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.

A novel fumigation moxibustion device has been designed to enable adjustable and controllable moxa smoke temperature, maintaining a relatively stable fumigation temperature while improving the utilization efficiency of moxa smoke. The device consists of five main components: a temperature control chamber, fumigation outlet, temperature measurement module, moxa smoke filtration chamber, and elastic band. It is compact, refined, and easy to operate. The device allows users to set the desired fumigation temperature according to therapeutic needs and simultaneously filters and eliminates residual moxa smoke after treatment. This design addresses the challenges of traditional fumigation moxibustion therapy, including unstable moxa smoke temperature, difficulty in regulation, low utilization efficiency, and high dependence on manual operation. It contributes to the promotion and application of fumigation moxibustion therapy and supports the establishment of a standardized moxibustion system.
Moxibustion/methods* ; Humans ; Equipment Design ; Fumigation ; Temperature

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 evaluate the efficacy of hypothermia therapy in neonates with hypoxic-ischemic encephalopathy(HIE)through the analysis of amplitude-integrated electroencephalography.Methods A retrospective analysis was conducted on 59 neonates with moderate to severe HIE.They were divided into observation group(n=31,hypothermia therapy)and control group(n=28,conventional therapy)according to different treatment protocols.Chi-square test,independent sample t-test,and one-factor Mann-Whitney U test were used for intergroup difference analysis.Results Significant differences between two groups were observed in lower boundary amplitude after 24 h of treatment,sleep-awake cycle after 72 h of treatment,and total scores after 72 h of treatment(P<0.05).After 48 and 72 h of treatment,the neonates in hypothermia therapy group had obviously lower neuro-specific enolase level than those in control group(P<0.05).Conclusion Early application of hypothermia therapy can significantly improve cerebral function in neonates with HIE and lower the neuro-specific enolase level.

Purpose To explore the feasibility of artificial intelligence compressed sensing(ACS)technique in ankle joint MRI.Materials and Methods From September to October 2023,32 healthy volunteers who underwent ankle joint scanning in Beijing Friendship Hospital,Capital Medical University were prospectively collected.MRI of the ankle joint based on ACS and parallel imaging(PI)technology was performed on 3.0T MR.The sagittal proton density weighted imaging(PDWI),coronary PDWI,transverse PDWI and sagittal T1WI were acquired,and all data were divided into test group and control group,with ACS to accelerate the multiples of 5(ACS 5.0)in test group,whereas PI speed ratio of 2(PI 2.0)in control group,respectively.The signal intensity of talus,achilles tendon and cartilage were measured,the signal intensity and standard deviation of the long hallux flexor were obtained,and the signal noise ratio(SNR)and contrast to noise ratio(CNR)were calculated via long hallux flexor as background noise.The data of objective and subjective evaluation of the two sequences were statistically analyzed,and the image quality of each sequence was evaluated via the standard reference of PI 2.0.Results SNR and CNR in ACS group were higher than those in PI group,and the anatomical structure of sagittal PDWI sequence between the two groups had statistical significance(t=-2.937,-1.981,-4.058,-3.879,P<0.05).There were significant differences in cartilage SNR and talus CNR in coronal PDWI sequence(t=-3.310,-3.567;P=0.002,P<0.001).In terms of axial PDWI sequence,there were statistically significant differences in talus CNR and cartilage CNR between ACS and PI groups(t=-4.270,-4.382,P<0.05).The subjective evaluation of the image quality scores of the two groups by the two diagnostic imaging doctors showed a strong observer consistency(Kappa=0.977,P=0.009).There was no significant difference in image quality scores between the two groups(Z=-0.248,-0.747,<0.001,-0.071,P>0.05).The total collection time of ACS group and PI group was 337 s and 610 s,respectively.Compared with PI group,the total scanning time of ACS group was shortened by 44.8%.Conclusion ACS based MRI of the ankle joint can not only shorten the scan time,but also ensure and further improve the image quality,with feasibility.

Objective:To investigate the effect of visceral obesity on the short-term curative effect of Da Vinci robotic-assisted radical resec-tion for rectal cancers.Methods:Clinical and pathological data of patients with rectal cancer undergoing Da Vinci robotic-assisted surgery,admitted to People's Hospital of Zhengzhou University and Cancer Hospital of Zhengzhou University from November 2019 to June 2022 were retrospectively analyzed.Visceral fat area(VFA)≥100 cm2 was used as the standard to define visceral obesity.Patients were categorized in-to visceral and non-visceral obesity groups.The short-term efficacy of the two groups was evaluated,and the influencing factors of post-operative complications were analyzed using univariate and multivariate Logistic regression.Results:Among a total of 169 patients,93 were included in the visceral obesity group and 76 in the non-visceral obesity group.There was no significant difference in the baseline data between the two groups(P>0.05).There was no conversion to laparotomy in the non-visceral obesity group,and the conversion rate was 1.1%(1/93)in the visceral obesity group.The second operation rate was 2.2%(2/93)in the visceral obesity group and 1.3%(1/76)in the non-visceral obesity group with no statistical difference between the two groups.There were no significant differences in the operation dur-ation,intraoperative blood loss,number of lymph node dissections,and total postoperative complication rate between the two groups(P>0.05).Multivariate Logistic regression analysis revealed that an NRS≥3 independently contributed as a risk factor for postoperative com-plications(OR=3.190,95%CI:1.240-8.210,P=0.016).Conclusions:An NRS≥3 is an independent risk factor for complications post-robotic rad-ical rectal cancer surgery.The robotic surgical platform can overcome obesity-related limitations and is equally safe and effective for pa-tients with visceral obesity presenting with rectal cancer.

Objective:To explore the genetic etiology of a child with severe combined immunodeficiency (SCID).Methods:Whole exome sequencing (WES) and copy number variation (CNV) analysis were carried out to screen potential variants in the proband. Suspected variants were validated by Sanger sequencing and qPCR.Results:WES showed that the proband harbored compound heterozygous variants of the DCLRE1C gene, namely deletion of exons 1-3 and c. 322G>A (p.Val108Met) in exon 5. The exon 1-3 deletion was derived from his father and was known to be pathogenic, while the c. 322G>A was derived from his mother and was unreported previously. Conclusion:The compound heterozygous variants of the DCLRE1C gene probably underlay the SCID in this child.

Intrauterine adhesion (IUA) is caused by damage of the basal layer of endometrium, which leads to fibrosis of the endometrium and the formation of adhesion, resulting in partial or complete occlusion of the uterine cavity, abnormal menstruation, infertility or recurrent miscarriage. The prevalence of IUA in women has been increasing in recent years, and the high recurrence rate of moderate to severe IUA makes IUA treatment more challenging. Iatrogenic endometrial injury is the main cause of IUA. However, the incidence of IUA and the severity of IUA vary among patients who have received similar uterine operations, suggesting that there may be other synergistic factors in the development of IUA. There is a certain correlation between the pathogenesis and the microbiota of the gential tract. In many IUA patients, it has been observed that the probiotics such as Lactobacillus in the vagina is significant reduced, and the pathogenic bacteria such as Gardnerella and Prevotella are excessive growth. The reproductive tract microbiota can be involved in the development and progression of IUA via impacting immune function and metabolism.
Humans ; Female