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Abstract Previous studies have shown a relationship between SPECC1 and the prognosis of breast cancer, indicating a potential function for SPECC1 in the initiation and progression of cancer. However, the role played by SPECC1 in other tumors is not yet known. Therefore, we used bioinformatics techniques to conduct a thorough investigation into the possible mechanism of SPECC1 in pan-cancer, analyzing data reported in the literature as well as databases such as GTEx and CCLE, cBioportal, TCGA, and UCSC XENA. Comparing the results with matching normal tissues, the majority of cancers, including pancreatic adenocarcinoma (PAAD) and breast invasive carcinoma (BRCA), exhibited higher levels of SPECC1, while hepatocellular carcinoma (HCC) showed lower expression levels. SPECC1 was also found to be genetically mutated in endometrial cancer, sarcoma, and esophageal cancer. The prognosis of lung adenocarcinoma, kidney papillary cell carcinoma, and breast cancer is highly correlated with dysregulation of SPECC1 expression. This work helps guide clinical therapy by highlighting the sensitivity of tumor-treating medicines and the prognostic importance of SPECC1 in various malignancies. KEGG pathway enrichment analysis revealed focused adhesion, collagen-containing extracellular matrix (collagen), and the primary enrichment domains for SPECC1-related genes. These findings were obtained through gene annotation (GO) examination of SPECC1 expression. Primary mediators of the cytokine-cytokine receptor interaction include PICOC1-associated genes, cell-substrate junction genes, and extracellular matrix containing collagen. PICOC1-associated genes primarily mediate the PI3K-AKT signaling pathway. Drug sensitivity assay showed that SPECC1 high-expressing cell lines were more sensitive to docetaxel, doxorubicin, etc. In conclusion, the current study shows how SPECC1 is expressed in different cancers and how this expression relates to the prognosis of the tumor. It also revealed the mutations and copy number variations of SPECC1 in various tumors and its potential involvement in cellular pathway regulatory networks and cytological processes. This study examines the relationship between immune genes, cellular infiltration, and immunological scores in the tumor microenvironment, which explain the severity of the disease. This study looks at the response of SPEC1 expression to anticancer therapy. Explains the prognostic significance and drug response of SPECC-1.

Abstract Background Diabetic kidney disease (DKD) is the major complication of diabetes concomitant with gut dysbiosis and glycometabolic disorder, which are strongly associated with bile acid (BA) metabolism. Yet studies investigating the BA metabolism involving in DKD pathogenesis are limited. This study aimed to explore the metabolomic profiling of BAs in DKD and analyze its association with DKD progression. Methods An ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method was established to quantify BAs in the plasma, fecal and urine samples of patients with DKD or T2DM and healthy individuals (n = 30 for each group). The key BAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and receiver-operating characteristic (ROC) curve. Polynomial regression and Pearson’s correlation analyses were performed to assess the correlation between the key BAs and the clinical indicators reflecting DKD progression. Results Metabolomic profiling of 50 kinds of BAs presented the markedly step-wise alterations of BAs in plasma and feces as well as the little in urine of patients with DKD. Eight kinds of BAs in the plasma, eight kinds in the feces and three kinds in the urine were abnormally expressed, accompanying with the increased conjugated/unconjugated ratios of cholic acid, deoxycholic acid, chenodeoxycholic acid, ursodeoxycholic acid and hyocholic acid in the plasma, and of cholic acid, chenodeoxycholic acid and lithocholic acid in the feces. Moreover, the increased plasma level of glycochenodeoxycholic acid, and the increased fecal levels of glycolithocholic acid, 7-ketodeoxycholic acid and chenodeoxycholic acid-3-β-D-glucuronide are strongly correlated with the clinical indicators reflecting DKD progression, including eGFR, 24 h urinary protein and 24 h urinary microalbumin. Conclusions Our study for the first time disclosed the specific alterations of BA metabolism reflecting the step-wise progression of DKD, providing the basis for early identification and therapeutical strategies for DKD.

IntroductionThis study aimed to elucidate the effects of outdoor air pollution and allergic conjunctivitis and population-based lagged effects of air pollution.MethodsWe included data on six major air pollutants, PM10, PM2.5, carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3), and 3325 allergic conjunctivitis outpatient visits in Urumqi, northwest China, from 1 January 2013 to 31 December 2020. We developed quasi-Poisson generalized linear regression models with distributed lagged nonlinear models (DLNM), and single and multi-pollutant models were constructed to investigate single-day and cumulative lagged effects in detail.ResultsOur results confirmed that elevated PM10 and NO2 levels are significantly associated with increased allergic conjunctivitis outpatient visits with lags of 2 and 3 days respectively, and subgroup analyses further suggest that the effects of PM10 and NO2 on allergic conjunctivitis are more pronounced during the warm season. Women are more sensitive to PM10 exposure and the effect of air pollution on allergic conjunctivitis is influenced by age (e.g., infancy and older people).DiscussionOur work provides the first time-series study in Urumqi, the world's furthest inland city from the ocean. Further implementation of specific outdoor air pollution controls such as the burning of fossil fuels like coal, as well as special population protection policies remain necessary. Multicenter studies with larger sample sizes are needed.

ZCL-278 is a selective small molecule specifically inhibiting the Cdc42-intersectin interaction, yet its in-vivo pharmacokinetic and pharmacodynamic properties against renal diseases had not been determined. Thus, our study explored the absorption, distribution and excretion of ZCL-278 as well as its pharmacological efficacy against chronic kidney disease (CKD). With the optimized detection method, absolute oral bioavailability of ZCL-278 was determined as 10.99 % in male and 17.34 % in female rats. ZCL-278 was rapidly and abundantly distributed in various tissues, especially the kidney and heart, while few excreted through urine and feces. In the adenine-induced CKD mice, the increased plasma creatinine and urea, the decreased body weight as well as the renal pathological alterations, including vacuolization of renal tubular epithelial cells, granular degeneration, cell flattening, luminal dilation, and cylindruria, were significantly ameliorated after ZCL-278 administration. Moreover, ZCL-278 could also reverse the increased intensities of renal inflammation and fibrosis in the CKD mice. These results clarified the pharmacokinetics of ZCL-278 in rats and preliminarily indicated that ZCL-278 has favorable pharmacodynamic properties for CKD primed for lead development and optimization, warranting further drug development.

IntroductionCisplatin is a widely used chemotherapeutic agent prescribed to treat solid tumors. However, its clinical application is limited because of cisplatin- induced nephrotoxicity. A known complication of cisplatin is acute kidney injury (AKI). Deletion polymorphisms of GSTM1 and GSTT1, members of the glutathione S-transferase family, are common in humans and are presumed to be associated with various kidney diseases. However, the specific roles and mechanisms of GSTM1 and GSTT1 in cisplatin induced AKI remain unclear.MethodsTo investigate the roles of GSTM1 and GSTT1 in cisplatin-induced AKI, we generated GSTM1 and GSTT1 knockout mice using CRISPR-Cas9 technology and assessed their kidney function under normal physiological conditions and cisplatin treatment. Using ELISA kits, we measured the levels of oxidative DNA and protein damage, along with MDA, SOD, GSH, and the GSH/GSSG ratio in wild-type and GSTM1/GSTT1 knockout mice following cisplatin treatment. Additionally, oxidative stress levels and the expression of ferroptosis-related proteins in kidney tissues were examined through Western blotting, qPCR, immunohistochemistry, and immunofluorescence techniques.ResultsHere, we found that GSTT1 and GSTM1 were downregulated in the renal tubular cells of AKI patients and cisplatin-treated mice. Compared with WT mice, Gstm1/Gstt1-DKO mice were phenotypically normal but developed more severe kidney dysfunction and exhibited increased ROS levels and severe ferroptosis after injecting cisplatin.DiscussionOur study revealed that GSTM1 and GSTT1 can protect renal tubular cells against cisplatin-induced nephrotoxicity and ferroptosis, and genetic screening for GSTM1 and GSTT1 polymorphisms can help determine a standard cisplatin dose for cancer patients undergoing chemotherapy.

