Objective:To assess performance advantages of voxel-less optimization(VoLO)algorithm of CyberKnife-based S7 treatment plan system for the optimization of stereotactic body radiation therapy(SBRT)for liver cancer.Methods:The case data of 20 patients with hepatocellular carcinoma from Chinese PLA General Hospital during June 2022 and April 2023 were retrospectively selected,which included 10 patients with large hepatocellular carcinoma and 10 patients with small hepatocellular carcinoma.All patients adopted respectively sequential optimization(SO)and VoLO to conduct optimization for plan.The optimized quality of plan and execution efficiency of two kinds of algorithms were assessed,and the influences of different tumor volumes also were considered.The planed quality assessment included dosimetric parameters of the target region and organ at risk(OAR).The assessment parameters of execution efficiency included the numbers of monitor units(MUs),nodes and beams,and estimated treatment time.Paired t-test method was adopted to analyze quality of plan and treatment efficiency.Results:On the aspect of the dose of target region,for small hepatocellular carcinoma,the conformity index(CI)value(1.08±0.05)of target region of VoLO algorithm was significantly better than(1.17±0.06)of SO algorithm(t=4.631,P<0.05).The gradient index(GI),coverage rate and dose by 95%(D95%)of VoLO algorithm were better than those of SO algorithm,while the differences were not significant(P>0.05).According to the defined standards of liver surgery,for large hepatocellular carcinoma,the differences in CI,GI,coverage rate and D95%of target region between two kinds of algorithms were significant(t=3.337,4.238,-3.359,-3.311,P<0.05),respectively.On the aspect of dosimetry for OAR,for the target region of large hepatocellular carcinoma,the differences of liver Dmean and D700 cm3 between two kinds of algorithms were significant(t=4.114,3.415,P<0.05).However,for small hepatocellular carcinoma,there was no significant statistical difference in dosimetry parameters of OAR between two kinds of algorithms(P>0.05).The execution efficiency of the plan of VoLO group was obviously higher than that of SO group,and the differences of MU number,node number,beam number and estimated treatment time between two groups were significant(t=12.661,4.423,5.024,9.487,P<0.05).Conclusion:The quality of VoLO plan is significantly better than that of SO,which has a significant improvement in execution efficiency of treatment.For the cases of large hepatocellular carcinoma with more complexity,the VoLO optimization shows better advantages on the aspect of dose on target region,and protection for normal liver.

Objective:To assess performance advantages of voxel-less optimization(VoLO)algorithm of CyberKnife-based S7 treatment plan system for the optimization of stereotactic body radiation therapy(SBRT)for liver cancer.Methods:The case data of 20 patients with hepatocellular carcinoma from Chinese PLA General Hospital during June 2022 and April 2023 were retrospectively selected,which included 10 patients with large hepatocellular carcinoma and 10 patients with small hepatocellular carcinoma.All patients adopted respectively sequential optimization(SO)and VoLO to conduct optimization for plan.The optimized quality of plan and execution efficiency of two kinds of algorithms were assessed,and the influences of different tumor volumes also were considered.The planed quality assessment included dosimetric parameters of the target region and organ at risk(OAR).The assessment parameters of execution efficiency included the numbers of monitor units(MUs),nodes and beams,and estimated treatment time.Paired t-test method was adopted to analyze quality of plan and treatment efficiency.Results:On the aspect of the dose of target region,for small hepatocellular carcinoma,the conformity index(CI)value(1.08±0.05)of target region of VoLO algorithm was significantly better than(1.17±0.06)of SO algorithm(t=4.631,P<0.05).The gradient index(GI),coverage rate and dose by 95%(D95%)of VoLO algorithm were better than those of SO algorithm,while the differences were not significant(P>0.05).According to the defined standards of liver surgery,for large hepatocellular carcinoma,the differences in CI,GI,coverage rate and D95%of target region between two kinds of algorithms were significant(t=3.337,4.238,-3.359,-3.311,P<0.05),respectively.On the aspect of dosimetry for OAR,for the target region of large hepatocellular carcinoma,the differences of liver Dmean and D700 cm3 between two kinds of algorithms were significant(t=4.114,3.415,P<0.05).However,for small hepatocellular carcinoma,there was no significant statistical difference in dosimetry parameters of OAR between two kinds of algorithms(P>0.05).The execution efficiency of the plan of VoLO group was obviously higher than that of SO group,and the differences of MU number,node number,beam number and estimated treatment time between two groups were significant(t=12.661,4.423,5.024,9.487,P<0.05).Conclusion:The quality of VoLO plan is significantly better than that of SO,which has a significant improvement in execution efficiency of treatment.For the cases of large hepatocellular carcinoma with more complexity,the VoLO optimization shows better advantages on the aspect of dose on target region,and protection for normal liver.

