BACKGROUND: Changes in N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels may not fully translate into patient-reported health status in patients with heart failure (HF). We aimed to evaluate the correlation between NT-proBNP levels and patient-reported health status changes at one month after discharge of patients, and their associations with risk of death and rehospitalization in patients with acute HF. METHODS: We used data from the China Patient-centered Evaluative Assessment of Cardiac Events Prospective Heart Failure Study (PEACE 5p-HF Study). Patient-reported health status was measured by the 12-item Kansas City Cardiomyopathy Questionnaire (KCCQ-12). Patients who were hospitalized for HF and completed the KCCQ-12 and NT-proBNP tests before and one month after discharge were eligible in our study. We stratified patients into different groups based on NT-proBNP levels (i.e., improved, stable, and deteriorated) and KCCQ-12 scores (i.e., not deteriorated and deteriorated). We also examined the associations of the joint NT-proBNP and KCCQ-12 change with the risk of one-year and four-year clinical outcomes. RESULTS: A total of 2461 patients were included in the analysis. The mean age was 64.06 ± 13.51 years, and 36.37% (895/2461) of the study population were female. Among patients with improved NT-proBNP levels, 115 (10.95%) patients had deteriorated KCCQ-12 scores. The correlation between the change in the KCCQ-12 score and NT-proBNP level was weak ( r2 = 0.002, P = 0.013). Stratification by changes in the KCCQ-12 score revealed subgroups with distinctive risks, such that patients with deteriorated KCCQ-12 scores in any of the NT-proBNP change groups exhibited an increased risk of one-year all-cause death than participants with not deteriorated KCCQ-12 scores in any of the NT-proBNP change groups. Patients with improved NT-proBNP levels and deteriorated KCCQ-12 scores presented greater risks of one-year all-cause death (hazard ratio [HR]: 2.45, 95% confidence interval [CI]: 1.34-4.48) than patients with stable NT-proBNP levels and not deteriorated KCCQ-12 scores (HR [95% CI], 1.77 [1.25-2.53]). CONCLUSIONS: A discrepancy between changes in NT-proBNP levels and KCCQ-12 scores was common. The change in NT-proBNP levels was not sufficient to characterize critical aspects related to HF during one month after discharge of patients. Changes in the KCCQ-12 score exhibit complementary information to NT-proBNP levels for the prediction of clinical outcomes in patients with acute HF. REGISTRATION www.clinicaltrials.gov (No. NCT02878811).
Aged ; Female ; Humans ; Male ; Middle Aged ; Health Status ; Heart Failure/metabolism* ; Natriuretic Peptide, Brain/metabolism* ; Peptide Fragments/metabolism* ; Prospective Studies

OBJECTIVE To evaluate the quality differences of Alpinia katsumadai from different habitats. METHODS High- performance liquid chromatography（HPLC） was used to establish the fingerprints of A. katsumadai from 18 batches of different habitats， and the quality of A. katsumadai from different habitats was comprehensively evaluated by similarity evaluation， cluster analysis （CA）， principal component analysis （PCA）， orthogonal partial least squares-discriminant analysis （OPLS-DA） and the content determination results of alpinetin， pinocembrin， cardamonin and alnustone in A. katsumadai. RESULTS The similarity of HPLC fingerprints for 18 batches of A. katsumadai was ＞0.9. Eleven common peaks were identified from the chromatogram， and four of them were specifically characterized. Both CA and PCA grouped 18 batches of A. katsumadai into 3 categories， extracting 2 principal components （the cumulative variance contribution rate reached 89.798%）. OPLS-DA identified 9 quality difference markers， namely the components corresponding to peaks 4， 9， 3， 2， 7 （pinocembrin）， 8 （cardamonin）， 6 （alpinetin）， 10 and 11 （alnustone）. The content of alpinetin， pinocembrin， cardamonin， and alnustone ranged from 4.507 1-11.579 7， 5.154 4-14.183 3， 5.109 5-13.588 3 and 4.494 6-11.277 2 mg/g， respectively. CONCLUSIONS The quality of A. katsumadai from different habitats is quite different， and the quality of A. katsumadai from Hainan is the best.

