A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.

OBJECTIVE: Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation. METHODS: This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics. RESULTS: The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p < 0.05). CONCLUSION: The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

A GPU-based fast Monte Carlo code that supports proton transport in magnetic field for radiation therapy.

This paper presents the effort to extend a previously reported code ARCHER, a GPU-based Monte Carlo (MC) code for coupled photon and electron transport, into protons including the consideration of magnetic fields. The proton transport is modeled using a Class-II condensed-history algorithm with continuous slowing-down approximation. The model includes ionization, multiple scattering, energy straggling, elastic and inelastic nuclear interactions, as well as deflection due to the Lorentz force in magnetic fields. An additional direction change is added for protons at the end of each step in the presence of the magnetic field. Secondary charge particles, except for protons, are terminated depositing kinetic energies locally, whereas secondary neutral particles are ignored. Each proton is transported step by step until its energy drops to below 0.5 MeV or when the proton leaves the phantom. The code is implemented using the compute unified device architecture (CUDA) platform for optimized GPU thread-level parallelism and efficiency. The code is validated by comparing it against TOPAS. Comparisons of dose distributions between our code and TOPAS for several exposure scenarios, ranging from single square beams in water to patient plan with magnetic fields, show good agreement. The 3D-gamma pass rate with a 2 mm/2% criterion in the region with dose greater than 10% of the maximum dose is computed to be over 99% for all tested cases. Using a single NVIDIA TITAN V GPU card, the computational time of ARCHER is found to range from 0.82 to 4.54 seconds for 1 × 107 proton histories. Compared to a few hours running on TOPAS, this speed improvement is significant. This work presents, for the first time, the performance of a GPU-based MC code to simulate proton transportation magnetic fields, demonstrating the feasibility of accurate and efficient dose calculations in potential magnetic resonance imaging (MRI)-guided proton therapy.

Adaptive assessment based on fractional CBCT images for cervical cancer.

PURPOSE: Anatomical and other changes during radiotherapy will cause inaccuracy of dose distributions, therefore the expectation for online adaptive radiation therapy (ART) is high in effectively reducing uncertainties due to intra-variation. However, ART requires extensive time and effort. This study investigated an adaptive assessment workflow based on fractional cone-beam computed tomography (CBCT) images. METHODS: Image registration, synthetic CT (sCT) generation, auto-segmentation, and dose calculation were implemented and integrated into ArcherQA Adaptive Check. The rigid registration was based on ITK open source. The deformable image registration (DIR) method was based on a 3D multistage registration network, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN). The auto-segmentation of organs at risk (OARs) on sCT images was finished by a deep learning-based auto-segmentation software, DeepViewer. The contours of targets were obtained by the structure-guided registration. Finally, the dose calculation was based on a GPU-based Monte Carlo (MC) dose code, ArcherQA. RESULTS: The dice similarity coefficient (DSCs) were over 0.86 for target volumes and over 0.79 for OARs. The gamma pass rate of ArcherQA versus Eclipse treatment planning system was more than 99% at the 2%/2 mm criterion with a low-dose threshold of 10%. The time for the whole process was less than 3 min. The dosimetric results of ArcherQA Adaptive Check were consistent with the Ethos scheduled plan, which can effectively identify the fractions that need the implementation of the Ethos adaptive plan. CONCLUSION: This study integrated AI-based technologies and GPU-based MC technology to evaluate the dose distributions using fractional CBCT images, demonstrating remarkably high efficiency and precision to support future ART processes.

Initial experience of visualized biliary cannulation during ERCP.

