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Introduction: We aimed to describe a case of bilateral keratoconjunctivitis after exposure to the toxic sap of Euphorbia lathyris. Case Report: A 76-year-old gentleman presented after exposure to E. lathyris whilst he was gardening. He had 6/12 visual acuity in his right eye, and 6/4 in his left. Examination revealed marked periocular dermatitis, conjunctival injection and corneal oedema in the right eye with diffuse punctate epithelial staining. He was treated with ocular irrigation, topical steroids, antibiotics, cycloplegics and lubricants. Over 48 h, his left eye started to become symptomatic. He developed bilateral corneal epithelial defects and anterior chamber inflammation. His visual acuity worsened to 6/36 right and 6/24 left. At his 3-week follow-up, there was marked improvement in the resolution of the toxic keratoconjunctivitis in both eyes. Conclusion: Toxic sap from E. lathyris can cause severe keratoconjunctivitis. Irrigation of both eyes despite unilateral symptoms and early follow-up should be considered signs of toxicity may only become evident after 24–48 h.

The bonding performance of CFRP-steel joints between carbon fiber reinforced polymer （CFRP） plate and steel is seriously affected by marine environment. In order to evaluate the bonding strength of CFRP-steel joints exposed to marine environment， a double-lap shear joint on five specinmens with different bonding lengths were corroded for 7 to 21 days in the NaCl solution. The technique of digital image correlation （DIC） was applied to measure the normal strain distribution on the surface of CFRP. Scanning electron microscope （SEM） and energy dispersive spectrometer （EDS） were used to observe microscopic information and monitor element content changes of CFRP surface. The results show that the contents of Na+ and Cl- on the CFRP surface are increased with the increase of corrosion period， which induces charge compensation. Electrolyte penetration into the interface leads to free volume expansion， resulting in a decrease in the interfacial binding strength. The adhesion failure at the interface of adhesive and CFRP is observed in the experiment. The corrosion is aggravated with the increase of time， leading to the decrease of the ultimate load and the ductility. The interface ductility decreases after corroding for 7 to 14 days and the strain softening disappears and exhibits characteristics of the brittle failure after corroding for 21 days. The finite element model was established based on the Fickian theory and bonding-slip constitutive parameters.The diffusion characteristics of seawater between bonding interfaces and the interface typeⅡ delamination process were simulated，and the numenical results are in good agreement with the expermental results.

Abstract With the aim to enhance prediction accuracy for nonlinear time series, this paper put forward an improved deep Echo State Network based on reservoir states reconstruction driven by a Self-Normalizing Activation (SNA) function as the replacement for the traditional Hyperbolic tangent activation function to reduce the model’s sensitivity to hyper-parameters. The Strategy was implemented in a two-state reconstruction process by first inputting the time series data to the model separately. Once, the time data passes through the reservoirs and is activated by the SNA activation function, the new state for the reservoirs is created. The state is input to the next layer, and the concatenate states module saves. Pairs of states are selected from the activated multi-layer reservoirs and input into the state reconstruction module. Multiple input states are transformed through the state reconstruction module and finally saved to the concatenate state module. Two evaluation metrics were used to benchmark against three other ESNs with SNA activation functions to achieve better prediction accuracy.

Food reserves are the “reservoir” and "regulator" for maintaining national food security. Considering multiple factors comprehensively such as the different degrees of self-sufficiency in grain production and population density, this paper selects three countries, namely the United States, Japan, and India, as cases to systematically sort out and compare the management experience and practices of governments in the reserve system, functional positioning, and collaborative mechanism of grain reserves, and summarized the characteristics of government grain reserve management in various countries, which can guide market expectations, stabilize domestic markets. Therefore, it is of great significance to enhance the ability and resilience of reserves to respond to major risks and challenges. To further clarify the logical relationship between various types of government grain reserves in China, and continuously improve the collaborative mechanism of government grain reserve management, this provides valuable optimization ideas for reference.

Summary:. The displacement of a lost contact lens into the upper fornix is an uncommon occurrence. This case report presents a rare incidence wherein a patient diagnosed with hemifacial atrophy was found to have concealed five soft contact lenses within the left upper fornix during autologous fat injection into the retrobulbar space.

