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Abstract Droplet Interface Bilayers (DIBs) constitute a commonly used model of artificial membranes for synthetic biology research applications. However, their practical use is often limited by their requirement to be surrounded by oil. Here we demonstrate in-situ bilayer manipulation of submillimeter, hydrogel-encapsulated droplet interface bilayers (eDIBs). Monolithic, Cyclic Olefin Copolymer/Nylon 3D-printed microfluidic devices facilitated the eDIB formation through high-order emulsification. By exposing the eDIB capsules to varying lysophosphatidylcholine (LPC) concentrations, we investigated the interaction of lysolipids with three-dimensional DIB networks. Micellar LPC concentrations triggered the bursting of encapsulated droplet networks, while at lower concentrations the droplet network endured structural changes, precisely affecting the membrane dimensions. This chemically-mediated manipulation of enclosed, 3D-orchestrated membrane mimics, facilitates the exploration of readily accessible compartmentalized artificial cellular machinery. Collectively, the droplet-based construct can pose as a chemically responsive soft material for studying membrane mechanics, and drug delivery, by controlling the cargo release from artificial cell chassis.

Abstract Protein kinases become an important source of potential drug targets. Developing new, efficient, and safe small-molecule kinase inhibitors has become an important topic in the field of drug research and development. In contrast with traditional wet experiments which are time-consuming and expensive, machine learning-based approaches for predicting small molecule inhibitors for protein kinases are time-saving and cost-effective, which are highly desired for us. However, the issue of sample scarcity (known active and inactive compounds are usually limited for most kinases) poses a challenge to the research and development of machine learning-based kinase inhibitors' active prediction methods. To alleviate the data scarcity problem in the prediction of kinase inhibitors, in this study, we present a novel Meta-learning-based inductive logistic matrix completion method for the Prediction of Kinase Inhibitors (MetaILMC). MetaILMC adopts a meta-learning framework to learn a well-generalized model from tasks with sufficient samples, which can fast adapt to new tasks with limited samples. As MetaILMC allows the effective transfer of the prior knowledge learned from kinases with sufficient samples to kinases with a small number of samples, the proposed model can produce accurate predictions for kinases with limited data. Experimental results show that MetaILMC has excellent performance for prediction tasks of kinases with few-shot samples and is significantly superior to the state-of-the-art multi-task learning in terms of AUC, AUPR, etc., various performance metrics. Case studies also provided for two drugs to predict Kinase Inhibitory scores, further validating the proposed method's effectiveness and feasibility. Scientific contribution Considering the potential correlation between activity prediction tasks for different kinases, we propose a novel meta learning algorithm MetaILMC, which learns a prior of strong generalization capacity during meta-training from the tasks with sufficient training samples, such that it can be easily and quickly adapted to the new tasks of the kinase with scarce data during meta-testing. Thus, MetaILMC can effectively alleviate the data scarcity problem in the prediction of kinase inhibitors.

In this study, a trisodium citrate (TSC)-assisted hydrothermal method is utilized to prepare three-dimensional hydroxyapatite (3D HA). Understanding the role of TSC in the preparation of 3D HA crystals may provide valuable methods to design advanced biomaterials. As one of the indexes of solution supersaturation, the initial pH (ipH) value can not only directly affect the nucleation rate, but also affect the growth of HA crystals. In this work, the effect of the ipH on the microstructure, particle size distribution, and specific surface area of the 3D HA is explored. Results showed that the morphology of 3D HA transformed from a bundle to a dumbbell ball and then a dumbbell with an increase in the ipH. A corresponding mechanism of such a structural evolution was proposed, providing inspiration for the fabrication of innovative 3D HA structures with enhanced biological functionality and performance.

Patients with lung adenocarcinoma (LUAD) are vulnerable to COVID-19. However, there is currently a lack of available treatments for LUAD patients infected with COVID-19. Jaranol is a naturally derived flavonoid that exhibits promising properties as an antiviral and antitumor agent. Therefore, this research intends to investigate the molecular targets and underlying mechanisms of jaranol for the treatment of COVID-19/LUAD. Through network pharmacology, 47 intersection target genes were identified for jaranol against COVID-19/LUAD, and an eight-gene risk score model with strong predictive accuracy for LUAD patients was constructed. Functional enrichment analysis demonstrated that the main mechanism of jaranol in combating COVID-19/LUAD involved the regulation of response to hormone, positive regulation of phosphorylation, and PI3K-Akt signaling pathway. Furthermore, the results of molecular docking analysis indicated that jaranol exhibited a significant affinity with eight hub targets (AKT1, SRC, EGFR, HSP90AA1, ESR1, PTGS2, PPARG, and MMP9) as well as three core COVID-19-related targets (ACE2, 3CLpro, and PLpro). The direct binding between jaranol and MMP9 was then validated in A549 lung cancer cells using cellular thermal shift assay (CETSA). These findings suggest that jaranol has a promising therapeutic effect against COVID-19/LUAD, providing a theoretical foundation and novel perspectives for future development as a potential pharmaceutical candidate.

The multiple species characteristics of traditional Chinese medicines (TCMs) are crucial for expanding TCMs sources, meeting the needs of the pharmaceutical industry and ensuring clinical requirements. It’s also one of the significant factors affecting the quality control of TCMs. Systematic differential analysis of original species in TCMs is an important link in achieving comprehensive quality control, ensuring the effectiveness and safety of clinical medication. The study aims to establish a reliable and efficient approach to screen combinatorial discriminatory quality markers for rapid differentiation of original species by metabolomics coupled with DNA barcoding as a case of Cimicifugae Rhizoma. DNA barcoding is used to identify the origin of Cimicifugae Rhizoma. The data-dependent acquisition mode integrated with the computerized intelligent filtering system was established for in-depth characterization of metabolites from Cimicifugae Rhizoma using ultra-high performance liquid chromatography to quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). The untargeted metabolomics combined with multivariate statistical analysis was performed to screen and identify the potential combinatorial discriminatory quality markers. Finally, quantitative analysis and predictive model of these markers were employed to validate the feasibility of this strategy to distinguish the original species. Based on the scores of variable importance in projection greater than 1.0 and t-test (p