Water use efficiency (WUE) is an important metric for assessing regional sustainability and the carbon-water balance of ecosystems. Drought is a significant stressor affecting the carbon-water cycle. Nevertheless, the response of WUE to different types of droughts remains poorly understood. This study analyzed the variability characteristics of WUE, meteorological (SPEI), hydrological (SRI), and agricultural (SSMI) droughts. Additionally, it investigated the response of WUE to these three types of droughts over the Mongolian Plateau (1982–2021). The results indicated a non-significant decreasing trend in WUE, and a remarkable drying trend in SPEI, SRI, and SSMI. WUE was positively correlated with three drought indices, with average R (WUE, SPEI), R (WUE, SRI), and R (WUE, SSMI) at 0.060, 0.051, and 0.028, respectively; this indicated that WUE decreased as drought intensified. Regional WUE proved most sensitive to SPEI and was observed in coniferous forests, broadleaf forests, meadow steppes, typical steppes, and alpine grasslands. Sand land vegetation and desert steppes showed the highest sensitivity to SRI, whereas shrubs and croplands exhibited the highest sensitivity to SSMI. More importantly, SRI caused more severe losses in WUE than SSMI and SPEI. These findings offer valuable insights for future carbon sink management and drought risk assessment.

The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.

Objective As the prevalence of diabetes steadily increases, the burden of diabetic kidney disease (DKD) is also intensifying. In response, we have utilized a 10-year diabetes cohort from our medical center to train machine learning-based models for predicting DKD and interpreting relevant factors. Methods Employing a large dataset from 73,101 hospitalized type 2 diabetes patients at The First Affiliated Hospital of Zhengzhou University, we analyzed demographic and medication data. Machine learning models, including XGBoost, CatBoost, LightGBM, Random Forest, AdaBoost, GBDT (gradient boosting decision tree), and SGD (stochastic gradient descent), were trained on these data, focusing on interpretability by SHAP. SHAP explains the output of the models by assigning an importance value to each feature for a particular prediction, enabling a clear understanding of how individual features influence the prediction outcomes. Results The XGBoost model achieved an area under the curve (AUC) of 0.95 and an area under the precision-recall curve (AUPR) of 0.76, while CatBoost recorded an AUC of 0.97 and an AUPR of 0.84. These results underscore the effectiveness of these models in predicting DKD in patients with type 2 diabetes. Conclusions This study provides a comprehensive approach for predicting DKD in patients with type 2 diabetes, employing machine learning techniques. The findings are crucial for the early detection and intervention of DKD, offering a roadmap for future research and healthcare strategies in diabetes management. Additionally, the presence of non-diabetic kidney diseases and diabetes with complications was identified as significant factors in the development of DKD.

Abstract Chronic kidney disease (CKD) has historically been a significant global health concern, profoundly impacting both life and well-being. In the process of CKD, with the gradual loss of renal function, the incidence of various life-threatening complications, such as cardiovascular diseases, cerebrovascular accident, infection and stroke, is also increasing rapidly. Unfortunately, existing treatments exhibit limited ability to halt the progression of kidney injury in CKD, emphasizing the urgent need to delve into the precise molecular mechanisms governing the occurrence and development of CKD while identifying novel therapeutic targets. Renal fibrosis, a typical pathological feature of CKD, plays a pivotal role in disrupting normal renal structures and the loss of renal function. Ferroptosis is a recently discovered iron-dependent form of cell death characterized by lipid peroxide accumulation. Ferroptosis has emerged as a potential key player in various diseases and the initiation of organ fibrosis. Substantial evidence suggests that ferroptosis may significantly contribute to the intricate interplay between CKD and its progression. This review comprehensively outlines the intricate relationship between CKD and ferroptosis in terms of iron metabolism and lipid peroxidation, and discusses the current landscape of pharmacological research on ferroptosis, shedding light on promising avenues for intervention. It further illustrates recent breakthroughs in ferroptosis-related regulatory mechanisms implicated in the progression of CKD, thereby providing new insights for CKD treatment. Video Abstract

Abstract Aim Cannabinoid receptors are components of the endocannabinoid system that affect various physiological functions. We aim to investigate the effect of cannabinoid receptor modulation on kidney disease. Methods PubMed, Web of Science databases, and EMBASE were searched. Articles selection, data extraction and quality assessment were independently performed by two investigators. The SYRCLE’s RoB tool was used to assess the risk of study bias, and pooled SMD using a random-effect model and 95% CIs were calculated. Subgroup analyses were conducted in preselected subgroups, and publication bias was evaluated. We compared the effects of CB1 and CB2 antagonists and/or knockout and agonists and/or genetic regulation on renal function, blood glucose levels, body weight, and pathological damage-related indicators in different models of chronic and acute kidney injury. Results The blockade or knockout of CB1 could significantly reduce blood urea nitrogen [SMD,− 1.67 (95% CI − 2.27 to − 1.07)], serum creatinine [SMD, − 1.88 (95% CI − 2.91 to − 0.85)], and albuminuria [SMD, − 1.60 (95% CI − 2.16 to − 1.04)] in renal dysfunction animals compared with the control group. The activation of CB2 group could significantly reduce serum creatinine [SMD, − 0.97 (95% CI − 1.83 to − 0.11)] and albuminuria [SMD, − 2.43 (95% CI − 4.63 to − 0.23)] in renal dysfunction animals compared with the control group. Conclusions The results suggest that targeting cannabinoid receptors, particularly CB1 antagonists and CB2 agonists, can improve kidney function and reduce inflammatory responses, exerting a renal protective effect and maintaining therapeutic potential in various types of kidney disease.

Based on the Michaelis-Menten reaction model with catalytic effects, a more comprehensive one-dimensional stochastic Langevin equation with immune surveillance for a tumor cell growth system is obtained by considering the fluctuations in growth rate and mortality rate. To explore the impact of environmental fluctuations on the growth of tumor cells, the analytical solution of the steady-state probability distribution function of the system is derived using the Liouville equation and Novikov theory, and the influence of noise intensity and correlation intensity on the steady-state probability distributional function are discussed. The results show that the three extreme values of the steady-state probability distribution function exhibit a structure of two peaks and one valley. Variations of the noise intensity, cross-correlation intensity and correlation time can modulate the probability distribution of the number of tumor cells, which provides theoretical guidance for determining treatment plans in clinical treatment. Furthermore, the increase of noise intensity will inhibit the growth of tumor cells when the number of tumor cells is relatively small, while the increase in noise intensity will further promote the growth of tumor cells when the number of tumor cells is relatively large. The color cross-correlated strength and cross-correlated time between noise also have a certain impact on tumor cell proliferation. The results help people understand the growth kinetics of tumor cells, which can a provide theoretical basis for clinical research on tumor cell growth.

Abstract Diabetic nephropathy (DN) is a serious microvascular complication of diabetes. Ferroptosis, a new form of cell death, plays a crucial role in the pathogenesis of DN. Renal tubular injury triggered by ferroptosis might be essential in this process. Numerous studies demonstrate that the vitamin D receptor (VDR) exerts beneficial effects by suppressing ferroptosis. However, the underlying mechanism has not been fully elucidated. Thus, they verified the nephroprotective effect of VDR activation and explored the mechanism by which VDR activation suppressed ferroptosis in db/db mice and high glucose‐cultured proximal tubular epithelial cells (PTECs). Paricalcitol (PAR) is a VDR agonist that can mitigate kidney injury and prevent renal dysfunction. PAR treatment could inhibit ferroptosis of PTECs through decreasing iron content, increasing glutathione (GSH) levels, reducing malondialdehyde (MDA) generation, decreasing the expression of positive ferroptosis mediator transferrin receptor 1 (TFR‐1), and enhancing the expression of negative ferroptosis mediators including ferritin heavy chain (FTH‐1), glutathione peroxidase 4 (GPX4), and cystine/glutamate antiporter solute carrier family 7 member 11 (SLC7A11). Mechanistically, VDR activation upregulated the NFE2‐related factor 2/heme oxygenase‐1 (Nrf2/HO‐1) signaling pathway to suppress ferroptosis in PTECs. These findings suggested that VDR activation inhibited ferroptosis of PTECs in DN via modulating the Nrf2/HO‐1 signaling pathway.

