Spatial-temporal combination and multi-head flow-attention network for traffic flow prediction.

Traffic flow prediction based on spatial-temporal data plays a vital role in traffic management. However, it still faces serious challenges due to the complex spatial-temporal correlation in nonlinear spatial-temporal data. Some previous methods have limited ability to capture spatial-temporal correlation, and ignore the quadratic complexity problem in the traditional attention mechanism. To this end, we propose a novel spatial-temporal combination and multi-head flow-attention network (STCMFA) to model the spatial-temporal correlation in road networks. Firstly, we design a temporal sequence multi-head flow attention (TS-MFA), in which the unique source competition mechanism and sink allocation mechanism make the model avoid attention degradation without being affected by inductive biases. Secondly, we use GRU instead of the linear layer in traditional attention to map the input sequence, which further enhances the temporal modeling ability of the model. Finally, we combine the GCN with the TS-MFA module to capture the spatial-temporal correlation, and introduce residual mechanism and feature aggregation strategy to further improve the performance of STCMFA. Extensive experiments on four real-world traffic datasets show that our model has excellent performance and is always significantly better than other baselines.

Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Monocyte-to-Lymphocyte Ratio (MLR) as Biomarkers in Diagnosis Evaluation of Acute Exacerbation of Chronic Obstructive Pulmonary Disease: A Retrospective, Observational Study.

Purpose: Hierarchical management is advocated in China to effectively manage chronic obstructive pulmonary disease (COPD) patients and reduce the incidence and mortality of acute exacerbation of COPD (AE-COPD). However, primary and community hospitals often have limited access to advanced equipment and technology. Complete blood count (CBC), which is commonly used in these hospitals, offers the advantages of being cost-effective and easily accessible. This study aims to evaluate the significance of routine blood indicators in aiding of diagnosing AE-COPD. Patients and Methods: In this research, we enrolled a total of 112 patients diagnosed with AE-COPD, 92 patients with stable COPD, and a control group comprising 60 healthy individuals. Clinical characteristics, CBC parameters, and serum CRP levels were collected within two hours. To assess the associations between NLR/PLR/MLR and CRP by Spearman correlation test. The diagnostic accuracy of NLR, PLR and MLR in AE-COPD was assessed using Receiver Operating Characteristic Curve (ROC) and the area under the curve (AUC). Binary Logistic Regression analysis was conducted for the indicators of NLR, PLR and MLR. Results: We found that patients with AE-COPD had significantly higher levels of NLR, PLR and MLR in contrast to patients with stable COPD. Additionally, the study revealed a noteworthy correlation between CRP and NLR (rs=0.5319, P<0.001), PLR (rs=0.4424, P<0.001), and MLR (rs=0.4628, P<0.001). By utilizing specific cut-off values, the amalgamation of NLR, PLR and MLR augmented diagnostic sensitivity. Binary logistic regression analysis demonstrated that heightened NLR and MLR act as risk factors for the progression of AE-COPD. Conclusion: The increasing levels of NLR, PLR and MLR could function as biomarkers, akin to CRP, for diagnosis and assessment of acute exacerbations among COPD patients. Further research is required to validate this concept.

Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study.

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung cancer (NSCLC), and identifying candidates likely responding to this advanced treatment is of important clinical significance. The current multi-institutional study aims to develop a deep learning model to predict pathologic complete response (pCR) to neoadjuvant immunotherapy in NSCLC based on computed tomography (CT) imaging and further prob the biologic foundation of the proposed deep learning signature. A total of 248 participants administrated with neoadjuvant immunotherapy followed by surgery for NSCLC at Ruijin Hospital, Ningbo Hwamei Hospital, and Affiliated Hospital of Zunyi Medical University from January 2019 to September 2023 were enrolled. The imaging data within 2 weeks prior to neoadjuvant chemoimmunotherapy were retrospectively extracted. Patients from Ruijin Hospital were grouped as the training set (n = 104) and the validation set (n = 69) at the 6:4 ratio, and other participants from Ningbo Hwamei Hospital and Affiliated Hospital of Zunyi Medical University served as an external cohort (n = 75). For the entire population, pCR was obtained in 29.4% (n = 73) of cases. The areas under the curve (AUCs) of our deep learning signature for pCR prediction were 0.775 (95% confidence interval [CI]: 0.649 - 0.901) and 0.743 (95% CI: 0.618 - 0.869) in the validation set and the external cohort, significantly superior than 0.579 (95% CI: 0.468 - 0.689) and 0.569 (95% CI: 0.454 - 0.683) of the clinical model. Furthermore, higher deep learning scores correlated to the upregulation for pathways of cell metabolism and more antitumor immune infiltration in microenvironment. Our developed deep learning model is capable of predicting pCR to neoadjuvant chemoimmunotherapy in patients with NSCLC.

PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study.

PURPOSE: No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction. METHODS: A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups. RESULTS: The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS). CONCLUSION: The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.

[18F]FDG PET-CT radiomics signature to predict pathological complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicenter study.

OBJECTIVES: This study aims to develop and validate a radiomics model based on 18F-fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) images to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: One hundred eighty-five patients receiving neoadjuvant chemoimmunotherapy for NSCLC at 5 centers from January 2019 to December 2022 were included and divided into a training cohort and a validation cohort. Radiomics models were constructed via the least absolute shrinkage and selection operator (LASSO) method. The performances of models were evaluated by the area under the receiver operating characteristic curve (AUC). In addition, genetic analyses were conducted to reveal the underlying biological basis of the radiomics score. RESULTS: After the LASSO process, 9 PET-CT radiomics features were selected for pCR prediction. In the validation cohort, the ability of PET-CT radiomics model to predict pCR was shown to have an AUC of 0.818 (95% confidence interval [CI], 0.711, 0.925), which was better than the PET radiomics model (0.728 [95% CI, 0.610, 0.846]), CT radiomics model (0.732 [95% CI, 0.607, 0.857]), and maximum standard uptake value (0.603 [95% CI, 0.473, 0.733]) (p < 0.05). Moreover, a high radiomics score was related to the upregulation of pathways suppressing tumor proliferation and the infiltration of antitumor immune cell. CONCLUSION: The proposed PET-CT radiomics model was capable of predicting pCR to neoadjuvant chemoimmunotherapy in NSCLC patients. CLINICAL RELEVANCE STATEMENT: This study indicated that the generated 18F-fluorodeoxyglucose positron emission tomography-computed tomography radiomics model could predict pathological complete response to neoadjuvant chemoimmunotherapy, implying the potential of our radiomics model to personalize the neoadjuvant chemoimmunotherapy in lung cancer patients. KEY POINTS: • Recognizing patients potentially benefiting neoadjuvant chemoimmunotherapy is critical for individualized therapy of lung cancer. • [18F]FDG PET-CT radiomics could predict pathological complete response to neoadjuvant immunotherapy in non-small cell lung cancer. • [18F]FDG PET-CT radiomics model could personalize neoadjuvant chemoimmunotherapy in lung cancer patients.

PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer.

Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.

Soil organic carbon stability and exogenous nitrogen fertilizer influence the priming effect of paddy soil under long-term exposure to elevated atmospheric CO2.

Soil organic carbon (SOC) stability and dynamics are greatly influenced by long-term elevated atmospheric CO2 [CO2]. The priming effect (PE) is vital in SOC stability and dynamics, but its role in paddy soil under long-term elevated [CO2] remains unclear. To examine how SOC stability changed in paddy soil after long-term elevated atmospheric CO2 enrichment, the PE was quantified through a 13C-glucose-induced experiment with different N levels for topsoil (0-20 cm) from paddy free-air CO2 enrichment (FACE) platform. Compared with the ambient CO2 concentration ([CO2]), 10 years of elevated [CO2] (500 µmol·mol-1) significantly increased SOC and TN content by 18.4% and 19.0%, respectively, while the C/N ratio was not changed. The labile C fractions including dissolved organic carbon (DOC) and readily oxidizable organic carbon (ROC), but excluding microbial biomass C (MBC), accumulated faster than SOC in paddy soil, which implied the reduced SOC stability for long-term elevated [CO2] enrichment. With the decline of SOC stability, the exogenously induced cumulative specific PE (PE per gram of SOC) remarkably increased by 41.1-72.7% for elevated [CO2] fumigation. The cumulative PE, especially the cumulative specific PE, was found significantly linearly correlated with the ROC content or ROC/SOC ratio (labile SOC pool). Furthermore, the application of nitrogen fertilizer slowed down the PE under elevated [CO2] condition. Our results showed that long-term elevated [CO2] enrichment reduced SOC stability and, together with exogenous nitrogen fertilizer, regulated the PE in paddy soil.

