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.

Sixteen compounds were isolated from the solid rice culture of the endophytic fungus Alternaria alstroemeriae WZ-419 derived from Pseudostellariae Radix, using silica gel, ODS, Sephadex LH-20 column chromatography, and preparative HPLC. Their structures were elucidated based on physicochemical properties and comprehensive spectroscopic analyses, and identified as alterpenoid A (1), tricycloalternarene A (2), altenusin (3), 4'-epialtenuene (4), altenuene (5), alternariol (6), altenuisol (7), 3-hydroxyalternariol-5-O-methylether (8), alternariol-9-methyl ether (9), alternariol 1′-hydroxy-9-methyl ether (10), stemphyperylenol (11), dihydroalterperylenol (12), alterinone A (13), 4-hydroxy-3-methoxy-5-(2E,6E-3,7,11-trimethyldodeca-2,6,10-trien-1-yl) benzoic acid (14), hexylitaconic acid (15), and altechromone A (16). Among them, compound 1 and 13 were new, and compounds 14 and 15 were isolated from the genus Alternaria for the first time. The antibacterial activities of compounds 1–16 were evaluated in vitro. Compounds 3, 6–8, 10, 11, 13, and 14 exhibited notable inhibitory effects against Gram-positive bacteria, with minimal inhibitory concentrations ranging from 8 to 64 µg/mL.This study isolated a series of antimicrobial metabolites from the endophytic fungi of Pseudostellariae Radix, providing new insights into their bioactive constituents and laying a foundation for further pharmacological investigations.

Objective To develop albumin-binding indocyanine green (ICG) and assess its potential for near-infrared fluorescence imaging and intraoperative navigation in lung cancer. Methods ABD-tri was recombinantly expressed by genetic engineering. Its albumin-binding capability was determined using size-exclusion chromatography, and its albumin-dependent binding to lung cancer cells was evaluated by flow cytometry. ICG was conjugated to ABD-tri to generate the fluorescent probe ABD-tri-ICG. The potential of ABD-tri-ICG for near-infrared fluorescence imaging and imaging-guided tumor resection was evaluated in mice bearing subcutaneous tumor grafts of lung cancer. Results ABD-tri was highly expressed in Escherichia coli (E. coli) and was purified to homogeneity via a simple affinity chromatography. ABD-tri bound both human and murine serum albumin, contributing to its binding to lung cancer cells. ICG was effectively conjugated to ABD-tri to produce the fluorescence probe ABD-tri-ICG after mixing and incubation at room temperature for 1 h. In mice bearing lung cancer tumor grafts, intravenously injected ABD-tri-ICG enabled clear visualization of tumors with diameters ranging from 5 to 7 mm within 0.5-24 h post-injection. The tumor grafts were resected under the guidance of ABD-tri-ICG-mediated near-infrared fluorescence imaging. Conclusion Intravenous injection of ABD-tri-ICG allows rapid and sustained visualization of lung cancer tumor grafts and enables intraoperative navigation in mice, warranting further evaluation on the clinical translation of ABD-tri-ICG.

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.

Laboratory animal science is an emerging interdisciplinary field supporting life science and medical research, and a key component of talent cultivation and technological innovation in medical colleges. Currently, this field faces challenges such as a significant imbalance between talent supply and demand and a weak systematic training system. To respond to national strategies and societal needs, Shandong First Medical University established the School of Laboratory Animal through industry-education integration during the 14th Five-Year Plan period. The school is based on The Medical Laboratory Technology major, cultivates application-oriented, interdisciplinary professionals in laboratory animal science, and has constructed a three-in-one training system integrating "course learning–scientific research–industry practice". Specific measures for talent cultivation include: optimizing the general education, professional courses, and intensive practical modules in the talent training program, and establishing the "Yellow River Class" integrating industry and education; implementing an undergraduate mentor system, and conducting scientific research training based on The Model Animal Research and Development Engineering Laboratory; collaborating with bases across the entire industrial chain of laboratory animal production, research, application, and quality control, appointing industry mentors, and strengthening practical teaching. Practice has shown that this system has been remarkably effective: a total of 320 undergraduate students have been enrolled since 2020; the employment rate of undergraduate graduates for two consecutive cohorts has reached 100%, and the postgraduate enrollment rate has exceeded 50%; undergraduate students have won numerous national and provincial awards in academic competitions, and have obtained multiple patents and published papers. In the future, the school will further integrate the advantages of medicine, agriculture, and science, optimize the depth and breadth of courses, strengthen the construction of faculty and teaching materials, and improve the undergraduate-master's integrated training mechanism. This article can provide a reference for the training of professionals in laboratory animal science and related biomedical fields in medical colleges.

