BACKGROUND:Hippocampal injury caused by aortic coarctation has been poorly studied,and combined detection of tumor necrosis factor α,nuclear factor κB and ionized calcium binding adaptor molecule-1 expression in aortic dissection has not been reported.OBJECTIVE:To observe histomorphologic changes in the hippocampus of a mouse model of aortic dissection and investigate the expression and significance of tumor necrosis factor alpha,nuclear factor kappaB and ionized calcium binding adaptor molecule-1 in the hippocampus of aortic dissection mice.METHODS:Sixteen healthy 3-week-old male C57BL/6 mice were randomly divided into two groups:control group and aortic dissection group,with eight mice in each group.In the aortic dissection group,mice were given β-aminopropionitrile monofumarate as drinking water for 4 weeks,and the angiotensin Ⅱ microinfiltration pump was then implanted to establish an animal model of aortic dissection.Mice in the control group were given normal diet and water.After the model was established,the maximum diameter of the ascending aorta was measured,hematoxylin-eosin staining and EVG staining were performed to evaluate the model formation rate,and the levels of inflammatory factors tumor necrosis factor α and interleukin 6 in serum were detected by enzyme-linked immunosorbent assay.The hippocampus was dissected and stained with hematoxylin-eosin to observe the pathological changes of the hippocampus in brain sections.The protein expression of tumor necrosis factor α,nuclear factor κB and ionized calcium binding adaptor molecule-1 was detected by western blot analysis.RESULTS AND CONCLUSION:(1)Compared with the control group,the maximum diameter of the ascending aorta in the aortic dissection group was significantly enlarged.(2)Hematoxylin-eosin staining of the aorta showed obvious thickening of the middle aorta and destruction and disorder of the aortic wall structure in mice.Neurons in the CA1 and CA3 regions of mice were sparsely arranged,reduced in size,and showed pyknosis with deeply stained nuclei.(3)Serum levels of inflammatory factors tumor necrosis factor α and interleukin 6 were increased in the aortic dissection group compared with the control group(P＜0.01).(4)The expression levels of tumor necrosis factor α,nuclear factor κB,phosphorylated nuclear factor κB,and ionized calcium binding adaptor molecule-1 in the hippocampus were increased in the aortic dissection group compared with the control group(P＜0.05).To conclude,microglial activation and increased expression of tumor necrosis factor α and nuclear factor κB may be involved in hippocampal neuron injury in aortic dissection mice.

BACKGROUND:Hippocampal injury caused by aortic coarctation has been poorly studied,and combined detection of tumor necrosis factor α,nuclear factor κB and ionized calcium binding adaptor molecule-1 expression in aortic dissection has not been reported.OBJECTIVE:To observe histomorphologic changes in the hippocampus of a mouse model of aortic dissection and investigate the expression and significance of tumor necrosis factor alpha,nuclear factor kappaB and ionized calcium binding adaptor molecule-1 in the hippocampus of aortic dissection mice.METHODS:Sixteen healthy 3-week-old male C57BL/6 mice were randomly divided into two groups:control group and aortic dissection group,with eight mice in each group.In the aortic dissection group,mice were given β-aminopropionitrile monofumarate as drinking water for 4 weeks,and the angiotensin Ⅱ microinfiltration pump was then implanted to establish an animal model of aortic dissection.Mice in the control group were given normal diet and water.After the model was established,the maximum diameter of the ascending aorta was measured,hematoxylin-eosin staining and EVG staining were performed to evaluate the model formation rate,and the levels of inflammatory factors tumor necrosis factor α and interleukin 6 in serum were detected by enzyme-linked immunosorbent assay.The hippocampus was dissected and stained with hematoxylin-eosin to observe the pathological changes of the hippocampus in brain sections.The protein expression of tumor necrosis factor α,nuclear factor κB and ionized calcium binding adaptor molecule-1 was detected by western blot analysis.RESULTS AND CONCLUSION:(1)Compared with the control group,the maximum diameter of the ascending aorta in the aortic dissection group was significantly enlarged.(2)Hematoxylin-eosin staining of the aorta showed obvious thickening of the middle aorta and destruction and disorder of the aortic wall structure in mice.Neurons in the CA1 and CA3 regions of mice were sparsely arranged,reduced in size,and showed pyknosis with deeply stained nuclei.(3)Serum levels of inflammatory factors tumor necrosis factor α and interleukin 6 were increased in the aortic dissection group compared with the control group(P＜0.01).(4)The expression levels of tumor necrosis factor α,nuclear factor κB,phosphorylated nuclear factor κB,and ionized calcium binding adaptor molecule-1 in the hippocampus were increased in the aortic dissection group compared with the control group(P＜0.05).To conclude,microglial activation and increased expression of tumor necrosis factor α and nuclear factor κB may be involved in hippocampal neuron injury in aortic dissection mice.

