ObjectiveTo investigate the first-diagnosed reporting rate and annual changing trends of chronic obstructive pulmonary disease (COPD) among adult residents in Changzhou City from 2020 to 2024, and to provide a scientific basis for the prevention and control of COPD in this region. MethodsData on first-diagnosed cases of COPD from 2020 to 2024 were collected from the Changzhou Health Data Management Platform, along with resident population data stratified by age group and sex for the corresponding years. Stratified analyses were conducted by diagnosis year, sex, and age group to calculate crude first-diagnosed reporting rate and age-specific first-diagnosed reporting rate. The age-standardized first-diagnosed reporting rate (ASFDRR) was calculated using data from the Seventh National Population Census of China (2020) as the standard population. A logarithmic-linear model was employed to estimate the average annual percentage change (AAPC) in ASFDRR, along with its 95%CI and P-value. ResultsFrom 2020 to 2024, a total of 12 925 first-diagnosed COPD cases of adult residents were reported in Changzhou City. The overall crude first-diagnosed reporting rate increased from 37.91/100 000 in 2020 to 100.68/100 000 in 2024. The overall ASFDRR rose from 116.83/100 000 in 2020 to 274.59/100 000 in 2024, with an AAPC of 26.55% (95%CI: 5.98%‒51.11%). Among these,9 831 new cases were male, with the crude first-diagnosed reporting rate increasing from 57.07/100 000 to 159.29/100 000, and the ASFDRR increasing from 92.54/100 000 to 223.82 / 100 000, with an AAPC of 27.58% (95%CI: 6.21%‒53.26%). There were 3 094 new female cases, with the crude first-diagnosed reporting rate rising from 19.53/100 000 to 45.39/100 000, and the ASFDRR increasing from 26.01/100 000 to 55.33/100 000, with an AAPC of 22.90% (95%CI: 5.19%‒43.59%). Both crude and age-standardized first-diagnosed reporting rates of COPD for the total population and different gender groups showed a statistically significant upward trend as age increased (all P<0.05). The reporting peaks were primarily concentrated in the 80 years old age group. ConclusionThe age-standardized first-diagnosed reporting rate of COPD in Changzhou City exhibited a rapid upward trend from 2020 to 2024. ASFDRR was higher in males than that in females, and the elderly population constituted the main reported group. This trend warrants close attention, emphasizing the need to strengthen screening, early intervention, and standardized management for high-risk populations.

Metabolic dysfunction-associated fatty liver disease （MAFLD） has become one of the most prevalent chronic liver diseases worldwide， posing a serious challenge to public health. In this context， the integration of artificial intelligence （AI）， especially intelligent diagnosis and treatment and digital health interventions based on machine learning， can break through the limitations of traditional methods， realize efficient screening of multi-dimensional data such as key genes， biomarkers， and biochemical metabolites， and achieve revolutionary breakthroughs in risk prediction， subtype identification， and therapeutic effect assessment for MAFLD. This article systematically reviews the ground-breaking application of machine learning models in driving the innovation of clinical diagnosis and precise risk prediction of MAFLD， conducts a comprehensive comparative analysis of digital health practice cases of MAFLD in China and globally， and deeply analyzes their advantages and limitations in terms of research subjects， interventions， and management team. Studies have shown that the deep integration of digital health and long-term management of MAFLD is becoming the key engine driving the transformation of disease management modes towards an intelligent， individualized， and precise era， but there are various ethical and technical issues that need to be addressed urgently.

