Mitophagy and clear cell renal cell carcinoma: insights from single-cell and spatial transcriptomics analysis.

Background: Clear Cell Renal Cell Carcinoma (ccRCC) is the most common type of kidney cancer, characterized by high heterogeneity and complexity. Recent studies have identified mitochondrial defects and autophagy as key players in the development of ccRCC. This study aims to delve into the changes in mitophagic activity within ccRCC and its impact on the tumor microenvironment, revealing its role in tumor cell metabolism, development, and survival strategies. Methods: Comprehensive analysis of ccRCC tumor tissues using single cell sequencing and spatial transcriptomics to reveal the role of mitophagy in ccRCC. Mitophagy was determined to be altered among renal clear cells by gene set scoring. Key mitophagy cell populations and key prognostic genes were identified using NMF analysis and survival analysis approaches. The role of UBB in ccRCC was also demonstrated by in vitro experiments. Results: Compared to normal kidney tissue, various cell types within ccRCC tumor tissues exhibited significantly increased levels of mitophagy, especially renal clear cells. Key genes associated with increased mitophagy levels, such as UBC, UBA52, TOMM7, UBB, MAP1LC3B, and CSNK2B, were identified, with their high expression closely linked to poor patient prognosis. Particularly, the ubiquitination process involving the UBB gene was found to be crucial for mitophagy and its quality control. Conclusion: This study highlights the central role of mitophagy and its regulatory factors in the development of ccRCC, revealing the significance of the UBB gene and its associated ubiquitination process in disease progression.

Causal relationship between immune cells and hepatocellular carcinoma: a Mendelian randomisation study.

Background: Hepatocellular carcinoma (HCC), the predominant malignancy of the digestive tract, ranks as the third most common cause of cancer-related mortality globally, significantly impeding human health and lifespan. Emerging immunotherapeutic approaches have ignited fresh optimism for patient outcomes. This investigation probes the link between 731 immune cell phenotypes and HCC through Mendelian Randomization and single-cell sequencing, aiming to unearth viable drug targets and dissect HCC's etiology. Methods: We conducted an exhaustive two-sample Mendelian Randomization analysis to ascertain the causal links between immune cell features and HCC, utilizing publicly accessible genetic datasets to explore the causal connections of 731 immune cell traits with HCC susceptibility. The integrity, diversity, and potential horizontal pleiotropy of these findings were rigorously assessed through extensive sensitivity analyses. Furthermore, single-cell sequencing was employed to penetrate the pathogenic underpinnings of HCC. Results: Establishing a significance threshold of pval_Inverse.variance.weighted at 0.05, our study pinpointed five immune characteristics potentially elevating HCC risk: B cell % CD3- lymphocyte (TBNK panel), CD25 on IgD+ (B cell panel), HVEM on TD CD4+ (Maturation stages of T cell panel), CD14 on CD14+ CD16- monocyte (Monocyte panel), CD4 on CD39+ activated Treg ( Treg panel). Conversely, various cellular phenotypes tied to BAFF-R expression emerged as protective elements. Single-cell sequencing unveiled profound immune cell phenotype interactions, highlighting marked disparities in cell communication and metabolic activities. Conclusion: Leveraging MR and scRNA-seq techniques, our study elucidates potential associations between 731 immune cell phenotypes and HCC, offering a window into the molecular interplays among cellular phenotypes, and addressing the limitations of mono-antibody therapeutic targets.

Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling.

Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Acupuncture therapy on myofascial pain syndrome: a systematic review and meta-analysis.

