Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis.

Background: Membranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive. Methods: In this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancer-related genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer. Results: We conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN). ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers. Conclusion: This evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.

Analytical and experimental analysis of concrete temperature and energy considering open-air environmental variations.

Longwave radiation is an important open-air environmental factor that can significantly affect the temperature of concrete, but it has often been ignored in the temperature analysis of open-air concrete structures. In this article, an improved analytical model of concrete temperature was proposed by considering solar radiation, thermal convection, thermal conduction and especially longwave radiation. Temperature monitoring of an open-air concrete block was carried out to verify the proposed model and analyze the heat energy characteristics of open-air concrete. As demonstrated by the open-air experiment, under the influence of longwave radiation, the temperature at the top of the concrete block could decrease rapidly at night and even become lower than the minimum temperature at its bottom. Compared with the analytical model that ignores longwave radiation, the improved model that includes it better matches the measured temperature. According to the energy analysis, although solar radiation controls the transient variation in heat energy, the heat exchange caused by longwave radiation were more than that caused by convection on sunlit surfaces, which indicates the importance of considering longwave radiation.

Evaluafion of the efficacy of wall shear stress in carotid artery stenting.

Objective: To characterize the value of carotid wall shear stress (WSS) following carotid artery stenting (CAS) in patients with carotid stenosis. Methods: Twenty-eight patients with carotid stenosis treated with CAS between March 2021 to May 2022 in the eighth medical center of the PLA General Hospital were selected for our study. Carotid ultrasound was performed before the operation, one week post-operation, and six months post-operation. Carotid artery WSS was detected by blood flow vector imaging, and the changes in WSS before and after the operation were collected. Genetic testing of drugs was detected for patients with restenosis. Results: Pre-operative WSS of the proximal, narrowest region, and distal carotid arteries in patients with ischemic carotid artery stenosis was 7.88 ± 3.18Pa, 14.36 ± 6.66Pa, and 1.55 ± 1.15Pa, respectively. Comparatively, pre-operative WSS of the proximal, narrowest region and distal carotid arteries in patients without ischemic symptoms was 5.02 ± 1.99Pa, 9.68 ± 4.23Pa, and 1.10 ± 0.68Pa, respectively, with a significant difference between the two groups (p < 0.001). Overall WSS of the proximal, narrowest region, and distal carotid arteries in patients before CAS was 6.68 ± 3.0Pa, 12.47 ± 5.98Pa, and 1.39 ± 0. 96Pa. WSS of the proximal, narrowest region, and distal carotid was 4.15 ± 1.42Pa, 6.71 ± 2.64Pa, and1.86 ± 1.13Pa one week after CAS, compared to 4.44 ± 1.91Pa, 7.90 ± 4.38Pa, and 2. 36 ± 1.09Pa six months after CAS. WSS of the proximal and narrowest region of the carotid artery was reduced after carotid stenting, and the difference was statistically significant (p < 0.001). There was no statistically significant difference in WSS between one week and six months after stenting (P > 0.05). Conclusion: We employed early carotid WSS as a means of evaluating the efficacy of carotid artery stenting. Changes in carotid WSS are closely associated with carotid artery stenosis, providing valuable hemodynamic information for CAS treatment. This technique holds great application value in pre-operative evaluation and long-term follow-up.

Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia.

Purpose: This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods: In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results: For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion: The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.

Prevalence and prognosis of malignancy in THSD7A-associated membranous nephropathy: a systematic literature review and clinical case study.

