Background Polycyclic aromatic hydrocarbons (PAHs) are widely known atmospheric pollutants, which can cause serious harm to human body and ecological environment. Objective To analyze the concentrations and sources of PAHs in atmospheric PM_2.5 in Zhifu District and Longkou City during the heating period in Yantai. Methods Two monitoring sites in Zhifu District and Longkou City of Yantai were selected, and PM_2.5 sample collection was carried out from April 2023 to March 2024. Gas chromatography-mass spectrometry was applied to analyze the concentrations of PAHs in atmospheric PM_2.5 during the heating period (from November 2023 to March 2024) and the non-heating period (from April to October 2023). The concentrations of PAHs in the two periods were compared and the sources of PAHs during the heating period were analyzed by characteristic ratio method. Results During the heating period, the total concentration ranges of PAHs in atmospheric PM_2.5 at the monitoring sites in Zhifu District and Longkou City of Yantai were (1.59-23.70) ng·m⁻³ and (2.08-149.72) ng·m⁻³ respectively, and the medians (M) and the 25th and 75th percentiles (P₂₅, P₇₅) were 4.99 (2.61, 8.09) ng·m⁻³ and 15.46 (8.15, 29.05) ng·m⁻³ respectively. The PAHs concentrations in Longkou City were significantly higher than those in Zhifu District in all months (P<0.05). The highest median total concentrations of PAHs in both sites were reported in January (8.14 ng·m⁻³ and 81.56 ng·m⁻³ respectively). In the non-heating period, the M (P₂₅, P₇₅) of the total PAHs concentrations at the two sites were 1.59 (1.59, 2.78) ng·m⁻³ and 4.11 (2.94, 7.97) ng·m⁻³ respectively, much lower than those in the heating period (P<0.01). The order of composition of PAHs by ring number in PM_2.5 at both sites was 4-ring> 5-ring> 6-ring, with the 4-ring contributing the largest proportion (65.33% and 46.39% respectively). Fluoranthene had the highest concentration among PAHs at both sites, with concentrations M (P₂₅, P₇₅) of 1.29 (0.51, 1.78) ng·m⁻³ and 2.32 (1.30, 3.82) ng·m⁻³ respectively. The characteristic ratios of fluoranthene/(fluoranthene + pyrene), fluoranthene/pyrene, benzo[a]anthracene/(benzo[a]anthracene + chrysene), pyrene/benzo[a]pyrene, benzo[a]pyrene/(benzo[a]pyrene + chrysene), benzo[a]pyrene/benzo[g,h,i]pyrene, and indeno[1,2,3-c,d]pyrene/(indeno[1,2,3-c,d]pyrene + benzo[g,h,i]pyrene) in Zhifu District were 0.62, 1.65, 0.41, 4.48, 0.50, 0.93 and 0.47 respectively, and those in Longkou were 0.57, 1.35, 0.40, 2.89, 0.29, 0.79 and 0.47 respectively. The results showed that PAHs pollutants were generated by combination of coal combustion, vehicle exhaust emissions and gasoline combustion. Conclusion During the heating period in Yantai area, the total concentration of PAHs in atmospheric PM_2.5 is significantly higher than that in the non-heating period. Among them, during the heating period, the pollution level in Longkou City is significantly higher than that in Zhifu District. The local PAHs may be sourced from mixed pollution, and the main sources include gasoline, and coal combustion and vehicle exhaust emissions.

Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

With the continuous development of digital technology, three-dimensional (3D) facial scanning technology is becoming more and more widely used in dentistry, where it enhances the precision of various processes of clinical diagnosis and treatment, and provides new perspectives and tools for dental research and application. Nowadays, 3D facial scanning technology meets clinical needs in terms of accuracy, and most facial scanners have an accuracy of 140-1 330 μm, and the 3D facial scanning related applications of smartphones can also meet the clinical application needs to a certain extent. In the clinical field, the 3D scanner can be combined with a variety of technologies to achieve multi-modal data fusion and create virtual patients, and its prediction rate in obstructive sleep apnea hypopnea syndrome is as high as 91%, which has great clinical application value. This article aims to explore the research status, clinical applications, and accuracy analysis of 3D facial scanning techniques in the field of dentistry.

With the continuous development of digital technology, three-dimensional (3D) facial scanning technology is becoming more and more widely used in dentistry, where it enhances the precision of various processes of clinical diagnosis and treatment, and provides new perspectives and tools for dental research and application. Nowadays, 3D facial scanning technology meets clinical needs in terms of accuracy, and most facial scanners have an accuracy of 140-1 330 μm, and the 3D facial scanning related applications of smartphones can also meet the clinical application needs to a certain extent. In the clinical field, the 3D scanner can be combined with a variety of technologies to achieve multi-modal data fusion and create virtual patients, and its prediction rate in obstructive sleep apnea hypopnea syndrome is as high as 91%, which has great clinical application value. This article aims to explore the research status, clinical applications, and accuracy analysis of 3D facial scanning techniques in the field of dentistry.

