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 feasibility of deep learning reconstruction (DLR) algorithm combined with a dual-low protocol (low radiation dose and low contrast medium dose) for thoracoabdominal aortic CT angiography (CTA).Methods:This cross-sectional study prospectively enrolled 56 patients suspected of aortic diseases who underwent aortic CTA at China-Japan Friendship Hospital from June 2023 to June 2024. All patients were randomly divided into two groups: Group A (28 cases) underwent CTA with a tube voltage of 100 kVp, automatic tube current modulation (noise index=10), and a contrast agent dose of 80 ml (flow rate 5 ml/s), with images reconstructed using the three-dimensional adaptive iterative dose reduction algorithm (AIDR). Group B (28 cases) underwent CTA with a tube voltage of 80 kVp, automatic tube current modulation (noise index=25), and a contrast agent dose of 40 ml (flow rate 3.5 ml/s), with images reconstructed using either the deep learning reconstruction algorithm-Advanced intelligent Clear-IQ Engine (AiCE subgroup) or the AIDR (AIDR subgroup). Two physicians evaluated the image quality of the three groups subjectively and objectively. Objective evaluation metrics included CT values, image noise (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at the ascending aorta, carina-level descending aorta, celiac trunk-origin abdominal aorta, and common iliac bifurcation abdominal aorta carina. Subjective evaluation metrics included image quality and noise scores. Comparisons among the three datasets (Group A, AiCE subgroup, AIDR subgroup) were performed using one-way ANOVA or the Kruskal-Wallis test, with appropriate post-hoc tests for pairwise comparisons.Results:No significant differences were observed in CT values of the ascending aorta, descending aorta, and abdominal aorta between Group A and the AiCE subgroup or the AIDR subgroup ( P0.05). However, significant overall differences were found in SD, SNR, and CNR values for the ascending aorta, descending aorta, and abdominal aorta ( P0.05). Pairwise comparisons revealed that, except for no significant differences in SD, SNR, and CNR values of the ascending and descending aorta between Group A and the AiCE subgroup, and no significant difference in SNR values of the ascending and abdominal aorta between Group A and the AIDR subgroup ( P0.05), all other intergroup comparisons showed statistically significant differences ( P0.05). Significant overall differences were also observed in image quality and noise scores between Group A and the AiCE and AIDR subgroups ( P0.05). Except for no significant differences in image quality and noise scores between Group A and the AiCE subgroup ( P0.05), all other pairwise comparisons showed statistically significant differences ( P0.05). Conclusions:The application of deep learning reconstruction algorithm combined with a dual-low protocol in thoracoabdominal aortic CTA can reduce radiation dose and contrast agent dose while maintaining diagnostic image quality, demonstrating significant clinical value for widespread adoption.

The cultivation of innovation ability is not only the essential requirement of graduate education,but also the strate-gic demand of the development of the communist party and our country,and is of great significance to the realization of the Chinese dream of the great rejuvenation of the Chinese nation.Curriculum ideology and politics should run through the whole process of post-graduate innovation ability training.However,the curriculum ideology and politics and postgraduate innovation ability training lack deep integration.It's important for postgraduates'growth and scientific research innovation that the curriculum ideology and politics covers the whole process of scientific research activities.Therefore,this paper focuses on the design and specific implementation schemes of the curriculum ideology and politics on the postgraduate innovative ability training at the respiratory disease research team in the department of medical immunology.It makes a basis for optimizing postgraduate curriculum ideology and politics teaching in the future,which also provides ideas for cultivating innovative talents with both morality and ability in medical specialty.