BACKGROUND : Selective biliary cannulation is the most challenging step in endoscopic retrograde cholangiopancreatography (ERCP) because only indirect radiographic images can be obtained. Therefore, we developed a novel endoscopic retrograde direct cholangioscopy (ERDC) technology to facilitate visible biliary cannulation. METHODS : In this case series, we used ERDC to treat 21 patients with common bile duct stones who were enrolled consecutively between July 2022 and December 2022. The procedure details and complications were recorded, and all patients were followed up for 3 months after the procedure. The learning curve effect was analyzed by comparing the early and later cases. RESULTS : Biliary cannulation was successful in all patients, and the stones were removed completely. The median (interquartile range [IQR]) time for cholangioscopy-guided biliary cannulation was 240.0 (10.0-430.0) seconds, and the median (IQR) number of cannulation procedures was 2 (1-5). Despite there being one episode of post-ERCP pancreatitis, one of cholangitis, and three patients developing asymptomatic hyperamylasemia, all of the patients recovered after symptomatic treatment, being discharged and with no serious adverse events occurring during the 3-month follow-up period. Compared with the early cases, the number of intubations and the use of guidewire guidance decreased in later cases. CONCLUSION : Our research confirms that ERDC is a feasible technology for biliary cannulation under direct vision.

Investigation of total skin helical tomotherapy using a 3D-printed total skin bolus.

OBJECTIVE: To investigate the effectiveness of using a 3D-printed total skin bolus in total skin helical tomotherapy for the treatment of mycosis fungoides. MATERIALS AND METHODS: A 65-year-old female patient with a 3-year history of mycosis fungoides underwent treatment using an in-house desktop fused deposition modelling printer to create a total skin bolus made of a 5-mm-thick flexible material, which increased the skin dose through dose building. The patient's scan was segmented into upper and lower sections, with the division line placed 10 cm above the patella. The prescription was to deliver 24 Gy over 24 fractions, given 5 times per week. The plan parameters consisted of a field width of 5 cm, pitch of 0.287 and modulation factor of 3. The complete block was placed 4 cm away from the planned target region to reduce the area of the internal organs at risk, especially the bone marrow. Dose delivery accuracy was verified using point dose verification with a "Cheese" phantom (Gammex RMI, Middleton, WI), 3D plane dose verification with ArcCHECK (Model 1220, Sun Nuclear, Melbourne, FL), and multipoint film dose verification. Megavoltage computed tomography guidance was also utilized to ensure the accuracy of the setup and treatment. RESULTS: A 5-mm-thick 3D-printed suit was used as a bolus to achieve a target volume coverage of 95% of the prescribed dose. The conformity index and homogeneity index of the lower segment were slightly better than those of the upper segment. As the distance from the skin increased, the dose to the bone marrow gradually decreased, and the dose to other organs at risk remained within clinical requirements. The point dose verification deviation was less than 1%, the 3D plane dose verification was greater than 90%, and the multipoint film dose verification was less than 3%, all of which confirmed the accuracy of the delivered dose. The total treatment time was approximately 1.5 h, which included 0.5 h of wearing the 3D-printed suit and 1 h with the beam on. Patients only experienced mild fatigue, nausea or vomiting, low-grade fever, and grade III bone marrow suppression. CONCLUSION: The use of a 3D-printed suit for total skin helical tomotherapy can result in a uniform dose distribution, short treatment time, simple implementation process, good clinical outcomes, and low toxicity. This study presents an alternative treatment approach that can potentially yield improved clinical outcomes for mycosis fungoides.

Rapid Discovery of Substances with Anticancer Potential from Marine Fungi Based on a One Strain-Many Compounds Strategy and UPLC-QTOF-MS.

The secondary metabolites of marine fungi with rich chemical diversity and biological activity are an important and exciting target for natural product research. This study aimed to investigate the fungal community in Quanzhou Bay, Fujian, and identified 28 strains of marine fungi. A total of 28 strains of marine fungi were screened for small-scale fermentation by the OSMAC (One Strain-Many Compounds) strategy, and 77 EtOAc crude extracts were obtained and assayed for cancer cell inhibition rate. A total of six strains of marine fungi (P-WZ-2, P-WZ-3-2, P-WZ-4, P-WZ-5, P56, and P341) with significant changes in cancer cell inhibition induced by the OSMAC strategy were analysed by UPLC-QTOF-MS. The ACD/MS Structure ID Suite software was used to predict the possible structures with inhibitory effects on cancer cells. A total of 23 compounds were identified, of which 10 compounds have been reported to have potential anticancer activity or cytotoxicity. In this study, the OSMAC strategy was combined with an untargeted metabolomics approach based on UPLC-QTOF-MS to efficiently analyse the effect of changes in culture conditions on anticancer potentials and to rapidly find active substances that inhibit cancer cell growth.

