Objective To explore the prevalence of Helicobacter pylori (Hp) infection and its association with dietary and lifestyle habits among the adult physical examination population in Xuzhou area. Methods Retrospectively selected the physical examination population who underwent HP testing at our hospital's physical examination center from May 2021 to December 2023 as the research object. The prevalence of Hp infection in the population was analyzed based on the physical examination results. A questionnaire survey was used to collect information on the eating and living habits of all study subjects. Logistic regression was used to analyze the relationship between eating and living habits and Hp infection. Results A total of 1 354 physical examination people were included in the study, and the Hp infection rate was 37.30% (505/1354). The difference in Hp infection rates among people of different age groups is statistically significant (P<0.05), with the middle-aged population (41-59 years old) having the highest Hp positive infection rate (45.38%).High salt (41.11%), hot diet (40.56%), history of smoking (45.23%) and drinking (43.80%), less consumption of fruits and vegetables (43.73%), irregular exercise (41.29%), irregular diet People who frequently eat out (43.56%) and eat out frequently (42.57%) have a higher Hp infection rate (P<0.05).After adjusting for demographic factors such as gender, age, place of residence and education level, multivariate Logistic regression results showed that high-salt diet (OR=3.975, 95%CI: 2.670-5.917) and hot diet (OR=3.357, 95%CI: 2.291-4.919), smoking (OR=1.458, 95%CI: 1.082-1.964), drinking alcohol (OR=1.654, 95%CI: 1.279-2.138), eating fruits and vegetables (OR=1.759, 95%CI: 1.345-2.301), regular exercise (OR=1.822, 95%CI: 1.371-2.421), regular diet (OR=1.893, 95%CI: 1.391-2.575), eating out (OR=1.690, 95%CI: 1.277-2.237) were associated with the risk of Hp infection (P<0.05). Conclusion The positive infection rate of Hp among the physical examination population in Xuzhou is slightly lower than the average epidemic level in China. Cultivating healthy eating and living habits can effectively reduce the risk of Hp infection.

ObjectiveTo investigate the therapeutic effects and potential mechanisms of Atractylodes chinensis volatile oil microemulsion in relieving depression， thus establishing a theoretical foundation and a new approach for developing it as a novel adjunctive antidepressant. MethodsSixty SD male rats were assigned into four groups： control， model （chronic unpredictable mild stress）， positive drug （flupentixol hydrochloride， 1.8 mg·kg^-1）， and low-， medium-， high-dose （16.2， 32.4， 64.8 mg·kg^-1） A. chinensis volatile oil microemulsion. The sucrose preference test， open field test， tail suspension test， and forced swimming test were conducted to measure the sucrose preference rate， total exercise distance， average speed， resting time， tail suspension time， and swimming immobility time before and after treatment. The morphology of the rat brain tissue was visualized by hematoxylin-eosin （HE） staining. The levels of 5-hydroxytryptamine （5-HT）， norepinephrine （NE）， and cortisol （CORT） in the hippocampal tissue were measured by enzyme-linked immunosorbent assay （ELISA）. Real-time PCR was used to detect mRNA level differences of cyclic adenosine monophosphate （cAMP）， protein kinase A （PKA）， cyclic adenosine monophosphate response element-binding protein （CREB）， brain-derived neurotrophic factor （BDNF）， and tyrosine kinase B （TrkB） in the hippocampus. Western blot was employed to assess protein level variations of cAMP， PKA， CREB， BDNF， and TrkB in the same tissue samples. ResultsCompared with that in the blank group， the body mass of rats in low-， medium-， high-dose A. chinensis volatile oil emulsion groups decreased （P<0.05）， indicating that the modeling was successful. Compared with the model group， medium-， high-dose A. chinensis volatile oil emulsion shortened the tail suspension time， swimming immobility time， and resting time （P<0.05， P<0.01）， while increasing the sucrose preference rate， total exercise distance， and average speed （P<0.01）. No significant changes were observed in the low-dose A. chinensis volatile oil emulsion group. ELISA results indicated that CORT concentrations in the hippocampal tissue of medium and high-dose A. chinensis volatile oil emulsion groups decreased （P<0.01）. In the high-dose group， 5-HT and NE concentrations increased （P<0.05， P<0.01）， while they had no significant changes in the low-dose group. Real-time PCR results revealed that the mRNA levels of cAMP， PKA， and CREB in the hippocampus of the medium-dose Beicangzhu volatile oil emulsion group increased （P<0.05， P<0.01）， and those of cAMP， PKA， CREB， BDNF， and TrkB were upregulated in the high-dose Beicangshu volatile oil microemulsion group （P<0.01）. Western blot and immunofluorescence results demonstrated that the protein levels of cAMP， PKA， and TrkB in the hippocampal tissue of the low-dose A. chinensis volatile oil microemulsion group were up-regulated （P<0.05）. The medium-dose Beicangzhu volatile oil emulsion group exhibited increases in protein levels of cAMP， PKA， BDNF， and TrkB （P<0.05， P<0.01）， while the high-dose group showed elevationsin protein levels of cAMP， PKA， CREB， BDNF， and TrkB （P<0.05， P<0.01）. ConclusionBeicangzhu volatile oil emulsion demonstrates certain antidepressant efficacy by inhibiting CORT expression， upregulating the expression of 5-HT， NE， cAMP， PKA， CREB， BDNF， and TrkB， activating the CREB/BDNF signaling pathway to improve the cerebral protection.