Progress and Perspective of Glass-Ceramic Solid-State Electrolytes for Lithium Batteries.

The all-solid-state lithium battery (ASSLIB) is one of the key points of future lithium battery technology development. Because solid-state electrolytes (SSEs) have higher safety performance than liquid electrolytes, and they can promote the application of Li-metal anodes to endow batteries with higher energy density. Glass-ceramic SSEs with excellent ionic conductivity and mechanical strength are one of the main focuses of SSE research. In this review paper, we discuss recent advances in the synthesis and characterization of glass-ceramic SSEs. Additionally, some discussions on the interface problems commonly found in glass-ceramic SSEs and their solutions are provided. At the end of this review, some drawbacks of glass-ceramic SSEs are summarized, and future development directions are prospected. We hope that this review paper can help the development of glass-ceramic solid-state electrolytes.

Does elevated CO2 enhance the arsenic uptake by rice? Yes or maybe: Evidences from FACE experiments.

Elevated CO2 (eCO2) strongly affects rice yield and quality in arsenic (As) paddy soils. However, understanding of the As accumulation in rice under coupled stress of eCO2 and soil As is still limited while data are scarce. It greatly limits the prediction for future rice safety. This study investigated the As uptake by rice grown in different As paddy soils under two CO2 conditions (ambient and ambient +200 µmol mol-1) in the free-air CO2 enrichment (FACE) system. Results showed that eCO2 lowered soil Eh at the tillering stage and caused higher concentrations of dissolved As and Fe2+ in soil pore water. Compared with the control, the increased As transfer abilities in rice straws under eCO2 contributed to the higher As accumulation in rice grains, and their total As concentrations were increased by 10.3-31.2%. Besides, the increased amounts of iron plaque (IP) under eCO2 failed to effectively inhibit the As uptake by rice due to the difference in critical stage between As immobilized by IP (mainly in maturing stage) and uptake by rice roots (about 50% contribution before filling stage). Risk assessments suggest that eCO2 enhanced the human health risks of As intake from rice grains produced in low-As paddy soils (<30 mg kg-1). In order to alleviate the As threats to rice under eCO2, we consider that proper soil drainage before filling stage to improve soil Eh can serve as an effective way to reduce As uptake by rice. Pursuing appropriate rice varieties to reduce the As transfer ability may be the other positive strategy.

Elevated CO2 and temperature under future climate change increase severity of rice sheath blight.

Sheath blight (ShB), caused by Rhizoctonia solani, is one of the major threats to rice (Oryza sativa L.) production. However, it is not clear how the risk of rice ShB will respond to elevated CO2 and temperature under future climate change. Here, we conducted, field experiments of inoculated R. solani under combinations of two CO2 levels (ambient and enriched up to 590 µmol mol-1) and two temperature levels (ambient and increased by 2.0°C) in temperature by free-air CO2 enrichment (T-FACE) system for two cultivars (a susceptible cultivar, Lemont and a resistant cultivar, YSBR1). Results indicate that for the inoculation of plants with R. solani, the vertical length of ShB lesions for cv. Lemont was significantly longer than that for cv. YSBR1 under four CO2 and temperature treatments. The vertical length of ShB lesions was significantly increased by elevated temperature, but not by elevated CO2, for both cultivars. The vertical length of ShB lesions under the combination of elevated CO2 and elevated temperature was increased by 21-38% for cv. Lemont and by -1-6% for cv. YSBR1. A significant increase in MDA level was related to a significant increase in the vertical length of ShB lesions under the combination of elevated CO2 and elevated temperature. Elevated CO2 could not compensate for the negative effect of elevated temperature on yield of both cultivars under future climate change. Rice yield and biomass were further decreased by 2.0-2.5% and 2.9-4.2% by an increase in the severity of ShB under the combination of elevated CO2 and elevated temperature. Thus, reasonable agronomic management practices are required to improve both resistance to ShB disease and grain yield for rice under future climate change.

Effects of elevated CO2 on the Cd uptake by rice in Cd-contaminated paddy soils.

