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Abstract Disulfidptosis, a newly identified programmed cell death pathway in prostate cancer (PCa), is closely associated with intracellular disulfide stress and glycolysis. This study aims to elucidate the roles of disulfidptosis-related genes (DRGs) in the pathogenesis and progression of PCa, with the goal of improving diagnostic and therapeutic approaches. We analyzed PCa datasets and normal tissue transcriptome data from TCGA, GEO, and MSKCC. Using consensus clustering analysis and LASSO regression, we developed a risk scoring model, which was validated in an independent cohort. The model's predictive accuracy was confirmed through Kaplan–Meier curves, receiver operating characteristic (ROC) curves, and nomograms. Additionally, we explored the relationship between the risk score and immune cell infiltration, and examined the tumor microenvironment and somatic mutations across different risk groups. We also investigated responses to immunotherapy and drug sensitivity. Our analysis identified two disulfidosis subtypes with significant differences in survival, immune environments, and treatment responses. According to our risk score, the high-risk group exhibited poorer progression-free survival (PFS) and higher tumor mutational burden (TMB), associated with increased immune suppression. Functional enrichment analysis linked high-risk features to key cancer pathways, including the IL-17 signaling pathway. Moreover, drug sensitivity analysis revealed varied responses to chemotherapy, suggesting the potential for disulfidosis-based personalized treatment strategies. Notably, we identified PROK1 as a crucial prognostic marker in PCa, with its reduced expression correlating with disease progression. In summary, our study comprehensively assessed the clinical implications of DRGs in PCa progression and prognosis, offering vital insights for tailored precision medicine approaches.

Abstract Purpose To perform a meta-analysis to compare clinical outcomes of anterior cruciate ligament (ACL) repair and ACL reconstruction for acute ACL rupture. Method We searched Pubmed, Embase, the Cochrane Library, and Web of Science databases to seek relevant studies. Clinical outcomes included failure rate, hardware removal rate, anteroposterior (AP) knee laxity, and patient-reported outcomes. In addition, subgroup analysis was carried out according to repair techniques, rupture locations, and study designs. Funnel plots were used to detect publication bias. All statistical analysis was performed using STATA (version 14.2, StataCorp). Results A total of 10 articles were included in this study, comprising 5 randomized controlled trials (RCTs) and 5 cohort studies, involving a total of 549 patients. We found no statistical differences between the ACL repair and ACL reconstruction in the following outcomes: failure rate, AP knee laxity, International Knee Documentation Committee (IKDC) score, Lysholm score, Knee Injury and Osteoarthritis Outcome (KOOS) Score, and Tegner score. However, the ACL repair group had a higher hardware removal rate. Except for AP knee laxity results on different repair techniques, there was no statistical difference in other subgroup analyses. Conclusion Compared with ACL reconstruction, ACL repair shows similar results in clinical outcomes, and it is promising to be an effective alternative treatment for acute ACL rupture. Larger samples and higher-quality studies are needed to support our results and further explore the advantages of ACL repair in other aspects. Level of evidence Level III.

Abstract. Sarcopenia is an age-related disease that mainly involves decreases in muscle mass, muscle strength and muscle function. At the same time, the body fat content increases with aging, especially the visceral fat content. Adipose tissue is an endocrine organ that secretes biologically active factors called adipokines, which act on local and distant tissues. Studies have revealed that some adipokines exert regulatory effects on muscle, such as higher serum leptin levels causing a decrease in muscle function and adiponectin inhibits the transcriptional activity of Forkhead box O3 (FoxO3) by activating peroxisome proliferators-activated receptor-γ coactivator -1α (PGC-1α) and sensitizing cells to insulin, thereby repressing atrophy-related genes (atrogin-1 and muscle RING finger 1 [MuRF1]) to prevent the loss of muscle mass. Here, we describe the effects on muscle of adipokines produced by adipose tissue, such as leptin, adiponectin, resistin, mucin and lipocalin-2, and discuss the importance of these adipokines for understanding the development of sarcopenia.