Objective To analyze the factors affecting the prognosis of patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) undergoing immunotherapy and construct an individualized prognostic nomogram prediction model. Methods A retrospective analysis was conducted on the clinical data of 385 patients with driver gene-negative, locally advanced or metastatic NSCLC who received first-line immune checkpoint inhibitors. Univariate and multivariate Cox regression analyses were used to identify prognostic risk factors, and a prognostic nomogram model was established. The predictive performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC), and calibration curves. The cutoff value of the nomogram was calculated to stratify patients by risk. Survival curves were calculated by Kaplan-Meier analysis. Results Age (HR=1.775, 95%CI: 1.265-2.490), degree of differentiation (HR=0.365, 95%CI: 0.257-0.519), low PD-L1 expression (HR=0.661, 95%CI: 0.455-0.960), high PD-L1 expression (HR=0.423, 95%CI: 0.297-0.603), SCC-Ag (HR=1.549, 95%CI: 1.109-2.163), and CONUT score (HR=2.527, 95%CI: 1.797-3.554) were independent risk factors affecting overall survival (OS) of patients with NSCLC undergoing immunotherapy. The nomogram prediction model constructed on the basis of these factors had a C-index of 0.767. Time-dependent ROC curves for survival showed that the AUCs for 1-, 2-, and 3-year OS were 0.830, 0.853, and 0.886, respectively. Calibration curves indicated that the nomogram-predicted survival rates were in good agreement with the actual outcomes. The cutoff value for the study’s nomogram prediction model was 136.60 points, and survival curves showed statistically significant differences between different risk groups (P<0.05). Conclusion The nomogram model established in this study can effectively predict the prognosis of patients with driver gene-negative locally advanced or metastatic NSCLC treated with first-line immunosuppressive therapy. It provides a new tool for assessing prognosis and aids clinicians in formulating individualized treatment plans.

In the context of burgeoning economic trade, there has been a remarkable increase in the number of ships travelling between domestic and international ports. Inbound and outbound cargo ships, cruise liners, ferries, and other types of vessels have emerged as critical vehicles for the dispersal of vector organisms and the spread of infectious disease agents. Public health concerns associated with these vessels represent a significant threat to human welfare. In this paper, we comprehensively review the hazards posed by vector organisms on ships, the latest advances in prevention and control strategies, and proposes appropriate countermeasures. The aim is to provide effective guidance for the prevention and control of public health security issues caused by ship-borne vector organisms.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

BackgroundObstructive sleep apnea （OSA） represents a prevalent sleep disordered breathing condition characterized by complex pathophysiology. The arousal threshold （ArTH）， a core non-anatomical contributor to OSA pathogenesis， is intimately tied to the disease severity and clinical phenotypes. To date， research regarding factors associated with ArTH has yielded inconsistent findings， and ArTH profiles and disparities across populations with different genetic backgrounds are not fully elucidated. ObjectiveTo explore the differences of ArTH in OSA patients with different genetic backgrounds and analyze the key factors affecting ArTH， thereby providing evidence for understanding OSA pathophysiology and formulating targeted treatment regimens. MethodsA total of 285 patients who met the diagnostic criteria for OSA， as defined by the Multidisciplinary Diagnosis and Treatment Guidelines for Adult Obstructive Sleep Apnea， were retrospectively enrolled in this study. All participants underwent overnight polysomnography （PSG） at the Sleep Medicine Center of Inner Mongolia Mental Health Center from December 2022 to May 2024. Based on the study design， the cohort was stratified into two distinct genetic background subgroups （group A and group B）. Demographic and clinical characteristics， the Epworth Sleepiness Scale （ESS） score， and overnight PSG data were collected. The apnea hypopnea index （AHI）， the lowest pulse oxygen saturation （LSpO₂）， and fraction of hypopneas （F_Hypopneas） were utilized as surrogate indicators to estimate ArTH in OSA patients. The influencing factors of low ArTH were tested by binary Logistic regression analysis. ResultsAmong the 285 OSA patients， there were 227 cases （79.65%） in group A and 58 cases （20.35%） in group B. Comparisons between the two genetic background subgroups revealed no statistically significant differences in the proportion of low ArTH， ESS score， PSG parameters， and the three markers for low ArTH （AHI<30 events/h， LSpO₂>82.5%， F_Hypopneas>58.3%） （P>0.05）. Binary Logistic regression analysis identified sex （OR=2.421， 95% CI： 1.070–5.478）， BMI （OR=0.847， 95% CI： 0.770–0.932）， N1 sleep duration （OR=0.974， 95% CI： 0.963–0.985）， and hypertension （OR=0.348， 95% CI： 0.143–0.848） as independent factors associated with low ArTH in the total cohort. Stratified analysis by genetic backgrounds revealed that sex（OR=3.799， 95% CI： 1.389–10.392）， BMI（OR=0.819， 95% CI：0.723–0.929）， and N1 sleep duration（OR=0.973， 95% CI： 0.961–0.986） were independent factors of low ArTH in group A. In contrast， only N1 sleep duration （OR=0.951， 95% CI： 0.911–0.993） remained a significant factor in group B. ConclusionArthur may have conservative characteristics in patients with OSA with different genetic backgrounds， but the pathophysiological mechanism of OSA may have population heterogeneity. ［Funded by Inner Mongolia Autonomous Region Natural Science Fundation Project （number， 2024QN08050）； Intra-institutional Scientific Research Project of Inner Mongolia Mental Health Center （number， 2022QNWN0010）］

