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Boosting and stabilizing oxygen evolution reaction through Ru single atoms anchored amorphous NiMoOx electrocatalyst
A long-term self-driven metronomic photodynamic system for cancer therapy
Abstract Metronomic photodynamic therapy is a long-term, low-dose treatment strategy that employs optical devices with continuous photosensitizer administration and requires stable device attachment with a consistent power source. These factors significantly limit patient mobility. Currently, no metronomic photodynamic therapy modality can operate independent of external devices, underscoring the critical need for in vivo light sources that function without external energy inputs. In this study, we integrate self-luminous bacteria with a photosensitizer in alginate microcapsules to create a self-driven metronomic photodynamic therapy that can be securely implanted within a tumour, thereby enabling continuous light emission without requiring an external energy source or ongoing replenishment of photosensitive reactants. By harnessing nutrients from the tumour microenvironment, this system sustains the generation of reactive oxygen species. A single injection effectively eliminates larger tumours (>300 mm3) in an opaque melanoma mouse model and transplanted hepatocarcinoma rabbit model. Self-driven metronomic photodynamic therapy demonstrates advantages over traditional photodynamic therapy, indicating its potential as a versatile therapeutic approach for cancer treatment with deeply situated lesions.
Stereo-random oligonucleotides enable efficient recruitment of ADAR in vitro and in vivo
Abstract Site-directed RNA editing is a promising and potentially safer alternative to genome editing. Previous methods have been developed that recruit the endogenously and ubiquitously expressed ADAR enzymes to initiate site-specific A-to-I edits, but often suffer from low efficacy or dependency on viral delivery. Chemically modified oligonucleotides may be a promising alternative, but the approach still lacks systematic in-depth studies. Furthermore, the best characterized platform uses stereo-pure backbone chemistry, which is not widely used, commercially unavailable and challenging to manufacture. Here, we report on single-stranded oligonucleotides of 30-60 nt length, which are fully chemically stabilized by applying commercially available, classical RNA drug modifications, like 2´-O-methyl, 2´-fluoro, and DNA on a stereo-random phosphate/phosphorothioate backbone. We demonstrate our so-called RESTORE 2.0 oligonucleotides to induce the correction of pathogenic point mutations, efficacy after GalNAc-mediated uptake into human primary hepatocytes, and proof of in-vivo efficacy in mice upon lipid nanoparticle-mediated delivery. The discovered design principles may increase the accessibility of site-directed RNA base editing to expand and support further research in this field.
Publisher Correction: Oxygen spillover on supported Pt-cluster for anti-CO-poisoning hydrogen oxidation
Molecular insights into how the motions of the β-barrel and POTRA domains of BamA are coupled for efficient function
Persistence and clearance of oral human papillomavirus among a multi-national cohort of men
MTCH2 modulates CPT1 activity to regulate lipid metabolism of adipocytes
Abstract Metabolic disorders, including obesity and metabolic-associated steatohepatitis, arise from a chronic energy surplus. Thus, enhancing energy dissipation through increased respiration holds significant therapeutic potential for metabolic disorders. Through a comprehensive analysis of human and murine adipose tissues, along with a functional screen, we identify mitochondrial carrier homolog 2, a mitochondrial outer membrane protein, as a pivotal regulator of mitochondrial metabolism. Intriguingly, its expression in adipose tissue is a strong determinant of obesity in humans. Adipocyte-specific ablation of mitochondrial carrier homolog 2 improves mitochondrial function and whole-body energy expenditure, independent of uncoupling protein 1. Furthermore, mitochondrial carrier homolog 2 regulates mitochondrial influx of free fatty acids by modulating the sensitivity of carnitine palmitoyltransferase 1 to malonyl-CoA through direct physical interaction, leading to enhanced energy expenditure in adipocytes/adipose tissue. Here we show mitochondrial carrier homolog 2 functions as a negative regulator of energy metabolism in adipocytes and represents a potential target for treating obesity and related metabolic disorders.
A library of avian proteins improves palaeoproteomic taxonomic identification and reveals widespread intraspecies variability
Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders
Abstract Technological devices play a central role in adolescents’ life. Despite concerns about negative effects of excessive screen time, there is little knowledge of screen behaviors’ genetic architecture. Using self-reports from adolescents in the Norwegian Mother, Father, and Child Cohort Study ( n = 18,490), we performed genome-wide association analysis for four screen behaviors: time spent (1) watching television; (2) gaming; (3) sitting/lying down with a screen device; and (4) using social media. The resulting summary statistics were analysed using the conditional false discovery rate (condFDR) approach to increase genetic discovery. We also estimated SNP-heritabilities of the screen behaviors and genetic correlations with eight psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, anorexia nervosa, cannabis use disorder and alcohol use disorder), and educational attainment. Screen behaviors displayed significant SNP-heritabilities (0.048–0.12). We observed significant genetic correlations between screen behaviors and psychiatric disorders (r g range: 0.21–0.42). Educational attainment demonstrated negative genetic correlation with screen behaviors, most strongly with social media use (r g = − 0.69). CondFDR analysis identified three novel loci associated with social media use. Thus, we show that screen behaviors are heritable, polygenic traits that partly share genetic signal with mental disorders and educational attainment.