OBJECTIVE: To analyze the time characteristics of the Ethos online adaptive radiotherapy (OART) process in clinical practice and provide guidance for the comprehensive optimization of each stage of adaptive radiotherapy. METHODS: The study involved 61 patients with cervical, rectal, gastric, lung, esophageal, and breast cancers who underwent Ethos OART. The mean ± standard deviation of segmental time, total time, and target volume for these patients were tracked. The time characteristics for different cancer types were evaluated, and the average time for target and organ at risk (OAR) modifications was compared with the average target volume for each cancer type. RESULTS: Cervical cancer born the longest total treatment time, while breast cancer had the shortest. For all cancer types except breast cancer, the modification time for target and OAR was the most time-consuming segment. The average time for target and OAR modifications aligned with the trend of the average target volume. CONCLUSION The total treatment time for various cancers ranges from 15 to 35 minutes, indicating room for improvement.
Humans ; Radiotherapy Planning, Computer-Assisted/methods* ; Neoplasms/radiotherapy* ; Female

Objective To compare the clinical efficacy of abdominal ultrasound-guided endoscopic retrograde appendicitis therapy(ERAT)with laparoscopic appendectomy(LA)for acute uncomplicated appendicitis using propensity score matching.Methods The clinical data of 441 patients with acute uncomplicated appendicitis admitted to the Third People's Hospital of Yunnan Province from March 2020 to April 2023 were collected.The cases were classified based on the differences in surgical method and divided into the ERAT group(n = 30)and LA group(n = 411).The clinical efficacy of patients was compared between the two groups after reducing confounding bias by propensity score matching(PSM).Results After PSM,a total of 30 pairs of patients in the two groups were successfully matched,and the baseline data of the two groups met the requirements for comparability.At 24 hours after the operation,the ERAT group exhibited lower white blood cells,neutrophil counts,and C-reactive protein levels compared to the LA group,and these differences were statistically significant(P<0.05).There was no significant difference in the operation time and total effective rate between the ERAT group and the LA group(P>0.05).However,the ERAT group had lower intraoperative blood loss and shorter pain relief time compared to the LA group,and these differences were statistically significant(P<0.05).Conclusion Abdominal ultrasound-guided endoscopic retrograde appendicitis treatment is an effective,safe,and feasible technique with good prospects for the treatment of acute uncomplicated appendicitis.

Objective To construct a risk prediction model for ventilator-associated pneumonia(VAP)in trauma patients and evaluate its efficacy.Methods A single-center retrospective study was conducted,trauma patients admitted to the department of emergency intensive care unit(EICU)of Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine from January 1,2018 to January 1,2023 were selected as the study subjects,and the patients were divided into VAP group and non-VAP group.Differences between the two groups in variables including demographic characteristics,clinical data,and clinical scores.To prevent overfitting,differences between the groups were reduced using LASSO regression.Multifactor Logistic regression was used to identify risk factors for VAP in trauma patients and construct a risk prediction model.The model's discrimination was evaluated using the receiver operator characteristic curve(ROC curve)and area under the curve(AUC).The calibration curve was drawn and Hosmer-Lemeshow test were performed to evaluate the calibration degree of the model.Decision curve analysis(DCA)and clinical impact curve(CIC)were used to analyse the model's net benefit at different probability thresholds.Results A total of 888 trauma patients were included,among which 166 cases(18.7%)were diagnosed with VAP.Compared to the non-VAP group,the VAP group showed a significant increase in age,age-adjusted Charlson comorbidity index(aCCI)scores,white blood cell count(WBC),sequential organ failure assessment(SOFA)scores,length of ICU stay,and the proportion of patients with chest trauma,traumatic brain injury,and spinal cord injury.In contrast,hemoglobin(Hb),Glasgow coma scale(GCS)scores,and body mass index(BMI)were significantly lower in the VAP group(all P<0.05).Using LASSO regression,four variables were identified as important predictors for the occurrence of VAP in trauma patients:length of ICU stay,aCCI,WBC,and SOFA score.Multivariate Logistic regression showed that length of ICU stay[odds ratio(OR)and 95%confidence interval(95%CI)was 1.094(1.070-1.117)],aCCI[OR(95%CI)was 1.135(1.065-1.210)],WBC[OR(95%CI)was 1.139(1.104-1.176)],and SOFA score[OR(95%CI)was 1.137(1.080-1.197)]were independent risk factors for the occurrence of VAP in trauma patients(all P<0.05).Based on these influencing factors,a predictive model for VAP occurrence was constructed.ROC curve analysis showed that the AUC for predicting VAP occurrence in trauma patients was 0.876,with a 95%CI was 0.850-0.903,a sensitivity of 86.14%,and a specificity of 75.17%,indicating that the model has a high discriminative ability.Hosmer-Lemeshow test:χ2=7.7,P=0.2,Cox&Snell R2=0.236,Nagelkerke R2=0.387,the calibration curve was very close to the diagonal,and the mean absolute error(MAE)=0.03,indicating the model's predictions were highly consistent with actual clinical observations.The DCA and CIC curves indicate that within the threshold probability of<70%,using this model to identify high-risk groups for VAP in trauma patients and making clinical decisions can provide benefits in clinical practice.Conclusion The risk prediction model of VAP in trauma patients constructed in this study has high discrimination and calibration,which can provide reference for medical personnel to identify high-risk groups of VAP among trauma patients at an early stage and provide targeted intervention measures.

