Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

End-stage dilated cardiomyopathy belongs to the irreversible cardiac decompensation stage,and neither drugs nor cardiac resynchronization therapy can improve the symptoms of heart failure in patients.Orthotopic heart transplantation is a surgical procedure that involves removing the diseased heart of the recipient and implanting the donor heart in its original position.With the advancements in surgical transplantation techniques and immunosuppressive therapy,it has become an effective treatment for end-stage heart disease.Coronary artery disease after heart transplantation is one of the issues that need attention after heart transplantation.This article reports a 68-year-old male who suffered from recurrent heart failure and ventricular tachycardia due to"dilated cardiomyopathy"and underwent allogeneic orthotopic heart transplantation 5 years ago.The patient underwent coronary angiography and intravascular ultrasound examination under local anesthesia.This case has certain guiding significance for studying the progression of coronary artery disease in heart transplant patients.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

End-stage dilated cardiomyopathy belongs to the irreversible cardiac decompensation stage,and neither drugs nor cardiac resynchronization therapy can improve the symptoms of heart failure in patients.Orthotopic heart transplantation is a surgical procedure that involves removing the diseased heart of the recipient and implanting the donor heart in its original position.With the advancements in surgical transplantation techniques and immunosuppressive therapy,it has become an effective treatment for end-stage heart disease.Coronary artery disease after heart transplantation is one of the issues that need attention after heart transplantation.This article reports a 68-year-old male who suffered from recurrent heart failure and ventricular tachycardia due to"dilated cardiomyopathy"and underwent allogeneic orthotopic heart transplantation 5 years ago.The patient underwent coronary angiography and intravascular ultrasound examination under local anesthesia.This case has certain guiding significance for studying the progression of coronary artery disease in heart transplant patients.

Objective:To investigate the ocular pathogen spectrum and drug sensitivity characteristics of patients to be diagnosed with infectious keratitis within 10 years in two tertiary hospitals in Nantong City, Jiangsu Province, eastern China.Methods:A cross-sectional study was conducted.Microbial culture specimens from consecutive 1 404 patients with suspected infectious keratitis admitted to the Department of Ophthalmology at the First People's Hospital of Nantong City and Nantong University Affiliated Hospital from January 2014 to October 2023 were collected.The patients' general data, etiology and drug sensitivity test results were analyzed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Nantong First People's Hospital (No.2021KT273) and Nantong University Affiliated Hospital (No.2019-K068).Results:Among 1 404 patients with suspected infectious keratitis, the positive rate of microbial culture was 37.04%(520/1 404).A total of 537 strains were isolated and cultured, with fungi accounting for 69.09%(371/537) and bacteria accounting for 30.91%(166/537).The common bacterial genera in fungal keratitis were Fusarium (47.17%, 175/371), Alternaria (15.90%, 59/371), Aspergillus (14.56%, 54/371), Certospora (10.78%, 40/371) and Penicillium (3.50%, 13/371).The annual composition ratio of Fusarium showed a downward trend, while Certospora showed an upward trend.The common bacteria in bacterial keratitis were Staphylococcus epidermidis (24.10%, 40/166), Streptococcus pneumoniae (17.47%, 29/166), Pseudomonas aeruginosa (13.25%, 22/166), Staphylococcus aureus (6.63%, 11/166) and Corynebacterium macginleyi (4.22%, 7/166).The annual composition ratio of Pseudomonas aeruginosa showed an increasing trend.The resistance rates of gram-positive bacteria to levofloxacin and vancomycin were 36.26% and 0% respectively, and the resistance rates of gram-negative bacteria to aminoglycosides and ceftazidime were <10%.A total of 61 bacterial strains (40.94%) were multi-drug resistant. Conclusions:Fusarium is common in fungal keratitis, and Corynebacterium macginleyi in bacterial keratitis may be a microbial feature in Nantong City.Levofloxacin may no longer be suitable as a first-line antibiotic for topical ocular use.

