Objective:To rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Objective:To rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Background::Influenza and pneumococcal vaccination are a priority in patients with chronic obstructive pulmonary disease (COPD). However, limited information is available on vaccination coverage among patients with acute exacerbations of COPD (AECOPD) in China. This study aimed to determine the rates and associated factors of influenza and pneumococcal vaccination in patients hospitalized with AECOPD.Methods::Baseline data from a national, multicenter, hospital-based study that included adult inpatients with AECOPD between 2017 and 2021 were analyzed. The outcomes of interest were the influenza vaccination in the past year and the pneumococcal vaccination in the past 5 years. To ensure national representativeness, rates were weighted according to the distribution of hospital levels and types enrolled in this study. Multivariable Poisson regression based on mixed-effects models were used to determine the associated factors. The independent variables included the region and hospital features where the participants were located, sociodemographic characteristics (age, sex, rural/urban residence, education, etc.), and clinical indicators (COPD disease history, lung function parameters, comorbidities, etc.). The treatment profiles of the vaccinated and unvaccinated participants were compared.Results::Of 6949 eligible participants, the weighted rates of influenza/pneumococcal, influenza, and pneumococcal vaccination were 2.72% (95% confidence interval [CI]: 2.34%-3.10%), 2.09% (95% CI: 1.76%-2.43%), and 1.25% (95% CI: 0.99%-1.51%), respectively. In multivariable models, age ≥60 years (60-69 years, odds ratio [OR]: 1.90, 95% CI: 1.11-3.25; ≥80 years, OR: 2.00, 95% CI: 1.06-3.78), geographical regions (Northern China relative to Eastern China, OR: 5.09, 95% CI: 1.96-13.21), urban residence (OR: 1.69, 95% CI: 1.07-2.66), a higher education level (junior high school, OR: 1.77, 95% CI: 1.21-2.58; senior high school or above, OR: 2.61, 95% CI: 1.69-4.03), former smoking (OR: 1.79, 95% CI: 1.15-2.79), and regular inhaled medication treatment (OR: 3.28, 95% CI: 2.29-4.70) were positively associated with vaccination. Patients who had experienced severe exacerbations in the past year were less likely to be vaccinated (OR: 0.65, 95% CI: 0.45-0.96). Compared with unvaccinated participants, vaccinated participants adhered better to pharmacological and non-pharmacological treatment.Conclusions::Influenza and pneumococcal vaccination coverage are extremely low. Urgent measures are necessary to increase vaccination coverage among inpatients with AECOPD in China.

Objective:To propose a deep learning network model 2D-PE-GAN to automatically delineate the target area of nasopharyngeal carcinoma and improve the efficiency of target area delineation.Methods:The model adopted the architecture of generative adversarial networks which used a UNet similar structure as the generator, and 2D-PE-block was added after each layer of convolution operation of the generator to improve the accuracy of delineation. The experimental data included CT images from 130 cases of nasopharyngeal carcinoma. The images were preprocessed before model training. In addition, three models of UNet, GAN, and GAN with an attention mechanism were compared, and Dice similarity coefficient, Hausdorff distance, accuracy, Matthews correlation coefficient, Jaccard distance were employed to evaluate network performance.Results:Compared with UNet, GAN and GAN with the attention mechanism, the average Dice similarity coefficient of 2D-PE-GAN network segmentation of CTV was increased by 26%, 4% and 2%. The average Dice similarity coefficient of GTV segmentation was increased by 21%, 4%, 2%, respectively. Compared with the GAN network with the attention mechanism, the parameters and time of 2D-PE-GAN were reduced by 0.16% and 18%, respectively.Conclusions:Compared with the above three networks, 2D-PE-GAN network can increase the segmentation accuracy of nasopharyngeal carcinoma target area delineation. At the same time, compared with the attention mechanism with similar reasons, 2D-PE-GAN network can reduce the occupation of computing resources when the segmentation accuracy is not much different.

