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SummarySuccessful fracturing operations heavily rely on the performance of fracturing fluids (FFs), which mainly include high sand-carrying capacity (SCC) and significant drag reduction (DR). Herein, a functional copolymer, SRP-6, was synthesized by introducing a zwitterionic surfactant, 4-((3-((3-(docos-10-enamido)propyl)dimethylammonio)-2-hydroxypropyl)amino)benzenesulfonate (SVBS), during polymerization, and its structure was characterized by Fourier transform infrared and 1H nuclear magnetic resonance (NMR). SRP-6 shows superior solubility, hydrodynamic volume, and microstructure in salinity water, thanks to the hydrophilicity of multiple functional groups in the copolymer structure, and the SVBS-induced pseudointerpenetrated network as well as the uniform distribution of multiple functional monomers. Furthermore, the SVBS, as a physical crosslinker, imparts the SRP-6/SVBS solution with outstanding rheological performance, SCC, and DR. In addition, the contribution of elasticity and viscosity to SCC was calculated. These results collectively suggest that the SRP-6/SVBS solution could be potentially used as a high-performance FF for the development of tight reservoirs.

Due to its limited intelligence and abilities, machine learning is currently unable to handle various situations thus cannot completely replace humans in real-world applications. Because humans exhibit robustness and adaptability in complex scenarios, it is crucial to introduce humans into the training loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learning algorithms. In this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developed for policy training in an end-to-end autonomous driving case. With our newly designed mechanism for control transfer between humans and automation, humans are able to intervene and correct the agent's unreasonable actions in real time when necessary during the model training process. Based on this human-in-the-loop guidance mechanism, an improved actor-critic architecture with modified policy and value networks is developed. The fast convergence of the proposed Hug-DRL allows real-time human guidance actions to be fused into the agent's training loop, further improving the efficiency and performance of DRL. The developed method is validated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning approaches. The results suggest that the proposed method can effectively enhance the training efficiency and performance of the DRL algorithm under human guidance without imposing specific requirements on participants’ expertise or experience. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University Published version This work was supported in part by the SUG-NAP Grant of Nanyang Technological University and the A*STAR Grant (W1925d0046), Singapore.

Making safe and human-like decisions is an essential capability of autonomous driving systems, and learning-based behavior planning presents a promising pathway toward achieving this objective. Distinguished from existing learning-based methods that directly output decisions, this work introduces a predictive behavior planning framework that learns to predict and evaluate from human driving data. This framework consists of three components: a behavior generation module that produces a diverse set of candidate behaviors in the form of trajectory proposals, a conditional motion prediction network that predicts future trajectories of other agents based on each proposal, and a scoring module that evaluates the candidate plans using maximum entropy inverse reinforcement learning (IRL). We validate the proposed framework on a large-scale real-world urban driving dataset through comprehensive experiments. The results show that the conditional prediction model can predict distinct and reasonable future trajectories given different trajectory proposals and the IRL-based scoring module can select plans that are close to human driving. The proposed framework outperforms other baseline methods in terms of similarity to human driving trajectories. Additionally, we find that the conditional prediction model improves both prediction and planning performance compared to the non-conditional model. Lastly, we note that learning the scoring module is crucial for aligning the evaluations with human drivers.

In this article, through screening Bilibili 5 years(2018-2022) in the first quarter of positive advertising revenue overall revenue in the annual report data mining sector revenue data bi li bi commercialization trend of miles, through the two plates data policy interpretation, combined with the analysis of the existing advertising marketing model B stand UP main business single, advertising the advantages and disadvantages of take-off, video invitation, Put forward strategic advertising marketing suggestions, and carry out combined advertising marketing under the premise of realizing multi-win, de-marketing formalization and content tolerance, to reverse its profit status and provide direction for Bilibili’s future development.