Valeriana jatamansi Jones (V. jatamansi) is an ethnomedicinal herb widely used worldwide for its excellent anti-inflammatory activity. However, the quality markers that were correlated with the anti-inflammatory activity in V. jatamansi remained unclear. This study aimed to identify combinatorial quality markers based on the anti-inflammatory effects of V. jatamansi for enhancing its quality control. The chemical constituents of V. jatamansi were identify by UPLC-Q-TOF-MS, and 36 compounds were identified. The anti-inflammatory mechanism was predicted by network pharmacology, STAT3 and JAK1 were selected as key targets. The anti-inflammatory activity and predicted targets were validated by using lipopolysaccharide (LPS)-stimulated RAW264.7 cells in vitro. A combination of bivariate correlation analysis (BCA) and grey correlation analysis (GCA) was applied to evaluate the correlation between the chemical spectrum and the anti-inflammatory activities. Hesperidin, acevaltrate and valtrate were screened as potential quality markers. These three compounds exhibited anti-inflammatory effects by inhibiting the production of inflammatory mediators (NO, IL-6, IL-1β and TNF-α) and suppressing the expression of the two key targets (JAK1 and STAT3). The quantitative analysis results showed that the contents of the combinatorial quality markers were positively correlated with the anti-inflammatory activity of V. jatamansi. The contribution rate of the combinatorial quality markers reached 45.0 %. This study provides a comprehensive strategy for the quality control of V. jatamansi based on combinatorial quality markers.

Optical fiber gratings can be appropriately packaged and integrated for the real-time monitoring of the structural health of buildings or composite-material films. In this work, a tilted fiber Bragg grating at the micron scale was proposed, designed, and optimized via simulation model analysis using OptiFDTD software. The effects of the grating inclination, grating period, and grating length on the transmission spectrum of the tilted microfiber Bragg grating (TMFBG) were studied. The transmission spectrum’s responses to different refractive indices were simulated and compared. A TFBG was uniformly etched with hydrofluoric acid based on the chemical etching method, and several TMFBGs with different diameters were prepared. The refractive index-sensing characteristics of the TMFBGs with different structures were studied. It was found that the sensitivity of the etched TFBG was greatly improved from 0.964 nm/RIU to 6.368 nm/RIU for the higher-order cladding mode, and from 0.294 nm/RIU to 2.353 nm/RIU for the lower-order cladding mode, being approximately six times and eight times higher, respectively.

With the development and popularization of Intelligent Transportation Systems (ITS), Vehicle Ad-Hoc Networks (VANETs) have attracted extensive attention as a key technology. In order to achieve real-time monitoring, VANET technology enables vehicles to collect real-time traffic updates through information collection devices and transmit this information to Roadside Units (RSUs), which are processed and integrated by an information processing center. However, high vehicle density leads to a conflict between minimizing the interval for vehicles to send Basic Safety Messages (BSMs) to RSUs and the limited communication resources of VANETs. To address this issue, we propose a MAC protocol based on the 802.11 CSMA/CA mechanism with the Preemptive-Resume Priority scheme. The arbitration device provides preemptive service to data packets with higher priority levels, thereby reducing data transmission delay. Moreover, queuing theory is employed to calculate the total delay for vehicles to send BSMs to a drone receiver, minimizing the BSM transmission interval and achieving minimal delay to meet safety driving requirements. The effectiveness and superiority of this mechanism and algorithm are demonstrated through simulation experiments.

Abstract The combination of super‐resolution microscopy and synthetic fluorescence probes has emerged as a universal tool to monitor dynamic biological events at the nanometer scale. However, the limited site‐specificity and fluorogenicity of synthetic fluorescent probes make it still difficult to realize long‐term super‐resolution imaging. Herein, we introduce a dynamic aggregation mediated SNAP‐tag fluorogenic probe, BGAN‐Aze, which can specifically bind to various SNAP‐tag fusion proteins with 41‐fold fluorescence enhancement. The equilibrium between the non‐fluorescent aggregate/dimer (A–D) and the fluorescent monomer (M) of BGAN‐Aze acts as an effective method to reduce the fluorescence background and endow BGAN‐Aze with the capability of conducting washing‐free super‐resolution imaging of various intracellular and extracellular proteins. Using this probe, we monitored multiple dynamic biological events, such as MMC, mitophagy, the fusion of nucleolus, and the growth and contact of filopodia. We expect that BGAN‐Aze will become a widely used SNAP‐tag for super‐resolution imaging of dynamic biological events and the A–D–M equilibrium can be a general strategy for designing fluorogenic probes.

The nasal cavity three-dimensional structure reconstruction is significant for the diagnosis and treatment of nasal and nasopharynx diseases. Currently, most systems for nasal cavity three-dimensional structure reconstruction are based on a long-source acoustic tube. However, the long-source acoustic tube has the weaknesses of low portability and high cost. To address these issues, a nasal cavity three-dimensional structure reconstruction system based on a short-source acoustic tube was designed in this research. The designed system comprises the lower computer of a nasal acoustic signal acquisition device and the upper computer of nasal acoustic signal analysis software. The lower computer of a nasal acoustic signal acquisition device consists of a signal processing unit, a detection unit, a microcontroller, and a USB communication interface. It is used to collect the original acoustic signal in the nasal cavity. The upper computer of nasal acoustic signal analysis software consists of the nasal cavity acoustic signal preprocessing method and the nasal cavity three-dimensional structure hierarchical reconstruction method. The nasal cavity acoustic signal preprocessing method is used to eliminate the DC offset component and suppress the high-frequency noise. Then, the nasal cavity three-dimensional structure hierarchical reconstruction method is used to establish the relationship between the cross-sectional area of the nasal cavity and the depth of the nasal cavity. In order to verify the effectiveness of the system designed in this research, the straight tube model and the simulated nasal cavity model were selected as test subjects, and the depth–cross-sectional area was selected as the evaluation indicator of the accuracy of the nasal cavity three-dimensional structure reconstruction result. The experimental results show that the root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 0.6284 cm2 to 0.0201 cm2 and 47.6467% to 1.8248%, respectively, under the straight tube model by using the nasal cavity acoustic signal preprocessing method. The root mean squared error and the mean relative error of the depth–cross-sectional area were reduced from 4.7730 cm2 to 0.1510 cm2 and 107.8128% to 3.3096%, respectively, under the simulated nasal cavity model. Meanwhile, this system can increase the effective measurement depth of the nasal cavity from 7 cm to 13 cm based on the preprocessing method. The results demonstrated that the designed system can not only reconstruct and visualize a nasal cavity three-dimensional structure with high precision but also generate comprehensive test reports. Therefore, this system can provide a basis for further auxiliary diagnosis of the disease and facilitate doctors in explaining the physiological structure of the nasal cavity to patients.