Sedative potential of palmatine chloride in thiopental sodium-induced chicks: evidence from in vivo and in Silico studies
Corpse flowers waft out stinky compounds as fast as landfills do
Pythagorean fuzzy MAIRCA CRITIC for energy price and demand forecasting involving eco economic factors for sustainable economy
Abstract Sustainable economies require effective energy planning that goes beyond relying on functioning forecasting models to comprehend energy dynamics, and also provides well-defined decision-making (DM) models that can address risk, ambiguity, and conflicting eco-economic objectives. This type of strategic planning requires an integrated assessment approach that can evaluate forecasting choices in an uncertain and dynamic environment. This paper presents a new and modified methodology for ranking energy forecasting models within a Pythagorean Fuzzy Set (PFS) system by integrating the CRITIC (Criteria Importance Through Inter-Criteria Correlation) weighting framework and the MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) ranking scheme. In the suggested framework, expert uncertainty and vagueness are represented by the PFS environment. In contrast, some of the leading eco-economic indicators are objectively weighted using CRITIC, and forecasting model alternatives are prioritized based on MAIRCA. A comparative study is conducted on a hypothetical data set that represents realistic energy system capabilities, including adaptability, carbon policy integration, and computing efficiency. The findings suggest that the framework contributes to consistent, interpretable, and uncertainty-aware rankings, and the Deep Q-Network (DQN) model was ranked to be the most effective alternative. The study contributes to the development of more sophisticated decision-support mechanisms to facilitate sustainable energy planning, enabling informed and balanced decisions as the eco-economic climate evolves rapidly.
Integrated machine learning analysis of proteomic and transcriptomic data identifies healing associated targets in diabetic wound repair
Grid infrastructure and renewables integration for singapore energy transition
Predicting road traffic accident severity from imbalanced data using VAE attention and GCN
Ontology-driven integration of advertised and operational capabilities in robots
Abstract The adaptability of robotic systems is expanding the horizons of manufacturing flexibility. However, fully leveraging the potential of these systems poses considerable challenges. A key requirement is the ability to understand and model their diverse capabilities through a standardized and semantically well-defined framework. In this paper, we introduce the Robotic Capability Ontology (RCO), developed through a systematic investigation of various types of robotic capabilities, including those related to function, quality, and process performance. We define two types of capabilities: Advertised capabilities, as specified by manufacturers, and Operational capabilities, which reflect real-world performance. The RCO framework provides an ontology-based approach to representing these capabilities in a structured and interpretable manner. Within the manufacturing context, RCO serves as a reference ontology that bridges manufacturer specifications and empirical performance data to support more accurate, explainable, and interoperable representations of robotic capabilities.
A 7-year experience in adenoidectomy with endoscopic radiofrequency volume reduction
Fault analysis addressing the combined influence of high penetration of DFIG, SCIG, PMSG wind farms, and PV farms in power grid integration
Proof-of-concept of bayesian latent class modelling usefulness for assessing diagnostic tests in absence of diagnostic standards in mental health
Abstract This study aimed at demonstrating the feasibility, utility and relevance of the Bayesian Latent Class Modelling (BLCM), not assuming a gold standard, when assessing the diagnostic accuracy of the first hetero-assessment test for early detection of occupational burnout (EDTB) by healthcare professionals and the OLdenburg Burnout Inventory (OLBI). We used available data from OLBI and EDTB completed for 100 Belgian and 42 Swiss patients before and after medical consultations. We applied the Hui-Walter framework for two tests and two populations and ran models with minimally informative priors, with and without conditional dependency between diagnostic sensitivities and specificities. We further performed sensitivity analysis by replacing one of the minimally informative priors with the distribution beta 1,2 at each time for all priors. We also performed the sensitivity analysis using literature-based informative priors for OLBI. Using the BLCM without conditional dependency, the diagnostic sensitivity and specificity of the EDTB were 0.91 (0.77-1.00) and 0.82 (0.59-1.00), respectively. The sensitivity analysis did not yield any significant changes in these results. The EDTB’s sensitivity and specificity obtained by a BLCM approach are better compared to the previous studies when EDTB was evaluated against OLBI, considered as a gold standard. These findings show the utility and relevance of BLCM in the absence of a gold standard.