Objective To explore the impact of number of positive regional lymph nodes (nPRLN) in N1 stage on the prognosis of non-small cell lung cancer (NSCLC) patients. Methods Patients with TxN1M0 stage NSCLC who underwent lobectomy and mediastinal lymph node dissection from 2010 to 2015 were screened from SEER database (17 Regs, 2022nov sub). The optimal cutoff value of nPRLN was determined using X-tile software, and patients were divided into 2 groups according to the cutoff value: a nPRLN≤optimal cutoff group and a nPRLN>optimal cutoff group. The influence of confounding factors was minimized by propensity score matching (PSM) at a ratio of 1 : 1. Kaplan-Meier curves and Cox proportional hazards models were used to evaluate overall survival (OS) and lung cancer-specific survival (LCSS) of patients. Results A total of 1316 patients with TxN1M0 stage NSCLC were included, including 662 males and 654 females, with a median age of 67 (60, 73) years. The optimal cutoff value of nPRLN was 3, with 1165 patients in the nPRLN≤3 group and 151 patients in the nPRLN>3 group. After PSM, there were 138 patients in each group. Regardless of before or after PSM, OS and LCSS of patients in the nPRLN≤3 group were superior to those in the nPRLN>3 group (P<0.001). N1 stage nPRLN>3 was an independent prognostic risk factor for OS [HR=1.52, 95%CI (1.22, 1.89), P<0.001] and LCSS [HR=1.72, 95%CI (1.36, 2.18), P<0.001]. Conclusion N1 stage nPRLN>3 is an independent prognostic risk factor for NSCLC patients in TxN1M0 stage, which may provide new evidence for future revision of TNM staging N1 stage subclassification.

ObjectiveTo investigate the risk factors for plastic bronchitis （PB） in children with Mycoplasma pneumoniae pneumonia （MPP） and to construct a predictive model. MethodsPropensity score matching （1∶4 nearest neighbor matching） was used to balance baseline data between the two groups. Univariate and multivariate Logistic regression analyses were performed to identify independent risk factors. Various machine learning algorithms were applied to build predictive models， and model performance was evaluated using receiver operating characteristic curves， decision curves， and calibration curves. SHapley Additive exPlanations （SHAP） method was used for model interpretation. ResultsAfter matching， baseline characteristics were comparable between the two groups. Compared with the non-PB group， the PB group had significantly longer fever duration， higher incidence of pleural effusion and right upper lobe involvement， and higher levels of procalcitonin （PCT）， D-dimer， lactate dehydrogenase （LDH）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， immunoglobulin E （IgE）， and red blood cell count in bronchoalveolar lavage fluid （BALF） （all P<0.05）， while lymphocyte and eosinophil counts in blood were significantly lower （P<0.05）. Multivariate Logistic regression showed that PCT>0.207 μg/L， LDH>386.25 U/L， right upper lobe involvement， and red blood cell count in BALF>1 500×10⁶ were independent risk factors. Among machine learning models， the gradient boosting machine （GBM） model achieved an area under the curve of 0.786 in the validation set， demonstrating the best generalization ability. SHAP analysis validated the importance of the above variables. ConclusionThe four indicators above have independent predictive value for PB occurrence， and the GBM model shows certain clinical risk stratification capability.

Objective To establish an individualized nomogram model and evaluate its efficacy to provide a possible evaluation basis for the prognosis of lower third and abdominal part of oesophageal adenocarcinoma (EAC). Methods Lower third and abdominal part of EAC patients from 2010 to 2015 were chosen from the SEER Research Plus Database (17 Regs, 2022nov sub). The patients were randomly allocated to the training cohort and the internal validation cohort with a ratio of 7∶3 using bootstrap resampling. The Cox proportional hazards regression analysis was used to determine significant contributors to overall survival (OS) in EAC patients, which would be elected to construct the nomogram prediction model. C-index, calibration curve and receiver operating characteristic (ROC) curve were performed to evaluate its efficacy. Finally, the efficacy to evaluate the OS of EAC patients was compared between the nomogram prediction model and TNM staging system. Results In total, 3945 patients with lower third and abdominal part of EAC were enrolled, including 3475 males and 470 females with a median age of 65 (57-72) years. The 2761 patients were allocated to the training cohort and the remaining 1184 patients to the internal validation cohort. In the training and the internal validation cohorts, the C-index of the nomogram model was 0.705 and 0.713, respectively. Meanwhile, the calibration curve also suggested that the nomogram model had a strong capability of predicting 1-, 3-, and 5-year OS rates of EAC patients. The nomogram also had a higher efficacy than the TNM staging system in predicting 1-, 3-, and 5-year OS rates of EAC patients. Conclusion This nomogram prediction model has a high efficiency for predicting OS in the patients with lower third and abdominal part of EAC, which is higher than that of the current TNM staging system.