BACKGROUND: This study aims to investigate the incidence and prognosis of malignancy in individuals with thrombospondin type-1 domain-containing 7A (THSD7A)-associated membranous nephropathy (MN). METHODS: First, we performed a systematic literature review of prevalence of malignancy in THSD7A-associated MN. Then, we conducted a retrospective analysis of 454 patients diagnosed with MN through renal biopsy at our hospital between January 2016 and December 2020. We assessed the presence of serum anti-THSD7A antibodies and performed immunohistochemical staining of renal tissue for THSD7A. Subsequently, we followed patients with THSD7A-associated MN for a minimum of 3-5 years, collecting their clinical, pathological characteristics, and prognosis. Additionally, we conducted a literature review on patients with THSD7A-associated MN in conjunction with malignancy. RESULTS: We identified a total of nine articles containing comprehensive data on THSD7A-associated MN and malignancy. Among 235 patients with THSD7A-positive MN, 36 individuals had concurrent malignancies, resulting in a malignancy prevalence of 13.3% (95% CI: 8.9-17.7%). In our center, we followed up with 15 patients diagnosed with THSD7A-associated MN and observed three cases of concomitant tumors: two cases of lung adenocarcinoma and one case of small cell lung cancer with multiple metastases. The prevalence of malignancy in our cohort was 20%. Notably, we detected positive THSD7A staining in both renal and lung cancer tissues in one patient with small cell lung cancer. CONCLUSIONS: Patients with THSD7A-associated MN should undergo vigilant follow-up assessments, with a particular focus on actively seeking potential tumorigenic lesions to prevent misdiagnosis or oversight.

Sub-features orthogonal decoupling: Detecting bone wall absence via a small number of abnormal examples for temporal CT images.

The absence of bone wall located in the jugular bulb and sigmoid sinus of the temporal bone is one of the important reasons for pulsatile tinnitus. Automatic and accurate detection of these abnormal singes in CT slices has important theoretical significance and clinical value. Due to the shortage of abnormal samples, imbalanced samples, small inter-class differences, and low interpretability, existing deep-learning methods are greatly challenged. In this paper, we proposed a sub-features orthogonal decoupling model, which can effectively disentangle the representation features into class-specific sub-features and class-independent sub-features in a latent space. The former contains the discriminative information, while, the latter preserves information for image reconstruction. In addition, the proposed method can generate image samples using category conversion by combining the different class-specific sub-features and the class-independent sub-features, achieving corresponding mapping between deep features and images of specific classes. The proposed model improves the interpretability of the deep model and provides image synthesis methods for downstream tasks. The effectiveness of the method was verified in the detection of bone wall absence in the temporal bone jugular bulb and sigmoid sinus.

Unraveling DDIT4 in the VDR-mTOR pathway: a novel target for drug discovery in diabetic kidney disease.

Introduction: Diabetic kidney disease (DKD) necessitates innovative therapeutic strategies. This study delves into the role of DNA damage-inducing transcription factor 4 (DDIT4) within the VDR-mTOR pathway, aiming to identify a novel target for DKD drug discovery. Methods: Transcriptome data from the Gene Expression Omnibus Database were analyzed to assess the expression of mTOR and VDR expression in human renal tissues. Clinical samples from DKD patients and minimal change disease (MCD) controls were examined, and a DKD animal model using 20-week-old db/db mice was established. DDIT4 plasmid transfection was employed to modulate the VDR-mTOR pathway, with its components evaluated using immunohistochemistry, real-time quantitative PCR (qRT-PCR), Western blotting, and enzyme-linked immunosorbent assay (ELISA). Results: Changes in the expression of the VDR-mTOR pathway were observed in both DKD patients and the animal model. Overexpression of DDIT4 increased VDR expression and decreased levels of mTOR, p70s6k, and 4E-BP1. Furthermore, DDIT4 treatment regulated autophagy by upregulating LC3I expression and downregulating LC3II expression. Notably, DDIT4 alleviated oxidative stress by reducing the levels of lipid peroxidation product MDA, while simultaneously increasing the levels of superoxide dismutase (SOD) and glutathione (GSH), underscoring the role of DDIT4 in the pathological process of DKD and its potential as a therapeutic target. Conclusion: Unraveling DDIT4's involvement in the VDR-mTOR pathway provides insights for innovative DKD drug discovery, emphasizing its potential as a therapeutic target for future interventions.