Purpose: Traditional Chinese medicine (TCM) therapies, especially acupuncture, have received increasing attention in the field of pain management. This meta-analysis evaluated the effectiveness of acupuncture in the treatment of myofascial pain syndrome. Methods: A comprehensive search was conducted across a number of databases, including PubMed, Cochrane Library, WOS, CNKI, WANFANG, Sinomed, and VIP. Furthermore, articles of studies published from the inception of these databases until November 22, 2023, were examined. This systematic review and meta-analysis encompassed all randomized controlled trials (RCTs) on acupuncture for myofascial pain syndromes, without language or date restrictions. Based on the mean difference (MD) of symptom change, we critically assessed the outcomes reported in these trials. The quality of evidence was assessed using the Cochrane Risk of Bias Tool. The study is registered with PROSPERO under registration number CRD42023484933. Results: Our analysis included 10 RCTs in which 852 patients were divided into two groups: an acupuncture group (427) and a control group (425). The results of the study showed that acupuncture was significantly more effective than the control group in treating myofascial pain syndromes, which was reflected in a greater decrease in VAS scores (MD = -1.29, 95% [-1.65, -0.94], p < 0.00001). In addition, the improvement in PRI and PPI was more pronounced in the acupuncture group (PRI: MD = -2.04, 95% [-3.76, -0.32], p = 0.02) (PPI: MD = -1.03, 95% [-1.26, -0.79], p < 0.00001) compared to the control group. These results suggest that acupuncture is effective in reducing myofascial pain. It is necessary to further study the optimal acupoints and treatment time to achieve the best therapeutic effect. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023484933.

The calculated electronic and optical properties of ß-Ga2O3 based on the first principles.

INTRODUCTION: The electronic and optical properties of ß-Ga2O3 have been investigated by CASTEP using first principles. It is found that ß-Ga2O3 has an indirect band gap and the conduction band base is located at the Γ point. The stability of ß-Ga2O3 is demonstrated by the calculation of elastic constants, and the ductility of ß-Ga2O3 is demonstrated by the ratio of Poisson's ratio to shear modulus. The optical property analysis shows that ß-Ga2O3 has a high absorption capacity in the ultraviolet region, but a low absorption capacity in visible and infrared light. CONTEXT: The structure, optical, and electronic properties of ß-Ga2O3 are calculated and analyzed based on first-principles calculation. The optimized structures of ß-Ga2O3 are in good agreement with previously studied. In this paper, the elastic, electronic, and optical properties of ß-Ga2O3 are calculated. METHODS: The CASTEP code was employed to execute these calculations in the present work, where the exchange-correlation interactions were treated in the generalized gradient approximation (GGA) using the Perdew-Burke-Ernzerhof (PBE) functional in the geometry optimizations and electronic and elastic properties.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics.

BACKGROUND: Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. METHODS: Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. RESULTS: Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. CONCLUSION: Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

Clinical Significance of Disulfidptosis-related Genes and Functional Analysis in Gastric Cancer.

Background: Worldwide, gastric cancer (GC) remains intractable due to its poor prognosis and high morbidity and mortality. Disulfidptosis is a novel kind of cell death mediated by abnormal accumulation of intracellular disulphides. The correlation between disulfidptosis and GC is still unknown. Therefore, it is necessary to elucidate the pathogenesis and mechanism of disulfidptosis and GC for clinical diagnosis and intervention. Methods: RNA-sequencing data from several public data portals and clinical samples were collected. We compared the expression levels of four key genes of disulfidptosis, including SLC7A11, SLC3A2, RPN1, and NCKAP1, in GC and selected prognostic genes to build a novel GC prognosis-related nomogram model. The biological functions and immune landscape of the identified prognostic genes were explored. Results: Overexpressed NCKAP1 and SLC7A11 were prognostic disulfidptosis-related genes in GC. We combined these genes and several clinicopathological factors to build a prognostic nomogram model for GC. Meanwhile, the ROC curves showed that NCKAP1 and SLC7A11 were promising biomarkers for GC screening. The biological and cellular functions were focused on actin activities, GTPase and immunoreaction. The tumour immune microenvironment and immune therapy targets were identified. Competing endogenous RNA network was built to explore the downstream regulatory mechanisms. Finally, the elevated NCKAP1 and SLC7A11 expression in GC was validated via qRT-PCR in a cell line and tissue line. Conclusion: In conclusion, NCKAP1 and SLC7A11 are promising prognostic and diagnostic biomarkers for GC that correlate with the activities of actin, energy metabolism of GTPase, immune infiltration and immunotherapy.