Abstract Background The increasing number of chronic diseases consumes a large amount of health resources and puts a huge burden on health service system. The integrated management of chronic diseases in Sanming City aims to improve the efficiency and quality of chronic disease management through the collaboration between different levels of medical institutions. Aim The aim of the present study was to use the theory of planned behaviour (TPB) to examine the intention and behaviours of interinstitutional collaboration in chronic disease management (ICCDM) among healthcare personnel. Methods A cross-sectional study of 274 health care personnel was conducted in medical institutions in Fujian Province, China, from March 2022 to April 2022. A self-administered questionnaire based on TPB theory was applied to measure the participants’ ICCDM behaviours. Results The proposed TPB model revealed that attitude was significantly and positively associated with behaviour intention, and behaviour intention and perceived behavioural control were significant predictors of ICCDM behaviour. Conclusion TPB provides insights into ICCDM behaviour. Due to the fact that attitude, perceived behavioural control, and behavioural intention towards ICCDM behaviour were demonstrated to be significant predictors of ICCDM behaviour, these factors may be a promising focus of ICCDM interventions in the integrated management of chronic diseases in China.

Deep tooth decay approaching the pulp may develop into pulpitis; to prevent this, pulp cells need to balance the rapid immune response to avoid rapid swelling of the pulp. Current treatment of deep decay that approaches the pulp involves the application of drugs that induce low‐level inflammation in the dental pulp to promote its repair, but this treatment is sometimes insufficient. However, the unsuccessful treatment often resulted in pulpitis. The C5a‐C5aR is the initial stage of the immune cascade response. Blocking the binding of C5a‐C5aR can slow the immune response in the narrow pulp cavity, so that dental pulp cells have enough time to proliferate, migrate, and differentiate. In this study, we compared lipoteichoic acid (LTA) and lipopolysaccharides (LPS) at different concentrations and time points and used the C5aR antagonist W54011 to block the C5a‐C5aR axis. The blocking effect was detected by analyzing the expression of C5a, C5aR, interleukin (IL)‐6, and Toll‐like receptors 2 and 4 (TLR‐2, 4). Next, we determined the optimal concentration and duration of LTA and LPS treatment in combination with W54011. Based on our results, we selected 1.0 μg·mL−1 LPS treatment for 48 h to generate an inflammatory model of human dental pulp cells. We then regrouped the cells and conducted expression analyses to monitor the expression of C5a, C5aR, IL‐6, and TLR‐4 at the protein and mRNA levels. LPS stimulation for 48 h and treatment with W54011 for 48 h effectively inhibited inflammation and did not affect C5a expression. This study provides a basis for follow‐up studies of W54011 in dental pulp cells.

Abstract Sudden sensorineural hearing loss is a common and frequently occurring condition in otolaryngology. Existing studies have shown that sudden sensorineural hearing loss is closely associated with mutations in genes for inherited deafness. To identify these genes associated with deafness, researchers have mostly used biological experiments, which are accurate but time-consuming and laborious. In this paper, we proposed a computational method based on machine learning to predict deafness-associated genes. The model is based on several basic backpropagation neural networks (BPNNs), which were cascaded as multiple-level BPNN models. The cascaded BPNN model showed a stronger ability for screening deafness-associated genes than the conventional BPNN. A total of 211 of 214 deafness-associated genes from the deafness variant database (DVD v9.0) were used as positive data, and 2110 genes extracted from chromosomes were used as negative data to train our model. The test achieved a mean AUC higher than 0.98. Furthermore, to illustrate the predictive performance of the model for suspected deafness-associated genes, we analyzed the remaining 17,711 genes in the human genome and screened the 20 genes with the highest scores as highly suspected deafness-associated genes. Among these 20 predicted genes, three genes were mentioned as deafness-associated genes in the literature. The analysis showed that our approach has the potential to screen out highly suspected deafness-associated genes from a large number of genes, and our predictions could be valuable for future research and discovery of deafness-associated genes.