Unraveling the interplay of ferroptosis and immune dysregulation in diabetic kidney disease: a comprehensive molecular analysis.

BACKGROUND: Diabetic kidney disease (DKD) is a primary microvascular complication of diabetes with limited therapeutic effects. Delving into the pathogenic mechanisms of DKD and identifying new therapeutic targets is crucial. Emerging studies reveal the implication of ferroptosis and immune dysregulation in the pathogenesis of DKD, however, the precise relationship between them remains not fully elucidated. Investigating their interplay is pivotal to unraveling the pathogenesis of diabetic kidney disease, offering insights crucial for targeted interventions and improved patient outcomes. METHODS: Integrated analysis, Consensus clustering, Machine learning including Generalized Linear Models (GLM), RandomForest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (xGB), Artificial neural network (ANN) methods of DKD glomerular mRNA sequencing were performed to screen DKD-related ferroptosis genes.CIBERSORT, ESTIMATE and ssGSEA algorithm were used to assess the infiltration of immune cells between DKD and control groups and in two distinct ferroptosis phenotypes. The ferroptosis hub genes were verified in patients with DKD and in the db/db spontaneous type 2 diabetes mouse model via immunohistochemical and Western blotting analyses in mouse podocyte MPC5 and mesangial SV40-MES-13 cells under high-glucose (HG) conditions. RESULTS: We obtained 16 differentially expressed ferroptosis related genes and patients with DKD were clustered into two subgroups by consensus clustering. Five ferroptosis genes (DUSP1,ZFP36,PDK4,CD44 and RGS4) were identified to construct a diagnostic model with a good diagnosis performance in external validation. Analysis of immune infiltration revealed immune heterogeneity between DKD patients and controls.Moreover, a notable differentiation in immune landscape, comprised of Immune cells, ESTIMATE Score, Immune Score and Stromal Score was observed between two FRG clusters. GSVA analysis indicated that autophagy, apoptosis and complement activation can participate in the regulation of ferroptosis phenotypes. Experiment results showed that ZFP36 was significantly overexpressed in both tissue and cells while CD44 was on the contrary.Meanwhile,spearman analysis showed both ZFP36 and CD44 has a strong correlation with different immune cells,especially macrophage. CONCLUSION: The regulation of the immune landscape in DKD is significantly influenced by the focal point on ferroptosis. Newly identified ferroptosis markers, CD44 and ZFP36, are poised to play essential roles through their interactions with macrophages, adding substantial value to this regulatory landscape.

Comparison of Machine Learning Models Using Diffusion-Weighted Images for Pathological Grade of Intrahepatic Mass-Forming Cholangiocarcinoma.

Is the radiomic approach, utilizing diffusion-weighted imaging (DWI), capable of predicting the various pathological grades of intrahepatic mass-forming cholangiocarcinoma (IMCC)? Furthermore, which model demonstrates superior performance among the diverse algorithms currently available? The objective of our study is to develop DWI radiomic models based on different machine learning algorithms and identify the optimal prediction model. We undertook a retrospective analysis of the DWI data of 77 patients with IMCC confirmed by pathological testing. Fifty-seven patients initially included in the study were randomly assigned to either the training set or the validation set in a ratio of 7:3. We established four different classifier models, namely random forest (RF), support vector machines (SVM), logistic regression (LR), and gradient boosting decision tree (GBDT), by manually contouring the region of interest and extracting prominent radiomic features. An external validation of the model was performed with the DWI data of 20 patients with IMCC who were subsequently included in the study. The area under the receiver operating curve (AUC), accuracy (ACC), precision (PRE), sensitivity (REC), and F1 score were used to evaluate the diagnostic performance of the model. Following the process of feature selection, a total of nine features were retained, with skewness being the most crucial radiomic feature demonstrating the highest diagnostic performance, followed by Gray Level Co-occurrence Matrix lmc1 (glcm-lmc1) and kurtosis, whose diagnostic performances were slightly inferior to skewness. Skewness and kurtosis showed a negative correlation with the pathological grading of IMCC, while glcm-lmc1 exhibited a positive correlation with the IMCC pathological grade. Compared with the other three models, the SVM radiomic model had the best diagnostic performance with an AUC of 0.957, an accuracy of 88.2%, a sensitivity of 85.7%, a precision of 85.7%, and an F1 score of 85.7% in the training set, as well as an AUC of 0.829, an accuracy of 76.5%, a sensitivity of 71.4%, a precision of 71.4%, and an F1 score of 71.4% in the external validation set. The DWI-based radiomic model proved to be efficacious in predicting the pathological grade of IMCC. The model with the SVM classifier algorithm had the best prediction efficiency and robustness. Consequently, this SVM-based model can be further explored as an option for a non-invasive preoperative prediction method in clinical practice.

