Objective: To investigate the epidemiological and spatio-temporal characteristics of influenza in Jiaxing City, Zhejiang Province, so as to provide insights into perfecting the prevention and control strategies of influenza. Methods: Data of influenza in Jiaxing City from 2019 to 2023 were collected from the Chinese Disease Prevention and Control Information System. Population data of the same period were collected from the Zhejiang Health Information Network Reporting System. The epidemiological characteristics of influenza were analyzed using descriptive analysis. Vector map information was collected from the Open Street Map, and the spatio-temporal clustering characteristics of influenza were analyzed using spatial autocorrelation and spatio-temporal scanning. Results: A total of 181 501 cases of influenza were reported in Jiaxing City from 2019 to 2023, with an average annual reported incidence of 653.93/105. The majority of cases were aged 5 to <15 years (59 785 cases, 32.94%). The majority of the occupations were students (78 239 cases, 43.11%) and pre-school children (33 715 cases, 18.58%). The county (city, district) with the highest reported incidence was Haining City (1 451.70/105), and the town (street) with the highest reported incidence was Chang'an Town (1 932.78/105). Spatial autocorrelation analysis showed that the incidence of influenza in Jiaxing City from 2019 to 2023 had positive spatial correlations (all Moran's I>0, all P<0.05), with a high-high clustering in the southern region. Spatio-temporal scanning analysis showed that there was a spatio-temporal clustering of influenza in Jiaxing City from 2019 to 2023, with the southern region being the primary-type clustering area and the period between November and January of the following year being the clustering time. Conclusion There was a significant spatio-temporal clustering of influenza in Jiaxing City from 2019 to 2023, with winter being the peak season and the southern region being the primary area.

Objective: To investigate the epidemiological characteristics of pertussis in Jiaxing City from 2004 to 2023 and spatio-temporal clustering characteristics from 2022 to 2023, so as to provide insights into formulation of pertussis control measures. Methods: Data of pertussis cases in Jiaxing City from 2004 to 2023 were collected through the Infectious Disease Report Information System of Chinese Disease Prevention and Control Information System. The epidemiological characteristics of pertussis cases in Jiaxing City from 2004 to 2023 were descriptively analyzed, and the spatio-temporal clustering characteristics from 2022 to 2023 were analyzed using spatio-temporal scanning. Results: A total of 478 pertussis cases were reported in Jiaxing City from 2004 to 2023, with an average annual reported incidence of 0.53/105. The reported incidence showed an upward trend from 2004 to 2023 (P<0.05), with the highest in 2022 (3.17/105). Higher incidence of pertussis was reported in June to August (149 cases, 31.17%) and November to December (112 cases, 23.43%). There was no statistically significant difference in the reported incidence between males and females (0.56/105 vs. 0.50/105, P>0.05). The cases aged under one year accounted for the highest proportion, with 199 cases (41.63%). Haining City (0.68/105), Jiashan County (0.64/105) and Tongxiang City (0.60/105) ranked the top three in the reported incidence of pertussis. Spatio-temporal scanning analysis showed that from 2022 to 2023, the primary clustering area of pertussis was centered in Daqiao Town of Nanhu District, covering 27 towns (streets) in Nanhu District, Jiashan County, Xiuzhou District and Pinghu City, and the clustering time was from November to December, 2023. Conclusions The reported incidence of pertussis was at a low level in Jiaxing City, but showed an upward trend from 2004 to 2023. The incidence of pertussis was higher among infants under one year of age, peaked in June to August and November to December, and was concentrated in Nanhu District and its surrounding areas.

