Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

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.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

Objective To analyze the technical essentials of single-microport assisted micro-uni-port thoracoscopic surgery, and to investigate its surgical efficacy and promotional value. Methods Clinical data of patients who consecutively underwent radical upper lobectomy in the Department of Thoracic Surgery of Xi'an International Medical Center Hospital from March 2023 to June 2024 were retrospectively analyzed. According to the surgical approach, patients were divided into two groups: the single-microport assisted group (underwent upper lobectomy via single-microport assisted micro-uni-port thoracoscopy) and the traditional uniportal group (underwent traditional uniportal thoracoscopic lobectomy). Clinical outcomes were compared between the two groups. Results A total of 62 patients were enrolled. There were 30 patients in the single-microport assisted group, with a mean age of (57.4±10.8) years, and 32 patients in the traditional uniportal thoracoscopic group, with a mean age of (57.6±8.7) years. The baseline data were comparable between the two groups. All patients in both groups successfully underwent minimally invasive surgery without conversion to thoracotomy. The operative time in the single-microport assisted group was significantly shorter than that in the traditional uniportal group [(146.03±30.79) min vs. (171.41±36.41) min, P=0.004]. However, there were no statistically significant differences between the two groups in intraoperative blood loss, number of dissected lymph nodes, duration of chest tube drainage, postoperative pain score, postoperative hospital stay, hospitalization cost, or incidence of postoperative complications (all P>0.05). Conclusion Single-microport assisted micro-uni-port thoracoscopic surgery for upper lobectomy can maximally integrate the advantages of three-port and uni-port VATS while effectively avoiding their disadvantages. It significantly shortens the operative time without increasing postoperative pain or complications, and represents a more minimally invasive, safer, and more convenient surgical approach.

Objective To observe the effects of wheat grain moxibustion on the hippocampal ERK/CREB-BDNF/TrkB signaling pathway and neurotransmitters in rats with perimenopausal depressive disorder(PDD);To explore its mechanism in treating PDD.Methods Totally 24 female SD rats were randomly divided into sham-operation group,model group,Western medicine group and wheat grain moxibustion group,with 6 rats in each group.The PDD rat model was established by bilateral ovariectomy and chronic unpredictable mild stress.The wheat grain moxibustion group received moxibustion at the"Zhongwan","Xiawan","Qihai","Guanyuan"acupoints,with a conical moxa cone for intervention,6 cones per acupoint;the Western medicine group was given fluoxetine hydrochloride solution 1.8 mg/kg by gavage,once a day,for consecutive 21 d.Before and after modeling and after intervention,the body mass,sucrose water preference rate and immobility time in the forced swimming test of the rats were recorded;Transmission electron microscopy was used to observe the synaptic structure in hippocampal tissue;ELISA was used to detect the expressions of 5-HT,GABA and Glu in hippocampal tissue;Western blot was used to detect the expressions of ERK,p-ERK,CREB,p-CREB,BDNF and TrkB proteins in hippocampal tissue.Results Compared with the sham-operation group,the body mass and sucrose water preference rate of the model group decreased,and the immobility time in the forced swimming test was prolonged(P<0.01);the number of synaptic vesicles in hippocampus decreased,the thickness of the postsynaptic density decreased,the aggregation of transduction-related proteins decreased,the synaptic cleft increased;the contents of 5-HT and GABA in hippocampal tissue decreased,the content of Glu increased,and the expressions of p-ERK,p-CREB,BDNF and TrkB proteins decreased(P<0.01).Compared with the model group,the body mass and sucrose water preference rate of the rats in Western medicine group and wheat grain moxibustion group increased,and the immobility time in the forced swimming test was shortened(P<0.01);the area of the postsynaptic density in the hippocampus increased,the synaptic cleft reduced,the contents of 5-HT and GABA in hippocampal tissue increased,the content of Glu decreased,and the expressions of p-ERK,p-CREB,BDNF and TrkB proteins increased(P<0.01).Conclusion Wheat grain moxibustion can effectively alleviate depressive-like behavior in PDD rats,and its mechanism may be related to regulating neurotransmitter expressions and mediating the ERK/CREB-BDNF/TrkB signaling pathway,thereby regulating hippocampal neuroplasticity.

Acupuncture treatment of chronic pain combined with depression (CPDC) is the result of a multi-target, multi-pathway approach. Acupuncture can treat CPDC by inhibiting the activation of glial cells, regulating the release of inflammatory mediators, regulating the expressions of neurotransmitters, changing the plasticity of neural synapses, regulating related epigenetic effects, regulating the microbiota-brain-gut axis, inhibiting nerve cell apoptosis, and antagonizing oxidative stress. The mechanism of its effect mainly involves anti-inflammatory related signaling pathways, regulation of neural synapse-related signaling pathways, and exerts its therapeutic effect through hippocampus, cerebral cortex, and amygdala.

Colorectal cancer is one of the common malignancies of the digestive system.Laparo-scopic surgery,as a minimally invasive procedure,has advantages such as minimal trauma,rapid re-covery,and shortened hospital stay,gradually becoming a main choice for treatment of digestive system diseases.However,anastomotic leakage remains one of the serious complications following this surger-y,impacting patients'recovery and prognosis.This review aimed to summarize and analyze recent re-search on anastomotic leakage after laparoscopic colorectal cancer surgery,exploring its pathogenesis,risk factors,preventive strategies,treatment and nursing measures,in order to provide a scientific ba-sis and guidance for clinical practice.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Toxoplasmosis is a zoonotic disease caused by infection with Toxoplasma gondii. Congenital toxoplasmosis is a common form of intrauterine infection and is associated with severe neurological sequelae such as cerebral palsy and cognitive impairment. This report presented the magnetic resonance imaging (MRI) features of a fetal intracranial toxoplasmosis case, including bilateral ventriculomegaly, multiple intracranial cystic lesions, and parenchymal calcifications, without concurrent retinal abnormalities or hepatosplenomegaly. Post-termination analyses confirmed the presence of T.gondii DNA in amniotic fluid and umbilical venous blood, with histopathology revealing necrosis and eosinophilic infiltration. MRI demonstrates superior soft-tissue resolution in evaluating the extent of cerebral lesions and parenchymal damage, underscoring its diagnostic value in fetal toxoplasmic encephalopathy.

Bisphenol A (BPA) is an environmental chemical that is widely exposed in human daily life. It has attracted much attention because of its estrogenic effect. Polycystic ovary syndrome (PCOS) is the most common endocrine metabolic disease in women of reproductive age. Its pathogenesis involves the interaction of genetic, environmental and endocrine disorders. In recent years, more and more studies have shown that BPA plays an important role in the occurrence and development of PCOS. BPA promotes the development of PCOS by interfering with insulin sensitivity, immune response and changing ovarian structure and function. In addition, BPA exposure levels are associated with hyperandrogenism, insulin resistance, obesity, dyslipidemia, and decreased ovarian reserve in PCOS patients. This review summarizes the effects of BPA on PCOS in terms of ovarian function, glucose and lipid metabolism, and inflammatory response, so as to provide theoretical basis for clinical intervention and prevention of BPA in the development of PCOS.