Corrigendum: Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis.

[This corrects the article DOI: 10.3389/fimmu.2023.1181697.].

PMAIP1 regulates autophagy in osteoblasts via the AMPK/mTOR pathway in osteoporosis.

Osteoporosis (OP) is a highly prevalent disorder characterized by low bone mass that severely reduces patient quality of life. Although numerous treatments for OP have been introduced in clinic, many have side effects and high costs. Therefore, there is still an unmet need for optimal solutions. Here, raw signal analysis was used to identify potential high-risk factors for OP, and the biological functions and possible mechanisms of action (MOAs) of these factors were explored via gene set enrichment analysis (GSEA). Subsequently, molecular biological experiments were performed to verify and analyze the discovered risk factors in vitro and in vivo. PMAIP1 was identified as a potential risk factor for OP and significantly suppressed autophagy in osteoblasts via the AMPK/mTOR pathway, thereby inhibiting the proliferation and differentiation of osteoblasts. Furthermore, we constructed an ovariectomy (OVX) model of OP in rats and simultaneously applied si-PMAIP1 for in vivo interference. si-PMAIP1 upregulated the expression of LC3B and p-AMPK and downregulated the expression of p-mTOR, and these effects were reversed by the autophagy inhibitor. Micro-CT revealed that, si-PMAIP1 significantly inhibited the development of osteoporosis in OVX model rats, and this therapeutic effect was attenuated by treatment with an autophagy inhibitor. This study explored the role and mechanism of PMAIP1 in OP and demonstrated that PMAIP1 may serve as a novel target for OP treatment.

Spatiotemporal pattern of coastal water pollution and its driving factors: implications for improving water environment along Hainan Island, China.

In the context of human activities and climate change, the gradual degradation of coastal water quality seriously threatens the balance of coastal and marine ecosystems. However, the spatiotemporal patterns of coastal water quality and its driving factors were still not well understood. Based on 31 water quality parameters from 2015 to 2020, a new approach of optimizing water quality index (WQI) model was proposed to quantitatively assess the spatial and temporal water quality along tropical Hainan Island, China. In addition, pollution sources were further identified by factor analysis and the effects of pollution source on water quality was finally quantitatively in our study. The results showed that the average water quality was moderate. Water quality at 86.36% of the monitoring stations was good while 13.53% of the monitoring stations has bad or very bad water quality. Besides, the coastal water quality had spatial and seasonal variation, along Hainan Island, China. The water quality at "bad" level was mainly appeared in the coastal waters along large cities (Haikou and Sanya) and some aquaculture regions. Seasonally, the average water quality in March, October and November was worse than in other months. Factor analysis revealed that water quality in this region was mostly affected by urbanization, planting and breeding factor, industrial factor, and they played the different role in different coastal zones. Waters at 10.23% of monitoring stations were at the greatest risk of deterioration due to severe pressure from environmental factors. Our study has significant important references for improving water quality and managing coastal water environment.

TXNIP deficiency attenuates renal fibrosis by modulating mTORC1/TFEB-mediated autophagy in diabetic kidney disease.

Thioredoxin-interacting protein (TXNIP) is an important regulatory protein for thioredoxin (TRX) that elicits the generation of reactive oxygen species (ROS) by inhibiting the redox function of TRX. Abundant evidence suggests that TXNIP is involved in the fibrotic process of diabetic kidney disease (DKD). However, the potential mechanism of TXNIP in DKD is not yet well understood. In this study, we found that TXNIP knockout suppressed renal fibrosis and activation of mammalian target of rapamycin complex 1 (mTORC1) and restored transcription factor EB (TFEB) and autophagy activation in diabetic kidneys. Simultaneously, TXNIP interference inhibited epithelial-to-mesenchymal transformation (EMT), collagen I and fibronectin expression, and mTORC1 activation, increased TFEB nuclear translocation, and promoted autophagy restoration in HK-2 cells exposed to high glucose (HG). Rapamycin, an inhibitor of mTORC1, increased TFEB nuclear translocation and autophagy in HK-2 cells under HG conditions. Moreover, the TFEB activators, curcumin analog C1 and trehalose, effectively restored HG-induced autophagy, and abrogated HG-induced EMT and collagen I and fibronectin expression in HK-2 cells. Taken together, these findings suggest that TXNIP deficiency ameliorates renal fibrosis by regulating mTORC1/TFEB-mediated autophagy in diabetic kidney diseases.