Recent advancements in intelligent diagnosis rely heavily on data-driven methods. However, these methods often encounter challenges in adequately addressing class imbalances in the context of the fault diagnosis of mechanical systems. This paper proposes the MeanRadius-SMOTE graph neural network (MRS-GNN), a novel framework designed to synthesize node representations in GNNs to effectively mitigate this issue. Through integrating the MeanRadius-SMOTE oversampling technique into the GNN architecture, the MRS-GNN demonstrates an enhanced capability to learn from under-represented classes while preserving the intrinsic connectivity patterns of the graph data. Comprehensive testing on various datasets demonstrates the superiority of the MRS-GNN over traditional methods in terms of classification accuracy and handling class imbalances. The experimental results on three publicly available fault diagnosis datasets show that the MRS-GNN improves the classification accuracy by 18 percentage points compared to some popular methods. Furthermore, the MRS-GNN exhibits a higher robustness in extreme imbalance scenarios, achieving an AUC-ROC value of 0.904 when the imbalance rate is 0.4. This framework not only enhances the fault diagnosis accuracy but also offers a scalable solution applicable to diverse mechanical and complex systems, demonstrating its utility and adaptability in various operating environments and fault conditions.

Abstract Background The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML). Methods This study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots. Results The random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively. Conclusions The PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study. Trial registration Chinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.

Introduction Anterior cruciate ligament (ACL) rupture is one of the most common knee injuries in sports, and the gold standard for treating ACL rupture is tendon graft reconstruction. Internal brace technology is being used nowadays for ligament repair; however, more relevant in vivo clinical evidence is required for using internal brace technology in ACL reconstruction (ACLR). We conducted a randomised controlled trial to investigate the clinical efficacy of internal brace technology in ACLR.Methods and analysis This randomised, parallel-controlled trial included patients with ACL rupture who underwent inpatient surgery at the Department of Orthopaedics, Xiangya Hospital, Central South University. Random number table method was used to assign the participants to either the test or the control group. The test group underwent ACLR using the internal brace technique, whereas the control group underwent standard ACLR. Uniform postoperative rehabilitation protocol was used for both the groups. Patient-reported outcomes included preoperative baseline and postoperative recovery at 1, 3, 6, 12 and 24 months. The primary outcome was International Knee Documentation Committee function from baseline (ACL rupture) to 6 months postoperatively. Secondary outcomes included (1) other patient outcome reporting metrics, Lysholm knee score, Knee Injury and Osteoarthritis Outcome Score and Visual Analog Scale; (2) the use of Kneelax3 knee stabiliser to assess knee stability; (3) occurrence of adverse events, such as graft refraction or symptomatic instability, postoperative infection and contralateral injury and (4) magnetic resonance images at 12 and 24 months after ACLR.Ethics and dissemination This trial was approved by the Medical Ethics Committee of the Xiangya Hospital of Central South University on 26 October 2021. Data will be published in peer-reviewed journals and presented at national and international conferences.Trial registration number ChiCTR2200057526.

Soil surveying and mapping provide important support for environmental science research on soil and other resources. Due to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy. Therefore, there is an urgent need for accurate and up-to-date soil maps. Soil has a high correlation with its corresponding environmental factors in space, and typical samples contain an appropriate soil–environment relationship of soil types. Understanding how to extract typical samples according to environmental factors and determine the implied soil–environment relationship is the key to updating soil maps. In this study, a hierarchical typical sample extraction method based on land use type and environmental factors was designed. According to the corresponding relationship between the soil type and the land use type (ST-LU), the outdate soil map patches caused by changes in land use were excluded, follow by typical samples being extracted according to the peak intervals of the soil–environmental factor histograms. Additionally, feature selection was performed through variance analysis and mutual information, and four machine learning models were used to predict soil types. In addition, the influence of environmental factors on soil prediction was discussed, in terms of variable importance analysis. Using an overall common validation set, the results show that the prediction accuracy using typical samples for learning in the modeling set is above 0.8, while the prediction accuracy when using random samples is only about 0.4. Compared with the original soil map, the accuracy and resolution of the predicted soil maps based on typical samples are greatly improved. In general, typical samples can effectively explore the actual soil–environment knowledge implied in the soil type map. By extracting typical samples from historical soil type map and combining them with high-resolution remote sensing data, we can generate new soil type maps with high accuracy and short update cycle. This can provide some references for typical sampling design and soil type prediction.

