ObjectiveTo analyze the epidemiological characteristics of latent tuberculosis infection (LTBI) among newly detained populations in eastern China, to identify high-risk groups, and to provide a scientific basis for formulating tuberculosis prevention and control strategies in the prison system. MethodsA cross-sectional study was conducted among the newly admitted detainees in two prisons in eastern China in 2022. Data on demographic characteristics, behavioral risk factors and previous disease history of the research subjects were collected through a structured questionnaire survey. The LTBI status of the detainees was determined using the QuantiFERON-TB Gold In-Tube (QFT-GIT) method. Lasso regression was used to screen variables, followed by multivariate logistic regression analysis to investigate the influencing factors of LTBI. ResultsA total of 305 detainees were included in the study. The median age of detainees was 35 (31, 43) years. The study population was predominantly male (67.21%), of Han ethnicity (95.41%), had a junior or senior high school education (59.34%), and was unemployed (31.80%). A history of smoking was reported by 52.79% of participants, while 57.70% reported no alcohol consumption. The majority had no history of hypertension (85.90%), diabetes mellitus (93.77%), human immunodeficiency virus (HIV) infection (97.38%), familial genetic diseases (95.08%), surgery or trauma (73.77%), drug use (92.79%), or hepatitis (93.77%). The LTBI rate was 14.75%. After comparing the demographic characteristics of LTBI and non-infected groups, it was found that smoking history (χ²=7.40, P=0.025), drug use history (χ²=5.49, P=0.019), and HIV infection (χ²=8.12, P=0.004) were statistically correlated with LTBI infection. The results of multivariate logistic regression analyses showed that smoking [adjusted odds ratio (aOR)=4.08, 95%CI: 1.60‒10.42, P=0.003], HIV infection (aOR=11.57, 95%CI: 2.50‒53.51, P=0.002) and drug use (aOR=3.04, 95%CI: 1.02‒9.09, P=0.046) were risk factors for LTBI. ConclusionThe LTBI rate among newly detainees in two prisons in eastern China is slightly lower than that among long-term detainees. Early screening and intervention should be implemented for newly detainees, with particular attention focused on high-risk groups such as those with a history of smoking, HIV infection, or drug use.

Objective:To investigate the protective effect of a composite hydrogel against hydrogen peroxide(H2O2)-induced oxidative stress injury in the cardiomyocytes,and to clarify its possible mechanism.Methods:The mice were subcutaneously injected with 100 μL of hydrogel.After normal feeding for 1,14,and 28 d,the mice were sacrificed.Tissue samples were collected and subjected to HE staining to observe the histocompatibity of the hydrogel.The primary cardiomyocytes isolated from 1-day-old SD rats were used to establish an oxidative stress injury model.The primary cardiomyocyties were divided into control,H2O2 and H2O2+Hydrogel groups.The primary cardiomyocytes in control group were cultured normally,the primary cardiomyocytes in H2O2 group were treated with 200 μmol·L-1 H2O2 for 24 h,and the primary cardiomyocytes in H2O2+Hydrogel group were incubated with 1 g·L-1 composite hydrogel and 200 μmol·L-1 H2O2 for 24 h.The viabilities of cardiomyocytes in various groups were assessed by cell counting kit-8(CCK-8)method.Dihydroethidium(DHE)and 2',7'-dichlorodihydrofluorescein diacetate(DCFH-DA)staining were used to assess the reactive oxygen species(ROS)levels in the cells.The expressions of filamentous actin(F-actin)in the cells in various groups were detected by phalloidin fluorescence staining;the expressions of connexin 43(Cx43)and cardiac troponin T(cTnT)proteins in the cardiomyocytes in various groups were detected by immunofluorescence method.The apoptotic rates of cardiomyocytes in various groups were assessed with TUNEL staining method.The expression levels of apoptosis-related proteins B-cell lymphoma-2(Bcl-2)and Bcl-2-associated X protein(Bax)in the cardiomyocytes in various groups were assessed by Western blotting method.Results:The HE staining results showed that the inflammatory cells around the implanted hydrogel were less infiltrated,and the inflammatory reaction of subcutaneous implantation was less.Compared with control group,the viability of cardiomyocytes in H2O2 group was significantly decreased(P＜0.05),the level of ROS in the cells was increased(P＜0.05),the expression levels of Cx43,cTnT and F-actin proteins in the cells were decreased(P＜0.001),the apoptosis rate of cardiomyocytes were decreased(P＜0.01),the expression level of Bcl-2 protein in the cells was increased(P＜0.001),and the expression level of Bax protein was decreased(P＜0.01).Compared with H2O2 group,the viability of cardiomyocytes was significantly increased(P＜0.05),the level of ROS in the cells was decreased(P＜0.01),the expression levels of cTnT,Cx43 and F-actin proteins were increased(P＜0.01),the apoptotic rate of cardiomyocytes were significantly decreased(P＜0.001),the expression level of Bcl-2 protein in the cells were decreased(P＜0.01),and the expression level of Bax protein was increased(P＜0.01).Conclusion:Hydrogel may promote the expression of cardiomyocyte-related proteins by scavenging ROS in the environment and inhibit the cardiomyocyte apoptosis to achieve the protective effect on the cardiomyocytes under oxidative stress.