WDFF-Net: Weighted Dual-branch Feature Fusion Network for Polyp Segmentation with Object-aware Attention Mechanism.

Colon polyps in colonoscopy images exhibit significant differences in color, size, shape, appearance, and location, posing significant challenges to accurate polyp segmentation. In this paper, a Weighted Dual-branch Feature Fusion Network is proposed for Polyp Segmentation, named WDFF-Net, which adopts HarDNet68 as the backbone network. Firstly, a dual-branch feature fusion network architecture is constructed, which includes a shared feature extractor and two feature fusion branches, i.e. Progressive Feature Fusion (PFF) branch and Scale-aware Feature Fusion (SFF) branch. The branches fuse the deep features of multiple layers for different purposes and with different fusion ways. The PFF branch is to address the under-segmentation or over-segmentation problems of flat polyps with low-edge contrast by iteratively fusing the features from low, medium, and high layers. The SFF branch is to tackle the the problem of drastic variations in polyp size and shape, especially the missed segmentation problem for small polyps. These two branches are complementary and play different roles, in improving segmentation accuracy. Secondly, an Object-aware Attention Mechanism (OAM) is proposed to enhance the features of the target regions and suppress those of the background regions, to interfere with the segmentation performance. Thirdly, a weighted dual-branch the segmentation loss function is specifically designed, which dynamically assigns the weight factors of the loss functions for two branches to optimize their collaborative training. Experimental results on five public colon polyp datasets demonstrate that, the proposed WDFF-Net can achieve a superior segmentation performance with lower model complexity and faster inference speed, while maintaining good generalization ability.

DnaK duplication and specialization in bacteria correlates with increased proteome complexity.

The chaperone 70 kDa heat shock protein (Hsp70) is important for cells from bacteria to humans to maintain proteostasis, and all eukaryotes and several prokaryotes encode Hsp70 paralogs. Although the mechanisms of Hsp70 function have been clearly illuminated, the function and evolution of Hsp70 paralogs is not well studied. DnaK is a highly conserved bacterial Hsp70 family. Here, we show that dnaK is present in 98.9% of bacterial genomes, and 6.4% of them possess two or more DnaK paralogs. We found that the duplication of dnaK is positively correlated with an increase in proteomic complexity (proteome size, number of domains). We identified the interactomes of the two DnaK paralogs of Myxococcus xanthus DK1622 (MxDnaKs), which revealed that they are mostly nonoverlapping, although both prefer α and ß domain proteins. Consistent with the entire M. xanthus proteome, MxDnaK substrates have both significantly more multi-domain proteins and a higher isoelectric point than that of Escherichia coli, which encodes a single DnaK homolog. MxDnaK1 is transcriptionally upregulated in response to heat shock and prefers to bind cytosolic proteins, while MxDnaK2 is downregulated by heat shock and is more associated with membrane proteins. Using domain swapping, we show that the nucleotide-binding domain and the substrate-binding ß domain are responsible for the significant differences in DnaK interactomes, and the nucleotide binding domain also determines the dimerization of MxDnaK2, but not MxDnaK1. Our work suggests that bacterial DnaK has been duplicated in order to deal with a more complex proteome, and that this allows evolution of distinct domains to deal with different subsets of target proteins.IMPORTANCEAll eukaryotic and ~40% of prokaryotic species encode multiple 70 kDa heat shock protein (Hsp70) homologs with similar but diversified functions. Here, we show that duplication of canonical Hsp70 (DnaK in prokaryotes) correlates with increasing proteomic complexity and evolution of particular regions of the protein. Using the Myxococcus xanthus DnaK duplicates as a case, we found that their substrate spectrums are mostly nonoverlapping, and are both consistent to that of Escherichia coli DnaK in structural and molecular characteristics, but show differential enrichment of membrane proteins. Domain/region swapping demonstrated that the nucleotide-binding domain and the ß substrate-binding domain (SBDß), but not the SBDα or disordered C-terminal tail region, are responsible for this functional divergence. This work provides the first direct evidence for regional evolution of DnaK paralogs.

