Air pollution and upper respiratory diseases: an examination among medically insured populations in Wuhan, China.

Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ground-level ozone (O3), and sulfur dioxide (SO2) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO2, SO2, PM2.5, and PM10) were significantly linked to an elevated risk of URTI, particularly during the previous 0-3 days and previous 0-4 days. Additionally, NO2 and SO2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations.

Contamination and Health Risk Assessment of Multiple Mycotoxins in Edible and Medicinal Plants.

Edible and medicinal plants (EMPs) are widely used but are easily infected by harmful fungi which produce mycotoxins. Herein, 127 samples from 11 provinces were collected to investigate 15 mycotoxins based on geographic, demographic, processing, and risk characteristics. A total of 13 mycotoxins were detected, and aflatoxin B1 (0.56~97.00 µg/kg), deoxynivalenol (9.41~1570.35 µg/kg), fumonisin B1 (8.25~1875.77 µg/kg), fumonisin B2 (2.74~543.01 µg/kg), ochratoxin A (0.62~19.30 µg/kg), and zearalenone (1.64~2376.58 µg/kg) occurred more frequently. Mycotoxin levels and species were significantly different by region, types of EMPs, and method of processing. The margin of exposure (MOE) values was well below the safe MOE (10,000). AFB1 exposure from Coix seed and malt consumption in China was of high health concern. The hazard Index (HI) method showed the range of 113.15~130.73% for malt, indicating a public health concern. In conclusion, EMPs should be concerned because of the cumulative effects of co-occurred mycotoxins, and safety management strategies should be developed in follow-up studies.

A multi-scale keypoint estimation network with self-supervision for spinal curvature assessment of idiopathic scoliosis from the imperfect dataset.

Idiopathic scoliosis (IS) is a common lifetime disease, which exhibits an obvious deformity of spinal curvature to seriously affect heart and lung function. Accurate radiographic assessment of spinal curvature is vitally important for the clinical diagnosis and treatment planning of idiopathic scoliosis. Deep learning algorithms have been widely adopted to the medical image analysis with the remarkable advancement in computer vision. The automated methods can improve the efficiency of clinical diagnosis to relieve the burden of doctors, which have advantage in dealing with the tedious and repetitive tasks. However, existing methods usually require sufficiently large training datasets with strict annotation, which are costly and laborious especially for medical images. Moreover, the medical images of serious IS always contain the blurry and occlusive parts, which would make the accurate and robust estimation of the spinal curvature more difficult. In this paper, a dot annotation approach is presented to train the spinal curvature assessment model, rather than using strict annotation of IS X-ray images. We develop a multi-scale keypoint estimation network to reduce the requirement for large training datasets, in which the Squeeze-and-Excitation (SE) blocks are incorporated to improve the representational capacity of the model. Then, a self-supervision module is designed to alleviate the blurry and occlusive problem, and we use the two-view radiographic assessments of IS to generate a 3D spinal curvature. Finally, extensive experiments are conducted on a collected clinical dataset, in which we obtain 81.5 AP and the average Ed between the predicted keypoints and the ground truths is 0.43, making an improvement over the mainstream approaches.

Structural Color Spectral Response of Dense Structures of Discoidal Particles Generated by Evaporative Assembly.

Structural color─optical response due to light diffraction or scattering from submicrometer-scale structures─is a promising means for sustainable coloration. To expand the functionality of structural color, we introduce discoidal shape anisotropy into colloidal particles and characterize how structural color reflection can be engineered. Uniaxial compression of spheres is used to prepare discoids with varying shape anisotropy and particle size. Discoids are assembled into thin films by evaporation. We find that structural color of assembled films displays components due to diffuse backscattering and multilayer reflection. As discoids become more anisotropic, the assembled structure is more disordered. The multilayer reflection is suppressed─peak height becomes smaller and peak width broader; thus, the color is predominantly from diffuse backscattering. Finally, the discoid structural color can be tuned by varying particle size and has low dependence on viewing angle. We corroborate our results by comparing experimental microstructures and measured reflection spectra with Monte Carlo simulations and calculated spectra by finite-difference time-domain simulation. Our findings demonstrate that the two tunable geometries of discoids─size and aspect ratio─generate different effects on spectral response and therefore can function as independent design parameters that expand possibilities for producing noniridescent structural color.

CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients.

OBJECTIVE: This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients. MATERIALS METHODS: Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, follow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, nonlesion lung volume (NLLV) (lung volume - lesion volume), and fraction of nonlesion lung volume (%NLLV) (nonlesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and NLLV, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built random forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic curves (AUC) and that of RF regressors using the root-mean-square error. RESULTS: Patients were classified into three groups of disease severity: moderate (n = 25), severe (n = 47) and critical (n = 27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe, and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, and 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, and 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n = 3), partial recovery with residual pulmonary damage (n = 80), prolonged recovery (n = 15), and death (n = 1). The %NLLV in three severity groups were 92.18 ± 9.89%, 82.94 ± 16.49%, and 66.19 ± 24.15% with p value <0.05 among each two groups. The AUCs of RF classifiers using hybrid models were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than either radiomics models or clinical models (p < 0.05). The root-mean-square errors of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 ± 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 ± 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 ± 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 ± 0.67 weeks), respectively. The AUCs for prediction of ICU treatment and prognosis (partial recovery vs prolonged recovery) were 0.945 and 0.960, respectively. CONCLUSION: CT quantification and machine-learning models show great potentials for assisting decision-making in the management of COVID-19 patients by assessing disease severity and predicting clinical outcomes.

A method of rapid quantification of patient-specific organ doses for CT using deep-learning-based multi-organ segmentation and GPU-accelerated Monte Carlo dose computing.

PURPOSE: One technical barrier to patient-specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient-specific multi-organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient-specific organ doses for CT - in a similar manner as radiation therapy treatment planning - will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep-learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU-based Monte Carlo rapid organ dose calculation. METHODS: A deep convolutional neural network (CNN) based on the U-Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used: The lung CT segmentation challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, each consisting of five segmented organs, and the Pancreas-CT (PCT) dataset, which contains 43 abdominal CT scan patients each consisting of eight segmented organs. A fivefold cross-validation method is performed on both sets of data. Dice similarity coefficients (DSCs) are used to evaluate the segmentation performance against the ground truth. A GPU-based Monte Carlo dose code, ARCHER, is used to calculate patient-specific CT organ doses. The proposed method is evaluated in terms of relative dose errors (RDEs). To demonstrate the potential improvement of the new method, organ dose results are compared against those obtained for population-average patient phantoms used in an off-line dose reporting software, VirtualDose, at Massachusetts General Hospital. RESULTS: The median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.92 (heart), 0.86 (spinal cord), 0.76 (esophagus) for the LCTSC dataset, along with 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.90 (stomach), 0.87 (gall bladder), 0.80 (pancreas), 0.75 (esophagus), and 0.61 (duodenum) for the PCT dataset. Comparing with organ dose results from population-averaged phantoms, the new patient-specific method achieved smaller absolute RDEs (mean ± standard deviation) for all organs: 1.8% ± 1.4% (vs 16.0% ± 11.8%) for the lung, 0.8% ± 0.7% (vs 34.0% ± 31.1%) for the heart, 1.6% ± 1.7% (vs 45.7% ± 29.3%) for the esophagus, 0.6% ± 1.2% (vs 15.8% ± 12.7%) for the spleen, 1.2% ± 1.0% (vs 18.1% ± 15.7%) for the pancreas, 0.9% ± 0.6% (vs 20.0% ± 15.2%) for the left kidney, 1.7% ± 3.1% (vs 19.1% ± 9.8%) for the gallbladder, 0.3% ± 0.3% (vs 24.2% ± 18.7%) for the liver, and 1.6% ± 1.7% (vs 19.3% ± 13.6%) for the stomach. The trained automatic segmentation tool takes <5 s per patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel to the segmentation process using the GPU-accelerated ARCHER code take <4 s per patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the database. CONCLUSION: This work shows the feasibility to perform combined automatic patient-specific multi-organ segmentation of CT images and rapid GPU-based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency.