OBJECTIVE:Elevated blood pressure increases the risk of cardiovascular diseases.Isometric exercise training has been shown to significantly reduce resting blood pressure,but the factors influencing its effectiveness remain unclear,and specific application guidelines are yet to be established.This study aims to evaluate the impact of isometric exercise training on resting blood pressure through meta-analysis,explore its moderating factors,and provide evidence-based recommendations based on its dose-response relationship.METHODS:Following the PRISMA guidelines,a systematic search was conducted in PubMed,Embase,Cochrane Library,Scopus,and Web of Science databases using keywords"Isometric exercise training,""Systolic blood pressure,"and"Diastolic blood pressure,"covering literature up to September 2024.Randomized controlled trials involving isometric exercise training and resting blood pressure were included.Three independent researchers performed literature screening and data extraction,assessing bias risk and quality grades using the Risk of Bias 2.0 tool and GRADE framework.Main effect pooling,publication bias assessment,subgroup,and regression analysis were conducted using R software(version 4.3.4).RESULTS:A total of 28 articles(comprising 32 randomized controlled trials)involving 977 participants were included.(1)Meta-analysis results indicated that isometric exercise training significantly reduced resting systolic blood pressure(MD=-8.01,95％CI=-9.22 to-6.80,P＜0.01,I2=18.20％,low evidence grade)and diastolic blood pressure(MD=-3.46,95％CI=-4.64 to-2.28,P＜0.01,I2=0％,moderate evidence grade)compared to no exercise.(2)Subgroup analysis results revealed significant influences of gender,health status,exercise modality,frequency,intensity,duration,sets per session,rest duration,and baseline blood pressure on the main effects for both systolic(P＜0.01)and diastolic blood pressure(P＜0.05).(3)Regression analysis results did not show any significant influencing factors,but body mass index(β=-4.11,P=0.091)showed a significant negative trend on the main effect for systolic blood pressure.(4)No significant publication bias was observed in the meta-analysis results(P＞0.05).CONCLUSION:(1)Isometric exercise training significantly lowers systolic(low evidence grade)and diastolic(moderate evidence grade)blood pressure with clinically meaningful thresholds.(2)Participant characteristics(gender,health status,baseline blood pressure,and body mass index)and isometric exercise training protocols(modality,frequency,intensity,duration,cycle,sets per session,and rest duration)influence its antihypertensive effects.(3)The article recommends the optimal blood pressure management prescription:three sessions per week,with four sets per session,each set lasting 2 minutes with a 2-minute rest,at an intensity of 95％HRpeak using isometric wall squat exercises;the intervention period can be adjusted around a 6-week node.Future high-quality research is urgently needed to further validate and support these conclusions.