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential for safe and efficient operation of connected automated vehicles under complex driving situations. Two main challenges for this task are to handle the varying number of heterogeneous target agents and jointly consider multiple factors that would affect their future motions. This is because different kinds of agents have different motion patterns, and their behaviors are jointly affected by their individual dynamics, their interactions with surrounding agents, as well as the traffic infrastructures. A trajectory prediction method handling these challenges will benefit the downstream decision-making and planning modules of autonomous vehicles. To meet these challenges, we propose a three-channel framework together with a novel Heterogeneous Edge-enhanced graph ATtention network (HEAT). Our framework is able to deal with the heterogeneity of the target agents and traffic participants involved. Specifically, agents' dynamics are extracted from their historical states using type-specific encoders. The inter-agent interactions are represented with a directed edge-featured heterogeneous graph and processed by the designed HEAT network to extract interaction features. Besides, the map features are shared across all agents by introducing a selective gate-mechanism. And finally, the trajectories of multiple agents are predicted simultaneously. Validations using both urban and highway driving datasets show that the proposed model can realize simultaneous trajectory predictions for multiple agents under complex traffic situations, and achieve state-of-the-art performance with respect to prediction accuracy. The achieved final displacement error (FDE@3sec) is 0.66 meter under urban driving, demonstrating the feasibility and effectiveness of the proposed approach. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University This work was supported in part by A*STAR Singapore under Grant W1925d0046 and in part by Start-Up Grant, Nanyang Technological University, Singapore.

Idiopathic pulmonary fibrosis is a progressive fibrotic disease characterized by excessive deposition of (myo)fibroblast produced collagen fibrils in alveolar areas of the lung. Lysyl oxidases (LOXs) have been proposed to be the central enzymes that catalyze the cross-linking of collagen fibers. Here, we report that, while its expression is increased in fibrotic lungs, genetic ablation of LOXL2 only leads to a modest reduction of pathological collagen cross-linking but not fibrosis in the lung. On the other hand, loss of another LOX family member, LOXL4, markedly disrupts pathological collagen cross-linking and fibrosis in the lung. Furthermore, knockout of both Loxl2 and Loxl4 does not offer any additive antifibrotic effects when compared to Loxl4 deletion only, as LOXL4 deficiency decreases the expression of other LOX family members including Loxl2 . On the basis of these results, we propose that LOXL4 is the main LOX activity underlying pathological collagen cross-linking and lung fibrosis.

PurposeIn this study, an eco-friendly cotton fabric (CF) treatment method was proposed to induce anti-ultraviolet and flame retardant properties, and a new application of tannic acid (TA) and phytic acid (PA) in ultraviolet protection and flame retardant fabric was put forward.Design/methodology/approachBy combining diethylenetriamine, PA and TA on CF, a chemical reaction intumescent flame retardant CF with anti-ultraviolet and anti-flame retardance was developed.FindingsThe flame retardant and ultraviolet resistance of CF were characterized by LOI, vertical combustion, cone calorimetry and ultraviolet resistance testing. SEM, XPS, FTIR and other tests were used to analyze the chemical composition, surface morphology and residual carbon after combustion of the CF, and it was confirmed that the modified CF has good ultraviolet resistance and flame retardant performance.Originality/valueIn this study, an eco-friendly CF treatment method was proposed to induce anti-ultraviolet and flame retardant properties, and a new application of TA and PA in ultraviolet protection and flame retardant fabric was put forward.

Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful modeling capability. However, two limitations exist in the current deep learning-based OCTA reconstruction methods: (a) the angiogram information extraction is only limited to the locally consecutive B-scans; and (b) all reconstruction models are confined to the 2D convolutional network architectures, lacking effective temporal modeling. As a result, the valuable neighborhood information and inherent temporal characteristics of OCTA are not fully utilized. In this paper, we designed a neighborhood information-fused Pseudo-3D U-Net (NI-P3D-U) for OCTA reconstruction.The proposed NI-P3D-U was investigated on an in vivo animal dataset by a cross-validation strategy under both fully supervised learning and weakly supervised learning pipelines. To demonstrate the OCTA reconstruction capability of the proposed NI-P3D-U, we compared it with several state-of-the-art methods.The results showed that the proposed network outperformed the state-of-the-art deep learning-based OCTA algorithms in terms of visual quality and quantitative metrics, and demonstrated an effective generalization for different training strategies (fully supervised and weakly supervised) and imaging protocols. Meanwhile, the idea of neighborhood information fusion was also expanded to other network architectures, resulting in significant improvements.These investigations indicate that the proposed network, which combines the neighborhood information strategy with temporal modeling architecture, is well capable of performing OCTA reconstruction, and has a certain potential for clinical applications.