New capabilities of the Monte Carlo dose engine ARCHER-RT: Clinical validation of the Varian TrueBeam machine for VMAT external beam radiotherapy.

PURPOSE: The Monte Carlo radiation transport method is considered the most accurate approach for absorbed dose calculations in external beam radiation therapy. In this study, an efficient and accurate source model of the Varian TrueBeam 6X STx Linac is developed and integrated with a fast Monte Carlo photon-electron transport absorbed dose engine, ARCHER-RT, which is capable of being executed on CPUs, NVIDIA GPUs, and AMD GPUs. This capability of fast yet accurate radiation dose calculation is essential for clinical utility of this new technology. This paper describes the software and algorithmic developments made to the ARCHER-RT absorbed dose engine. METHODS: AMD's Heterogeneous-Compute Interface for Portability (HIP) was implemented in ARCHER-RT to allow for device independent execution on NVIDIA and AMD GPUs. Architecture-specific atomic-add algorithms have been identified and both more accurate single-precision and double-precision computational absorbed dose calculation methods have been added to ARCHER-RT and validated through a test case to evaluate the accuracy and performance of the algorithms. The validity of the source model and the radiation transport physics were benchmarked against Monte Carlo simulations performed with EGSnrc. Secondary dose-check physics plans, and a clinical prostate treatment plan were calculated to demonstrate the applicability of the platform for clinical use. Absorbed dose difference maps and gamma analyses were conducted to establish the accuracy and consistency between the two Monte Carlo models. Timing studies were conducted on a CPU, an NVIDIA GPU, and an AMD GPU to evaluate the computational speed of ARCHER-RT. RESULTS: Percent depth doses were computed for different field sizes ranging from 1.5 cm2  × 1.5 cm2 to 22 cm2  × 40cm2 and the two codes agreed for all points outside high gradient regions within 3%. Axial profiles computed for a 10 cm2  × 10 cm2 field for multiple depths agreed for all points outside high gradient regions within 2%. The test case investigating the impact of native single-precision compared to double-precision showed differences in voxels as large as 71.47% and the implementation of KAS single-precision reduced the difference to less than 0.01%. The 3%/3mm gamma pass rates for an MPPG5a multileaf collimator (MLC) test case and a clinical VMAT prostate plan were 94.2% and 98.4% respectively. Timing studies demonstrated the calculation of a VMAT plan was completed in 50.3, 187.9, and 216.8 s on an NVIDIA GPU, AMD GPU, and Intel CPU, respectively. CONCLUSION: ARCHER-RT is capable of patient-specific VMAT external beam photon absorbed dose calculations and its potential has been demonstrated by benchmarking against a well validated EGSnrc model of a Varian TrueBeam. Additionally, the implementation of AMD's HIP has shown the flexibility of the ARCHER-RT platform for device independent calculations. This work demonstrates the significant addition of functionality added to ARCHER-RT framework which has marked utility for both research and clinical applications and demonstrates further that Monte Carlo-based absorbed dose engines like ARCHER-RT have the potential for widespread clinical implementation.

Influences of operator head posture and protective eyewear on eye lens doses in interventional radiology: A Monte Carlo Study.