The double-sidewall guide pit method finds extensive application in the construction of large cross-section tunnels in soft rock strata due to its minimal disruption to the surrounding rock, thereby enhancing tunnel stability. To investigate the loading and deformation patterns of the surrounding rock and tunnel support using the double-sidewall guide pit method, this study compares the impacts of various construction sequences on surface settlement, surrounding rock stress, and lining stress using indoor model tests. The experimental results show that after excavating the upper guide hole on one side, the excavation of the lower guide hole on the same side is carried out. The upper and lower support structures form a closed loop, and the structure can better constrain the surrounding rock and control the deformation of the surrounding rock, whereas the lower structure can share the stresses suffered by the upper structure. Therefore, compared with the upper and lower excavation methods, the surface settlement caused by the left and right excavation methods is smaller, the disturbance to the surrounding rock is smaller, and the supporting structure is more evenly and stably stressed in the excavation process.

Adhesion G protein-coupled receptor G2 (ADGRG2) is an orphan adhesion G protein-coupled receptor (GPCR), which performs a tumor-promoting role in certain cancers; however, it has not been systematically investigated in hepatocellular carcinoma (HCC). In the current study, we utilized multiple databases to analyze the expression and diagnostic and prognostic value of ADGRG2 in HCC and its correlation with immune infiltration and inflammatory factors. The function and upstream regulatory miRNA of ADGRG2 were validated through qPCR, Western blot, CCK8, wound healing, and dual luciferase assays. It turned out that ADGRG2 was significantly higher in HCC and had a poor survival rate, especially in AFP ≤ 400 ng/mL subgroups. Functional enrichment analysis suggested that ADGRG2 may be involved in cancer pathways and immune-related pathways. In vitro, siRNA-mediated ADGRG2 silencing could inhibit the proliferation and migration of Huh7 and HepG2 cells. There was a highly significant positive correlation between ADGRG2 and neutrophils. Moreover, NET-related genes were filtered and confirmed, such as ENO1 and S100A9. Meanwhile, the high expression of ADGRG2 was also accompanied by the highest number of inflammatory cytokines, chemokines, and chemokine receptors and good immunotherapy efficacy. Finally, AGDGR2 may be sensitive to two drugs (PIK-93 and NPK76-II-72-1) and can be targeted by miR-326. In conclusion, ADGRG2 may serve as a novel biomarker and drug target for HCC diagnosis, immunotherapy, and prognosis and was related to neutrophils and the inflammatory process of liver cancer development.

Abstract Natural products (NPs) are a valuable source for anti-inflammatory drug discovery. However, they are limited by the unpredictability of the structures and functions. Therefore, computational and data-driven pre-evaluation could enable more efficient NP-inspired drug development. Since NPs possess structural features that differ from synthetic compounds, models trained with synthetic compounds may not perform well with NPs. There is also an urgent demand for well-curated databases and user-friendly predictive tools. We presented a comprehensive online web platform (InflamNat, http://www.inflamnat.com/ or http://39.104.56.4/ ) for anti-inflammatory natural product research. InflamNat is a database containing the physicochemical properties, cellular anti-inflammatory bioactivities, and molecular targets of 1351 NPs that tested on their anti-inflammatory activities. InflamNat provides two machine learning-based predictive tools specifically designed for NPs that (a) predict the anti-inflammatory activity of NPs, and (b) predict the compound-target relationship for compounds and targets collected in the database but lacking existing relationship data. A novel multi-tokenization transformer model (MTT) was proposed as the sequential encoder for both predictive tools to obtain a high-quality representation of sequential data. The experimental results showed that the proposed predictive tools achieved an AUC value of 0.842 and 0.872 in the prediction of anti-inflammatory activity and compound-target interactions, respectively.

A simple and efficient vortex-assisted matrix solid phase dispersion with a ultra-high-performance liquid chromatography-triple quadrupole mass spectrometer (VA-MSPD-UHPLC-MS/MS) was applied for simultaneous extraction and determination of seven alkaloids and three organic acids from Uncariae Ramulas Cum Unicis. The optimal extraction conditions of the target components were obtained by Box-Behnken design (BBD) combined with response surface methodology (RSM). The results of the method validation showed that this analytical method displayed good linearity with a correlation coefficient (r) no lower than 0.9990. The recoveries of ten active ingredients from Uncariae Ramulas Cum Unicis ranged from 95.9% to 103% (RSD ≤ 2.77%). The RSDs of intra-day and inter-day precisions were all below 2.97%. The present method exhibited not only lower solvent and sample usage, but also shorter sample processing and analysis time. Consequently, the developed VA-MSPD-UHPLC-MS/MS method could be successfully and effectively used for the extraction and analysis of ten active components from Uncariae Ramulas Cum Unicis.

Carrier aggregation (CA) technology in the Fifth-Generation (5G) network can be considered as a major feature for high-rate data services, which can be combined with the concept of 5G in unlicensed band (5G-U) for data offloading to further enhance the efficiency of the scarce licensed spectral resource. However, the introduction of cellular services in an unlicensed spectrum can force unlicensed band users to silent mode. Meanwhile, the coexistence among different radio access technologies operating on the same frequency band is a complex issue, especially considering the requirement diversity of quality of service (QoS) in networking. In this paper, we propose an optimal resource allocation mechanism to maximize system performance satisfaction for multiple types of services with different QoS requirements in the 5G-U network with a CA feature to address the challenge. Additionally, the performance of services in Wi-Fi system is protected by reserving the minimum spectrum resource when offloading for the 5G-U network. Analytical results on the mean ergodic achievable rate of Wi-Fi services and the mean packet delay of 5G services are provided to maximize the system performance satisfaction. Numerical results demonstrate the effectiveness of the proposed algorithm, wherein optimal spectrum and power allocation can contribute to the maximum system satisfaction in a 5G-U network with a CA feature.