Prognostic gene landscapes and therapeutic insights in sepsis-induced coagulopathy.

BACKGROUND: Sepsis is a common and critical condition encountered in clinical practice that can lead to multi-organ dysfunction. Sepsis-induced coagulopathy (SIC) significantly affects patient outcomes. However, the precise mechanisms remain unclear, making the identification of effective prognostic and therapeutic targets imperative. METHODS: The analysis of transcriptome data from the whole blood of sepsis patients, facilitated the identification of key genes implicated in coagulation. Then we developed a prognostic model and a nomogram to predict patient survival. Consensus clustering classified sepsis patients into three subgroups for comparative analysis of immune function and immune cell infiltration. Single-cell sequencing elucidated alterations in intercellular communication between platelets and immune cells in sepsis, as well as the role of the coagulation-related gene FYN. Real-time quantitative PCR determined the mRNA levels of critical coagulation genes in septic rats' blood. Finally, administration of a FYN agonist to septic rats was observed for its effects on coagulation functions and survival. RESULTS: This study identified four pivotal genes-CFD, FYN, ITGAM, and VSIG4-as significant predictors of survival in patients with sepsis. Among them, CFD, FYN, and ITGAM were underexpressed, while VSIG4 was upregulated in patients with sepsis. Moreover, a nomogram that incorporates the coagulation-related genes (CoRGs) risk score with clinical features of patients accurately predicted survival probabilities. Subgroup analysis of CoRGs expression delineated three molecular sepsis subtypes, each with distinct prognoses and immune profiles. Single-cell sequencing shed light on heightened communication between platelets and monocytes, T cells, and plasmacytoid dendritic cells, alongside reduced interactions with neutrophils in sepsis. The collagen signaling pathway was found to be essential in this dynamic. FYN may affect platelet function by modulating factors such as ELF1, PTCRA, and RASGRP2. The administration of the FYN agonist can effectively improve coagulation dysfunction and survival in septic rats. CONCLUSIONS: The research identifies CoRGs as crucial prognostic markers for sepsis, highlighting the FYN gene's central role in coagulation disorders associated with the condition and suggesting novel therapeutic intervention strategies.

Metabolomics and machine learning approaches for diagnostic and prognostic biomarkers screening in sepsis.

BACKGROUND: Sepsis is a life-threatening disease with a poor prognosis, and metabolic disorders play a crucial role in its development. This study aims to identify key metabolites that may be associated with the accurate diagnosis and prognosis of sepsis. METHODS: Septic patients and healthy individuals were enrolled to investigate metabolic changes using non-targeted liquid chromatography-high-resolution mass spectrometry metabolomics. Machine learning algorithms were subsequently employed to identify key differentially expressed metabolites (DEMs). Prognostic-related DEMs were then identified using univariate and multivariate Cox regression analyses. The septic rat model was established to verify the effect of phenylalanine metabolism-related gene MAOA on survival and mean arterial pressure after sepsis. RESULTS: A total of 532 DEMs were identified between healthy control and septic patients using metabolomics. The main pathways affected by these DEMs were amino acid biosynthesis, phenylalanine metabolism, tyrosine metabolism, glycine, serine and threonine metabolism, and arginine and proline metabolism. To identify sepsis diagnosis-related biomarkers, support vector machine (SVM) and random forest (RF) algorithms were employed, leading to the identification of four biomarkers. Additionally, analysis of transcriptome data from sepsis patients in the GEO database revealed a significant up-regulation of the phenylalanine metabolism-related gene MAOA in sepsis. Further investigation showed that inhibition of MAOA using the inhibitor RS-8359 reduced phenylalanine levels and improved mean arterial pressure and survival rate in septic rats. Finally, using univariate and multivariate cox regression analysis, six DEMs were identified as prognostic markers for sepsis. CONCLUSIONS: This study employed metabolomics and machine learning algorithms to identify differential metabolites that are associated with the diagnosis and prognosis of sepsis patients. Unraveling the relationship between metabolic characteristics and sepsis provides new insights into the underlying biological mechanisms, which could potentially assist in the diagnosis and treatment of sepsis. TRIAL REGISTRATION: This human study was approved by the Ethics Committee of the Research Institute of Surgery (2021-179) and was registered by the Chinese Clinical Trial Registry (Date: 09/12/2021, ChiCTR2200055772).