Objective To explore the brain damage of SD rats under different time points of hypobaric hypoxia exposure.Methods A rat high-altitube cerebral edema(HACE)model was constructed by simulating an altitude of 6 000 m in a hypobaric hypoxia animal experimental chamber.Thirty-six SD male rats were randomly divided into the control group and the hypobaric hypoxia exposure 3,7 and 14 d groups,with 9 rats in each group.Except for the control group,the rats in each group were continuously exposed to hypobaric hypoxia for 3,7,and 14 d.At the end of the modeling period,serum was collected by blood sampling via the abdominal aorta,and brain tissue samples were taken.The wet-to-dry ratio(W/D)of brain tissue was calculated,and the levels of relevant oxidative enzymes in serum and brain tissue were measured.The expression levels of hypoxia-inducible factor-1α(HIF-1α)and aquaporin 4(AQP4)mRNAs in brain tissue were detected by real-time fluorescence quantitative polymerase chain reaction.Results The W/D of brain tissues in the control group and the group exposed to hypobaric hypoxia for 3,7 and 14 d were 4.46±0.12,4.98±0.16,5.07±0.18 and 4.95±0.07;the superoxide dismutase contents were(111.86±2.45),(90.73±1.48),(79.64±2.56)and(55.33±1.45)U·g-1;the glutathione contents were(126.91±5.18),(125.26±1.53),(56.20±2.17)and(122.73±1.78)μg·mL-1;the malondialdehyde contents were(230.94±2.00),(362.65±3.28),(407.34±3.47)and(237.50±1.59)nmol·g-1;the relative expression levels of HIF-1 α mRNA were 1.00±0,2.99±0.49,4.72±0.49 and 1.91±0.28;the relative expression levels of AQP4 mRNA were 1.00±0,2.62±0.34,8.38±0.84 and 5.27±0.42,respectively.Statistically significant differences were found between the above indexes in the 3,7 and 14 d of hypobaric hypoxia exposure group compared with the control group(P＜0.05,P＜0.01).Conclusion Different time of hypobaric hypoxia exposure can up-regulate the expression of AQPs proteins in HACE rats and cause the disruption of the blood-brain barrier,and the HACE model constructed in the hypobaric hypoxia chamber with 6 000 m intervention for 7 d was more stable.

Objective:To investigate the ocular pathogen spectrum and drug sensitivity characteristics of patients to be diagnosed with infectious keratitis within 10 years in two tertiary hospitals in Nantong City, Jiangsu Province, eastern China.Methods:A cross-sectional study was conducted.Microbial culture specimens from consecutive 1 404 patients with suspected infectious keratitis admitted to the Department of Ophthalmology at the First People's Hospital of Nantong City and Nantong University Affiliated Hospital from January 2014 to October 2023 were collected.The patients' general data, etiology and drug sensitivity test results were analyzed.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Nantong First People's Hospital (No.2021KT273) and Nantong University Affiliated Hospital (No.2019-K068).Results:Among 1 404 patients with suspected infectious keratitis, the positive rate of microbial culture was 37.04%(520/1 404).A total of 537 strains were isolated and cultured, with fungi accounting for 69.09%(371/537) and bacteria accounting for 30.91%(166/537).The common bacterial genera in fungal keratitis were Fusarium (47.17%, 175/371), Alternaria (15.90%, 59/371), Aspergillus (14.56%, 54/371), Certospora (10.78%, 40/371) and Penicillium (3.50%, 13/371).The annual composition ratio of Fusarium showed a downward trend, while Certospora showed an upward trend.The common bacteria in bacterial keratitis were Staphylococcus epidermidis (24.10%, 40/166), Streptococcus pneumoniae (17.47%, 29/166), Pseudomonas aeruginosa (13.25%, 22/166), Staphylococcus aureus (6.63%, 11/166) and Corynebacterium macginleyi (4.22%, 7/166).The annual composition ratio of Pseudomonas aeruginosa showed an increasing trend.The resistance rates of gram-positive bacteria to levofloxacin and vancomycin were 36.26% and 0% respectively, and the resistance rates of gram-negative bacteria to aminoglycosides and ceftazidime were <10%.A total of 61 bacterial strains (40.94%) were multi-drug resistant. Conclusions:Fusarium is common in fungal keratitis, and Corynebacterium macginleyi in bacterial keratitis may be a microbial feature in Nantong City.Levofloxacin may no longer be suitable as a first-line antibiotic for topical ocular use.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective: To explore the key factors affecting the selection and effectiveness of bladder management modalities in patients with spinal cord injury，so as to provide reference for the optimization of individualized bladder management strategies. Methods: The clinical and follow-up data of 78 patients with spinal cord injury treated in our hospital during Jan.1,2013 and Dec.31,2022 were retrospectively analyzed.The distribution of bladder management modalities among different grades of injuries was analyzed. Bowker symmetry test was used to evaluate the difference between bladder management modalities at discharge and at the end of follow-up. Multiple linear regression was used to explore the influencing factors of bladder management effects. Plotting Kaplan-Meier survival curves were adopted to calculate the median time of changes in bladder management. Results: At discharge，there were 9 cases of self-catheterization，19 cases of intermittent catheterization，22 cases of reflexive voiding，26 cases of long-term catheterization，and 2 cases using urinary collector.At the end of follow-up，there were 15 cases of self-catheterization，8 cases of intermittent catheterization，34 cases of reflexive voiding，14 cases of long-term catheterization，and 7 cases using urinary collector.There was a significant difference between the modalities of bladder management at discharge and at the end of follow-up (χ =21.43，P=0.018).Multiple linear regression showed a significant decrease of 8.60 in the total neurogenic bladder symptom score (NBSS) for grade D injuries compared with grade A injuries (P=0.026). The median time to bladder management change was 7.93 months (95%CI:5.44-9.44), with approximately 50% of patients experiencing a change in bladder management within 8 months after discharge. Conclusion: The modalities of bladder management changed significantly after discharge.The grade of injury was a key factor affecting the effectiveness of bladder management.Higher grade was associated with worse effectiveness of bladder management.