Objective:To explore the value of the vesical imaging-reporting and data system (VI-RADS) score based on multiparametric MRI (mpMRI) combined with quantitative tumor MRI parameters in assessing the muscle invasion of bladder cancer.Methods:The study was a case-control study. The data of 87 bladder cancer patients confirmed by pathology who underwent mpMRI of the bladder were retrospectively collected from the First Medical Center of Chinese PLA General Hospital between January 2019 and April 2023 The pathological findings were used as the gold standard to categorize them into the muscle invasive bladder cancer (MIBC) group (29 cases) and non-muscle invasive bladder cancer (NMIBC) group (58 cases). Quantitative parameters were measured based on preoperative mpMRI images, including the length of tumor bladder wall contact, the perpendicular distance between the bladder tumor and the tangent of the bladder wall, the maximal diameter of the bladder tumor, and the volume of the bladder tumor. Bladder cancer was classified according to the VI-RADS scoring criteria. The Mann-Whitney U test was used for intergroup comparisons. Multivariate logistic regression analysis was performed to obtain the independent risk factors related to muscle invasion of bladder cancer and to establish the model. The receiver operating characteristic curves were analyzed for MRI quantitative parameters and logistic regression models, and area under the curve (AUC) comparisons were performed using the DeLong test. Results:The differences in tumor bladder wall contact length, perpendicular distance from the tumor to the tangent line of the bladder wall, maximum diameter, bladder tumor volume, and the VI-RADS scores were statistically significant between the MIBC group and the NMIBC group ( P<0.05). Multifactorial logistic regression analysis showed that tumor bladder wall contact length ( OR=21.07, 95% CI 3.56-124.89, P=0.001) and VI-RADS score ( OR=11.90, 95% CI 3.53-40.12, P<0.001) were the independent risk factors for evaluating the muscle invasion of bladder cancer. The difference between the VI-RADS score and the tumor bladder wall contact length for assessing muscular infiltration of bladder cancer had AUCs of 0.802 (95% CI 0.704-0.899) and 0.759 (95% CI 0.652-0.865). The combined model of VI-RADS score combined with tumor bladder wall contact length had an AUC of 0.891 (95% CI 0.812-0.970), which was higher than the diagnostic efficacy of applying tumor bladder wall contact length or VI-RADS score alone ( Z=3.05, 2.37, P=0.002, 0.018). Conclusion:Tumor contact length with the bladder wall is an independent risk factor for assessing muscle invasion of bladder cancer and the combination of VI-RADS score may enhances diagnostic accuracy.

Objective To explore the risk factors of apathy and correlations with cognitive function in patients with hypertension combined with cerebral small vessel disease(CSVD).Methods Totally 141 patients with hypertension combined with CSVD were prospectively enrolled and were divided into apathy group(n=43)and non-apathy group(n= 98)according to neuropsychiatric inventory-apathy scale(NPI-Apathy)scores.The general data,imaging marker scores and total imaging burden scores were compared between groups.In hypertension combined with CSVD patients,multivariate logistic regression analysis was performed to screen the independent risk factors of apathy,and Spearman correlation analysis was also performed to observe the correlation of apathy and cognitive function.Results The patients'age,high density lipoprotein cholesterol(HDL-C),Fazekas scores of lateral periventricular white matter hyper-intensity(WMH),cerebral microbleed of depth/infratentorial and total imaging burden scores of apathy group were all higher,while mini-mental state examination(MMSE)and Montreal cognitive assessment(MoCA)scores were both lower than those of non-apathy group(all P＜0.05).HDL-C and Fazekas scores of lateral periventricular WMH were both independent risk factors for apathy(both P＜0.05),while NPI-Apathy scores were moderately negatively correlated with cognitive function in patients with hypertension combined with CSVD(r=-0.543,-0.484,both P＜0.001).Conclusion HDL-C and Fazekas scores of lateral periventricular WMH were both independent risk factors for apathy in patients with hypertension combined with CSVD.The more severe the apathy,the lower the cognitive function.

The accurate assessment of burn injuries is complex and difficult. The emergence of some new skin imaging techniques, such as hyperspectral imaging, unilateral magnetic resonance imaging, laser Doppler perfusion imaging, etc., has made great progress in research on burn injury assessment. With the continuous progress of imaging technology and medical imaging, medical imaging technologies such as digital radiography, computed tomography, magnetic resonance imaging, and ultrasonography, and so on, have played an important role in burn injury assessment. However, there are still relatively few research reports on burn injury imaging, which may be due to the fact that the imaging techniques and diagnostic experience used for burn injury assessment are not yet fully popular in some medical institutions, and the imaging manifestations related to burns are complex and lack of specificity. This article mainly reviews the application research progress of various imaging techniques in the assessment of burn injury in recent years, aiming to explore the application value of various imaging techniques in burn injury assessment.

Cerebrovascular disease is a kind of diseases in which cerebral vessels were damaged by various causes,resulting in transient or permanent dysfunction of brain tissue.Deep medullary veins are important intracranial draining veins,which impact venous return of deep brain parenchyma and play an important role in assessing the severity of cerebrovascular diseases and guiding treatment strategies.The research progresses of MRI for evaluating deep medullary vein related cerebrovascular diseases were reviewed in this article.

