Identification of prognostic stemness-related genes in kidney renal papillary cell carcinoma.

BACKGROUND: Kidney renal papillary cell carcinoma (KIRP) is the second most prevalent malignant cancer originating from the renal epithelium. Nowadays, cancer stem cells and stemness-related genes (SRGs) are revealed to play important roles in the carcinogenesis and metastasis of various tumors. Consequently, we aim to investigate the underlying mechanisms of SRGs in KIRP. METHODS: RNA-seq profiles of 141 KIRP samples were downloaded from the TCGA database, based on which we calculated the mRNA expression-based stemness index (mRNAsi). Next, we selected the differentially expressed genes (DEGs) between low- and high-mRNAsi groups. Then, we utilized weighted gene correlation network analysis (WGCNA) and univariate Cox analysis to identify prognostic SRGs. Afterwards, SRGs were included in the multivariate Cox regression analysis to establish a prognostic model. In addition, a regulatory network was constructed by Pearson correlation analysis, incorporating key genes, upstream transcription factors (TFs), and downstream signaling pathways. Finally, we used Connectivity map analysis to identify the potential inhibitors. RESULTS: In total, 1124 genes were characterized as DEGs between low- and high-RNAsi groups. Based on six prognostic SRGs (CCKBR, GPR50, GDNF, SPOCK3, KC877982.1, and MYO15A), a prediction model was established with an area under curve of 0.861. Furthermore, among the TFs, genes, and signaling pathways that had significant correlations, the CBX2-ASPH-Notch signaling pathway was the most significantly correlated. Finally, resveratrol might be a potential inhibitor for KIRP. CONCLUSIONS: We suggested that CBX2 could regulate ASPH through activation of the Notch signaling pathway, which might be correlated with the carcinogenesis, development, and unfavorable prognosis of KIRP.

Advances in the study of tissue-engineered retinal pigment epithelial cell sheets.

Regarded as the most promising treatment modality for retinal degenerative diseases, retinal pigment epithelium cell replacement therapy holds significant potential. Common retinal degenerative diseases, including Age-related Macular Degeneration, are frequently characterized by damage to the unit comprising photoreceptors, retinal pigment epithelium, and Bruch's membrane. The selection of appropriate tissue engineering materials, in conjunction with retinal pigment epithelial cells, for graft preparation, can offer an effective treatment for retinal degenerative diseases. This article presents an overview of the research conducted on retinal pigment epithelial cell tissue engineering, outlining the challenges and future prospects.

Preoperative neoadjuvant targeted therapy remodels intra-tumoral heterogeneity of clear-cell renal cell carcinoma and ferroptosis inhibition induces resistance progression.

Neoadjuvant tyrosine kinase inhibitor (TKI) therapy is an important treatment option for advanced renal cell carcinoma (RCC). Many RCC patients may fail to respond or be resistant to TKI therapy. We aimed to explore the key mechanisms of neoadjuvant therapy résistance. We obtained tumor samples from matched pre-treatment biopsy and post-treatment surgical samples and performed single-cell RNA sequencing. Sunitinib-resistant ccRCC cell lines were established. Ferroptosis was detected by ferrous ion and lipid peroxidation levels. Tumor growth and resistance to Sunitinib was validated in vitro and vivo. Immunohistochemistry was used to validate the levels key genes and lipid peroxidation. Multi-center cohorts were included, including TCGA, ICGC, Checkmate-025 and IMmotion151 clinical trial. Survival analysis was performed to identify the associated clinical and genomic variables. Intratumoral heterogeneity was first described in the whole neoadjuvant management. The signature of endothelial cells was correlated with drug sensitivity and progression-free survival. Ferroptosis was shown to be the key biological program in malignant cell resistance. We observed tissue lipid peroxidation was negatively correlated with IL6 and tumor response. TKI-resistant cell line was established. SLC7A11 knockdown promoted cell growth and lipid peroxidation, increased the ferroptosis level, and suppressed the growth of tumor xenografts significantly (P<0.01). IL6 could reverse the ferroptosis and malignant behavior caused by SLC7A11(-) via JAK2/STAT3 pathway, which was rescued by the ferroptosis inducer Erastin. Our data indicate that ferroptosis is a novel strategy for advanced RCC treatment, which activated by IL6, providing a new idea for resistance to TKIs.

