Objective:To evaluate the feasibility of using the domestic ArcherQA system for fast and simplified independent verification of CyberKnife (CK) stereotactic radiotherapy (SRT) plans.Methods:SRT plans of 57 patients treated with CK at Tianjin Medical University Cancer Institute and Hospital from August 2021 to August 2022 were retrospectively analyzed, including 15 intracranial, 30 pulmonary, and 12 abdominal tumors cases. Point-dose and planar-dose verifications were performed using an ionization chamber and radiochromic films embedded in a homogeneous phantom, and the results were compared with those calculated by the treatment planning system (TPS). The localization CT images and corresponding SRT plans were imported into the ArcherQA system for independent dose verification and analysis. The correlation between ArcherQA results and phantom measurements was analyzed, with comparisons of target mean dose differences and γ pass rates.Results:Phantom measurement results showed, the measured point-dose differences for intracranial, lung, and abdominal plans were -0.94% ± 3.22%, 1.92% ± 2.05%, and 2.12% ± 0.77%, respectively. The mean dose differences in target dose calculation between ArcherQA and TPS: intracranial in the gross tumor volume (GTV) regions were 0.34% ± 2.21%, lung tumor GTV were -2.47% ± 2.46%, and abdominal tumor GTV were 0.80% ± 2.61%, respectively. Among them, the abdominal GTV region showed the highest correlation between ArcherQA and measured results ( r=0.78). The average two-dimensional γ pass rates (2 mm/2%, threshold=10%) measured using phantom films were 95.92% ± 2.35% for intracranial, 95.70% ± 2.74% for lung, and 96.74% ± 3.41% for abdominal tumors plans, respectively. The three-dimensional ArcherQA results showed comparable γ pass rates (1 mm/2%, threshold=10%) for lung and abdominal GTV and PTV regions, with similar medians and data dispersion to film measurements. Conclusions:The ArcherQA system enables rapid and efficient independent dose verification of CK SRT plans without the need for additional hardware. The verification results show good correlation with phantom measurements, supporting its potential as an auxiliary quality assurance tool in clinical CK SRT implementation.

OBJECTIVE: To investigate the relationship between physical activity and genetic risk and their combined effects on the risk of developing chronic obstructive pulmonary disease. METHODS: This prospective cohort study included 318,085 biobank participants from the UK. Physical activity was assessed using the short form of the International Physical Activity Questionnaire. The participants were stratified into low-, intermediate-, and high-genetic-risk groups based on their polygenic risk scores. Multivariate Cox regression models and multiplicative interaction analyses were used. RESULTS: During a median follow-up period of 13 years, 9,209 participants were diagnosed with chronic obstructive pulmonary disease. For low genetic risk, compared to low physical activity, the hazard ratios ( HRs) for moderate and high physical activity were 0.853 (95% confidence interval [ CI]: 0.748-0.972) and 0.831 (95% CI: 0.727-0.950), respectively. For intermediate genetic risk, the HRs were 0.829 (95% CI: 0.758-0.905) and 0.835 (95% CI: 0.764-0.914), respectively. For participants with high genetic risk, the HRs were 0.809 (95% CI: 0.746-0.877) and 0.818 (95% CI: 0.754-0.888), respectively. A significant interaction was observed between genetic risk and physical activity. CONCLUSION Moderate or high levels of physical activity were associated with a lower risk of developing chronic obstructive pulmonary disease across all genetic risk groups, highlighting the need to tailor activity interventions for genetically susceptible individuals.
Humans ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Exercise ; Male ; Female ; Middle Aged ; Prospective Studies ; Aged ; Genetic Predisposition to Disease ; Risk Factors ; United Kingdom/epidemiology* ; Incidence ; Adult

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 In view of the potential threat of adverse drug reaction(ADR)to patients'health and the limitations of traditional monitoring methods,this study investigates the construction method of an ADR knowledge graph based on a knowledge-driven technology path to promote the intelligent upgrading of pharmacovigilance field.Methods In this study,an adverse drug reaction knowledge base was established,and the data within the knowledge base were preprocessed.The BERT-CRF model was utilized to identify and extract the relevant entities,relationships and attribute information,and knowledge alignment was carried out by combining the rule method and statistical method.The knowledge fusion data was stored in the Neo4j graph database to construct the knowledge graph.Results Based on the established knowledge base,this study explored the method of constructing knowledge graph of adverse drug reaction by knowledge-driven technology,laying the groundwork for data mining and the development of intelligent auxiliary system tools based on knowledge graph.Conclusions The study on the construction method of an ADR knowledge graph based on a knowledge-driven technology path provides a new perspective and technical path for the intelligent upgrading of pharmacovigilance field.It holds important theoretical and practical significance for improving drug safety,protecting patients'health,and promoting the healthy development of pharmaceutical industry.

