Breast cancer classification in point-of-care ultrasound imaging-the impact of training data.

Purpose: The survival rate of breast cancer for women in low- and middle-income countries is poor compared with that in high-income countries. Point-of-care ultrasound (POCUS) combined with deep learning could potentially be a suitable solution enabling early detection of breast cancer. We aim to improve a classification network dedicated to classifying POCUS images by comparing different techniques for increasing the amount of training data.

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces.

Performing reliable computer simulations of elementary processes occurring at metal-water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Due to the large system sizes needed for studying processes at liquid water-metal interfaces, these systems can currently not be described using density functional theory (DFT).

Artificial intelligence for detection of prostate cancer in biopsies during active surveillance.

Objectives: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS).

Patients And Methods: A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

Background: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.

Influence of Surface Chemistry on Host/Guest Interactions: A Model Study on Redox-Sensitive β-Cyclodextrin/Ferrocene Complexes.

While host/guest interactions are widely used to control molecular assembly on surfaces, quantitative information on the effect of surface chemistry on their efficiency is lacking. To address this question, we combined electrochemical characterization with quartz crystal microbalance with dissipation monitoring to study host/guest interactions between surface-attached ferrocene (Fc) guests and soluble β-cyclodextrin (β-CD) hosts. We identified several parameters that influence the redox response, β-CD complexation ability, and repellent properties of Fc monolayers, including the method of Fc grafting, the linker connecting Fc with the surface, and the diluting molecule used to tune Fc surface density.

Population Balance Equations for Reactive Separation in Polymer Upcycling.

Many polymer upcycling efforts aim to convert plastic waste into high-value liquid hydrocarbons. However, the subsequent cleavage of middle distillates to light gases can be problematic. The reactor often contains a vapor phase (light gases and middle distillates) and a liquid phase (molten polymers and waxes with a suspended or dissolved catalyst).

Invariant Molecular Representations for Heterogeneous Catalysis.

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used.

Benchmarking the Accuracy of Density Functional Theory against the Random Phase Approximation for the Ethane Dehydrogenation Network on Pt(111).

The Random Phase Approximation (RPA) is conceptually the most accurate Density Functional Approximation method, able to simultaneously predict both adsorbate and surface energies accurately; however, this work questions its superiority over DFT for catalytic application on hydrocarbon systems. This work uses microkinetic modeling to benchmark the accuracy of DFT functionals against that of RPA for the ethane dehydrogenation reaction on Pt(111). Eight different functionals, with and without dispersion corrections, across the GGA, meta-GGA and hybrid classes are evaluated: PBE, PBE-D3, RPBE, RPBE-D3, BEEF-vdW, SCAN, SCAN-rVV10, and HSE06.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

Background: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions.

Unlocking the Potential of A-Site Ca-Doped LaCoFeO: A Redox-Stable Cathode Material Enabling High Current Density in Direct CO Electrolysis.

Massive carbon dioxide (CO) emission from recent human industrialization has affected the global ecosystem and raised great concern for environmental sustainability. The solid oxide electrolysis cell (SOEC) is a promising energy conversion device capable of efficiently converting CO into valuable chemicals using renewable energy sources. However, Sr-containing cathode materials face the challenge of Sr carbonation during CO electrolysis, which greatly affects the energy conversion efficiency and long-term stability.

Theoretical Investigation of the Electrochemical Oxidation of H and CO Fuels on a Ruddlesden-Popper SrLaFeO Anode.

The electrochemical oxidation of H and CO fuels have been investigated on the Ruddlesden-Popper layered perovskite SrLaFeO (SLF) under anodic solid oxide fuel cell conditions using periodic density functional theory and microkinetic modeling techniques. Two distinct FeO-plane-terminated surface models differing in terms of the underlying rock salt layer (SrO or LaO) are used to identify the active site and limiting factors for the electro-oxidation of H, CO, and syngas fuels. Microkinetic modeling predicted an order of magnitude higher turnover frequency for the electro-oxidation of H compared to CO for SLF at short-circuit conditions.

Distinguishing Plasmin-Generating Microvesicles: Tiny Messengers Involved in Fibrinolysis and Proteolysis.

A number of stressors and inflammatory mediators (cytokines, proteases, oxidative stress mediators) released during inflammation or ischemia stimulate and activate cells in blood, the vessel wall or tissues. The most well-known functional and phenotypic responses of activated cells are (1) the immediate expression and/or release of stored or newly synthesized bioactive molecules, and (2) membrane blebbing followed by release of microvesicles. An ultimate response, namely the formation of extracellular traps by neutrophils (NETs), is outside the scope of this work.

Grafted dinuclear zinc complexes for selective recognition of phosphatidylserine: Application to the capture of extracellular membrane microvesicles.

Microvesicles (MVs) are key markers in human body fluids that reflect cellular activation related to diseases as thrombosis. These MVs display phosphatidylserine at the outer leaflet of their plasma membrane as specific recognition moieties. The work reported in this manuscript focuses on the development of an original method where MVs are captured by bimetallic zinc complexes.

Liquid-Phase Effects on Adsorption Processes in Heterogeneous Catalysis.