PURPOSE: To quantify the effects of operator head posture and different types of protective eyewear on the eye lens dose to operators in interventional radiology (IR). METHODS: A deformable computational human phantom, Rensselaer Polytechnic Institute (RPI) Adult Male, consisting of a high-resolution eye model, was used to simulate a radiologist who is performing an interventional radiology procedure. The radiologist phantom was deformed to a set of different head postures. Three different protective eyewear models were incorporated into the posture-deformed radiologist phantom. The eye lens dose of the radiologist was calculated using the Monte Carlo code, MCNP. Effects of the radiologist's head posture and different types of protective eyewear on eye lens doses were studied. The relationship between efficacy of protective eyewear and the radiologist's head posture was investigated. Effects of other parameters on efficacy of protective eyewear were also studied, including the angular position of the radiologist, the gap between the eyewear and the face of the radiologist, and the lead equivalent thickness. RESULTS: The dose to both lenses decreased by 80% as the head posture moved from looking downward to looking upward. Sports wrap glasses were found to reduce doses further than the other two studied models. The efficacy of eyewear was found to be related to radiologist's head posture as well. When the radiologist was looking up, the protective eyewear almost provided no protection to both lenses. Other factors such as the face-to-eyewear distance and the lead equivalent thickness were also found to have an impact on the efficacy of protective eyewear. The dose reduction factor (DRF), defined as the ratio of the dose to the lens without protection to that with protection, decreased from 4.25 to 1.07 as the face-to-eyewear distance increased. The DRF almost doubled when the lead equivalent thickness increased from 0.07 to 0.35 mm. However, further increase in lead equivalent thickness showed little improvement in dose reduction. CONCLUSION: The radiologist's head posture has a significant influence on the eye lens dose in IR. Sports wrap protective eyewear which conforms to the curve of the face is essential for the radiation protection of the eye lens. However, the radiologist's head posture and other exposure parameters should be considered when evaluating the protection of the radiologist's eyes.

The association of financial incentives for low density lipoprotein cholesterol reduction with patient activation and motivation.

There is growing interest in using financial incentives for patients to improve medication adherence, but few studies have evaluated whether financial incentives are associated with patients' activation and motivation. We analyzed survey data collected as part of a randomized clinical trial conducted from 2011 to 2014 of four financial incentive interventions to reduce low density lipoprotein cholesterol (LDL-C) among patients at risk for atherosclerotic cardiovascular disease. The main trial included 1503 patients aged 18-80 and recruited from primary care practices affiliated with three health systems. Participants were randomized into four groups: patient financial incentives, primary care physicians (PCPs) incentives, patients and PCPs shared incentives, or no incentives for LDL-C control. Patient Activation Measure (PAM) and Treatment Self Regulation Questionnaire (TSRQ) surveys were administered at baseline and 12 months. Clinical outcomes were change in LDL-C at 12 and 15 months and average medication adherence as measured by electronic pill bottle opening. Mean changes in PAM and TSRQ scores were compared between patients eligible and not eligible for incentives. Clinical outcomes were tested against baseline and change in psychosocial measures using bivariate and multivariate regression. Change in PAM score and TSRQ autonomous subscore did not differ significantly between patients eligible and not eligible for incentives. Lower baseline and greater increase in TSRQ autonomous subscore were predictive of greater 15-month decrease in LDL-C. A financial incentive intervention to improve LDL-C control was not associated with changes in patients' activation or autonomous motivation. Increases in patient autonomous motivation are predictive of long-term LDL-C control.

DEVELOPMENT OF A SET OF MESH-BASED AND AGE-DEPENDENT CHINESE PHANTOMS AND APPLICATION FOR CT DOSE CALCULATIONS.

Phantoms for organ dose calculations are essential in radiation protection dosimetry. This article describes the development of a set of mesh-based and age-dependent phantoms for Chinese populations using reference data recommended by the Chinese government and by the International Atomic Energy Agency (IAEA). Existing mesh-based RPI adult male (RPI-AM) and RPI adult female (RPI-AF) phantoms were deformed to form new phantoms according to anatomical data for the height and weight of Chinese individuals of 5 years old male, 5 years old female, 10 years old male, 10 years old female,15 years old male, 15 years old female, adult male and adult female-named USTC-5 M, USTC-5F, USTC-10M, USTC-10F, USTC-15M, USTC-15F, USTC-AM and USTC-AF, respectively. Following procedures to ensure the accuracy, more than 120 organs/tissues in each model were adjusted to match the Chinese reference parameters and the mass errors were within 0.5%. To demonstrate the usefulness, these new set of phantoms were combined with a fully validated model of the GE LightSpeed Pro 16 multi-detector computed tomography (MDCT) scanner and the GPU-based ARCHER Monte Carlo code to compute organ doses from CT examinations. Organ doses for adult models were then compared with the data of RPI-AM and RPI-AF under the same conditions. The absorbed doses and the effective doses of RPI phantoms are found to be lower than these of the USTC adult phantoms whose body sizes are smaller. Comparisons for the doses among different ages and genders were also made. It was found that teenagers receive more radiation doses than adults do. Such Chinese-specific phantoms are clearly better suited in organ dose studies for the Chinese individuals than phantoms designed for western populations. As already demonstrated, data derived from age-specific Chinese phantoms can help CT operators and designers to optimize image quality and doses.