Prevalence and immunopathologic characteristics of seropositive and seronegative hepatitis B virus-associated membranous nephropathy: a retrospective study of 420 biopsy-proven cases.

BACKGROUND: This retrospective study aims to investigate the prevalence and immunopathologic characteristics of seropositive and seronegative hepatitis B virus-associated membranous nephropathy (HBV-MN). METHODS: Clinicopathologic and serologic records of 420 patients with histologically confirmed HBV-MN between January 2014 and July 2021 were examined to determine the prevalence of seropositive and seronegative HBV-MN. Serum anti-PLA2R antibody testing was conducted on 280 patients with HBV-associated membranous nephropathy (HBV-MN) from August 2018 to July 2021. Immunopathologic characteristics of HBV-MN patients and anti-PLA2R antibody positivity were analyzed. RESULTS: Among 420 pathologically confirmed HBV-MN patients, 230 (54.8%) were seropositive for HBV. The seropositive group exhibited higher blood creatinine values and incidence of liver function abnormalities than the seronegative group (p < .05). Serum anti-PLA2R antibody testing on 280 HBV-MN patients revealed a total positive rate of 44.6%, with the seronegative group showing a significantly higher rate (62.6%) compared to the seropositive group (32.1%) (p < .01). The anti-PLA2R antibody-positive group displayed higher levels of urine protein (p < .05), serum cholesterol (p < .01), and IgG4 subtypes (p < .05) compared to the negative group. Additionally, the positive group had significantly lower levels of serum albumin and IgG than the negative group (p < .01). CONCLUSIONS: This comprehensive study reveals a significantly higher prevalence of seronegative HBV-MN than previously thought. The blood creatinine values and incidence of liver function abnormalities was higher in the serology-positive group than in the serology-negative group. Notably, the seronegative group displayed a higher positive rate of anti-PLA2R antibodies compared to the seropositive group, indicating distinctive clinical and immunopathologic features.

Pulmonary embolism and hemorrhage after displacement of angiographic catheter tip to pulmonary artery: A case report and literature review.

Pulmonary embolism and massive hemoptysis caused by intravascular foreign bodies have rarely been reported. We report a case of an end-stage renal disease patient in which the tip of the angiographic catheter fell off into the pulmonary artery during endovascular interventional opening when the patient underwent vascular access occlusion for dialysis. During the operation, the foreign body was displaced repeatedly and finally anchored to the posterior basal segment branch of the right lower pulmonary artery. A pulmonary embolism occurred during the operation, and massive hemoptysis and hemorrhagic shock occurred after anticoagulation and thrombolytic therapy. After receiving anti-shock and symptomatic treatment, the patient gradually recovered. After six months of follow-up, no pulmonary embolism or pulmonary infarction occurred. Our case report presents an alternative approach to extracting a foreign object from the pulmonary artery by locating the foreign object within the vascular terminations, without resorting to forceful removal. This method mitigates the potential risks of pulmonary embolism and bleeding associated with forceful extraction.