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). MATERIALS AND METHODS: In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. RESULTS: The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. CONCLUSION: The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

Spraying performance and deposition characteristics of an improved air-assisted nozzle with induction charging.

Electrostatic spraying technology can improve the efficiency of pesticide deposition on the surface of leaves and reduce the environmental pollution caused by pesticide drift, which has an important prospect in agricultural pesticide application. To improve the deposition and penetration of droplets in the crop canopy, we designed and optimized an air-assisted electrostatic nozzle and conducted the spraying performance experiment. Parameters, such as charge-to-mass ratio (CMR) and particle size, were tested and analyzed to obtain the suitable operating parameters of nozzle. The results proved that the improved air-assisted electrostatic nozzle has good atomization and chargeability. There is a good charging effect with a charging voltage of 3,000-5,000 V, the CMR increased 127.8% from 0.86 to 1.97 mC/kg as the charge voltage increases from 1,000 to 4,000 V, at an air pressure of 1.0 bar and liquid flow rate of 200 ml/min. Furthermore, we designed a multi-factor orthogonal experiment, which was conducted using a four-factor, three-level design to investigate the effects of operational parameters and canopy characteristics on droplet deposition and penetration. Analysis of variance (ANOVA) and F-test were performed on the experiment results. The results showed that the factor effect on droplet penetration, in descending order, was as follows: spray distance, leaf area index, air pressure, and air pressure × spray distance. The factor effect on abaxial leaf deposition, in descending order, was as follows: air pressure, spray distance, air pressure × charge voltage, spray distance × charge voltage, and charge voltage. For optimal droplet penetration and abaxial leaf deposition, option A 3 B 1 D 2 (air pressure 1.5 bar, spray distance 0.2 m, charge voltage 2,500 V) is recommend. The spray nozzle atomization performance and deposition regulation were studied by experimental methods to determine the optimal values of operating parameters to provide a reference for electrostatic spray system development.

Enhancing malignancy prediction in thyroid nodules: A multimodal ultrasound radiomics approach in TI-RADS category 4 lesions.

PURPOSE: To explore the diagnostic value of intralesional and perilesional radiomics based on multimodal ultrasound (US) images in predicting the malignant ACR TIRADS 4 thyroid nodules (TNs). METHODS: A total of 297 cases of TNs in patients who underwent preoperative thyroid grayscale US and shear wave elastography (STE) were enrolled (training cohort: n = 150, internal validation cohort: n = 77, external validation cohort: n = 70). Regions of interests (ROIs) were delineated on grayscale US images and STE images, and then an isotropic expansion of 1.0, 1.5, 2.0, 2.5, and 3.0 mm was applied. Predictive models were established using recursive feature elimination-support vector machines (RFE-SVM) based on radiomics features calculated by random forest. RESULTS: The perilesional ROI1.5mm expansion achieved the highest area under curve (AUC) (AUC: 0.753 for grayscale US, 0.728 for STE; 95% confidence interval (CI): 0.664-0.743, 0.684-0.739, respectively). The joint model had the highest AUC values of 0.936 in the training dataset, 0.926 in internal dataset, and 0.893 in external dataset. The calibration curve showed good consistency and the decision curve indicated a greater clinical net benefit of the joint model. CONCLUSION: Joint model containing perilesional radiomics (1.5 mm) had significant value in predicting the malignant ACR TIRADS 4 TNs.

Porphyrin-Thiophene Based Conjugated Polymer Cathode with High Capacity for Lithium-Organic Batteries.

Organic electrode materials are promising for next-generation energy storage materials due to their environmental friendliness and sustainable renewability. However, problems such as their high solubility in electrolytes and low intrinsic conductivity have always plagued their further application. Polymerization to form conjugated organic polymers can not only inhibit the dissolution of organic electrodes in the electrolyte, but also enhance the intrinsic conductivity of organic molecules. Herein, we synthesized a new conjugated organic polymer (COPs) COP500-CuT2TP (poly [5,10,15,20-tetra(2,2'-bithiophen-5-yl) porphyrinato] copper (II)) by electrochemical polymerization method. Due to the self-exfoliation behavior, the porphyrin cathode exhibited a reversible discharge capacity of 420 mAh g-1, and a high specific energy of 900 Wh Kg-1 with a first coulombic efficiency of 96 % at 100 mA g-1. Excellent cycling stability up to 8000 cycles without capacity loss was achieved even at a high current density of 5 A g-1. This highly conjugated structure promotes COP500-CuT2TP combined high energy density, high power density, and good cycling stability, which would open new opportunity for the designable and versatile organic electrodes for electrochemical energy storage.