A novel label-free immunosensor for the quantitative detection of human cardiovascular diseases biomarker GDF 15 was prepared using the three-dimensional braided composite A@M as the signal amplification platform for the first time.

Supple Fig 1, Supple Table 1-8, and Supple Ref. Supplementary Figure S1. 2100 Bioanalyzer spectra of total DNA extracted from CSF samples. Supplementary Table S1. Sample collection information. Supplementary Table S2. Genes/regions included in the LungPlasma panel. Supplementary Table S3. Genes included in the customized SeqCap EZ Choice Library. Supplementary Table S4. List of hotspot mutations and structural rearrangements. Supplementary Table S5. Co-existence of EGFR and PIK3CA mutations as reported in NSCLC patient datasets Supplementary Table S6. Occurrence of somatic protein-changing variants in primary tumor and CSF. Supplementary Table S7. Genes and biological pathways with recurrent somatic protein-changing variants in primary tumor or CSF. Supplementary Table S8. Comparison of EGFR mutation type(s) in CSF cfDNA and pellet DNA as detected by droplet digital PCR (ddPCR).

Purpose: Leptomeningeal metastasis (LM) is a detrimental complication of non–small cell lung cancer (NSCLC) and associated with poor prognosis. However, the underlying mechanisms of the metastasis process are still poorly understood.Experimental Design: We performed next-generation panel sequencing of primary tumor tissue, cerebrospinal fluid (CSF), and matched normal controls from epidermal growth factor receptor (EGFR) mutation-positive NSCLC patients with LM.Results: The status of EGFR-activating mutations was highly concordant between primary tumor and CSF. PIK3CA aberrations were high in these patients, implicating an association with LM risk. Intriguingly, low overlapping of somatic protein-changing variants was observed between paired CSF and primary lesions, exhibiting tumor heterogeneity and genetic divergence. Moreover, genes with CSF-recurrent genomic alterations were predominantly involved in cell-cycle regulation and DNA-damage response (DDR), suggesting a role of the pathway in LM development.Conclusions: Our study has shed light on the genomic variations of NSCLC-LM, demonstrated genetic heterogeneity and divergence, uncovered involvement of cell-cycle and DDR pathway, and paved the way for potential therapeutic approaches to this unmet medical need. Clin Cancer Res; 24(1); 209–16. ©2017 AACR.

In recent years, rapid urbanisation has caused a large volume of construction and demolition waste in China. Although China has introduced some documents and policies to manage the waste, these documents are not operable or detailed enough to guide the practice further. It is of great importance to analyse critical success factors (CSFs) that could contribute to the successful development of construction and demolition waste management in China. A survey questionnaire was delivered to 150 participants, with 121 responses returned. Eighteen CSFs were identified and evaluated. The results showed that five factors – namely, ‘polluter-pays principle’, ‘subsidies for recycling activities’, ‘waste minimisation at the planning stage’, ‘value-added tax deduction’ and ‘penalties on violations’ – were considered the most important factors. Additionally, differences among the perspectives of three groups of related stakeholders (researchers, industry practitioners and government officers) on the criticality of the 18 CSFs were investigated. ‘City plan for land use’ was considered to have significant disagreement among researchers and government officers. However, the perceptions of industry practitioners were significantly different from those of researchers and government officers. The disagreements might be caused by the different interests, needs and objectives of related stakeholders. The findings of this study will likely prompt the policymakers in China to consider relevant CSFs to enhance the performance of construction and demolition waste management.