In recent years, generative artificial intelligence (AI) technologies have achieved remarkable progress in the early screening, risk prediction, disease progression assessment, and clinical trial design of glaucoma.Using advanced algorithms, such as generative adversarial networks, variational autoencoders, and diffusion models, researchers have synthesized high-quality structural images of the optic disc, macular region, and retinal nerve fiber layer, which effectively alleviates the limitations of scarce clinical imaging data and label imbalance.These methods have substantially improved the accuracy and generalization of deep learning models in visual field defect prediction, structure-function mapping, and longitudinal disease progression simulation.Meanwhile, multimodal generative approaches that integrate imaging data, visual field tests, and clinical features have facilitated individualized prediction of glaucoma progression.In addition, large language models have shown preliminary potential in ophthalmic image interpretation, clinical text information extraction, and decision support, providing new insights into intelligent ophthalmic diagnosis and treatment.However, the clinical implementation of generative AI in glaucoma faces challenges.The pathological authenticity and cross-device consistency of generated images require further validation, which may affect the reliability of early glaucoma detection.The heterogeneous characteristics of different glaucoma subtypes, such as open-angle and angle-closure glaucoma, also limit the generalization of synthetic data.Moreover, issues related to model interpretability (" black-box" nature), artifact generation, data privacy, and ethical governance remain key barriers to clinical translation.In the future, it is expected that establishing large-scale training frameworks that incorporate multicenter, multimodal, and multiethnic datasets will enhance model robustness and clinical applicability.Furthermore, generative AI may contribute to remote ophthalmic care and personalized precision therapy by enhancing low-quality image, reconstructing missing data, and simulating dynamic disease courses.This article reviews the current applications, core technologies, and challenges of generative AI in glaucoma diagnosis and management, and discusses its future directions and translational potential in clinical ophthalmology.

Objective To evaluate the value of postoperative radiotherapy (PORT) in patients with stage ⅢA-N2 non-small cell lung cancer who received complete resection and chemotherapy. Methods Patients with stage ⅢA-N2 non-small cell lung cancer who received complete resection and chemotherapy were chosen from the SEER Research Plus Database [17 Registries, November 2012 Submission (2000-2019)]. The patients were divided into a PORT group and a non-PORT group according to whether the PORT was used. To balance baseline characteristics between non-PORT and PORT groups, R software was used to conduct a propensity score matching (PSM) with a ratio of 1 : 1 and a matching tolerance of 0.01. Both the Cox regression analysis and Kaplan-Meier survival analysis were conducted to evaluate the value of PORT in terms of overall survival (OS) and disease-specific survival (DSS). Results In total, 2468 patients with stage ⅢA-N2 non-small cell lung cancer were enrolled, including 1078 males and 1390 females with a median age of 65 (58-71) years. There were 1336 patients in the PORT group, and 1132 patients in the non-PORT group. Cox regression analysis showed that PORT was not significantly associated with OS (multivariate analysis: HR=1.051, 95%CI 0.949-1.164, P=0.338) and DSS (multivariate analysis: HR=1.094, 95%CI 0.976-1.225, P=0.123). No statistical difference was found in the OS or DSS between non-PORT group and PORT group after PSM analysis (P＞0.05). Conclusion PORT does not have a survival benefit for patients with stage ⅢA-N2 non-small cell lung cancer who received complete resection and chemotherapy.

In recent years, generative artificial intelligence (AI) technologies have achieved remarkable progress in the early screening, risk prediction, disease progression assessment, and clinical trial design of glaucoma.Using advanced algorithms, such as generative adversarial networks, variational autoencoders, and diffusion models, researchers have synthesized high-quality structural images of the optic disc, macular region, and retinal nerve fiber layer, which effectively alleviates the limitations of scarce clinical imaging data and label imbalance.These methods have substantially improved the accuracy and generalization of deep learning models in visual field defect prediction, structure-function mapping, and longitudinal disease progression simulation.Meanwhile, multimodal generative approaches that integrate imaging data, visual field tests, and clinical features have facilitated individualized prediction of glaucoma progression.In addition, large language models have shown preliminary potential in ophthalmic image interpretation, clinical text information extraction, and decision support, providing new insights into intelligent ophthalmic diagnosis and treatment.However, the clinical implementation of generative AI in glaucoma faces challenges.The pathological authenticity and cross-device consistency of generated images require further validation, which may affect the reliability of early glaucoma detection.The heterogeneous characteristics of different glaucoma subtypes, such as open-angle and angle-closure glaucoma, also limit the generalization of synthetic data.Moreover, issues related to model interpretability (" black-box" nature), artifact generation, data privacy, and ethical governance remain key barriers to clinical translation.In the future, it is expected that establishing large-scale training frameworks that incorporate multicenter, multimodal, and multiethnic datasets will enhance model robustness and clinical applicability.Furthermore, generative AI may contribute to remote ophthalmic care and personalized precision therapy by enhancing low-quality image, reconstructing missing data, and simulating dynamic disease courses.This article reviews the current applications, core technologies, and challenges of generative AI in glaucoma diagnosis and management, and discusses its future directions and translational potential in clinical ophthalmology.