The water pump is a piece of crucial electromechanical equipment to ensure the safety of tunnels. Therefore, it’s essential to master the performance trend of pumps to prevent the occurrence of failure. In this paper, essential information and failure records of pumps in 15 operating tunnels for many years were collected. According to the data characteristics, a data-filling model based on XGBoost is developed to address the issue of the censored data. Considering that most pumps are still in operation, a failure prediction model based on Random Survival Forest (RSF) is designed by incorporating survival analysis principles. The proposed Pump Failure Trend Prediction Model (PFTPM) overcomes difficulties caused by the lack of previous data and the small number of old pumps. We identify two phases of failure: the first phase exhibits a bathtub-shaped failure rate curve, while the second phase is characterized by a lower failure risk. The importance of considering rainfall, pump operating time, and performance changes for effective maintenance planning is emphasized. Furthermore, we summarize the failure evolution law of various types of pumps to amend maintenance cycle in the existing specification. Overall, this paper integrates innovative big-data technologies into the traditional maintenance data of tunnel pumps.

Information hiding is an important technique for security-related applications, and it can be used wildly in many scenarios. Secret data embedding methods are proposed in spatial, frequency, and other domains by researchers in recent years. After investigating the existing information hiding schemes, we find that there is still room to improve the data embedding performance. Inspired by some works of literature, we propose a new scheme based on the modulus function with Minimum Pixel Modification (MPM22) for hiding secret data into carriers. The main idea of the scheme is to embed confidential bits into cover-image pixels while minimizing modification to original pixels. The correctness and high performance of the scheme are proved in mathematics and comprehensive experiments. Experimental result shows MPM22 has the largest embedding capacity (3 36.1 dB@k=3, PSNR>30.5 dB @k=4)。 The experimental result also confirms that our proposed scheme has higher performance than other schemes.

Abstract Background This study aimed to assess the fertility intentions of young people after the announcement of the three–child policy in China and to determine whether knowledge about reproductive, maternal, newborn, and child health (RMNCH) services or support, childbearing- and childbirth-related anxiety, and parenthood–related anxiety influence fertility intentions. Methods A cross-sectional Internet-based survey was conducted on a nationwide sample of young people aged 18 to 28 years old in education institutions. Factors associated with fertility intentions were analysed using partial least squares structural equation modelling (PLS-SEM). Results Only 4.2% of males and 1.7% of females intended to have three children or more. On the whole, the majority (40.3%) reported the intention to have two children. The mean and standard deviation (SD) for the total knowledge RMNCH support and/or services knowledge score was 9.5 (SD ± 8.9), out of a possible score of 39. The median and interquartile range (IQR) of childbearing– and childbirth-related anxiety score was 8.0 (IQR = 6.0–9.0), out of a possible score of 10. The median and IQR of parenthood–related anxiety score among the males was 6.0 (IQR = 4.0–9.0) and for females was 7.0 (IQR = 5.0–9.0). Results from PLS-SEM revealed that a higher level of knowledge of RMNCH support and/or services is significantly associated with higher fertility intentions. Both childbearing- and childbirth-related anxiety and parenthood–related anxiety were inversely associated with fertility intentions. Conclusion Raising awareness about RMNCH supportive measures and easing birth- and parenting anxiety are imperative to enhance birth rates. Future policies should pay more attention to these determinants to achieve their intended goal of boosting population growth.

The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model’s applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications.

The Super Tau Charm Facility (STCF) proposed in China is a newgeneration electron–positron collider with center-of-mass energies covering 2-7 GeV and a peak luminosity of 0.5 1035 cm−2 s−1. The offline software of STCF (OSCAR) is developed to support the offline data processing, including detector simulation, reconstruction, calibration as well as physics analysis. To meet STCF’s specific requirements, OSCAR is designed and developed based on the SNiPER framework, a lightweight common software for HEP experiments. Besides the commonly used software such as Geant4 and ROOT, several state-ofthe-art software packages and tools in the HEP community are incorporated as well, such as the Detector Description Toolkit (DD4hep), the plain-old-data I/O (podio) and Intel Thread Building Blocks (TBB) etc. This paper will present the overall design as well as some implementation details of OSCAR, including the event data management, paralleled data processing based on SNiPER and TBB as well as the geometry management system based on DD4hep. Currently, OSCAR is fully functioning to facilitate the conceptual design of the STCF detector and the study of its physics potential.