BACKGROUND:Spondylolisthesis is a common disease,and there is a lack of effective drugs to treat it.There is still a need to further define the pathogenesis and screen out more suitable therapeutic targets for spondylolisthesis.Mendelian randomization analysis can be used to explore the drugable genes associated with spondylolisthesis and provide valuable guidance for the development of more effective and targeted therapeutic drugs.OBJECTIVE:To explore potential therapeutic targets and effective drugs for spondylolisthesis by means of pharmaceutically available genome-wide Mendelian randomization analysis.METHODS:Using the Finnish database,eQTLGen consortium,drug signature database,drug-gene interaction database,protein-protein interaction database,organic small molecule biological activity database and protein structure database,which contains genome and health information of half a million Finns,data on druggable genes were subjected to two-sample Mendelian randomization analysis and co-localization analysis with data from genome-wide association studies of spondylolisthesis to identify genes highly associated with spondylolisthesis.In addition,GO and KEGG enrichment analysis,protein network construction,drug prediction and molecular docking were performed to provide valuable guidance for the development of more effective and targeted therapeutic agents.RESULTS AND CONCLUSION:In this study,we identified 34 potential drug target genes that were significantly associated with spondylolisthesis,particularly the gene APOBEC3G.This gene showed a significant association with spondylolisthesis outcomes through Mendelian analysis and co-localization analysis,suggesting that APOBEC3G may be a priority therapeutic target.As for other potential mechanisms and drugs,we still need to conduct more in-depth research to determine their roles.This study used a database from a European population,which can be used as a reference for the study of population genetics in China.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveZheng’s San Qi San (ZSQS) power, a classic traditional Chinese medicine (TCM) formula, is used for treating soft tissue injuries involving muscles, tendons, and ligaments. However, its underlying therapeutic mechanisms remain unclear. This study aimed to screen and identify pharmaceutically active ingredients and their candidate biomolecule targets, and further elucidate the molecular mechanism of ZSQS in the treatment of skeletal muscle injury. MethodsNetwork pharmacology was employed to construct “ZSQS-component-target”, “protein-protein interaction (PPI)” and “active ingredient-core protein-pathway” networks to predict the key active ingredients and potential core targets of ZSQS for skeletal muscle injury. The predicted results were then validated via microarray data from the GEO database. Molecular docking was then performed to assess the binding ability between the screened active ingredients of ZSQS and the candidate core targets. Moreover, liquid chromatography-mass spectrometry (LC-MS) was used for qualitative and quantitative analysis to verify the active components of the drug and ZSQS serum. Finally, an animal model of eccentric exercise-induced skeletal muscle injury and a myotube cell model of oxidative stress-induced injury were established to validate the effects of ZSQS and its interventional effects on the biological functions of critical targets, thereby demonstrating the potential therapeutic mechanism of ZSQS. ResultsAmong the 111 active components identified in ZSQS and their corresponding 204 targets related to the skeletal muscle injury repair process, 14 core targets (including AKT1) and 4 core active components (quercetin, luteolin, kaempferol, and β‑sitosterol) were screened out, while the corresponding metabolites of quercetin, luteolin and kaempferol were detected in the ZSQS serum. Among these targets, 5 candidate genes (IL-6, CASP3, HIF1A, STAT3, and JUN) overlapped with the differential expression screening results with GEO data, and IL-6 was confirmed to be enriched in the PI3K/AKT pathway. Combined with the prediction results of the AKT expression levels, these findings suggest that the phosphorylation level of AKT1 plays a core role in the therapeutic mechanism of ZSQS. Molecular docking analysis further revealed that the PH domain of AKT1 had high binding energy with all 4 core active components, as verified by LC-MS. Finally, animal model studies have shown the promoting effect of ZSQS administration on skeletal muscle injury repair and its possible antioxidant damage mechanism. Cell model studies further demonstrated that ZSQS-containing serum, core active ingredient combination therapy, and quercetin monomer could increase the phosphorylation level of AKT, promote the nuclear translocation of Nrf2, upregulate the expression of downstream antioxidant enzymes (SOD, GPx, and GR), and inhibit the expression of inflammatory factors (IL-6 and TNF-α), thereby alleviating oxidative stress and the inflammatory response. ConclusionZSQS alleviates skeletal muscle injury mainly by activating the AKT/Nrf2 signaling pathway, enhancing cellular antioxidant and anti-inflammatory capabilities. The results of this study provide a scientific basis for the clinical application and modernized development of ZSQS.