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Keywords: adverse outcome; D-dimer; prediction; pregnancy; reference interval EN adverse outcome D-dimer prediction pregnancy reference interval e99 e101 3 02/06/21 20210301 NES 210301 To the Editor, Pregnancy is a complicated physiological process, during which the balance of the hemostatic systems is tipped toward a hypercoagulable state [[1]]. Unlike postpartum hemorrhage and preeclampsia, much fewer reports were focused on the associations between D-dimer and preterm birth and fetal distress. Further, the elevation of D-dimer was found to be associated with increased risks of preeclampsia, fetal distress, postpartum hemorrhage and a decreased risk of preterm delivery in our retrospective study. [Extracted from the article]

Procedural knowledge is crucial for understanding and performing concrete real-world tasks. Yet, despite the importance of procedural knowledge, research into procedural knowledge understanding is still under-developed. In particular, videos contain rich semantics that are important for understanding procedural knowledge, but have traditionally been less explored than natural language texts for understanding procedural knowledge. Motivated by harnessing procedural knowledge from videos for task assistance (i.e., assisting people in performing procedural tasks), we present the first comprehensive survey of procedural video datasets. Through systematically surveying 23 procedural video datasets, including both instructional and non-instructional videos, in a conceptual framework for task assistance, we seek to understand the trends and gaps in existing datasets, as well as to gain insights into the future of such datasets. This survey examines the current state of procedural video datasets, in terms of their data, content and annotation characteristics, as well as processing function and evaluation. The survey also identifies and suggests a number of possible directions to bring this area to the next level. • Human knowledge can be divided into declarative and procedural knowledge. Beyond declarative knowledge, this paper focuses on procedural knowledge which is crucial for understanding and performing concrete real-world tasks. • This is the first comprehensive survey of procedural video datasets, depicting series of actions performed in some constrained but non-unique order to achieve some intended high-level goal. Comprising instructional and non-instructional videos, procedural videos are rich in fine-grained procedural semantics that supplements text-based procedural guides. • Through systematically surveying 23 procedural videos in a conceptual framework for task assistance comprising procedure creation, procedure tracking and contextual assistance, we seek to understand the trends and gaps in existing datasets, as well as to gain insights into the future of such datasets. • The procedural video datasets are comprehensively examined in terms of their data, content, and annotation characteristics, together with their processing functions and evaluations. Challenges and potential directions for dataset and research to bring this area to the next level are identified and discussed. [ABSTRACT FROM AUTHOR]

• In this paper, we propose a novel method termed as Cross-modal discriminant Adversarial Network (CAN) to learn a latent discriminant space for cross-modal data, which is with a novel network structure and a novel learning mechanism (CDM). In brief, CDM projects the generated features of all modalities into a latent common space and gives the positive/negative feedback to adversarial learning. Therefore, our method could reduce the modality discrepancy, while preserving the discriminative information into the common space. • To improve our CDM, a novel objective function is presented to learn the common space in which the within-class samples should be compacted and the betweenclass samples should be scattered. Furthermore, the transformations of the CDM can be analytically solved from the generated features, thus escaping from the trap of local minimal. • To avoid the trivial solutions of directly optimizing the CDM objective function, a novel logarithmic eigenvalue-based loss is proposed. Another advantage of the proposed loss is that it could push as much discrimination as possible into all latent directions of CDM transformations instead of only the dominant ones. Preprint submitted Cross-modal retrieval aims at retrieving relevant points across different modalities, such as retrieving images via texts. One key challenge of cross-modal retrieval is narrowing the heterogeneous gap across diverse modalities. To overcome this challenge, we propose a novel method termed as Cross-modal discriminant Adversarial Network (CAN). Taking bi-modal data as a showcase, CAN consists of two parallel modality-specific generators, two modality-specific discriminators, and a Cross-modal Discriminant Mechanism (CDM). To be specific, the generators project diverse modalities into a latent cross-modal discriminant space. Meanwhile, the discriminators compete against the generators to alleviate the heterogeneous discrepancy in this space, i.e. , the generators try to generate unified features to confuse the discriminators, and the discriminators aim to classify the generated results. To further remove the redundancy and preserve the discrimination, we propose CDM to project the generated results into a single common space, accompanying with a novel eigenvalue-based loss. Thanks to the eigenvalue-based loss, CDM could push as much discriminative power as possible into all latent directions. To demonstrate the effectiveness of our CAN, comprehensive experiments are conducted on four multimedia datasets comparing with 15 state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Multiview clustering has gained increasing attention recently due to its ability to deal with multiple sources (views) data and explore complementary information between different views. Among various methods, multiview subspace clustering methods provide encouraging performance. They mainly integrate the multiview information in the space where the data points lie. Hence, their performance may be deteriorated because of noises existing in each individual view or inconsistent between heterogeneous features. For multiview clustering, the basic premise is that there exists a shared partition among all views. Therefore, the natural space for multiview clustering should be all partitions. Orthogonal to existing methods, we propose to fuse multiview information in partition level following two intuitive assumptions: (i) each partition is a perturbation of the consensus clustering; (ii) the partition that is close to the consensus clustering should be assigned a large weight. Finally, we propose a unified multiview subspace clustering model which incorporates the graph learning from each view, the generation of basic partitions, and the fusion of consensus partition. These three components are seamlessly integrated and can be iteratively boosted by each other towards an overall optimal solution. Experiments on four benchmark datasets demonstrate the efficacy of our approach against the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

