Real-time acquisition of pulmonary ventilation and perfusion information through thoracic electrical impedance tomography (EIT) holds significant clinical value. This study proposes a novel method based on the rime (RIME) algorithm-optimized variational mode decomposition (VMD) to separate lung ventilation and perfusion signals directly from raw voltage data prior to EIT image reconstruction, enabling independent imaging of both parameters. To validate this approach, EIT data were collected from 16 healthy volunteers under normal breathing and inspiratory breath-holding conditions. The RIME algorithm was employed to optimize VMD parameters by minimizing envelope entropy as the fitness function. The optimized VMD was then applied to separate raw data across all measurement channels in EIT, with spectral analysis identifying relevant components to reconstruct ventilation and perfusion signals. Results demonstrated that the structural similarity index (SSIM) between perfusion images derived from normal breathing and breath-holding states averaged approximately 84% across all 16 subjects, significantly outperforming traditional frequency-domain filtering methods in perfusion imaging accuracy. This method offers a promising technical advancement for real-time monitoring of pulmonary ventilation and perfusion, holding significant value for advancing the clinical application of EIT in the diagnosis and treatment of respiratory diseases.
Humans ; Electric Impedance ; Algorithms ; Tomography/methods* ; Pulmonary Ventilation/physiology* ; Lung/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Adult

Objective:To investigate human factors underlying radiotherapy planning safety incidents and quantitatively explore their interrelationships.Methods:A total of 1 619 safety incidents recorded in the automated plan checking system developed by West China Hospital of Sichuan University were utilized. Human factors were identified and statistically analyzed using the Human Factors Analysis and Classification System (HFACS). A Bayesian network model was developed and combined with sensitivity analysis for quantitative assessment.Results:Key contributing factors included organizational processes (12.89%), inadequate supervision (11.85%), and personnel factors (13.50%). Utilizing the established HFACS Bayesian network hybrid model in conjunction with sensitivity analysis, it has been found that the most significant influences on skill‐based errors and decision errors were condition of operators and environmental factors, with corresponding indices of 0.96 and 0.76. Additionally, personnel factors had the greatest impact on routine, with an index of 3.51.Conclusions:Key contributing factors span all HFACS levels, with organizational processes, supervision, personnel, and condition of operators each playing a significant role. Upstream factors — such as organizational climate, environment factors, and personnel factors — strongly influence downstream risks. These offer actionable insights for developing targeted safety protocols.

BACKGROUND:The progression of chronic oral diseases is closely related to the continuous inflammatory response.Anti-inflammatory peptides are expected to become a substitute for traditional anti-inflammatory drugs due to their rich sources,easy absorption by the body and low side effects.OBJECTIVE:To review the types,anti-inflammatory mechanisms,and their application in oral related diseases.METHODS:CNKI,PubMed,and Web of Science were retrieved,with"polypeptide,anti-inflammatory,immunomodulation,oral inflammatory diseases"as Chinese and English search terms.111 articles related to the classification of anti-inflammatory peptides,anti-inflammatory mechanisms,and application of oral inflammatory diseases were selected for review.RESULTS AND CONCLUSION:(1)Anti-inflammatory peptides are abundant in nature,which can be extracted from plants,animals,and microorganisms.In addition to naturally occurring peptides and protein hydrolysates,peptides synthesized by chemical modification,computer simulation design,and genetic recombination technology can also exert anti-inflammatory effects.The composition,position,and properties of amino acids affect their anti-inflammatory activity.(2)Because the anti-inflammatory mechanism of anti-inflammatory peptides is still unclear,the activity verification is mostly cell experiments,and there is a lack of animal models,clinical trials and other further studies.(3)In the treatment of oral inflammatory diseases(including periodontitis,oral mucositis,caries,pulpitis,suppurative osteomyelitis of the jaw,and peri-implantitis),anti-inflammatory peptides can inhibit the release of inflammatory factors such as interleukin 6,interleukin 1β,and tumor necrosis factor α in oral tissues,regulate inflammatory responses,improve the chronic inflammatory environment,reduce tissue destruction and structural loss,and promote bone tissue regeneration,providing new ideas for the treatment of oral inflammatory diseases.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective: To construct and to validate a nomogram prediction model based on Lipoprotein-associated phospholipase A2(Lp-PLA2) and Lipoprotein(a) [LP(a) ]for predicting the risk of major adverse cardiovascular events(MACE) in patients with coronary heart disease(CHD). Methods: A retrospective analysis was conducted on the clinical data of 442 patients with coronary heart disease(CHD). Among them,411 patients who completed follow-up were randomly divided into a training set(288 cases) and a validation set(123 cases) at a 7 ∶ 3 ratio.Independent risk factors for major adverse cardiovascular events(MACE) in CHD patients were screened through Lasso regression analysis and Cox regression analysis,and a nomogram prediction model was constructed. The predictive performance of the model was evaluated using time-dependent receiver operating characteristic curves(ROC),calibration curves,and decision curve analysis. Results: Variables were screened through Lasso regression and Cox regression analysis. The final model included nine independent predictors,namely age,smoking history,clinical phenotype of CHD,the number of coronary artery lesions,Gensini score,BNP,Lp-PLA2,LP(a), and the history of statin use. The area under the ROC curve in the training set was 0. 897,0. 885,and 0. 909 at 1,2,and 3 years,respectively; The area under the ROC curve in the validation set was 0. 885,0. 881,and 0. 923 at 1,2,and 3 years,respectively. These results demonstrated that the model had excellent discriminatory power. The calibration curves and decision curves demonstrated that the model had high clinical practicality in predicting the occurrence of MACE in CHD patients. Conclusion The nomogram prediction model based on LP-PLA2,LP(a)and other risk factors provides an effective tool for the prognosis assessment of CHD patients,facilitating the early identification of high-risk patients and enabling individualized intervention.

