[摘 要] 目的：探讨嗜酸性粒细胞（Eos）和其他循环血细胞和炎症指标在预测小细胞肺癌（SCLC）免疫治疗疗效和免疫相关不良反应（irAE）中的价值。方法:回顾分析2013年8月至2023年7月期间海军军医大学第一附属医院呼吸科收治的410例SCLC患者临床信息；在化疗或免疫治疗前，以及治疗后的3个周期，分别检测患者全血细胞计数和细胞因子等指标；记录irAE的发生时间、类型、分级，以及随访情况。结果: 接受化疗联合免疫检查点抑制剂（ICI）治疗（简称联合治疗）的患者116例，其中一线联合治疗患者91例、后线联合治疗25例。联合组患者的总有效率（ORR）为44.8%，疾病控制率（DCR）为90.5%，单用化疗（单化）组患者的ORR为38.4%，DCR为85.0%。联合组中位PFS为8.9（7.2～10.5）个月，中位OS为17.7（13.9～21.5）个月。将联合组与单化组行倾向得分匹配（PSM）法配对，对比二组治疗后3周期的绝对嗜酸性粒细胞计数（AEC）水平、相对嗜酸性粒细胞计数（REC）水平；计算历次复查Eos水平与基线Eos水平的比值（AECT1/0、AECT2/0、AECT3/0、RECT1/0、RECT2/0、RECT3/0），联合组的AECT3/0和RECT3/0显著高于单化组。单因素分析表明，基线AEC和REC的升高与较好的治疗至失败时间（TTF）和OS显著相关（P < 0.05）；治疗后Eos水平与基线水平的比例（AECT3/0、RECT3/0）与较好的PFS和TTF显著相关（P < 0.05）；RECT3/0升高同样与OS改善显著相关（P < 0.05）。多因素分析提示，AECT3/0 > 0.41与较好的PFS和TTF显著相关（P < 0.05）；当RECT3/0 > 0.32时，与较好的PFS、TTF、OS均显著相关（P < 0.05）；RECT3/0 > 0.27仅与较好的TTF、OS显著相关（P < 0.05）。亚组分析发现，缓解组的RECT3/0显著高于非缓解组（P < 0.05）。一线应用ICI与二线/后线应用ICI患者的PFS、TTF、OS无统计学差异，但一线应用ICI时ORR（50% vs 25%，P < 0.05）和DCR（93.48% vs 79.17%，P < 0.05）显著优于二线/后线。116例联合治疗患者发生43例irAE（35.34%）, 最常见的为免疫相关性皮炎8.62%；Ⅲ级以上的irAE共17例（14.66%）；因irAE停药10例（8.62%），死亡2例;发生irAE的患者PFS较未发生irAE的患者显著延长（P < 0.05），而TTF和OS无统计学差异。发生irAE患者的RECT3较未发生irAE患者显著升高（P < 0.05），AECT3/0 > 0.29的患者irAE发生率显著增高（P < 0.05）。结论: Eos是SCLC接受ICI治疗的保护性因素，通过监测AECT3/0、RECT3/0，并结合患者的临床病理生理特征、细胞因子和炎症标志物水平进行综合评估，可有效预测SCLC患者的免疫治疗疗效和irAE的发生。

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.

Objective To develop a symptom and function assessment scale for patients after Nuss procedure for pectus excavatum and to test its reliability and validity. Methods Following the principles and procedures of patient-reported outcome (PRO) scale development stipulated by the U.S. Food and Drug Administration, an initial draft was formed through literature analysis, qualitative interviews, and Delphi expert consultation. The preliminary draft was used to conduct a pre-survey on patients who underwent Nuss procedure for pectus excavatum at Guangdong Provincial People's Hospital, and the reliability and validity of the scale were tested. Results A preliminary PRO-based symptom and function scale was constructed, covering two domains: symptoms and impact on daily functions. The symptom dimension included six items: chest tightness, palpitations, pain, shortness of breath, foreign body sensation of the steel plate, and distress; while the impact on daily functions included four items: difficulty in getting out of bed or lying down, raising arms, bending over, and standing or sitting for a long time. A total of 73 patients who underwent Nuss procedure for pectus excavatum were included in the questionnaire survey, with 70 valid questionnaires collected, including 64 males and 6 females, with 56 patients aged 12-17 years and 14 patients ≥18 years. Through exploratory factor analysis, two common factors were extracted, with a cumulative variance contribution rate of 70%. The Cronbach’s α coefficient of the scale was 0.917. Conclusion The scale developed in this study has good reliability and validity, high reliability and stability, and can be used as an evaluation tool for the recovery status of patients after Nuss procedure for pectus excavatum.