Objective To explore the key environmental factors influencing the distribution and spatially stratified heterogeneity of the suitable habitats of Ranunculus ternatus Thunb.;To predict its potential suitable habitats within China;To provide theoretical guidance for the conservation of its wild resources,artificial introduction and cultivation,and production planning.Methods Based on the MaxEnt model,a distribution prediction model was established using 148 selected species distribution information and 28 environmental factor data.The ENMeval package in R language was used to optimize the model parameters.Quantitative analysis(q-value ranking)of environmental factors affecting spatial differentiation in potential suitable areas using geographic detector factor detection and interaction detection.Results After optimization,the accuracy of model was improved and the degree of overfitting was reduced.The average value of the area under the curve(AUC)was 0.940.The contribution rate of environmental factors and the jackknife test showed that the key environmental factors influencing the distribution of Ranunculus ternatus Thunb.mainly included the precipitation in March,the precipitation in November,elevation,the solar radiation intensity in October,the solar radiation intensity in December,and the average temperature in January.The results of raster calculation showed that the area of highly suitable habitats of Ranunculus ternatus Thunb.was approximately 4.105×104 km2,the area of moderately suitable habitats was approximately 4.243×105 km2,and the area of lowly suitable habitats was approximately 6.385×105 km2,accounting for about 3.8%,38.4%and 57.8%of the total area of suitable habitats,respectively.Conclusion This study can provide a basis for the conservation and sustainabele utilization of the wild resources of Ranunculus ternatus Thunb.

Objective:To understand the epidemiological characteristics and spatiotemporal distribution of hepatitis E in Zhejiang Province from 2008 to 2023 and provide scientific evidence for the prevention and control of hepatitis E.Methods:The incidence data of hepatitis E in Zhejiang from 2008 to 2023 were obtained from China Disease Control and Prevention Information System. Joinpoint regression model was used to analyze the trends in incidence rates. Software ArcGIS 10.8 was used to analyze the spatial distribution and autocorrelation of hepatitis E cases, while software SaTScan 10.1 was used to investigate spatial clustering at district level.Results:A total of 33 027 cases of hepatitis E were reported in Zhejiang from 2008 to 2023. The average annual incidence rate was 3.64/100 000. The majority of the cases reported were men (21 739), and higher average annual incidence rate was observed in the age group 50-74 years (32.04/100 000). The overall reported incidence rate declined first and then increased in 2020, showing a seasonal distribution with a high incidence from January to May. Notably, the incidence was particularly high in Hangzhou (6.54/100 000) and Quzhou (5.56/100 000). Spatial autocorrelation analyses revealed that the incidence of hepatitis E exhibited high-high clustering in all the years, with northwestern Zhejiang as main affected area. Prior to 2020, the high-high clustering area in northwestern Zhejiang became smaller, followed by an expansion trend in 2020-2022. Spatiotemporal clustering analyses identified seven clustering areas from 2010 to 2019, one was in 16 counties (cities, districts) in northwestern Zhejiang. And the clustering areas along the coastline shifted from the north to the south.Conclusions:The incidence of hepatitis E in Zhejiang exhibited distinct spatiotemporal clustering pattern. Consequently, targeted prevention and control measures could be taken for hepatitis E in high-risk areas in Zhejiang, including northwestern and coastal areas.

Objective To explore the environmental factors influencing the distribution of Pulsatilla chinensis(Bge.)Regel and to predict its potential habitat in China;To provide reference for the assessment of its wild resources and the introduction of its cultivation.Methods Totally 230 pieces of information on the distribution of sample sites were collected.Combined with data of 48 environmental factors,MaxEnt model and ArcGIS 10.8 software were used to select the dominant environmental factors that affect their distribution,and visual analysis of potential suitable areas was conducted.Results The prediction accuracy of the established model met the requirements(AUC＞0.9).The main environmental factors affecting the distribution of Pulsatilla chinensis(Bge.)Regel were the precipitation in the wettest season,the average temperature in November,the altitude,the standard deviation of the seasonal variation of temperature,the precipitation in May and the average temperature in August,with a cumulative contribution rate of 84.3％.The prediction results showed that it was mainly distributed in the North China and Northeast China,with a total suitability zone of about 2 382 461.60 km2,of which the low suitability zone was about 1 263 497.00 km2,the medium suitability zone was about 582 879.20 km2,and the high suitability zone was about 536 085.40 km2,accounting for approximately 53.03％,24.47％and 22.50％of the total suitability zone.Conclusion The results of the model are consistent with the literature records,which can provide theoretical reference for the sustainable utilization of Pulsatilla chinensis(Bge.)Regel.resources.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

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.