Detector studies for future experiments rely on advanced software tools to estimate performance and optimize their design and technology choices. The Key4hep project provides a flexible turnkey solution for the full experiment life-cycle based on established community tools such as ROOT, Geant4, DD4hep, Gaudi, podio and spack. Members of the CEPC, CLIC, EIC, FCC, and ILC communities have joined to develop this framework and have merged, or are in the progress of merging, their respective software environments into the Key4hep stack. These proceedings will give an overview over the recent progress in the Key4hep project: covering the developments towards adaptation of state-of-theart tools for simulation (DD4hep, Gaussino), track and calorimeter reconstruction (ACTS, CLUE), particle flow (PandoraPFA), analysis via RDataFrame, and visualization with Phoenix, as well as tools for testing and validation.

Track reconstruction is one of the most important and challenging tasks in the offline data processing of collider experiments. For the BESIII detector working in the tau-charm energy region, plenty of efforts were made previously to improve the tracking performance with traditional methods, such as template matching and Hough transform etc. However, for difficult tracking tasks, such as the tracking of low momentum tracks, tracks from secondary vertices and tracks with high noise level, there is still large room for improvement. In this contribution, we demonstrate a novel tracking algorithm based on machine learning method. In this method, a hit pattern map representing the connectivity between drift cells is established using an enormous MC sample, based on which we design an optimal method of graph construction, then an edgeclassifying Graph Neural Network is trained to distinguish the hit-on-track from noise hits. Finally, a clustering method based on DBSCAN and RANSAC is developed to cluster hits from multiple tracks. Track fitting algorithm based on GENFIT2 is also studied to obtain the track parameters, where deterministic annealing filter are implemented to deal with ambiguities and potential noises. The preliminary results on BESIII MC sample presents promising performance, showing potential to apply this method to other trackers based on drift chamber as well, such as the CEPC and STCF detectors under pre-study.

Particle identification (PID) is one of the most fundamental tools in various physics research conducted in collider experiments. In recent years, machine learning methods have gradually become one of the mainstream methods in the PID field of high-energy physics experiments, often providing superior performance. The emergence of quantum machine learning may potential arm a powerful new toolbox for machine learning. In this work, targeting at the π±/K± discrimination problem at the STCF experiment, a convolutional neural network (CNN) in the endcap PID system is developed. By combining the hit position and arrival time of each Cherenkov photon at the sensors, a two-dimensional pixel map is constructed as the CNN input. The preliminary results show that the CNN model has a promising performance. In addition, based on the classical CNN, a quantum convolution neural network (QCNN) is developed as well, exploring possible quantum advantages provided by quantum machine learning methods.

In recent years, quantum computing shows significant potentials in many areas. In this proceeding, we revisit the observation of the Zc(3900) resonance with quantum machine learning techniques, specifically quantum support vector machine (QSVM). Meanwhile, the outcomes are compared with classical support vector machine (SVM) method. With the IBM Qiskit toolkit, the QSVM method achieves a competitive signal and background classification accuracy compared to classical methods. This study emphasizes the potential of quantum machine learning in high-energy physics research, and it reveals the feasibility of applying quantum computing in future physics data analysis.

The STCF physics program will provide an unique platform for studies of hadron physics, strong interactions and searches for new physics beyond the Standard Model in the τ-charm region. To deliver those physics programs, the charged particles at STCF are required to be reconstructed with high efficiency and excellent momentum resolution. In particular, charged particles with transverse momentum down to 50 MeV are required to be reconstructed. The tracking performance at STCF is studied using A Common Tracking Software (ACTS) based on the information of the STCF tracking system, a µRWELL-based inner tracker and a drift chamber. We demonstrated the first application of ACTS for a drift chamber. The implementation and tracking performance are presented.

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to measure the neutrino mass ordering (NMO) using a 20-kton liquid scintillator (LS) detector. Besides the precise measurement of the reactor neutrino’s oscillation spectrum, an atmospheric neutrino oscillation measurement in JUNO offers independent sensitivity for NMO, which can potentially increase JUNO’s total sensitivity in a joint analysis. In this contribution, we present a novel multi-purpose reconstruction method for atmospheric neutrinos in JUNO at few-GeV based on a machine learning technique. This method extracts features related to event topology from PMT waveforms and uses them as inputs to machine learning models. A preliminary study based on the JUNO simulation shows good performances for event directionality reconstruction and neutrino flavor identification. This method also has a great application potential for similar LS detectors.

