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE: To reveal the molecular basis of knee osteoarthritis (KOA) with Yang deficiency and blood stasis syndrome by analyzing the gene expression profiles in synovial fluid and blood of KOA patients with this syndrome. METHODS: A total of 80 KOA patients were recruited from October 2022 to June 2024, including 40 cases in the non-Yang deficiency and blood stasis group (27 males and 13 females), with an average age of (61.75±3.45) years old;and 40 cases in the Yang deficiency and blood stasis group (22 males and 18 females), with an average age of (62.00±2.76) years old. The levels of body mass index (BMI), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fibrinogen, total cholesterol, and D-dimer were recorded and summarized. Blood and synovial fluid samples from patients were collected for gene expression profile microarray sequencing, and then PCR and immunohistochemistry were used for clinical verification on the patients' synovial fluid and cartilage samples. RESULTS: Logistic regression analysis showed that compared with KOA patients with non-Yang deficiency and blood stasis syndrome, those with Yang deficiency and blood stasis syndrome had increased BMI, LDL, fibrinogen, total cholesterol, and D-dimer, and decreased HDL, with a clear correlation between the two groups. There were 562 differential genes in the blood, among which 322 were up-regulated and 240 were down-regulated;755 differential genes were found in the synovial fluid, with 350 up-regulated and 405 down-regulated. KEGG signaling pathway analysis of synovial fluid revealed changes in lipid metabolism-related pathways, including cholesterol metabolism, fatty acid metabolism, and PPARG signaling pathway. Analysis of the involved differential genes identified 6 genes in synovial fluid that were closely related to lipid metabolism, namely LRP1, LPL, ACOT6, TM6SF2, DGKK, and PPARG. Subsequently, PCR and immunohistochemical verification were performed using synovial fluid and cartilage samples, and the results were consistent with those of microarray sequencing. CONCLUSION This study explores the clinical and genomic correlation between traditional Chinese medicine syndromes and knee osteoarthritis from the perspective of lipid metabolism, and proves that abnormal lipid metabolism is closely related to KOA with Yang deficiency and blood stasis syndrome from both clinical and basic aspects.
Humans ; Male ; Female ; Middle Aged ; Synovial Fluid/metabolism* ; Osteoarthritis, Knee/metabolism* ; Yang Deficiency/complications* ; Aged

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

Qualitative and quantitative analysis methods for chemical components of different processed products of Corni Fructus were established to systematically characterize and identify these components, and the content of the main differential components was determined. The chemical components of different processed products of Corni Fructus were collected using ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS). Through analysis of self-built databases, literature, and reference standards, a total of 93 components were obtained, including 19 iridoids, 15 flavonoids, 16 organic acids, eight triterpenoids, eight tannins, four amino acids, two polysaccharides, five olefins, and 16 other compounds. Additionally, by using multivariate statistical methods, the differential components between different processed products of Corni Fructus were screened under the conditions of VIP>1.0 and FC<0.5 or FC>2.0 and P<0.05. The PCA and OPLS-DA results showed differences in the chemical components between different processed products of Corni Fructus. A total of 21 differential components were screened, including tartaric acid, morroniside, and rutin. On this basis, ultra-high performance liquid chromatography-triple quadrupole tandem mass spectrometry(UPLC-QqQ-MS/MS) was used to determine the content of 10 main common differential components, including gallic acid, morroniside, ursolic acid, loganin, swertiamarin, rutin, 5-hydroxymethylfurfural, cornuside Ⅰ, quercetin, and oleanolic acid. The above 10 components showed a good linear relationship within the determined concentration range, with the precision, stability, repeatability, and sample recovery rate all meeting the requirements. Compared with that in Corni Fructus, the content of iridoid glycosides in wine-prepared Corni Fructus and wine-and honey-prepared Corni Fructus decreased, while the content of gallic acid, rutin, quercetin, 5-hydroxymethylfurfural, ursolic acid, and oleanolic acid increased. Compared with wine-prepared Corni Fructus, wine-and honey-prepared Corni Fructus showed varying degrees of increase in all other components, except for a slight decrease in gallic acid content. In summary, this study clarified the influence of different processing methods on the chemical components of Corni Fructus, providing a theoretical basis for the scientific connotation, overall quality evaluation, and clinically rational application of Corni Fructus processing in the future.
Tandem Mass Spectrometry/methods* ; Chromatography, High Pressure Liquid/methods* ; Cornus/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Fruit/chemistry*