Clinical application and improvement of a CNN-based autosegmentation model for clinical target volumes in cervical cancer radiotherapy.

OBJECTIVE: Clinical target volume (CTV) autosegmentation for cervical cancer is desirable for radiation therapy. Data heterogeneity and interobserver variability (IOV) limit the clinical adaptability of such methods. The adaptive method is proposed to improve the adaptability of CNN-based autosegmentation of CTV contours in cervical cancer. METHODS: This study included 400 cervical cancer treatment planning cases with CTV delineated by radiation oncologists from three hospitals. The datasets were divided into five subdatasets (80 cases each). The cases in datasets 1, 2, and 3 were delineated by physicians A, B, and C, respectively. The cases in datasets 4 and 5 were delineated by multiple physicians. Dataset 1 was divided into training (50 cases), validation (10 cases), and testing (20 cases) cohorts, and they were used to construct the pretrained model. Datasets 2-5 were regarded as host datasets to evaluate the accuracy of the pretrained model. In the adaptive process, the pretrained model was fine-tuned to measure improvements by gradually adding more training cases selected from the host datasets. The accuracy of the autosegmentation model on each host dataset was evaluated using the corresponding test cases. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD_95) were used to evaluate the accuracy. RESULTS: Before and after adaptive improvements, the average DSC values on the host datasets were 0.818 versus 0.882, 0.763 versus 0.810, 0.727 versus 0.772, and 0.679 versus 0.789, which are improvements of 7.82%, 6.16%, 6.19%, and 16.05%, respectively. The average HD_95 values were 11.143 mm versus 6.853 mm, 22.402 mm versus 14.076 mm, 28.145 mm versus 16.437 mm, and 33.034 mm versus 16.441 mm, which are improvements of 37.94%, 37.17%, 41.60%, and 50.23%, respectively. CONCLUSION: The proposed method improved the adaptability of the CNN-based autosegmentation model when applied to host datasets.

Associations among sexuality-related factors, recent two-week morbidity and annual hospitalization in female migrant workers: a cross-sectional study in southern China.

To explore the influence of sexuality-related factors on recent two-week morbidity and annual hospitalization in female migrant workers, 880 Chinese rural-to-urban female migrant workers aged 16-57 years were studied. Clustered logistic regression analyses revealed that women who never or seldom experienced lubrication difficulties had a lower risk of recent two-week morbidity (adjusted odds ratio [OR] = 0.32, 95% confidence interval [CI] = 0.17-0.60, P< 0.001; adjusted OR = 0.35, 95% CI = 0.18-0.69, P= 0.003) than those who always experienced lubrication difficulties; women who never felt a lack of sexual interest had a significantly lower risk of annual hospitalization (adjusted OR = 0.40, 95% CI = 0.20-0.79, P= 0.009) than those who always or seldom lacked sexual interest, and women who never felt sexual satisfaction had a higher risk of annual hospitalization (adjusted OR = 3.08, 95% CI = 1.75-5.42, P< 0.001) than those who always or seldom experienced sexual satisfaction. The independent contributions of sexuality-related factors to the risk of recent two-week morbidity and annual hospitalization were 5.8% and 29.5%, respectively. This study suggests that sexuality may have a modest influence on recent two-week morbidity and a dominant impact on annual hospitalization.

Pulmonary toxicity and RNA sequencing analyses of mouse in response to exposure to cellulose nanofibrils.