The rising atmospheric CO2 is a major driver for climate change, directly affects rice production. Cadmium (Cd) in paddy soils also serves as a persistent concern. Currently, few studies consider the rice response to coupled stresses of elevated CO2 (eCO2) and soil Cd. Experimental evidence understanding the effects and mechanisms of eCO2 on Cd uptake by rice is lacking yet. In a free-air CO2 enrichment (FACE) system, a 3-year pot experiment was conducted to explore the Cd uptake by rice under two CO2 conditions (ambient and ambient + 200 µmol·mol-1) using combinations of in-situ Cd-contaminated soils and associated rice varieties. Results showed that more low-crystalline Fe oxides (Feh) in iron plaque (IP) were deposited on root surface with the increased dissolved Fe2+ due to lower soil redox status under eCO2. The Cd accumulation in rice was hindered due to more Cd associated with Feh (Feh-Cd) rather than uptake by roots. Taken together, the relative effects of eCO2 on Cd uptake by rice were consistent across years under different Cd-contaminated soils. Our findings will help to better understand the Cd uptake by rice under future climate conditions, and thus push the development of climate-crop-soil models and accurate prediction for food security.

BC-DUnet-based segmentation of fine cracks in bridges under a complex background.

Crack is the external expression form of potential safety risks in bridge construction. Currently, automatic detection and segmentation of bridge cracks remains the top priority of civil engineers. With the development of image segmentation techniques based on convolutional neural networks, new opportunities emerge in bridge crack detection. Traditional bridge crack detection methods are vulnerable to complex background and small cracks, which is difficult to achieve effective segmentation. This study presents a bridge crack segmentation method based on a densely connected U-Net network (BC-DUnet) with a background elimination module and cross-attention mechanism. First, a dense connected feature extraction model (DCFEM) integrating the advantages of DenseNet is proposed, which can effectively enhance the main feature information of small cracks. Second, the background elimination module (BEM) is proposed, which can filter the excess information by assigning different weights to retain the main feature information of the crack. Finally, a cross-attention mechanism (CAM) is proposed to enhance the capture of long-term dependent information and further improve the pixel-level representation of the model. Finally, 98.18% of the Pixel Accuracy was obtained by comparing experiments with traditional networks such as FCN and Unet, and the IOU value was increased by 14.12% and 4.04% over FCN and Unet, respectively. In our non-traditional networks such as HU-ResNet and F U N-4s, SAM-DUnet has better and higher accuracy and generalization is not prone to overfitting. The BC-DUnet network proposed here can eliminate the influence of complex background on the segmentation accuracy of bridge cracks, improve the detection efficiency of bridge cracks, reduce the detection cost, and have practical application value.

Effects of laser paint stripping on oxide film damage of 2024 aluminium alloy aircraft skin.

Paint layer was stripped from the 2024 aluminium alloy aircraft skin by either 1000 grit sandpaper or laser with 150 ps pulse width while the laser paint stripping (LPS) process was recorded by a high-speed camera. The surface and cross-section morphologies, chemical compositions and chemical valences of obtained the paint stripping samples were also characterise. The corrosion resistance was determined by the Potentiodynamic Polarization Curve (PPC). On mechanical paint stripping (MPS) samples, a large amount of scratches remained. Surface roughness increased and the oxide film was removed completely. The trace of the laser scan was observable on the surface of LPS samples. Recrystallisation occurred on the LPS surface and eventually formed arrayed micro and sub-micro structures. The oxide film is mainly composed of Al2O3 with a thickness about 2.10 µm. The corrosion current density of mechanical and LPS samples are 3.66 ×10-2 mA·cm-2 and 6.66×10-5 mA·cm-2, respectively. Comparing to MPS which removed all the oxide film and damaged the substrate metal, LPS only damaged the oxide film mildly without damaging metal substrate. The remaining oxide film contributes to a higher corrosion resistance of the LPS sample.

The impact of global dimming on crop yields is determined by the source-sink imbalance of carbon during grain filling.