BESIII is designed to study physics in the τ-charm energy region utilizing the high luminosity BEPCII. For collision physics experiments like the BESIII experiment, particle identification (PID) is one of the most important and commonly used tools for physics analysis. The effective µ/π identification performance is of great significance for most of BESIII physics analysis. However, due to the close masses of these two particles, as well as the intrinsic correlation between multiple detector information, traditional methods at BESIII is facing challenges in µ/π identification. In recent decades, machine learning (ML) techniques have been rapidly developed and have shown successful applications in HEP experiments. The PID based on ML provides powerful capability of combining more detection information from all sub-detectors with the data-driven approach. In this article, targeting at the µ/π identification problem at the BESIII experiment, we have developed a new PID algorithm based on the gradient boosted decision tree (BDT) model. Preliminary results show that the XGBoost classifier provides obviously higher discrimination power than traditional methods. In addition, based on the substantial amount of high-quality data taken by the BESIII detector, a method of evaluating and suppressing the systematical error of the ML model is also introduced, which is critical for applying the model to physics studies.

CH4 is one of the most important greenhouse gases responsible for climate change. The effect of algal decomposition on CH4 emission fluxes in aquaculture environments is unknown. This study determined the relative abundance of methanogens in aquaculture in northern China. We determined various parameters in the water and sediment after the decomposition of Chaetomorpha valida. The results showed that the relative abundance of methanogens increased significantly during the decomposition of C. valida. The average CH4 emission flux was 50.64 mg m-2 h-1 when the density of C. valida was 25 mg cm-3, which was 11 times higher than that at a density of 0 mg cm-3. The concentrations of dissolved oxygen (DO), nitrite, nitrate and sulfate in the aquaculture pond significantly decreased during the decomposition of C. valida. The formation of a strong anaerobic reduction environment promoted the formation and release of CH4. Our results confirmed that the decomposition of C. valida enhanced the CH4 emission flux. The average CH4 emission fluxes ranged from 4.62 to 50.64 mg m-2 h-1. It is particularly important to remove decomposed algae in a timely manner and control greenhouse gas emissions from aquaculture ponds to mitigate climate change.

Talaromyces amestolkiae is an important fungal species owing to its ubiquity in soils, plants, air, and food. In this study, we identified a novel six-segmented polymycovirus, Talaromyces amestolkiae polymycovirus 1 (TaPmV-1). Each of the double-stranded (ds) RNA segments of TaPmV-1 contained a single open reading frame, and the proteins encoded by dsRNA1, dsRNA2, dsRNA3, and dsRNA 5 shared significant amino acid identities of 56, 40, 47, and 43%, respectively, with the corresponding proteins of Aspergillus fumigatus polymycovirus-1(AfuPmV-1). DsRNA1, dsRNA3, and dsRNA5 of TaPmV-1 encoded an RNA-dependent RNA polymerase (RdRp), a viral methyltransferase, and a PAS-rich protein, respectively. The functions of the proteins encoded by dsRNA2, dsRNA4, and dsRNA6 have not been elucidated. Comparison of the virus-infected strain LSH3 with virus-cured strain LSHVF revealed that infection with TaPmV-l may reduce the production of red pigments and induce the clustering of fungal sclerotia. Furthermore, transcriptomic analyses demonstrated that infection with TaPmV-l downregulated the expression of transcripts related to metabolism, and may correlate with the reduced production of red pigments and clustering of sclerotia in T. amestolkiae. These results of this study provide novel insights into the mechanism of fungal gene regulation by polymycovirus infections at the transcriptome level, and this study is the first to report a novel polymycovirus of T. amestolkiae.