Acid sphingomyelinase mediates ferroptosis induced by high glucose via autophagic degradation of GPX4 in type 2 diabetic osteoporosis.

BACKGROUND: Ferroptosis has been implicated in the pathological process of type 2 diabetic osteoporosis (T2DOP), although the specific underlying mechanisms remain largely unknown. This study aimed to clarify the role and possible mechanism of acid sphingomyelinase (ASM)-mediated osteoblast ferroptosis in T2DOP. METHODS: We treated hFob1.19 cells with normal glucose (NG) and different concentrations of high glucose (HG, 26.25 mM, 35 mM, or 43.75 mM) for 48 h. We then measured cell viability and osteogenic function, quantified ferroptosis and autophagy levels, and measured the levels of ASM and ceramide in the cells. To further investigate the specific mechanism, we examined these indicators by knocking down ASM expression, hydroxychloroquine (HCQ) treatment, or N-acetylcysteine (NAC) treatment. Moreover, a T2DOP rat model was induced and microcomputed tomography was used to observe the bone microstructure. We also evaluated the serum levels of iron metabolism-associated factors, ceramide and lipid peroxidation (LPO) and measured the expression of ASM, LC3 and GPX4 in bone tissues. RESULTS: HG inhibited the viability and osteogenic function of osteoblasts by inducing ferroptosis in a concentration-dependent manner. Furthermore, the expression of ASM and ceramide and autophagy levels were increased by HG treatment, and these factors were required for the HG-induced reactive oxygen species (ROS) generation and LPO. Similarly, inhibiting intracellular ROS also reduced HG-induced ASM activation and autophagy. ASM-mediated activation of autophagy was crucial for HG-induced degradation of GPX4, and inhibiting ASM improved osteogenic function by decreasing HG-induced autophagy, GPX4 degradation, LPO and subsequent ferroptosis. We also found that inhibiting ASM could alleviated ferroptosis and autophagy and improved osteogenic function in a T2DOP rat model. CONCLUSION: ASM-mediated autophagy activation induces osteoblast ferroptosis under HG conditions through the degradation of GPX4, providing a novel mechanistic insight into the treatment and prevention of T2DOP.

VDAC2 malonylation participates in sepsis-induced myocardial dysfunction via mitochondrial-related ferroptosis.

Sepsis-induced myocardial dysfunction (SIMD) is a prevalent and severe form of organ dysfunction with elusive underlying mechanisms and limited treatment options. In this study, the cecal ligation and puncture and lipopolysaccharide (LPS) were used to reproduce sepsis model in vitro and vivo. The level of voltage-dependent anion channel 2 (VDAC2) malonylation and myocardial malonyl-CoA were detected by mass spectrometry and LC-MS-based metabolomics. Role of VDAC2 malonylation on cardiomyocytes ferroptosis and treatment effect of mitochondrial targeting nano material TPP-AAV were observed. The results showed that VDAC2 lysine malonylation was significantly elevated after sepsis. In addition, the regulation of VDAC2 lysine 46 (K46) malonylation by K46E and K46Q mutation affected mitochondrial-related ferroptosis and myocardial injury. The molecular dynamic simulation and circular dichroism further demonstrated that VDAC2 malonylation altered the N-terminus structure of the VDAC2 channel, causing mitochondrial dysfunction, increasing mitochondrial ROS levels, and leading to ferroptosis. Malonyl-CoA was identified as the primary inducer of VDAC2 malonylation. Furthermore, the inhibition of malonyl-CoA using ND-630 or ACC2 knock-down significantly reduced the malonylation of VDAC2, decreased the occurrence of ferroptosis in cardiomyocytes, and alleviated SIMD. The study also found that the inhibition of VDAC2 malonylation by synthesizing mitochondria targeting nano material TPP-AAV could further alleviate ferroptosis and myocardial dysfunction following sepsis. In summary, our findings indicated that VDAC2 malonylation plays a crucial role in SIMD and that targeting VDAC2 malonylation could be a potential treatment strategy for SIMD.