A state-of-the-art review on biomass-derived carbon materials for supercapacitor applications: From precursor selection to design optimization.

Biomass-derived carbon materials have the characteristics of a wide range of precursor sources, controllable carbon nano-dimension, large specific surface area and abundant heteroatoms doping. At present, biomass-derived carbon materials have been widely used in electrochemical energy storage devices, especially the research and development of biomass-derived carbon materials for supercapacitors has become mature and in-depth. Therefore, it is of importance to summarize the advanced technologies and strategies for optimizing biomass-derived carbon materials for supercapacitors, which will effectively promote the further development of high-performance supercapacitors. In this review, the recent research progress of biomass-derived carbon materials is provided in detail, including the selection of biomass precursors, the design of carbon nano-dimension and the theory of heteroatom doping. Besides, the preparation methods of biomass-derived carbon materials and the related processes of optimizing the electrochemical performance are also summarized. This review ends with the perspectives for future research directions and challenges in the field of biomass-derived carbon materials for electrochemical applications. This review aims to provide helpful reference information for the nano-dimensional design and electrochemical performance optimization of biomass-derived carbon materials for the practical application of supercapacitors.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms.

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

Structures, cold pressure lines, and electronic properties of cubic Al2O and AlO: First-principles calculations.

Aluminized explosive has attracted more and more attention in recent years because of its high explosive heat and high power. Al2O and AlO are indispensable aluminum oxides in the explosion process of aluminized explosives. The study of the physical properties of solid Al2O and AlO under pressure may play an important role in the understanding of the explosion mechanism of aluminized explosives. CONTEXT: The structures, cold-pressed lines and electronic properties of cubic Al2O and AlO are calculated and analyzed based on first-principles calculation in this paper. The optimized structures of Al2O and AlO are in good agreement with those previously studied. The cold pressure line shows that the specific volumes of Al2O and AlO decrease with increasing pressure. The peak values and peak positions of density of state of Al2O and AlO change greatly under pressure. METHODS: The CASTEP code was used to execute these calculations throughout the present work, where the plane-wave basis set and norm conserving pseudopotential were employed.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks.

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay.

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.

Background: Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications. Methods: In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method. Results: Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age. Conclusion: Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

Clinical nutrition service capacity of 445 secondary or above hospitals in Sichuan, China: the 2021 annual survey.

BACKGROUND AND OBJECTIVES: To investigate the capacity of clinical nutrition services in secondary and tertiary hospitals in the Sichuan Province, China. METHODS AND STUDY DESIGN: Convenience sampling was used. E-questionnaires were distributed to all eligible medical institutions in Sichuan through the official network of provincial and municipal clinical nutrition quality control centers. The data obtained were sorted in Microsoft Excel and analyzed by SPSS. RESULTS: A total of 519 questionnaires were returned, of which 455 were valid. Only 228 hospitals were accessible to clinical nutrition services, of which 127 hospitals had independently set up clinical nutrition departments (CNDs). The ratio of clinical nutritionists to beds was 1:214. During the last decade, the rate of constructing new CNDs was maintained at approximately 5 units/year. A total of 72.4% of hospitals managed their clinical nutrition units as part of their medical technology departments. The specialist number ratio of senior, associate, intermediate and junior is approximately 1:4:8:10. There were 5 common charges for clinical nutrition. CONCLUSIONS: The sample representation was limited, and the capacity of clinical nutrition services may have been overestimated. Secondary and tertiary hospitals in Sichuan are currently in the second high tide of department establishment, with a positive trend of departmental affiliation standardization and a basic formation of a talent echelon.

Electrons and phonons of the discharge products in the lithium-oxygen and lithium-sulfur batteries from first-principles calculations.