With the rapid advancement of artificial intelligence (AI) technology, particularly breakthroughs in large models, AI applications in healthcare are becoming increasingly widespread.Ophthalmology, as a critical branch of medicine, has emerged as a clinical specialty with comprehensive AI research and applications, especially in the AI-driven analysis of ophthalmic imaging.Currently, AI in ophthalmology is undergoing profound transformation driven by large model technology.AI large models, with their robust data comprehension and multimodal interaction capabilities, offer new possibilities for the diagnosis, treatment, and health education of ophthalmic diseases, powerfully propelling the evolution of current AI paradigms in ophthalmology.This article explores the novel opportunities brought by the extensive application of AI large models in ophthalmic research and practice, and provides recommendations for ophthalmologists and insights for industry development.

Objective:To evaluate the short-term and long-term outcomes of patients with locally advanced low rectal cancer who achieved clinical complete response (cCR) or near-clinical complete response (near-cCR) after neoadjuvant chemoradiotherapy (nCRT) and then underwent local excision.Methods:This was a descriptive case series study. Clinical data of patients with low rectal cancer who received neoadjuvant therapy, achieved cCR or near-cCR, underwent local excision, and had complete postoperative follow-up data were retrospectively analyzed. The study period was from May, 2015 to October, 2024, and the patients were treated at Fujian Medical University Union Hospital. Indications for local excision in this study were as follows: pathologically confirmed rectal adenocarcinoma, with the lower edge of the tumor ≤ 6 cm from the anal verge; maximum diameter of the lesion ≤ 2 cm after nCRT; no regional lymph node metastasis detected by transrectal endoscopic ultrasound (ERUS), pelvic magnetic resonance imaging (MRI), or positron emission tomography-computed tomography (PET-CT) after nCRT; MRI showing fibrosis of the primary lesion with a small amount of high signal on diffusion-weighted imaging (DWI), consistent with ymrT0-1 stage; serum carcinoembryonic antigen level within the normal range (< 5 μg/L) after nCRT; complicated with severe underlying diseases such as cardiovascular and cerebrovascular diseases and assessed as unable to tolerate radical surgery through comprehensive evaluation; and signed informed consent for local excision. The contraindications were: colonoscopic pathology indicating poorly differentiated adenocarcinoma or signet ring cell carcinoma; suspected lateral lymph node metastasis before neoadjuvant therapy; patients with residual lesions exceeding 3 cm in range after treatment. A total of 153 patients were included in this study, including 84 males and 69 females. The median age was 62 years, and the median distance from the tumor to the anal verge after neoadjuvant therapy was 4.0 cm. The short-term efficacy indicators of this study included postoperative complications of local excision and postoperative pathological results, and the long-term efficacy indicators included oncological prognosis (3-year cumulative local recurrence rate, 3-year cumulative distant metastasis rate, 3-year progression-free survival, and 3-year overall survival) and anal function at 1 year after surgery evaluated using the Low Anterior Resection Syndrome (LARS) scale where the total score is 42 points such that 0-20 points indicate no LARS, 21-29 points indicate mild LARS, and 30-42 points indicate severe LARS.Results:Postoperative pathology showed 122 cases (79.7%) of ypT0 stage, 10 cases (6.5%) of ypT1 stage, 18 cases (11.8%) of ypT2 stage, and 3 cases (2.0%) of ypT3 stage. The incidence of surgery-related complications was 42.5% (65/153), and the main complications included perianal pain (39.9%, 61/153), intestinal wall incision dehiscence (21.6%, 33/153), and intestinal wall incision infection (18.3%, 28/153). The proportion of patients who received hypofractionated radiotherapy before surgery and developed intestinal wall incision dehiscence was 65.2% (15/23), which was higher than that in the conventional long-course (13.6%, 16/118) and short-course radiotherapy groups (16.7%,2/12) (χ 2=30.55, P<0.001); of the 20 patients who received additional immunotherapy before surgery, 13 developed intestinal wall incision dehiscence was 65.0%, which was higher than that in the group without additional immunotherapy [15.0%(20/133),χ 2=25.66, P<0.001]. The median follow-up time of the entire group was 35.4 months. During the follow-up period, there were 9 cases of postoperative local recurrence, with a 3-year cumulative local recurrence rate of 7.9% and 5 cases of distant metastasis, with a 3-year cumulative distant metastasis rate of 5.0%. The 3-year progression-free survival rate was 89.0%, and the 3-year overall survival rate was 95.9%. At 1 year after surgery, 10 cases (10.5%, 10/95) had severe anal dysfunction, and the median LARS score of the entire group was 5.0 (range: 0-41.0) points. Conclusions:For patients with locally advanced low rectal cancer who achieve cCR or near-cCR after neoadjuvant therapy, local excision results in favorable oncological prognosis and anal function preservation effects; however, the incidence of complications is relatively high.

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.