Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
.
Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Objective To evaluate the application value of the second generation snap shot freeze(SSF2)combined with artificial intelligence reconstruction in free heart rate coronary computed tomography angiography(CCTA).Methods The examination data of 37 patients undergoing CCTA were divided into two groups for reconstruction.Group A,reconstruction by artificial intelligence after SSF2 algorithm correction;group B,original images automatically split and multi-phase reconstruction by artificial intelligence.Image quality were compared on volume rendering(VR),curve planar reformation(CPR),maximum intensity projection(MIP)image,subjective evaluation,objective scoring,and signal-to-noise ratio(SNR).Independent samples t-test and Chi-square test were used,and P<0.05 was considered statistically significant.Results There were statistically significant differences in the image quality score and SNR of the two reconstruction methods(P=0.009).Group A scored better,with higher signal intensity,lower noise intensity,and better SNR.The difference in the number of right coronary artery(RCA)analyzable segments between the two groups was statistically significant(P<0.05).The excellent and good rate of subjective evaluation of coronary artery segments in group A RCA(98.6%)was higher than that in group B(69.6%).Conclusion Using SSF2 combined with artificial intelligence reconstruction technology can significantly improve the image quality of CCTA,improve the success rate of CCTA examination,and improve the overall work efficiency.

Objective To systematically evaluate the effect and safety of neoadjuvant PD-1/PD-L1 inhibitors combined with chemotherapy for resectable non-small cell lung cancer (NSCLC). Methods The PubMed, EMbase, The Cochrane Library, CNKI, and Wanfang data were searched by computer to identify relevant studies on anti PD-1 /PD-L1 combined with chemotherapy for resectable NSCLC from inception to March 2023. Two authors independently screened the literature, extracted the data, and evaluated the risk of bias in the included studies. The single-arm study was evaluated for quality using the methodological index for non-randomized studies (MINORS). Meta-analysis was conducted by RevMan 5.4 software. Results Twenty-six studies with 965 patients were included in this meta-analysis. MINORS scores of single-arm studies were ≥12 points. The meta-analysis results showed that the pooled pathologic complete response, major pathologic response, and objective response rates as well as partial response, surgical rate and R0 surgical resection rate of neoadjuvant PD-1/PD-L1 inhibitors combined with chemotherapy were 39% [RD=0.39, 95%CI (0.31, 0.47) ], 59% [RD=0.59, 95%CI (0.53, 0.65) ], 72% [RD=0.72, 95%CI (0.65, 0.80) ], 62% [RD=0.62, 95%CI (0.56, 0.69) ], 86% [RD=0.86, 95%CI (0.81, 0.92) ], and 94% [RD=0.94, 95%CI (0.92, 0.97) ], respectively. In terms of safety, the rate of adverse events (AEs) was 65% [RD=0.65, 95%CI (0.52, 0.78) ], and the rate of grade 3 to 5 AEs was 16% [RD=0.16, 95%CI (0.10, 0.23) ]. Conclusion The combination of neoadjuvant PD-1/PD-L1 inhibitors with chemotherapy has good efficacy and safety in the treatment of patients with resectable NSCLC.