In recent years, the advancement of deep learning technology has led to excellent performance in synthetic aperture radar (SAR) automatic target recognition (ATR) technology. However, due to the interference of speckle noise, the task of classifying SAR images remains challenging. To address this issue, a multi-scale local–global feature fusion network (MFN) integrating a convolution neural network (CNN) and a transformer network was proposed in this study. The proposed network comprises three branches: a CovNeXt-SimAM branch, a Swin Transformer branch, and a multi-scale feature fusion branch. The CovNeXt-SimAM branch extracts local texture detail features of the SAR images at different scales. By incorporating the SimAM attention mechanism to the CNN block, the feature extraction capability of the model was enhanced from the perspective of spatial and channel attention. Additionally, the Swin Transformer branch was employed to extract SAR image global semantic information at different scales. Finally, the multi-scale feature fusion branch was used to fuse local features and global semantic information. Moreover, to overcome the problem of poor accuracy and inefficiency of the model due to empirically determined model hyperparameters, the Bayesian hyperparameter optimization algorithm was used to determine the optimal model hyperparameters. The model proposed in this study achieved average recognition accuracies of 99.26% and 94.27% for SAR vehicle targets under standard operating conditions (SOCs) and extended operating conditions (EOCs), respectively, on the MSTAR dataset. Compared with the baseline model, the recognition accuracy has been improved by 12.74% and 25.26%, respectively. The results demonstrated that Bayes-MFN reduces the inter-class distance of the SAR images, resulting in more compact classification features and less interference from speckle noise. Compared with other mainstream models, the Bayes-MFN model exhibited the best classification performance.

A series of ultra-toughened sustainable blends were prepared from poly(lactic acid) (PLA) and bromobutyl rubber-based ionomers (i-BIIRs) via reactive blending, in which dicumyl peroxide (DCP) and Joncryl®ADR-4440 (ADR) were used as reactive blending additives. The miscibility, phase morphology and mechanical property of the PLA/i-BIIRs blends were thoroughly investigated through DMA, SEM, tensile and impact tests. The influence of different ionic groups and the effects of DCP and ADR on the compatibility between the phases, phase structure and mechanical properties were analyzed. The introduction of the imidazolium-based ionic groups and the reactive agents enable the i-BIIRs play multiple roles as effective compatibilizers and toughening agents, leading to improved interfacial compatibility and high toughness of the blends. The mechanical properties test showed that the PLA/i-BIIRs blends exhibit excellent toughness: impact strength and the elongation at break of AR-OH(30)+AD reached 95 kJ/m2 and 286%, respectively. The impact fracture surface showed the large-scale plastic deformation of the PLA matrix in the blends, resulting in greatly absorbing the impact energy. The results proved that simultaneously applying reactive blend and multiple intermolecular interactions methods is an effective toughening strategy for toughening modification of the PLA blends.

C-N bond formation is one of the most commonly used reactions in medicinal chemistry. Herein, we report an efficient Pd-promoted hydroamination reaction between DNA-conjugated aryl alkenes and a wide scope of aliphatic amines. The described reactions are demonstrated in good to excellent conversions to furnish C (sp3)–N bonds on DNA. This DNA-compatible transformation has strong potentials for the application into DNA-encoded library synthesis.

Pile-raft foundation reinforcement will have a certain impact on the adjacent existing line foundation, which will affect the normal service of the subgrade, and even lead to the instability of the subgrade. Until now, there have been few studies on adjacent construction problems, and there are few field experimental data available for us to consult. Therefore, this study relies on the construction project of Shanghai–Nanjing intercity high-speed railway close to the existing line, using in situ monitoring methods, such as stress shovels, horizontal strain gauges, and inclinometers combined with finite-element calculation and rail-inspection vehicle-data analysis. The stress, displacement, and geometric linearity of the adjacent existing line foundation during the reinforcement construction of a pile-raft foundation were studied. Our aim was to measure the impact and optimize the existing roadbed-protection measures employed during the construction period. We found that the cumulative horizontal displacement of the existing line foundation slope toe during the construction period was 24.25 mm, and the lateral displacement rate was less than 0.59 mm/d. The distance between the two lines was 9 m. The horizontal stress of the foundation soil in the depth range varied according to extrusion and retraction with the different construction stages, and the extrusion stress was less than 10 kPa. The horizontal stress changes in different construction stages were not obvious; the track quality index (TQI) in the existing track inspection during the construction period increased by 129.58%, and the existing track geometric linearity fluctuated greatly. According to the test results, the excavation stage of the subgrade foundation pit was the most dangerous stage of the existing subgrade during the construction of the new line pile-raft foundation. Although the change of the horizontal stress in different construction stages was not obvious, the horizontal displacement of the slope toe was sensitive to the construction process. Therefore, it could be used as a key indicator to monitor the deformation and stability of the existing subgrade. The correction coefficient was obtained by combining the detection data of the track-inspection vehicle with the finite element calculation data, based on which the accurate estimation of the horizontal displacement of the slope toe after the excavation of the foundation pit was realized. The monitoring and evaluation method of the stability of the existing line foundation under the influence of the pile-raft foundation reinforcement construction was preliminarily established through field monitoring and the analysis of the track-inspection data. Based on this method, relevant early warning values were proposed. The test results and engineering measures ensured the safe operation of the existing line foundation. This has important theoretical significance for guiding the construction of a new subgrade of adjacent existing lines.

Recently, the use of semiconductor-based photocatalytic technology as an effective way to mitigate the environmental crisis attracted considerable interest. Here, the S-scheme BiOBr/CdS heterojunction with abundant oxygen vacancies (Vo-BiOBr/CdS) was prepared by the solvothermal method using ethylene glycol as a solvent. The photocatalytic activity of the heterojunction was investigated by degrading rhodamine B (RhB) and methylene blue (MB) under 5 W light-emitting diode (LED) light. Notably, the degradation rate of RhB and MB reached 97% and 93% in 60 min, respectively, which were better than that of BiOBr, CdS, and BiOBr/CdS. It was due to the construction of the heterojunction and the introduction of Vo, which facilitated the spatial separation of carriers and enhanced the visible-light harvest. The radical trapping experiment suggested that superoxide radicals (·O2−) acted as the main active species. Based on valence balance spectra, Mott-Schottky(M-S) spectra, and DFT theoretical calculations, the photocatalytic mechanism of the S-scheme heterojunction was proposed. This research provides a novel strategy for designing efficient photocatalysts by constructing S-scheme heterojunctions and introducing oxygen vacancies for solving environmental pollution.