Aqueous solvation free energies of adsorption have recently been measured for phenol adsorption on Pt(111). Endergonic solvent effects of ∼1 eV suggest solvents dramatically influence a metal catalyst's activity with significant implications for the catalyst design. However, measurements are indirect and involve adsorption isotherm models, which potentially reduces the reliability of the extracted energy values.

Conformational transition in SPR experiments: impact of spacer length, immobilization mode and aptamer density on signal sign and amplitude.

Surface plasmon resonance (SPR) is an optical, real-time and label-free technique which represents a standard to study biomolecular interactions. While SPR signals are usually positive upon recognition, a few cases of negative signals have been reported because of significant conformational transition of the receptor upon the recognition of the target. In this study, we reported on the observation of negative or null SPR signals for an aptamer recognition with its low molecular weight target.

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera.

Purpose: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning.

Methods: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI.

Unraveling Unique Surface Chemistry of Transition Metal Nitrides in Controlling Selective C-O Bond Scission Pathways of Glycerol.

Controlled C-O bond scission is an important step for upgrading glycerol, a major byproduct from the continuously increasing biodiesel production. Transition metal nitride catalysts have been identified as promising hydrodeoxygenation (HDO) catalysts, but fundamental understanding regarding the active sites of the catalysts and reaction mechanism remains unclear. This work demonstrates a fundamental surface science study of MoN and Cu/MoN for the selective HDO reaction of glycerol, using a combination of model surface experiments and first-principles calculations.

Size-Controlled Nanoparticles Embedded in a Mesoporous Architecture Leading to Efficient and Selective Hydrogenolysis of Polyolefins.

A catalytic architecture, comprising a mesoporous silica shell surrounding platinum nanoparticles (NPs) supported on a solid silica sphere (mSiO/Pt-/SiO; is the mean NP diameter), catalyzes hydrogenolysis of melt-phase polyethylene (PE) into a narrow C-centered distribution of hydrocarbons in high yield using very low Pt loadings (∼10 g Pt/g PE). During catalysis, a polymer chain enters a pore and contacts a Pt NP where the C-C bond cleavage occurs and then the smaller fragment exits the pore. mSiO/Pt/SiO resists sintering or leaching of Pt and provides high yields of liquids; however, many structural and chemical effects on catalysis are not yet resolved.

Probing surface-adsorbate interactions through active particle dynamics.

Adsorbate molecules present in a reaction mixture may bind to and block catalytic sites. Measurement of the surface coverage of these molecules via adsorption isotherms is critical for modeling and design of catalytic reactions on surfaces. However, it is challenging to measure isotherms in solution in a way that is directly relevant to catalytic activity under reaction conditions, particularly since adsorbates may bind with an enormous range of surface affinity parameters.

Negative SPR Signals during Low Molecular Weight Analyte Recognition.

Surface plasmon resonance (SPR) is a powerful technique for studying biomolecular interactions mainly due to its sensitivity and real-time and label free advantages. While SPR signals are usually positive, only a few studies have reported sensorgrams with negative signals. The aim of the present work is to investigate and to explain the observation of negative SPR signals with the hypothesis that it reflects major changes in ligand conformation resulting from target binding.

An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies.

Background: Gleason grading is the standard diagnostic method for prostate cancer and is essential for determining prognosis and treatment. The dearth of expert pathologists, the inter- and intraobserver variability, as well as the labour intensity of Gleason grading all necessitate the development of a user-friendly tool for robust standardisation.

Objective: To develop an artificial intelligence (AI) algorithm, based on machine learning and convolutional neural networks, as a tool for improved standardisation in Gleason grading in prostate cancer biopsies.

Dependency of solvation effects on metal identity in surface reactions.

Solvent interactions with adsorbed moieties involved in surface reactions are often believed to be similar for different metal surfaces. However, solvents alter the electronic structures of surface atoms, which in turn affects their interaction with adsorbed moieties. To reveal the importance of metal identity on aqueous solvent effects in heterogeneous catalysis, we studied solvent effects on the activation free energies of the O-H and C-H bond cleavages of ethylene glycol over the (111) facet of six transition metals (Ni, Pd, Pt, Cu, Ag, Au) using an explicit solvation approach based on a hybrid quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface.

Direct Detection of Low-Molecular-Weight Compounds in 2D and 3D Aptasensors by Biolayer Interferometry.

The direct biolayer interferometry (BLI) measurement of low-molecular-weight (LMW) analytes (<200 Da) still represents a challenge, in particular, when low receptor densities are used. BLI is a powerful optical technique for the label-free, real-time characterization and quantification of biomolecular interactions at interfaces. We demonstrate herein that the quantification of biomolecular recognition is possible by BLI using either 2D-like or 3D platforms for aptamer ligand immobilization.

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications.

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them.

Upcycling Single-Use Polyethylene into High-Quality Liquid Products.

Our civilization relies on synthetic polymers for all aspects of modern life; yet, inefficient recycling and extremely slow environmental degradation of plastics are causing increasing concern about their widespread use. After a single use, many of these materials are currently treated as waste, underutilizing their inherent chemical and energy value. In this study, energy-rich polyethylene (PE) macromolecules are catalytically transformed into value-added products by hydrogenolysis using well-dispersed Pt nanoparticles (NPs) supported on SrTiO perovskite nanocuboids by atomic layer deposition.