Synthesis and assessment of the antioxidant and antitumor properties of asymmetric curcumin analogues.

In this study, 12 asymmetric curcumin (CUR) analogues and 5 symmetric curcumin derivatives were synthesized, the antioxidant activity of these derivatives were evaluated by radicals 1,1-diphenyl-2-picryl-hydrazyl (DPPH) assay, 2,2-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) assay, ROO (TRAP) assay and O(2-) (NET) assay and anti-proliferative activities of these analogues were assessed against the human hepatoma cell line (SMMC-7721), the human breast cancer cell line (MCF-7) and the human prostate cancer cell lines (PC-3). Most of the asymmetric compounds showed stronger antioxidant activities than Vitamin C (Vc). Curcumin analogues reducing free radicals contain two reaction mechanisms: H-atom and electron transfer mechanisms. Compound 14 showed the most significant antioxidant activity compared with curcumin and other derivatives. Shorted the carbon chain of 14 can reduce the O-H bond dissociation enthalpy (BED) to improve the antioxidant activity. The antioxidant activity of 25 was similar to curcumin. All of the compounds performed better in an anti-proliferate assay than curcumin, especially compound 25, which exhibited the preferential cytotoxic activity against MCF-7 cells(25, IC50 = 9.11 µM, curcumin, IC50 = 70.2 µM). Considering these data, future studies should be performed to assess the therapeutic values of these asymmetric curcumin analogues.

ARCHERRT - a GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: software development and application to helical tomotherapy.

PURPOSE: Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHERRT is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head & neck. METHODS: To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHERRT. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improve the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHERRT and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. RESULTS: For the water phantom, the depth dose curve and dose profiles from ARCHERRT agree well with DOSXYZnrc. For clinical cases, results from ARCHERRT are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head & neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to specific architecture of GPU, modified Woodcock tracking algorithm performed inferior to the original one. ARCHERRT achieves a fast speed for PSF-based dose calculations. With a single M2090 card, the simulations cost about 60, 50, 80 s for three cases, respectively, with the 1% statistical error in the PTV. Using the latest K40 card, the simulations are 1.7-1.8 times faster. More impressively, six M2090 cards could finish the simulations in 8.9-13.4 s. For comparison, the same simulations on Intel E5-2620 (12 hyperthreading) cost about 500-800 s. CONCLUSIONS: ARCHERRT was developed successfully to perform fast and accurate MC dose calculation for radiotherapy using PSFs and patient CT phantoms.

Factors influencing the success of rural cataract surgery programs in China: the study of hospital administration and relative productivity (SHARP).

PURPOSE: To explore factors potentially influencing the success or failure of rural Chinese hospitals in increasing cataract surgical output and quality. METHODS: Focus groups (FGs, n = 10) were conducted with hospital administrators, doctors, and nurses at 28 county hospitals in Guangdong Province. Discussions explored respondents' views on increasing surgical volume and quality and improving patient satisfaction. Respondents numerically ranked possible strategies to increase surgical volume and quality and patient satisfaction. FG transcripts were independently coded by two reviewers utilizing the constant comparative method following the grounded theory approach, and numerical responses were scored and ranked. RESULTS: Ten FGs and 77 ranking questionnaires were completed by 33 administrators, 23 doctors, and 21 nurses. Kappa values for the two coders were greater than 0.7 for all three groups. All groups identified a critical need for enhanced management training for hospital directors. Doctors and nurses suggested reducing surgical fees to enhance uptake, although administrators were resistant to this. Although doctors saw the need to improve equipment, administrators felt current material conditions were adequate. Respondents agreed that patient satisfaction was generally high, and did not view increasing patient satisfaction as a priority. CONCLUSIONS: Our findings highlight agreements and disagreements among the three stakeholder groups about improving surgical output and quality, which can inform strategies to improve cataract programs in rural China. Respondents' beliefs about high patient satisfaction are not in accord with other studies in the area, highlighting a potential area for intervention.