Background: Ferroptosis was proposed to be a type of programmed cell death in 2012. Ferroptosis plays a significant role in a variety of illnesses. Objective: To better understand the direction of future research, we performed a bibliometric analysis to identify research hotspots with a focus on ferroptosis. Methods: The search terms [TI = “ferroptosis” OR (“GSH” AND “GPX4”) OR “lipid peroxidation” OR “iron homeostasis” OR “iron metabolism”] AND [PY = “2012–2022”] AND [DT = “Article OR Review”] AND [LA = “English”] were used to retrieve publications related to ferroptosis for a bibliometric analysis. We utilized Microsoft Excel to calculate the frequency and proportion of the published articles, VOSviewer to perform a co-occurrence analysis and for visualizing the data, CiteSpace to obtain a timeline of keywords and institutions, and RStudio to calculate citation metrics. As indicated by the analysis, indicators such as the number of publications, the most productive authors and coauthorship status, the distribution of publications by country, favoured journals, the most influential institutions and the most frequently cited documents are reported in this article. Results: A total of 8009 publications were retrieved from the WOS core collection, and 197 papers published in 2023 were removed from this analysis. The remaining 7812 papers, which included 118 in the WOS collection, were incorporated into the bibliometric study. Conclusion: The number of annual scientific publications on ferroptosis have been increasing each year. The academic communities represented by Tang, Daolin, Stockwell, Brent R., Wang, Fudi, and Conrad, Marcus were the most authoritative. China, USA, and Germany were the front-runners in the field of ferroptosis. Free Radical Biology and Medicine was the largest contributor of ferroptosis-related research, and Cell and Nature were the most influential journals to publish articles on ferroptosis. Columbia Univ and Univ Pittsburgh were the institutions that received the most attention. Recent research on ferroptosis has been focused on molecular mechanisms, particularly those in the contexts of various diseases, which will be a hotspot of future research. In addition, interdisciplinary ferroptosis and big-data research is expected to be a new frontier.

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.

PurposeTo determine the effect of local infiltration anesthesia (LIA) at the donor site combined with a femoral nerve block (FNB) on short-term postoperative pain, functional outcomes, and rehabilitation after arthroscopic hamstring tendon autograft anterior cruciate ligament reconstruction (ACLR).MethodsThis study was a single center, randomized controlled trial. Seventy-three subjects with ACL rupture were enrolled. Participants were randomly allocated to two groups, 47 in the experimental group (Group A) and 26 in the control group (Group B). All operations were performed under FNB. In Group A, 10 ml of 1% ropivacaine was injected precisely at the hamstring donor site. Patients in Group B were treated with the same amount of saline. Preoperatively and postoperatively, pain scores based on the numerical rating scale (NRS) and consumption of opioids were recorded. In addition, knee functions were assessed by the International Knee Documentation Committee Subjective Knee Form (IKDC), the Lysholm score, and the Knee injury and Osteoarthritis Outcome Score (KOOS) preoperatively and postoperatively at 1 and 3 months. In addition, we applied the KNEELAX3 arthrometer to evaluate the stability of the knee preoperatively and postoperatively so that subjective and objective knee conditions were obtained to help us assess knee recovery in a comprehensive manner.ResultsThe hamstring donor-site block reduced pain within the first 12 postoperative hours. There were no significant differences between two groups in pain intensity preoperatively and equal to or greater than 24 hours postoperatively. Furthermore, there were no differences between the groups concerning knee functions preoperatively or in the short-term follow-up at 1 and 3 months.ConclusionLIA at the donor site can effectively improve the early postoperative pain of patients after ACLR and reduce the use of opioids without affecting the functional outcomes of the surgery.