Abstract Background Inter-joint coordination is an important factor affecting postural stability, and its variability increases after fatigue. This study aimed to investigate the coordination pattern of lower limb joints during the sit-to-stand (Si-St) and stand-to-sit (St-Si) tasks in stroke patients and explore the influence of duration on inter-joint coordination. Methods Thirteen stroke hemiplegia patients (five with left paretic and eight right paretic) and thirteen age-matched healthy subjects were recruited. The Si-St and St-Si tasks were performed while each subject’s joint kinematics were recorded using a three-dimensional motion capture system. Sagittal joint angles of the bilateral hip, knee and ankle joints as well as the movement duration were extracted. The angle-angle diagrams for the hip-knee, hip-ankle and knee-ankle joint were plotted to assess the inter-joint coordination. The inter-joint coordination was quantified using geometric characteristics of the angle-angle diagrams, including perimeter, area and dimensionless ratio. The coefficient of variation (CV) was performed to compare variability of the coordination parameters. Results There were no significant differences in the perimeter, area and dimensionless ratio values of the bilateral hip-knee, hip-ankle and knee-ankle inter-joints during Si-St and St-Si tasks in the stroke group. The perimeter values of bilateral hip-knee and knee-ankle inter-joints in the stroke group were lower (P

Abstract Acute kidney injury (AKI) is a prevalent pathological condition that is characterized by a precipitous decline in renal function. In recent years, a growing body of studies have demonstrated that renal maladaptation following AKI results in chronic kidney disease (CKD). Therefore, targeting the transition of AKI to CKD displays excellent therapeutic potential. However, the mechanism of AKI to CKD is mediated by multifactor, and there is still a lack of effective treatments. Ferroptosis, a novel nonapoptotic form of cell death, is believed to have a role in the AKI to CKD progression. In this study, we retrospectively examined the history and characteristics of ferroptosis, summarized ferroptosis’s research progress in AKI and CKD, and discussed how ferroptosis participates in regulating the pathological mechanism in the progression of AKI to CKD. Furthermore, we highlighted the limitations of present research and projected the future evolution of ferroptosis. We hope this work will provide clues for further studies of ferroptosis in AKI to CKD and contribute to the study of effective therapeutic targets to prevent the progression of kidney diseases.

Abstract In this study, we explored inter-ocular asymmetry (between the two eyes of the same patient) using optical coherence tomography angiography (OCTA) in patients with diabetes mellitus (DM) at different retinopathy stages. A total of 258 patients were divided into four groups: no DM, DM without diabetic retinopathy (DR), non-proliferative DR (NPDR), and proliferative DR (PDR). Superficial and deep vessel density (SVD, DVD), superficial and deep perfusion density (SPD, DPD), foveal avascular zone (FAZ) area, perimeter and circularity were calculated, and asymmetry index (AI) was used to evaluate the asymmetry of two eyes of the same subject. AIs of SPD, SVD, FAZ area and FAZ perimeter in the PDR group were larger than all other 3 groups (all p

The characteristics of 21 tropical cyclones (TCs) and associated high winds in Shanghai from 2005 to 2020 are analyzed based on the TC best track data, surface observations, and ERA5 reanalysis dataset. Results indicate that 95% of the TCs causing high winds in Shanghai were generated during July to September, and located to the south of Shanghai for ~80% of the impact time. Divided into four categories according to tracks, the TCs show significant discrepancies in genesis month and location, intensity, size, and duration. The findings of high winds in Shanghai highlight the combination effect of TC intensity, track, and the distance between TCs and Shanghai normalized by TC size (NDSH-TC). The maximum 10-min sustained (Vmax) and extreme 3-s wind speeds (Vex) in Shanghai only increase slightly with TC intensity, due to the fact that the stronger TCs are usually farther away from Shanghai. The Vmax and Vex differ in response to different TC tracks and reach the maximum value under TCs moving northward across the land west of Shanghai and the sea east of Shanghai, respectively. The urban and coastal wind speeds are the smallest and the largest respectively, indicating that the largest underlying surface roughness is associated with the most rapid urban construction. The wind speeds under the influence of TCs in four quadrants of Shanghai also present different variations with the NDSH-TC. Additionally, the wind direction in Shanghai is regularly related to the relative position between the TC center and Shanghai. A case study combined with TC In-Fa (2106) verified the validity of the conclusions, which can provide a reference for TC forecasters to make more accurate wind field predictions.

The Augmented Transformer U-Net (AugTransU-Net) is proposed to address limitations in existing transformer-related U-Net models for brain tumor segmentation. While previous models effectively capture long-range dependencies and global context, these works ignore the hierarchy to a certain degree and need more feature diversity as depth increases. The proposed AugTransU-Net integrates two advanced transformer modules into different positions within a U-shaped architecture to overcome these issues. The fundamental innovation lies in constructing improved augmentation transformer modules that incorporate Augmented Shortcuts into standard transformer blocks. These augmented modules are strategically placed at the bottleneck of the segmentation network, forming multi-head self-attention blocks and circulant projections, aiming to maintain feature diversity and enhance feature interaction and diversity. Furthermore, paired attention modules operate from low to high layers throughout the network, establishing long-range relationships in both spatial and channel dimensions. This allows each layer to comprehend the overall brain tumor structure and capture semantic information at critical locations. Experimental results demonstrate the effectiveness and competitiveness of AugTransU-Net in comparison to representative works. The model achieves Dice values of 89.7%/89.8%, 78.2%/78.6%, and 80.4%/81.9% for whole tumor (WT), enhancing tumor (ET) and tumor core (TC) segmentation on the BraTS2019-2020 validation datasets, respectively. The code for AugTransU-Net will be made publicly available at https://github.com/MuqinZ/AugTransUnet.

Soil microorganisms play a crucial role in wetland ecosystems, but the specific mechanisms underlying their diversity and network stability across lakeshore wetlands, as well as their importance in microbial ecology, are still poorly understood. Here, we analyzed the biogeographical pattern and network stability of lakeshore wetland microbial communities across a regional scale, and identified the primary factors that influence their composition. The results revealed that α-diversity of wetland bacteria decreased linearly with increasing longitude and latitude, whereas the α-diversity of fungal showed an increasing trend in lakeshore wetlands. Species richness was the principal way that the biogeographical pattern of α-diversity was expressed. The β-diversity of soil microbial in lakeshore wetlands has a significant geographical attenuation pattern. Fungal communities were influenced by stochastic spatial diffusion, while bacterial communities were influenced by deterministic environmental filtration as they transition from semi-arid to arid regions. Community network stability was specifically determined by bacterial and fungal diversity. Structural equation modelling revealed that spatial distance and climate indirectly affect bacterial diversity by influencing soil and plant diversity, which in turn affects feedback network stability. These findings show that the geographical pattern of soil microbial diversity in lakeshore wetlands influences network stability at the spatial scale, offering insights into the adaptation of wetland ecosystem diversity and stability at the regional level.

Purpose: This study aims to explore the dynamic balance of human beings and investigate the relationship between functional structure as well as functional connectivity. Through a comprehensive bibliometric and visual analysis of the research literature from 1995 to 2022, we quantitatively display the development of the dynamic balance and brain structure as well as functional connection. Our objective is to present new trends and frontiers in the study of dynamic balance and brain function through bibliometrics software, providing valuable insights for future research in this domain. Methods: The literature on dynamic balance, brain structure and functional connectivity between 1995 and 2022 was retrieved from the Web of Science database. We employed CiteSpace software to analyze various aspects, including the year of publication, journal, authors, keywords, institutions, countries, and references. Based on the analysis results, a co-reference map was generated to visually observe research hotspots and knowledge structures. Results: A total of 1533 records were retrieved during the survey period (1995–2022), with a gradually increase in the number of annual publications. Notably, the data suggests a notable increase in publications between 2020 and 2021. The number of publications increased by 20 % from 2020 to 2021. The journal “Proceedings of the National Academy of Sciences (PNAS)” emerged as the most prolific journal. Among the cited authors, Deco and Gustavo ranked at the top. Key research terms in this field include ''neural network'', ''functional connectivity'', ''dynamic'', ''model'' and ''brain''. Particularly, the keyword ''neural network'' exhibited the strongest growth. The analysis of keywords cluster revealed the top 10 clusters of research themes. Oxford University stood out as the most productive institution, while the United States held the greatest influence with the highest number of publications and centrality. The reference cluster analysis further demonstrated the top 10 clusters in the literature. Conclusion: Through the use of CiteSpace software, this study performed a comprehensive bibliometric and visual analysis of the Web of Science research literature on human dynamic balance and brain structural as well as functional connectivity over the past few decades. This may help researchers identify new perspectives on potential collaborators as well as collaborating institutions, hot topics, and research frontiers in the research field. The results provided an intuitive displayed overview of research trends, hotspots and frontiers in this field, facilitating a general understanding of its progression. Through unremitting efforts, it provides valuable guidance and reference for future research work.

