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: To investigate the association of special family structure and physical activity with psychological sub health among secondary vocational school students, so as to provide reference to inform mental health promotion in the population. Methods: From September to December 2024, a convenience sample was drawn from 16 schools including 5 141 secondary vocational school students across 8 provinces (municipalities) in China (Fujian, Chongqing, Guangdong, Guangxi, Hubei, Jiangxi, Shanghai, Zhejiang). A self developed questionnaire, Multidimensional Sub health Questionnaire of Adolescents and International Physical Activity Questionnaire Short Form were used to investigate and evaluate basic information, family structure, psychological sub health status and physical activity. Descriptive statistics and Logistic regression analysis were performed to explore relationships of special family structures and physical activity with psychological sub health in secondary vocational school students. Results: The detection rate of psychological sub health among secondary vocational school students was 11.0%. The reporting rates for secondary vocational school students who lost their father or lost their mother, whose parents divorced, whose parents remarried, and those from special family structures were 2.8%, 0.8%, 11.8%, 3.9%, and 14.7%, respectively. Moreover, the detection rates of psychological sub health and its sub dimensions significantly differed among secondary vocational school students grouped by family type (divorced parental families, remarried parental families and special family structures)( χ 2=5.90-22.67, all P <0.05). Binary Logistic regression indicated that maternal bereavement was associated with a higher risk of conduct problems [ AOR ( 95% CI )= 2.90(1.15-7.33)], and parental divorce was associated with a higher risk of psychological sub health ( AOR= 2.71 , 95%CI =1.04- 7.06 ) among secondary vocational school students (both P <0.05). Interaction analysis showed that both students with a special family structure and insufficient physical activity [ AOR (95% CI )=1.99(1.44-2.75)] and those with a non-special family structure and insufficient physical activity [ AOR (95% CI )=1.81(1.48-2.22)] were associated with increased risks of psychological sub health among secondary vocational school students (both P <0.01). Conclusion Special family structure and physical activity are associated with psychological sub health among secondary vocational school students; early identification and assessment based interventions for those with a special family structure should be strengthened, while school based physical activity should be implemented to increase physical activity levels,thereby reducing the risk of psychological sub health.

Objective: To explore the effects of a classroom based transdiagnostic preventive intervention program for emotional disorders on depressive and anxiety symptoms, and resilience among junior high school students, so as to provide reference for promoting adolescent mental health. Methods: From October to December 2023, 72 students from a junior high school in Beijing were recruited as research subjects and divided into an intervention group ( n =36) and a control group ( n =36). The intervention group participated in 8 sessions (40 mins per session) of transdiagnostic prevention intervention course for emotional disorders; the control group students participated in research based learning lessons or class meetings. Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Resilience Scale-8 (RS-8) were used to evaluate junior high school students. The Wilcoxon signed rank test was used to compare the differences in depressive and anxiety symptoms, and resilience scores between the intervention group and the control group before and after the intervention. PROCESS macro was used to test the mediating effect. Results: There were no statistically significant differences in the scores of depressive and anxiety symptoms, and resilience before and after intervention in the intervention group ( Z =-1.07, 0.07, 1.43, all P >0.05). In the control group, the depressive symptoms score was higher after the intervention than before, and the resilience score was lower than before the intervention ( Z = 2.23 , -2.53, both P <0.05). After intervention, the depressive symptoms score in the intervention group was lower than that in the control group, and the resilience score in the intervention group was higher than that in the control group ( Z =-2.09, 3.33, both P < 0.05). The increase in the scores of resilience played a complete mediating role between the intervention and depressive symptoms, with the mediating effect value being -0.98 (95% CI =-2.47 to -0.11), accounting for 30.98% of the total effect. Conclusion Transdiagnostic preventive intervention program for emotional disorders based on classroom could prevent the deterioration of depressive symptom in junior high school students, and maintaining the level of resilience is an important factor for the intervention effect.

Although video-assisted thoracoscopic surgery (VATS) offers clear advantages, including minimal trauma and rapid recovery, stable intraoperative visualization still depends heavily on the camera assistant. Traditional manual endoscope holding is therefore susceptible to several limitations, including image instability, assistant fatigue, and poor coordination with the primary surgeon. Operating in complex and confined anatomical spaces with a rigid thoracoscope further increases the demands on hand-eye coordination. Thoracoscopic camera-holding technology has progressively evolved from passive support systems to active camera holders and robot-assisted thoracic surgery (RATS) platforms, and is now advancing toward artificial intelligence-driven tracking and intelligent control. Based on this, a series of devices, including multifunctional intelligent assistant arms and active camera-holding arms for thoracic surgery, have been developed and have attracted increasing attention. This article reviews the development of intelligent camera-holding assistant arms and discusses related advances and future directions in the field.

