Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Considering the classification problems of electroencephalogram(EEG)signals and their nonlinear,non-stationary characteristics,multivariate variational mode decomposition(MVMD)is introduced to process steady-state visual evoked potential(SSVEP)signals.Herein a novel classification algorithm for SSVEP called MVMDMS-CCA which combines a new approach for mode selection with canonical correlation analysis(CCA)algorithm is presented.MVMDMS-CCA method uses the signal-to-noise ratio to determine the key parameter K in MVMD,and then performs MVMD decomposition.Mode selection is carried out by setting a threshold using the maximal information coefficient(MIC)method,and the modes not meeting the threshold are adaptively denoised using wavelet denoising.A new combination of modes is constructed and input into the CCA algorithm to achieve SSVEP signal classification.The proposed method is validated on a self-collected EEG dataset,and it achieves an average classification accuracy of 93.23%under a 3 s window,showing 5.78%higher than standard CCA and 1.51%higher than the improved filter bank CCA.MVMDMS-CCA can effectively extract SSVEP components from EEG signals while suppressing noises,providing a new perspective for the research of SSVEP decoding algorithms.

Objective To investigate the effect of acute cold stimulation on the staufen1-mediated mRNA decay(SMD)pathway in brown adipose tissue of mice and the downstream regulated target genes.Methods Mice were subjected to acute cold stimulation(CS)at 4℃.After 48 hours,the brown adipose tissue of mice was extracted to detect the expression of genes including as Stau1,Ucp1 and Pparγ,and compared with mice in room temperature control group(RT).Transcriptomic sequencing was performed on the brown adipose tissue of mice in the CS group and in the RT group,and the functional enrichment analysis of differential genes was performed on the sequencing re-sults.The Stau1 gene was knocked out in the brown adipocytes of mice using CRISPR-Cas9 technology,and the ex-pression of thermogenic genes after knockout was analyzed.Results Acute cold stimulation induced the expression of Stau1 gene and promoted the degradation of downstream target genes Serpineb1,Klf2 and c-Jun in the SMD pathway(P＜0.05).After Stau1 knockout,the glycolipid metabolism pathway of brown adipocytes in mice was significantly up-regulated,and the expression of thermogenesis-related genes Ucp1,Prdm16,ATP5o,Dio2 and Pgc1α was up-regulated(P＜0.05).Conclusions Acute cold stimulation promotes the SMD pathway in brown adipose tissue of mice,and SMD pathway mainly regulates the metabolic and thermogenic pathways in brown adipocytes.

Considering the classification problems of electroencephalogram(EEG)signals and their nonlinear,non-stationary characteristics,multivariate variational mode decomposition(MVMD)is introduced to process steady-state visual evoked potential(SSVEP)signals.Herein a novel classification algorithm for SSVEP called MVMDMS-CCA which combines a new approach for mode selection with canonical correlation analysis(CCA)algorithm is presented.MVMDMS-CCA method uses the signal-to-noise ratio to determine the key parameter K in MVMD,and then performs MVMD decomposition.Mode selection is carried out by setting a threshold using the maximal information coefficient(MIC)method,and the modes not meeting the threshold are adaptively denoised using wavelet denoising.A new combination of modes is constructed and input into the CCA algorithm to achieve SSVEP signal classification.The proposed method is validated on a self-collected EEG dataset,and it achieves an average classification accuracy of 93.23%under a 3 s window,showing 5.78%higher than standard CCA and 1.51%higher than the improved filter bank CCA.MVMDMS-CCA can effectively extract SSVEP components from EEG signals while suppressing noises,providing a new perspective for the research of SSVEP decoding algorithms.