The Qaidam Block, located at the northern Qinghai–Tibet Plateau, is a pivotal area in unraveling the closure time of the Kunlun Ocean basin which might have recorded the transformation process between the Proto-Tethys and Paleo-Tethys Ocean basins. However, the late Triassic position of the Qaidam Block remains enigmatic, largely due to the scarcity of paleomagnetic data essential for quantitatively determining its paleolatitude. The widespread presence of the Elashan formation, particularly along the southern periphery of the Qaidam block, presents good material for conducting paleomagnetic work. Nevertheless, the primary magnetic carriers preserved within the Elashan formation might be influenced by multiple tectonic thermal events, particularly those associated with collisions between southern blocks and the Qaidam Block. Here we present rock magnetism and magnetic fabrics studies to identify the content and composition of magnetic minerals within the Elashan formation. The rock magnetic and petrologic results show that the magnetic carriers in the samples from the Elashan formation are dominated by magnetite with a small amount of goethite, pyrrhotite, and hematite. The results of Anisotropy in Magnetic Susceptibility indicate that the south of the Longwalangku section might not be obviously influenced by the tectonic events. Our results also provided guidance for future paleomagnetic research, emphasizing the importance of conducting further sampling away from adjacent faults, particularly in the southern Longwalangku area.

Background: Physical activity contributes to both physiological and psychosocial benefits for children and adolescents with disabilities. However, the prevalence of physical inactivity is notably higher among disabled young people compared to their healthy peers. Despite this, there is a lack of constructed knowledge structure, evolutionary path, research hotspots, and frontiers in studies related to physical activity in young people with disabilities.Methods: The literature related to the research of physical activity in children and adolescents with disabilities was retrieved from the core collection of the Web of Science. The annual publication numbers and the timing, frequency, and centrality of the co-occurrence network with respect to journals, countries, institutions, authors, references, and keywords were analyzed. Additionally, clustering analysis and burst analysis were performed on the references and keywords. All analyses were conducted using CiteSpace. Results: A total of 1308 related articles were included. The knowledge structure of research on the physical activity of disabled children and adolescents, including annual publication numbers, influencing journals, countries, institutions, authors, references, and keywords along with their respective collaborative networks, has been constructed. Furthermore, the research foundation, current hot topics, and research frontiers have been identified by analyzing references and keywords. Conclusions: Current research hotspots include interventions, therapies, and programs aimed at enhancing specific skills, as well as addressing the satisfaction of competence to improve motivation and the effectiveness of physical activity. There is also a focus on the development of scales for quantitative studies. Future directions may be toward personalized interventions or programs to enhance physical activity levels among youth with disabilities.

A reasonable inflating strategy must be developed for filling an onboard hydrogen storage tank with hydrogen gas. The inflow hydrogen temperature has always been a constant value in filling cases. However, in our opinion, the optimal inflow hydrogen temperature is not supposed to be a fixed value but a value that constantly changes and adjusts with filling time, i.e., the inflow hydrogen temperature is a function of the filling time. How to determine this functional relationship is a critical problem to be addressed. Herein, an approach is introduced. A dual-zone model is presented to research the thermal effect during the process of charging hydrogen storage tanks. Based on the numerical results of the dual-zone model, the charging process was divided into three stages, allowing us to obtain data for 1331 filling cases. Then, a back propagation (BP) neural network model was built to analyze the data, and the implicit relationship between the inflow hydrogen temperatures and maximum hydrogen temperature pressure could be deduced. With this implicit relationship, the critical values of the inflow hydrogen temperatures can be obtained from the critical situation. Suppose the inflow hydrogen temperatures in a practical case are higher than the critical values. In that case, the highest hydrogen temperature in the tank will exceed the limited safety value of 358 K. In contrast, if the inflow hydrogen temperatures are lower than the critical values, then more energy will be needed to precool the inlet hydrogen temperature. Thus, theoretically, the critical inflow hydrogen temperatures should be at their optimal values.

Background: Ciprofol, a novel sedative–hypnotic drug, has been approved for its use in inducing and maintaining general anesthesia, as well as for providing sedation.Methods: In this phase I, single-center, parallel, controlled, open-label clinical trial, our objective was to analyze the pharmacokinetics (PK), pharmacodynamics (PD), and safety of ciprofol emulsion in 24 participants with mild and moderate renal impairment (n = 8 per group) and matched healthy participants (n = 8). An initial loading infusion of ciprofol was administered at 0.4 mg/kg for 1 min, followed by a maintenance infusion at a rate of 0.4 mg/kg/h for 30 min. We collected plasma and urine samples from the participants to assess the PK of ciprofol and its metabolite M4. The evaluation of PD involved using a modified observer’s alertness/sedation scale (MOAA/S) in combination with bispectral index (BIS) monitoring. Safety assessments were conducted throughout the trial process.Results: The plasma concentration–time curve of ciprofol in participants with renal impairment was similar to that in participants with normal kidney function. The area under the curve (AUC) and maximum concentration (Cmax) of total and unbound ciprofol in plasma for participants with renal impairment were only slightly higher (0.7–1.2-fold) than those in participants with normal renal function. In contrast, for the metabolite M4, AUC values were 1.3- and 2.1-fold greater in participants with mild and moderate renal impairment, respectively, than in healthy controls. However, renal impairment had no significant impact on the PD parameters. The study found that ciprofol was well-tolerated, with all adverse events (AEs) reported being mild or moderate in severity.Conclusion: Based on these findings, we can conclude that no dosage adjustment of ciprofol is necessary for patients with mild–moderate renal impairment who receive the injection.Clinical Trial Registration:https://clinicaltrials.gov, identifier NCT04142970.

Common reed (Phragmites australis) is a widespread grass species that exhibits a high degree of intraspecific variation for functional traits along environmental gradients. However, the mechanisms underlying intraspecific variation and adaptation strategies in response to environmental gradients on a regional scale remain poorly understood. In this study, we measured leaf, stem, and root traits of common reed in the lakeshore wetlands of the arid and semi-arid regions of the Inner Mongolia Plateau aiming to reveal the regional-scale variation for functional traits in this species, and the corresponding potentially influencing factors. Additionally, we aimed to reveal the ecological adaptation strategies of common reed in different regions using the plant economics spectrum (PES) theory. The results showed that functional-trait variation followed significant latitudinal and longitudinal patterns. Furthermore, we found that these variations are primarily driven by temperature-mediated climatic differences, such as aridity, induced by geographical distance. In contrast, soil properties and the combined effects of climate and soil had relatively minor effects on such properties. In the case of common reed, the PES theory applies to the functional traits at the organ, as well as at the whole-plant level, and different ecological adaptation strategies across arid and semi-arid regions were confirmed. The extent of utilization and assimilation of resources by this species in arid regions was a conservative one, whereas in semi-arid regions, an acquisition strategy prevailed. This study provides new insights into intraspecific variations for functional traits in common reed on a regional scale, the driving factors involved, and the ecological adaptation strategies used by the species. Moreover, it provided a theoretical foundation for wetland biodiversity conservation and ecological restoration.

Abstract Background RNA methylation is a widely known post-transcriptional regulation which exists in many cancer and immune system diseases. However, the potential role and crosstalk of five types RNA methylation regulators in diabetic nephropathy (DN) and immune microenvironment remain unclear. Methods The mRNA expression of 37 RNA modification regulators and RNA modification regulators related genes were identified in 112 samples from 5 Gene Expression Omnibus datasets. Nonnegative Matrix Factorization clustering method was performed to determine RNA modification patterns. The ssGSEA algorithms and the expression of human leukocyte antigen were employed to assess the immune microenvironment characteristics. Risk model based on differentially expression genes responsible for the modification regulators was constructed to evaluate its predictive capability in DN patients. Furthermore, the results were validated by using immunofluorescence co-localizations and protein experiments in vitro. Results We found 24 RNA methylation regulators were significant differently expressed in glomeruli in DN group compared with control group. Four methylation-related genes and six RNA regulators were introduced into riskScore model using univariate Logistic regression and integrated LASSO regression, which could precisely distinguish the DN and healthy individuals. Group with high-risk score was associated with high immune infiltration. Three distinct RNA modification patterns were identified, which has significant differences in immune microenvironment, biological pathway and eGFR. Validation analyses showed the METTL3, ADAR1, DNMT1 were upregulated whereas YTHDC1 was downregulated in DN podocyte cell lines comparing with cells cultured by the normal glucose. Conclusion Our study reveals that RNA methylation regulators and immune infiltration regulation play critical roles in the pathogenesis of DN. The bioinformatic analyses combine with verification in vitro could provide robust evidence for identification of predictive RNA methylation regulators in DN.

