A Single-Cell Transcriptome Profiling of Triptolide-Induced Nephrotoxicity in Mice.

Triptolide (TP), an active component isolated from the traditional Chinese herb Tripterygium wilfordii Hook F (TWHF), shows great promise for treating inflammation-related diseases. However, its potential nephrotoxic effects remain concerning. The mechanism underlying TP-induced nephrotoxicity is inadequately elucidated, particularly at single-cell resolution. Hence, single-cell RNA sequencing (scRNA-seq) of kidney tissues from control and TP-treated mice is performed to generate a thorough description of the renal cell atlas upon TP treatment. Heterogeneous responses of nephron epithelial cells are observed after TP exposure, attributing differential susceptibility of cell subtypes to excessive reactive oxygen species and increased inflammatory responses. Moreover, TP disrupts vascular function by activating endothelial cell immunity and damaging fibroblasts. Severe immune cell damage and the activation of pro-inflammatory Macro_C1 cells are also observed with TP treatment. Additionally, ligand-receptor crosstalk analysis reveals that the SPP1 (osteopontin) signaling pathway targeting Macro_C1 cells is triggered by TP treatment, which may promote the infiltration of Macro_C1 cells to exacerbate renal toxicity. Overall, this study provides comprehensive information on the transcriptomic profiles and cellular composition of TP-associated nephrotoxicity at single-cell resolution, which can strengthen the understanding of the pathogenesis of TP-induced nephrotoxicity and provide valuable clues for the discovery of new therapeutic targets to ameliorate TP-associated nephrotoxicity.

Single-cell transcriptome analysis uncovers underlying mechanisms of acute liver injury induced by tripterygium glycosides tablet in mice.

Tripterygium glycosides tablet (TGT), the classical commercial drug of Tripterygium wilfordii Hook. F. has been effectively used in the treatment of rheumatoid arthritis, nephrotic syndrome, leprosy, Behcet's syndrome, leprosy reaction and autoimmune hepatitis. However, due to its narrow and limited treatment window, TGT-induced organ toxicity (among which liver injury accounts for about 40% of clinical reports) has gained increasing attention. The present study aimed to clarify the cellular and molecular events underlying TGT-induced acute liver injury using single-cell RNA sequencing (scRNA-seq) technology. The TGT-induced acute liver injury mouse model was constructed through short-term TGT exposure and further verified by hematoxylin-eosin staining and liver function-related serum indicators, including alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase and total bilirubin. Using the mouse model, we identified 15 specific subtypes of cells in the liver tissue, including endothelial cells, hepatocytes, cholangiocytes, and hepatic stellate cells. Further analysis indicated that TGT caused a significant inflammatory response in liver endothelial cells at different spatial locations; led to marked inflammatory response, apoptosis and fatty acid metabolism dysfunction in hepatocytes; activated hepatic stellate cells; brought about the activation, inflammation, and phagocytosis of liver capsular macrophages cells; resulted in immune dysfunction of liver lymphocytes; disturbed the intercellular crosstalk in liver microenvironment by regulating various signaling pathways. Thus, these findings elaborate the mechanism underlying TGT-induced acute liver injury, provide new insights into the safe and rational applications in the clinic, and complement the identification of new biomarkers and therapeutic targets for liver protection.

Assessing lateral femoral condyle cartilage prior to medial UKA: MRI vs. Valgus stress radiograph.

BACKGROUND: The cartilage quality of the lateral compartment needs to be clarified prior to medial unicompartmental knee arthroplasty (UKA). Valgus stress radiograph has been recommended as the preferred tool. Some studies also show that magnetic resonance imaging (MRI) has a higher diagnostic value. So, we conducted this study to compare whether valgus stress radiographic lateral joint space width (LJSW) and MRI grading can accurately reflect cartilage quality and its screening value for UKA-suitable patients. METHODS: One hundred and thirty eight knees proposed for UKA were enrolled prospectively. Valgus stress radiograph was taken to measure LJSW. LJSW > 4 mm was considered normal and suitable for UKA. For weight-bearing area cartilage of lateral femoral condyle, Recht grade was assessed by MRI preoperatively. Recht grades ≤ 2 were treated as non-high-grade injuries while Recht grades > 2 were treated as high-grade injuries. Outerbridge grade was the gold standard and was assessed intraoperatively. Patients with Outerbridge grades 0-2 (non-high-grade injuries) underwent UKA, and patients with Outerbridge grades 3-4 (high-grade injuries) underwent total knee arthroplasty (TKA). The diagnostic parameters of valgus stress radiograph and MRI for the selection of UKA candidates were calculated, and receiver operating characteristic curves were drawn. P < 0.05 was considered significant. RESULTS: Of 138 knees, 120 underwent UKAs, and 18 underwent TKAs. In terms of selecting UKA candidates, the sensitivity was close between MRI (95.0%) and valgus stress radiograph (96.7%), and the specificity, accuracy, positive predictive value and negative predictive value of MRI (94.4%, 94.9%, 99.1%, 73.9%, respectively) were higher than that of valgus stress radiograph (5.9%, 85.5%, 88.0%, 20.0%, respectively). The difference in area under the curve (AUC) between MRI (0.950) and LJSW (0.602) was significant (P = 0.001). CONCLUSION: Compared with valgus stress radiograph, MRI has excellent evaluation value in diagnosing lateral weight-bearing cartilage injuries and can be used as a reliable tool for selecting suitable UKA patients.