Irisin Ameliorates Renal Tubulointerstitial Fibrosis by Regulating the Smad4/ß-Catenin Pathway in Diabetic Mice.

Background: The primary pathophysiology of diabetic kidney disease (DKD) is tubulointerstitial fibrosis (TIF), and an essential contributing element is excessive extracellular matrix deposition. Irisin is a polypeptide formed by splitting fibronectin type III domain containing 5 (FNDC5), which participates in a number of physiological and pathological processes. Methods: The purpose of this article is to examine irisin's function in DKD and analyze both its in vitro and in vivo effects. The Gene Expression Omnibus (GEO) database was used to download GSE30122, GSE104954, and GSE99325. Analysis of renal tubule samples from nondiabetic and diabetic mice identified 94 differentially expressed genes (DEGs). The transforming growth factor beta receptor 2 (TGFBR2), irisin, and TGF-ß1 were utilized as DEGs to examine the impact of irisin on TIF in diabetic kidney tissue, according to the datasets retrieved from the GEO database and Nephroseq database. Additionally, the therapeutic impact of irisin was also examined using Western blot, RT-qPCR, immunofluorescence, immunohistochemistry, and kits for detecting mouse biochemical indices. Results: In vitro, the findings demonstrated that irisin not only down-regulated the expression of Smad4 and ß-catenin but also reduced the expression of proteins linked to fibrosis, the epithelial-mesenchymal transition (EMT), and mitochondrial dysfunction in HK-2 cells maintained in high glucose (HG) environment. In vivo, overexpressed FNDC5 plasmid was injected into diabetic mice to enhance its expression. Our studies found that overexpressed FNDC5 plasmid not only reversed the biochemical parameters and renal morphological characteristics of diabetic mice but also alleviated EMT and TIF by inhibiting Smad4/ß-catenin signaling pathway. Conclusion: The above experimental results revealed that irisin could reduce TIF in diabetic mice via regulating the Smad4/ß-catenin pathway.

Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis.

Background: To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis. Methods: The lipid metabolism-related hub genes were screened using machine learning algorithms, and the immune cell infiltration of these hub genes were assessed by CIBERSORT and Single-sample GSEA. Next, the immune function of these hub genes at the single-cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, the support vector machine-recursive feature elimination (SVM-RFE) algorithm was conducted to compare the significantly altered metabolites critical to hub genes between SP and HC. Furthermore, the role of the key hub gene was verified in sepsis rats and LPS-induced cardiomyocytes, respectively. Results: A total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism (MAPK14, EPHX2, BMX, FCER1A, and PAFAH2) were screened. Then, we found an immunosuppressive microenvironment in sepsis. The role of hub genes in immune cells was further confirmed by the single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and were associated with MAPK14. Finally, inhibiting MAPK14 decreased the levels of inflammatory cytokines and improved the survival and myocardial injury of sepsis. Conclusion: The lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients.

Role of SUMOylation of STAT1 in tubular epithelial­mesenchymal transition induced by high glucose.

Tubulointerstitial fibrosis (TIF) is an important pathological change that occurs during the development of diabetic kidney disease. The epithelial­mesenchymal transition (EMT) of renal tubular epithelial cells is a manifestation of TIF. STAT1, a member of the STAT family of transcription factors, can be modified by the small ubiquitin­related modifier (SUMO), thus affecting the activity of STAT1. The present study investigated the role of STAT1 SUMOylation in high glucose­induced tubular EMT by western blotting, immunocytochemistry, immunofluorescence, co­immunoprecipitation and dual luciferase reporter analysis. The results indicated that in the process of high glucose­induced EMT, STAT1 activation protected the cells from EMT. However, high glucose also increased the SUMOylation of STAT1, which prevented STAT1 from exerting an effective protective role by inhibiting its activity.