In order to be able to make a good decision, we need to evaluate the uncertainty information of multiple attributes of different alternatives that obey the normal distribution, and the interrelationship among multiple attributes should be considered in the process of evaluating. This paper aims to propose a new multiple attribute decision-making (MADM) method, which uses a new aggregation operator to evaluate the uncertainty information that obey normal distribution comprehensively. We firstly extended Schweizer-Sklar (SS) t-norm (TN) and t-conorm (TCN) to q-rung orthopair normal fuzzy number (q-RONFN) and defined the Schweizer-Skla operational laws of q-rung orthopair normal fuzzy set (q-RONFs). Secondly, we developed q-rung orthopair normal fuzzy Maclaurin symmetric mean aggregation operators based on SS operations considering that the Maclaurin symmetric mean operator can reflect the interrelationship among multiple input variables. Furthermore, we discussed some desirable properties of the above operators, such as monotonicity, commutativity, and idempotency. Lastly, we proposed a novel MADM method based on developed aggregation operators. A numerical example on enterprise partner selection is given to testify the effectiveness of the developed method. The results of analysis indicated that our proposed aggregation operators have stronger information aggregation ability and are more general and flexible for MADM problems.

Spasticity is a complex neurological disorder, causing significant physical disabilities and affecting patients' independence and quality of daily lives. Current spasticity assessment methods are questioned for their non-standardized measurement protocols, limited reliabilities, and capabilities in distinguishing neuron or non-neuron factors in upper motor neuron lesion. A series of new approaches are developed for improving the effectiveness of current clinical used spasticity assessment methods with the developing technology in biosensors, robotics, medical imaging, biomechanics, telemedicine, and artificial intelligence. We investigated the reliabilities and effectiveness of current spasticity measures employed in clinical environments and the newly developed approaches, published from 2016 to date, which have the potential to be used in clinical environments. The new spasticity scales, taking advantage of quantified information such as torque, or echo intensity, the velocity-dependent feature and patients' self-reported information, grade spasticity semi-quantitatively, have competitive or better reliability than previous spasticity scales. Medical imaging technologies, including near-infrared spectroscopy, magnetic resonance imaging, ultrasound and thermography, can measure muscle hemodynamics and metabolism, muscle tissue properties, or temperature of tissue. Medical imaging-based methods are feasible to provide quantitative information in assessing and monitoring muscle spasticity. Portable devices, robotic based equipment or myotonometry, using information from angular, inertial, torque or surface EMG sensors, can quantify spasticity with the help of machine learning algorithms. However, spasticity measures using those devices are normally not physiological sound. Repetitive peripheral magnetic stimulation can assess patients with severe spasticity, which lost voluntary contractions. Neuromusculoskeletal modeling evaluates the neural and non-neural properties and may gain insights into the underlying pathology of spasticity muscles. Telemedicine technology enables outpatient spasticity assessment. The newly developed spasticity methods aim to standardize experimental protocols and outcome measures and enable quantified, accurate, and intelligent assessment. However, more work is needed to investigate and improve the effectiveness and accuracy of spasticity assessment.

B cells and the humoral immunity are important players in the pathogenesis of autoimmune diseases. BAFF (also known as BLYS) and a proliferation-inducing ligand APRIL are required for the maintenance of the B-cell pool and humoral immunity. BAFF and APRIL can promote B-cell differentiation, maturation, and plasma cell antibody secretion. BAFF/APRIL overexpression has been identified in several autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematosus, IgA nephropathy, etc. Telitacicept, a novel fully human TACI-Fc fusion protein that binds both BAFF and APRIL, was approved in China in March 2021 for the treatment of systemic lupus erythematosus at a recommended dose of 160 mg/w subcutaneously and is in clinical trials for the treatment of multiple indications in other autoimmune diseases. In this review, we explored telitacicept’s mechanism of action and clinical data. In addition, the immune features of autoimmune nephropathy were discussed, emphasizing lupus nephritis, IgA nephropathy, and membranous nephropathy.

Background and aimsDietary factors play an important role in the development of chronic kidney disease (CKD). However, evidence on the relationship of beans consumption with CKD remains limited and inconclusive, especially in the middle-and low-income populations. The current study aimed to investigate the relationships of beans intake with indicators of kidney injury and CKD prevalence in rural adults.MethodsA total of 20,733 rural adults from the Henan Rural Cohort Study in 2018–2022 were included. The total beans intake was collected using a validated food frequency questionnaire. Indicators of kidney injury and CKD was determined by the estimated glomerular filtration rate and the urinary albumin to creatinine ratio. Generalized linear regression and logistic regression models were applied to estimate the relationship of beans intake with continuous and dichotomized indicators of renal function, respectively.ResultsOf the 20,733 participants, 2,676 (12.91%) subjects were identified as CKD patients. After adjusting for potential confounders, participants in the higher quartiles of beans intake had a lower prevalence of CKD (odds ratio and 95% confidence interval, OR (95%CI); Q2: 0.968(0.866–1.082); Q3: 0.836(0.744–0.939); Q4: 0.854(0.751–0.970)) and albuminuria (Q2: 0.982(0.875–1.102); Q3: 0.846(0.750–0.954); Q4: 0.852 (0.746–0.973)), compared with the Q1. Per 50 g/day increment in beans intake was significantly associated with a 5 and 4% decreased prevalence of albuminuria and CKD, respectively. These inverse relationships were also significant in the subgroups of men, elder, and high-income participants (p

Abstract Soil freeze–thaw processes lead to high nitrous oxide (N2O) emissions and exacerbate the greenhouse effect. The wetlands of the Inner Mongolia Plateau are in the pronounced seasonal freeze–thaw zone, but the effect of spring thaw on N2O emissions and related microbial mechanisms is still unclear. We investigated the effects of different periods (freeze, freeze–thaw, and thaw) on soil bacterial community diversity and composition and greenhouse gas emissions during the spring freeze–thaw in the XiLin River riparian wetlands in China by amplicon sequencing and static dark box methods. The results showed that the freeze–thaw periods predominantly impact on the diversity and composition of the bacterial communities. The phyla composition of the soil bacteria communities of the three periods is similar in level, with Proteobacteria, Chloroflexi, Actinobacteria, and Acidobacteria dominating the microbial communities. The alpha‐diversity of bacterial communities in different periods varies that the freezing period is higher than that of the freeze–thaw period (p

BackgroundPeritonitis is considered as one of the most serious complications that cause hospitalization in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). There is limited evidence on the impact of the parathyroid hormone (PTH) on the first peritoneal dialysis (PD)-associated peritonitis episode. We aimed to investigate the influence of serum intact parathyroid hormone (iPTH) on peritonitis in patients undergoing PD.MethodsThis was a retrospective cohort study. Patients undergoing initial CAPD from a single center in China were enrolled. The baseline characteristics and clinical information were recorded. The primary outcome of interest was the occurrence of the first PD-associated peritonitis episode. Five Cox proportional hazard models were constructed in each group set. In group set 1, all participants were divided into three subgroups by tertiles of the serum concentration of iPTH; in group set 2, all participants were divided into three subgroups based on the serum concentration of iPTH with 150 pg/ml interval (300 pg/ml). Hazard ratios and 95% confidence intervals (CIs) were calculated for each model. The multivariate linear regression analysis elimination procedure assessed the association between the clinical characteristics at baseline and the iPTH levels. Restricted cubic spline models were constructed, and stratified analyses were also conducted.ResultsA total of 582 patients undergoing initial PD (40% women; mean age, 45.1 ± 11.5 years) from a single center in China were recruited. The median follow-up duration was 25.3 months. Multivariate Cox regression analysis showed that, in the fully adjusted model, a higher serum iPTH level (tertile 3, iPTH >300 pg/ml) was significantly associated with a higher risk of PD-associated peritonitis at 3 years [tertile 3: hazard ratio (HR) = 1.53, 95%CI = 1.03–2.55, p = 0.03; iPTH > 300 pg/ml: HR = 1.57, 95%CI = 1.08–2.27, p = 0.02]. The hazard ratio for every 100 pg/ml increase in serum iPTH level was 1.12 (95%CI = 1.05–1.20, p