OBJECTIVE: The growing applications of nanocelluloses in the fields of advanced nanocomposites, electronics, and medical devices necessitate investigation of their potential adverse effects on human health. The lungs are the primary and the most important route for the entry of nanocelluloses into the human body in occupational settings. However, data on the pulmonary toxicity of cellulose nanofibrils (CNFs) and its molecular mechanism are limited. This study investigated the pulmonary toxicity of CNFs and its genomic expression using the RNA sequencing approach. MATERIALS AND METHODS: Female C57BL/6 mice were administered CNFs at 50 µg/mouse by oropharyngeal aspiration. Samples were collected at 3 and 14 days after exposure to CNFs (DAEC). RESULTS: At three DAEC, the microscopic sections of lungs revealed a significant inflammatory response. In terms of gene expression alterations, 94 genes were up-regulated, and 107 genes were down-regulated. Most of these differentially expressed genes were involved in the inflammatory and immune responses, including chemokines, NK cells, killer cell lectin-like receptors, CD antigens, T cell-specific GTPases, immunity-related GTPase family M members, and interferon-induced proteins encoding genes. However, only 9 and 26 genes at 14 DAEC were significantly up- and down-regulated, respectively. CONCLUSIONS: The pathological analysis of lung sections and the analysis of sequencing data suggested that the homeostasis of mice lungs was restored at 14 DAEC. The findings of this study provide insights into the pulmonary toxicity, and underlying toxicological mechanisms, caused by exposure to CNFs, and are useful for the assessment of the potential toxicity of nanocelluloses.

End-to-end unsupervised cycle-consistent fully convolutional network for 3D pelvic CT-MR deformable registration.

OBJECTIVE: To improve the efficiency of computed tomography (CT)-magnetic resonance (MR) deformable image registration while ensuring the registration accuracy. METHODS: Two fully convolutional networks (FCNs) for generating spatial deformable grids were proposed using the Cycle-Consistent method to ensure the deformed image consistency with the reference image data. In all, 74 pelvic cases consisting of both MR and CT images were studied, among which 64 cases were used as training data and 10 cases as the testing data. All training data were standardized and normalized, following simple image preparation to remove the redundant air. Dice coefficients and average surface distance (ASD) were calculated for regions of interest (ROI) of CT-MR image pairs, before and after the registration. The performance of the proposed method (FCN with Cycle-Consistent) was compared with that of Elastix software, MIM software, and FCN without cycle-consistent. RESULTS: The results show that the proposed method achieved the best performance among the four registration methods tested in terms of registration accuracy and the method was more stable than others in general. In terms of average registration time, Elastix took 64 s, MIM software took 28 s, and the proposed method was found to be significantly faster, taking <0.1 s. CONCLUSION: The proposed method not only ensures the accuracy of deformable image registration but also greatly reduces the time required for image registration and improves the efficiency of the registration process. In addition, compared with other deep learning methods, the proposed method is completely unsupervised and end-to-end.

A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer.

PURPOSE: To develop and test a three-dimensional (3D) deep learning model for predicting 3D voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT). METHODS: A total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net_B was constructed to predict 3D dose distributions. Eight types of 3D matrices from CT images, contoured structures, and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: (a) The dice similarity coefficients (DSCs) of different isodose volumes, the average dose difference of all voxels within the body, and 3%/5 mm global gamma passing rates of organs at risks (OARs) and planned target volume (PTV) were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; (b) The dosimetric index (DI) including homogeneity index, conformity index, V50 , V45 for PTV and OARs between predicted and clinical truth were statistically analyzed with the paired-samples t test. The model was also compared with 3D U-Net and the same architecture model without beam configurations input (named as 3D U-Res-Net_O). RESULTS: The 3D U-Res-Net_B model predicted 3D dose distributions accurately. For the 22 testing cases, the average prediction bias ranged from -1.94% to 1.58%, and the overall mean absolute errors (MAEs) was 3.92 ± 4.16%; there was no statistically significant difference for nearly all DIs. The model had a DSCs value above 0.9 for most isodose volumes, and global 3D gamma passing rates varying from 0.81 to 0.90 for PTV and OARs, clearly outperforming 3D U-Res-Net_O and being slightly superior to 3D U-Net. CONCLUSIONS: This study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.

A fast robust optimizer for intensity modulated proton therapy using GPU.

