OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective:To apply statistical process control (SPC) techniques to the quality assurance of non-normal radiotherapy plans through Johnson transformation, establishing patient-specific tolerance and action limits based on treatment sites and dose/distance assessment criteria, thereby enhancing the intensity-modulated radiation therapy (IMRT) verification accuracy and dose delivery precision.Methods:In this study, 951 gamma analysis data of patient-specific quality assurance (PSQA) executed on the Halcyon accelerator platform were selected and categorized into six groups based on treatment sites, including brain (102 cases), head and neck (100 cases), breast (229 cases), lung (154 cases), esophagus (223 cases), and pelvic (143 cases) groups. The six groups of data were statistically analyzed through Anderson-Darling normality tests ( α = 0.05) using Minitab 21 software. Non-normal data were transformed into normal data through Johnson transformation and then were used to establish treatment site-specific tolerance and action limits, which were compared with the Shewhart control charts based on normal distributions. Results:The PSQA result of the six groups all exhibited non-normal distributions ( P < 0.05). Through Johnson transformation, the tolerance and action limits for the head and neck, breast, lung, esophagus, and pelvic areas under the 3%/2 mm criterion ranged from 95.13% to 96.16% and 94.19% to 95.91%, respectively. In contrast, the tolerance and action limits ranged from 91.15% to 94.86% and 89.94% to 94.78% under the 2%/2 mm criterion. Directly applying Shewhart control charts without normality assumptions yielded higher tolerance limits compared to the application of Johnson transformation, increasing the false positive rate in the non-normal PSQA process. Conclusions:Applying the SPC techniques directly to a non-normal process can lead to an increased false alarm rate and wrong process interpretation. The SPC techniques combined with Johnson transformation enable more effective monitoring of a non-normal PSQA process, facilitating timely identification of potential factors that may lead to an out-of-control process based on the treatment site-specific limits.

Objective To propose and validate a decomposition method based on half-projection reconstruction for dual-energy cone beam CT(DE CBCT),thereby providing a potentially feasible low-dose imaging solution for anatomical monitoring and dose reconstruction optimization in adaptive radiotherapy.Methods Dual-energy scans were performed on a Gammex phantom using the on-board kilovoltage CBCT system of a VitalBeam accelerator at acquisition frame rates of 15 and 7 frames per second(f/s).Images were reconstructed from the projection data,and dual-energy decomposition was applied to the 7 f/s dual-energy images to derive relative electron density(RED)and stopping power ratio(SPR)using weighted formulas and empirical functions,followed by accuracy evaluation.Additionally,the weighted CT dose index was calculated for different scanning parameters.Results Dual-energy decomposition effectively suppressed image artifacts,with RED and SPR errors remaining below 2.82%and 2.56%,respectively.Compared with the traditional dual-scan method which required high-and low-energy acquisitions,the weighted CT dose index of the half-projection DE CBCT was reduced by 11.60 mGy(a 52.90%reduction).Furthermore,it was 2.58 mGy lower than the dose of the full-projection high-energy CBCT alone(a 19.98%reduction)and only 1.31 mGy higher than that of the low-energy CBCT(a 14.52%increase).Conclusion The proposed method effectively suppresses image artifacts while maintaining high accuracy in RED and SPR under low radiation dose conditions,demonstrating its potential value for scenarios requiring frequent image guidance,such as adaptive radiotherapy.

Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective:To apply statistical process control (SPC) techniques to the quality assurance of non-normal radiotherapy plans through Johnson transformation, establishing patient-specific tolerance and action limits based on treatment sites and dose/distance assessment criteria, thereby enhancing the intensity-modulated radiation therapy (IMRT) verification accuracy and dose delivery precision.Methods:In this study, 951 gamma analysis data of patient-specific quality assurance (PSQA) executed on the Halcyon accelerator platform were selected and categorized into six groups based on treatment sites, including brain (102 cases), head and neck (100 cases), breast (229 cases), lung (154 cases), esophagus (223 cases), and pelvic (143 cases) groups. The six groups of data were statistically analyzed through Anderson-Darling normality tests ( α = 0.05) using Minitab 21 software. Non-normal data were transformed into normal data through Johnson transformation and then were used to establish treatment site-specific tolerance and action limits, which were compared with the Shewhart control charts based on normal distributions. Results:The PSQA result of the six groups all exhibited non-normal distributions ( P < 0.05). Through Johnson transformation, the tolerance and action limits for the head and neck, breast, lung, esophagus, and pelvic areas under the 3%/2 mm criterion ranged from 95.13% to 96.16% and 94.19% to 95.91%, respectively. In contrast, the tolerance and action limits ranged from 91.15% to 94.86% and 89.94% to 94.78% under the 2%/2 mm criterion. Directly applying Shewhart control charts without normality assumptions yielded higher tolerance limits compared to the application of Johnson transformation, increasing the false positive rate in the non-normal PSQA process. Conclusions:Applying the SPC techniques directly to a non-normal process can lead to an increased false alarm rate and wrong process interpretation. The SPC techniques combined with Johnson transformation enable more effective monitoring of a non-normal PSQA process, facilitating timely identification of potential factors that may lead to an out-of-control process based on the treatment site-specific limits.