Single-cell RNA transcriptome landscape of murine liver following systemic administration of mesoporous silica nanoparticles.

Due to the unique physicochemical properties, mesoporous silica nanoparticles (MONs) have been widely utilized in biomedical fields for drug delivery, gene therapy, disease diagnosis and imaging. With the extensive applications and large-scale production of MONs, the potential effects of MONs on human health are gaining increased attention. To better understand the cellular and molecular mechanisms underlying the effects of MONs on the mouse liver, we profiled the transcriptome of 63,783 single cells from mouse livers following weekly intravenous administration of MONs for 2 weeks. The results showed that the proportion of endothelial cells and CD4+ T cells was increased, whereas that of Kupffer cells was decreased, in a dose-dependent manner after MONs treatment in the mouse liver. We also observed that the proportion of inflammation-related Kupffer cell subtype and wound healing-related hepatocyte subtype were elevated, but the number of hepatocytes with detoxification characteristics was reduced after MONs treatment. The cell-cell communication network revealed that there was more crosstalk between cholangiocytes and Kupffer cells, liver capsular macrophages, hepatic stellate cells, and endothelial cells following MONs treatment. Furthermore, we identified key ligand-receptor pairs between crucial subtypes after MONs treatment that are known to promote liver fibrosis. Collectively, our study explored the effects of MONs on mouse liver at a single-cell level and provides comprehensive information on the potential hepatotoxicity of MONs.

Improved YOLOv5 infrared tank target detection method under ground background.

The detection precision of infrared seeker directly affects the guidance precision of infrared guidance system. To solve the problem of low target detection accuracy caused by the change of imaging scale, complex ground background and inconspicuous infrared target characteristics when infrared image seeker detects ground tank targets. In this paper, a You Only Look Once, Transform Head Squeeze-and-Excitation (YOLOv5s-THSE) model is proposed based on the YOLOv5s model. A multi-head attention mechanism is added to the backbone and neck of the network, and deeper target features are extracted using the multi-head attention mechanism. The Cross Stage Partial, Squeeze-and-Exclusion module is added to the neck of the network to suppress the complex background and make the model pay more attention to the target. A small object detection head is introduced into the head of the network, and the CIoU loss function is used in the model to improve the detection accuracy of small objects and obtain more stable training regression. Through these several improvement measures, the background of the infrared target is suppressed, and the detection ability of infrared tank targets is improved. Experiments on infrared tank target datasets show that our proposed model can effectively improve the detection performance of infrared tank targets under ground background compared with existing methods, such as YOLOv5s, YOLOv5s + SE, and YOLOV 5 s + Convective Block Attention Module.

Single-cell transcriptome analysis reveals liver injury induced by glyphosate in mice.

BACKGROUND: Glyphosate (GLY), as the active ingredient of the most widely used herbicide worldwide, is commonly detected in the environment and living organisms, including humans. Its toxicity and carcinogenicity in mammals remain controversial. Several studies have demonstrated the hepatotoxicity of GLY; however, the underlying cellular and molecular mechanisms are still largely unknown. METHODS: Using single-cell RNA sequencing (scRNA-seq), immunofluorescent staining, and in vivo animal studies, we analyzed the liver tissues from untreated and GLY-treated mice. RESULTS: We generated the first scRNA-seq atlas of GLY-exposed mouse liver. GLY induced varied cell composition, shared or cell-type-specific transcriptional alterations, and dysregulated cell-cell communication and thus exerted hepatotoxicity effects. The oxidative stress and inflammatory response were commonly upregulated in several cell types. We also observed activation and upregulated phagocytosis in macrophages, as well as proliferation and extracellular matrix overproduction in hepatic stellate cells. CONCLUSIONS: Our study provides a comprehensive single-cell transcriptional picture of the toxic effect of GLY in the liver, which offers novel insights into the molecular mechanisms of the GLY-associated hepatotoxicity.

SILAC-based chemoproteomics reveals a neoligan analogue as an anti-inflammatory agent targeting IRGM to ameliorate cytokine storm.