Background and objective Lung cancer is the cancer with the highest incidence and mortality rates in China,and non-small cell lung cancer(NSCLC)accounts for 80％-85％of all malignant lung tumors.Currently,surgical treat-ment remains the primary treatment modality for lung cancer.In recent years,the effectiveness of immune checkpoint inhibi-tors for NSCLC has become a consensus,and neoadjuvant immunochemotherapy(nICT)has shown promising efficacy and safety in early to intermediate stage NSCLC.However,there are fewer studies related to nICT for locally advanced NSCLC.This study aims to evaluate the efficacy and safety of nICT therapy in locally advanced resectable NSCLC.Methods 85 con-firmed resectable stage ⅢA and ⅢB patients treated in the Department of Thoracic Surgery,Second Hospital of Lanzhou University,from January 2021 to April 2024,were divided into the nICT group(n=32)and the surgery alone group(n=53).Clinical baseline data,perioperative indicators,postoperative complications,imaging response rate,pathological response rate,incidence of adverse events,and quality of life were compared between the two groups.Results There were no statisti-cally significant differences in clinical baseline data between the two groups(P＞0.05).Incidence of choosing thoracotomy was higher in the nICT group than in the surgery alone group(P=0.002).There were no significant differences in surgical time,intraoperative blood loss,number of dissected lymph nodes,duration of chest tube placement,postoperative hospital stay,and R0 resection rate between the two groups(P＞0.05).The overall incidence of postoperative complications was 31.25％in the nICT group and 22.64％in the surgery alone group,with no statistically significant difference(P=0.380).In the nICT group,the objective response rate(ORR)was 84.38％,with 5 cases of complete response(CR)(15.63％),22 cases of partial response(PR)(68.75％),15 cases of pathological response rate(pCR)(46.88％),and 11 cases of major pathological reaponse(MPR)(34.38％).During nICT treatment,12 cases(37.50％)experienced grade 3 treatment-related adverse events,no death induced by adverse events or immune related adverse events.Moreover,the symptoms of the patients were improved after nICT treat-ment.Conclusion Neoadjuvant immunochemotherapy shows promising efficacy in locally advanced resectable NSCLC,with manageable treatment-related adverse events.It is a safe and feasible neoadjuvant treatment modality for locally advanced resectable NSCLC.

Objective:To study the effects of fluoride exposure on skeletal development of zebrafish larvae and its possible molecular mechanisms.Methods:Six hours post fertilization(6 hpf) wild-type zebrafish embryos were selected and exposed to sodium fluoride [NaF, control group (0 mg/L NaF), low fluoride group (25 mg/L NaF) and high fluoride group (100 mg/L NaF)] for 9 days. Fluorine ion selective electrode was used to detect the overall fluorine content of zebrafish larvaes, and the death and development of zebrafish larvaes were observed and counted. Bone mineralization and chondrogenesis of the zebrafish larvaes were analyzed by alizarin red staining and alcin blue staining, respectively. The expression levels of sry-related-high-mobilty-group box 9a (Sox9a), osteprotegerin (OPG) and receptor-activator of nuclear factor kappa beta ligant (RANKL) were analyzed by real-time quantitative PCR.Results:Compared with control group [(0.12 ± 0.01) μg/140 larvaes], the overall fluorine contents of zebrafish larvaes in low fluoride group [(0.28 ± 0.03) μg/140 larvaes] and high fluoride group [(0.64 ± 0.10) μg/140 larvaes] were significantly higher, and the differences were statistically significant ( P < 0.05). Compared with control group, zebrafish larvaes in high fluoride group had shorter body length, higher swim bladder loss rate and higher spinal curvature rate ( P < 0.05). The alizarin red staining area, integrated optical density (IOD) and the number of mineralized vertebrae were higher in low fluoride group, while the alcin blue staining area of cartilage formation was lower ( P < 0.05). In the high fluoride group, alizarin red staining area, IOD and the number of mineralized vertebrae were lower, while the alcin blue staining area of cartilage formation was higher ( P < 0.05). Compared with control group, the expression levels of OPG mRNA and OPG/RANKL mRNA in low fluoride group were higher ( P < 0.05); the expression level of RANKL mRNA was higher in high fluoride group, while the expression level of OPG/RANKL mRNA was lower ( P < 0.05). Conclusion:A short period of fluoride exposure from zebrafish embryo to zebrafish larvae can cause abnormal bone development of zebrafish larvae, which may be related to endochondral osteogenesis and OPG/RANKL pathway.