Objective To investigate the relationship between plasma von willebrand factory-lytic protease (ADAMTS13) level and the severity of the disease and its prognostic value in patients with endometriosis (EMS). Methods 130 patients with EMS treated in the hospital from January 2019 to December 2022 were retrospectively selected. Plasma ADAMTS13 level was detected by enzyme-linked immunosorbent assay (ELISA),and its correla-tion with American Fertility Society (r-AFS) stage and endometriosis Health Status Scale (EHP-30) score was analyzed. The 1-year prognosis was recorded,and the risk factors of poor prognosis were analyzed by logistic regression. Receiver operating characteristic curve (ROC) was plotted to evaluate the predictive value of plasma ADAMTS13 levels in patients with EMS. Results The plasma ADAMTS13 level of EMS patients decreased signifi-cantly with the increase of r-AFS stage,and was negatively correlated with the score of EHP-30 (P<0.001). Multiple factors showed that low plasma ADAMTS13 level,bilateral location of pelvic lesions and high r-AFS stage were independent risk factors for poor prognosis (all P<0.05). ROC analysis showed that the area under the curve (AUC) of plasma ADAMTS13 was 0.889 (cut-off point 13.315,sensitivity 0.931,specificity 0.690). Conclusion Plasma ADAMTS13 level is negatively correlated with the severity of EMS,and is an independent risk factor for poor prognosis of EMS patients,and has a certain predictive value for prognosis of EMS patients.

Objective To investigate the diagnostic accuracy and clinical applicability of the Linknet-VGG16 deep learning algorithm for measuring the effective optical zone(EOZ)after corneal refractive surgery.Methods This single-center retrospective cohort study included 69 patients(69 eyes)who underwent femtosecond laser-assisted in situ kerato-mileusis(FS-LASIK)(34 eyes)or small incision lenticule extraction(SMILE)(35 eyes)at the Refractive Surgery Center of Affiliated Zhengzhou Aier Eye Hospital of Henan University from June 2023 to June 2024.Data from the right eyes of all patients were selected for statistical analysis.During the surgery,patients in the FS-LASIK group adopted the VisuMax fem-tosecond laser system combined with the Amaris 750S excimer laser system,while those in the SMILE group only used the VisuMax femtosecond laser system.A total of 276 Pentacam images were re-examined postoperatively.A Linknet segmenta-tion model based on the VGG16 encoder was constructed,and image normalization techniques were applied to accelerate model convergence.Model performance was assessed using accuracy,intersection over union(IoU),and the Dice coeffi-cient.The traditional EOZ measurement method based on corneal tangential curvature served as the reference standard.Bland-Altman analysis was conducted to evaluate consistency across all images and within each group,and the time effi-ciency of both methods was compared.Results Six representative medical image segmentation architectures(U-Net,U-Net++,DeepLabv3-ResNet50,DeepLabv3+-ResNet50,Unet-Densenet169,and Linknet-VGG16)were systematically evaluated.The Linknet-VGG16 model demonstrated superior performance over the other 5 models in pixel-level accuracy,IoU and Dice coefficient,which were 99.83％,99.48％and 99.74％,respectively.Although there was no significant differ-ence in accuracy and Dice coefficient between Linknet-VGG16 and U-Net models(whose accuracy was 99.82％and Dice coefficient was 99.72％),the inference speed of the U-Net model(62.46 ms)was 31.76％slower than that of the Linknet-VGG16 model(42.62 ms).The evaluation results of a clinically applicable comprehensive scoring model(weights:accura-cy 20％,IoU 20％,Dice coefficient 20％,speed 25％,model size 15％)showed that the Linknet-VGG16 model achieved a score of 88.01,surpassing other architectures(U-Net:86.29;DeepLabv3+-ResNet50:80.41;DeepLabv3-ResNet50:73.82;U-Net++:73.22;Unet-Densenet169:66.66).Bland-Altman analysis revealed that the mean difference of the 136 images in the FS-LASIK group was 0.01 mm[95％limits of agreement(LoA):-0.36 to 0.35 mm],with 96.3％of data points falling within the LoA.The mean difference of the 140 images in the SMILE group was-0.01 mm(95％LoA:-0.36 to 0.33 mum),with 95.7％of data points falling within the LoA.The mean difference of all 276 images was 0.00 mm(95％LoA:-0.36 to 0.34 mm),with 96.4％of data points falling within the LoA.These results indicated excellent consistency.The average measurement time per image using the traditional EOZ measurement method was 13.00 minutes,whereas the deep learning model required only 3.22 seconds.Conclusion The traditional EOZ measurement method based on corne-al tangential curvature exhibits good consistency with the fully automatic EOZ measurement method based on deep learning algorithms,achieving high image recognition accuracy.Additionally,the deep learning algorithm significantly reduces measurement time,compared with the traditional method based on corneal tangential curvature.