Self-guided disentangled representation learning for single image dehazing.

Image dehazing has received extensive research attention as images collected in hazy weather are limited by low visibility and information dropout. Recently, disentangled representation learning has made excellent progress in various vision tasks. However, existing networks for low-level vision tasks lack efficient feature interaction and delivery mechanisms in the disentanglement process or an evaluation mechanism for the degree of decoupling in the reconstruction process, rendering direct application to image dehazing challenging. We propose a self-guided disentangled representation learning (SGDRL) algorithm with a self-guided disentangled network to realize multi-level progressive feature decoupling through sharing and interaction. The self-guided disentangled (SGD) network extracts image features using the multi-layer backbone network, and attribute features are weighted using the self-guided attention mechanism for the backbone features. In addition, we introduce a disentanglement-guided (DG) module to evaluate the degree of feature decomposition and guide the feature fusion process in the reconstruction stage. Accordingly, we develop SGDRL-based unsupervised and semi-supervised single image dehazing networks. Extensive experiments demonstrate the superiority of the proposed method for real-world image dehazing. The source code is available at https://github.com/dehazing/SGDRL.

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance.

Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multi-dimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively.

ULK1 confers neuroprotection by regulating microglial/macrophages activation after ischemic stroke.

Microglial activation and autophagy play a critical role in the progression of ischemic stroke and contribute to the regulation of neuroinflammation. Unc-51-like kinase 1 (ULK1) is the primary autophagy kinase involved in autophagosome formation. However, the impact of ULK1 on neuroprotection and microglial activation after ischemic stroke remains unclear. In this study, we established a photothrombotic stroke model, and administered SBI-0206965 (SBI), an ULK1 inhibitor, and LYN-1604 hydrochloride (LYN), an ULK1 agonist, to modulate ULK1 activity in vivo. We assessed sensorimotor deficits, neuronal apoptosis, and microglial/macrophage activation to evaluate the neurofunctional outcome. Immunofluorescence results revealed ULK1 was primarily localized in the microglia of the infarct area following ischemia. Upregulating ULK1 through LYN treatment significantly reduced infarct volume, improved motor function, promoted the increase of anti-inflammatory microglia. In conclusion, ULK1 facilitated neuronal repair and promoted the formation of anti-inflammatory microglia pathway after ischemic injury.

Experimental Study on the Dilatancy and Energy Evolution Behaviors of Red-Bed Rocks under Unloading Conditions.

Surrounding rock deformation and consequent support failure are the most prominent issues in red-bed rock tunnel engineering and are mainly caused by the effects of unloading, rheology, and swelling. This study investigated the mechanical responses of two kinds of red-bed mudstone and sandstone under unloading conditions via laboratory observation. Volume dilation was observed on the rocks during unloading, and the dilatancy stress was linear with the initial confining pressure. However, the ratios of dilatancy stress to peak stress of the two rocks kept at a range from 0.8 to 0.9, regardless of confining pressures. Both the elastic strain energy and the dissipated energy evolved synchronously with the stress-strain curve and exhibited conspicuous confining pressure dependence. Special attention was paid to the evolution behavior of the dilatancy angle. The dilatancy angle changed linearly during unloading. When the confining pressure was 10 MPa, the dilatancy angle of mudstone decreased from 26.8° to 12.5° whereas the dilatancy angle of sandstone increased from 34.6° to 51.1°; when the confining pressure rose to 25 MPa, the dilatancy angle of mudstone and sandstone decreased from 45.8° to 17.4° and increased from 21.7° to 39.5°, respectively. To further understand the evolution of the dilatancy angle, we discussed the links between the variable dilatancy angle and the processes of rock deformation and energy dissipation.

The tumor immune microenvironment of nasopharyngeal carcinoma after gemcitabine plus cisplatin treatment.