ATF3 Regulates Osteogenic Function by Mediating Osteoblast Ferroptosis in Type 2 Diabetic Osteoporosis.

Purpose: Osteoporosis is a complication of type 2 diabetes, and it is characterized by reduced bone mass, augmented bone fragility, and increased risk of fracture, thus reducing patient quality of life, especially in the elderly. Ferroptosis has been implicated in the pathological process of type 2 diabetic osteoporosis (T2DOP), but the specific underlying mechanisms remain largely unknown. This study clarified the role of activating transcription factor 3 (ATF3) in T2DOP and explored its specific regulatory mechanism, providing a new treatment target for T2DOP. Methods: We cultured hFob1.19 cells in high glucose (HG, 35 mM) and knocked down ATF3 using short hairpin RNA (shRNA). We then measured cell viability, assessed morphology, quantified the expression of ATF3 and glutathione peroxidase 4 (GPX4), detected the levels of reactive oxygen species (ROS) and lipid peroxides, and determined the osteogenic function of osteoblasts. Cystine/glutamate antiporter (system Xc-) activity was evaluated by determining the expression of SLC7A11 and the levels of glutathione (GSH) and extracellular glutamate. We constructed a T2DOP rat model and observed the effect of ATF3 on ferroptosis and T2DOP by knocking down ATF3 using small interfering RNA (siRNA). Then, we evaluated the levels of iron metabolism, lipid peroxidation, and bone turnover in serum, detected the expression of ATF3, SLC7A11, and GPX4 in bone tissues, and assessed bone microstructure using microcomputed tomography. Results: ATF3 expression was increased in osteoblasts under HG condition and in T2DOP rats. Inhibiting the function of ATF3 increased GPX4 levels and reduced the accumulation of ROS and lipid peroxides. These changes inhibited the ferroptosis of osteoblasts and improved osteogenic function. In addition, HG induced ATF3 upregulation, resulting in decreased SLC7A11 expression and lower levels of intracellular GSH and extracellular glutamate. Conclusion: Osteoblast ferroptosis under HG conditions is induced by ATF3-mediated inhibition of system Xc- activity, and these events contribute to T2DOP pathogenesis.

A unified model for high resolution mapping of global lake (>1 ha) clarity using Landsat imagery data.

Lake clarity, usually measured by Secchi disc depth (SDD), is a reliable proxy of lakes trophic status due to its close link with total suspended matter, chlorophyll-a, and nutrients. Trained with in-situ measured SDD and match-up Landsat images, we established various regression models to estimate SDD for global lakes. We selected a unified model which demonstrated good spatiotemporal transferability, and has potential to map SDD in different years with good quality of Landsat top-of-atmosphere (TOA) images embedded in Google Earth Engine (GEE). The unified model was successfully calibrated (n = 3586 data points, R2 = 0.84, MAPE = 29.8%) against SDD measured in 2235 lakes across the world, and the validation (n = 1779, R2 = 0.76, MAPE = 38.8%) also exhibited stable performance. The unified model was tuned to historical SDD measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, L8-OLI) launched over the past four decades (1984-2020), thus confirming its temporal stability. Global SDD was mapped using GEE with OLI TOA products mainly acquired in 2019 to examine the spatial variation of lake water clarity (lake surface area ≥ 1 ha) all over the world. Worldwide, lake water clarity averaged 3.13 ± 1.71 m in 2019, but exhibited remarkable spatial variability due to catchment hydrological and landscape settings, lake morphology, elevation and anthropogenic impact. Inland waters in Europe (4.18 ± 1.82 m) and North America (3.84 ± 1.77 m) had the highest clarity due to greater water depth combined with less human disturbance in the high latitude regions. Lakes in South America (2.50 ± 2.33 m), Asia (2.44 ± 1.63 m) and Africa (2.36 ± 0.72 m) displayed intermediate clarity. Lakes in Oceania (1.97 ± 1.48 m) exhibited the lowest clarity for all continents except Antarctica. Further, we used the mapped SDD to evaluate water trophic status using the Carlson trophic state index. Our results indicate that, in 2019, about 63.6% of the lake areas and 47.8% of total lake numbers (2,219,627/4,646,056) were oligotrophic for global lakes, while about 23.6% areal percent and 37.1% of lake numbers are eutrophic mostly as a result of their being located in agricultural and urban-dominated drainage basins. This study, for the first time, provides water clarity information for lakes with area ≥ 1 ha all over the world with 30-m resolution and facilitates the understanding of the water clarity relevant to TSM (r = 0.95), Chl-a (r = 0.73), total phosphorus (r = 0.75), total nitrogen (r = 0.60), which could further provide water clarity data and technical support for trophic level evaluations as well. This unified model could serve as a powerful research tool for long-term monitoring of aquatic ecosystems and assessing their resilience to anthropogenic disturbance and climate change-related stressors.