With the increasing density of high voltage transmission systems, the potential risks and hazards of environmental electric fields (EFs) generated by these systems to surrounding organisms is becoming a source of public concern. To evaluate the effect of environmental EFs on plants, we used soybean as a model and systematically evaluated the effect of continuous exposure to different intensities (0 kV/m, 2 kV/m, and 10 kV/m) of power frequency EFs on agronomic characters, yield, nutrient contents, protective enzyme activities, and gene transcription. We found that the effects on soybean were more pronounced when plants were exposed to EF during development (especially at the seedling stage) than when they were exposed at maturity. The functional leaf number, stem diameter, plant dry weight, and pod number were largely unaffected by EF, while the germination rate and protective enzyme activities increased with increasing EF intensity. In plants exposed to low-intensity EF (2 kV/m), some agronomic characters, including chlorophyll content, plant height, and bean dry weight, as well as the soluble sugar and total protein contents, were significantly higher than those of plants exposed to high-intensity EF (10 kV/m) and control plants (0 kV/m). Through transcriptome analysis, we found that 2,977 genes were significantly up-regulated and 1,462 genes were down-regulated when plants were exposed to EF. These differentially expressed genes mainly encode ribosome proteins and related enzymes involved in carbon metabolism pathway, providing a novel perspective for understanding molecular mechanisms underpinning the responses to EF stress in soybean. Image 1 • EF generated by high-voltage transmission line may have ecological impact. • Low-intensity EF shown to increase photosynthesis efficiency and soybean yield. • EF treatment shown to increase the protective enzyme activity of soybean. • Differentially expressed genes induced by EF mainly encode ribosome proteins and carbon metabolism enzymes. [ABSTRACT FROM AUTHOR]

• In this population-based cohort study, the laboratory coagulation test results and perinatal medical records were retrieved and analyzed from 29,328 singleton and 840 twin pregnant women. • Increased FIB, DD and decreased PT, APTT and TT were observed as the gestational age went up in the singleton pregnancy, suggesting a hypercoagulation hemostasis. An enhanced procoagulant state was achieved in the twin pregnancy. • The incidence of adverse perinatal outcomes was remarkably associated with the maternal levels of FIB, PT, TT, APTT and DD in the third trimester, which may be applied in early recognition of women at high risk of adverse outcomes due to coagulopathy. During pregnancy, complex physiological changes take place in the hemostatic system, resulting in a hypercoagulable state. With the established trimester-specific reference intervals (RIs) of the coagulation tests, we investigated the associations between disturbance of hemostasis and adverse pregnant outcomes in a population-based cohort study. The first- and third-trimester coagulation tests results were retrieved from 29,328 singleton and 840 twin pregnant women for regular antenatal check-ups from November 30th, 2017 to January 31st, 2021. The trimester-specific RIs for fibrinogen (FIB), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), d -dimer (DD) were estimated using both the direct observational and the indirect Hoffmann methods. The associations between the coagulation tests and the risks of developing pregnancy complications as well as adverse perinatal outcomes were assessed using the logistic regression analysis. Increased FIB, DD and decreased PT, APTT and TT were observed as the gestational age increases in the singleton pregnancy. An enhanced procoagulant state, marked by significant elevation of FIB, DD and reduction of PT, APTT and TT, was observed in the twin pregnancy. The subjects with anormal PT, APTT, TT, DD, tend to have increased risks of developing peri - and postpartum complications such as preterm birth, fetal growth restriction. The incidence of adverse perinatal outcomes was remarkably associated with the maternal increased levels of FIB, PT, TT, APTT and DD in the third trimester, which may be applied in early identification of women at high risk of adverse outcomes due to coagulopathy. [ABSTRACT FROM AUTHOR]