Objective To investigate the relationship of anxiety and depression mood with cognitive function in elderly patients with Parkinson's disease. Methods A total of 511 elderly Parkinson's disease patients who visited the 907th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army from October 2024 to November 2025 were selected as the study subjects. The patients were divided into a cognitive impairment group (n=98) and a non-cognitive impairment group (n=413) based on the presence of cognitive impairment. The patient related data were collected. Disease severity was assessed using the modified Hoehn-Yahr (H-Y) staging scale, anxiety levels were evaluated with the Beck Anxiety Inventory (BAI), depressive symptoms were measured using the Geriatric Depression Scale (GDS), and cognitive function was assessed with the Mini-Mental State Examination. Logistic regression analysis was performed to identify risk factors for cognitive impairment in elderly Parkinson's patients, and Pearson correlation analysis was conducted to examine the relationship between anxiety/depression and cognitive function. Results Educational level (OR=5.348, 95% CI: 2.278-10.482), marital status (OR=6.056, 95% CI: 2.932-12.508), H-Y stage (OR=2.189, 95% CI: 1.433-3.345), BAI score (OR=1.270, 95% CI: 1.188-1.358), and GDS score (OR=1.340, 95% CI: 1.236-1.453) were all risk factors for cognitive impairment in elderly Parkinson's patients (P<0.05). Pearson correlation analysis showed that both BAI score and GDS score were negatively correlated with cognitive function (r=-0.383 and -0.400, respectively, P<0.001). Conclusion Educational level, marital status, H-Y stage, BAI score, and GDS score are all influencing factors for cognitive impairment in elderly Parkinson's disease patients, with both BAI scores and GDS scores being negatively correlated with cognitive function.

To systematically evaluate the impact of the "group-based " medical assistance program for Xizang, conducted by Peking Union Medical College Hospital, on the implementation outcomes of standardized residency training (SRT) at the Xizang Autonomous Region People′s Hospital, and to provide evidence-based decision-making references and policy optimization recommendations for medical education support models in border ethnic minority regions.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder. Some NIID patients may present with paroxysmal neurological symptoms, posing challenges for clinical diagnosis. Thisarticle reports a 68-year-old male NIID patient presenting predominantly with stroke-like episodes. The patient was admitted with sudden-onset right-sided limb weakness, and had a prior history of cognitive decline and bradykinesia. Brain magnetic resonance imaging revealed severe subcortical white-matter hyperintensities in bilateral cerebral hemispheres on fluid-attenuated inversion recovery sequences, as well as characteristic "ribbon sign" at the corticomedullary junction on diffusion-weighted imaging. Genetic testing identified an abnormal GGC repeat expansion of 112 times in the NOTCH2NLC gene, confirming the diagnosis of NIID. In addition, we reviewed previously reported NIID cases with stroke-like episodes as the predominant clinical manifestation, aiming to provide references for the clinical diagnosis and treatment of this disease.

Objective To explore the necessity of routine chest X-ray (CXR) examination after lung surgery based on patient symptom burden. Methods A retrospective study was conducted on the patients who underwent thoracoscopic lung resection at the Department of Thoracic Surgery of Guangdong Provincial People's Hospital from March 2020 to April 2023. Routine CXR findings obtained within 24 h post-operation, after chest tube removal, and at 1-month post-discharge, as well as subsequent clinical interventions triggered by these examinations, were collected and analyzed. The Perioperative Symptom Assessment Lung (PSA-Lung) inventory was used to quantify symptom burden including pain, dyspnea, and sleep disturbance. Results A total of 2 101 patients were collected, including 986 males and 1 115 females, with a median age of 56 years. The rate of clinical interventions triggered by routine CXR examinations was very low, being 1.0%, 0.5%, and 1.3% within 24 h post-operation, within 12 h after extubation, and at 1-month post-discharge, respectively. One month after discharge, patients in the intervention group exhibited significantly higher scores for dyspnea (3 points vs. 2 points, P=0.015), pain (2 points vs. 1 point, P=0.039), and sleep disturbance (3 points vs. 2 points, P=0.036) compared with the non-intervention group. Conclusion Very few routine postoperative CXR examinations change patients’ management, and patients who need extra-intervention tended to have more severe symptom burden after surgery.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.

Objective: The variety and efficacy of biomarkers available that may be used objectively to diagnose major depressive disorder (MDD) in adults are unclear. This systematic review aims to identify and evaluate the variety of objective markers used to diagnose MDD in adults. Methods: The search strategy was applied via PubMed and PsycINFO over the past 10 years (2013–2023) to capture the latest available evidence supporting the use of biomarkers to diagnose MDD. Data was reported through narrative synthesis. Results: Forty-two studies were included in the review. Findings were synthesised based on the following measures: blood, neuroimagingeurophysiology, urine, dermatological, auditory, vocal, cerebrospinal fluid and combinatory—and evaluated based on its sensitivity/specificity and area under the curve values. The best predictors of blood (MYT1 gene), neuroimagingeurophysiological (5-HT1A auto-receptor binding in the dorsal and median raphe), urinary (combined albumin, AMBP, HSPB, APOA1), cerebrospinal fluid-based (neuron specific enolase, microRNA) biomarkers were found to be closely linked to the pathophysiology of MDD. Conclusion A large variety of biomarkers were available to diagnose MDD, with the best performing biomarkers intrinsically related to the pathophysiology of MDD. Potential for future research lies in investigating the joint sensitivity of the best performing biomarkers identified via machine learning methods and establishing the causal effect between these biomarkers and MDD.