Total suspended solids characterization and management implications for lakes in East China.

In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, -4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007-2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between -50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20, 11, 17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were significantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%-68%). Human activities (fertilizer usage, domestic wastewater, industrial wastewater) and natural factors (precipitation, wind speed and NDVI) played relatively important roles to TSS changes in 14 and 15 of the 91 basins, respectively. Beyond the six factors in this study, other unanalyzed factors (such as lake depth and soil texture) also had some impacts on the distribution of TSS in the study area.

Combined Treatment of Bone Marrow Mesenchymal Stem Cells and Fasudil Promotes Neurovascular Remodeling and Neurological Function Recovery in Ischemic Stroke.

Stroke remains a highly deadly and disabling disease with limited treatment tragedies due to the limitations of available treatments; novel therapies for stroke are needed. In this article, the synergistic results of dual bone marrow mesenchymal stem cells (BMSC) and fasudil treatment in rat models of ischemic stroke still require further identification. Sprague-Dawley rats were used to construct the middle cerebral artery, occlusion models. BMSCs were incubated with fasudil, and MTT was performed to evaluate cell proliferation. The rats were treated with fasudil + BMSC, BMSC, fasudil, and saline. Blood samples were collected for complete blood count analysis and measurement of serum TNF-α levels. The neurological functions were evaluated. After the rats were sacrificed, immunohistochemical staining and TTC staining was performed. Fasudil promoted the proliferation of BMSCs and induced their differentiation into neuron-like cells. BMSCs increased the proportion of neutrophils; nevertheless, fasudil counteracted the neutrophil increase. The TUJ-1/MAP2/VIII factor expression in the fasudil + BMSC group was significantly higher than that in the other groups. The number of GFAP-positive cells decreased in the fasudil + BMSC and BMSC alone groups. The infarct volume in the fasudil + BMSC and BMSC alone groups was significantly lower than in the fasudil alone and control groups. Both BMSCs and fasudil exert neurorestorative effects in rat models of cerebral ischemia. Fasudil neutralizes the pro-inflammatory effects of BMSCs, while BMSCs and fasudil together had synergistic effects promoting neurovascular remodeling and neurological function recovery in stroke. A combination of BMSCs and fasudil provides a promising method for the treatment of ischemic stroke.

Songhua River basin's improving water quality since 2005 based on Landsat observation of water clarity.

Water clarity, denoted by the Secchi disk depth (SDD), is one of the most important indicators for monitoring water quality. In the Songhua River basin (SHRB), few studies have used Landsat to monitor long-term (3-4 decades) changes in lake SDD and explore the impact of natural and human factors on SDD interannual variation at the watershed scale. Lakes in the SHRB are of great significance to local populations. Understanding the spatiotemporal dynamics of SDD could help policymakers manage, protect, and predict lake water quality. We utilized the Landsat red/blue band ratio in the Google Earth Engine to estimate the SDD of 77 lakes and generated annual mean SDD maps from 1990 to 2018. The results of the SDD interannual changes showed that the water quality in the SHRB has improved since 2005. Specifically, the SDD in the SHRB displayed a significant increasing trend (p < 0.05) from 0.29 m in 2005 to 0.37 m in 2018. Moreover, the number of lakes displaying a significant increasing trend for SDD increased from 18 between 1990 and 2005 to 31 between 2005 and 2018. We also found that use of chemical fertilizer significantly impacted lakes, followed by wastewater discharge and normalized difference vegetation index. Improvements in the quantity and ability of wastewater discharge treatment and increased vegetation cover have alleviated water pollution; however, the non-point pollution of agriculture still poses a threat to some lakes in the SHRB. Therefore, more efforts should be made to further improve the aquatic ecological environment of SHRBs.

Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm.

Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-a concentrations. In this study, a total of 273 samples were collected from 45 typical lakes across China during 2017-2019. Here, we proposed applicable machine learning algorithms (i.e., linear regression model (LR), support vector machine model (SVM) and Catboost model (CB)), which integrate a broad scale dataset of lake biogeochemical characteristics using Multispectral Imager (MSI) product to seamlessly retrieve the Chl-a concentration. A K-means clustering approach was used to cluster the 273 normalized water leaving reflectance spectra [Rrs (λ)] extracted from MSI imagery with Case 2 Regional Coast Colour (CR2CC) processor into three groups. The pH, electrical conductivity (EC), total suspended matter (TSM) and dissolved organic carbon (DOC) from three clustering groups had significant differences (p < 0.05**), indicating that water quality parameters have an integrated impact on Rrs(λ)-spectra. The results of machine learning algorithms integrating demonstrated that SVM obtained a better degree of measured- and derived- fitting (calibration: slope = 0.81, R2 = 0.91; validation: slope = 1.21, R2 = 0.88). On the contrary, the documented nine Chl-a algorithms gave poor results (fitting 1:1 linear slope < 0.4 and R2 < 0.70) with synchronous train and test datasets. It demonstrated that machine learning provides a robust model for quantifying Chl-a concentration. Further, considering three Rrs(λ) clustering groups by k-means, Chl-a SVM model indicated that cluster 1 group gave a better retrieving performance (slope = 0.71, R2 = 0.78), followed by cluster 3 group (slope = 0.77, R2 = 0.64) and cluster 2 group (slope = 0.67, R2 = 0.50). These are related to the low TSM and high DOC levels for cluster-1 and cluster-3 Rrs(λ) spectra, which reduce the influence of particle in red bands for Rrs(λ) signal. Our results highlighted the quantification of lake Chl-a concentrations using MSI imagery and SVM, which can realize the large-scale monitoring and more appropriate for medium/low Chl-a level. The remote estimation of Chl-a based on artificial intelligence can provide an effective and robust way to monitor the lake eutrophication on a macro-scale; and offer a better approach to elucidate the response of lake ecosystems to global change.

Quantifying total suspended matter (TSM) in waters using Landsat images during 1984-2018 across the Songnen Plain, Northeast China.

Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change.

Spatiotemporal patterns of urban thermal environment and comfort across 180 cities in summer under China's rapid urbanization.

BACKGROUND: China is considered as the largest and most rapidly urbanizing nation in the world. However, possible changes of urban thermal environment and comfort under the rapid urbanization in China still remain poorly understood at a national scale. METHODS: Based on the data collected from 180 cities in 1990, 2005, and 2015 in China, the spatiotemporal patterns of urban thermal environment and comfort in summer and their relationships with urbanization variables were investigated in this study. RESULTS: Our results indicate that urban thermal environment has changed greatly during the 25 years. Furthermore, the changes of urban climate in different regions are inconsistent. The Physiological Equivalent Temperature (PET) at most cities (81%) in China increased from 1990 to 2015, which suggested that urban thermal comfort in China was also deteriorating during the 25 years. However, while the PET of some cities in China began to decrease from 2005 to 2015, there were still 33% of cities that had positive trends,which mainly located in North region. Urbanization resulted in a significant influence on urban climate. Compared to southern cities, northern cities were more sensitive to urbanization impact. The most important contribution to increasing of PET for urbanization variables is gross domestic product, followed by urban population. The analysis results reveal changing patterns of urban thermal comfort in China during summer season. It can help urban government and managers improve urban thermal environment and comfort.