Objective To explore the correlation between glycolysis related indexes of peripheral blood mononuclear cells(PBMCs)[glucose transporter 1(GLUT1),fructose-2,6-diphosphatase 3(PFKFB3)and lactate kinase M2(PKM2)]and intracranial pressure and clinical prognosis of patients with hypertensive cerebral hemorrhage(HICH)who broke into the ventricle.Methods 85 HICH patients hospitalized in our hospital from January 2021 to June 2022 were retrospectively collected as the research participants.The levels of PKM2,GLUT1 and PFKFB3 in PBMCs were analyzed by enzyme-linked immunosorbent assay kit.Six months after the onset of HICH,the short-term outcome was evaluated by modified Rankin scale(mRS),and the patients were divided into good prognosis(mRS score≤2)and poor prognosis(mRS score≥3).Results The levels of PKM2,PFKFB3 and GLUT1 in PBMCs of HICH patients with ventricular rupture were further lower than those of the patients without ventricular rupture(P<0.05).Six months after discharge,8 patients(22.9%)with HICH in non-ventricular rupture group had poor prognosis,while 28 patients(56.0%)with HICH in ventricular rupture group had poor prognosis,and the difference was statistically significant(χ2=9.263,P=0.002).Compared with the group with good prognosis,the levels of PKM2 and GLUT1 in PBMCs of HICH patients with poor prognosis were lower(P<0.05).Compared with the good prognosis group,the levels of PKM2 and GLUT1 in PBMCs of HICH patients with poor prognosis group were lower in HICH patients with ventricular rupture(P<0.05).In HICH patients who broke into the ventricle,the AUC of PKM2 and GLUT1 in predicting the poor prognosis of HICH patients were 0.879(95%CI:0.807～0.951)and 0.897(95%CI:0.831～0.963),respectively.Conclusion The levels of PKM2 and GLUT1 in PBMCs of patients with HICH breaking into ventricles decrease significantly,and are negatively correlated with intracranial pressure,which can be used to predict the short-term prognosis of the patients.

BACKGROUND: Senescent human skin primary fibroblast (FB) models have been established for studying aging-related, proliferative, and inflammatory skin diseases. The aim of this study was to compare the transcriptome characteristics of human primary dermal FBs from children and the elderly with four senescence models. METHODS: Human skin primary FBs were obtained from healthy children (FB-C) and elderly donors (FB-E). Senescence models were generated by ultraviolet B irradiation (FB-UVB), D-galactose stimulation (FB-D-gal), atazanavir treatment (FB-ATV), and replication exhaustion induction (FB-P30). Flow cytometry, immunofluorescence staining, real-time quantitative polymerase chain reaction, co-culturing with immune cells, and bulk RNA sequencing were used for systematic comparisons of the models. RESULTS: In comparison with FB-C, FB-E showed elevated expression of senescence-related genes related to the skin barrier and extracellular matrix, proinflammatory factors, chemokines, oxidative stress, and complement factors. In comparison with FB-E, FB-UVB and FB-ATV showed higher levels of senescence and expression of the genes related to the senescence-associated secretory phenotype (SASP), and their shaped immune microenvironment highly facilitated the activation of downstream immune cells, including T cells, macrophages, and natural killer cells. FB-P30 was most similar to FB-E in terms of general transcriptome features, such as FB migration and proliferation, and aging-related characteristics. FB-D-gal showed the lowest expression levels of senescence-related genes. In comparisons with the single-cell RNA sequencing results, FB-E showed almost complete simulation of the transcriptional spectrum of FBs in elderly patients with atopic dermatitis, followed by FB-P30 and FB-UVB. FB-E and FB-P30 showed higher similarity with the FBs in keloids. CONCLUSIONS Each senescent FB model exhibited different characteristics. In addition to showing upregulated expression of natural senescence features, FB-UVB and FB-ATV showed high expression levels of senescence-related genes, including those involved in the SASP, and FB-P30 showed the greatest similarity with FB-E. However, D-galactose-stimulated FBs did not clearly present aging characteristics.
Humans ; Fibroblasts/drug effects* ; Cellular Senescence/physiology* ; Skin/metabolism* ; Child ; Transcriptome/genetics* ; Aged ; Ultraviolet Rays ; Cells, Cultured ; Galactose/pharmacology*