Objective To investigate the expression and clinical significance of S100A14 and LOXL2 in papillary thyroid carcinoma tissues. Methods Paraffin blocks from 90 cases of thyroid lesion were collected to make tissue microarray. The expression of S100A14 and LOXL2 in 30 cases of nodular goiter (NG), 30 cases of classic papillary thyroid carcinoma (CPTC) and 30 cases of high invasion papillary thyroid carcinoma were detected by immunohistochemistry. The correlation of S100A14 and LOXL2 expression with the clinicopathological characteristics of papillary thyroid carcinoma patients was investigated. Results S100A4 and LOXL2 were highly expressed in papillary thyroid carcinoma. There was a gradually increasing trend from the expression in NG group, CPTC group to high invasive group. The positive rate of S100A14 in papillary thyroid carcinoma with lymph node metastasis was higher than that without lymph node metastasis (P < 0.05). There was significant difference of S100A14 and LOXL2 expression between CPTC group and high invasion group (P < 0.05). S100A14 expression was positively correlated with LOXL2 expression (r=0.332). Conclusion The high expression of S100A14 and LOXL2 may be related to the malignant biological behavior and invasiveness of papillary thyroid carcinoma, and S100A14 may promote lymph node metastasis.

Objective To assess the willingness and factors influencing the choice of primary healthcare (PHC) institutions among patients with chronic conditions in China.Design A nationwide population-based study with binary logistic regression was conducted and used to estimate the ORs of the influencing factors of health-seeking at PHC institutions using the Anderson model as a theoretical framework.Setting The China Family Panel Studies (CFPS) database.Participants The study sample included 7967 patients with chronic conditions identified from the 2016 and 2018 CFPS databases.Results From 2016 to 2018, the rate of choosing PHC institutions for patients with chronic conditions dropped from 51.0% to 47.7%. The logistic regression results showed that patients with low family income (OR value of >60 000 group was 0.57, 95% CI 0.43 to 0.74), low education level (OR value of bachelor degree or above was 0.54, 95% CI 0.35 to 0.83;), older age (OR value of >65 group was 1.31, 95% CI 1.08 to 1.60;), hypertension and diabetes (OR 1.26, 95% CI 1.13 to 1.41), living in rural areas (OR value of urban was 0.47, 95% CI 0.38 to 0.60), immigrating from rural to urban areas (OR 1.64, 95% CI 1.26 to 2.13), reporting good health (OR value of very good was 1.33, 95% CI 1.05 to 1.68) and those from areas with a high proportion of PHC institutions (OR 1.05, 95% CI 1.02 to 1.07) were more inclined to choose PHC institutions. Conversely, patients with urban employee health insurance (OR 0.62, 95% CI 0.49 to 0.80) and more than one chronic disease (OR 0,83, 95% CI 0.75 to 0.92) preferred choosing a hospital.Conclusions The patients’ willingness to choose PHC institutions was low. The health-seeking preference of patients with chronic conditions is derived from medical needs and is influenced by the predisposing factors and tendencies of enabling resources. Measures should be taken to improve the capacity of PHC institutions.

Hypertrophic scar causes serious functional and cosmetic problem, but no treatment method is known to achieve a satisfactory therapeutic effect. However, mesenchymal stem cells show a possible cure prospect. Here, we investigated the effect of interleukin-10-modified adipose-derived mesenchymal stem cells (IL-10-ADMSC) on the formation of hypertrophic scar. In vitro, IL-10-ADMSC could highly express IL-10 and exhibited stronger inhibition of hypertrophic scar fibroblasts (HSFs) proliferation, migration, and extracellular matrix synthesis (the expression of collagen I, collagen III, FN, and α-SMA protein) than ADMSC. In vivo, we found that IL-10-ADMSC speeded up wound healing time and reduced scar area and scar outstanding height. Same as in vitro, IL-10-ADMSC also exhibited stronger inhibition of extracellular matrix synthesis (the expression of collagen I, collagen III protein) in wound than ADMSC. In addition, we also found that IL-10-ADMSC is also a stronger inhibitory effect on inflammation in wound than ADMSC, and IL-10-ADMSC inhibited TGF-β/Smads and NF-κB pathway. In conclusion, IL-10-ADMSC demonstrated the ability to prevent hypertrophic scar formation. And its possible molecular mechanism might be related to IL-10-ADMSC inhibiting the proliferation and migration of the synthesis of extracellular matrix of HSFs, and IL-10-ADMSC inhibited the inflammation during the wound healing.