Objective:To investigate the feasibility of deep learning reconstruction (DLR) algorithm combined with a dual-low protocol (low radiation dose and low contrast medium dose) for thoracoabdominal aortic CT angiography (CTA).Methods:This cross-sectional study prospectively enrolled 56 patients suspected of aortic diseases who underwent aortic CTA at China-Japan Friendship Hospital from June 2023 to June 2024. All patients were randomly divided into two groups: Group A (28 cases) underwent CTA with a tube voltage of 100 kVp, automatic tube current modulation (noise index=10), and a contrast agent dose of 80 ml (flow rate 5 ml/s), with images reconstructed using the three-dimensional adaptive iterative dose reduction algorithm (AIDR). Group B (28 cases) underwent CTA with a tube voltage of 80 kVp, automatic tube current modulation (noise index=25), and a contrast agent dose of 40 ml (flow rate 3.5 ml/s), with images reconstructed using either the deep learning reconstruction algorithm-Advanced intelligent Clear-IQ Engine (AiCE subgroup) or the AIDR (AIDR subgroup). Two physicians evaluated the image quality of the three groups subjectively and objectively. Objective evaluation metrics included CT values, image noise (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at the ascending aorta, carina-level descending aorta, celiac trunk-origin abdominal aorta, and common iliac bifurcation abdominal aorta carina. Subjective evaluation metrics included image quality and noise scores. Comparisons among the three datasets (Group A, AiCE subgroup, AIDR subgroup) were performed using one-way ANOVA or the Kruskal-Wallis test, with appropriate post-hoc tests for pairwise comparisons.Results:No significant differences were observed in CT values of the ascending aorta, descending aorta, and abdominal aorta between Group A and the AiCE subgroup or the AIDR subgroup ( P0.05). However, significant overall differences were found in SD, SNR, and CNR values for the ascending aorta, descending aorta, and abdominal aorta ( P0.05). Pairwise comparisons revealed that, except for no significant differences in SD, SNR, and CNR values of the ascending and descending aorta between Group A and the AiCE subgroup, and no significant difference in SNR values of the ascending and abdominal aorta between Group A and the AIDR subgroup ( P0.05), all other intergroup comparisons showed statistically significant differences ( P0.05). Significant overall differences were also observed in image quality and noise scores between Group A and the AiCE and AIDR subgroups ( P0.05). Except for no significant differences in image quality and noise scores between Group A and the AiCE subgroup ( P0.05), all other pairwise comparisons showed statistically significant differences ( P0.05). Conclusions:The application of deep learning reconstruction algorithm combined with a dual-low protocol in thoracoabdominal aortic CTA can reduce radiation dose and contrast agent dose while maintaining diagnostic image quality, demonstrating significant clinical value for widespread adoption.

Objective To develop a Nomogram clinical prediction model for the pathological occurrence of extraprostatic extension(EPE)after radical prostatectomy in prostate cancer patients,using simplified site-specific magnetic resonance imaging(MRI)indicators and other clinical parameters.Methods A total of 181 prostate cancer patients[mean age(69.0±7.3)years]who underwent radical prostatectomy were included.These patients had received 3-Tesla multi-parametric magnetic resonance imaging(3-T mpMRI)within 6 months prior to surgery.Based on mpMRI measurements[capsular contact length(CCL)>15 mm,capsular bulging/irregularities,diameter of index lesion(dIL),and evident extraprostatic extension(eEPE)],the dIL?sEPE grading system was derived.The optimal cut-off value of dIL(denoted as dIL)was determined using the Youden J index,and categorized it into a binary variable.A Logistic regression model was established based on the dIL?sEPE grading and clinical scores.The predictive performance of clinical indicators,MRI indicators,and combined clinical and MRI indicators were compared.Finally,a clinical prediction model integrating both clinical and MRI data was developed.Results Pathological EPE was found in 46 out of 181 cases(25.4% ).A Nomogram prediction model for EPE was established with a combination of the dIL?sEPE grading and clinical indicators.Conclusion The combination of dIL?sEPE grading with clinical indicators accurately predicts extracapsular extension in prostate cancer.The Nomogram model that established,based on MRI imaging characteristics and clinical indicators has good performance and is easy to use.It is beneficial to stratifying management for prostate cancer patients,and it provides valuable guidance for patients suitable for nerve-sparing surgery.

Objective:This study aims to establish an animal model that accurately replicates the clinical symptoms and pathologi-cal changes of late-stage human TMJOA,with the goal of providing a standardized and reliable animal experimental method for sub-sequent research on the disease.Methods:Forty-eight male New Zealand rabbits aged 4 to 6 months were randomly divided into a model group and a sham group.The animals in the model group underwent bilateral temporomandibular joint disc perforation surgery,while the animals in the sham group underwent a sham surgery.The modeling effects were assessed at 4,8,and 12 weeks post-surgery using nociceptive behavior assessments,passive mouth opening measurements,histological analysis(HE,Safranin O-fast green staining),immunohistochemistry,immunofluorescence staining,and magnetic resonance imaging(MRI).Results:Animals in the model group exhibited distinct TMJOA symptoms,including joint pain and restricted mouth opening.Histologically,typical osteo-arthritis changes were evident.The Mankin osteoarthritis score was significantly higher(P＜O.05).Conclusion:The TMJ disc perfo-ration model resmbled the same clinical manifestations and pathological changes seen in human TMJOA.