Objective To propose and validate a decomposition method based on half-projection reconstruction for dual-energy cone beam CT(DE CBCT),thereby providing a potentially feasible low-dose imaging solution for anatomical monitoring and dose reconstruction optimization in adaptive radiotherapy.Methods Dual-energy scans were performed on a Gammex phantom using the on-board kilovoltage CBCT system of a VitalBeam accelerator at acquisition frame rates of 15 and 7 frames per second(f/s).Images were reconstructed from the projection data,and dual-energy decomposition was applied to the 7 f/s dual-energy images to derive relative electron density(RED)and stopping power ratio(SPR)using weighted formulas and empirical functions,followed by accuracy evaluation.Additionally,the weighted CT dose index was calculated for different scanning parameters.Results Dual-energy decomposition effectively suppressed image artifacts,with RED and SPR errors remaining below 2.82%and 2.56%,respectively.Compared with the traditional dual-scan method which required high-and low-energy acquisitions,the weighted CT dose index of the half-projection DE CBCT was reduced by 11.60 mGy(a 52.90%reduction).Furthermore,it was 2.58 mGy lower than the dose of the full-projection high-energy CBCT alone(a 19.98%reduction)and only 1.31 mGy higher than that of the low-energy CBCT(a 14.52%increase).Conclusion The proposed method effectively suppresses image artifacts while maintaining high accuracy in RED and SPR under low radiation dose conditions,demonstrating its potential value for scenarios requiring frequent image guidance,such as adaptive radiotherapy.

Immunotherapy has become a mainstream treatment for cancer.However,the immunosuppressive tumor microenvironment hinders the antigen extraction and presentation by immune cells,resulting in insufficient infiltration and activation of cytotoxic T cells.Moreover,patient variability and the systemic distribution of immunotherapeutic agents can cause toxic side effects,such as excessive activ-ation or immunosuppression.The extratumoral toxicity and challenges in penetrating the tumor microenvironment hinder the effectiveness of immunotherapy on solid tumors.Recently,researchers have developed immunotherapy platforms using polymers,nucleic acids,and cells capable of logical processing based on signal inputs.These platforms can target tumors and perform logical processing based on various in-puts from the tumor microenvironment or external signals.This review briefly introduces logic-gated platforms designed using synthetic nanocarriers,nucleic acids,and chimeric antigen receptor T-cells(CAR-T)and discusses research progress in immunotherapy,aiming to provide foresight by comparing the advantages and disadvantages of each platform.

OBJECTIVE:To study the improvement effects of Rouganbao granules on liver fibrosis of model rats,and to provide experimental evidence for further development of this preparation. METHODS:Totally 45 male SD rats were divided into normal group,model group,positive control group(Fuzheng huayu capsules,0.4 g/kg)and Rouganbao granules high-dose and low-dose groups(16.8,8.4 g/kg by crude drug)according to random number table,with 9 rats in each group. Except for normal group,other groups were given CCl4sub cutaneously and high lipid diet+15% ethanol solution for 12 weeks to establish liver fibrosis model. After modeling,medication groups were given relevant medicine intragastrically,once a day,for consecutive 12 weeks. Normal group and model group were given corresponding volume of water intragastrically. The pathology change of liver tissues of rats was observed by inverted microscope. The levels of liver function indexes (ALT,AST,ALB) and liver fibrosis indexes(HA,Ⅳ-C,LN,Ⅲ-PC)were determined by ELISA. RESULTS:Compared with model group,liver cell vacuolation and liver fibrosis of rats were improved significantly in Rouganbao granule high-dose and low-dose groups;central vein was clearly visible,and no dilation and atrophy were found,especially in high-dose group;the serum levels of ALT and AST were decreased significantly,while the level of ALB was increased significantly(P＜0.05 or P＜0.01),of which the protective effect for liver was similar to positive control drug but better in increasing ALB(P＜0.05 or P＜0.01);the serum levels of HA,Ⅳ-C,LN and Ⅲ-PC were all decreased in varying degree,especially in high-dose group(P＜0.05 or P＜0.01),of which the effect of relieving liver fibrosis was similar to positive control drug. CONCLUSIONS:Rouganbao granules can improve liver function and delay the progression of liver fibrosis in model rats.