Robust optimization has been shown to be effective for stabilizing treatment planning in intensity modulated proton therapy (IMPT), but existing algorithms for the optimization process is time-consuming. This paper describes a fast robust optimization tool that takes advantage of the GPU parallel computing technologies. The new robust optimization model is based on nine boundary dose distributions - two for ±range uncertainties, six for ±set-up uncertainties along anteroposterior (A-P), lateral (R-L) and superior-inferior (S-I) directions, and one for nominal situation. The nine boundary influence matrices were calculated using an in-house finite size pencil beam dose engine, while the conjugate gradient method was applied to minimize the objective function. The proton dose calculation algorithm and the conjugate gradient method were tuned for heterogeneous platforms involving the CPU host and GPU device. Three clinical cases - one head and neck cancer case, one lung cancer case, and one prostate cancer case - were investigated to demonstrate the clinical feasibility of the proposed robust optimizer. Compared with results from Varian Eclipse (version 13.3), the proposed method is found to be conducive to robust treatment planning that is less sensitive to range and setup uncertainties. The three tested cases show that targets can achieve high dose uniformity while organs at risks (OARs) are in better protection against setup and range errors. Based on the CPU + GPU heterogeneous platform, the execution times of the head and neck cancer case and the prostate cancer case are much less than half of Eclipse, while the run time of the lung cancer case is similar to that of Eclipse. The fast robust optimizer developed in this study can improve the reliability of traditional proton treatment planning in a much faster speed, thus making it possible for clinical utility.

Evaluation of deep learning-based auto-segmentation algorithms for delineating clinical target volume and organs at risk involving data for 125 cervical cancer patients.

OBJECTIVE: To evaluate the accuracy of a deep learning-based auto-segmentation mode to that of manual contouring by one medical resident, where both entities tried to mimic the delineation "habits" of the same clinical senior physician. METHODS: This study included 125 cervical cancer patients whose clinical target volumes (CTVs) and organs at risk (OARs) were delineated by the same senior physician. Of these 125 cases, 100 were used for model training and the remaining 25 for model testing. In addition, the medical resident instructed by the senior physician for approximately 8 months delineated the CTVs and OARs for the testing cases. The dice similarity coefficient (DSC) and the Hausdorff Distance (HD) were used to evaluate the delineation accuracy for CTV, bladder, rectum, small intestine, femoral-head-left, and femoral-head-right. RESULTS: The DSC values of the auto-segmentation model and manual contouring by the resident were, respectively, 0.86 and 0.83 for the CTV (P < 0.05), 0.91 and 0.91 for the bladder (P > 0.05), 0.88 and 0.84 for the femoral-head-right (P < 0.05), 0.88 and 0.84 for the femoral-head-left (P < 0.05), 0.86 and 0.81 for the small intestine (P < 0.05), and 0.81 and 0.84 for the rectum (P > 0.05). The HD (mm) values were, respectively, 14.84 and 18.37 for the CTV (P < 0.05), 7.82 and 7.63 for the bladder (P > 0.05), 6.18 and 6.75 for the femoral-head-right (P > 0.05), 6.17 and 6.31 for the femoral-head-left (P > 0.05), 22.21 and 26.70 for the small intestine (P > 0.05), and 7.04 and 6.13 for the rectum (P > 0.05). The auto-segmentation model took approximately 2 min to delineate the CTV and OARs while the resident took approximately 90 min to complete the same task. CONCLUSION: The auto-segmentation model was as accurate as the medical resident but with much better efficiency in this study. Furthermore, the auto-segmentation approach offers additional perceivable advantages of being consistent and ever improving when compared with manual approaches.

Work engagement as a mediator between organizational commitment and job satisfaction among community health-care workers in China: a cross-sectional study.

The purpose of this study was to explore levels of organizational commitment, job satisfaction and work engagement among community health-care workers in China, and to examine spatial relationships of variables. Data were collected by Organizational Commitment Scale, Job Satisfaction Scale and Utrecht Work Engagement Scale from 1404 community health-care workers in Guangzhou and Shenzhen cities. Structural equation model was used to analyze relationships among three variables. Medium levels of organizational commitment, job satisfaction and work engagement were found among community health-care workers. Organizational commitment was positively correlated to work engagement (r = 0.564) and job satisfaction (r = 0.550). The path analysis indicated that total effect (ß = 0.598) of organizational commitment on job satisfaction (R2 = 0.52) consisted of a direct effect (ß = 0.264) and an indirect effect (ß = 0.334), which was mediated positively by work engagement. Improvement in work engagement may lead to higher level of job satisfaction and organizational commitment.