• A novel hybrid approach which integrates a Faster R-CNN for crack patch detection, a DCNN for crack orientation recognition, and a Bayesian algorithm for integration. It provides a novel framework to combine deep learning models and Bayesian analysis to address challenging vision problems where the deep learning approaches with simple end-to-end learning strategy might not be effective. • A distinctive approach to apply Faster R-CNN for the challenging task of crack detection by training it to detect crack patches of suitable SNR, and a semi-automatic method to annotate crack patches of suitable scales to train a Faster R-CNN. • A new Bayesian integration algorithm based on local spatial proximity, orientation consistency and alignment consistency to connect associated neighboring crack patches and suppress false detections, as well as an efficient algorithm to learn the optimal parameters. Vision-based crack detection is of crucial importance in various industries, and it is very challenging due to weak signals in noisy backgrounds. In this paper, we propose a novel hybrid approach for crack detection in raw images, which combines deep learning models and Bayesian probabilistic analysis for robust crack detection. First, we re-train a state-of-the-art object detector (e.g. a Faster R-CNN) to detect crack patches of suitable SNR (signal-noise-ratio). We design a semi-automatic method to generate ground truths of crack patches along crack lines for training. To further improve the accuracy of crack detections over the whole image, we propose a Bayesian integration algorithm to suppress false detections. Specifically, we use a deep CNN to recognize the orientation of the crack segment in each detected patch. Then, a Bayesian probability is computed on the accumulated evidence from detected adjacent patches within a neighborhood based on spatial proximity, orientation consistency and alignment consistency. The patch which lacks local supports is suppressed as false detection. An algorithm to learn the parameters of Bayesian integration is also derived. Extensive experiments and evaluations are performed on a new comprehensive dataset of crack images. The results show that our approach outperforms the state-of-the-art baseline approach on deep CNN classifier. Ablation experiments are also conducted to show the effectiveness of proposed techniques. [ABSTRACT FROM AUTHOR]

3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a sophisticated "detect-then-describe" pipeline, which builds explicit relation modules upon a 3D detector with numerous hand-crafted components. While these methods have achieved initial success, the cascade pipeline tends to accumulate errors because of duplicated and inaccurate box estimations and messy 3D scenes. In this paper, we first propose Vote2Cap-DETR, a simple-yet-effective transformer framework that decouples the decoding process of caption generation and object localization through parallel decoding. Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture. To this end, we propose an advanced version, Vote2Cap-DETR++, which decouples the queries into localization and caption queries to capture task-specific features. Additionally, we introduce the iterative spatial refinement strategy to vote queries for faster convergence and better localization performance. We also insert additional spatial information to the caption head for more accurate descriptions. Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate Vote2Cap-DETR and Vote2Cap-DETR++ surpass conventional "detect-then-describe" methods by a large margin. We have made the code available at https://github.com/ch3cook-fdu/Vote2Cap-DETR.

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Chinese National Cancer Center. Published by Elsevier B.V.)

Data-free knowledge distillation aims to learn a small student network from a large pre-trained teacher network without the aid of original training data. Recent works propose to gather alternative data from the Internet for training student network. In a more realistic scenario, the data on the Internet contains two types of label noise, namely: 1) closed-set label noise, where some examples belong to the known categories but are mislabeled; and 2) open-set label noise, where the true labels of some mislabeled examples are outside the known categories. However, the latter is largely ignored by existing works, leading to limited student network performance. Therefore, this paper proposes a novel data-free knowledge distillation paradigm by utilizing a webly-collected dataset under universal label noise, which means both closed-set and open-set label noise should be tackled. Specifically, we first split the collected noisy dataset into clean set, closed noisy set, and open noisy set based on the prediction uncertainty of various data types. For the closed-set noisy examples, their labels are refined by teacher network. Meanwhile, a noise-robust hybrid contrastive learning is performed on the clean set and refined closed noisy set to encourage student network to learn the categorical and instance knowledge inherited by teacher network. For the open-set noisy examples unexplored by previous work, we regard them as unlabeled and conduct self-supervised learning on them to enrich the supervision signal for student network. Intensive experimental results on image classification tasks demonstrate that our approach can achieve superior performance to state-of-the-art data-free knowledge distillation methods.