Objective To observe the value of follicular thyroid imaging reporting and data system(F-TIRADS)for differentiating benign and malignant follicular thyroid lesions.Methods Totally 502 patients with follicular thyroid lesions were retrospectively enrolled,including 104 patients with single malignant lesion(malignant group,containing 77 follicular thyroid carcinomas[FTC]and 27 follicular variant of papillary thyroid carcinomas[FVPTC])and 398 patients with 416 benign lesions(benign group,containing 197 follicular thyroid adenomas[FTA]and 219 thyroid adenomatous hyperplasia).Ultrasonic features of lesions were recorded,and F-TIRADS scores were assigned by 1 junior and 1 senior ultrasound physicians.Taken histopathology results as gold standard,receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy for differentiating benign and malignant follicular thyroid lesions using F-TIRADS.Results Significant differences of composition,internal echo,boundary,calcification and trabecular structure of lesions were found between groups(all P＜0.001).Taken F-TIRADS score≥ 7 as the optimal cut-off value,the sensitivity,specificity,accuracy,positive predictive value and negative predictive value for differentiating benign and malignant follicular thyroid lesions by the junior physician was 76.92％,77.40％,77.31％,93.06％and 45.98％,while by the senior physician was 78.84％,81.25％,80.76％,93.89％and 51.25％,respectively.The efficacy of the latter was higher than of the former(AUC was 0.827 and 0.859,respectively,P＜0.05).Conclusion F-TIRADS could effectively identifying benign and malignant follicular thyroid lesions.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

Objective To develop a toolkit suitable for assisting community health institutions in the early identification and inter-vention of mild cognitive impairment(MCI)among older adults.Methods A literature review was conducted to construct a draft of the identification and intervention toolkit.Tools with an expert approval rate above 70%were included after expert consultation.The final version of the toolkit was developed by integrating these tools with officially recommended tools in China.Results The expert consultation yielded an authority coefficient of 0.84.The finalized toolkit included the assessment tools of Mini-Mental State Examination,Montreal Cognitive Assessment,General Practitioner Assessment of Cognition,Cognitive Abilities Screening Instrument and Clock Drawing Test,and 18 intervention measures in-cluding pharmacological treatment,cognitive training and psychological interventions,etc.Conclusion The MCI Identification-Intervention Toolkit may serve as a reference for guiding the identification and inter-vention of MCI among older adults for community health institutions.