A randomized, controlled trial of an intervention promoting cataract surgery acceptance in rural China: the Guangzhou Uptake of Surgery Trial (GUSTO).

PURPOSE: To evaluate an educational intervention promoting acceptance of cataract surgery in rural China using a randomized controlled design. METHODS: Patients aged 50 years or older with presenting visual acuity (PVA) less than 6/18 in one or both eyes due to cataract were recruited from 26 screening sessions (13 intervention, 13 control) conducted by five rural hospitals in Guangdong, China. At intervention sessions, subjects were shown a 5-minute informational video, and counseled about cataract, surgery, and surgical cost. During screening, all subjects answered questionnaires on knowledge and attitudes about cataract, their finances, and transportation, and were referred for definitive examination if eligible. Study outcomes were acceptance of surgery (principal outcome) and hospital follow-up. RESULTS: Subjects in the intervention group were younger than controls (P = 0.01), but the groups did not otherwise differ. Among 212 intervention patients and 222 controls, no differences in knowledge and attitude regarding cataract were found. Surgery was accepted by 31.1% of intervention patients and 34.2% of controls (P > 0.50). Predictors of acceptance included younger age, worse logMAR PVA, knowing that cataract can be treated surgically only, greater anticipated loss in income from hospitalization, and greater house floor space per person. Membership in the intervention group was not associated with accepting surgery (odds ratio [OR] = 1.11, 95% confidence interval [CI] 0.67-1.84) or hospital follow-up (OR = 1.03, 95% CI = 0.63-1.67). CONCLUSIONS: Educational interventions that successfully impart the knowledge that cataract can be only treated surgically may be more effective in increasing uptake in this setting. (ClinicalTrials.gov number, NCT01123928.).

Extension of RPI-adult male and female computational phantoms to obese patients and a Monte Carlo study of the effect on CT imaging dose.

Although it is known that obesity has a profound effect on x-ray computed tomography (CT) image quality and patient organ dose, quantitative data describing this relationship are not currently available. This study examines the effect of obesity on the calculated radiation dose to organs and tissues from CT using newly developed phantoms representing overweight and obese patients. These phantoms were derived from the previously developed RPI-adult male and female computational phantoms. The result was a set of ten phantoms (five males, five females) with body mass indexes ranging from 23.5 (normal body weight) to 46.4 kg m(-2) (morbidly obese). The phantoms were modeled using triangular mesh geometry and include specified amounts of the subcutaneous adipose tissue and visceral adipose tissue. The mesh-based phantoms were then voxelized and defined in the Monte Carlo N-Particle Extended code to calculate organ doses from CT imaging. Chest-abdomen-pelvis scanning protocols for a GE LightSpeed 16 scanner operating at 120 and 140 kVp were considered. It was found that for the same scanner operating parameters, radiation doses to organs deep in the abdomen (e.g., colon) can be up to 59% smaller for obese individuals compared to those of normal body weight. This effect was found to be less significant for shallow organs. On the other hand, increasing the tube potential from 120 to 140 kVp for the same obese individual resulted in increased organ doses by as much as 56% for organs within the scan field (e.g., stomach) and 62% for those out of the scan field (e.g., thyroid), respectively. As higher tube currents are often used for larger patients to maintain image quality, it was of interest to quantify the associated effective dose. It was found from this study that when the mAs was doubled for the obese level-I, obese level-II and morbidly-obese phantoms, the effective dose relative to that of the normal weight phantom increased by 57%, 42% and 23%, respectively. This set of new obese phantoms can be used in the future to study the optimization of image quality and radiation dose for patients of different weight classifications. Our ultimate goal is to compile all the data derived from these phantoms into a comprehensive dosimetry database defined in the VirtualDose software.