Global dimming reduces incident global radiation but increases the fraction of diffuse radiation, and thus affects crop yields; however, the underlying mechanisms of such an effect have not been revealed. We hypothesized that crop source-sink imbalance of either carbon (C) or nitrogen (N) during grain filling is a key factor underlying the effect of global dimming on yields. We presented a practical framework to assess both C and N source-sink relationships, using data of biomass and N accumulation from periodical sampling conducted in field experiments for wheat and rice from 2013 to 2016. We found a fertilization effect of the increased diffuse radiation fraction under global dimming, which alleviated the negative impact of decreased global radiation on source supply and sink growth, but the source supply and sink growth were still decreased by dimming, for both C and N. In wheat, the C source supply decreased more than the C sink demand, and as a result, crops remobilized more pre-heading C reserves, in response to dimming. However, these responses were converse in rice, which presumably stemmed from the more increment in radiation use efficiency and the more limited sink size in rice than wheat. The global dimming affected source supply and sink growth of C more significantly than that of N. Therefore, yields in both crops were dependent more on the source-sink imbalance of C than that of N during grain filling. Our revealed source-sink relationships, and their differences and similarities between wheat and rice, provide a basis for designing strategies to alleviate the impact of global dimming on crop productivity.

The acclimation of leaf photosynthesis of wheat and rice to seasonal temperature changes in T-FACE environments.

Crops show considerable capacity to adjust their photosynthetic characteristics to seasonal changes in temperature. However, how photosynthesis acclimates to changes in seasonal temperature under future climate conditions has not been revealed. We measured leaf photosynthesis (An ) of wheat (Triticum aestivum L.) and rice (Oryza sativa L.) grown under four combinations of two levels of CO2 (ambient and enriched up to 500 µmol/mol) and two levels of canopy temperature (ambient and increased by 1.5-2.0°C) in temperature by free-air CO2 enrichment (T-FACE) systems. Parameters of a biochemical C3 -photosynthesis model and of a stomatal conductance (gs ) model were estimated for the four conditions and for several crop stages. Some biochemical parameters related to electron transport and most gs parameters showed acclimation to seasonal growth temperature in both crops. The acclimation response did not differ much between wheat and rice, nor among the four treatments of the T-FACE systems, when the difference in the seasonal growth temperature was accounted for. The relationships between biochemical parameters and leaf nitrogen content were consistent across leaf ranks, developmental stages, and treatment conditions. The acclimation had a strong impact on gs model parameters: when parameter values of a particular stage were used, the model failed to correctly estimate gs values of other stages. Further analysis using the coupled gs -biochemical photosynthesis model showed that ignoring the acclimation effect did not result in critical errors in estimating leaf photosynthesis under future climate, as long as parameter values were measured or derived from data obtained before flowering.

The fertilization effect of global dimming on crop yields is not attributed to an improved light interception.

Global dimming, a decadal decrease in incident global radiation, is often accompanied with an increase in the diffuse radiation fraction, and, therefore, the impact of global dimming on crop production is hard to predict. A popular approach to quantify this impact is the statistical analysis of historical climate and crop data, or use of dynamic crop simulation modelling approach. Here, we show that statistical analysis of historical data did not provide plausible values for the effect of diffuse radiation versus direct radiation on rice or wheat yield. In contrast, our field experimental study of 3 years demonstrated a fertilization effect of increased diffuse radiation fraction, which partly offset yield losses caused by decreased global radiation, in both crops. The fertilization effect was not attributed to any improved canopy light interception but mainly to the increased radiation use efficiency (RUE). The increased RUE was explained not only by the saturating shape of photosynthetic light response curves but also by plant acclimation to dimming that gradually increased leaf nitrogen concentration. Crop harvest index slightly decreased under dimming, thereby discounting the fertilization effect on crop yields. These results challenge existing modelling paradigms, which assume that the fertilization effect on crop yields is mainly attributed to an improved light interception. Further studies on the physiological mechanism of plant acclimation are required to better quantify the global dimming impact on agroecosystem productivity under future climate change.

Human Mesenchymal Stem Cell-Treated Regulatory CD23+CD43+ B Cells Alleviate Intestinal Inflammation.