This paper presents a novel method for supervised multi-view representation learning, which projects multiple views into a latent common space while preserving the discrimination and intrinsic structure of each view. Specifically, an apriori discriminant similarity graph is first constructed based on labels and pairwise relationships of multi-view inputs. Then, view-specific networks progressively map inputs to common representations whose affinity approximates the constructed graph. To achieve graph consistency, discrimination, and cross-view invariance, the similarity graph is enforced to meet the following constraints: 1) pairwise relationship should be consistent between the input space and common space for each view; 2) within-class similarity is larger than any between-class similarity for each view; 3) the inter-view samples from the same (or different) classes are mutually similar (or dissimilar). Consequently, the intrinsic structure and discrimination are preserved in the latent common space using an apriori approximation schema. Moreover, we present a sampling strategy to approach a sub-graph sampled from the whole similarity structure instead of approximating the graph of the whole dataset explicitly, thus benefiting lower space complexity and the capability of handling large-scale multi-view datasets. Extensive experiments show the promising performance of our method on five datasets by comparing it with 18 state-of-the-art methods.

Dynamically evolving adhesions between cells and extracellular matrix (ECM) transmit time-varying signals that control cytoskeletal dynamics and cell fate. Dynamic cell adhesion and ECM stiffness regulate cellular mechanosensing cooperatively, but it has not previously been possible to characterize their individual effects because of challenges with controlling these factors independently. Therefore, a DNA-driven molecular system is developed wherein the integrin-binding ligand RGD can be reversibly presented and removed to achieve cyclic cell attachment/detachment on substrates of defined stiffness. Using this culture system, it is discovered that cyclic adhesion accelerates F-actin kinetics and nuclear mechanosensing in human mesenchymal stem cells (hMSCs), with the result that hysteresis can completely change how hMSCs transduce ECM stiffness. Results are dramatically different from well-known results for mechanotransduction on static substrates, but are consistent with a mathematical model of F-actin fragments retaining structure following loss of integrin ligation and participating in subsequent repolymerization. These findings suggest that cyclic integrin-mediated adhesion alters the mechanosensing of ECM stiffness by hMSCs through transient, hysteretic memory that is stored in F-actin., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Context: Adrenal incidentaloma (AI) is commonly discovered on cross-sectional imaging. Mild autonomous cortisol secretion is the most common functional disorder detected in AI., Objective: To delineate the association between radiological characteristics of benign adrenocortical tumors and hypothalamus-pituitary-adrenal (HPA) axis., Methods: In the study, 494 patients diagnosed with benign unilateral adrenocortical tumors were included. Mild autonomous cortisol secretion (MACS) was diagnosed when cortisol after 1mg-dexamethasone suppression test (1-mg DST) was in the range of 1.8-5ug/dl. Non-functional adrenocortical tumor (NFAT) was diagnosed as cortisol following 1-mg DST less than 1.8ug/dL. We performed Logistics regression and causal mediation analyses, looking for associations between radiological characteristics and the HPA axis., Results: Of 494 patients, 352 (71.3%) with NFAT and 142 (28.7%) with MACS were included. Patients with MACS had a higher tumor diameter, thinner contralateral adrenal gland, and lower plasma ACTH and serum DHEAS than those with NFAT. ACTH (OR 0.978, 0.962-0.993) and tumor diameter (OR 1.857, 95%CI, 1.357-2.540) were independent factors associated with decreased serum DHEAS (all P<0.05). ACTH was also associated with decreased contralateral adrenal diameter significantly (OR 0.973, 95%CI, 0.957-0.988, P=0.001). Causal mediation analysis showed ACTH mediated the effect significantly for the association between 1-mg DST results and DHEAS level (P mediation< 0.001, proportion=22.3%). Meanwhile, we found ACTH mediated 39.7% of the effects of 1-mg DST on contralateral adrenal diameter (P mediation =0.012)., Conclusions: Patients with MACS had thinner contralateral adrenal glands and disturbed HPA axes compared with NFAT. ACTH may partially be involved in mediating the mild autonomous cortisol secretion to DHEAS and the contralateral adrenal gland., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhu, Wu, Su, Jiang, Zhou, Jiang, Zhang, Zhong and Wang.)