Objective To evaluate the role of spinal cord tumor necrosis factor receptor-associated factor 6 (TRAF6)/nuclear factor kappa B (NF-κB)signaling pathway in the development of diabetic neuropathic pain (DNP)in rats.Methods Clean-grade healthy adult male Sprague-Dawley rats,aged 2 months,weighing 180-230 g,in which IT catheters were implanted,were used in this study.Streptozotocin 60 mg/kg was intraperitoneally injected after IT catheterization to establish the model of DNP.Twelve DNP rats were divided into 2 groups (n =6 each) by a random number table method:DNP group and DNP plus TRAF6 inhibitor group (group DTR).Another 6 age-matched Sprague-Dawley rats were used as normal control group (group NC).The rats in group DC and group DTR received IT injection of dimethyl sulfoxide 10 μl and TRAF6 inhibition 10 μg,respectively,once a day for 7 consecutive days starting from day 21 after establishing the model.The mechanical paw withdrawal threshold (MWT) was determined before establishing the model (T1),on 7,14 and 21 days after establishing the model (T2-4),and on 1,4 and 7 days after IT injection (T5-7).The rats were sacrificed after the last MWT measurement,and the L3-5 segments of the spinal cord were removed for determination of the expression of TRAF6 and NFκB p65 by Western blot.Results Compared with group NC,the MWT at T3-7 in group DC and at T3-6 in group DTR was significantly decreased,and the expression of spinal TRAF6 and NF-κB p65 was up-regulated in DC and DTR groups (P＜0.05).Compared with group DC,the MWT was significantly increased at T6-7,and the expression of spinal TRAF6 and NF-κB p65 was down-regulated in group DTR (P ＜ 0.05).Conclusion Spinal cord TRAF6/NF-κB signaling pathway is involved in the development of DNP in rats.

Objective To compare the anesthetic effect of fentanyl and sufentanil in vocal cord tumors excision in elderly patients.Methods Sixty elderly patients from the First Hospital of Shanxi Medical University scheduled for elective excision of vocal cord tumors were randomly divided into fentanyl group (group F) and sufentanil group(group SF) according to the random number table method, and each group had 30 cases. The dose was determined by its efficiency ratio of analgesic (10:1) followed by anesthesia induction with compound etomidate and cisatracurium. Besides, injection remifentanil and propofol with pump was used for anesthesia maintenance during the operation. Haemodynamics indicators of group F and group SF were recorded at different time point, including before (T0), after induction of anesthesia (T1), immediately after intubation (T2), during the resection of tumor (T3), before extubation (T4) and after extubation (T5). Time to resuscitation, time to extubation, the cooperation at extubation, the sedation scores of 10 minutes after extubation (according to Ramsay sedation level score), and the complications of anesthesia were recorded respectively. Results In the group SF, T0pressure: (135±6) mmHg / (82±6) mmHg (1 mmHg = 0.133 kPa), heart rate:(80±7)/min;in the group F,T0pressure:(129±7)mmHg/(79±6)mmHg,heart rate:(80±7)/min.In the group SF,T1pressure:(102±5)mmHg/(61±4)mmHg,heart rate:(64±4)/min;in the group F,T1pressure:(100±6) mmHg / (61±4) mmHg, heart rate: (61±3) /min. T3heart rate: (89±5) /min (group SF), (92±5) /min (group F). Compared with T0, the pressure and heart rate of T1were decreased in both groups, and the heart rate of T3was increased (P < 0.05). The time to resuscitation and time to extubation in the group F was shorter than that in the group SF[(5.5±1.8)min vs.(10.4±3.1)min,(8.6±2.4)min vs.(15.4±2.6)min,both P<0.05].Ramsay score in the group F was lower than that in the group SF[(2.4±0.3) scores vs. (3.2±0.5) scores, P<0.05]. There were no significant differences in the cooperation at extubation, incidence rate of vomiting and nausea between the two groups (all P > 0.05). Intraoperative awareness and postoperative irritability were not found in both groups.Conclusions The equivalent dose of fentanyl and sufentanil induction are safe and effective for elderly patients with excision of vocal cord tumors. The patients can get more rapid revival and higher revival quality after fentanyl induction.

