Search

The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has clarified its clinical significance in predicting outcomes and efficacy. However, there are no studies on the systematic analysis of TME characteristics in bladder cancer. In this study, we comprehensively evaluated the TME invasion pattern of bladder cancer in 1,889 patients, defined three different TME phenotypes, and found that different subtypes were associated with the clinical prognosis and pathological characteristics of bladder cancer. We further explored the signaling pathways, cancer-immunity cycle, copy number, and somatic mutation differences among the different subtypes and used the principal component analysis algorithm to calculate the immune cell (IC) score, a tool for comprehensive evaluation of TME. Univariate and multivariate Cox regression analyses showed that ICscore is a reliable and independent prognostic biomarker. In addition, the use of anti-programmed death-ligand (PD-L1) treatment cohort, receiver operating characteristic (ROC) curve, Tumor Immune Dysfunction and Exclusion (TIDE), Subnetwork Mappings in Alignment of Pathways (SubMAP), and other algorithms confirmed that ICscore is a reliable prognostic biomarker for immune checkpoint inhibitor response. Patients with higher ICscore showed a significant therapeutic advantage in immunotherapy. In conclusion, this study improves our understanding of the characteristics of TME infiltration in bladder cancer and provides guidance for more effective personalized immunotherapy strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest., (Copyright © 2021 Chen, Chen, He, Cheng, Zhu, Xiao, Lan, Wang and Cao.)

Background: Hepatocellular carcinoma (HCC) is a highly prevalent and deadly cancer, with limited treatment options for advanced-stage patients. Disulfidptosis is a recently identified mechanism of programmed cell death that occurs in SLC7A11 high-expressing cells due to glucose starvation-induced disintegration of the cellular disulfide skeleton. We aimed to explore the potential of disulfidptosis, as a prognostic and therapeutic marker in HCC. Methods: We classified HCC patients into two disulfidptosis subtypes (C1 and C2) based on the transcriptional profiles of 31 disulfrgs using a non-negative matrix factorization (NMF) algorithm. Further, five genes (NEIL3, MMP1, STC2, ADH4 and CFHR3) were screened by Cox regression analysis and machine learning algorithm to construct a disulfidptosis scoring system (disulfS). Cell proliferation assay, F-actin staining and PBMC co-culture model were used to validate that disulfidptosis occurs in HCC and correlates with immunotherapy response. Results: Our results suggests that the low disulfidptosis subtype (C2) demonstrated better overall survival (OS) and progression-free survival (PFS) prognosis, along with lower levels of immunosuppressive cell infiltration and activation of the glycine/serine/threonine metabolic pathway. Additionally, the low disulfidptosis group showed better responses to immunotherapy and potential antagonism with sorafenib treatment. As a total survival risk factor, disulfS demonstrated high predictive efficacy in multiple validation cohorts. We demonstrated the presence of disulfidptosis in HCC cells and its possible relevance to immunotherapeutic sensitization. Conclusion: The present study indicates that novel biomarkers related to disulfidptosis may serve as useful clinical diagnostic indicators for liver cancer, enabling the prediction of prognosis and identification of potential treatment targets. [ABSTRACT FROM AUTHOR]

Machine learning is increasingly integrated into chemistry research by guiding experimental procedures, correlating structure and function, interpreting large experimental datasets, to distill scientific insights that might be challenging with traditional methods. Such applications, however, largely focus on gaining insights via big data and/or big computation, while neglecting the valuable chemical prior knowledge dwelling in chemists' minds. In this paper, we introduce an Electrochemistry‐Informed Neural Network (ECINN) by explicitly embedding electrochemistry priors including the Butler–Volmer (BV), Nernst and diffusion equations on the backbone of neural networks for multi‐task discovery of electrochemistry parameters. We applied the ECINN to voltammetry experiments of Fe2+/Fe3+ ${{\rm F}{{\rm e}}^{2+}/{\rm F}{{\rm e}}^{3+}}$ and RuNH362+/RuNH363+ ${{\rm R}{\rm u}{\left({\rm N}{{\rm H}}_{3}\right)}_{6}^{2+{\rm \ }}/{\rm R}{\rm u}{\left({\rm N}{{\rm H}}_{3}\right)}_{6}^{3+{\rm \ }}}$ redox couples to discover electrode kinetics and mass transport parameters. Notably, ECINN seamlessly integrated mass transport with BV to analyze the entire voltammogram to infer transfer coefficients directly, so offering a new approach to Tafel analysis by outdating various mass transport correction methods. In addition, ECINN can help discover the nature of electron transfer and is shown to refute incorrect physics if imposed. This work encourages chemists to embed their domain knowledge into machine learning models to start a new paradigm of chemistry‐informed machine learning for better accountability, interpretability, and generalization. [ABSTRACT FROM AUTHOR]

Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data. [ABSTRACT FROM AUTHOR]

[Display omitted] • Experimenter independent approach to discriminating membrane vs pinhole diffusion. • Random forest model gives high accuracy. • Applied to work of Bard and Leddy [10]. • Prediction mirror independent conclusion of Bard and Leddy. • Interplay of ML with human checks is essential! Current – time transients resulting from potential step measurements at electrodes covered with thin films are typically used to characterise the diffusive transport of the electroactive species to the electrode as being either via partition into and then within the membrane, or alternatively exclusively via pinholes. The transients expected in the two cases are often closely similar. We use ML to provide a user independent analysis of the transient to indicate the most probable mechanism. The limitations and strengths of this approach are critically discussed. [ABSTRACT FROM AUTHOR]