Objective To observe the value of follicular thyroid imaging reporting and data system(F-TIRADS)for differentiating benign and malignant follicular thyroid lesions.Methods Totally 502 patients with follicular thyroid lesions were retrospectively enrolled,including 104 patients with single malignant lesion(malignant group,containing 77 follicular thyroid carcinomas[FTC]and 27 follicular variant of papillary thyroid carcinomas[FVPTC])and 398 patients with 416 benign lesions(benign group,containing 197 follicular thyroid adenomas[FTA]and 219 thyroid adenomatous hyperplasia).Ultrasonic features of lesions were recorded,and F-TIRADS scores were assigned by 1 junior and 1 senior ultrasound physicians.Taken histopathology results as gold standard,receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy for differentiating benign and malignant follicular thyroid lesions using F-TIRADS.Results Significant differences of composition,internal echo,boundary,calcification and trabecular structure of lesions were found between groups(all P＜0.001).Taken F-TIRADS score≥ 7 as the optimal cut-off value,the sensitivity,specificity,accuracy,positive predictive value and negative predictive value for differentiating benign and malignant follicular thyroid lesions by the junior physician was 76.92％,77.40％,77.31％,93.06％and 45.98％,while by the senior physician was 78.84％,81.25％,80.76％,93.89％and 51.25％,respectively.The efficacy of the latter was higher than of the former(AUC was 0.827 and 0.859,respectively,P＜0.05).Conclusion F-TIRADS could effectively identifying benign and malignant follicular thyroid lesions.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

Objective To develop a value evaluation index system for health management and logistics positions in pub-lic hospitals,providing a research tool for future performance evaluations and salary reform.Methods Literature review,focus group interviews,and the point-factor method were employed to establish an initial index pool.A public hospital in Guangzhou was selected as a case study.Delphi expert consultation was then utilized to refine and finalize the indicators for the evaluation system.Results For the two rounds of Delphi expert consultation,the response rates were 87.5％and 100.0％,with the expert authority coefficient of 0.812.The Kendall's W coordination coefficients were 0.796 and 0.624 for the first round,and 0.747 and 0.918 for the second,(all＜0.001).The coefficients of variation ranged from 0.157 to 0.265 for the first round and from 0.108 to 0.230 for the second round.Experts provided four suggestions,leading to the removal of one secondary indicator and modifications to the definitions of three others.Finally,the evaluation system consists of 4 primary indicators and 12 secondary indicators.Conclusion This system can provide a research tool and reference for the follow-up performance evaluation,improve the salary reform of public hospitals,and promote the high-quality development of public hospitals.

Pasteurella is a genus of zoonotic pathogens that are widely distributed in both healthy and diseased ani-mals.Reports of human infections caused by wild animal bites are rare.This paper reports the first case of Pasteu-rella multocida infection caused by a snow leopard bite in China.The strains were identified using the DL-96 Ⅱ bac-terial identification system,BRUKER automatic microbial mass spectrometer,and Autof ms 1000 mass spectrome-ter.Antimicrobial susceptibility testing was performed using the DL-96 Ⅱ bacterial assay system.After wound de-bridement,surgical incision and drainage,and combined antimicrobial treatment,the patient recovered and was dis-charged from the hospital.This paper aims to improve clinical health care workers'understanding on this bacterium.

Objective:To evaluate the performance of the neural network model in segmenting gout tophus in the first metatarsophalangeal（MTP1）joint ultrasound images.Methods:A total of 1 218 tophus images from 381 patients who underwent MTP1 ultrasound examinations in the Affiliated Hospital of Qingdao University between May 2023 and December 2024 were prospectively collected. The images were divided into training，validation，and test sets in a ratio of 7∶2∶1. Multiple neural network models were trained to automatically identify and segment tophus in the images，with physician-annotated tophus regions serving as the reference standard. Model performance was evaluated in the test set，and the impact of tophus characteristics（e.g.，echogenicity，size，and presence of bone erosion）on segmentation efficacy was analyzed.Results:In the test set，CMUNeXt demonstrated superior tophus segmentation performance versus Unet，Unet++，TransUnet，and CMU-Net，achieving an accuracy of 99.1%，precision of 79.1%，recall of 84.6%，intersection over union of 68.8%，and Dice similarity coefficient of 80.2%. Logistic regression identified tophus echogenicity，size，and bone erosion as independent efficacy factors OR（95% CI）=7.275（1.598-33.129），21.303（4.282-105.985），13.520（3.617-50.530），0.076（0.007-0.823）（all P<0.05）. Hypoechoic tophus demonstrated significantly superior segmentation performance compared to mixed-echoic and isoechoic tophus（all P<0.05），and lesions with larger maximum diameters（>10 mm）were segmented more effectively than smaller tophus（ P<0.05）. Conclusions:The CMUNeXt model enables accurate identification and segmentation of tophus in MTP1 ultrasound images，particularly excelling for larger and hypoechoic lesions. This approach holds significant promise for AI-assisted diagnosis of MTP1 gouty arthritis.