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Abstract Electrochromic devices are applied extensively to camouflages, smart windows, heat insulation layers, and automobile rearview mirrors, etc. The amorphous WO3 is a very attractive electrochromic material, whereas it suffers from degradation of optical modulation and reversibility on ion exchange owing to those deep trapped ions with irreversible reaction behavior. Herein, we designed and, by using magnetron sputtering, prepared a composite film with TiO2/WO3/TiO2 double heterojunctions, which is capable of eliminating the deep trapped ions by itself under ultraviolet light (UV) assistance. The electrochromic device based on this composite film, after being recovery by short-time UV irradiation, can maintain a high transmission modulation of 94.72% after 7000 cycles of the voltammetry measurement. This feature allows the device to maintain its initial electrochromic performance after prolonged use. Moreover, the double heterojunction structure can reduce colouring time and enormously improve the colouration efficiency (CE) of electrochromic devices. Experimental research shows that when the thickness of the bottom and upper TiO2 layer of the WO3 film was 145.5 nm and 97.0 nm, respectively, the CE of electrochromic devices reached a perfectly high value (479.3 cm2/C), being much higher than that of WO3 devices (69.5 cm2/C). Functions of the TiO2/WO3/TiO2 double heterojunction in electrochromic device were investigated by combining theoretical analysis and experiment validation, and these results provide a general framework for developing and designing superior electrochromic materials and devices.

Abstract Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

Objectives Risk prediction for patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM/DM-ILD.Methods This prognostic study analysed patients with PM/DM-ILD admitted to Nanjing Drum Hospital from 2016 to 2021. The primary outcome was mortality within 1 year. We used a least absolute shrinkage and selection operator (LASSO) logistic regression model to identify predictive laboratory indicators. These indicators were used to create a laboratory risk score, and we developed a mortality risk prediction model by incorporating clinical factors. The evaluation of model performance encompassed discrimination, calibration, clinical utility and practical application for risk prediction and prognosis.Results Overall, 418 patients with PM/DM-ILD were enrolled and randomly divided into development (n=282) and validation (n=136) cohorts. LASSO logistic regression identified four optimal features in the development cohort, forming a laboratory risk score: C reactive protein, lactate dehydrogenase, CD3+CD4+ T cell counts and PO2/FiO2. The final prediction model integrated age, arthralgia, anti-melanoma differentiation-associated gene 5 antibody status, high-resolution CT pattern and the laboratory risk score. The prediction model exhibited robust discrimination (area under the receiver operating characteristic: 0.869, 95% CI 0.811 to 0.910), excellent calibration and valuable clinical utility. Patients were categorised into three risk groups with distinct mortality rates. The internal validation, sensitivity analyses and comparative assessments against previous models further confirmed the robustness of the prediction model.Conclusions We developed and validated an evidence-based mortality risk prediction model with simple, readily accessible clinical variables in patients with PM/DM-ILD, which may inform clinical decision-making.

Introduction: Systemic lupus erythematosus (SLE) is a prototypic autoimmune disorder with a variable clinical course, ranging from mild to severe forms. It mainly occurs in women, especially those of fertile age. The aim of the study was to systematically analyze the associations of perinatal disease activity with adverse outcomes of Chinese patients with SLE and their offspring. Material and methods: Data of prenatal SLE patients and healthy pregnant woman admitted to our hospital during the period October 2001 to January 2018 were retrospectively collected, and the status of offspring of SLE patients was followed up in March 2020. Disease activity was evaluated by SLE disease activity index 2000 (SLEDAI-2k), and those with scores > 6 were defined as having active disease. Results: In total, 198 deliveries of 194 SLE patients and 199 deliveries of healthy women were documented. Maternal and fetal adverse outcomes occurred in 74 (37.4%) and 90 (45.5%) deliveries of SLE patients, respectively, which were significantly higher than those of healthy subjects. Among SLE patients, the active group had higher rates of gestational hypertension (p < 0.001), preeclampsia/eclampsia (p < 0.001), low birth weight (p < 0.001), premature birth (p < 0.001) and fetal growth restriction (FGR) (p < 0.01) than the inactive group. Multivariate logistic analysis revealed that perinatal renal activity was associated with gestational hypertension (OR = 4.43, p < 0.001), preeclampsia/eclampsia (OR = 9.14, p < 0.001), low birth weight (OR = 2.24, p < 0.05) and premature birth (OR = 4.20, p < 0.001). Compared with the general population, offspring of SLE patients had relatively high rates of eczema (50/142, 35.2%) and congenital heart disease (6/142, 4.2%), which were irrelevant to perinatal disease activity, but related to specific antibodies. Conclusions: For perinatal women with SLE, renal activity is associated with a variety of adverse pregnancy outcomes. However, maternal perinatal disease activity does not seem to affect the growth of their offspring. [ABSTRACT FROM AUTHOR]