Hepatocellular carcinoma (HCC) has a high mortality rate worldwide, and there are still many problems in the early diagnosis, molecular targeted therapy, and immunotherapy. It is necessary to explore valuable diagnostic markers and new therapeutic targets in HCC. Zinc finger protein 385A (ZNF385A) and zinc finger protein 346 (ZNF346) represent a unique class of RNA-binding Cys2 His2 (C2H2) zinc finger proteins that are involved in the regulation of cell cycle and apoptosis, but little is known of their roles in HCC. Based on multiple databases and analysis tools, we explored the expression, clinical relation, prognostic value, possible biological function, and pathways of ZNF385A and ZNF346, and their relationship with immune infiltration. Our results revealed that ZNF385A and ZNF346 were highly expressed and were associated with poor prognosis in HCC. Hepatitis B virus (HBV) infection may lead to the overexpression of ZNF385A and ZNF346, which was accompanied by elevated apoptosis and chronic inflammation. Moreover, ZNF385A and ZNF346 were positively correlated with immune-suppressive cells, inflammatory cytokines, immune checkpoint genes, and poor immunotherapy efficacy. Finally, the knockdown of ZNF385A and ZNF346 was observed to negatively affect the proliferation and migration of HepG2 cells in vitro. In conclusion, ZNF385A and ZNF346 are promising candidate biomarkers for the diagnosis, prognosis, and response to immunotherapy in HCC, and this study may help to understand the tumor microenvironment (TME) of liver cancer, and to develop new therapeutic targets.

In the field of adversarial attacks, the generative adversarial network (GAN) has shown better performance. There have been few studies applying it to malware sample supplementation, due to the complexity of handling discrete data. More importantly, unbalanced malware family samples interfere with the analytical power of malware detection models and mislead malware classification. To address the problem of the impact of malware family imbalance on accuracy, a selection feature conditional Wasserstein generative adversarial network (SFCWGAN) and bidirectional temporal convolutional network (BiTCN) are proposed. First, we extract the features of malware Opcode and API sequences and use Word2Vec to represent features, emphasizing the semantic logic between API tuning and Opcode calling sequences. Second, the Spearman correlation coefficient and the whale optimization algorithm extreme gradient boosting (WOA-XGBoost) algorithm are combined to select features, filter out invalid features, and simplify structure. Finally, we propose a GAN-based sequence feature generation algorithm. Samples were generated using the conditional Wasserstein generative adversarial network (CWGAN) on the imbalanced malware family dataset, added to the trainset to supplement the samples, and trained on BiTCN. In comparison, in tests on the Kaggle and DataCon datasets, the model achieved detection accuracies of 99.56% and 96.93%, respectively, which were 0.18% and 2.98% higher than the models of other methods.

Objective: To prepare and validate the chemical structure of the cefotaxime dimer and cefotaxime trimer impurities available from pharmaceutical industry.Methods: A polymer stock solution of cefotaxime sodium was obtained using a concentrated solution degradation method. The cefotaxime dimer and trimer impurities were separated and purified by preparative reversed-high-performance liquid chromatograph (RP-HPLC), and the pure cefotaxime dimer and trimer were obtained by a freeze-drying method. The two impurities were characterized by infrared (IR) spectroscopy, ultraviolet (UV) spectroscopy, mass spectroscopy, and nuclear magnetic resonance (NMR, including one-dimensional and two-dimensional NMR). The groups corresponding to the characteristic absorption peaks in the UV and IR spectra were analyzed, and the 1H and 13C NMR signals were assigned.Results: The cefotaxime dimer was isolated and purified from the actual sample of industrial medicines, and chemical structure of the dimer is the same as in the dimers investigated earlier. The polymerization sites and stereoscopic configuration of trimer impurity was validated for the first time.Conclusion: This study is of great significance for the study of the structures and quality control of polymer impurities in cephalosporin drugs. Promoting the polymerization sites study and providing a technical basis for allergic study of polymer impurities.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a debilitating disease that induces mental stress, lower urinary symptoms, and pelvic pain, therefore resulting in a decline in quality of life. The present diagnoses and treatments still lead to unsatisfactory outcomes, and novel diagnostic and therapeutic modalities are needed. Although our understanding of the etiology and pathophysiology of IC/BPS is growing, the altered permeability of the impaired urothelium, the sensitized nerves on the bladder wall, and the chronic or intermittent sensory pain with inaccurate location, as well as pathologic angiogenesis, fibrosis, and Hunner lesions, all act as barriers to better diagnoses and treatments. This study aimed to summarize the comprehensive information on IC/BPS research, thereby promoting the progress of IC/BPS in the aspects of diagnosis, treatment, and prognosis. According to diverse international guidelines, the etiology of IC/BPS is associated with multiple factors, while the presence of Hunner lesions could largely distinguish the pathology, diagnosis, and treatment of non-Hunner lesions in IC/BPS patients. On the basis of the diagnosis of exclusion, the diverse present diagnostic and therapeutic procedures are undergoing a transition from a single approach to multimodal strategies targeting different potential phenotypes recommended by different guidelines. Investigations into the mechanisms involved in urinary symptoms, pain sensation, and bladder fibrosis indicate the pathophysiology of IC/BPS for further potential strategies, both in diagnosis and treatment. An overview of IC/BPS in terms of epidemiology, etiology, pathology, diagnosis, treatment, and fundamental research is provided with the latest evidence. On the basis of shared decision-making, a multimodal strategy of diagnosis and treatment targeting potential phenotypes for individual patients with IC/BPS would be of great benefit for the entire process of management. The complexity and emerging evidence on IC/BPS elicit more relevant studies and research and could optimize the management of IC/BPS patients.

Generally, ground-coupled floor heat transfer is supposed as annual periodic, which is reasonable for conventional buildings. However, for emergency housing with a short life cycle, the influence of initial soil temperature needs to be considered. In a previous study, the Wiener–Hopf technique was introduced to solve the two-dimensional transient heat transfer equation with mixed Dirichlet and Robin boundary conditions. Based on that, an analytical solution of the dynamic heat transfer equation with initial soil temperature conditions was obtained. Since the solution was in the form of a double integral, its numerical evaluation method was also analyzed to improve computational efficiency. The accuracy and efficiency of the solution were validated by the finite volume method. Then, the effects of initial soil temperatures in different seasons, soil heat conductivities, and floor insulation on ground-coupled heat transfer were discussed. Results showed significant temperature differences between the current solution and the annual periodic solutions (long-time solutions), especially in hot and cold climates. Moreover, the larger the thermal capacity of the soil, the bigger temperature differences occurred. Therefore, this study is expected to provide a theoretical foundation for the indoor environment prediction and optimization design of emergency temporary housing.