Objective:To construct a model for predicting early recurrence of hepatocellular carcinoma (HCC) patients after radiofrequency ablation by different machine learning models based on multimodal MRI and clinical indicators, and to evaluate the predictive efficacy of the model.Methods:The data of patients with HCC who underwent radiofrequency ablation in Fourth Medical Center of Chinese PLA General Hospital and the First Medical Center of Chinese PLA General Hospital from January 2015 to December 2021 were retrospectively analyzed. A total of 169 patients with HCC were enrolled, including 152 males and 17 females, aged (57.2±9.2) years. The training set ( n=135) and the test set ( n=34) were randomly divided according to 8∶2. There were 49 cases recurrence in training set and 12 cases recurrence in test set. Based on the training set, the clinical influencing factors of early recurrence in patients with HCC after radiofrequency ablation were screened by univariated and multivariate logistic analysis, and the imaging features were sequentially screened by variance threshold method, select K-best and LASSO regression. Support vector machine (SVM), logistic regression and random forest (RFOREST) were used to construct the prediction models of early postoperative recurrence with simple imagomics alone or combined clinical features, respectively, and the receiver operating characteristic (ROC) curve was used to evaluate the prediction efficiency of the models. Results:Multivariate logistic regression analysis showed that preoperative alpha-fetoprotein >20 μg/L, platelet count >140×10 9 and tumor location were the influential factors for early recurrence of HCC patients after radiofrequency ablation (all P<0.05). Through variance threshold analysis, select K-best and LASSO regression, 16 optimal image omics features were selected. SVM, logistic regression and RFOREST were used to construct a simple imaging omics model for predicting early recurrence of HCC patients after radiofrequency ablation. The areas under ROC curve of the test set were 0.826, 0.830 and 0.826, respectively. And the areas under ROC curve of the constructed imagomics combined clinical model of test set were 0.830, 0.830 and 0.909, respectively. The area under ROC curve of RFOREST in the test set was better than that of SVM and logistic regression ( Z=2.19, 3.98, P=0.008, 0.008). Conclusion:The combined model constructed by SVM, logistic regression and RFOREST based on clinical indicators and image omics features is effective in predicting the early recurrence of patients with HCC after radiofrequency ablation, and the model constructed by RFOREST is the best.

Objective:To evaluate the accuracy of an artificial intelligence (AI) model in measuring key angles on pelvic radiographs of the hip and assess its effectiveness in diagnosing developmental dysplasia of the hip (DDH) and borderline developmental dysplasia of the hip (BDDH).Methods:A retrospective analysis was conducted using anteroposterior pelvic X-ray films from 1,029 patients with suspected DDH. The data were collected from the Department of Radiology, Fourth Medical Center of the Chinese PLA General Hospital. Among the patients, 273 were male, and 756 were female, with an average age of 57.01 ± 18.16 years (range, 12-88 years). The dataset was randomly divided into a training set (720 cases), a test set (206 cases), and a validation set (103 cases). Two radiologists identified and marked key anatomical points of the hip joint to establish the training dataset, which was then used to develop a deep learning-based AI model capable of locating these key anatomical positions. Using the identified anatomical points, the AI model automatically measured and calculated the Sharp angle, center-edge (CE) angle, and T?nnis angle in the test dataset. The measurement results from the AI model were compared with those of the radiologists to evaluate the model's accuracy. The validation set was used to optimize model parameters, and the test dataset was used to evaluate the diagnostic performance of DDH. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic efficacy of the AI model for DDH and BDDH.Results:The accuracy rates of the AI model in measuring the left Sharp angle, CE angle, and T?nnis angle for diagnosing DDH were 89.8%, 90.1%, and 86.8%, respectively. For the right side, the accuracy rates were 93.7%, 92.2%, and 80.5%, respectively. There were no statistically significant differences in the mean values of the Sharp, T?nnis, and CE angles between manual and AI measurements ( P>0.05). Pearson correlation tests and intraclass correlation coefficient (ICC) analyses revealed high consistency between AI and manual measurements of the Sharp angle, T?nnis angle, and CE angle, with r-values and ICC values exceeding 0.75. Additionally, the AI model performed measurements significantly faster (1.7±0.1 s) than radiologists (88.1±8.4 s and 90.3±7.4 s, P<0.001). The areas under the ROC curves (AUCs) for diagnosing DDH using the Sharp angle, CE angle, and T?nnis angle measured by the AI model were 0.883, 0.922, and 0.908 (left side) and 0.924, 0.871, and 0.922 (right side), respectively. For diagnosing BDDH, the AUCs of the left and right CE angles measured by the AI model were 0.787 and 0.676, respectively. Kappa test results indicated good agreement between the AI model and manual measurements as well as final clinical diagnoses. For the CE angle, the κ value of the AI model was 0.663, while κ values for the Sharp and T?nnis angles were all greater than 0.800. Conclusion:The convolutional neural network-based AI model effectively and automatically measures the Sharp, CE, and T?nnis angles and demonstrates high diagnostic efficacy for DDH and BDDH.