In this article, an improved short-term speed prediction method is proposed to predict short-term future speed and analyze future energy consumption of intelligent fuel cell vehicles. The short-term future speed is predicted by the proposed Inflated 3-D Inception long short-term memory (LSTM) network, which takes the spatiotemporal-vision information and vehicle motion states. Specifically, the spatiotemporal-vision-based deep neural network utilizes image sequences captured by a front-facing camera as environmental information and historical speed series as motion information to improve the prediction accuracy. Then, a case study of the proposed speed prediction method, with rule-based energy management strategy to calculate future energy consumption, is presented. The simulation results show that short-term speed prediction based on the Inflated 3-D Inception LSTM network can achieve high accuracy of speed prediction in various traffic densities, as well as low prediction errors of future energy consumption including the hydrogen consumption and state-of-charge attenuation. This work was supported in part by the National Key Research and Development Program under Grant 2018YFB0105402 and Grant 2018YFB0105703, in part by the Fundamental Research Funds for the Central Universities under Grant 2019CDXYQC0003, Grant 244005202014, 2019, and Grant 2018CDXYTW0031, in part by the National Natural Science Foundation of China under Grant 51806024, in part by the Chongqing Research Program of Foundation and Advanced Technology under Grant cstc2017jcyjAX0276, and in part by the Venture and Innovation Support Program for Chongqing Overseas Returnees under Grant cx2018051.

Previous small-size studies reported BRAF-mutated NSCLC patients have comparable sensitivity to immune checkpoint inhibitors (ICIs). However, how BRAF mutation affects the tumor immune microenvironment (TIME) is unknown. We performed Nanostring-panel RNA sequencing to evaluate TIME in 57 BRAF mutated and wild-type (WT) NSCLC specimens (cohort A). The efficacy of ICI monotherapy or combined therapies was determined in 417 patients with WT and BRAF mutated NSCLC (cohort B). We found that BRAF-mutant tumors had similar ratios of CD8+ T cells to Tregs, the balance of cytotoxicity gene expression signatures and immune suppressive features, and similar ICI-response-related biomarkers to WT NSCLC. A similar TIME pattern was observed between the BRAF V600E and Non-V600E subgroups of NSCLC. The further retrospective study confirmed that treatment with ICI monotherapy or combined therapies resulted in similar overall survival (OS) (HR: 0.85; 95% CI, 0.56 to 1.30; p = 0.47) and progress-free survival (PFS) (HR: 1.02; 95% CI, 0.72 to 1.44; p = 0.91) of patients with WT (n = 358) and BRAF mutant (n = 59) NSCLC. Similarly, both patients with BRAF V600E or Non-V600E NSCLC had similar responses to immunotherapy. Our findings support that BRAF mutation did not modulate TIME in NSCLC and therapeutic responses to ICIs. Patients with NSCLC harboring BRAF mutation should not be denied treatment with ICIs.

The heterogeneous Fenton system has drawn great attention in recent years due to its effective degradation of polluted water capability without limitation of the pH range and avoiding excess ferric hydroxide sludge. Therefore, simple chemical precipitation and vacuum filtration method for manufacturing the heterogeneous Fenton aramid nanofibers (ANFs)/ferrous oxalate (FeC2O4) composite membrane catalysts with excellent degradation of methylene blue (MB) is reported in the study. The morphology and structure of materials synthesized were characterized by scanning electron microscope (SEM), X-ray energy spectrum analysis (EDS), infrared spectrometer (FTIR), and X-ray diffraction (XRD) equipment. The 10 ppm MB degradation efficiency of composite catalyst and ferrous oxalate (FeC2O4) within 15 min were 94.5% and 91.6%, respectively. The content of methylene blue was measured by a UV-Vis spectrophotometer. Moreover, the dye degradation efficiency still could achieve 92% after five cycles, indicating the composite catalyst with excellent chemical stability and reusability. Simultaneously, the composite catalyst membrane can degrade not only MB but also rhodamine B (RB), orange II (O II), and methyl orange (MO). This study represents a new avenue for the fabrication of heterogeneous Fenton catalysts and will contribute to dye wastewater purification, especially in the degradation of methylene blue.