Rationale: Mesenchymal stem cells (MSCs) have been demonstrated to ameliorate inflammatory bowel disease by their actions on multiple immune cells, especially on regulatory B cells (Breg cells). However, the phenotypes and functions of human MSCs (hMSCs)-treated Breg cell subsets are not yet clear. Methods: Purified B cells were cocultured with MSCs and the phenotypes and immunomodulatory functions of the B cells were analyzed by FACS and proliferation assays in vitro. Also, a trinitrobenzenesulfonic acid-induced mouse colitis model was employed to detect the function of MSC-treated Breg cells in vivo. Results: We demonstrated that coculturing with hMSCs significantly enhanced the immunomodulatory activity of B cells by up-regulating IL-10 expression. We then identified that a novel regulatory B cell population characterized by CD23 and CD43 phenotypic markers could be induced by hMSCs. The CD23+CD43+ Breg cells substantially inhibited the inflammatory cytokine secretion and proliferation of T cells through an IL-10-dependent pathway. More significantly, intraperitoneal injection of hMSCs ameliorated the clinical and histopathological severity in the mouse experimental colitis model, accompanied by an increase in the number of CD23+CD43+ Breg cells. The adoptive transfer of CD23+CD43+ B cells effectively alleviated murine colitis, as compared with the CD23-CD43- B cells. Treatment with CD23+CD43+ B cells, and not hMSCs, substantially improved the symptoms of colitis in B cell-depleted mice. Conclusion: the novel CD23+CD43+ Breg cell subset appears to be involved in the immunomodulatory function of hMSCs and sheds new light on elucidating the therapeutic mechanism of hMSCs for the treatment of inflammation-related diseases.

Correction to: Human umbilical cord-derived mesenchymal stem cells protect against experimental colitis via CD5+ B regulatory cells.

The original article [1] contains a duplication error within Figure 3.

Stanniocalcin-2 contributes to mesenchymal stromal cells attenuating murine contact hypersensitivity mainly via reducing CD8+ Tc1 cells.

Mesenchymal stromal cells (MSCs) have been demonstrated to ameliorate allergic contact dermatitis (ACD), a typical T-cell-mediated disorder. However, the underlying mechanisms behind the MSC-based treatment for ACD have not yet been fully elucidated. The stanniocalcins (STCs) comprise a family of secreted glycoprotein hormones that act as important anti-inflammatory proteins. Here, we investigated the roles of STCs in MSC-mediated T-cell suppression and their potential role in the MSC-based treatment for ACD. Gene expression profiling revealed that STC2, but not STC1, was highly expressed in MSCs. STC2 knockdown in MSCs significantly impaired their effects in reducing TNF-α- and IFN-γ-producing CD8+ T cells. Importantly, silencing the STC2 expression in MSCs abated their therapeutic effect on contact hypersensitivity (CHS) in mice, mainly restoring the generation and infiltration of IFN-γ-producing CD8+ T cells (Tc1 cells). Mechanistically, STC2 co-localized with heme oxygenase 1 (HO-1) in MSCs, and contributed to MSC-mediated reduction of CD8+ Tc1 cells via regulating HO-1 activity. Together, these findings newly identify STC2 as the first stanniocalcin responsible for mediating the immunomodulatory effects of MSCs on allogeneic T cells and STC2 contribute to MSC-based treatment for ACD mainly via reducing the CD8+ Tc1 cells.

Mesenchymal Stromal Cells-Derived ß2-Microglobulin Promotes Epithelial-Mesenchymal Transition of Esophageal Squamous Cell Carcinoma Cells.

Mesenchymal stromal cells (MSCs) have been considered as one of the pivotal type of cells composing the tumor microenvironment. Although contact-dependent mechanisms and paracrine factors are thought to collaborate in governing the MSCs-based effects on tumors progression, the underlying mechanisms remain largely unknown. In particular, the involvement of MSCs-derived cytokines in the epithelial-mesenchymal transition (EMT) of esophageal squamous cell carcinoma (ESCC) has not been clarified. In this study, we observed that ß2-Microglobulin (B2M) is highly expressed in MSCs but scarcely in ESCC cells. Based on the previously described EMT promoting effect of B2M, we investigated the in vitro effect of MSCs-derived B2M on the EMT of ESCC cells, and discovered its subsequent enhancing effects on cell mobility and tumor-initiation. Further xenograft transplantation experiments confirmed the in vivo induction of tumor-initiation by MSCs-derived B2M. Noteworthy, we showed that the B2M expression positively correlated with poor prognosis. The fact that B2M is primarily expressed by the stroma of the ESCC tissue strengthens our hypothesis that in ESCC, MSCs-derived B2M promotes tumor-initiation and invasion via enhancing EMT, resulting in an adverse prognosis for the patients. Our results will be valuable for the prediction of the development and treatment of ESCC.