Objective To construct a risk prediction model for ventilator-associated pneumonia(VAP)in trauma patients and evaluate its efficacy.Methods A single-center retrospective study was conducted,trauma patients admitted to the department of emergency intensive care unit(EICU)of Hebei Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine from January 1,2018 to January 1,2023 were selected as the study subjects,and the patients were divided into VAP group and non-VAP group.Differences between the two groups in variables including demographic characteristics,clinical data,and clinical scores.To prevent overfitting,differences between the groups were reduced using LASSO regression.Multifactor Logistic regression was used to identify risk factors for VAP in trauma patients and construct a risk prediction model.The model's discrimination was evaluated using the receiver operator characteristic curve(ROC curve)and area under the curve(AUC).The calibration curve was drawn and Hosmer-Lemeshow test were performed to evaluate the calibration degree of the model.Decision curve analysis(DCA)and clinical impact curve(CIC)were used to analyse the model's net benefit at different probability thresholds.Results A total of 888 trauma patients were included,among which 166 cases(18.7%)were diagnosed with VAP.Compared to the non-VAP group,the VAP group showed a significant increase in age,age-adjusted Charlson comorbidity index(aCCI)scores,white blood cell count(WBC),sequential organ failure assessment(SOFA)scores,length of ICU stay,and the proportion of patients with chest trauma,traumatic brain injury,and spinal cord injury.In contrast,hemoglobin(Hb),Glasgow coma scale(GCS)scores,and body mass index(BMI)were significantly lower in the VAP group(all P<0.05).Using LASSO regression,four variables were identified as important predictors for the occurrence of VAP in trauma patients:length of ICU stay,aCCI,WBC,and SOFA score.Multivariate Logistic regression showed that length of ICU stay[odds ratio(OR)and 95%confidence interval(95%CI)was 1.094(1.070-1.117)],aCCI[OR(95%CI)was 1.135(1.065-1.210)],WBC[OR(95%CI)was 1.139(1.104-1.176)],and SOFA score[OR(95%CI)was 1.137(1.080-1.197)]were independent risk factors for the occurrence of VAP in trauma patients(all P<0.05).Based on these influencing factors,a predictive model for VAP occurrence was constructed.ROC curve analysis showed that the AUC for predicting VAP occurrence in trauma patients was 0.876,with a 95%CI was 0.850-0.903,a sensitivity of 86.14%,and a specificity of 75.17%,indicating that the model has a high discriminative ability.Hosmer-Lemeshow test:χ2=7.7,P=0.2,Cox&Snell R2=0.236,Nagelkerke R2=0.387,the calibration curve was very close to the diagonal,and the mean absolute error(MAE)=0.03,indicating the model's predictions were highly consistent with actual clinical observations.The DCA and CIC curves indicate that within the threshold probability of<70%,using this model to identify high-risk groups for VAP in trauma patients and making clinical decisions can provide benefits in clinical practice.Conclusion The risk prediction model of VAP in trauma patients constructed in this study has high discrimination and calibration,which can provide reference for medical personnel to identify high-risk groups of VAP among trauma patients at an early stage and provide targeted intervention measures.