With the aims of safe, smart and sustainable future mobility, a personalized approach of trajectory planning and control based on user preferences is developed for lane-change of autonomous vehicles in this paper. First, a safe area during the lane change process is identified by using constraint Delaunay triangulation. Then, an improved rapidly-exploring Random Trees ( i -RRT) is developed with B-spline to generate the feasible trajectory cluster, which is subject to the safe area boundaries and the vehicle dynamics. To extract a personalized trajectory from this cluster, we firstly adopt the fuzzy linguistic preference relation (FLPR) method to identify users’ preferences on driving, which can be reflected by their subjective objectives including driving safety, ride comfort and vehicle stability. Then, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve the multi-objective optimisation problem formulated by considering the user preferences. The algorithms proposed above are integrated, and both simulation and experimental validation are conducted under lane-change scenarios of autonomous driving. Simulation and experiment results show that proposed approach is able to successfully realize personalized trajectory planning and lane-change control, satisfying users’ various preferences and simultaneously ensure vehicle safety, demonstrating its feasibility and effectiveness.

Hydrogels and organogels, as two crucial representatives of soft materials, have attracted immense interest. However, they develop independently along two parallel lines, and these gels with single networks have their inherent drawbacks. For example, hydrogels tend to freeze, and organogels are usually brittle. Herein, organogels were incorporated into a hydrogel matrix for the synthesis of organohydrogels GOHs through polymerization in Pickering emulsion. The rigid organogel domains contribute to enhancing the strength of organohydrogels. Besides this, the organogels derived from 12-HAS self-assembly behavior exhibit a gel-sol transition when the temperature reaches 70 °C, thus leading to a thermo-softening behavior in the GOHs. Due to the phase transition of organogel domains and the elastic hydrogel network, the resultant organohydrogels demonstrate high-strain shape-memory performance (over 1000%) which could help achieve full recovery in seconds. Consequently, GOHs are endowed with the potential of practical application in soft robots, wearable devices, and biological materials.

The performance of speed prediction-based energy management strategy (EMS) for fuel cell vehicles (FCVs) highly relies on the accuracy of predicted speed sequences. Therefore, the future speed sequences are estimated by Inflated 3D Inception long short-term memory (LSTM) network, which can use the historical speed and image information to improve the accuracy of speed prediction. Meanwhile, the energy economy and powertrain system durability are the objectives of real-time optimization. For optimizing energy economy and powertrain system durability of FCVs, the real-time optimization of EMS using the Inflated 3D Inception LSTM network-based speed prediction is proposed. To do this, the mathematical models including energy economy and powertrain system durability of FCVs are developed at the beginning. Then, based on the predicted speed sequences, a real-time optimization method with sequential quadratic programming (SQP) algorithm is proposed to minimize the energy consumption and take into consideration powertrain system degradation in the prediction horizon. Simulation results show that the proposed EMS can significantly reduce the total cost of energy consumption and powertrain system degradation.

The performance and behavior of DNBT under RMD simulation at high temperature (2600 K, 2900 K, 3200 K and 3500 K).

In order to deal with the problem of the exploitation of low permeability reservoirs and to improve fracturing fluid performance, a carbon-dot-based nano-crosslinker was developed in which the same cross-linking point can be linked with multiple guar gum molecular chains; it also shows good stability. HRTEM and dynamic light scattering (DLS) studies were used to observe the morphology, crystal plane spacing (0.34 nm), and particle size distribution (1.20–1.42 nm) of the carbon dot crosslinker. Elemental mapping, Fourier transform infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS) were used to prove the successful doping with B and N. Then, rheological testing was carried out with carbon-dot-based fracturing fluid (CDG), and the results showed that the viscosity of CDG remained above 170 mPa s at 120 °C and a shear rate of 170 s−1, performing better than organic boron fracturing fluid. Then, a Cole–Cole model was established based on the viscoelastic data from 25–120 °C; the results showed that the radius of curvature of the Cole–Cole plot increased with a decrease in temperature, and the system has strong compatibility. Displacement experiments were also used to characterize the fluorescence tracing properties. This work proves that the preparation of carbon dots to act as a crosslinker and fluorescent tracer was successful, and it also provides new insight for the application of carbon dots.