Diesel engine have been applied to transportation due to its high efficiency, high output power, and high reliability. However, the emissions of diesel engines have become a serious problem and various technologies have been applied to reduce such emissions. Non-thermal plasma (NTP) is a notable potential measure for diesel engine emission control. The research investigated the effect of double dielectric barrier discharge (DDBD) NTP on diesel engine emission with different discharge voltages and duty ratios. The results shown that CO emission increases as the discharge voltage and duty ratio increases, with a maximum increasing rate of 8.59%. However, NOX, formaldehyde, acetaldehyde, VOCs, and PM2.5 emissions reduced, with a maximum reduction rate of 65.97%, 34.65%, 30.73%, 35.94%, and 85.21%, respectively. The research found that NTP can be used to control diesel engine emission, more importantly, it can achieve the reduction of NOX and PM emission simultaneously.

The Boltzmann machine (BM) and restricted Boltzmann machine (RBM) models are representative stochastic neural networks, in which neuron states are determined by stochastic activation functions. They are widely used in many applications. However, when analog circuits are used to realize a neural network, noise are not avoidable. The noise could come from external environments, such as power supplies and thermal noise, and they will affect the neurons’ stochastic behaviour in the BM and RBM models. Hence it is important to theoretically study how the noise affect the operation of the BM and RBM models. To best of our knowledge, there are little works related to the analysis on noisy BMs and noisy RBMs. This paper considers that there are additive noise in the inputs of the neurons, and theoretically studies the behaviors of the two models under this imperfect condition. It is found that the effect of additive noise is similar to increasing the temperature factors of the two models. Since the input noise may make the networks to have wrong stochastic behaviour, there is Kullback Leibler (KL) divergence loss in noisy BMs and noisy RBMs. Based on the Gaussian-distributed noise assumption, a noise compensation method is proposed to suppress the effect of additive noise. Experiments show that the proposed noise compensation method can greatly suppress the KL divergence loss. In addition, from the experimental results, our method is also effective for handling non-Gaussian noise.

BackgroundTesticular cancer is the most common solid malignancy in young men. Given the many histological classifications of testicular tumors, seminoma is one of the most treatable cancers. The survival rate in early-stage disease was more than 90%. Thus, seminoma at the cellular and molecular levels, especially at the single-cell level, is worth studying.MethodsWe performed a single-cell RNA sequencing (scRNA-seq) study on a patient who was diagnosed with testicular seminoma with lymph node metastasis. This study presented tumor tissue, PBMC, pelvic and renal hilus lymph node in a total of 18,206 high-quality single-cell transcriptome information. The characteristics of metastatic cell lineage were revealed by the comparison between different tumor cell subtypes at the scRNA level.ResultsA single-cell map of testicular seminoma with lymph node metastasis was constructed by scRNA-seq. We discovered the gene expression characteristics of the tumor cells in testicular seminoma, especially metastatic tumor cells. KRT8 and KRT18 were commonly expressed in the three tumor cell subtypes. However, TCF7L1, SCG3 and SV2C were the specifically expressed genes of tumor cell subtypes in primary tumor sites. Some molecular markers specifically expressed by the metastatic cell lineage, such as POU5F1, were identified.ConclusionsWe revealed the molecular characteristics of testicular seminoma at the single-cell level, especially the metastatic tumor cells. This study could provide new insights into the diagnosis and treatment of testicular seminoma.

Bioaerosols are atmospheric particles with a biological trace, such as viruses, bacteria, fungi, and plant material such as pollen and plant debris. In this study, we analyzed the biological information in bioaerosols using next generation sequencing of the trace DNA. The samples were collected using an Andersen air sampler and separated into two groups according to particulate matter (PM) size: small (PM2.5) and large (PM10). Amplification and sequencing of the bacterial 16S rDNA gene, prokaryotic internal transcribed spacer 1 (ITS1) region and DNA sequence of a plant chloroplast gene (rbcL) were carried out using several sets of specific primers targeting animal and plant sequences. Lots of bacterial information was detected from the bioaerosols. The most abundant bacteria in several samples were of the Actinobacteria (class), Alphaproteobacteria, Bacilli, and Clostridia. For the animal detection using internal transcribed spacer 1, only uncultured fungi were detected in more than half of the hits, with a high number of Cladosporium sp. in the samples. For the plant identification, the ITS1 information only matched fungal species. However, targeting of the rbcL region revealed diverse plant information, such as Medicago papillosa. In conclusion, traces of bacteria, fungi, and plants could be detected in the bioaerosols, but not of animals using our primers.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models—random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)—were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