【Objective】 To investigate the effects of biomimetic bone trabecular with the same porosity and pore size and regular porous structure on the adhesion, proliferation, and differentiation of osteoblasts, so as to provide theoretical basis for the improvement of osseointegration performance of titanium alloy implants. 【Methods】 The biomimetic bone trabecular and regular porous structures with the same porosity and pore size were generated by computer-aided software, and then processed into disc-shaped Ti6Al4V scaffolds with a diameter of 10 mm and a height of 3 mm by selective laser melting technology. MC3T3-E1 cells, the precursor cells of mouse osteoblasts in the logarithmic growth phase, were seeded on two kinds of scaffolds and divided into biomimetic bone trabecular group and regular porous structure group. After 3 hours of culture, acridine orange staining and phalloidin /DAPI staining were used to evaluate the number of cell adhesion. After 3 days of culture, the scaffolds were examined by scanning electron microscopy to evaluate the adhesion state of cells. After 1, 3, and 5 days of culture, the scaffolds were taken for CCK8 detection to observe the proliferation of cells. After 7 and 14 days of differentiation, alkaline phosphatase (ALP) activity was detected. After 14 days of differentiation, the expressions of osteogenesis-related genes (ALP, OCN, RUNX2) were detected by RT-PCR. After 30 days of differentiation, the scaffolds were stained with alizarin red and 100 g/L cetylpyridinium chloride was used to dissolve mineralized nodules. Calcium salt deposition was qualitatively and quantitatively detected to evaluate cell differentiation. 【Results】 The results of acridine orange and phalloidin /DAPI staining showed that the biomimetic trabecular Ti6Al4V scaffold adhered to more MC3T3-E1 cells than the regular porous structure, and the cytoskeleton of the former scaffold was more densely distributed. The results of scanning electron microscopy showed that the pseudopodia of MC3T3-E1 cells on the biomimetic bone trabecular Ti6Al4V scaffold were longer and the extension state was better than that of the regular porous structure. CCK8 test showed that the proliferation of MC3T3-E1 cells on the biomimetic trabecular bone titanium alloy scaffold was significantly higher than that on the regular porous structure on the 3rd and 5th day, and the difference gradually increased with the increase of time, with statistical significance (P<0.05). The results of cell differentiation test showed that ALP activity on the bionic trabecular scaffold was higher than that on the regular porous structure (P<0.05). The expressions of osteogenic genes (ALP, OCN, RUNX2) in MC3T3-E1 cells on the biomimetic bone trabecular titanium alloy scaffold were significantly higher than those on the regular porous structure (P<0.05). After 30 days of induction, the amount of calcium salt deposited in the bionic trabecular titanium alloy scaffold was significantly larger than that in the regular porous structure (P<0.05). 【Conclusion】 The biomimetic bone trabecular with a porosity of 65% and an equivalent pore size of 600 μm is more conducive to the adhesion, proliferation and differentiation of mouse osteoblast precursor cells MC3T3-E1 on the titanium alloy scaffold than the regular porous structure with the same porosity and pore size. It is theoretically more conducive to improving the osseointegration performance of titanium alloy implants.

Mechanical allodynia (MA), including punctate and dynamic forms, is a common and debilitating symptom suffered by millions of chronic pain patients. Some peripheral injuries result in the development of bilateral MA, while most injuries usually led to unilateral MA. To date, the control of such laterality remains poorly understood. Here, to study the role of microglia in the control of MA laterality, we used genetic strategies to deplete microglia and tested both dynamic and punctate forms of MA in mice. Surprisingly, the depletion of central microglia did not prevent the induction of bilateral dynamic and punctate MA. Moreover, in dorsal root ganglion-dorsal root-sagittal spinal cord slice preparations we recorded the low-threshold Aβ-fiber stimulation-evoked inputs and outputs of superficial dorsal horn neurons. Consistent with behavioral results, microglial depletion did not prevent the opening of bilateral gates for Aβ pathways in the superficial dorsal horn. This study challenges the role of microglia in the control of MA laterality in mice. Future studies are needed to further understand whether the role of microglia in the control of MA laterality is etiology-or species-specific.
Mice ; Animals ; Hyperalgesia/metabolism* ; Microglia/metabolism* ; Disease Models, Animal ; Spinal Cord/metabolism* ; Spinal Cord Dorsal Horn/metabolism* ; Ganglia, Spinal/metabolism*