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems.

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Correlation analysis of EGFR gene mutation abundance and the efficacy of targeted therapy with osimertinib in nonsmall cell lung cancer-a case control study.

BACKGROUND: In nonsmall cell lung cancer (NSCLC), epidermal growth factor receptor (EGFR) mutation is the primary cancer-causing mutation. But whether the practical effectiveness of EGFR tyrosine kinase inhibitors (TKIs) can be influenced by plasma EGFR mutation abundance when treating patients with advanced NSCLC remains unanswered. Therefore, this research was intended to reveal the connection between plasma EGFR mutation abundance and clinical outcomes in osimertinib-treated patients with advanced NSCLC. METHODS: A total of 120 patients with advanced NSCLC were retrospectively analyzed, and 56 patients with EGFR-mutation-positive NSCLC receiving osimertinib first-line therapy were eventually screened and included. The baseline status and abundance of plasma EGFR in patients with NSCLC were detected by cSMART, and the ratio of 0.1 was the critical value. Imaging examinations were performed every 8-12 weeks for the assessment of tumor response. The relationship between baseline EGFR mutation abundance and clinical outcomes of TKI therapy was analyzed. RESULTS: The objective response rates (ORR) of EGFR-mutant patients in the high-/low-abundance groups were 69.2% and 40.0%, respectively. The high abundance group had an obviously higher ORR than the low abundance group (P = 0.029). A much longer median progression-free survival (mPFS) was demonstrated in patients with high mutation abundance than in patients with low abundance (11.2 months vs 7.1 months, P = 0.0133). As for the median overall survival (mOS), it showed the same trend as mPFS in patients from different groups (15.5 vs 10.7 months, P = 0.0028). The role of plasma mutation abundance as an independent prognostic factor for both PFS (hazard ratios [HR]: 0.30, P = 0.006) and OS (HR: 0.35, P = 0.004) was demonstrated by multivariate Cox regression analysis. CONCLUSION: There is a close connection between plasma EGFR mutation abundance and survival benefit in patients with NSCLC, which can be used for predicting the efficacy of EGFR-TKI targeted therapy. Our study is expected to provide a research basis for screening patients to whom the EGFR-TKI therapy is beneficial.

Vacancy-induced catalytic mechanism for alcohol electrooxidation on nickel-based electrocatalyst.

Owing to outstanding performances, nickel-based electrocatalysts are commonly used in electrochemical alcohol oxidation reactions (AORs), and the active phase is usually vacancy-rich nickel oxide/hydroxide (NiOx Hy ) species. However, researchers are not aware of the catalytic role of atom vacancy in AORs. Here, we study vacancy-induced catalytic mechanisms for AORs on NiOx Hy species. As to AORs on oxygen-vacancy-poor ß-Ni(OH)2 , the only redox mediator is electrooxidation-induced electrophilic lattice oxygen species, which can only catalyze the dehydrogenation process (e.g., the electrooxidation of primary alcohol to carboxylic acid) instead of the C-C bond cleavage. Hence, vicinal diol electrooxidation reaction involving the C-C bond cleavage is not feasible with oxygen-vacancy-poor ß-Ni(OH)2 . Only through oxygen vacancy-induced adsorbed oxygen-mediated mechanism, can oxygen-vacancy-rich NiOx Hy species catalyze the electrooxidation of vicinal diol to carboxylic acid and formic acid accompanied with the C-C bond cleavage. Crucially, we examine how vacancies and vacancy-induced catalytic mechanisms work during AORs on NiOx Hy species.

Breaking solvation dominance of ethylene carbonate via molecular charge engineering enables lower temperature battery.