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.

To investigate the impact of mild-to-moderate frailty on the long-term prognosis of hospitalized elderly patients with type 2 diabetes mellitus(T2DM).

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.

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 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 assess the consistency of ultrasound and dual-energy computed tomography (DECT) in the diagnosis of gouty arthritis(GA), reasons of the differences were further analyzed.Methods:The ultrasound and DECT images of 150 knee joints from 147 patients diagnosed with gout at the Gout Specialty Clinic of Qingdao University Affiliated Hospital from February 2022 to October 2023 were retrospectively analyzed. According to anatomy, the knee joint was anatomically segmented into five regions: intra-articular, anterior, posterior, medial, and lateral.Location of monosodium urate (MSU) deposition was meticulously recorded. The Kappa consistency test was employed to assess the consistency of the two examination results in different regions of the knee joint. The McNemar chi-square test was utilized to conduct a differential analysis between DECT and ultrasound results.Results:Double contour sign(DCS) (81.2%, 92/112) was the most common intra-articular ultrasound sign in knee joints with GA. In the extra-articular region, MSU was commonly deposited in and around the popliteal tendon (ultrasound: 51.6%, 66/128; DECT: 54.7%, 70/128). Corresponding MSU deposits on DECT were found in 9 of 92 joints with DCS and in 9 of 49 joints with aggregates detected on ultrasound.In the assessment of MSU deposits, ultrasound showed an overall higher positive rate than DECT (87.3% vs. 72.3%, P=0.001), with poor consistency between the two examinations (Kappa=0.153). In distinct anatomical regions, ultrasound and DECT showed high consistency in the medial (Kappa=0.697) and lateral (Kappa=0.718) sides and the difference was not statistically significant ( P>0.05). Intra-articular (Kappa=0.289) and anterior (Kappa=0.303) regions exhibited only fair consistency, with statistically significant diagnostic differences ( P<0.05). When exclusively assessing cases with tophus, ultrasound and DECT demonstrated high consistency in the medial and lateral aspects(Kappa=0.685, 0.748) without statistical difference ( P>0.05). In the anterior region, the consistency between the two examinations was moderate (Kappa=0.256), while in the intra-articular region, the consistency of the two methods was lower (Kappa=0.147), and the differences was statistically significant ( P<0.001). Conclusions:Both ultrasound and DECT exhibit good diagnostic capabilities for gouty knee arthritis.However, the consistency between the two techniques varies in different anatomical locations. Clinical assessment should be tailored based on the specific anatomical position. DECT has an advantage in evaluating intra-articular MSU deposits, while ultrasound is more sensitive to detect early and scattered MSU deposits.