This paper investigates the flight dynamics of the aircraft with wing asymmetric damage and the fault-tolerant control problem to improve the stability and flight quality of damaged aircraft. A high-fidelity wing asymmetric damaged aircraft nonlinear model is developed, as well as the impact of wing asymmetric damage on the physical and aerodynamic properties of the aircraft is also analyzed. The trim strategies for damaged aircraft are investigated to achieve a rapid estimation of trim states after damage occurs. This paper presents a robust cascaded nonlinear fault-tolerant control framework that integrates the incremental nonlinear dynamic inversion control with improved piecewise-constant-based nonlinear L1 adaptive control for the stability control to enhance the stability and tracking performance of the damaged aircraft. Theoretical analysis proves that the presented fault-control structure is robust to disturbances and can decouple rapidity and robustness while guaranteeing steady-state and transient performance. Finally, the hardware-in-the-loop flight control experiment platform is developed to validate the cascaded nonlinear fault-tolerant controller. In the experiment, the proposed controller is verified under wing asymmetric damage and compared with existing methods. Experimental results show that the proposed fault-tolerant control is able to overcome wing asymmetric damage and significantly improve the tracking performance of the damaged aircraft even with 27.2% of the severe damage to the left-wing., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

The abnormal activation of epidermal growth factor receptor (EGFR) drives the development of non-small cell lung cancer (NSCLC). The EGFR-targeting tyrosine kinase inhibitor osimertinib is frequently used to clinically treat NSCLC and exhibits marked efficacy in patients with NSCLC who have an EGFR mutation. However, free osimertinib administration exhibits an inadequate response in vivo, with only ∼3% patients demonstrating a complete clinical response. Consequently, we designed a biomimetic nanoparticle (CMNP @Osi ) comprising a polymeric nanoparticle core and tumor cell-derived membrane-coated shell that combines membrane-mediated homologous and molecular targeting for targeted drug delivery, thereby supporting a dual-target strategy for enhancing osimertinib efficacy. After intravenous injection, CMNP @Osi accumulates at tumor sites and displays enhanced uptake into cancer cells based on homologous targeting. Osimertinib is subsequently released into the cytoplasm, where it suppresses the phosphorylation of upstream EGFR and the downstream AKT signaling pathway and inhibits the proliferation of NSCLC cells. Thus, this dual-targeting strategy using a biomimetic nanocarrier can enhance molecular-targeted drug delivery and improve clinical efficacy., (© 2023 The Authors.)

Background: As folates are essential for embryonic development and growth, it is necessary to accurately determine the levels of folates in plasma and red blood cells (RBCs) for clinical intervention. The aims of this study were to develop and validate a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantitation of folates in plasma and RBCs and to examine the association between plasma and RBC folate concentrations and gestational diabetes mellitus (GDM), gestational hypertension (GH) and preeclampsia (PE)., Methods: With the in-house developed LC-MS/MS, a retrospective cross-sectional study was conducted. The healthy pregnant women of first- ( n  = 147), second- ( n  = 84) and third-trimester ( n  = 141) or the women diagnosed with GDM ( n  = 84), GH ( n  = 58) or PE ( n  = 23), that were aged between 22 and 46 years old and registered at our institute, were subjected for measurement of folic acid (FA) and 5-methyltetrahydrofolate (5-MTHF), followed by appropriate statistical association analysis., Results: The assay for simultaneous quantitation of FA and 5-MTHF in plasma and RBCs was linear, stable, with imprecision less than 15% and recoveries within ±10%. The lower limits of quantification for FA and 5-MTHF measurement in whole blood were 0.57 and 1.09 nmol/L, and in plasma were 0.5 and 1 nmol/L, respectively. In the association analysis, the patients with lower RBC folate level (<906 nmol/L) presented higher risks of PE development (OR 4.861 [95% CI 1.411-16.505]) by logistic regression and restricted cubic spline (RCS) regression in a nonlinear fashion. In addition, higher level of plasma folates in pregnancy was significantly associated with GH risk but may be protective for the development of GDM., Conclusions: The in-house developed LC-MS/MS method for folates and metabolites in plasma or RBC showed satisfactory analytical performance for clinical application. Further, the levels of folates and metabolites were diversely associated with GDM, GH and PE development.