Objective To explore the characteristics of tumor immune microenvironment of pancreatic ductal adenocarcinoma(PDAC)based on the differentially expressed genes associated with macrophages in early and late tumor tissues of PDAC patients,so as to provide new targets for early diagnosis and treatment of PDAC.Methods Three early-stage and 4 late-stage tumor samples were collected from PDAC patients.Tissue transcriptome data were analyzed by single-cell sequencing technology.With The Cancer Genome Atlas(TCGA)database,macrophage related genes differentially expressed in early and late PDAC related to prognosis were obtained.The prognostic risk scoring model was constructed using the least absolute shrinkage and selection operator(LASSO)-Cox method.The risk genes associated with PDAC prognosis were screened using the Gene Expression Profiling Interaction Analysis 2(GEPIA2)online survival analysis tool.Their immune infiltration was analyzed using the cell-type identification by estimating relative subsets of RNA transcripts(CIBERSORT)method,and their expression in different subpopulations of macrophages at different periods was resolved by t-distributed stochastic nearest-neighbour embedding(tSNE)clustering downscaling and the Monocle package.The protein-protein interactions and immunohistochemistry were further analyzed with the help of STRING database and The Human Protein Atlas(HPA)database.Results A total of 48 differentially expressed macrophage-related genes in early and late PDAC were found to be associated with prognosis.Eleven differentially expressed macrophage genes were screened as PDAC prognostic genes,and they were used to construct a prognostic risk scoring model for PDAC.Two prognostic genes with significant risk indicators were screened:2'-5'-oligoadenylate synthetase 2(OAS2)was mainly expressed in secreted phosphoprotein 1(SPP1)-positive macrophages,and assembly factor for spindle microtubules(ASPM)was mainly expressed in proliferation marker protein Ki-67(MKI67)-positive macrophages.There were significant differences in the expression of OAS2 in PDAC macrophages at different stages.Immune infiltration studies showed that OAS2 and ASPM were highly expressed in regulatory T cells and M2-type macrophages(both P＜0.05).Protein-protein interaction network showed the expression relationship between OAS2 and ASPM and other co-expressed proteins.Immunohistochemistry results showed that the expression of OAS2 was higher in tumor tissues than in normal tissues.Conclusion OAS2 and ASPM are both poor prognostic genes that are specifically expressed and exert negative immune effects in SPP1+macrophages and MKI67+macrophages,respectively,promoting the progression of PDAC and ultimately leading to a poor prognosis,and it is expected to provide new targets for the prevention and treatment of PDAC.

Objective:To investigate the effects of short-term olfactory training and visual stimulation on olfactory recognition function and quality of life in patients following functional endoscopic sinus surgery (FESS).Methods:A total of 80 patients who underwent FESS in the Department of Otorhinolaryngology Head and Neck Surgery at the Affiliated Hospital of Xuzhou Medical University between December 2023 and February 2024 were enrolled in this study. The cohort comprised 67 males and 13 females, aged from 17 to 75 years. Participants were randomly allocated to either a control group ( n=40) or an experimental group ( n=40). The participants in control group received routine postoperative management, including nasal irrigation, oral antibiotics and glucocorticoids, topical budesonide nasal spray, and hypertonic saline solution for 12 weeks. In addition to the standard care, the participants in experimental group underwent olfactory-visual stimulation training starting 24 hours postoperatively, lasting for 2 weeks. The olfactory identification test (OIT), visual analogue scale (VAS) for olfaction and Questionnaire of Olfactory Disorders of Life (QOD-QoL, hereafter referred to as QOD) were administered preoperatively. Follow-up assessments were performed 2 weeks and 3 months postoperatively, with the same tests repeated. Data were analyzed using SPSS 27.0 and GraphPad Prism 7 statistical software. Results:There was no significant difference in preoperative OIT, VAS, and QOD scores between the two groups. The olfactory recognition function of some patients was improved after removing the packing material 24 hours after FESS surgery. The OIT scores of 2 weeks post-surgery were significantly higher than preoperative values in both groups (the experimental group Z=-4.73, P<0.001; the control group Z=-4.73, P<0.001). Participants in both groups showed improvements in olfactory VAS and QOD scores (experimental group Z value was -2.88 and -5.45, P<0.01 and<0.001, respectively; the control group Z value was -4.42 and -5.50, respectively, both P<0.001). However, there was no significant difference in the VAS score between the two groups ( Z=-0.68, P=0.499). The paticipants in experimental group showed greater improvement in OIT and greater reduction in QOD scores compared to the control group ( Z=-2.19, P=0.029; Z=-2.99, P=0.003). There was no significant difference in the decrease of olfactory VAS between the two groups ( Z=-0.02, P=0.988). There were no statistically significant differences of all patients in VAS, OIT and QOD scores at 2 weeks and 3 months after surgery (experimental group Z value was -0.91, -0.90 and -1.43, respectively, all P>0.05; control group Z value was -1.21, -0.84 and -0.91, respectively, all P>0.05). At 3 months post-surgery, the OIT scores in the experimental group were higher, the QOD scores were lower than those in the control group ( Z=-2.89, P=0.004; Z=-2.87, P=0.004). Conclusion:Short-term olfactory-visual stimulation training in the early postoperative period of FESS significantly improves the olfactory recognition function and enhances the quality of life of patients.