Pancreatic cancer is highly malignant and difficult to treat. The implantation of radioactive seed has opened up a new treatment option for pancreatic cancer. Although the implantation of radioactive seed in the treatment of pancreatic cancer has achieved good results, due to the special anatomical location of pancreatic cancer and the dense distribution of important tissues and organs around the tumor, the operation of seed implantation for pancreatic cancer has become a clinical treatment challenge. We applied the method of 3D printing template navigation and constraining the direction of the puncture needle. The puncture needle followed the safe puncture path designed in the preoperative treatment planning system, effectively avoiding important tissues and organs, and implanted radioactive seeds in the tumor. The operation of seed implantation was simplified, homogenized and made safe. In this group of cases, the proportion of successful particle implantation using 3D printing template navigation was 89.5%. All cases met the preoperative treatment plan in postoperative dose verification and achieved the purpose of radioactive seed implantation treatment.

Objective:To investigate the clinical efficacy of compound sabal berry tablets on overactive bladder symptoms in patients with benign prostatic hyperplasia after transurethral laser enucleation of the prostate.Methods:This study was a prospective study. Eighty patients with benign prostatic hyperplasia who underwent laser enucleation at Tongliao People's Hospital from January 2024 to December 2024 were included in this study. The patients were randomly divided into a study group and a control group using the random number table method, with 40 patients per group. The control group received 0.2 mg of tolterodine tartrate tablets twice a day after surgery. The study group was given compound sabal berry tablets (0.5 g orally three times a day) in addition to the treatment provided to the control group. Both groups of patients were treated for 4 weeks after surgery. The clinical efficacy of the two groups was compared, including the International Prostate Symptom Score (IPSS), Overactive Bladder Symptom Score (OABSS), Maximum Postoperative Urinary Flow Rate (Qmax), Post-Void Residual (PVR), and the incidence of postoperative bladder irritative symptoms.Results:The differences in the preoperative indicators, including IPSS, OABSS, Qmax, and PVR, between the two groups were not statistically significant (all P > 0.05). Preoperatively, in the control group, Qmax was (8.64 ± 2.83) mL/s, IPSS was (25.10 ± 4.37), OABSS was (10.52 ± 1.87), and PVR was (80.70 ± 6.34) mL; in the study group, the respective values were (9.12 ± 2.95) mL/s, (24.60 ± 4.53), (10.83 ± 1.73), and (80.10 ± 5.61) mL. Postoperatively, in the control group, Qmax was (20.30 ± 3.65) mL/s, IPSS was (8.50 ± 1.58), OABSS was (4.09 ± 0.52), and PVR was (9.70 ± 2.48) mL, while in the study group, the respective values were (21.40 ± 4.38) mL/s, (7.40 ± 1.76), (1.71 ± 0.36), and (9.00 ± 1.75) mL. Postoperatively, both groups showed a significant increase in Qmax, while IPSS, OABSS, and PVR all significantly decreased (all P < 0.05). Postoperatively, the IPSS and OABSS in the study group were significantly lower than those in the control group ( t = -3.28, -25.89, both P < 0.05). However, there were no statistically significant differences in Qmax and PVR between the two groups (both P > 0.05). The incidence of bladder irritative symptoms in the study group [12.50% (5/40)] was significantly lower than that in the control group [35.00% (14/40), χ2 = 8.64, P < 0.05]. Conclusions:Compound sabal berry tablets can reduce postoperative prostate symptoms and overactive bladder symptoms in patients undergoing transurethral laser enucleation of the prostate for benign prostatic hyperplasia, demonstrating a certain clinical efficacy.

Objective To investigate the status and influencing factors of type 2 diabetes mellitus(T2DM)patients' fear of complications,and to provide a reference for formulating targeted intervention measures.Methods From April to November 2024,370 patients with T2DM in 2 tertiary general hospitals in Daqing City were selected by convenience sampling method.General data questionnaire,Fear of Complications Questionnaire,Self-Perceived Burden Scale,Psychological Capital Questionnaire,Mishel Uncertainty in Illness Scale and Family Apgar Index Questionnaire were used for investigation.Univariate analysis and binary Logistic regression were performed to analyze the influencing factors.Results A total of 364 valid questionnaires were collected,with an effective recovery rate of 98.38％.The score of Fear of Complications Questionnaire was(23.47±7.47),and the incidence of fear of complications was 22.25％.Logistic regression analysis showed that medical payment methods,the number of complications,positive psychological capital and family care were the influencing factors of FoC in T2DM patients.Conclusion The fear of complications in T2DM patients is at a moderate level.Nursing staff should pay attention to the early assessment of patients' fear of complications,promptly identify and take effective measures to reduce the level of patients' fear of complications,improve their quality of life.

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.