Low temperatures severely impair the performance of lithium-ion batteries, which demand powerful electrolytes with wide liquidity ranges, facilitated ion diffusion, and lower desolvation energy. The keys lie in establishing mild interactions between Li+ and solvent molecules internally, which are hard to achieve in commercial ethylene-carbonate based electrolytes. Herein, we tailor the solvation structure with low-ε solvent-dominated coordination, and unlock ethylene-carbonate via electronegativity regulation of carbonyl oxygen. The modified electrolyte exhibits high ion conductivity (1.46 mS·cm-1) at -90 °C, and remains liquid at -110 °C. Consequently, 4.5 V graphite-based pouch cells achieve ~98% capacity over 200 cycles at -10 °C without lithium dendrite. These cells also retain ~60% of their room-temperature discharge capacity at -70 °C, and miraculously retain discharge functionality even at ~-100 °C after being fully charged at 25 °C. This strategy of disrupting solvation dominance of ethylene-carbonate through molecular charge engineering, opens new avenues for advanced electrolyte design.

A Novel Molecular Classification Method for Glioblastoma Based on Tumor Cell Differentiation Trajectories.

The latest 2021 WHO classification redefines glioblastoma (GBM) as the hierarchical reporting standard by eliminating glioblastoma, IDH-mutant and only retaining the tumor entity of "glioblastoma, IDH-wild type." Knowing that subclassification of tumors based on molecular features is supposed to facilitate the therapeutic choice and increase the response rate in cancer patients, it is necessary to carry out molecular classification of the newly defined GBM. Although differentiation trajectory inference based on single-cell sequencing (scRNA-seq) data holds great promise for identifying cell heterogeneity, it has not been used in the study of GBM molecular classification. Single-cell transcriptome sequencing data from 10 GBM samples were used to identify molecular classification based on differentiation trajectories. The expressions of identified features were validated by public bulk RNA-sequencing data. Clinical feasibility of the classification system was examined in tissue samples by immunohistochemical (IHC) staining and immunofluorescence, and their clinical significance was investigated in public cohorts and clinical samples with complete clinical follow-up information. By analyzing scRNA-seq data of 10 GBM samples, four differentiation trajectories from the glioblastoma stem cell-like (GSCL) cluster were identified, based on which malignant cells were classified into five characteristic subclusters. Each cluster exhibited different potential drug sensitivities, pathways, functions, and transcriptional modules. The classification model was further examined in TCGA and CGGA datasets. According to the different abundance of five characteristic cell clusters, the patients were classified into five groups which we named Ac-G, Class-G, Neo-G, Opc-G, and Undiff-G groups. It was found that the Undiff-G group exhibited the worst overall survival (OS) in both TCGA and CGGA cohorts. In addition, the classification model was verified by IHC staining in 137 GBM samples to further clarify the difference in OS between the five groups. Furthermore, the novel biomarkers of glioblastoma stem cells (GSCs) were also described. In summary, we identified five classifications of GBM and found that they exhibited distinct drug sensitivities and different prognoses, suggesting that the new grouping system may be able to provide important prognostic information and have certain guiding significance for the treatment of GBM, and identified the GSCL cluster in GBM tissues and described its characteristic program, which may help develop new potential therapeutic targets for GSCs in GBM.

Molecular subtyping and characterization of clear cell renal cell carcinoma by tumor differentiation trajectories.

Previous bulk RNA sequencing or whole genome sequencing on clear cell renal cell carcinoma (ccRCC) subtyping mainly focused on ccRCC cell origin or the complex tumor microenvironment (TME). Based on the single-cell RNA sequencing (scRNA-seq) data of 11 primary ccRCC specimens, cancer stem-cell-like subsets could be differentiated into five trajectories, whereby we further classified ccRCC cells into three groups with diverse molecular features. These three ccRCC subgroups showed significantly different outcomes and potential targets to tyrosine kinase inhibitors (TKIs) or immune checkpoint inhibitors (ICIs). Tumor cells in three differentiation directions exhibited distinct interactions with other subsets in the ccRCC niches. The subtyping model was examined through immunohistochemistry staining in our ccRCC cohort and validated the same classification effect as the public patients. All these findings help gain a deeper understanding about the pathogenesis of ccRCC and provide useful clues for optimizing therapeutic schemes based on the molecular subtype analysis.

