Lung cancer， with persistently high incidence and mortality rates， remains a significant global health challenge. By taking the study on the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School as an example， the diagnosis and treatment system for lung cancer with phlegm-dampness and blood stasis syndrome， which was formed in response to the humid and foggy environment of the Sichuan Basin， possesses unique value. However， traditional inheritance modes face challenges such as fragmentation， lack of standardization， and insufficient quantification， which hinder the promotion and application of this experience. This research focused on how to leverage multimodal data and artificial intelligence-generated content （AIGC） to achieve precise analysis， intelligent inheritance， and clinical innovation of the experience in diagnosis and treatment of lung cancer with phlegm-dampness and blood stasis syndrome by distinguished traditional Chinese medicine physicians of the Sichuan School. By integrating multimodal data （encompassing four diagnostic methods of traditional Chinese medicine， modern medical imaging， clinical laboratory tests， molecular biology， and regional environmental information）， a precise diagnosis and treatment system integrating macro and micro perspectives for the "disease， syndrome， and pathogenesis" was constructed. The research yielded the following results： （1） In precise syndrome differentiation， the objective quantification of the phlegm-dampness and blood stasis syndrome was achieved. By constructing a "four diagnostic methods， imaging， and molecule" correlation model， the study revealed intrinsic links between tongue and pulse parameters and the tumor microenvironment， as well as between regional climatic factors and syndrome characteristics， enabling real-time dynamic monitoring of efficacy. （2） In elucidating patterns， the study systematically explored the syndrome differentiation thoughts of Sichuan School physicians， such as the timing of purgation and tonification. A "pathogenesis， syndrome complex， and prescriptions and herb" network model was constructed， which accurately elucidated the synergistic action mechanisms of core herb pairs and quantified the dynamic compatibility patterns of reinforcing healthy Qi and eliminating pathogenic factors. （3） In intelligent empowerment， an auxiliary system integrating intelligent syndrome differentiation， treatment plan generation， and efficacy evaluation was built. This system can fuse regional characteristics with individual data， dynamically generate and optimize personalized prescriptions aligned with the experience of Sichuan School， and predict efficacy trends and potential adverse reactions. The integration of multimodal data and AIGC can effectively facilitate the structured inheritance and clinical translation of distinguished physicians' experience. The established intelligent diagnosis and treatment model integrating traditional Chinese medicine and Western medicine demonstrates clear potential in prolonging patients' progression-free survival， alleviating symptoms， and reducing adverse reactions to treatment. This study provides a referential methodological framework for the traditional Chinese medicine experience in diagnosis and treatment of lung cancer， especially the empirical inheritance and modernized development of regional academic schools. It contributes to advancing clinical diagnosis and treatment toward greater precision and personalization.

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 This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

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.

Objective To investigate the correlation between gut microbiota characteristics and immunotherapy efficacy in patients with advanced microsatellite stable (MSS) advanced gastrointestinal tumors. Methods Thirty patients with advanced MSS gastrointestinal tumors, including fourteen cases of gastric cancer and sixteen cases of colorectal cancer, who were admitted from November 1, 2022, to July 31, 2025 were enrolled. All patients underwent gut microbiota testing and received corresponding interventions on the basis of the results, followed by treatment with antiangiogenic agents combined with immunotherapy. Some patients additionally received probiotics or fecal microbiota transplantation. The primary endpoints were disease control rate (DCR) and objective response rate (ORR). The secondary endpoints were progression-free survival (PFS) and overall survival (OS). Univariate and multivariate Cox regression analyses were used to assess potential prognostic factors. Results Among the thirty patients, no complete response was observed. Two cases of partial response (PR), twelve cases of stable disease, and sixteen cases of progressive disease were recorded. The ORR was 6.7%, and the DCR was 46.7%. The median PFS was 6.4 months, and the median OS was 8.3 months. Both PR patients were classified as having a medium risk of gut microbiota dysbiosis and exhibited a Bacteroides-dominated enterotype. Multivariate analysis indicated that tumor type, liver metastasis, and bone metastasis were potential prognostic factors for PFS and OS. Conclusion Patients with a medium risk of gut dysbiosis may exhibit a relatively favorable response to immunotherapy, suggesting that gut microbiota status could serve as a potential biomarker for predicting treatment efficacy in advanced MSS gastrointestinal tumors.

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.

The core pathogenesis of attention deficit hyperactivity disorder （ADHD） lies in deficiency， stasis and stagnation. Deficiency arises from kidney essence depletion and spleen dysfunction in transportation and transformation， leading to inadequate nourishment of the marrow sea. Stasis caused by qi deficiency leads to obstruction in channels and collaterals， resulting in obstructed marrow transport. Stagnation is associated with the excess of the five minds transforming into fire， which scorches the brain orifices and leads to loss of control over marrow utilisation. Based on this， a "supplementation-unblocking-regulation" therapeutic approach is proposed. For deficiency， the focus is on supplementing kidney and fortifying spleen， and replenishing the marrow sea. For stasis， the priority is to unblock and open the orifices， and clear the marrow channels. For stagnation， the core is to clear fire and contain the mind， regulate and restore vital activity. In clinical practice， it is necessary to identify the primary and secondary pathogenic mechanisms and apply dynamic， combined treatment， integrating Chinese herbal medicine， acupuncture， and guiding exercises throughout the process， aiming to provide a reference for the diagnosis and treatment of ADHD with traditional Chinese medical.