Chemical constituents of n-butanol fraction of ethanol extract from the leaves of Cyclocarya paliurus (Batalin) Iljinskaja were studied. Ten compounds were purified by silica gel, MCI, ODS, Sephadex LH-20 column chromatography and semi-preparative high-performance liquid chromatography. Based on the physicochemical properties and spectroscopic data, these compounds were identified as (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(3′′,5′′-dimethoxy-(E)-p-coumaroyl)]-β-D-glucopyranoside (1), (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(E)-feruloyl]-β-D-glucopyranoside (2), (1S,3R,4S,5R)-4,5-di-O-caffeoyl quinic acid methyl ester (3), (1R,3R,4S,5R)-3,4-di-O-caffeoyl quinic acid methyl ester (4), (3R,5R)-3,4,5-tri-O-caffeoylquinic acid methyl ester (5), (1R,3R,4S,5R)-3-O-caffeoyl-5-O-p-coumaroyl quinic acid methyl ester (6), (1R,3R,4S,5R)-3-O-p-coumaroy-4-O-caffeoyl quinic acid methyl ester (7), (1R,3R,4S,5R)-3-O-caffeoyl-4-O-p-coumaroy quinic acid methyl ester (8), (3R,5R)-3,5-di-O-caffeoyl quinic acid methyl ester (9) and (1S,3R,4R,5R)-4-O-caffeoyl-5-O-feruloyl quinic acid methyl ester (10). Among them, compound 1 was a new compound, and compounds 3–10 were isolated from the genus Cyclocarya for the first time.

Intra-abdominal Hernia with Bowel Obstruction Triggering Multi-organ Failure: The Role of Preoperative Enteritis This report details the clinical course of a 41-year-old male who developed fulminant multiple organ dysfunction syndrome following emergency surgery for a strangulated intra-abdominal hernia. Preoperative manifestations, including diarrhea and marked leukocytosis, pointed towards an underlying infectious enteritis. We posited that surgical release of the obstructed, ischemic bowel segment precipitated a massive systemic influx of endotoxins and inflammatory mediators, triggering septic shock and rapid sequential organ failure. This case underscored the critical need to suspect concomitant gastrointestinal infection in patients presenting with mechanical bowel obstruction accompanied by infectious signs. Aggressive perioperative sepsis management encompassing early empiric antimicrobial therapy, vigilant hemodynamic monitoring, and preparedness for advanced organ support is essential to mitigate this severe complication.

Background: This study was performed to characterize the incidence, costs, and risk factors associated with renal and cardiovascular adverse outcomes following primary percutaneous coronary intervention (pPCI) in patients with ST-elevation myocardial infarction (STEMI). Methods: Patients with STEMI who underwent pPCI using iso-osmolar contrast were enrolled at 39 centers. The incidence of acute kidney injury (AKI) and major adverse renal and cardiovascular events (MARCE) was analyzed, as well as inpatient costs. Logistic regression analysis was performed to identify risk factors. Results: Among 2,293 patients, the incidence of AKI and MARCE within 72 hours post-pPCI was 4.14% (n = 95) and 4.40% (n = 101), respectively. AKI and/or MARCE were associated with systolic blood pressure (AKI: odds ratio [OR], 1.009; 95% confidence interval [CI], 1.000–1.018), hypertension (AKI: OR, 1.815; 95% CI, 1.133–2.906; MARCE: OR, 1.760;95% CI, 1.118–2.769), anterior wall infarction (AKI: OR, 1.895; 95% CI, 1.196–3.004; MARCE:OR, 1.939; 95% CI, 1.240–3.032), Killip class (AKI: OR, 1.465; 95% CI, 1.117–1.922; MARCE:OR, 1.467; 95% CI, 1.131–1.903), and serum creatinine (SCr; MARCE: OR, 1.006; 95% CI, 1.000–1.012). Hospitalization costs for patients with STEMI who developed AKI or MARCE were significantly higher than for those without AKI (9,595 ± 5,795 vs. 8,279 ± 3,872 USD, P = 0.003) or without MARCE (9,890 ± 5,616 vs. 8,255 ± 3,859 USD, P < 0.001). Conclusions In patients with STEMI undergoing pPCI with iso-osmolar contrast, the incidence of AKI and MARCE was associated with higher hospitalization costs. Systolic blood pressure, hypertension, anterior wall infarction, Killip class, and SCr were identified as risk factors for these outcomes.

To summarize the clinical characteristics, antibody spectrum and neuroelectrophysiological features of autoimmune nodopathy(AN), and to explore the phenotypic differences among different antibody-positive subgroups.