Roflumilast, a phosphodiesterase-4 inhibitor, improves hyperoxia-induced lung injury via anti-inflammation.

Roflumilast is an inhibitor of phosphodiesterase-4 (PDE4) and can suppress the hydrolysis of cAMP in inflammatory cells, conferring anti-inflammatory effects. This study aimed to investigate the protective effects of roflumilast on hyperoxia-induced acute lung injury (HALI) in a rat model. Male Sprague-Dawley rats were randomly assigned into: control group; HALI group; 2.5 mg/kg roflumilast group; and 5 mg/kg roflumilast group. Rats were pressurized to 250 kPa with pure oxygen to induce lung injury. In the roflumilast groups, rats were orally administered with roflumilast at 2.5 or 5 mg/kg once before hyperoxia exposure and once daily for two days after exposure. Rats were sacrificed 72 hours after hyperoxia exposure. The lung tissues were collected for the detection of lung water content, inflammatory cytokines and NF-κB/p-NF-κB protein expression, and the bronchoalveolar lavage fluid was harvested for the measurement of protein concentration and lactate dehydrogenase activity. Results showed roflumilast at different doses could significantly reduce lung edema, improve lung pathology and reduce the expression of inflammatory cytokines in the lung. The protective effects seemed to be related to the dose of roflumilast. Our study indicates roflumilast has the potential as a medication for the treatment of HALI.

Hedging crash risk in optimal portfolio selection.

When almost all underlying assets suddenly lose a certain part of their nominal value in a market crash, the diversification effect of portfolios in a normal market condition no longer works. We integrate the crash risk into portfolio management and investigate performance measures, hedging and optimization of portfolio selection involving derivatives. A suitable convex conic programming framework based on parametric approximation method is proposed to make the problem a tractable one. Simulation analysis and empirical study are performed to test the proposed approach.

The association between multimorbidity and health-related quality of life: a cross-sectional survey among community middle-aged and elderly residents in southern China.

BACKGROUND: Multimorbidity is common among the middle-aged and elderly residents. And it is associated to the reduction of health-related quality of life (HRQoL), including physical and psychological dimensions. However, there are few studies that have paid attention to the HRQoL of residents with multimorbidity in China. Therefore, this study aims to investigate the relationships between different multimorbidity patterns and HRQoL among middle-aged and elderly adults in China. METHODS: Based on a cross-sectional survey, the information regarding 18,137 adults, who were at least 45 years of age, was collected through interviews. Self-perceived HRQoL was assessed with the EQ-5D-3 L instrument, and the EQ-5D-3 L index score was calculated using the Chinese EQ-5D-3 L value set. The Tobit regression was used to explore the impacts of multimorbidity groups on HRQoL. RESULTS: Of 18,137 respondents, more than a fifth (3773,20.8%) of people had multimorbidity. Mean (SD) of EQ-5D index and VAS values were 0.95(0.14) and 76.02(13.66), respectively. Significant correlations were found between a lower HRQoL and increasing numbers of chronic conditions (P < 0.001). Most of chronic diseases co-occurred frequently, and the association between hypertension and diabetes mellitus was the strongest (adjusted OR = 3.82). The most prevalent disease is hypertension (5052,27.9%), and the most prevalent chronic diseases pair is hypertension and diabetes mellitus (841,4.6%). Among those chronic diseases with high prevalence, the effects on HRQoL ranged from chronic pain to hypertension (adjust b = - 0.036 to - 0.008). In the common multimorbidity patterns, co-occurrence of chronic pain and bone disease (adjust b = - 0.039) had the greatest impact on HRQoL. CONCLUSIONS: The HRQoL of middle-aged and elderly adults declines by multimorbidity. More attention should be paid to the HRQoL of residents with multimorbidity in China. The effect of different multimorbidity patterns on HRQoL is not simply added by two diseases, but changes by the different combination. Identifying different multimorbidity patterns of residents can provide more targeted measures to improve the HRQoL.