Gemcitabine plus cisplatin (GP) chemotherapy is the standard of care for nasopharyngeal carcinoma (NPC). However, the mechanisms underpinning its clinical activity are unclear. Here, using single-cell RNA sequencing and T cell and B cell receptor sequencing of matched, treatment-naive and post-GP chemotherapy NPC samples (n = 15 pairs), we show that GP chemotherapy activated an innate-like B cell (ILB)-dominant antitumor immune response. DNA fragments induced by chemotherapy activated the STING type-I-interferon-dependent pathway to increase major histocompatibility complex class I expression in cancer cells, and simultaneously induced ILB via Toll-like receptor 9 signaling. ILB further expanded follicular helper and helper type 1 T cells via the ICOSL-ICOS axis and subsequently enhanced cytotoxic T cells in tertiary lymphoid organ-like structures after chemotherapy that were deficient for germinal centers. ILB frequency was positively associated with overall and disease-free survival in a phase 3 trial of patients with NPC receiving GP chemotherapy ( NCT01872962 , n = 139). It also served as a predictor for favorable outcomes in patients with NPC treated with GP and immunotherapy combined treatment (n = 380). Collectively, our study provides a high-resolution map of the tumor immune microenvironment after GP chemotherapy and uncovers a role for B cell-centered antitumor immunity. We also identify and validate ILB as a potential biomarker for GP-based treatment in NPC, which could improve patient management.

Symmetry-Driven Assembly of a Penta-Shell Keplerate Cuprofullerene for Metallofullerene Frameworks.

Two metallofullerene frameworks (MFFs) constructed from a penta-shell Keplerate cuprofullerene chloride, C60 @Cu24 @Cl44 @Cu12 @Cl12 , have been successfully prepared via a C60 -templated symmetry-driven strategy. The icosahedral cuprofullerene chloride is assembled on a C60 molecule through [η2 -(C=C)]-CuI and CuI -Cl coordination bonds, resulting in the penta-shell Keplerate with the C60 core canopied by 24 Cu, 44 Cl, 12 Cu and 12 Cl atoms that fulfill the tic@rco@oae@ico@ico penta-shell polyhedral configuration. By sharing the outmost-shell Cl atoms, the cuprofullerene chlorides are connected into 2D or 3D (snf net) frameworks. TD-DFT calculations reveal that the charge transfer from the outmost CuI and Cl to C60 core is responsible for their light absorption expansion to near-infrared region, implying anionic halogenation may be an effective strategy to tune the light absorption properties of metallofullerene materials.

Neutrophil extracellular trap is an important connection between hemodialysis and acute myocardial infarction.

The incidence of acute myocardial infarction (AMI) in hemodialysis (HD) patients is high and the prognosis is extremely poor. However, the potential connection between HD and AMI, and its regulatory mechanisms remain unclear. In this study, the gene expression profiles of HD (GSE15072) and AMI (GSE66360) were downloaded from the Gene Expression Omnibus database, common differentially expressed genes (DEGs) were obtained using the limma R package, the biological functions were analyzed according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, machine learning was conducted to identify hub genes. Receiver operating characteristic curves and gene set enrichment analyses were used to explore the characters and biological function of hub genes, networks were used for candidate identification of transcription factor (TF), microRNA (miRNA), and drug. After a total of 255 common DEGs were selected, GO and KEGG analyses indicated that neutrophil extracellular trap (NET) may be a potential connection between HD and AMI, LILRB2, S100A12, CYBB, ITGAM, and PPIF were finally identified as hub genes. The area under curve of LILRB2, S100A12, and PPIF was higher than 0.8 in both datasets. Networks show the relationship between hub genes, TF, and miRNA, also the relationship between potential drugs and protein. In conclusion, NETs may be the potential connection between AMI and HD. The potential hub gene, signaling pathways, and drugs provided by this study may contribute to future AMI prevention and intervention in HD patients.