BACKGROUND: Lung cancer is the cancer with the highest mortality at home and abroad at present. The detection of lung nodules is a key step to reducing the mortality of lung cancer. Artificial intelligence-assisted diagnosis system presents as the state of the art in the area of nodule detection, differentiation between benign and malignant and diagnosis of invasive subtypes, however, a validation with clinical data is necessary for further application. Therefore, the aim of this study is to evaluate the effectiveness of artificial intelligence-assisted diagnosis system in predicting the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules. METHODS: Clinical data of 223 patients with early-stage lung adenocarcinoma appearing as pulmonary nodules admitted to the Lanzhou University Second Hospital from January 1st, 2016 to December 31th, 2021 were retrospectively analyzed, which were divided into invasive adenocarcinoma group (n=170) and non-invasive adenocarcinoma group (n=53), and the non-invasive adenocarcinoma group was subdivided into minimally invasive adenocarcinoma group (n=31) and preinvasive lesions group (n=22). The malignant probability and imaging characteristics of each group were compared to analyze their predictive ability for the invasive subtypes of early-stage lung adenocarcinoma. The concordance between qualitative diagnostic results of artificial intelligence-assisted diagnosis of the invasive subtypes of early-stage lung adenocarcinoma and postoperative pathology was then analyzed. RESULTS: In different invasive subtypes of early-stage lung adenocarcinoma, the mean CT value of pulmonary nodules (P<0.001), diameter (P<0.001), volume (P<0.001), malignant probability (P<0.001), pleural retraction sign (P<0.001), lobulation (P<0.001), spiculation (P<0.001) were significantly different. At the same time, it was also found that with the increased invasiveness of different invasive subtypes of early-stage lung adenocarcinoma, the proportion of dominant signs of each group gradually increased. On the issue of binary classification, the sensitivity, specificity, and area under the curve (AUC) values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 81.76%, 92.45% and 0.871 respectively. On the issue of three classification, the accuracy, recall rate, F1 score, and AUC values of the artificial intelligence-assisted diagnosis system for the qualitative diagnosis of invasive subtypes of early-stage lung adenocarcinoma were 83.86%, 85.03%, 76.46% and 0.879 respectively. CONCLUSIONS Artificial intelligence-assisted diagnosis system could predict the invasive subtypes of early‑stage lung adenocarcinoma appearing as pulmonary nodules, and has a certain predictive value. With the optimization of algorithms and the improvement of data, it may provide guidance for individualized treatment of patients.
Adenocarcinoma/pathology* ; Adenocarcinoma of Lung/pathology* ; Artificial Intelligence ; Humans ; Lung Neoplasms/pathology* ; Multiple Pulmonary Nodules ; Neoplasm Invasiveness ; Retrospective Studies

Purpose: Drug resistance is one of the main causes of chemotherapy failure in patients with small cell lung cancer (SCLC), and extensive biological studies into chemotherapy drug resistance are required. Materials and Methods: In this study, we performed lncRNA microarray, in vitro functional assays, in vivo models and cDNA microarray to evaluate the impact of lncRNA in SCLC chemoresistance. Results: The results showed that KCNQ1OT1 expression was upregulated in SCLC tissues and was a poor prognostic factor for patients with SCLC. Knockdown of KCNQ1OT1 inhibited cell proliferation, migration, chemoresistance and promoted apoptosis of SCLC cells. Mechanistic investigation showed that KCNQ1OT1 can activate transforming growth factor-β1 mediated epithelial-to-mesenchymal transition in SCLC cells. Conclusion Taken together, our study revealed the role of KCNQ1OT1 in the progression and chemoresistance of SCLC, and suggested KCNQ1OT1 as a potential diagnostic and prognostic biomarker in SCLC clinical management.

Objective To investigate the characteristics of knee kinematics and ground reaction force (GRF), as well as the stress state of cartilage and meniscus in the process of lateral incisions at different cutting angles under expected conditions. Methods Kinematics and GRF data of 14 subjects at 45°, 90° and 135° cutting angle respectively under expected conditions were collected. The knee joint reaction force was obtained through the inverse dynamics calculation of Visual 3D. Based on three-dimensional (3D) finite element model of the knee joint, the contact process at 3 lateral cutting angles was simulated. ResultsUnder expected conditions, there were significant differences in knee joint kinematics characteristics at 3 cutting angles during contact process(P＜0.001), and the knee flexion increased with the cutting angle increasing; the vertical GRF decreased significantly with the cutting angle increasing (P＜0.001), while the horizontal GRF showed the opposite trend; for 3 cutting angles, the peak contact stress of patellar cartilage and femoral cartilage was larger at 90° cutting angle, the peak principal stress at anterior cruciate ligament (ACL) contact point was also larger at 90° cutting angle, and the following was at 135° and 45° cutting angle, respectively; the peak contact stress of lateral femoral cartilage was larger than that of medial femoral cartilage at 3 cutting angles. Conclusions The risk of knee joint injury is higher at 90° cutting angle, and the stress state of knee joint at 135° cutting angles is better than that at 90° cutting angle, and the risk of knee joint injury does not increase with the increase of cutting angle under expected conditions.