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 establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

ObjectiveCell adhesion is a critical process that regulates cellular physiological functions. Quantitative characterization of adhesion dynamics is essential for elucidating the intrinsic mechanical mechanisms underlying cellular activities. Although atomic force microscopy-based single-cell force spectroscopy is widely used for single-cell adhesion measurements, it requires complex chemical modifications for preparation of live-cell probes, leading to limitations such as cumbersome operation, low throughput, and potential impacts on cell viability. Fluidic force microscopy, which combines atomic force microscopy with microfluidic probes, is a technique allowing the operation of force-controlled nanopipettes in aqueous environments. By applying negative or positive pressure via a pressure controller, a single living cell can be captured onto or released from the cantilever under physiological conditions. This procedure offers a simple workflow and high assay throughput for single-cell adhesion measurements without the need for chemical functionalization. In this study, fluidic force microscopy-based single-cell force spectroscopy was adopted to achieve long-term quantitative characterization of single-cell adhesion dynamics in a simpler and more efficient manner, comparing the dynamic differences in adhesion establishment between two cell lines with different differentiation levels. MethodsHEK 293T and hTERT RPE-1 cells were non-invasively captured on the cantilever of a fluidic force microscope via its integrated microfluidic system during 40 h of adhesion culture. Cell-substrate detachment assays were performed, and force-distance curves were recorded to extract key mechanical adhesion parameters, including adhesion force, adhesion energy, and maximum detachment distance. These measurements were combined with real-time monitoring of cell spreading area to systematically characterize the dynamic evolution of single-cell adhesion. ResultshTERT RPE-1 cells rapidly entered a stable adhesion phase within 1 h after seeding, with both area-normalized adhesion force and area-normalized adhesion energy reaching peak values. In contrast, HEK 293T cells required 4 h to achieve stable adhesion. Subsequently, the adhesion force, adhesion energy and maximum detachment distance of hTERT RPE-1 and HEK 293T cells stabilized at approximately 240 nN vs. 30 nN, 2.2 pJ vs. 0.12 pJ and 6 μm vs. 4 μm, respectively. hTERT RPE-1 cells reached the peak of area-normalized adhesion parameters earlier than HEK 293T cells, with their peak area-normalized adhesion force and area-normalized adhesion energy being substantially elevated relative to HEK 293T cells. HEK 293T cells presented stronger linear correlations among adhesion energy, maximum detachment distance and adhesion force compared with hTERT RPE-1 cells. For both cell lines, cell spreading area exhibited a weak correlation with adhesion force. Whereas the area-normalized adhesion parameters of HEK 293T cells remained relatively constant throughout the adhesion process, hTERT RPE-1 cells exhibited elevated values in the early phase, followed by a gradual decline. These results indicated distinct dynamic adhesion patterns between the two cell types, with hTERT RPE-1 cells exhibiting stronger adhesion strength and higher adhesion efficiency. ConclusionIn this study, fluidic force microscopy-based single-cell force spectroscopy was successfully applied to perform long-term in situ quantitative measurement of the adhesion dynamics in single adherent cells. The approach revealed divergent adhesion patterns between HEK 293T and hTERT RPE-1 cells, suggesting a close association between cell differentiation and adhesion behaviors. These findings provide quantitative mechanical evidence for further understanding the underlying mechanisms of cell adhesion.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Objective We reviewed and analyzed the epidemiological data of 83 imported dengue fever cases reported in Fengtai District, Beijing, from 2007 to 2025, to delineate their epidemiological characteristics and guide the dengue fever prevention and control strategy in the future. Methods Data on dengue fever cases in Fengtai District from 2007 to 2025 were collected to identify the epidemic factors and characteristics using descriptive epidemiological method. Results All 83 dengue fever cases reported in this region over the past two decades have been imported from abroad or other domestic areas, with Southeast Asian countries being identified as the primary source of imported infections, accounting for 42 cases (50.60%), followed by 15 cases (18.07%) from Chinese provinces other than Beijing. The remaining import sources included 13 cases from South Asia,8 from African countries, and each from Oceania, South America, and Europe. Disease onset was found concentrated between May and November. The sex ratio (male-to-female) is 1.59:1. The age of onset ranged from 14 to 61 years old, with a median age of 36. The largest proportion of cases was observed among cadres and office workers (31 cases, 37.35%), followed by businessmen (13 cases, 15.67%), and householders or the unemployed (10 cases,12.05%). Travel visit was identified as the primary reason for their exposure history. Conclusions All dengue fever cases reported in Fengtai from 2007 to 2025 were imported, with an increasing trend of imported cases in Beijing originating from other Chinese provinces. It is essential to strengthen health education for foreign travelers and returning residents, enhance clinical diagnosis and treatment training for medical personnel, eliminate mosquito breeding sites, and reduce mosquito density through active patriotic health campaigns.