Although cancer immunotherapy has made great strides in the clinic, it is still hindered by the tumor immunosuppressive microenvironment (TIME). The stimulator of interferon genes (STING) pathway which can modulate TIME effectively has emerged as a promising therapeutic recently. However, the delivery of most STING agonists, specifically cyclic dinucleotides (CDNs), is performed intratumorally due to their insufficient pharmacological properties, such as weak permeability across cell membranes and vulnerability to nuclease degradation. To expand the clinical applicability of CDNs, a novel pH-sensitive polycationic polymer-modified lipid nanoparticle (LNP-B) system was developed for intravenous delivery of CDNs. LNP-B significantly extended the circulation of CDNs and enhanced the accumulation of CDNs within the tumor, spleen, and tumor-draining lymph nodes compared with free CDNs thereby triggering the STING pathway of dendritic cells and repolarizing pro-tumor macrophages. These events subsequently gave rise to potent anti-tumor immune reactions and substantial inhibition of tumors in CT26 colon cancer-bearing mouse models. In addition, due to the acid-sensitive property of the polycationic polymer, the delivery system of LNP-B was more biocompatible and safer compared with lipid nanoparticles formulated with an indissociable cationic DOTAP (LNP-D). These findings suggest that LNP-B has great potential in the intravenous delivery of CDNs for tumor immunotherapy.

Objective To investigate the relationship between plasma von willebrand factory-lytic protease (ADAMTS13) level and the severity of the disease and its prognostic value in patients with endometriosis (EMS). Methods 130 patients with EMS treated in the hospital from January 2019 to December 2022 were retrospectively selected. Plasma ADAMTS13 level was detected by enzyme-linked immunosorbent assay (ELISA),and its correla-tion with American Fertility Society (r-AFS) stage and endometriosis Health Status Scale (EHP-30) score was analyzed. The 1-year prognosis was recorded,and the risk factors of poor prognosis were analyzed by logistic regression. Receiver operating characteristic curve (ROC) was plotted to evaluate the predictive value of plasma ADAMTS13 levels in patients with EMS. Results The plasma ADAMTS13 level of EMS patients decreased signifi-cantly with the increase of r-AFS stage,and was negatively correlated with the score of EHP-30 (P<0.001). Multiple factors showed that low plasma ADAMTS13 level,bilateral location of pelvic lesions and high r-AFS stage were independent risk factors for poor prognosis (all P<0.05). ROC analysis showed that the area under the curve (AUC) of plasma ADAMTS13 was 0.889 (cut-off point 13.315,sensitivity 0.931,specificity 0.690). Conclusion Plasma ADAMTS13 level is negatively correlated with the severity of EMS,and is an independent risk factor for poor prognosis of EMS patients,and has a certain predictive value for prognosis of EMS patients.

Objective:To study the optimal anastomotic angle of end-to-side anastomosis autogenous arteriovenous fistula (AVF).Methods:A case-report and case-series design was used to obtain clinical data on 10 patients with diabetic nephropathy from Department of Nephrology, the 905th Hospital of the Chinese People′s Liberation Army Navy from June 2024 to February 2025. The models of "radial artery-cephalic vein" end-to-side anastomosis in the forearm with anastomotic angles of 30°, 40°and 50°were established. Numerical simulation was used to analyze the blood flow in the model, and to study the effect of different anastomotic angles on blood flow. Wall shear stress (WSS), cross section flow velocity and flow rate, and relative residence time (RRT) were studied in the model. The Whitney test with Holm correction was used to evaluate the difference in the median RRT between the three angle models.Results:At the moment of 0.65 s, the area fraction of low wall shear stress (LWSS) in the 30° model was 7.7%, which was reduced by 2.4% and 3.7% compared to the 40°and 50°models, respectively. At the time of 0.2 s, the area proportions of high wall shear stress (HWSS) in the 30°, 40°and 50°models were 54.4%, 43.9% and 37.4%, respectively. At 0.2 s, the maximum cross section flow velocity reached 4.07, 3.84 and 3.67 m/s for the 30°, 40°and 50°models, respectively. In the cycle, the maximum mean flow velocity for the 30°model reached 1.20 m/s. The mean flow rates of the 30°, 40°and 50°models in the J-5 cross section were 349, 316, and 328 ml/min, respectivly. For patient 6, the area proportions of the RRT>1 region were 11.97%, 14.84% and 15.22% for the 30°, 40°and 50°models, respectively.Conclusions:The optimal anastomotic angle of "radial artery-cephalic vein" for end-to-side anastomosis AVF surgery in patients with diabetic nephropathy is 40°.

