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.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

Artificial intelligence (AI) has made preliminary advances in the management of postoperative follow up for lung cancer; however, a systematic review of its application value across the entire follow-up continuum remains lacking. Taking the four core dimensions of postoperative follow-up management as its framework, including surveillance candidate selection, follow-up interval optimization, follow-up protocol design, and follow-up modality selection, this review examines the progress of AI in patient risk stratification, surveillance frequency optimization, content design, and supportive platform development. The review further delineates the current challenges and future directions for AI in this domain, and ultimately seeks to promote the standardized and scaled application of AI in postoperative follow-up management for lung cancer, with the goal of establishing a "care beyond hospitalization" life-cycle management framework that improves the long-term quality of survival for patients undergoing lung cancer surgery.

ObjectiveTo evaluate the larvicidal effect of ultrasonic waves against Aedes albopictus larvae at various exposure parameters (distance and time), and to preliminarily investigate the effects of different containers (water volume and depth) and ultrasonic devices on the larvicidal efficacy, so as to provide experimental evidence for the application of ultrasound-based mosquito control technology. MethodsUnder controlled laboratory conditions, two commercially available ultrasonic devices [40 kHz/300 W and (50±5) kHz/70 W] were used to systematically examine the effects of exposure distance (20, 40, 60, 80, and 100 cm), exposure duration (1, 3, 6, 15 and 60 min), larval instar(Ⅰ‒Ⅱ and Ⅲ‒Ⅳ）, and container type on the immediate mortality of Aedes albopictus larvae collected from urban areas of Shanghai. Statistical analysis of the mortality rates of larvae among different experimental groups was performed using the chi-square test. ResultsUltrasonic exposure exerted a detectable larvicidal effect against Aedes albopictus larvae of all instars. For early-instar larvae, exposure time significantly affected mortality at all tested distances (20, 40, 60, 80 and 100 cm), with χ² values ranging from 25.57 to 32.74 ( all P<0.001). No significant distance effect was observed in the first and second instar larvae after short-term exposure (1 and 3 min; P>0.05). However, a distance-dependent effect emerged from 6 min onward (P<0.05). During short-term exposure (1 min and 3 min) at close range (20 cm and 40 cm), the immediate mortality of the third and fourth instar larvae was higher than that of the first and second instar larvae. Conversely, during prolonged exposure (15 min and 60 min), the first and second instar larvae exhibited significantly higher mortality than that of the third and fourth instar larvae. These results indicated that larval mortality was jointly influenced by exposure duration and distance. Preliminary experiments indicated that larvicidal efficacy in shallow water was lower than that in deep water. No significant difference in larvicidal effect was observed between the two ultrasonic devices with different frequencies and power outputs. ConclusionUnder laboratory conditions, ultrasound can effectively kill Aedes albopictus larvae, and the larvicidal effect is jointly influenced by larval instar, exposure distance, and aquatic environment. Compact ultrasonic devices show potential for field application in biological mosquito control.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.

A 70-year-old female patient presented with fatigue and edema for 3 months and was found to have elevated serum creatinine for 3 weeks. During the course of the disease, she had fever and cough. Examinations revealed multiple ground-glass opacities in both lungs and positivity for myeloperoxidase-anti-neutrophil cytoplasmic antibodies (ANCA), leading to a diagnosis of ANCA-associated vasculitis. The patient′s condition initially improved after pulse glucocorticoid therapy combined with cyclophosphamide. During treatment, however, the patient developed hematochezia, and colonoscopy revealed multiple colonic ulcers. Immunohistochemistry of colonic mucosal biopsy confirmed cytomegalovirus (CMV) positivity, establishing a diagnosis of CMV colitis. The patient was found to have concurrent Clostridioidesdifficile and pulmonary infections. During the disease course, the patient also developed deep vein thrombosis and roxadustat-associated central hypothyroidism. Given the presence of multiple comorbidities, rituximab was subsequently used for vasculitis treatment, resulting in sustained remission. This case highlights the importance of highly individualized treatment strategies for older patients with vasculitis, requiring adjustment of immunosuppressive therapy intensity based on disease progression.

As a cheap and effective antibiotic,sulfonamides are often used in animal husbandry.However,their residues in animal-derived foodstuffs will threaten human health.Consequently,a high-performance liquid chromatography(HPLC)method integrated with supramolecular solvent microextraction was successfully established for simultaneous quantification of sulfonamide residues sulfachlorpyridazine,sulfamethoxazole,sulfamethoxypyridazine and sulfadimethoxine in milk matrices.This approach exhibited prominent characteristics of operational simplicity,environmental sustainability,and high extraction efficiency.The supramolecular solvents prepared by tributyl octylphosphine tetrafluoroborate and tetrahydrofuran were employed as extraction solvents.The analytes underwent isolation and concentration via dispersive liquid-liquid microextraction(DLLME)prior to quantitative determination using high-performance liquid chromatography(HPLC).The composition and microscopic morphology of the supramolecular solvent were characterized through a series of analytical techniques,including phase diagram,Fourier transform infrared spectroscopy,scanning electron microscopy,and inverted fluorescence microscopy and so on.The density and pH value of supramolecular solvents were determined.The extraction conditions were optimized through the one-factor experiments.The experimental results demonstrated that under the optimal extraction conditions,the four kinds of sulfonamide antibiotics exhibited excellent linearity within respective detection range(R2 ≥ 0.9998)and the limits of detection(LOD)were 0.67-1.45 μg/L.Compared with literature methods,this approach offered some advantages such as simplicity of operation and less reagent consumption,and could be used for analysis and detection of sulfonamide antibiotic residues in milk samples.The present method provided technical support for food safety regulation and paved a new way for the application of supramolecular solvents in the field of extraction and separation.

Bongkrekic acid(BA)is a toxin with stable properties and no distinctive smell.It exists in common foods such as fermented edible grain products,potato products,spoiled tremella fuciformis and auricularia polytricha,as well as auricularia polytricha that has been soaked too long.It can easily cause food poisoning.At present,there is still a lack of complete method to detect BA,and no spe-cific antidote of BA has been found.Therefore,BA poisoning is easy to be misdiagnosed or missed diagnosed,and its mortality rate remains high.In recent years,studies have revealed the toxic mecha-nism of BA and found that BA can inactivate some enzymes containing thiol groups(-SH)and in-hibit the synthesis and transport of adenosine triphosphate(ATP),causing damage to liver,kidney,brain and other parenchymal organs.This article reviews the autopsy cases and literature of deaths caused by BA poisoning at home and abroad,systematically summarizes the epidemiology,clinical manifestations,pathological changes,toxicological mechanisms,detection methods,forensic diagnostic key points and challenges of BA in forensic medicine,with the aim of providing a reference for foren-sic identification of related cases.

Objective:To construct a training program for nurses in shared-decision making of breast cancer surgery based on core competence, and to provide reference for clinical training.Methods:From February to May 2023, through literature review, qualitative interview, group discussion, Delphi and analytic hierarchy process, the training program were determined.Results:Among 18 experts, there were 3 males and 15 females, aged (44.67±9.24) years old. The final training program included 6 first-level indicators(admission criteria, training content (theory + practice), training format, training hours, training instructors, training evaluation), 17 second-level indicators, and 57 third-level indicators. The effective response rates of 2 rounds of expert consultations were both 18/18, and the expert authority coefficients were both 0.933. The Kendall coordination coefficients of the first and second round were 0.160, 0.213 (both P<0.05). Conclusions:The training program of decision aid nurses in breast cancer surgery is scientific, practical and clinically valuable, and can provide reference for the development of standardized training for nurses.