Root systems are the key organs through which plants absorb water and nutrients and perceive the soil environment and thus are easily damaged by salt stress. Melatonin can alleviate stress-induced damage to roots. The present study investigated the effects of exogenous melatonin on the root physiology, transcriptome and metabolome of cotton seedlings under salt stress. Salt stress was observed to damage the cell structure and disorder the physiological system of cotton seedling roots. After subjecting melatonin-soaked seeds to salt stress, the activities of SOD, CAT and POD in cotton seedling roots increased by 10–25%, 50–60% and 50–60%, respectively. The accumulation of H2O2 and MDA were significantly decreased by 30–60% and 30–50%, respectively. The contents of soluble sugar, soluble protein and K+ increased by 15–30%, 15–30% and 20–50%, respectively, while the Na+ content was significantly reduced. Melatonin also increased auxin (by 20–40%), brassinosteroids (by 5–40%) and gibberellin (by 5–35%) and promoted melatonin content and root activity. Exogenous melatonin maintained the integrity of root cells and increased the number of organelles. Transcriptomic and metabolomic results showed that exogenous melatonin could mitigate the salt-stress-induced inhibition of plant root development by regulating the reactive oxygen species scavenging system; ABC transporter synthesis; plant hormone signal transduction, endogenous melatonin gene expression; and the expression of the transcription factors MYB, TGA and WRKY33. These results provide a new direction and empirical basis for improving crop salt tolerance with melatonin.

Laterite ore is one of the important sources of nickel (Ni). However, it is difficult to liberate Ni from ore structure during reduction roasting. This paper provided an effective way for a robust recovery of Ni from laterite ore by H2 reduction using sodium thiosulfate (Na2S2O3) as a promoter. . It was found that a Ni content of 9.97% and a Ni recovery of 99.24% were achieved with 20 wt% Na2S2O3 at 1,100°C. The promoting mechanism of Na2S2O3 in laterite ore reduction by H2 was also investigated. The thermogravimetric results suggested the formation of Na2Mg2SiO7, Na2SO3, Na2SO4, and S during the pyrolysis of laterite with Na2S2O3, among which the alkali metal salts could destroy the structures of nickel-bearing silicate minerals and hence release Ni, while S could participate in the formation of the low-melting-point eutectic phase of FeS-Fe. The formation of low-melting-point phases were further verified by the morphology analysis, which could improve the aggregation of Ni-Fe particles due to the capillary forces of FeS-Fe as well as the enhanced element migration by the liquid phase of sodium silicates during reduction.

With the increasing water pollution, traditional treatments cannot sufficiently remove pollutants, thereby prompting the development of photocatalysts. In this study, ZnO–carbon cloth (CC) and spherical ZnO/CdSe–CC heterostructures with different CdSe loadings were synthesized using an ultrasonic-hydrothermal method on flexible CC. Z20CdSe–CC (ZnO with 20 mg CdSe loaded on CC) exhibited the best visible-light-responsive photocatalytic performance, with approximately 83.5% methylene blue reduced in 180 min. In addition, the degradation efficiency of Z20CdSe–CC was maintained at 70.9% after three cycles in relation to that of the ZnO powder. The synergistic effect of CdSe and CC not only effectively widened the light absorption range of ZnO/CdSe–CC but also further promoted the effective transfer of carriers and realized an efficient photocatalytic degradation process. Therefore, the ZnO/CdSe–CC photocatalytic material with CC as the flexible substrate exhibited high photocatalytic activity and stability in environmental remediation. This provides a design idea for the development of an efficient and flexible photocatalytic material in line with the concept of green chemistry.

To solve the problem that small drones in the sky are easily confused with background objects and difficult to detect, according to the characteristics of irregular movement, small size, and changeable shape of drones, using a regional target recognition algorithm, the structure characteristics of Group Convolution (GC) in Resnext50 are absorbed. The optimized GC-faster-RCNN is obtained by improving the Fast-RCNN algorithm and the following methods are performed. First, a clustering method is used to analyze the dataset, and appropriate prior bounding box types are obtained. Second, the Resnext50 is used to replace the original feature extraction network, and the improved channel attention mechanism is integrated into its network output to enhance its feature map information. Then, we calculate its effective receptive field according to the Feature Pyramid Network (FPN) structure and redesign the prior bounding box of the corresponding size to construct a multi-scale detection network for small targets. Experiments show that the algorithm has a recognition accuracy of up to 94.8% under 1080 P image quality, and a recognition speed of 8FPS, which can effectively detect the positions of 1–5 small UAVs in a picture. This method provides an effective positioning detection for low-altitude UAVs.

Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1) Existing models are limited by the calculation method of graph convolution, and their computational cost will increase exponentially as the graph scale grows. (2) Stacking too many convolutional layers causes the over-smoothing issue and neglects the local graph structure. (3) Expanding the range of the neighborhood and the model depth together is difficult due to the orthogonal relationship between them. Inspired by personalized pagerank and auto-encoder, we conduct the node-wise graph clustering task in the undirected simple graph as the research direction and propose a Scalable Deep Network (SDN) for graph clustering via personalized pagerank. Specifically, we utilize the combination of multi-layer perceptrons and linear propagation layer based on personalized pagerank as the backbone network (i.e., the Quasi-GNN module) and employ a DNN module for auto-encoder to learn different dimensions embeddings. After that, SDN combines the two embeddings correspondingly; then, it utilizes a dual self-supervised module to constrain the training of the embedding and clustering process. Our proposed Quasi-GNN module reduces the computational costs of traditional GNN models in a decoupled approach and solves the orthogonal relationship between the model depth and the neighborhood range. Meanwhile, it also alleviates the degraded clustering effect caused by the over-smoothing issue. We conducted experiments on five widely used graph datasets. The experimental results demonstrate that our model achieves state-of-the-art performance.