Cannulation via the external jugular vein--An alternative to conventional peripherally inserted central catheterisation for paediatric patients.

PURPOSE: This study aimed to describe a peripherally inserted central catheterisation (PICC) for paediatric patients with inaccessible access and a high risk of general anaesthesia (GA). METHODS: This was a retrospective observational study involving all paediatric inpatients who performed the PICC via an EJV approach without GA between September 2014 and September 2021 in a provincial key clinical speciality. RESULTS: A total of 290 EJV line placement attempts were performed, and 29 were excluded due to missing placement results, resulting in a sample size of 261. The anatomical localisation, punctures, and catheterisation success rates for this practice were 100%, 100%, and 90.04%, respectively. The placement success rate in children younger than one year was 93.75% (45/48). The median line duration of use was 19 days, with a median length of catheter insertion of 13 cm. The most common complications were catheter malposition (n = 20) and dislodgement (n = 7). CONCLUSION: The PICC via an EJV approach without GA is a feasible and safe practice with acceptable success and complication rates, and low costs. It might be an attractive alternative for obtaining central vascular access for paediatric patients.

The use of D-dimer in the diagnosis and risk assessment of intracardiac thrombus among patients with dilated cardiomyopathy.

D-dimer is a biomarker of coagulation and fibrinolytic system activation in response to the body's hypercoagulable state. The study aims to investigate the usefulness of D-dimer in diagnosing and assessing the risk of intracardiac thrombus in patients with dilated cardiomyopathy (DCM). Consecutively enrolled in this study were patients with DCM who were admitted to our center for the first time. The diagnostic value was evaluated using the receiver operating characteristic (ROC) curve. Additionally, we used univariate and multivariate logistic regression to investigate the association between D-dimer and intracardiac thrombus. We also performed smooth curve fitting, threshold saturation effect analysis, and subgroup analysis. In total, 534 patients were enrolled in the study, and among them, 65 patients had intracardiac thrombus. Mural thrombus was the predominant type of thrombus, which was mainly located in the left ventricular apex. The optimal cut-off value of D-dimer for the diagnosis of intracardiac thrombus was 484 ng/mL, with a sensitivity and specificity of 0.769 and 0.646, respectively. In both unadjusted and adjusted logistic regression models, a positive association was found between D-dimer and intracardiac thrombus. Curve fitting and threshold effect analysis revealed two inflection points in the relationship between D-dimer and intracardiac thrombus (non-linear test: P = 0.032). When D-dimer was equal to 362 ng/mL, the odds ratio (OR) was 1, and the risk of thrombus gradually increased until it reached 4096 ng/mL, after which the trend no longer increased. Within this range, a twofold increase in D-dimer was associated with a 103.2% increased risk (OR = 2.032; 95% CI 1.293-3.193; P < 0.01). In the subgroup analysis, there was a significant interaction between D-dimer and BMI on intracardiac thrombus (P value for interaction was 0.013), and the risk was higher in patients with a BMI ≥ 25 kg/m2 (OR = 3.44; 95% CI 1.86-6.36; P < 0.01).

Prognostic value of genes related to cancer-associated fibroblasts in lung adenocarcinoma.

BACKGROUND: Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored. OBJECTIVE: This study was formulated to investigate the prognostic value of CAF-related genes in LUAD. METHODS: Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups. RESULTS: Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden. CONCLUSION: An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD.

Glycolysis-related biomarker TCIRG1 participates in regulation of renal cell carcinoma progression and tumor immune microenvironment by affecting aerobic glycolysis and AKT/mTOR signaling pathway.