Objective:To investigate the predictive value of radiomics features derived from dynamic contrast-enhanced MRI (DCE-MRI) based on habitat imaging technology for pathological complete response after neoadjuvant therapy (NAT) for breast cancer.Methods:All patients were female, aged 25-67 years. Patients were stratified into training ( n=83) and validation ( n=36) sets via stratified random sampling (7∶3 ratio). Pathological complete remission (pCR) and non-pathological complete remission (non-pCR) were defined using the Miller-Payne grading system. All patients underwent DCE-MRI before NAT. ITK-Snap software was used to outline the region of interest (ROI), the imaging histological features of the entire tumor region were extracted and screened, a traditional imaging histological model for predicting post-NAT pCR (ROI overall model) was constructed; the tumor region was divided into three subregions using habitat imaging technology, and the imaging histological features within ROI subregion 1, ROI subregion 2, and ROI subregion 3 were extracted and screened, and the habitat imaging model for predicting post-NAT pCR were constructed (ROI subregion 1 model, ROI subregion 2 model, ROI subregion 3 model). Univariate logistic regression identified clinical predictors of pCR for clinical model construction. Combined models integrating clinical predictors and habitat imaging features were established. The efficacy of each model in predicting pCR after NAT in breast cancer was evaluated using receiver operating characteristic curves and area under the curve (AUC), and the efficacy of clinical application of the models was evaluated using decision curve analysis (DCA). Results:Of the 119 patients, 74 were pCR patients, with 52 in the training set and 22 in the validation set, and 45 were non-pCR patients, with 31 in the training set and 14 in the validation set. Logistic regression analysis showed that human epidermal growth factor receptor 2 status ( OR=0.254, 95% CI 0.093-0.697, P=0.008) was an independent predictor of pCR after NAT, and this was used to construct a clinical prediction model. The predictive efficacy of ROI subregion 1 model and ROI subregion 2 model in the habitat model was higher than that of the traditional imaging histology model (ROI overall model), with AUCs of 0.805, 0.748,0.728 for the training set and 0.776,0.718,0.708 for the validation set, respectively. The combined clinical prediction model for predicting pCR after NAT in breast cancer had AUCs of 0.877 and 0.818 for the training and validation sets, respectively. DCA showed a higher net benefit for the combined model than for the traditional imaging histology model and the habitat imaging histology model. Conclusion:Compared with the traditional method of extracting the entire tumor region, extracting radiomics features from DCE-MRI subregions based on habitat imaging technology can improve the predictive performance of NAT efficacy in breast cancer.