Batteries have become a ubiquitous daily necessity, which are popularly applied to mobile phones and electric vehicles according to their size. Improving the battery cycle life and storage is important, but unexpected discharge products still restrict the upper limit of batter performance such as Li2O2, LiO2, and Li2S. In this study, we calculated electrons and phonons presenting the basic energy states in crystal using the first-principles calculations. The Li2O2 and Li2S are almost insulating due to the wide bandgap from their electronic structure, and doped-active p-orbital may be one of the pathways to improve crystal conduction due to the tendency of the density of states. The LiO2 is metallic, and the electronic structure and phonons show that the discharge products have an ionic feature. In addition, the ionic crystal can produce a larger DC permittivity because it possesses macroscopic polarisation. For Li2O2 and Li2S, the Raman peak of the O-O bonding is strong, while the Raman peak of the S-ion is very weak. The enhanced Raman peak of the S-ion presents a possibility to prevent the shuttle effect in Li-S batteries.

Phosphate ions functionalized spinel iron cobaltite derived from metal organic framework gel for high-performance asymmetric supercapacitors.

Spinel iron cobaltite (FeCo2O4) with high theoretical capacity is a promising positive electrode material for building high-performance supercapacitors. However, its inherent poor conductivity and deficient electrochemical active sites hinder the improvement of its electrochemical kinetics behavior. Herein, phosphate ions modified FeCo2O4 is obtained in the presence of oxygen vacancies (P-FeCo2O4-x) by a simple metal organic framework gel-derived strategy. Phosphate ions added on the surface of P-FeCo2O4-x greatly enhances its surface activity, thus prompting the faster charge storage kinetics of the electrode material. Due to its ample electrochemical active sites and rapid ion diffusion and electron mobility, the optimized P-FeCo2O4-x electrode delivers a superior specific capacity of 1568.8 F g-1 (784.4 C g-1) at a current density of 1 A/g and has an excellent cycling stability with 93.3 % initial capacity retention ratio after 5000 cycles. More impressively, the assembled asymmetric supercapacitor consisting of P-FeCo2O4-x and activated carbon which act as positive and negative electrode materials, respectively displays a favorable energy density of 60.2 Wh kg-1 at a power density of 800 W kg-1 and has a long cycling lifespan. These results demonstrate the potential importance of modifying the surface of spinel cobaltite with phosphate ions and incorporating oxygen defects in it as a facile strategy for enhancing the electrochemical kinetics of electrode materials.

Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma.

Background: Clear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC. Methods: Employing both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments. Results: By analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs. Conclusions: In this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

First-principles study of the structural phase transition process of solid nitrogen under pressure.

Due to the diversity of solid nitrogen structure, its phase transition has been a hot topic for many scientists. Herein, we first studied the structural softening of rhombohedral solid nitrogen under pressure using first-principles calculations. Then, a new criterion, Egret criterion, was proposed to predict the whole process from beginning to end of structural phase transition of solid nitrogen. Based on the discussion of acoustic phonons, we concluded that the phase transition of rhombohedral solid nitrogen starts from k-point F along the [- 1, - 1, 0] direction in a-axis, and the structural phase transition velocity is slow. Also, we use the Egret criterion proposed by us to predict the emergence of ξ-N2 and the stability of ξ-N2 at 17 GPa and 22 GPa, respectively, and this result is in good agreement with the phase diagram of nitrogen.

Electronic, optical, and vibrational properties of B3N3H6 from first-principles calculations.

The structural, electronic, optical, and vibrational properties of B3N3H6 have been calculated by means of the first-principles density functional theory (DFT) calculations within the generalized gradient approximation (GGA) and the local density approximation (LDA). The calculated structural parameters of B3N3H6 are in good agreement with experimental data. The obtained band structure of B3N3H6 shows that it has an indirect band gap with 5.007 eV, indicating that it presents insulation characteristic. The total and partial density of states (DOS) of B3N3H6 are given, which tell us the states of the orbital occupation. With the band structure and density of states, we have analyzed the optical properties including the complex dielectric function, refractive index, absorption, conductivity, loss function, and reflectivity. By the contrast, it is found that optical anisotropy is observed in the (001) direction and (100) direction. Moreover, the vibrational properties have been obtained and analyzed, showing that B3N3H6 is dynamically stable due to that there is no imaginary frequency. The frequencies associating with the vibrations are given, which show that B3N3H6 has a low mechanical modulus and thermal conductivity.