High-speed railways are built in deep soft-soil foundations interlayered with hard materials. However, the hard layers cannot be penetrated by conventional foundation treatment. Moreover, the project cost is sometimes prohibitive, which is an issue for post-construction subsidence control. The bag grouting pile is a special grouting-pile-reinforcement technique for treating soft-soil foundations interlayered with hard materials. This paper conducted tests on the Ningbo-Taizhou-Wenzhou ballast track passenger railway, combining a conventional mixing-pile-foundation (not through hard layer) processing station and the bag-grouting-pile-processing subgrade line. Using field tests combined with numerical calculation, the lateral displacement, settlement and pile–soil stress were tested to obtain the working properties of the grouting-pile-composite foundation. The variation in the law of lateral displacement, settlement, and pile–soil stress of the foundation was studied. The results showed that the post-construction subsidence of bag-grouting-pile- and conventional mixing-pile-foundation control was less than 15 cm and 30 cm, respectively, which met the design requirements. The results also showed that the design scheme was reasonable (load-sharing ratio of bag-pile-foundation pile soil-based test was reasonable). The numerical calculation showed a change in pile diameter. It also showed that pile spacing could not improve subsidence-control properties, bag grouting pile can promote pore-pressure dissipation and accelerate consolidation, and pile spacing can be increased to cut project costs incurred by the pile-bearing capacity.

REOB (Recycled Engine Oil Bottom) rejuvenated asphalt can solve the problems of environmental pollution and resource waste. In order to improve the performance of REOB rejuvenated asphalt, an SBS modifier was applied. An orthogonal test was carried out to optimize the preparation scheme of SBS/REOB-modified rejuvenated asphalt, and the physical rheological properties and microscopic mechanisms of modified rejuvenated asphalt with different SBS and REOB dosages were studied. The test results show that the optimal mixture of the SBS/REOB-modified and rejuvenated asphalt is 9% REOB + 4.5% SBS, the optimal shear time is 40 min, and the shear rate is 5000 r/min. SBS modification of the REOB rejuvenated asphalt can obtain the same excellent high temperature performance as ordinary SBS-modified asphalt, and the modification process can greatly improve the high-temperature deformation resistance of rejuvenated asphalt. After SBS modification of the REOB rejuvenated asphalt, its S value is significantly lower than the base asphalt and the REOB rejuvenated asphalt, and its m value is also greatly improved compared with the base asphalt and the REOB rejuvenated asphalt, indicating that the low-temperature performance of the modified rejuvenated asphalt is greatly improved compared with the rejuvenated asphalt and is better than the base asphalt. The fluorescence microscopic test revealed that both the modified rejuvenated asphalt and the SBS-modified new asphalt prepared in the laboratory showed a continuous asphalt phase, and the SBS phase of the modified rejuvenated asphalt could basically reach the level of finished modified asphalt.

In this paper, ZnO/NiO composites rich in oxygen vacancies are prepared by the solvothermal method and reduction method. In the test, through the use of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscope (TEM), diffuse reflectance spectroscopy (DRS), photoluminescence spectroscopy (PL), and electron paramagnetic resonance (EPR), we effectively prove the existence of phase, morphology and oxygen vacancies in the material. Through the photocatalysis test and gas sensitivity test, it is found that 10% Ni doped OZN-10 has the best photocatalytic activity and gas sensitivity characteristics. The degradation rate of methylene blue (MB) was 98%. The gas sensitivity test shows that OZN-10 has good selectivity, good response performance (3000 ppm, 27,887%) and excellent response recovery time (response time: 50 s, recovery time: 5–7 s) for saturated NH3 gas at standard atmospheric pressure (101.325 KPa) and room temperature (25 °C). The synergistic effect of oxygen vacancy as the center of a trap and p–n heterojunction forming an electric potential field at the interface is explained, and the mechanism of improving photocatalysis and gas sensitivity is analyzed. This work will provide an innovative vision for dual-performance oxygen vacancy modification of heterojunctions through photocatalysis.

The role of ethanol (C2H5OH) in pitting corrosion behavior of AISI 316L austenitic stainless steel was investigated in aqueous ethanolic solution with chloride. The pitting susceptibility and surface morphology of 316L in a series of ethanol-containing solutions were examined using X-ray photoelectron spectroscopy (XPS), optical microscopy with 3D stitching, immersion tests, and potentiodynamic polarization measurements. Results demonstrated that the ethanol concentration impacted little on the passive film stability while it dramatically influenced the pitting corrosion susceptibility. Corrosion rate of 316L after immersion tests first increased and then decreased as the concentration of ethanol increased from 0 to 10 M in ferric chloride solution. This, however, did not correspond to the breakdown potential which directly decreased from 489 to 249 mV as the water concentration decreased in ethanolic NaCl solutions. The pits density after both immersion and electrochemical tests showed that the initiation of pitting in ethanolic solution tended to occur at multiple points at the same time. The synergy effect on pitting behavior of hydrolysis enhancement and solubility reduction of metal cations due to the introduction of ethanol has also been discussed.

In order to ensure the safety of transmission lines, the use of unmanned aerial vehicle (UAV) images for automatic object detection has important application prospects, such as the detection of birds’ nests. The traditional bird’s nest detection methods mainly include the study of morphological characteristics of the bird’s nest. These methods have poor applicability and low accuracy. In this work, we propose a deep learning-based birds’ nests automatic detection framework—region of interest (ROI) mining faster region-based convolutional neural networks (RCNN). First, the prior dimensions of anchors are obtained by using k-means clustering to improve the accuracy of coordinate boxes generation. Second, in order to balance the number of foreground and background samples in the training process, the focal loss function is introduced in the region proposal network (RPN) classification stage. Finally, the ROI mining module is added to solve the class imbalance problem in the classification stage, combined with the characteristics of difficult-to-classify bird’s nest samples in the UAV images. After parameter optimization and experimental verification, the deep learning-based bird’s nest automatic detection framework proposed in this work achieves high detection accuracy. In addition, the mean average precision (mAP) and formula 1 (F1) score of the proposed method are higher than the original faster RCNN and cascade RCNN. Our comparative analysis verifies the effectiveness of the proposed method.

The standardization of grayscale display is essentially significant for image signal communication, transmission, and terminal reading. The key step of this standardization is establishing a traceable equipment of grayscale. As a relative value, grayscale is transferred to two different absolute values to satisfy different traceability methods, including optical density for hardcopy image and luminance for softcopy. For luminance, a generation equipment is designed to build the relationship between luminance and grayscale. In this work, novel equipment is established using digital light processing (DLP) by time-frequency modulation, and the corresponding uncertainty is analyzed. The experiment result shows that this digital equipment builds the relationship between grayscale and luminance in the range of 0.16-4000 cd/m2. It enables traceable measurement of grayscale to luminance on this equipment with high accuracy and can provide a standardized reference for the display of grayscale images in the fields of medicine, remote sensing, non-destructive testing, etc.