Optical coherence tomography (OCT) is a widely-used modality in clinical imaging, which suffers from the speckle noise inevitably. Deep learning has proven its superior capability in OCT image denoising, while the difficulty of acquiring a large number of well-registered OCT image pairs limits the developments of paired learning methods. To solve this problem, some unpaired learning methods have been proposed, where the denoising networks can be trained with unpaired OCT data. However, majority of them are modified from the cycleGAN framework. These cycleGAN-based methods train at least two generators and two discriminators, while only one generator is needed for the inference. The dual-generator and dual-discriminator structures of cycleGAN-based methods demand a large amount of computing resource, which may be redundant for OCT denoising tasks. In this work, we propose a novel triplet cross-fusion learning (TCFL) strategy for unpaired OCT image denoising. The model complexity of our strategy is much lower than those of the cycleGAN-based methods. During training, the clean components and the noise components from the triplet of three unpaired images are cross-fused, helping the network extract more speckle noise information to improve the denoising accuracy. Furthermore, the TCFL-based network which is trained with triplets can deal with limited training data scenarios. The results demonstrate that the TCFL strategy outperforms state-of-the-art unpaired methods both qualitatively and quantitatively, and even achieves denoising performance comparable with paired methods. Code is available at: https://github.com/gengmufeng/TCFL-OCT.

An ultraviolet (UV)-protective cotton fabric has been developed by combining tannic acid and copper ions (TA-Cu) on cotton fabrics. The structure and properties of TA-Cu and cotton fabrics finished...

The increasing discharge of oily wastewater and frequent oil spill accidents pose a great threat to our environment and cause a great waste of valuable resources. However, most existing methods to ...

Milling stability is a function of the tool point frequency response functions (FRFs), which vary with the movements of the moving parts within the whole machine tool work volume. The position-dependent tool point FRFs result in uncertain prediction of the stability lobe diagram (SLD) for chatter-free machining parameter selection. Taking the variations of modal parameters to represent the variations of tool point FRFs, this paper introduces the edge theorem to predict the robust milling chatter stability. The application of the edge theorem requires the minimum and maximum modal parameters within the machining space defined by the machining position and machining allowance information. Then, radial basis function artificial neural networks (RBFANNs) are used to predict the position-dependent modal parameters in X and Y directions based on the sample information of machining positions and related modal parameters at the tool point. Moreover, sample machining spaces are determined based on the aforementioned sample positions, and the trained RBFANNs are used to obtain corresponding sample extreme modal parameters. On this basis, RBFANNs for predicting the position and machining allowance-dependent extreme modal parameters can also be trained, and they are combined with the edge theorem and zero exclusion condition to calculate robust pairs of the spindle speed (n) and limiting axial cutting depth (aplim) and then plot the robust SLD (RSLD). A case study was performed on a real three-axial vertical machining center, and the plotted RSLD considering position variations was compared with the traditional SLD. Results of the chatter tests show that the RSLD can provide more reliable (ap, n) pairs to guarantee the milling stability, validating the feasibility of the proposed robust milling chatter stability prediction method.

Phase inversion composition methods and phase inversion temperature methods are the common methods for nanoemulsion formation. The mechanisms governing both PIC and PIT are the same: composition or temperature can trigger a change in the surfactant spontaneous curvature during the emulsification process. It is anticipated that pH may also induce a change in the spontaneous curvature of pH-responsive surfactants to prepare nanoemulsions. Therefore, fatty acid/amine complexes were synthesized through electrostatic interactions. Based on these complexes, nanoemulsions were successfully prepared by pH regulation. Electrical conductivity and pH measurements were employed to determine the phase inversion process. Dynamic light scattering, digital fluorescence microscopy, and transmission electron microscopy were employed to characterize the droplet size and morphology of the nanoemulsion. The effects of complex concentration, NaCl concentration, and pH of the system were investigated. The developed method, phase inversion pH (PIpH) method, is a moderate and easy-control method. Using this method, the size distributions of nanoemulsion are monomodal and narrow. Nanoemulsion prepared by PIpH has a unique pH-responsive behavior that can be controllably regulated among nanoemulsions, emulsions, and phase separation systems.