Hemoglobin (Hb) concentration is a key clinical biomarker for diagnosing and managing anemia, ischemic stroke, perioperative blood loss, and chronic diseases such as renal failure. Traditional venous blood sampling remains the gold standard due to its high accuracy, but its invasive nature limits frequent testing, real time monitoring, and large scale screening. This has driven growing interest in non-invasive Hb detection technologies over the past decade. Among these, optical methods are the most promising because of their safety, potential for continuous monitoring, and compatibility with portable or wearable devices. This paper systematically reviews major advances in optical non invasive Hb detection from the last ten years. We focus on near-infrared spectroscopy branches—photoplethysmography (PPG) and dynamic spectrum (DS)—and also cover color analysis/RGB imaging, Raman spectroscopy, and photoacoustic spectroscopy. For each technology, we explain its detection principles, analyze advantages and limitations, and summarize optimization strategies reported in recent literature. PPG, based on pulsatile blood volume changes, underpins many commercial continuous monitors. However, its accuracy is constrained by motion artifacts, individual physiological variations (e.g., skin tone, tissue thickness), and low AC signal to noise ratio. In contrast, DS—an advanced derivative of PPG—uses a differential principle to extract absorbance changes between systolic and diastolic peaks. This theoretically eliminates interference from static tissues (skin, bone, venous blood) and common mode noise (e.g., ambient light), positioning DS as a more robust framework for high precision Hb quantification. Beyond spectral methods, color analysis/RGB imaging offers a hardware minimalist approach. By analyzing images of vascular rich, thin tissues (e.g., conjunctiva, nail beds, palms), it enables Hb estimation using smartphone cameras. Recent advances have shifted from manual RGB feature extraction to deep learning models and spectral super resolution that reconstruct hyperspectral data from RGB inputs, significantly improving screening accuracy. Our academic perspective emphasizes critical and integrative analysis. We highlight persistent challenges that hinder clinical translation: profound individual biological variability (skin optics, microvascular architecture), sensitivity to measurement conditions (pressure, ambient light), and a lack of standardized validation protocols and multi center trials. A central thesis is that no single optical method is universally superior; each involves trade offs between accuracy, complexity, cost, and practicality. Looking forward, we posit that the next performance leap will come from multimodal information fusion—combining PPG, electrocardiogram (ECG), bioimpedance, or different optical modalities to compensate for individual differences and environmental noise. AI and deep learning are essential not only for image analysis but also for automated, end to end feature extraction from complex waveforms like PPG sequences. Advancing hardware (tunable lasers, quantum dot LEDs, novel sensor designs) is crucial to improve signal fidelity and portability. Finally, we advocate for clinical scenario specific optimization and rigorous standardized evaluation frameworks to gain regulatory approval (e.g., FDA, NMPA) and achieve widespread clinical acceptance. In conclusion, this review synthesizes a decade of progress. Optical non-invasive Hb detection has evolved from proof of concept studies to emerging products and validated screening tools, but the journey toward reliable, clinic ready quantitative devices continues. The convergence of smarter algorithms, fused sensing modalities, and focused clinical validation offers the most promising path to transform this potential into routine medical practice, ultimately enabling personalized, continuous, and accessible hematological management.

Objective To explore factors associated with early postoperative hormonal remission in patients with prolactinoma and to develop prediction models based on clinical and radiological features. Methods Data from 107 patients with prolactinoma who underwent transsphenoidal surgery at The First People’s Hospital of Huai’an, Nanjing Medical University between January 2020 and December 2024 was collected, included general clinical characteristics, preoperative laboratory indicators, and imaging features. Based on whether early postoperative prolactin (PRL) levels normalized, patients were divided into a remission group (n=76) and a non-remission group (n=31). Univariate logistic regression was used for preliminary evaluation of candidate variables, followed by LASSO regression for feature selection. Multiple machine learning models were constructed, including logistic regression, random forest, support vector machine, K-nearest neighbors, naive Bayes, decision tree, neural network, and gradient boosting decision tree (GBDT). All models were trained and evaluated using ten-fold cross-validation, with comprehensive assessment of model performance based on the area under the ROC curve (AUC), accuracy, sensitivity, specificity, precision, and F1 score. Results The maximum diameter of tumors in the non remission group was larger than that in the remission group (P=0.019), and the incidence of tumor stroke and preoperative PRL levels were significantly higher than those in the remission group (P＜0.001). Univariate analysis showed that sex, maximum tumor diameter, tumor stroke, and preoperative PRL levels were influencing factors for early postoperative hormone response in patients with prolactinoma (P＜0.05). The comparison results of machine learning models show that the neural network model performs the best (AUC=0.921) and has good clinical application value, followed by the GBDT model (AUC=0.893) and the support vector machine model (AUC=0.884). Other models also show certain predictive ability. Conclusions Sex, preoperative PRL levels, maximum tumor diameter, Hounsfield unit value, and tumor stroke are important factors affecting early hormone response after prolactinoma surgery. The machine learning model constructed based on the above variables has good predictive performance, and performs the best and has good clinical application value.