BACKGROUND: Renal cell carcinoma (RCC) is a hypermetabolic disease. Abnormal up-regulation of glycolytic signaling promotes tumor growth, and glycolytic metabolism is closely related to immunotherapy of renal cancer. The aim of the present study was to determine whether and how the glycolysis-related biomarker TCIRG1 affects aerobic glycolysis, the tumor microenvironment (TME) and malignant progression of clear cell renal cell carcinoma (ccRCC). METHODS: Based on The Cancer Genome Atlas (TCGA, n = 533) and the glycolysis-related gene set from MSigDB, we identified the glycolysis-related gene TCIRG1 by bioinformatics analysis, analyzed its immunological properties in ccRCC and observed how it affected the biological function and glycolytic metabolism using online databases such as TIMER 2.0, UALCAN, LinkedOmics and in vitro experiments. RESULTS: It was found that the expression of TCIRG1, was significantly increased in ccRCC tissue, and that high TCIRG1 expression was associated with poor overall survival (OS) and short progression-free interval (PFI). In addition, TCIRG1 expression was highly correlated with the infiltration immune cells, especially CD4+T cell Th1, CD8+T cell, NK cell, and M1 macrophage, and positively correlated with PDCD1, CTLA4 and other immunoinhibitors, CCL5, CXCR3 and other chemokines and chemokine receptors. More importantly, TCIRG1 may regulate aerobic glycolysis in ccRCC via the AKT/mTOR signaling pathway, thereby affecting the malignant progression of ccRCC cell lines. CONCLUSIONS: Our results demonstrate that the glycolysis-related biomarker TCIRG1 is a tumor-promoting factor by affecting aerobic glycolysis and tumor immune microenvironment in ccRCC, and this finding may provide a new idea for the treatment of ccRCC by combination of metabolic intervention and immunotherapy.

Retinal Organoid Models Show Heterozygous Rhodopsin Mutation Favors Endoplasmic Reticulum Stress-Induced Apoptosis in Rods.

Retinitis pigmentosa (RP) is a prevalent inherited retinal degenerative disease resulting from photoreceptor and pigment epithelial apoptosis. The Rhodopsin (RHO) is the most commonly associated pathogenic gene in RP. However, RHO mutations (c.512C>T P171L) have been infrequently reported, and the RP pathogenesis caused by these mutations remains unclear. The objective of this study was to investigate the impact of RHO (c.512C>T P171L) mutation on retinal cell differentiation and elucidate the underlying mechanisms of RP. An effective retinal organoid induction scheme for inhibiting the Wnt signaling pathway was selected for further experiments, and the established cell line chHES-406 was demonstrated to be heterozygous for RHO c.512C>T, with a normal karyotype and pluripotency potential. Furthermore, the development of chHES-406 organoids may be delayed, and apoptosis detection and co-localization revealed that chHES-406 organoids had more apoptotic cells than chHES-90 in the outer nuclear layer (ONL), mutant RHO protein was mislocalized in the endoplasmic reticulum (ER), and stress-related and apoptotic gene expression increased. Overall, our study elucidated a possible mechanism by which ER stress caused by RHO P171L protein mislocalization may lead to ONL cell apoptosis.

Yu-Shiba-Rusinov states assisted by asymmetric Coulomb repulsion in a bipartite molecular device.

Interfacing magnetism with superconducting condensates are promising candidates holding Majorana bound states with which fault-tolerant quantum computation could be implemented. Within this field, understanding the detailed dynamics is important both for fundamental reasons and for the development of innovative quantum technologies. Herein, motivated by a molecular magnet Tb2Pc3interacting with a superconducting Pb(111) substrate, which results in spin-orbital Yu-Shiba-Rusinov (YSR) states, as is affirmed by a theoretical simulation with the aid of the numerical renormalization group technique (see Xiaet al2022Nat. Commun.136388), we study the YSR states and quantum phase transitions (QPTs) in a bipartite molecular device adsorbed on ans-wave superconducting substrate. We highlight the effect of asymmetric Coulomb repulsion by computing the spectrum function and spin correlation function in various parameter regimes. We demonstrate that if one impurity is non-interacting, there are no YSR states in both impurities with any repulsion value in the other impurity. Whereas if the repulsion in one impurity is strong, the YSR states are observed in both impurities, and a QPT arises as the repulsion in the other impurity sweeps, assisted by the competition between the superconducting singlet (Cooper pair) and the Kondo singlet. The evolution of YSR states distinguishes from the single impurity case and can be well interpreted by the energy scales of the isotropic superconducting gap parameter, as well as the two Kondo temperatures. Our findings provide theoretical insights into the phase diagram of two magnetic impurities on a superconducting host and shine light on the effects induced by asymmetric Coulomb repulsion on many-body interactions.