Cytokine storm is a key feature of sepsis and severe stage of COVID-19, and the immunosuppression after excessive immune activation is a substantial hazard to human life. Both pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) are recognized by various pattern recognition receptors (PRRs), which lead to the immune response. A number of neolignan analogues were synthesized in this work and showed powerful anti-inflammation properties linked to the response to innate and adaptive immunity, as well as NP-7 showed considerable anti-inflammatory activity at 100 nM. On the sepsis model caused by cecum ligation and puncture (CLP) in C57BL/6J mice, NP-7 displayed a strong regulatory influence on cytokine release. Then a photo-affinity probe of NP-7 was synthesized and chemoproteomics based on stable isotope labeling with amino acids in cell cultures (SILAC) identified Immunity-related GTPase M (IRGM) as a target suppressing cytokine storm, which was verified by competitive pull-down, cellular thermal shift assay (CETSA), drug affinity responsive target stability (DARTS) and molecular dynamics simulations.

Staged open reduction and internal fixation with double-locking plates to treat bilateral distal femur periprosthetic fractures after total knee arthroplasty: A case report.

Background: The incidence of periprosthetic fractures after total knee arthroplasty (TKA) increases in parallel with the number of procedures. Comminuted fractures along the primary fracture line extending to the edge of the prosthesis are challenging, and bilateral fractures are rarely reported, especially with open injuries. Case presentation: A 65-year-old female had undergone bilateral TKA in our hospital 5 years before admission. She was admitted with a traumatic bilateral Rorabeck type II B distal femur periprosthetic fracture (closed right, open left, Gustilo II) and was treated with bilateral staged open reduction and internal fixation (ORIF) with double-locking plates. The patient experienced a prolonged delayed fracture union and finally healed around 21 months postoperatively. The function was satisfactory after 4 years of follow-up. Conclusion: ORIF with double-locking plates can be used to treat Rorabeck II B periprosthetic fracture where the primary fracture line extends beyond the edge of the prosthesis; however, there may be delayed healing or nonunion. Patients need to undergo long-term rehabilitation and endure long disability times and require good rehabilitation nursing care. Once they achieve bone healing, the treatment achieves bone preservation and substantial prosthesis survival.

GCdiscrimination: identification of gastric cancer based on a milliliter of blood.

Gastric cancer (GC) continues to be one of the major causes of cancer deaths worldwide. Meanwhile, liquid biopsies have received extensive attention in the screening and detection of cancer along with better understanding and clinical practice of biomarkers. In this work, 58 routine blood biochemical indices were tentatively used as integrated markers, which further expanded the scope of liquid biopsies and a discrimination system for GC consisting of 17 top-ranked indices, elaborated by random forest method was constructed to assist in preliminary assessment prior to histological and gastroscopic diagnosis based on the test data of a total of 2951 samples. The selected indices are composed of eight routine blood indices (MO%, IG#, IG%, EO%, P-LCR, RDW-SD, HCT and RDW-CV) and nine blood biochemical indices (TP, AMY, GLO, CK, CHO, CK-MB, TG, ALB and γ-GGT). The system presented a robust classification performance, which can quickly distinguish GC from other stomach diseases, different cancers and healthy people with sensitivity, specificity, total accuracy and area under the curve of 0.9067, 0.9216, 0.9138 and 0.9720 for the cross-validation set, respectively. Besides, this system can not only provide an innovative strategy to facilitate rapid and real-time GC identification, but also reveal the remote correlation between GC and these routine blood biochemical parameters, which helped to unravel the hidden association of these parameters with GC and serve as the basis for subsequent studies of the clinical value in prevention program and surveillance management for GC. The identification system, called GC discrimination, is now available online at http://lishuyan.lzu.edu.cn/GC/.

ABCModeller: an automatic data mining tool based on a consistent voting method with a user-friendly graphical interface.

In order to extract useful information from a huge amount of biological data nowadays, simple and convenient tools are urgently needed for data analysis and modeling. In this paper, an automatic data mining tool, termed as ABCModeller (Automatic Binary Classification Modeller), with a user-friendly graphical interface was developed here, which includes automated functions as data preprocessing, significant feature extraction, classification modeling, model evaluation and prediction. In order to enhance the generalization ability of the final model, a consistent voting method was built here in this tool with the utilization of three popular machine-learning algorithms, as artificial neural network, support vector machine and random forest. Besides, Fibonacci search and orthogonal experimental design methods were also employed here to automatically select significant features in the data space and optimal hyperparameters of the three algorithms to achieve the best model. The reliability of this tool has been verified through multiple benchmark data sets. In addition, with the advantage of a user-friendly graphical interface of this tool, users without any programming skills can easily obtain reliable models directly from original data, which can reduce the complexity of modeling and data mining, and contribute to the development of related research including but not limited to biology. The excitable file of this tool can be downloaded from http://lishuyan.lzu.edu.cn/ABCModeller.rar.