BackgroundRenal cell carcinoma (RCC) is the most common type of kidney cancer. Studying the pathogenesis of RCC is particularly important, because it could provide a direct guide for clinical treatment. Given that tumor heterogeneity is probably reflected at the mRNA level, the study of mRNA in RCC may reveal some potential tumor-specific markers, especially single-cell RNA sequencing (scRNA-seq).MethodsWe performed an exploratory study on three pathological types of RCC with a small sample size. This study presented clear-cell RCC (ccRCC), type 2 pRCC, and chRCC in a total of 30,263 high-quality single-cell transcriptome information from three pathological types of RCC. In addition, scRNA-seq was performed on normal kidneys. Tumor characteristics were well identified by the comparison between different pathological types of RCC and normal kidneys at the scRNA level.ResultsSome new tumor-specific markers for different pathologic types of RCC, such as SPOCK1, PTGIS, REG1A, CP and SPAG4 were identified and validated. We also discovered that NDUFA4L2 both highly expressed in tumor cells of ccRCC and type 2 pRCC. The presence of two different types of endothelial cells in ccRCC and type 2 pRCC was also identified and verified. An endothelial cell in ccRCC may be associated with fibroblasts and significantly expressed fibroblast markers, such as POSTN and COL3A1. At last, by applying scRNA-seq results, the activation of drug target pathways and sensitivity to drug responses was predicted in different pathological types of RCC.ConclusionsTaken together, these findings considerably enriched the single-cell transcriptomic information for RCC, thereby providing new insights into the diagnosis and treatment of RCC.

Sarcopenia is an age-related disease in which muscle mass, strength and function may decline with age or can be secondary to cachexia or malnutrition and can lead to weakness, falls and even death. With the increase in life expectancy, sarcopenia has become a major threat to the health of the elderly. Currently, our understanding of bone-muscle interactions is not limited to their mechanical coupling. Bone and muscle have been identified as secretory endocrine organs, and their interaction may affect the function of each. Both muscle-derived factors and osteokines can play a role in regulating muscle and bone metabolism via autocrine, paracrine and endocrine mechanisms. Herein, we comprehensively summarize the latest research progress on the effects of the osteokines FGF-23, IGF-1, RANKL and osteocalcin on muscle to explore whether these cytokines can be utilized to treat and prevent sarcopenia.

Bilateral renal cell carcinoma (RCC) is a rare disease that can be classified as either familial or sporadic. Studying the cellular molecular characteristics of sporadic bilateral RCC is important to provide guidance for clinical treatment. Cellular molecular characteristics can be expressed at the RNA level, especially at the single-cell degree. Single-cell RNA sequencing (scRNA-seq) was performed on bilateral clear cell RCC (ccRCC). A total of 3,575 and 3,568 high-quality single-cell transcriptome data were captured from the left and right tumour tissues, respectively. Gene characteristics were identified by comparing left and right tumours at the scRNA level. The complex cellular environment of bilateral ccRCC was presented by using scRNA-seq. Single-cell transcriptomic analysis revealed high similarity in gene expression among most of the cell types of bilateral RCCs but significant differences in gene expression among different site tumour cells. Additionally, the potential biological function of different tumour cell types was determined by gene ontology (GO) analysis. The transcriptome characteristics of tumour tissues in different locations at the single-cell transcriptome level were revealed through the scRNA-seq of bilateral sporadic ccRCC. This work provides new insights into the diagnosis and treatment of bilateral RCC.