The derivatives of ferrocene with redox properties are widely used. Some studies have used complex synthesis processes to obtain surfactants with redox properties. In order to simplify the synthesis process, FA-DMDA-Ox, a surfactant with redox and pH dual responses, was prepared by simple electrostatic interaction between ferrocenecarboxylic acid (FA) and N,N-dimethyldodecylamine (DMDA). A stable oil-in-water emulsion was prepared by using FA-DMDA-Ox at 25 °C. When sodium sulfite was added to the emulsion, the emulsion was demulsified. This was due to the oxidized ferrocene group that was reduced from the charged hydrophilic state to the uncharged hydrophobic state, which destroyed the original surface activity. In addition, when added HCl or NaOH to the emulsion changed pH, demulsification was caused by the dissociation of FA-DMDA-Ox.

Growth differentiation factor-15 (GDF-15) is a member of the transforming growth factor-β family. GDF-15 is overexpressed in cardiovascular diseases and has become a novel biomarker for these diseases. In this study, we fabricated a label-free electrochemical immunosensor for sensitive detection of GDF-15. Briefly, a three-dimensional braided composite of AuPtCu-SWCNTs@MoS

High-energy materials containing azole and furazan have revealed numerous properties; however, the underlying optical properties need to be solved. Meanwhile, the uncertainty for the choice of fluorescent matrix materials and the flexible situational conditions prompted us to estimate the optical and fluorescent properties of 5,5'-dinitro-2

Accumulating evidence indicates that inflammation and nutrition status are associated with clinical outcomes in patients with various malignancies. This study aimed to evaluate the prognostic significance of the pretreatment platelet to albumin ratio (PAR) in esophageal squamous cell carcinoma (ESCC) patients undergoing definitive radiotherapy. A total of 470 patients who underwent definitive radiotherapy with or without chemotherapy were enrolled. The optimal cut-off values of PAR and other indicators were determined by the X-tile. The Kaplan–Meier method, multivariate analyses Cox regression were conducted to identify the association between those indicators and the survival outcomes. The median follow-up time was 23.5 months. The optimal cut-off value of PAR was 5.7 × 109 and patients were stratified as the low PAR group and the high PAR group. In the univariate analysis, a low overall survival rate was significantly associated with T stage (P = 0.005), TNM stage (P P = 0.007), neutrophil to lymphocyte ratio (NLR) (P = 0.006), platelet to lymphocyte ratio (P P P P P = 0.001), adjuvant chemotherapy (P P = 0.033) were independent prognostic factors in ESCC treated with definitive radiotherapy. PAR is a novel, convenient, and inexpensive prognostic indicator for patients with ESCC undergoing definitive radiotherapy. Future validation from prospective larger-scale studies is warranted.

Background Programmed cell death protein 1 (PD-1) blockade combined with radiotherapy may be a promising strategy to improve the prognosis of patients with advanced non-small-cell lung cancer (NSCLC). However, the optimum radiotherapy scheme, timing, and location have not been fully determined. Methods We conducted a retrospective study of 57 patients receiving PD-1/PD-ligand 1 (PD-L1) inhibitors with radiotherapy for metastatic NSCLC in Zhejiang Cancer Hospital between June 2017 and December 2019. Data on overall survival (OS), progression-free survival (PFS), treatment response and adverse events were collected. Comparisons based on type of radiation, timing of radiotherapy and number of irradiated lesions were performed. Results The median OS was 25.9 (95% CI: 18.4-33.4) months, and the median PFS was 10.3 (95% CI: 8.9-11.8) months. The in-field and out-of-field overall response rates were 45.2% and 32.1%, respectively. Multisite radiotherapy was associated with longer PFS than single-site radiotherapy (19.50 vs. 9.10 months, P = 0.017). Furthermore, immunotherapy with concurrent radiotherapy tended to have superior outcomes compared with immunotherapy with sequential radiotherapy (OS: NR vs. 20.87 months, P = 0.079; PFS: 11.4 vs. 9.0 months, P = 0.153), as did immunotherapy with stereotactic radiotherapy compared with traditional radiotherapy (OS: 30.6 vs. 25.9 months, P = 0.840; PFS: 11.1 vs. 9.9 months, P = 0.803). The incidence of treatment-related adverse events (AEs) was 61.4%, but only 5.3% of patients experienced grade 3 AEs. Conclusions The combination of immunotherapy and radiotherapy demonstrated favourable survival and good tolerability in Chinese patients with advanced NSCLC.