Liq_ccRCC: Identification of Clear Cell Renal Cell Carcinoma Based on the Integration of Clinical Liquid Indices.

Currently, preoperative diagnosis and differentiation of renal clear cell carcinoma and other subtypes remain a serious challenge for doctors. The liquid biopsy technique and artificial intelligence have inspired the pursuit of distinguishing clear cell renal cell carcinoma using clinically available test data. In this work, a method called liq_ccRCC based on the integration of clinical blood and urine indices through machine learning approaches was successfully designed to achieve this goal. Clinically available biochemical blood data and urine indices were collected from 306 patients with renal cell carcinoma. Finally, the integration of 18 top-ranked clinical liquid indices (13 blood samples and 5 urine samples) was proven to be able to distinguish renal clear cell carcinoma from other subtypes of renal carcinoma by cross-valuation with an AUC of 0.9372. The successful introduction of this identification method suggests that subtype differentiation of renal cell carcinoma can be accomplished based on clinical liquid test data, which is noninvasive and easy to perform. It has huge potential to be developed as a promising innovation strategy for preoperative subtype differentiation of renal cell carcinoma with the advantages of convenience and real-time testing. liq_ccRCC is available online for the free test of readers at http://lishuyan.lzu.edu.cn/liq_ccRCC.

ATBdiscrimination: An in Silico Tool for Identification of Active Tuberculosis Disease Based on Routine Blood Test and T-SPOT.TB Detection Results.

Tuberculosis remains one of the deadliest infectious diseases worldwide. Only 5-15% of people infected with Mycobacterium tuberculosis develop active TB disease (ATB), while others remain latently infected (LTBI) during their lifetime, which has a completely different clinical treatment schedule. However, most current clinical diagnostic methods are based on the immune response of M. tuberculosis infections and cannot distinguish ATB from LTBIs. Thus, the rapid diagnosis of active or latent tuberculosis infections remains a serious challenge for clinicians. In this work, based on the test data of a total of 478 patients, 36 blood biochemical data were specially included with T-SPOT.TB detection results which are all from routine clinical practice as commercially available. Then a discrimination method to detect ATB infections was successfully developed based on these data by the random forest algorithm. This method presents a robust classification performance with AUC as 0.9256 and 0.8731 for the cross-validation set and the external validation set, respectively. This work suggests an innovative strategy for identification of ATB disease from a single drop of blood with advantages of being timely, efficient, and economical. It also provides valuable information for the comprehensive understanding of TB with deep associations between TB infection and routine blood test data. The web server of this identification method, called ATBdiscrimination, is now available online at http://lishuyan.lzu.edu.cn/ATB/ATBdiscrimination.html .

A Machine Learning Method for Identifying Lung Cancer Based on Routine Blood Indices: Qualitative Feasibility Study.

BACKGROUND: Liquid biopsies based on blood samples have been widely accepted as a diagnostic and monitoring tool for cancers, but extremely high sensitivity is frequently needed due to the very low levels of the specially selected DNA, RNA, or protein biomarkers that are released into blood. However, routine blood indices tests are frequently ordered by physicians, as they are easy to perform and are cost effective. In addition, machine learning is broadly accepted for its ability to decipher complicated connections between multiple sets of test data and diseases. OBJECTIVE: The aim of this study is to discover the potential association between lung cancer and routine blood indices and thereby help clinicians and patients to identify lung cancer based on these routine tests. METHODS: The machine learning method known as Random Forest was adopted to build an identification model between routine blood indices and lung cancer that would determine if they were potentially linked. Ten-fold cross-validation and further tests were utilized to evaluate the reliability of the identification model. RESULTS: In total, 277 patients with 49 types of routine blood indices were included in this study, including 183 patients with lung cancer and 94 patients without lung cancer. Throughout the course of the study, there was correlation found between the combination of 19 types of routine blood indices and lung cancer. Lung cancer patients could be identified from other patients, especially those with tuberculosis (which usually has similar clinical symptoms to lung cancer), with a sensitivity, specificity and total accuracy of 96.3%, 94.97% and 95.7% for the cross-validation results, respectively. This identification method is called the routine blood indices model for lung cancer, and it promises to be of help as a tool for both clinicians and patients for the identification of lung cancer based on routine blood indices. CONCLUSIONS: Lung cancer can be identified based on the combination of 19 types of routine blood indices, which implies that artificial intelligence can find the connections between a disease and the fundamental indices of blood, which could reduce the necessity of costly, elaborate blood test techniques for this purpose. It may also be possible that the combination of multiple indices obtained from routine blood tests may be connected to other diseases as well.