ObjectiveTo study on the different therapeutic effects and potential mechanisms of Rhei Radix et Rhizoma（RH） before and after steaming with wine on constipation model mice. MethodsFifty-four male ICR mice were randomly divided into control group， model group， lactulose group（1.5 mg·kg^-1）， high， medium and low dose groups of RH and RH steaming with wine（PRH）（8， 4， 1 g·kg^-1）. Except for the control group， the constipation model was replicated by gavage of loperamide hydrochloride（6 mg·kg^-1） in the other groups. After 2 weeks of modeling， each administration group was gavaged with the corresponding dose of drug solution， and the control and model groups were given an equal volume of normal saline， 1 time/d for 2 consecutive weeks. After administration， the feces were collected for 16S rRNA sequencing， the levels of gastrin（GAS）， motilin（MTL）， interleukin-6（IL-6）， γ-interferon（IFN-γ） in the colonic tissue were detected by enzyme-linked immunosorbent assay（ELISA）， the histopathological changes of colon were observed by hematoxylin-eosin（HE） staining， flow cytometry was used to detect the proportion changes of CD4⁺， CD8⁺ and regulatory T cell（Treg） in peripheral blood. ResultsCompared with the control group， the model group showed significantly decrease in fecal number in 24 h， fecal quality and fecal water rate（P<0.01）， the colon was seen to have necrotic shedding of mucosal epithelium， localized intestinal glands in the lamina propria were degenerated， necrotic and atrophied， a few lymphocytes were seen to infiltrate in the necrotic area in a scattered manner， the contents of GAS and MTL， the proportions of CD4⁺， CD8⁺ and Treg were significantly reduced（P<0.01）， the contents of IL-6 and IFN-γ were significantly elevated（P<0.05， P<0.01）. Compared with the model group， the fecal number in 24 h， fecal quality and fecal water rate of high-dose groups of RH and PRH were significantly increased（P<0.05， P<0.01）， the pathological damage of the colon was alleviated to varying degrees， the contents of GAS， MTL， IL-6 and IFN-γ were significantly regressed（P<0.05， P<0.01）， and the proportions of CD4⁺ and CD8⁺ were significantly increased（P<0.01）， although the proportion of Treg showed an upward trend， there was no significant difference. In addition， the results of intestinal flora showed that the number of amplicon sequence variant（ASV） and Alpha diversity were decreased in the model group compared with the control group， and there was a significant difference in Beta diversity， with a decrease in the relative abundance of Lactobacillus and an increase in the relative abundances of Bacillus and Helicobacter. Compared with the model group， the ASV number and Alpha diversity were increased in the high-dose groups of RH and PRH， and there was a trend of regression of Beta diversity to the control group， the relative abundance of Lactobacillus increased， and the relative abundances of Bacillus and Helicobacter decreased. ConclusionRH and PRH can improve dysbacteriosis， promote immune system activation， inhibit the release of inflammatory factors for enhancing the gastrointestinal function， which may be one of the potential mechanisms of their therapeutic effect on constipation.

Postoperative infection of internal fixation of closed fractures the lower limbs in adults represents a devastating complication, characterized by diagnostic challenges, prolonged treatment duration and high disability rates. Current management of these infections faces multiple challenges, such as difficulties in early accurate diagnosis, and various controversies about the treatment plan, leading to poor overall diagnosis and treatment results. To address these issues, based on evidence-based medicine and principles with emphasis on scientific rigor, clinical applicability and innovation, the Trauma Branch of the Chinese Medical Association, Orthopedic Branch of the Chinese Medical Doctor Association, Orthopedics Branch of the Chinese Medical Association, and Trauma Orthopedics and Polytrauma Group of the Resuscitation and Emergency Committee of the Chinese Medical Doctor Association have collaboratively organized a panel of relevant experts to develop the Guideline for diagnosis and treatment of infection after internal fixation of closed lower limb fractures in adults ( version 2025). The guideline proposed 10 recommendations, aiming to provide a foundation for standardized diagnosis and treatment of postoperative infection in adults with closed lower limb fractures.

Aim To establish the pyroptosis model of rat chondrocytes induced by tumor necrosis factor α(TNF-α)in order to study the effect of lysine acetyl-transferase 7(KAT7)on pyroptosis of chondrocytes.Methods Chondrocytes of rat knee joint were isolated by type Ⅱ collagenase digestion,and were identified by toluidine blue staining and Col Ⅱ immunofluorescence.CCK-8 was used to evaluate cell viability.Western blot was used to detect the expression of pyroptosis-related proteins NLRP3,GSDMD,caspase-8 and KAT7 in cells intervened with TNF-α,adenovirus overexpression of KAT7(KAT7-oe)and KAT7 inhibitor WM-3835.The microstructure of the cells was observed by scanning e-lectron microscopy.Pyroptosis was detected by TUNEL staining,and the expression of pyroptosis-related pro-tein and KAT7 was detected by immunofluorescence.Results Compared with the empty virus group,KAT7-oe inhibited cell viability,promoted the expression of pyroptosis-related proteins,and TNF-α enhanced this effect.At the same time,the expression of KAT7 and pyroptosis-related proteins in the TNF-α stimulation group increased,and WM-3835 reduced the related proteins expression.Electron microscopy showed that KAT7-oe caused cell swelling,deformation,membrane perforation and rupture,while WM-3835 could restore cell morphology.TUNEL staining and immunofluores-cence results also confirmed that KAT7-oe induced chondrocyte pyroptosis,and WM-3835 could down-reg-ulate the fluorescence of pyroptosis-related proteins.Conclusions The expression of KAT7 increases in rat chondrocyte pyroptosis model,and the intervention of KAT7 expression affects signal molecules related to py-roptosis pathway,suggesting that KAT7 may be related to chondrocyte pyroptosis.