The association between obesity indices and hypertension: Which index is the most notable indicator of hypertension in different age groups stratified by sex?

OBJECTIVE: This study aimed to investigate the most notable obesity index and its optimal cut-off point of hypertension in different age groups stratified by sexes among community residents in southern China. METHODS: In this cross-sectional study, 620 men and 631 women aged 18-59 years were enrolled. The independent-samples t-test and chi-square test were conducted to analyze continuous and categorical variables, respectively. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis assessed the association between the obesity indices and hypertension risk. RESULTS: Waist-stature ratio (WSR) and waist-hip ratio (WHR) were the most notable risk factors for hypertension in young men and women, respectively. The odds ratios (ORs) of hypertension risk increased with per standard deviation (SD) in WSR and WHR (WSR: OR = 2.877, 95% confidence interval [CI] = 1.602 to 5.167; WHR: OR = 10.683, 95%CI = 2.179 to 52.376). In the middle-aged group of both sexes, body mass index (BMI) was the most distinctive risk factor for hypertension, the ORs of hypertension risk increased with per SD in BMI (men: OR = 2.297, 95%CI = 1.683 to 3.136; women: OR = 1.810, 95%CI = 1.338 to 2.450). ROC curve analysis demonstrated WSR and PI were better indicators than other indices among young men, and WSR was the best marker among young women. However, BMI and WC were the most sensitive markers in middle-aged men and women, respectively. CONCLUSIONS: In this Chinese population, the association of obesity indices and hypertension is inconsistent in different age groups and sexes. It is important to choose appropriate indicators for specific groups of people.

Functional status and annual hospitalization in multimorbid and non-multimorbid older adults: a cross-sectional study in Southern China.

BACKGROUND: Hospitalization over the last one year, an indicator of health service utilization, is an important and costly resource in older adult care. However, data on the relationship between functional status and annual hospitalization among older Chinese people are sparse, particularly for those with and without multimorbidity. In this study,we aimed to examine the association between functional status and annual hospitalization among community-dwelling older adults in Southern China, and to explore the independent contributions of socio-demographic variables, lifestyle and health-related factors and functional status to hospitalization in multimorbid and non-multimorbid groups. METHODS: This cross-sectional, community-based survey, studied 2603 older adults aged 60 years and above. Functional status was assessed by Functional Independence Measure (FIM). The outcome variable was any hospitalization over the last one year (annual hospitalization). Clustered logistic regression was used to analyze the independent contributions of FIM domains to annual hospitalization. RESULTS: Only in the multimorbid group, did the risk of annual hospitalization decrease significantly with increasing FIM score in walk domain (adjusted OR = 0.80 per SD increase, 95% CI = 0.70-0.91, P = 0.001) and its independent contribution accounted for 24.62%, more than that of socio-demographic variables (18.46%). However, among individuals without multimorbidity, there were no significant associations between FIM domains and annual hospitalization; thus, no independent contribution to the risk of hospitalization was observed. CONCLUSIONS: There exist some degree of correlation between functional status and annual hospitalization among older adults in Southern China, which might be due to the presence of multimorbidity with advanced age.

Liquid crystalline tactoids: ordered structure, defective coalescence and evolution in confined geometries.

Tactoids are liquid crystalline microdroplets that spontaneously nucleate from isotropic dispersions, and transform into macroscopic anisotropic phases. These intermediate structures have been found in a range of molecular, polymeric and colloidal liquid crystals. Typically only studied by polarized optical microscopy, these ordered but easily deformable microdroplets are now emerging as interesting components for structural investigations and developing new materials. In this review, we highlight the structure, property and transformation of tactoids in different compositions, but especially cellulose nanocrystals. We have selected references that illustrate the diversity and most exciting developments in tactoid research, while capturing the historical development of this field.This article is part of a discussion meeting issue 'New horizons for cellulose nanotechnology'.