Objective:To evaluate the feasibility of using the domestic ArcherQA system for fast and simplified independent verification of CyberKnife (CK) stereotactic radiotherapy (SRT) plans.Methods:SRT plans of 57 patients treated with CK at Tianjin Medical University Cancer Institute and Hospital from August 2021 to August 2022 were retrospectively analyzed, including 15 intracranial, 30 pulmonary, and 12 abdominal tumors cases. Point-dose and planar-dose verifications were performed using an ionization chamber and radiochromic films embedded in a homogeneous phantom, and the results were compared with those calculated by the treatment planning system (TPS). The localization CT images and corresponding SRT plans were imported into the ArcherQA system for independent dose verification and analysis. The correlation between ArcherQA results and phantom measurements was analyzed, with comparisons of target mean dose differences and γ pass rates.Results:Phantom measurement results showed, the measured point-dose differences for intracranial, lung, and abdominal plans were -0.94% ± 3.22%, 1.92% ± 2.05%, and 2.12% ± 0.77%, respectively. The mean dose differences in target dose calculation between ArcherQA and TPS: intracranial in the gross tumor volume (GTV) regions were 0.34% ± 2.21%, lung tumor GTV were -2.47% ± 2.46%, and abdominal tumor GTV were 0.80% ± 2.61%, respectively. Among them, the abdominal GTV region showed the highest correlation between ArcherQA and measured results ( r=0.78). The average two-dimensional γ pass rates (2 mm/2%, threshold=10%) measured using phantom films were 95.92% ± 2.35% for intracranial, 95.70% ± 2.74% for lung, and 96.74% ± 3.41% for abdominal tumors plans, respectively. The three-dimensional ArcherQA results showed comparable γ pass rates (1 mm/2%, threshold=10%) for lung and abdominal GTV and PTV regions, with similar medians and data dispersion to film measurements. Conclusions:The ArcherQA system enables rapid and efficient independent dose verification of CK SRT plans without the need for additional hardware. The verification results show good correlation with phantom measurements, supporting its potential as an auxiliary quality assurance tool in clinical CK SRT implementation.

Objective:To investigate the association between anti-rituximab antibodies (ARA) and relapse after rituximab (RTX) therapy in children with frequently relapsing or steroid-dependent nephrotic syndrome (FRNS or SDNS).Methods:A retrospective cohort study was conducted. Clinical and laboratory data were collected from 48 FRNS or SDNS children treated with RTX in the Department of Pediatrics, General Hospital of Eastern Theater Command, between April 2024 and October 2024. Data included RTX dosing frequency, relapse events, peripheral CD20? B-cell counts, and ARA levels. With a 6-month observation period after the last RTX therapy, the children were divided into an ARA-positive group and an ARA-negative group based on ARA test results. Chi-square test, independent sample t-test, or Mann-Whitney U test were used to compare relapse rates and laboratory indicators between the two groups. The predictive value of ARA levels for relapse was evaluated using univariate receiver operating characteristic (ROC) curve analysis. Results:Among the 48 children (36 males, 12 females), the age of disease onset was 3.5 (2.0, 6.0) years, the ages at the first and last RTX treatments were 7.0 (5.0, 12.0) years and 9.5 (7.0, 13.0) years, respectively. The overall ARA positive rate was 29% (14/48). The relapse rate in the ARA-positive group was significantly higher than that in the negative group ( P<0.05). The ARA level was 0.01 (0.01, 5.88) μg/L, and all 12 children with ARA levels >5.88 μg/L relapsed. ROC curve analysis showed that ARA levels predicted relapse after RTX treatment in FRNS or SDNS children with an area under the curve (AUC) of 0.73, sensitivity of 0.50, specificity of 1.00, and an optimal cut-off value of 5.02 μg/L. All children received single-dose RTX therapy, with no significant difference in treatment frequency between the two groups ( P>0.05). At 3 months after the last rituximab therapy, CD20? B cell counts were significantly higher in the ARA-positive group ( P<0.05). During follow-up, 15% (7/48) of the children experienced infusion-related adverse reactions, with no significant difference in incidence between the two groups ( P>0.05). Conclusion:ARA is significantly associated with relapse in FRNS or SDNS children after RTX therapy.

The field(emergency)rapid inspection systems involving in the backpack,chest,vehicle and shelter had their research advances introduced and characteristics and deficiencies analyzed,and some improvement suggestions were put forward accordingly.It's pointed out the backpack,chest,vehicle and shelter be combined effectively to enhance the mobility and flexibility of field(emergency)rapid inspection systems.References were provided for the future enhancement and effecient operation of field(emergency)rapid inspection systems.[Chinese Medical Equipment Journal,2025,46(2):80-86]

Alzheimer's disease(AD)is an age-related neurodegenerative disease that mainly manifests clinically as progressive functional impairments in cognition,memory,and language.With the accelerated transition toward an older population in China,the number of people suffering from AD in China is increasing.The exact pathogenesis of AD remains unclear,with current therapeutic strategies mainly limited to symptomatic treatments.Animal models are important tools for preclinical research,enabling explorations of molecular mechanisms,behavioral functions,and treatment strategies of diseases.Future mechanistic research and drug development of AD should involve the establishment of animal models that are consistent with clinical pathological characteristics.This review summarizes the AD animal models commonly used in research,providing details on the strains,age,modeling method and doses.It also discusses research on traditional Chinese medicine(TCM)components and their pharmacodynamic mechanisms in related AD animal models,aiming to provide references for the development of new animal models and in-depth exploration of the specific pharmacological activities,targets,metabolic pathways,and clinical applications of each TCM component.

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.