Objective To investigate the diagnostic accuracy and clinical applicability of the Linknet-VGG16 deep learning algorithm for measuring the effective optical zone(EOZ)after corneal refractive surgery.Methods This single-center retrospective cohort study included 69 patients(69 eyes)who underwent femtosecond laser-assisted in situ kerato-mileusis(FS-LASIK)(34 eyes)or small incision lenticule extraction(SMILE)(35 eyes)at the Refractive Surgery Center of Affiliated Zhengzhou Aier Eye Hospital of Henan University from June 2023 to June 2024.Data from the right eyes of all patients were selected for statistical analysis.During the surgery,patients in the FS-LASIK group adopted the VisuMax fem-tosecond laser system combined with the Amaris 750S excimer laser system,while those in the SMILE group only used the VisuMax femtosecond laser system.A total of 276 Pentacam images were re-examined postoperatively.A Linknet segmenta-tion model based on the VGG16 encoder was constructed,and image normalization techniques were applied to accelerate model convergence.Model performance was assessed using accuracy,intersection over union(IoU),and the Dice coeffi-cient.The traditional EOZ measurement method based on corneal tangential curvature served as the reference standard.Bland-Altman analysis was conducted to evaluate consistency across all images and within each group,and the time effi-ciency of both methods was compared.Results Six representative medical image segmentation architectures(U-Net,U-Net++,DeepLabv3-ResNet50,DeepLabv3+-ResNet50,Unet-Densenet169,and Linknet-VGG16)were systematically evaluated.The Linknet-VGG16 model demonstrated superior performance over the other 5 models in pixel-level accuracy,IoU and Dice coefficient,which were 99.83％,99.48％and 99.74％,respectively.Although there was no significant differ-ence in accuracy and Dice coefficient between Linknet-VGG16 and U-Net models(whose accuracy was 99.82％and Dice coefficient was 99.72％),the inference speed of the U-Net model(62.46 ms)was 31.76％slower than that of the Linknet-VGG16 model(42.62 ms).The evaluation results of a clinically applicable comprehensive scoring model(weights:accura-cy 20％,IoU 20％,Dice coefficient 20％,speed 25％,model size 15％)showed that the Linknet-VGG16 model achieved a score of 88.01,surpassing other architectures(U-Net:86.29;DeepLabv3+-ResNet50:80.41;DeepLabv3-ResNet50:73.82;U-Net++:73.22;Unet-Densenet169:66.66).Bland-Altman analysis revealed that the mean difference of the 136 images in the FS-LASIK group was 0.01 mm[95％limits of agreement(LoA):-0.36 to 0.35 mm],with 96.3％of data points falling within the LoA.The mean difference of the 140 images in the SMILE group was-0.01 mm(95％LoA:-0.36 to 0.33 mum),with 95.7％of data points falling within the LoA.The mean difference of all 276 images was 0.00 mm(95％LoA:-0.36 to 0.34 mm),with 96.4％of data points falling within the LoA.These results indicated excellent consistency.The average measurement time per image using the traditional EOZ measurement method was 13.00 minutes,whereas the deep learning model required only 3.22 seconds.Conclusion The traditional EOZ measurement method based on corne-al tangential curvature exhibits good consistency with the fully automatic EOZ measurement method based on deep learning algorithms,achieving high image recognition accuracy.Additionally,the deep learning algorithm significantly reduces measurement time,compared with the traditional method based on corneal tangential curvature.