Objective:To investigate the risk factors for patients with hypertriglyceridemia-related acute pancreatitis (HTG-AP) developing into severe acute pancreatitis or experiencing organ failure.Methods:This retrospective cohort study collected clinical data from 2 429 patients diagnosed with acute pancreatitis from five hospitals in China between January 2019 and December 2023 using a pre-designed data collection form. The cohort included 1 516 males and 913 females,with an age of (50.2±16.5)years(range: 11 to 99 years). Among them,353 patients (16.1%) had HTG-AP,while 1 846 (83.9%) had non-HTG-AP. HTG-AP was defined as serum triglyceride levels>500 mg/dl with other etiologies excluded. Intergroup comparisons were performed using t-tests,Mann-Whitney U test or χ2 tests,respectively. Univariate and multivariate logistic regression analyses were conducted to assess risk factors for severe acute pancreatitis after adjusting for potential confounders,and a predictive model was developed and validated. Results:Compared with other etiologies,HTG-AP patients had a higher risk of progressing to SAP ( OR=1.415,95% CI: 0.866 to 2.312, P=0.017) and organ failure ( OR=1.256,95% CI: 1.015 to 1.554, P=0.036). Among HTG-AP patients,risk factors for SAP included body mass index ( OR=1.856,95% CI: 1.742 to 1.987, P=0.033),fasting blood glucose ( OR=1.128,95% CI: 1.036 to 1.229, P=0.006),white blood cell count( OR=1.162,95% CI: 1.055 to 1.281, P=0.002),and the presence of pleural effusion ( OR=13.151,95% CI: 4.330 to 19.946, P<0.01). A nomogram prediction model for SAP in HTG-AP was constructed based on these risk factors,demonstrating good discriminative ability with area under the curve values of 0.877 in the training set and 0.894 in the validation set,along with satisfactory calibration. Conclusions:HTG-AP patients are at higher risk of developing SAP and organ failure. The risk prediction model incorporating body mass index,fasting blood glucose,white blood cell count,and pleural effusion shows good predictive value for SAP.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

Objective:To integrate implicit templates with deep learning techniques, a novel neural network, the tooth-deformable deep implicit network (T-DDIN), was constructed to achieve high-precision shape completion of tooth defects in a personalized manner.Methods:A total of 550 intraoral scan models were collected from patients treated at the Department of Orthodontics and Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University (500 for training and 50 for testing), between March 2022 and March 2024. T-DDIN reconstructed defective tooth morphology using an implicit template and a latent encoding prediction network. During model evaluation, Class Ⅱ cavity defects and occlusal wear defects were simulated in the test set. Morphological restoration was performed using both traditional computer aided design (CAD) methods and the T-DDIN deep learning approach. The two methods were compared based on three-dimensional deviation, occlusal adjustment volumes, cusp angle deviation, and restoration time.Results:The T-DDIN group demonstrated significantly lower three-dimensional deviation for Class Ⅱ cavity defects and occlusal wear restoration [(0.14±0.05) and (0.16±0.09) mm], occlusal adjustment volumes [(0.44±0.03) and (0.49±0.03) mm 3], and difference value of the tooth cusp angles (5.69°±1.90° and 6.04°±0.53°) compared to the traditional CAD group (both P<0.001). No significant differences were observed within the T-DDIN group between the two defect types in terms of three-dimensional deviation ( P=0.098) or occlusal adjustment volume ( P=0.154) or difference value of the tooth cusp angles ( P=0.196). However, in the traditional CAD group, three-dimensional deviation, occlusal adjustment volume and difference value of the tooth cusp angles was significantly higher in occlusal wear restorations than in Class Ⅱ cavity defects restorations ( P<0.001). The T-DDIN group, which involved Class Ⅱ cavity defects and occlusal wear, demonstrated significantly less recovery time of morphology (37.2±7.7) and (39.4±6.2) s compared to the traditional CAD group ( P<0.001). Conclusions:T-DDIN demonstrated superior stability and accuracy in morphological reconstruction for various types of dental defects while significantly reducing restoration time.

Objective:To analyze the effects and mechanisms of ginseng polysaccharide in myocardial fibrosis and endothelial function in coronary heart disease model rats based on mitogen activated extracellular signal regulated kinase (MEK) and extracellular signal regulated kinase (ERK) signaling pathways.Methods:Totally 60 SD rats were divided into control group (10 rats) and model group (50 rats). A coronary heart disease model was established in modeling rats. The successfully modeled rats were divided into model group, drug control group, ginseng polysaccharide high- and low-dosage groups, with 10 rats in each group. The drug control group was orally administered with 2 mg/kg Lipitor solution, while the ginseng polysaccharide low- and high-dosage groups were orally administered with 100 and 200 mg/kg ginseng polysaccharide solution. The control group and model group were orally administered with equal volumes of physiological saline solution once a day for one week. HE staining and Masson staining were used to observe the morphological changes and pathological morphology of myocardial tissue. ELISA was used to detect the levels of NO, eNOS, ET-1, ADMA, TNF-α, CRP, and IL-6. Immunochemiluminescence was used to detect the expression levels of CK-MB and cTnl. Real time fluorescence quantification was used to identify the expression of MEK and ERK. Western blot was used to detect the expression of MEK, ERK, p-MEK, and p-ERK in myocardial tissue.Results:Compared with the model group, the levels of serum NO and eNOS increased in the drug control group and ginseng polysaccharide low- and high-dosage groups ( P<0.05), while the levels of ET-1, ADMA, TNF-α, CRP, IL-6, CK-MB, and cTnl decreased ( P<0.05), and the mRNA and protein expressions of MEK and ERK in myocardial tissue decreased ( P<0.05). Conclusions:Ginseng polysaccharides can improve the degree of myocardial fibrosis in rats, alleviate endothelial function and myocardial injury. Its mechanism may be related to the inhibition of the MEK/ERK signaling pathway.