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To investigate the predictive value of serum interleukin-17(IL-17)combined with eotaxin-3 for poor prognosis in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods A total of 213 patients with AECOPD admitted to Beijing Municipal Armed Police Force Hospital from May 2018 to July 2023 were selected as the disease group.According to the prognosis of patients,they were divided into good prognosis group(133 cases)and poor prognosis group(80 cases).At the same time,205 physical examination healthy people in Beijing Municipal Armed Police Force Hospital were selected as the healthy group.The serum levels of IL-17 and eotaxin-3 were detected by enzyme-linked immu-nosorbent assay.The clinical data of poor prognosis group and good prognosis group were compared.Pearson correlation analysis was used to analyze the correlation between serum IL-17 level and eotaxin-3 in AECOPD patients.Multivariate Logistic regression analysis was used to analyze the related factors affecting the progno-sis of AECOPD patients.The receiver operating characteristic(ROC)curve was used to evaluate the predic-tive value of serum IL-17 and eotaxin-3 levels for the prognosis of AECOPD patients.Results Compared with the healthy group,the serum levels of IL-17 and eotaxin-3 were increased in the disease group(P<0.05).Compared with the good prognosis group,the poor prognosis group had significant increases in serum IL-17 and eotaxin-3 levels(P<0.05).Serum IL-17 level was positively correlated with eotaxin-3 in AECOPD pa-tients(r=0.537,P<0.001).There were significant differences in Global Initiative for Chronic Obstructive Lung Disease(GOLD)grade,blood oxygen partial pressure(PaO2)and carbon dioxide partial pressure(PaCO2)between the poor prognosis group and the good prognosis group(P<0.05).GOLD grade,PaCO2,serum IL-17 and eotaxin-3 levels were risk factors for poor prognosis in patients with AECOPD(P<0.05),and PaO2 was a protective factor for poor prognosis in patients with AECOPD(P<0.05).The area under the curve of serum IL-17 and eotaxin-3 combined to predict the prognosis of AECOPD patients was 0.885,the sensitivity was 80.00%,and the specificity was 83.46%,which was better than that of IL-17 and eotaxin-3 a-lone(Zcombiation-IL-17=4.045,P<0.001,Zcombiation-eotaxin-3=3.254,P=0.001).Conclusion The serum levels of IL-17 and eotaxin-3 are increased in AECOPD patients.The combination of IL-17 and eotaxin-3 has predictive value for the prognosis of AECOPD patients.

Objective:To prepare decellularized extracellular matrix (dECM)-gelatin methacryloyl (GelMA) composite hydrogels and to study their effects on hepatocyte proliferation.Methods:Hepatic dECM was prepared by elution, and GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels were prepared by pepsin solubilization. The morphology of normal liver and dECM liver was observed by eyes and scanning electron microscopy using hematoxylin-eosin, Sirius red and periodate-Schiff staining, respectively. The internal structure of the dECM-GelMA composite hydrogels was observed by scanning electron microscopy, and the pore diameter was measured. Liver HL-7702 cells were co-cultured with GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels, and the cell proliferation viability was determined by cell counting kit-8. The expression of proliferating cell nuclear antigen (PCNA), Wnt family protein 5a (Wnt5a), β-catenin, extracellular-regulated protein kinase 1/2 (ERK1/2) and phosphorylated ERK1/2 (p-ERK1/2) were detected by Western blotting. Comparisons were made using independent sample t-test or one-factor analysis of variance. Results:After decellularization, the hepatocyte morphology showed rounded depressions, and the extracellular matrix structure was intact. The GelMA hydrogel and 10%, 30% and 50% dECM-GelMA composite hydrogels showed inernally porous structures. The pore diameter increased from (3.06±1.35) μm in the GelMA hydrogel to (16.01±4.02) μm in the 50% dECM-GelMA composite hydrogel. On the 3rd, 5th and 7th day, the relative cell proliferation was higher in the 50% dECM-GelMA composite hydrogel group than that in the GelMA hydrogel group (1.89±0.04 vs 1.53±0.01, 9.36±0.04 vs 3.89±0.09, 7.15±0.27 vs 4.89±0.15, all P<0.05). The relative expression levels of PCNA, Wnt5a, β-catenin, and p-ERK1/2/ERK1/2 proteins in the 50% dECM-GelMA composite hydrogel group were higher than those in the GelMA hydrogel group (2.14±0.04 vs 1.00±0.03, 2.36±0.09 vs 1.00±0.08, 1.45±0.03 vs 1.00±0.04, 1.43±0.04 vs 1.00±0.01, all P<0.05). Conclusions:A dECM-GelMA composite hydrogel can be prepared, which may promote hepatocyte proliferation by upregulating the phosphorylation of ERK1/2 and activating Wnt/β-catenin signaling pathway.