SF6 inevitably decomposes to various decomposition products under partial discharge in SF6-insulated equipment. Therefore, in order to ensure the running stability of the equipment, Pt-MoS2 monolayer adsorbent material was proposed to remove the main decomposition products: H2S and SO2, based on the density functional theory (DFT) calculation. The adsorption energy, charge transfer and structural parameters, density of states, and electron density difference were calculated to find the most stable adsorption structure and analyze the interaction mechanism of Pt-MoS2 monolayer to SO2 and H2S. It was found that Pt-MoS2 monolayer shows strong interaction to H2S and SO2 due the strong chemical activity of doped Pt atom. This study provides a theoretical basis to prepare Pt-MoS2 adsorbents for the SF6 decomposition components removal. Keywords: MoS2 monolayer, Pt doping, Adsorption, DFT calculations

Abstract Background Ischemic stroke-induced neuroinflammation is mainly mediated by microglial cells. The nuclear factor kappa B (NF-κB) pathway is the key transcriptional pathway that initiates inflammatory responses following cerebral ischemia. OTULIN, a critical negative regulator of the NF-κΒ signaling pathway, exerts robust effects on peripheral immune cell-mediated inflammation and is regarded as an essential mediator for repressing inflammation in vivo. The effect of OTULIN on inflammatory responses in the central nervous system (CNS) was previously unstudied. This current study investigated the anti-inflammatory effect of OTULIN both in vitro and in vivo in ischemic stroke models. Methods Sprague-Dawley (SD) rats were subjected to transient middle cerebral artery occlusion (tMCAO) or an intraperitoneal injection of lipopolysaccharide (LPS). Overexpression of the OTULIN gene was utilized to observe the effect of OTULIN on ischemic stroke outcomes. The effect of OTULIN overexpression on microglia-mediated neuroinflammation was examined in rat primary microglia (PM) and in the microglial cell line N9 after induction by oxygen-glucose deprivation (OGD)-treated neuronal medium. The activation and inflammatory responses of microglia were detected using immunofluorescence, ELISA, and qRT-PCR. The details of molecular mechanism were assessed using Western blotting. Results In the tMCAO rats, the focal cerebral ischemia/reperfusion injury induced a continuous increase in OTULIN expression within 72 h, and OTULIN expression was increased in activated microglial cells. OTULIN overexpression obviously decreased the cerebral infarct volume, improved the neurological function deficits, and reduced neuronal loss at 72 h after reperfusion, and it also inhibited the activation of microglia and attenuated the release of TNF-α, IL-1β, and IL-6 by suppressing the NF-κB pathway at 24 h after tMCAO. In vitro, OTULIN overexpression inhibited the microglia-mediated neuroinflammation by reducing the production of TNF-α, IL-1β, and IL-6 via depressing the NF-κB pathway in both PM and N9 cells. Conclusions OTULIN provides a potential therapeutic target for ischemic brain injury by ameliorating the excessive activation of microglial cells and neuroinflammation through repressing the NF-κB signaling pathway.

Background: An umbrella review was conducted to compare the effectiveness of extracorporeal shock wave therapy (ESWT) versus non-ESWT in the treatment of knee osteoarthritis (KOA). Materials and methods: Three databases including PubMed, Embase and Web of science were searched up to September 2023. Literature screening, quality evaluation, and data extraction were performed according to inclusion and exclusion criteria. Metaanalysis of outcome indicators was performed using Revman 5.4 software. Results: A total of eight meta-analysis were included in this umbrella review. All meta-analysis were graded against a Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) and scored between 8 and 11. Compared to the sham group, the ESWT group showed better results in WOMAC (Western Ontario and McMaster Universities Arthritis Index) [mean difference (MD)= -2.94, 95% CI: -5.52, -0.37, P= 0.03, I²= 60%], Visual Analog Scale (VAS) (MD = - 2.0, 95% CI: - 2.5, - 1.5, P< 0.01, I²=0%), range of motion (ROM) (MD =17.55, 95% CI: 13.49, 21.61, P< 0.00001, I²=0%), and Lequesne index (MD = - 2.85, 95% CI: - 3.64, - 2.07, P<0.00001, I²=48%). Conclusion: Based on the results of our analysis, ESWT is now an effective therapy for improving pain and function in patients with KOA. [ABSTRACT FROM AUTHOR]