Background and Aims: To compare the association of geriatric nutritional risk index (GNRI) and controlling nutritional status (CONUT) scores with malnutrition, and to study their association with clinical outcomes in older adult cancer patients., Methods: This retrospective analysis was conducted on 854 older adult cancer patients collected from 34 hospitals in 18 cities in China between June and September 2014. Anthropometric and hematological examination results at admission were collected, and subjective global assessment was used. Clinical outcomes, such as complications, length of hospital stays, and hospital costs, were recorded. Receiver operating characteristic curves were used to evaluate the accuracy of the two nutritional assessment tools for malnutrition. The association between GNRI and CONUT score and clinical outcomes was analyzed using the chi-square test, t-test, or rank sum test., Results: Among 854 patients with cancer, the prevalence of malnutrition was 42.7%. Compared with subjective global assessment, the GNRI had a significantly higher accuracy than the CONUT score in predicting malnutrition (area under the curve 0.704, 95% confidence interval, 0.658 - 0.750, P < 0.001). The GNRI was significantly associated with the occurrence of complications (χ2 = 4.985, P = 0.026), and low GNRI (≤98) was associated with a longer length of hospital stay (t = -2.179, P = 0.030)., Conclusions: The GNRI may be used to assess malnutrition in older adult cancer patients and can predict poor clinical outcomes in these patients., Competing Interests: The authors declare that they have no conflicts of interest.

Answering questions using multi-modal context is a challenging problem as it requires a deep integration of diverse data sources. Existing approaches only consider a subset of all possible interactions among data sources during one attention hop. In this paper, we present a Holistic Multi-modal Memory Network (HMMN) framework that fully considers interactions between different input sources (multi-modal context, question) at each hop. In addition, to hone in on relevant information, our framework takes answer choices into consideration during the context retrieval stage. Our HMMN framework effectively integrates information from the multi-modal context, question, and answer choices, enabling more informative context to be retrieved for question answering. Experimental results on the MovieQA and TVQA datasets validate the effectiveness of our HMMN framework. Extensive ablation studies show the importance of holistic reasoning and reveal the contributions of different attention strategies to model performance.

Biocompatible hydrogels with defined mechanical properties are critical to tissue engineering and regenerative medicine. Thiol-acrylate photopolymerized hydrogels have attracted special interest for their degradability and cytocompatibility, and for their tunable mechanical properties through controlling factors that affect reaction kinetics (e.g., photopolymerization, stoichiometry, temperature, and solvent choice). In this study, we hypothesized that the mechanical property of these hydrogels can be tuned by photoinitiators via photobleaching and by thiol-Michael addition reactions. To test this hypothesis, a multiscale mathematical model incorporating both photobleaching and thiol-Michael addition reactions was developed and validated. After validating the model, the effects of thiol concentration, light intensity, and pH values on hydrogel mechanics were investigated. Results revealed that hydrogel stiffness (i) was maximized at a light intensity-specific optimal concentration of thiol groups; (ii) increased with decreasing pH when synthesis occurred at low light intensity; and (iii) increased with decreasing light intensity when synthesis occurred at fixed precursor composition. The multiscale model revealed that the latter was due to higher initiation efficiency at lower light intensity. More broadly, the model provides a framework for predicting mechanical properties of hydrogels based upon the controllable kinetics of thiol-acrylate photopolymerization., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The aim of this study was to screen and investigate the anti-HBV and anti-tumor activities of separated compounds from Rabdosia serra (Maxim.) Hara to lay the basis for further isolate active entity. Three kinds of extractions from Rabdosia serra using different solvents (petroleum ether, acetidin, butyl alcohol) were prepared and used to analyze their anti-HBV activity in HepG2.2.15 cells for further separation. The cytotoxicity of each extraction was tested by MTT assay, the levels of HBsAg, HBeAg and HBV DNA in supernatants from HepG2.2.15 cells were detected by ELISA and real-time quantitative polymerase chain reaction (PCR). Then, the most effective extraction was further separated, the anti-HBV activities of separated compounds were also tested by MTT and ELISA, and three compounds with highest cytotoxicity were selected to further identify their anti-tumor activities on MCF-7, BGC-823 and HepG2 cells. Acetidin extraction C2 had the most effective anti-HBV activity that was used to be further separated, it led to statistically significant reduction in HBsAg and HBeAg secretion and HBV DNA. The separation of C2 resulted in 14 compounds, A3 and A5 markedly inhibited HBsAg secretion, while A9 inhibited HBeAg secretion in a dose-dependent manner with higher TI comparing with C2. A6, A7, A11 had different anti-tumor activity against different tumor cells. These data showed that the extraction and their separated effective compounds had strong inhibitory effect on HBV replication so as to have anti-HBV activity, and further separation and purification could enhance anti-HBV activity. Meanwhile, some compounds have high cytotoxicities on different tumor cells. Our study could provide a theoretical basis for the next clinical use and the development of potential and efficient drugs for HBV and tumor therapy from Rabdosia serra.