Abstract Arid area is very sensitive to global warming and are extremely vulnerable to climate change. Moreover, the water resources system in the arid area is fragile and will undergo tremendous changes with climate change. Therefore, the interaction of climate and hydrology in arid area has an important impact on the formation of regional microclimate and hydrological changes. Daihai Lake is a typical closed inland lake in arid area of China, and a key area for ecological protection in North China. In this paper, WRF-Hydro model is used to simulate the climate hydrological coupling situation of Daihai Basin from 1980 to 2020, and the coupling results are verified and calibrated by meteorological statistics, runoff calculation and remote sensing analysis. Based on the synopsis of climate and hydrology in the past 40 years, the causes and future trends of the hydrological elements in Daihai Basin are analyzed. Through the analysis, it is found that the interannual variation of precipitation in Daihai Basin is sharp, with 401.75 mm as the average from 1980 to 1994; and drastic fluctuations from 1995 to 2011, with a difference of nearly 400 mm between the interannual maximum and minimum; From 2012 to 2020, the fluctuation is small. Although the interannual variation of evaporation fluctuated, it showed an upward trend with a slope of 8.855 mm/year. The annual average temperature showed an obvious upward trend with a slope of 0.040 °C/year. From 1980 to 2020, the inflow of Daihai Lake shows a downward trend; Since 2013, the runoff into the lake has tended to be flat. Climate change and human activities are the decisive factors leading to the change of water quantity in Daihai, among which human activities play a greater role. Cultivated land irrigation and industrial water use are highly correlated with the lake discharge, and these two factors have a great influence on the lake discharge. If the current agricultural and industrial water consumption does not increase, Daihai still has a lifespan of nearly 120 years. If human activities do not change and any protective measures are not taken in time, under the background of global climate change, the flow of the Daihai Lake into the lake will be reduced to zero in 2025, and the Daihai Lake will completely dry up in 2031–2033. The study of climate hydrological coupling of long time series in Daihai Basin can not only make up for the lack of runoff data, but also provide the basis for water resources management, disaster prevention and mitigation.

The precise segmentation of tumor regions plays a pivotal role in the diagnosis and treatment of brain tumors. However, due to the variable location, size, and shape of brain tumors, the automatic segmentation of brain tumors is a relatively challenging application. Recently, U-Net related methods, which largely improve the segmentation accuracy of brain tumors, have become the mainstream of this task. Following merits of the 3D U-Net architecture, this work constructs a novel 3D U-Net model called SGEResU-Net to segment brain tumors. SGEResU-Net simultaneously embeds residual blocks and spatial group-wise enhance (SGE) attention blocks into a single 3D U-Net architecture, in which SGE attention blocks are employed to enhance the feature learning of semantic regions and reduce possible noise and interference with almost no extra parameters. Besides, the self-ensemble module is also utilized to improve the segmentation accuracy of brain tumors. Evaluation experiments on the Brain Tumor Segmentation (BraTS) Challenge 2020 and 2021 benchmarks demonstrate the effectiveness of the proposed SGEResU-Net for this medical application. Moreover, it achieves DSC values of 83.31, 91.64 and 86.85%, as well as Hausdorff distances (95%) of 19.278, 5.945 and 7.567 for the enhancing tumor, whole tumor, and tumor core on BraTS 2021 dataset, respectively.

Acute kidney injury (AKI), one of the most prevalent clinical diseases with a high incidence rate worldwide, is characterized by a rapid deterioration of renal function and further triggers the accumulation of metabolic waste and toxins, leading to complications and dysfunction of other organs. Multiple pathogenic factors, such as rhabdomyolysis, infection, nephrotoxic medications, and ischemia-reperfusion injury, contribute to the onset and progression of AKI. However, the detailed mechanism remains unclear. Ferroptosis, a recently identified mechanism of nonapoptotic cell death, is iron-dependent and caused by lipid peroxide accumulation in cells. A variety of studies have demonstrated that ferroptosis plays a significant role in AKI development, in contrast to other forms of cell death, such as apoptosis, necroptosis, and pyroptosis. In this review, we systemically summarized the definition, primary biochemical mechanisms, key regulators and associated pharmacological research progress of ferroptosis in AKI. We further discussed its therapeutic potential for the prevention of AKI, in the hope of providing a useful reference for further basic and clinical studies.

BackgroundGrowing evidence indicates that non-alcoholic fatty liver disease (NAFLD) is related to the occurrence and development of diabetic nephropathy (DN). This bioinformatics study aimed to explore optimal crosstalk genes and related pathways between NAFLD and DN.MethodsGene expression profiles were downloaded from Gene Expression Omnibus. CIBERSORT algorithm was employed to analyze the similarity of infiltrating immunocytes between the two diseases. Protein–protein interaction (PPI) co-expression network and functional enrichment analysis were conducted based on the identification of common differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were implemented to initially screen crosstalk genes. Machine learning models, including support vector machine, random forest model, and generalized linear model, were utilized to further identify the optimal crosstalk genes between DN and NAFLD. An integrated network containing crosstalk genes, transcription factors, and associated pathways was developed.ResultsFour gene expression datasets, including GSE66676 and GSE48452 for NAFLD and GSE30122 and GSE1009 for DN, were involved in this study. There were 80 common DEGs between the two diseases in total. The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. Among these genes, LPL and SPP1 were the most significant according to NAFLD-transcription factor network. Five hundred twenty-nine nodes and 1,113 edges comprised the PPI network of activated pathway-gene. In addition, 14 common pathways of these two diseases were recognized using Gene Ontology (GO) analysis; among them, regulation of the lipid metabolic process is closely related to both two diseases.ConclusionsThis study offers hints that NAFLD and DN have a common pathogenesis, and LPL and SPP1 are the most relevant crosstalk genes. Based on the common pathways and optimal crosstalk genes, our proposal carried out further research to disclose the etiology and pathology between the two diseases.

Diabetic nephropathy (DN) is the main cause of end-stage renal disease (ESRD), which is characterized by a series of abnormal changes such as glomerulosclerosis, podocyte loss, renal tubular atrophy and excessive deposition of extracellular matrix. Simultaneously, the occurrence of inflammatory reaction can promote the aggravation of DN-induced kidney injury. The most important processes in the canonical inflammasome pathway are inflammasome activation and membrane pore formation mediated by gasdermin family. Converging studies shows that pyroptosis can occur in renal intrinsic cells and participate in the development of DN, and its activation mechanism involves a variety of signaling pathways. Meanwhile, the activation of the NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome can not only lead to the occurrence of inflammatory response, but also induce pyroptosis. In addition, a number of drugs targeting pyroptosis-associated proteins have been shown to have potential for treating DN. Consequently, the pathogenesis of pyroptosis and several possible activation pathways of NLRP3 inflammasome were reviewed, and the potential drugs used to treat pyroptosis in DN were summarized in this review. Although relevant studies are still not thorough and comprehensive, these findings still have certain reference value for the understanding, treatment and prognosis of DN.

Changes in the weather will cause variations in the hydrological system. Arid areas, with fragile hydrological systems, are very sensitive to changes in the weather, so the coupling analysis of short-term weather and runoff in arid areas is of great significance. The Daihai Lake is a closed inland lake in an arid area of China. In this paper, Weather Research and Forecasting model mode-Hydrological module (WRF-HYDRO) is used to simulate the coupling of weather and hydrology in the Daihai Lake Basin. Regional optimization of WRF-HYDRO is carried out to determine the optimal parameters. The optimal WRF-HYDRO model is applied to couple the short-term weather and runoff in the Daihai Lake Basin to reproduce several rainstorm and flood events. It is found that runoff infiltration parameter (REFKDT) in WRF-HYDRO is the parameter that has the most severe effect on runoff in the Daihai Lake Basin. WRF-HYDRO can capture the rainstorm moment of the rainstorm events in the Daihai Lake Basin, especially the first rainstorm moment, and its simulation accuracy is good. WRF-HYDRO has a strong ability to capture flood peak, but there is a discrepancy between WRF-HYDRO flood peak and Soil Conservation Service Curve Number (SCS–CN) calculation result at the flood peak moment. The northern part of Zuoyun County should guard against the occurrence of flood disaster in wet season. The coupling of weather and hydrology can not only make up for the lack of runoff data in arid basins, but also provide a basis for water resources management and disaster prevention and mitigation in the basins.