Objective:To use the resting state functional network connectivity (FNC) method based on independent component analysis (ICA) to analyze the characteristics of FNC changes in patients with basal ganglia aphasia (BGA) after stroke, and to explore its occurrence and recovery mechanism under the intervention of acupuncture combined with language rehabilitation training.Methods:Using a prospective observational research method, 16 right-handed BGA patients who were treated at Beijing Hospital of Traditional Chinese Medicine, Capital Medical University from July 2021 to December 2022, as well as 14 healthy subjects matched in age, gender, education level, and handedness, were included. The resting state functional magnetic resonance imaging, demographic information, and Western Aphasia Examination data of healthy subjects and BGA patients before and after intervention were collected. The GIFT toolbox based on MATLAB platform was applied for ICA and resting state brain network FNC analysis. The FNC differences between BGA patients and healthy subjects were compared horizontally, and the FNC changes in BGA patients before and after intervention were compared vertically.Results:Compared with healthy subjects, post-stroke BGA patients showed decreased connectivity between the basal ganglia network, default network, and visual network before intervention, while increased connectivity between the auditory network, right frontoparietal network, and anterior cuneiform network; After the intervention of acupuncture combined with language rehabilitation training, the connectivity between the basal ganglia network, visual network, and anterior cuneiform network decreased, while the connectivity between the anterior convex network and bilateral frontoparietal network decreased, while the connectivity between the default network, auditory network, right frontoparietal network, and visual network increased. The BGA patient group showed enhanced connectivity between the basal ganglia network and the left frontoparietal network before and after intervention.Conclusions:The FNC changes between the basal ganglia network and other brain networks are key to reflecting the mechanism of BGA occurrence and language function recovery. Acupuncture combined with language rehabilitation training may improve language function by enhancing the connectivity between the basal ganglia network and the left frontoparietal network, and the redistribution of attention resources may also be one of the reasons for promoting language function recovery in BGA patients.

Owing to the inherent shortcomings of traditional therapeutic drugs in terms of inadequate therapeutic efficacy and toxicity in clinical treatment, nanomedicine designs have received widespread attention with significantly improved efficacy and reduced non-target side effects. Nanomedicines hold tremendous theranostic potential for treating, monitoring, diagnosing, and controlling various diseases and are attracting an unfathomable amount of input of research resources. Against the backdrop of an exponentially growing number of publications, it is imperative to help the audience get a panorama image of the research activities in the field of nanomedicines. Herein, this review elaborates on the development trends of nanomedicines, emerging nanocarriers, in vivo fate and safety of nanomedicines, and their extensive applications. Moreover, the potential challenges and the obstacles hindering the clinical translation of nanomedicines are also discussed. The elaboration on various aspects of the research trends of nanomedicines may help enlighten the readers and set the route for future endeavors.

CT image based organ segmentation is essential for radiotherapy treatment planning, and it is laborious and time consuming to outline the endangered organs and target areas before making radiation treatment plans. This study proposes a fully automated segmentation method based on fusion convolutional neural network to improve the efficiency of physicians in outlining the endangered organs and target areas. The CT images of 170 postoperative cervical cancer stage IB and IIA patients were selected for network training and automatic outlining of bladder, rectum, femoral head and CTV, and the neural network was used to localize easily distinguishable vessels around the target area to achieve more accurate outlining of CTV.
Female ; Humans ; Image Processing, Computer-Assisted ; Neural Networks, Computer ; Organs at Risk ; Pelvis ; Tomography, X-Ray Computed ; Uterine Cervical Neoplasms/surgery*

Drug-induced hyperglycemia/diabetes is a global issue. Some drugs induce hyperglycemia by activating the pregnane X receptor (PXR), but the mechanism is unclear. Here, we report that PXR activation induces hyperglycemia by impairing hepatic glucose metabolism due to inhibition of the hepatocyte nuclear factor 4-alpha (HNF4α)‒glucose transporter 2 (GLUT2) pathway. The PXR agonists atorvastatin and rifampicin significantly downregulated GLUT2 and HNF4α expression, and impaired glucose uptake and utilization in HepG2 cells. Overexpression of PXR downregulated GLUT2 and HNF4α expression, while silencing PXR upregulated HNF4α and GLUT2 expression. Silencing HNF4α decreased GLUT2 expression, while overexpressing HNF4α increased GLUT2 expression and glucose uptake. Silencing PXR or overexpressing HNF4α reversed the atorvastatin-induced decrease in GLUT2 expression and glucose uptake. In human primary hepatocytes, atorvastatin downregulated GLUT2 and HNF4α mRNA expression, which could be attenuated by silencing PXR. Silencing HNF4α downregulated GLUT2 mRNA expression. These findings were reproduced with mouse primary hepatocytes. Hnf4α plasmid increased Slc2a2 promoter activity. Hnf4α silencing or pregnenolone-16α-carbonitrile (PCN) suppressed the Slc2a2 promoter activity by decreasing HNF4α recruitment to the Slc2a2 promoter. Liver-specific Hnf4α deletion and PCN impaired glucose tolerance and hepatic glucose uptake, and decreased the expression of hepatic HNF4α and GLUT2. In conclusion, PXR activation impaired hepatic glucose metabolism partly by inhibiting the HNF4α‒GLUT2 pathway. These results highlight the molecular mechanisms by which PXR activators induce hyperglycemia/diabetes.