Objective To investigate the association of Chinese visceral adiposity index(CVAI)and high-sensitivity C-reactive protein(hs-CRP)with the risk of digestive malignancies in the Kailuan study population,and to provide a basis for the prevention and control of digestive malignancies in the population.Methods A prospective cohort study was conducted,and a total of 94 377 Kailuan workers who participated in the 2006 health examination,had no history of cancer,and had complete data on CVAI,CRP,and related covariates were selected as the observation cohort.According to the levels of CVAI and CRP,the subjects were divided into low CVAI+CRP≤3 mg/L group[CVAI(-)CRP(-)group],low CVAI+CRP>3 mg/L group[CVAI(-)CRP(+)group],high CVAI+CRP≤3 mg/L group[CVAI(+)CRP(-)group],and high CVAI+CRP>3 mg/L group[CVAI(+)CRP(+)group].An analysis of variance was used for comparison of normally distributed continuous data between groups,and the non-parametric Kruskal-Wallis H test was used for comparison of continuous data with skewed distribution between groups;the chi-square test was used for comparison of categorical data between groups.The Cox proportional-hazards regression model was used to assess the impact of CVAI and CRP alone or in combination on the risk of digestive malignancies.Results There were significant differences between the four groups in age,male/female ratio,total cholesterol,triglycerides,high-density lipoprotein cholesterol,systolic blood pressure,diastolic blood pressure,fasting blood glucose,high-sensitivity C-reactive protein,waist circumference,body mass index,marital status,alcohol consumption,smoking,reported income,and physical exercise(all P<0.05).During a mean follow-up time of 14.08±2.76 years,2 043 new-onset cases of digestive malignancies were identified by the end of follow-up on December 31,2021.The Cox proportional-hazards regression model showed that after adjustment for CRP and other factors,compared with the low CVAI group,the high CVAI group had a hazard ratio(HR)of 1.34(95%confidence interval[CI]:1.23-1.47)for the risk of digestive malignancies.After adjustment for CVAI and other factors,compared with the CRP≤3 mg/L group,the CRP>3 mg/L group had an HR of 1.14(95%CI:1.02-1.28)for the risk of digestive malignancies.Compared with the CVAI(-)CRP(-)group(n=40 978),the CVAI(-)CRP(+)group(n=6 210),the CVAI(+)CRP(-)group(n=36 502),and the CVAI(+)CRP(+)group(n=10 687)had an HR of 1.05(95%CI:1.01-1.09,P<0.05),1.32(95%CI:1.20-1.45,P<0.05),and 1.48(95%CI:1.28-1.70,P<0.05),respectively,for the risk of digestive malignancies.As for digestive malignancies at specific locations,the CVAI(+)CRP(+)group had an increased risk of liver cancer,gastric cancer,pancreatic cancer,colorectal cancer,and small intestinal cancer with an HR of 1.35(95%CI:1.05-1.81,P<0.05),1.48(95%CI:1.09-2.00,P<0.05),1.60(95%CI:1.07-2.41,P<0.05),1.76(1.40-2.21,P<0.05),and 3.85(95%CI:1.43-10.33,P<0.05),respectively.Conclusion A high level of CVAI,a high level of CRP,and high levels of CVAI and CRP in combination can all increase the risk of digestive malignancies,among which the high levels of CVAI and CRP in combination may lead to a higher risk.