Tumor segmentation using magnetic resonance imaging (MRI) plays a significant role in assisting brain tumor diagnosis and treatment. Recently, U-Net architecture with its variants have become prevalent in the field of brain tumor segmentation. However, the existing U-Net models mainly exploit coarse first-order features for tumor segmentation, and they seldom consider the more powerful second-order statistics of deep features. Therefore, in this work, we aim to explore the effectiveness of second-order statistical features for brain tumor segmentation application, and further propose a novel second-order residual brain tumor segmentation network, i.e., SoResU-Net. SoResU-Net utilizes a number of second-order modules to replace the original skip connection operations, thus augmenting the series of transformation operations and increasing the non-linearity of the segmentation network. Extensive experimental results on the BraTS 2018 and BraTS 2019 datasets demonstrate that SoResU-Net outperforms its baseline, especially on core tumor and enhancing tumor segmentation, illuminating the effectiveness of second-order statistical features for the brain tumor segmentation application.

The central region of Inner Mongolia is the northern ecological safety barrier of Beijing and even the whole country. It is one of the main sources of dust in North China, and air pollution control is the top priority in this region. In this study, the central region of Inner Mongolia was selected as the study area, multiple auxiliary variables were used to estimate the spatial distribution of PM2.5 concentration from 2016 to 2018 by geographically weighted regression, and the socioeconomic determinants of PM2.5 concentration were analyzed by geographic detectors. The results show that: 1) the established model can better estimate the spatial distribution of PM2.5 concentration in the study area, and the monthly mean correlation coefficient R of the verification parameters is stable, ranging from 0.58 to 0.66. 2) PM2.5 concentration in central Inner Mongolia showed significant temporal and spatial variation. The mean annual PM2.5 concentration along the Yellow River basin is the highest in the study area. PM2.5 concentration first increased and then decreased from 2016 to 2018. 3) Urban built-up area, permanent population and per capita GDP are the key factors affecting the spatial and temporal distribution of PM2.5 concentration in the study area. The results of this study provide theoretical basis and technical support for air pollution monitoring, management and prevention in central Inner Mongolia.

BackgroundDiabetic kidney disease (DKD) patients are facing an extremely high risk of cardiovascular disease (CVD), which is a major cause of death for DKD patients. We aimed to build a deep learning model to predict CVD risk among DKD patients and perform risk stratifying, which could help them perform early intervention and improve personal health management.MethodsA retrospective cohort study was conducted to assess the risk of the occurrence of composite cardiovascular disease, which includes coronary heart disease, cerebrovascular diseases, congestive heart failure, and peripheral artery disease, in DKD patients. A least absolute shrinkage and selection operator (LASSO) regression was used to perform the variable selection. A deep learning-based survival model called DeepSurv, based on a feed-forward neural network was developed to predict CVD risk among DKD patients. We compared the model performance with the conventional Cox proportional hazards (CPH) model and the Random survival forest (RSF) model using the concordance index (C-index), the area under the curve (AUC), and integrated Brier scores (IBS).ResultsWe recruited 890 patients diagnosed with DKD in this retrospective study. During a median follow-up of 10.4 months, there are 289 patients who sustained a subsequent CVD. Seven variables, including age, high density lipoprotein (HDL), hemoglobin (Hb), systolic blood pressure (SBP), smoking status, 24 h urinary protein excretion, and total cholesterol (TC), chosen by LASSO regression were used to develop the predictive model. The DeepSurv model showed the best performance, achieved a C-index of 0.767(95% confidence intervals [CI]: 0.717–0.817), AUC of 0.780(95%CI: 0.721–0.839), and IBS of 0.067 in the validation set. Then we used the cut-off value determined by ROC (receiver operating characteristic) curve to divide the patients into different risk groups. Moreover, the DeepSurv model was also applied to develop an online calculation tool for patients to conduct risk monitoring.ConclusionA deep-learning-based predictive model using seven clinical variables can effectively predict CVD risk among DKD patients and perform risk stratification. An online calculator allows its easy implementation.

Abstract Diabetic nephropathy (DN) is a serious complication in type 1 and type 2 diabetes, and renal interstitial fibrosis plays a key role in DN progression. Here, we aimed to probe into the role and potential mechanism of miR-483-5p in DN-induced renal interstitial fibrosis. In this study, we corroborated that miR-483-5p expression was lessened in type 1 and type 2 diabetic mice kidney tissues and high glucose (HG)-stimulated tubular epithelial cells (TECs), and raised in the exosomes derived from renal tissues in type 1 and type 2 diabetic mice. miR-483-5p restrained the expressions of fibrosis-related genes in vitro and renal interstitial fibrosis in vivo. Mechanistically, miR-483-5p bound both TIMP2 and MAPK1, and TIMP2 and MAPK1 were bound up with the regulation of miR-483-5p on renal TECs under HG conditions. Importantly, HNRNPA1-mediated exosomal sorting transported cellular miR-483-5p out of TECs into the urine. Our results expounded that HNRNPA1-mediated exosomal sorting transported cellular miR-483-5p out of TECs into the urine, thus lessening the restraint of cellular miR-483-5p on MAPK1 and TIMP2 mRNAs, and ultimately boosting extracellular matrix deposition and the progression of DN-induced renal interstitial fibrosis.

Forests worldwide store large quantities of carbon (C), particularly in soils as soil organic C (SOC). In northeastern China, two dominant forest types, secondary mixed forest (MF) and larch plantation forest (LF), cover extensive areas. However, we lack an understanding of the patterns and the mechanisms of SOC storage and stabilization in MF and LF, especially in deep soil layers. This research aims to illustrate the vertical distribution and mineral protection of SOC over soil depth; we also used δ13C values of soil fractions to evaluate SOC stability. Samples from the surface litter (Oi), organic layer (Oa+e), and 0–40 cm mineral soils were collected from both MF and LF plots. We used two different methods to separate bulk soils into distinguished fractions: (1) macro- and micro-aggregates and the non-aggregated fraction, and (2) particulate organic matter (POM) and mineral-associated organic matter (MAOM). The C concentrations, C stocks, and δ13C of all soil fractions were determined. Our findings were as follows: (1) SOC was mainly stored in mineral soils and was 13.6% lower in LF (8609 ± 1180 g C m−2) than MF (9969 ± 2084 g C m−2). (2) In both MF and LF, the SOC stock was mainly stored in aggregates (averaged 92.7%); macroaggregates dominated in the surface layers (Oa+e layer and 0–10 cm) but microaggregates dominated in the deep layers (10–20 cm and 20–40 cm). In mineral soils, MAOM was the dominant fraction of the C stock (averaged 81.6%). (3) The proportion of C distributed in microaggregates and MAOM increased from Oa+e to the 20–40 cm layer. (4) The C/N ratios and δ13C values of MAOM were smaller and heavier compared to those of POM. Our study demonstrated that in both forests, aggregate formation and mineral association predominantly contributed to SOC storage, and large stocks of SOC were distributed in the deep soil. The increasing proportion of SOC in microaggregates and MAOM along the soil depth, most likely derived from microbial turnover and microbial necromass, influenced SOC stability in both forest types.

BackgroundThe prevalence of diabetic microvascular diseases has increased significantly worldwide, the most common of which are diabetic nephropathy (DN) and diabetic retinopathy (DR). Microvascular endothelial cells are thought to be major targets of hyperglycemic damage, while the underlying mechanism of diffuse endothelial dysfunction in multiple organs needs to be further investigated.AimThe aim of this study is to explore the endothelial dysfunction mechanisms of serum exosomes (SExos) extracted from DR and DN (DRDN) patients.MethodsIn this study, human glomerular endothelial cells (HGECs) were used as the cell model. Metabolomics ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and proteomics tandem mass tag (TMT)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) together with bioinformatics, the correlation analysis, and the joint pathway analysis were employed to discover the underlying mechanisms of endothelial dysfunction caused by patient’s SExos.ResultsIt can be assumed that serum exosomes extracted by DRDN patients might cause endothelial dysfunction mainly by upregulating alpha subunit of the coagulation factor fibrinogen (FIBA) and downregulating 1-methylhistidine (1-MH). Bioinformatics analysis pointed to an important role in reducing excess cysteine and methionine metabolism.ConclusionFIBA overexpression and 1-MH loss may be linked to the pathogenicity of diabetic endothelial dysfunction in DR/DN, implying that a cohort study is needed to further investigate the role of FIBA and 1-MH in the development of DN and DR, as well as the related pathways between the two proteins.

