ABSTRACT
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.
ABSTRACT
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner. Our method incorporates a DL ensemble for initial parameter estimation, facilitating efficient downstream evolutionary sampling initialized with this DL-based prior. We showcase the effectiveness of integrating a rapid deep-learning algorithm with a high-precision evolution strategy in estimating brain tumor cell concentrations from magnetic resonance images. The DL-Prior plays a pivotal role, significantly constraining the effective sampling-parameter space. This reduction results in a fivefold convergence acceleration and a Dice-score of 95.
ABSTRACT
Electrochemical CO2 conversion is a key technology to promote the production of carbon-containing molecules, alongside reducing CO2 emissions leading to a closed carbon cycle economy. Over the past decade, the interest to develop selective and active electrochemical devices for electrochemical CO2 reduction emerged. However, most reports employ oxygen evolution reaction as an anodic half-cell reaction causing the system to suffer from sluggish kinetics with no production of value-added chemicals. Therefore, this study reports a conceptualized paired electrolyzer for simultaneous anodic and cathodic formate production at high currents. To achieve this, CO2 reduction was coupled with glycerol oxidation: a BiOBr-modified gas-diffusion cathode and a Nix B on Ni foam anode keep their selectivity for formate in the paired electrolyzer compared to the half-cell measurements. The paired reactor here reaches a combined Faradaic efficiency for formate of 141 % (45 % anode and 96 % cathode) at a current density of 200â mA cm-2 .
Subject(s)
Carbon Dioxide , Glycerol , Electrodes , FormatesABSTRACT
The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent 'imaging', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.
ABSTRACT
Complex functioning of the genome in the cell nucleus is controlled at different levels: (a) the DNA base sequence containing all relevant inherited information; (b) epigenetic pathways consisting of protein interactions and feedback loops; (c) the genome architecture and organization activating or suppressing genetic interactions between different parts of the genome. Most research so far has shed light on the puzzle pieces at these levels. This article, however, attempts an integrative approach to genome expression regulation incorporating these different layers. Under environmental stress or during cell development, differentiation towards specialized cell types, or to dysfunctional tumor, the cell nucleus seems to react as a whole through coordinated changes at all levels of control. This implies the need for a framework in which biological, chemical, and physical manifestations can serve as a basis for a coherent theory of gene self-organization. An international symposium held at the Biomedical Research and Study Center in Riga, Latvia, on 25 July 2022 addressed novel aspects of the abovementioned topic. The present article reviews the most recent results and conclusions of the state-of-the-art research in this multidisciplinary field of science, which were delivered and discussed by scholars at the Riga symposium.
Subject(s)
Cell Nucleus , Genome , Cell Nucleus/metabolism , Cell Differentiation/geneticsABSTRACT
Gas diffusion electrodes (GDEs) in CO2 reduction reaction (CO2RR) alleviate the mass transfer limitation of gaseous reagents, which is beneficial for reducing CO2 into valuable chemicals. GDEs offer higher current densities compared to electrodes immersed in the electrolyte. Disclosing the roles of different structural parameters in tuning the performance of the GDEs is essential to exert the potential of catalysts and to meet potential large-scale industrial applications of the CO2RR. A novel layer structure for the airbrush-type spray fabrication of GDEs was designed and optimised, comprising a carbon-based gas-diffusion layer, a PEEK fabric, a Ni mesh, a carbon-integrated catalyst layer, and a PTFE top layer. It was shown that adjusting the carbon material in the gas diffusion and the catalyst layer impacts the selectivity of the CO2RR due to the modulation of the pore network. This work disclosed a practical and scalable but also an easily transferable pathway for preparing GDEs and offered an idea of how to tune the significant parameters of GDEs for optimising their CO2RR performance.
ABSTRACT
Developing highly efficient and selective electrocatalysts for the CO2 reduction reaction to produce value-added chemicals has been intensively pursued. We report a series of Cux Oy Cz nanostructured electrocatalysts derived from a Cu-based MOF as porous self-sacrificial template. Blending catalysts with polytetrafluoroethylene (PTFE) on gas diffusion electrodes (GDEs) suppressed the competitive hydrogen evolution reaction. 25 to 50â wt % teflonized GDEs exhibited a Faradaic efficiency of ≈54 % for C2+ products at -80â mA cm-2 . The local OH- ions activity of PTFE-modified GDEs was assessed by means of closely positioning a Pt-nanoelectrode. A substantial increase in the OH- /H2 O activity ratio due to the locally generated OH- ions at increasing current densities was determined irrespective of the PTFE amount.
ABSTRACT
The electrochemical water splitting commonly involves the cathodic hydrogen and anodic oxygen evolution reactions (OER). The oxygen evolution reaction is more energetically demanding and kinetically sluggish and represents the bottleneck for a commercial competitiveness of electrochemical hydrogen production from water. Moreover, oxygen is essentially a waste product of low commercial value since the primary interest is to convert electrical energy into hydrogen as a storable energy carrier. We report on the anodic oxidation of 5-hydroxymethylfurfural (HMF) to afford the more valuable product 2,5-furandicarboxylic acid (FDCA) as a suitable alternative to the oxygen evolution reaction. Notably, HMF oxidation is thermodynamically more favorable than water oxidation and hence leads to an overall improved energy efficiency for H2 production. In addition, contrary to the "waste product O2", FDCA can be further utilized, e.g., for production of polyethylene 2,5-furandicarboxylate (PEF), a sustainable polymer analog to polyethylene terephthalate (PET) and thus represents a valuable product for the chemical industry with potential large scale use. Various cobalt-metalloid alloys (CoX; X = B, Si, P, Te, As) were investigated as potential catalysts for HMF oxidation. In this series, CoB required 180 mV less overpotential to reach a current density of 55 mA cm-2 relative to OER with the same electrode. Electrolysis of HMF using a CoB modified nickel foam electrode at 1.45 V vs RHE achieved close to 100% selective conversion of HMF to FDCA at 100% faradaic efficiency.
ABSTRACT
The electrochemical oxidation of the biorefinery product 5-(hydroxymethyl)furfural (HMF) to 2,5-furandicarboxylic acid (FDCA), an important platform chemical for the polymer industry, is receiving increasing interest. FDCA-based polymers such as polyethylene 2,5-furandicarboxylate (PEF) are sustainable candidates for replacing polyethylene terephthalate (PET). Herein, we report the highly efficient electrocatalytic oxidation of HMF to FDCA, using Ni foam modified with high-surface-area nickel boride (Nix B) as the electrode. Constant potential electrolysis in combination with HPLC revealed a high faradaic efficiency of close to 100 % towards the production of FDCA with a yield of 98.5 %. Operando electrochemistry coupled to ATR-IR spectroscopy indicated that HMF is oxidized preferentially via 5-hydroxymethyl-2-furancarboxylic acid rather than via 2,5-diformylfuran, which is in agreement with HPLC results. This study not only reports a low-cost active electrocatalyst material for the electrochemical oxidation of HMF to FDCA, but additionally provides insight into the reaction pathway.