TOD (transit-oriented development) is a planning concept that uses public transportation stations as the center of development, and it aims to integrate land use efficiency and transportation planning linkages to encourage the use of public transportation. The impact of metro TOD projects on urban transportation is multifaceted and complex, and the promotion of metro TOD ridership is an important topic in academic circles. However, the theoretical analysis framework of the impact mechanism of metro TOD ridership is still not perfect. Most studies ignore the TOD characteristics of the stations and the interaction between the station area’s land use and the station area functional linkage. Moreover, a few studies have focused on the mechanisms of the impact of TOD built environment factors on the spatial differentiation of station ridership, and the interactive effects of built environment factors. In this paper, the factors of a metro TOD station built environment were selected based on the node–place–linkage model expanded by the 5D principle of TOD, and a solution is provided for the computable transformation of the 5D principle. The GeoDetector method was used to detect the individual and interactive effects of the TOD built environment factors. The results show that the spatial distribution of the metro TOD station area ridership shows a core–peripheral structure and spatial heterogeneity, both on weekdays and weekends. Moreover, the individual effects of each factor can explain up to 49% and 35% of the traffic distribution on weekdays and weekends, respectively. In addition, the two-factor interactive effect has a stronger influence on metro ridership. The interactive effect can explain up to 72% and 77% of the traffic distribution on weekdays and weekends, respectively. Furthermore, the individual effects of each factor exhibited spatial heterogeneity in the local spaces, showing spatial facilitation and inhibition, respectively. Finally, the main policy recommendations are as follows: One of the important ways to guide the development of cities toward polycentric structure is to promote a TOD model in the peripheral areas of the cities. Building more public open spaces in TOD station areas and improving the collection and distribution capacity of the bus transport systems can effectively stimulate the ridership of metro stations.

The immune response to COVID-19 infection is variable. How COVID-19 influences clinical outcomes in hospitalized patients needs to be understood through readily obtainable biological materials, such as blood. We hypothesized that a high-density analysis of host (and pathogen) blood RNA in hospitalized patients with SARS-CoV-2 would provide mechanistic insights into the heterogeneity of response amongst COVID-19 patients when combined with advanced multidimensional bioinformatics for RNA. We enrolled 36 hospitalized COVID-19 patients (11 died) and 15 controls, collecting 74 blood PAXgene RNA tubes at multiple timepoints, one early and in 23 patients after treatment with various therapies. Total RNAseq was performed at high-density, with >160 million paired-end, 150 base pair reads per sample, representing the most sequenced bases per sample for any publicly deposited blood PAXgene tube study. There are 770 genes significantly altered in the blood of COVID-19 patients associated with antiviral defense, mitotic cell cycle, type I interferon signaling, and severe viral infections. Immune genes activated include those associated with neutrophil mechanisms, secretory granules, and neutrophil extracellular traps (NETs), along with decreased gene expression in lymphocytes and clonal expansion of the acquired immune response. Therapies such as convalescent serum and dexamethasone reduced many of the blood expression signatures of COVID-19. Severely ill or deceased patients are marked by various secondary infections, unique gene patterns, dysregulated innate response, and peripheral organ damage not otherwise found in the cohort. High-density transcriptomic data offers shared gene expression signatures, providing unique insights into the immune system and individualized signatures of patients that could be used to understand the patient’s clinical condition. Whole blood transcriptomics provides patient-level insights for immune activation, immune repertoire, and secondary infections that can further guide precision treatment.

Three chemical series of heterocyclic small molecules were designed and synthesized as the 3rd generation EGFR inhibitors. Detailed structure-activity relationship (SAR) studies and lead optimization (from 1a to 4d) were descripted. With the support of molecular modeling and evaluation through biochemical and cellular assays, liver microsomal/ plasma/ blood stability studies, preliminary rat PK screening and in vivo animal models, pyrrolopyrimidine-based compound 4d (abivertinib, AC0010), a novel potent and highly selective irreversible EGFR inhibitor, was discovered.Unlabelled ImageThis compound has been being clinically investigated in several countries including China, the United States, and Europe. Structurally distinct from those irreversible pyrimidine-based EGFR inhibitors (rociletinib, and recently approved osimertinib), abivertinib (AC0010) has demonstrated significant clinical benefit with good safety profiles (through a different metabolic pathway and possibly different resistance mechanism compared to both osimertinib and rociletinib) in NSCLC patients who had progressed on previous therapy, which provides an alternative therapeutic agent for NSCLC patients who develop acquired resistance to first generation EGFR TKIs.