Abstract The prevalence of kidney diseases is emerging as a public health problem. Stem cells (SCs), currently considered as a promising tool for therapeutic application, have aroused considerable interest and expectations. With self-renewal capabilities and great potential for proliferation and differentiation, stem cell therapy opens new avenues for the development of renal function and structural repair in kidney diseases. Mounting evidence suggests that stem cells exert a therapeutic effect mainly by replacing damaged tissues and paracrine pathways. The benefits of various types of SCs in acute kidney disease and chronic kidney disease have been demonstrated in preclinical studies, and preliminary results of clinical trials present its safety and tolerability. This review will focus on the stem cell-based therapy approaches for the treatment of kidney diseases, including various cell sources used, possible mechanisms involved, and outcomes that are generated so far, along with prospects and challenges in clinical application.

The advantages of the all-fiber optical current transformer include but are not limited to being small in size, having no magnetic saturation, exhibiting high measurement accuracy, and boasting strong electromagnetic interference resistance. However, the high cost of the all-fiber optical transformer limits its promotion and application in engineering. This paper proposes a design scheme of an independent double acquisition loop for the all-fiber optical current transformer based on the single optical path. Firstly, based on the closed-loop control mode and open-loop control mode, the two- channel sampling signal demand for relay protection, and the independent dual-acquisition loop design scheme of the all-fiber optical current transformer are proposed. Secondly, the reliability and economic feasibility of the scheme are demonstrated by an analysis of system failure and cost. The results show that the scheme can actualize the acquisition function of two independent all-fiber optical current transformer products on a single all-fiber current transformer in an integrated manner, which greatly reduces the cost of the all-fiber optical current transformer in engineering applications. Keywords: All-fiber optical current transformer, Acquisition circuit, Reliability

Angiotensin-I-converting enzyme (ACE) inhibitors are used extensively to control hypertension. In this study, a computer-assisted experimental approach was used to screen ACE-inhibiting peptides from X. sorbifolum seed meal (XSM). The process conditions for XSM hydrolysis were optimized through the orthogonal experimental method combined with a database. The optimal conditions for ACE inhibition included an alkaline protease dose of 5%, 45 °C, 15 min and pH 9.5. The hydrolysate was analyzed by LC-MS/MS, and 10 optimal peptides were screened. Molecular docking results revealed four peptides (GGLPGFDPA, IMAVLAIVL, ETYFIVR, and INPILLPK) with ACE inhibitory potential. At 0.1 mg/mL, the synthetic peptides GGLPGFDPA, ETYFIVR, and INPILLPK provided ACE inhibition rates of 24.89%, 67.02%, and 4.19%, respectively. GGLPGFDPA and ETYFIVR maintained high inhibitory activities during in vitro digestions. Therefore, the XSM protein may be a suitable material for preparing ACE inhibitory peptides, and computer-assisted experimental screening is an effective, accurate and promising method for discovering new active peptides.

Diabetic nephropathy is considered one of the most common microvascular complications of diabetes and the pathophysiology involves multiple factors. Progressive diabetic nephropathy is believed to be related to the structure and function of the tubular epithelial cells in the kidney. However, the role of lysine acetylation in lesions of the renal tubular epithelial cells arising from hyperglycemia is poorly understood. Consequently, in this study, we cultured mouse renal tubular epithelial cells in vitro under high glucose conditions and analyzed the acetylation levels of proteins by liquid chromatography-high-resolution mass spectrometry. We identified 48 upregulated proteins and downregulated 86 proteins. In addition, we identified 113 sites with higher acetylation levels and 374 sites with lower acetylation levels. Subcellular localization analysis showed that the majority of the acetylated proteins were located in the mitochondria (43.17%), nucleus (28.57%) and cytoplasm (16.19%). Enrichment analysis indicated that these acetylated proteins are primarily associated with oxidative phosphorylation, the citrate cycle (TCA cycle), metabolic pathways and carbon metabolism. In addition, we used the MCODE plug-in and the cytoHubba plug-in in Cytoscape software to analyze the PPI network and displayed the first four most compact MOCDEs and the top 10 hub genes from the differentially expressed proteins between global and acetylated proteomes. Finally, we extracted 37 conserved motifs from 4915 acetylated peptides. Collectively, this comprehensive analysis of the proteome reveals novel insights into the role of lysine acetylation in tubular epithelial cells and may make a valuable contribution towards the identification of the pathological mechanisms of diabetic nephropathy.

Background: Diabetic kidney disease (DKD) is a highly prevalent complication in patients with type 2 diabetes mellitus (T2DM). Patients with DKD exhibit changes in plasma levels of amino acids (AAs) due to insulin resistance, reduced protein intake, and impaired renal transport of AAs. The role of AAs in distinguishing DKD from T2DM and healthy controls has yet to be elucidated. This study aimed to investigate the metabolomic profiling of AAs in the plasma of patients with DKD.Methods: We established an ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method to detect the plasma levels of the 20 AAs in healthy controls (n = 112), patients with T2DM (n = 101), and patients with DKD (n = 101). The key AAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) models with loading plots, shared and unique structures (SUS) plots, and variable importance in projection (VIP) values. The discrimination accuracies of these key AAs were then determined by analyses of receiver-operating characteristic (ROC) curves.Results: Metabolomic profiling of plasma revealed significant alterations in levels of the 20 AAs in patients with DKD when compared to those in either patients with T2DM or healthy controls. Metabolomic profiling of the 20 AAs showed a visual separation of patients with DKD from patients with T2DM and healthy controls in OPLS-DA models. Based on loading plots, SUS plots, and VIP values in the OPLS-DA models, we identified valine and cysteine as potential contributors to the progression of DKD from patients with T2DM. Histidine was identified as a key mediator that could distinguish patients with DKD from healthy controls. Plasma levels of histidine and valine were decreased significantly in patients with DKD with a decline in kidney function, and had excellent performance in distinguishing patients with DKD from patients with T2DM and healthy controls according to ROC curves.Conclusion: Plasma levels of histidine and valine were identified as the main AAs that can distinguish patients with DKD. Our findings provide new options for the prevention, treatment, and management of DKD.

BackgroundThere is growing evidence to demonstrate that the epigenetic regulation of immune characteristics, especially for N6-methyladenosine (m6A) RNA methylation. However, how m6A methylation is involved in lupus nephritis (LN) is still unclear. This study aimed to determine the role of m6A RNA methylation and their association with the immune microenvironment in LN.MethodsIn total, 87 glomeruli (73 LN, 14 living healthy donors), 110 tubulointerstitium (95 LN, 15 living healthy donors), and 21 kidney whole tissue samples (14 LN, 7 controls) were included in our research to evaluate the expression levels of m6A regulators. CIBERSORT was used to assess the abundance of infiltrating immunocytes. The m6A regulator gene signature for LN was identified using LASSO-logistic regression and verified with external data. Consensus clustering algorithms were used for the unsupervised cluster analysis of m6A modification patterns in LN. Single-sample gene-set enrichment analysis and gene set variation analysis algorithms were employed to assess the activity of immune responses and other functional pathways. Weighted gene co-expression network analysis and protein-protein interaction networks were used to identify m6A methylation markers. Lastly, the Nephroseq V5 tool was used to analyze the correlation between m6A markers and renal function.ResultsWe found that the expression of m6A regulators was more significantly different in the glomeruli in LN compared with tubulointerstitium and whole kidney tissue. We established an m6A regulator signature, comprised of METTL3, WTAP, YTHDC2, YTHDF1, FMR1, and FTO, that can easily distinguish LN and healthy individuals. Two distinct m6A modification patterns based on 18 m6A regulators were determined, with significant differences in m6A regulator expression, immune microenvironment, biological functional pathways, and clinical characteristics. Activated NK cells, most immune responses, and HLA genes had strong correlations with m6A regulators. Seven m6A markers were identified and demonstrated a meaningful correlation with GFR, indicating that they are potential prognostic biomarkers.ConclusionThis study emphasized that m6A RNA methylation and the immune microenvironment are closely linked in LN. A better understanding of m6A modification patterns provide a basis for the development of novel therapeutic options for LN.