Objective To evaluate the effect of intrathecal cardamonin on diabetic neuropathic pain (DNP) in rats.Methods Healthy adult male Sprague-Dawley rats,aged 2 months,weighing 180-220 g,were included in the study.Diabetes mellitus was produced by intraperitoneal injection of streptozotocin 60 mg/kg after intrathecal catheterization.When decrease in pain threshold＞50％ of the baseline at 21 days after diabetes mellitus was produced,the induction of DNP was considered successful.Twenty-four rats with DNP were divided into 4 groups (n=6 each) using a random number table:group DNP,rapamycin group (group R),cardamonin 50 μg group (group D50) and cardamonin 100 μg group (group D100).Another 6 healthy age-matched rats were used as normal control (group C).In DNP,R,D50 and D100 groups,dimethyl sulfoxide 10 μl,rapamycin 5 μg and cardamonin 50 μg and 100 μg were intrathecally injected,respectively,once a day for 7 consecutive days starting from 21 days after successful estabiishment of the model.Mechanical paw withdrawal threshold (MWT) was measured before establishment of the model,on 7,14 and 21 days after establishment of the model,and on 1,4 and 7 days after intrathecal injection.The rats were sacrificed after the last MWT measurement,and their L3-5 segments of the spinal cord were removed for determination of the expression of S6K,p-S6K and synapsin Ⅱ by Western blot.Results Compared with group C,MWT was significantly decreased,and the expression of spinal S6K,p-S6K and synapsin Ⅱ was up-regulated in group DNP (P＜0.05 or 0.01).Compared with group DNP,MWT was significantly increased,and the expression of spinal S6K,p-S6K and synapsin Ⅱ was down-regulated in R,D50 and D100 groups (P＜0.05 or 0.01).Compared with group D50,MWT was significantly increased,and the expression of spinal S6K,p-S6K and synapsin Ⅱ was down-regulated in group D100 (P＜0.05).Conclusion Intrathecal cardamonin can relieve DNP in rats,and the mechanism is related to inhibiting spinal mammalian target of rapamycin activity and down-regulating the expression of synapsin Ⅱ.

Objective To observe the surface area temperature of dysmenorrhea rats with cold stagnation syndrome; To compare the different effects of Sanyinjiao (SP6) and Guanyuan (RN4).Methods Forty female SD rats were randomized into control group, model group, Sanyinjiao (SP6) group, and Guanyuan (RN4) group, 12 rats in each group. Whole body freezing method combined with estradiol benzoate injection was used to establish models. Sanyinjiao (SP6) group and Guanyuan (RN4) group received moxibustion at corresponding points for 3 times after modeling. Infrared thermal imaging was used to measure the skin temperature at the surface projection area of Sanyinjiao (SP6) and Xuehai (SP10) and Sanyinjiao-Xuehai lines before and after moxibustion.Results Compared with control group, the temperature gap between double sides of Sanyinjiao-Xuehai lines significantly increased in model group 5-30 min after moxibustion (P<0.05). 30 min after moxibustion, the temperature of right Sanyinjiao significantly decreased in other three groups (P<0.01). Compared with model group, the temperature gap between double sides of Sanyinjiao-Xuehai lines significantly decreased after 5-30 min in Sanyinjiao group (P<0.05), while Sanyinjiao-Xuehai lines significantly decreased after 10-30 min in Guanyuan group (P<0.05). Comparison between two moxibustion groups, the influence of Sanyinjiao group to temperature gap between double sides of Sanyinjiao-Xuehai lines was earlier than Guanyuan group (P<0.05).Conclusion Moxbustion can decrease the temperature gap between double sides of Sanyinjiao-Xuehai lines and ease the imbalance. And the influence of moxbustion SP6 to temperature gap between double sides of Sanyinjiao-Xuehai lines is earlier than moxibustion RN4.