Objective To investigate the application value of conventional MRI combined with diffusion tensor imaging(DTI)in evaluating the correlation between the texture composition of benign prostatic hyperplasia(BPH)and the International Prostate Symptom Score(IPSS).Methods Seventy patients with BPH confirmed by pathology were retrospectively analyzed and all patients underwent conventional MRI,DTI and IPSS before surgery.Evaluation metrics included:the mean signal intensity of T2WI(mean-SI-T2WI),apparent diffusion coefficient(ADC)and fractional anisotropy(FA)values.Independent samples t-test,partial correlation analysis,and receiver operating characteristic(ROC)curve were used to assess the correlation between the texture parameters of the prostate transition zone and IPSS.Results The mean-SI-T2WI was significantly negatively correlated with IPSS(r=-0.683,P<0.001);the average ADC value was slightly negatively correlated with IPSS(r=-0.467,P<0.001);and the average FA value was slightly positively correlated with IPSS(r=0.419,P<0.001).The predictive value of MRI texture parameters for IPSS in BPH patients,ranked from high to low,mean-SI-T2WI[area under the curve(AUC)=0.734],average ADC value(AUC=0.673),and average FA value(AUC=0.635);However,the combination of mean-SI-T2WI+ADC+FA(AUC=0.791)did not significantly improve the diagnostic efficacy by DeLong's test(P>0.05).Conclusion Mean-SI-T2WI,DWI and DTI can be used to evaluate the composition of the prostate,among which mean-SI-T2WI is the best,and the com-bination of them can not improve the diagnostic efficacy.

Spectral computed tomography (CT) leverages the varying linear attenuation coefficients of different substances across different energy levels of X-ray radiation. This capability allows for more precise identification of pathological tissues and their constituent structures. This technology has been widely applied in the diagnosis and differential diagnosis of various diseases. However, the application of spectral CT in radiotherapy is not as advanced as its established role in diagnostic radiology. In this comprehensive review, the fundamental physical principles underlying spectral CT were outlined, the techniques and methodologies for its implementation were illustrated, and its unique applications in the field of radiotherapy, along with its potential future development were discussed.

BACKGROUND:The progression of chronic oral diseases is closely related to the continuous inflammatory response.Anti-inflammatory peptides are expected to become a substitute for traditional anti-inflammatory drugs due to their rich sources,easy absorption by the body and low side effects.OBJECTIVE:To review the types,anti-inflammatory mechanisms,and their application in oral related diseases.METHODS:CNKI,PubMed,and Web of Science were retrieved,with"polypeptide,anti-inflammatory,immunomodulation,oral inflammatory diseases"as Chinese and English search terms.111 articles related to the classification of anti-inflammatory peptides,anti-inflammatory mechanisms,and application of oral inflammatory diseases were selected for review.RESULTS AND CONCLUSION:(1)Anti-inflammatory peptides are abundant in nature,which can be extracted from plants,animals,and microorganisms.In addition to naturally occurring peptides and protein hydrolysates,peptides synthesized by chemical modification,computer simulation design,and genetic recombination technology can also exert anti-inflammatory effects.The composition,position,and properties of amino acids affect their anti-inflammatory activity.(2)Because the anti-inflammatory mechanism of anti-inflammatory peptides is still unclear,the activity verification is mostly cell experiments,and there is a lack of animal models,clinical trials and other further studies.(3)In the treatment of oral inflammatory diseases(including periodontitis,oral mucositis,caries,pulpitis,suppurative osteomyelitis of the jaw,and peri-implantitis),anti-inflammatory peptides can inhibit the release of inflammatory factors such as interleukin 6,interleukin 1β,and tumor necrosis factor α in oral tissues,regulate inflammatory responses,improve the chronic inflammatory environment,reduce tissue destruction and structural loss,and promote bone tissue regeneration,providing new ideas for the treatment of oral inflammatory diseases.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.