Transplantation of olfactory ensheathing cells (OEC) is a promising therapy in spinal cord injury (SCI) treatment. However, the therapeutic efficacy of this method is unstable due to unknown reasons. Considering the alterations in the culture environment that occur during OEC preparation for transplantation, we hypothesize that these changes may cause variations in the curative effects of this method. In this study, we compared OEC cultured in medium containing different types and concentrations of serum. After purification and passage, the OEC were cultured for 7 days in different media containing 5%, 10%, 15% or 20% fetal bovine serum (FBS) or rat serum (RS), or the cells were cultured in FBS-containing medium first, followed by medium containing RS. In another group, the OEC were first cultured in 10% FBS for 3 days and then cultured with rat spinal cord explants with 10% RS for another 4 days. An MTT assay and P75 neurotrophin receptor immunofluorescence staining were used to examine cell viability and OEC numbers, respectively. The concentration of neurotrophin-3 (NT-3), which is secreted by OEC into the culture supernatant, was detected using the enzyme-linked immunosorbent assay (ELISA). RT-PCR was applied to investigate the NT-3 gene expression in OEC according to different groups. Compared with FBS, RS reduced OEC proliferation in relation to OEC counts (χ2 = 166.279, df = 1, p < 0.01), the optical density (OD) value in the MTT assay (χ2 = 34.730, df = 1, p < 0.01), and NT-3 concentration in the supernatant (χ2 = 242.997, df = 1, p < 0.01). OEC cultured with spinal cord explants secreted less NT-3 than OEC cultured alone (F = 9.611, df = 5.139, p < 0.01). Meanwhile, the order of application of different sera was not influential. There was statistically significant difference in NT-3 gene expression among different groups when the serum concentration was 15% (χ2 = 64.347, df = 1, p < 0.01). In conclusion, different serum conditions may be responsible for the variations in OEC proliferation and function.

A series of sulfamide and triazole benzodiazepines were obtained with the principle of bioisosterism. The p53-murine double minute 2 (MDM2) inhibitory activity and in vitro antitumor activity were evaluated. Most of the novel benzodiazepines exhibited moderate protein binding inhibitory activity. Particularly, triazole benzodiazepines showed good inhibitory activity and antitumor potency. Compound 16 had promising antitumor activity against the U-2 OS human osteosarcoma cell line with an IC50 value of 4.17 μM, which was much better than that of nutlin-3. The molecular docking model also successfully predicted that this class of compounds mimicked the three critical residues of p53 binding to MDM2.

Glioma is the most aggressive and lethal brain tumor in humans. Mutations of mitochondrial DNA (mtDNA) are commonly found in tumor cells and are closely associated with tumorigenesis and progress. However, glioma-specific inhibitors that reflect the unique feature of tumor cells are rare. Here we uncover RC-7, a ruthenium complex with strong red fluorescence, could bind with glioma mtDNA and then inhibited the growth of human glioma cells but not that of neuronal cells, liver, or endothelial cells. RC-7 significantly reduced energy production and increased the oxidative stress in the glioma cells. Administration of RC-7 into mice not only could be observed in the glioma mass of brain by fluorescence imaging, but also obviously prevented the growth of xenograft glioma and prolonged mouse survival days. The findings suggested the theranostic application of a novel type of complex through targeting the tumor mtDNA.

In visual diffuse transmission density measurement, it is hard to measure optical density (OD) up to 6.0 because the signal to noise is more than 10E-06. Thus, there are only two methods to find the measurement. One is using a highly sensitive detector with low background noise, and the other is improving the incident light flux with a wide spectrum, including visual scope. A new diffuse optics emitter was designed to realize OD measurements up to 6.0. It uses 235 optical fibers on a hemisphere to collect and feed in the incident flux, then emits this flux by a diffuse opal. Thus, an incident light with a high diffuse coefficient and high incident flux was realized for high OD measurement. This emitter has been used in the new national reference of National Institute of Metrology, China (NIM) for diffuse transmission optical density. According to the measurement result in this reference, the OD can be measured up to 6.6.

Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences.

Buildings that are designed with aggressive energy performance targets are defined broadly in this study as high-performance buildings. As the technology advances, some of these buildings have the potential to become zero-energy ready through the adoption of cost-effective measures, such as retro-commissioning and occupant behavior techniques. This study demonstrated the viability of an office building to achieve the zero-energy goal and intended to engage the owners of similar facilities. The case building was designed as a very low-energy building with an energy use intensity (EUI) goal of 42 kWh/(m2 a), and the actual EUI was 23.9 kWh/(m2 a). The calibrated simulation approach was employed in the study, and the results indicated that the case building can achieve the zero-energy goal by optimizing the controls of the HVAC (Heating, Ventilation and Air Conditioning) system, changing the occupant behavior and improving the performance of the photovoltaic system.

The purpose of this study was to investigate the possible protective effect of N-acetylserotonin (NAS) against acute hepatic ischemia-reperfusion (I/R) injury in mice. Adult male mice were randomly divided into three groups: sham, I/R, and I/R + NAS. The hepatic I/R injury model was generated by clamping the hepatic artery, portal vein, and common bile duct with a microvascular bulldog clamp for 30 min, and then removing the clamp and allowing reperfusion for 6 h. Morphologic changes and hepatocyte apoptosis were evaluated by hematoxylin-eosin (HE) and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining, respectively. Activated caspase-3 expression was evaluated by immunohistochemistry and Western blot. The activation of aspartate aminotransferase (AST), malondialdehyde (MDA), and superoxide dismutase (SOD) was evaluated by enzyme-linked immunosorbent assay (ELISA). The data show that NAS rescued hepatocyte morphological damage and dysfunction, decreased the number of apoptotic hepatocytes, and reduced caspase-3 activation. Our work demonstrates that NAS ameliorates hepatic IR injury.

In the last decade, great advances have been made in epidermal stem cell studies at the cellular and molecular level. These studies reported various subpopulations and differentiations existing in the epidermal stem cell. Although controversies and unknown issues remain, epidermal stem cells possess an immune-privileged property in transplantation together with easy accessibility, which is favorable for future clinical application. In this review, we will summarize the biological characteristics of epidermal stem cells, and their potential in orthopedic regenerative medicine. Epidermal stem cells play a critical role via cell replacement, and demonstrate significant translational potential in the treatment of orthopedic injuries and diseases, including treatment for wound healing, peripheral nerve and spinal cord injury, and even muscle and bone remodeling.