Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD.This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm.The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers.Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

Squaraine dyes derived from indolenine and benzo[e]indole as potential fluorescent probes for HSA detection and antifungal agents

Four squaraine dyes derived from 2,3,3-trimethylindolenine and 1,1,2-trimethyl-1H-benzo[e]indole with different combinations of barbituric groups attach to the central ring, having ester groups and alkyl chains in the nitrogen atoms of heterocyclic rings were synthesized. These dyes were fully characterized and their photophysical behavior was studied in ethanol and phosphate-buffered saline solution. Absorption and emission bands between 631 and 712 nm were detected, with the formation of aggregates in aqueous media, which is typical of this class of dyes. Tests carried out with 1,3-diphenylisobenzofuran allowed us to verify the ability of the dyes to produce singlet oxygen. The interaction of synthesized dyes with human serum albumin (HSA) was also evaluated, being demonstrated a linear correlation between fluorescence intensity and protein concentration. The antifungal potential of the dyes against the yeast Saccharomyces cerevisiae was evaluated using a broth microdilution assay. In order to test the photosensitizing capacity of the synthesized dyes, tests were carried out in the dark and with irradiation, using a custom-built light-emitting diode that emits close to the absorption wavelength of the studied dyes. The results showed that the interaction of dyes with HSA and the antifungal activity depends on the different structural modifications of the dyes.We thanks to Fundação para a Ciência e Tecnologia (FCT), Comissão de Coordenação e Desenvolvimento Regional do Norte (CCDR-N) and FEDER (European Fund for Regional Development)-COMPETEQREN-EU for financial support to the research centers CQ/UM (UIDB/00686/2020), CBMA (UID/BIA/04050/2020), CQ/VR (UID/QUI/UI0616/2019) and CICSUBI (POCI-01-0145-FEDER-007491), as well as PhD grants to V.S.D.G. (UMINHO/BD/43/2016) and J.C.C.F. (SFRH/BD/133207/2017)

Channel estimation method with improved performance for the UMTS-TDD mode

Channel estimation is an essential building block for UTRA-TDD high performance receivers. Once the performance of the channel estimator algorithm proposed by 3GPP is highly dependent on the time spreading between consecutive multi-path components, a Successive Multi-path channel Estimation Technique (SMET) that improves the time resolution is proposed in this paper. A SMET based maximum likelihood approach for vectorial channel estimation, to include the estimation of the direction-of-arrival, is also proposed. This algorithm solves efficiently the complex problem of DOA estimation of multiple users in a multi path propagation environment even when the number of required DOA's exceeds the number of antenna array elements. Another property of the proposed algorithm is its ability to resolve signals from different users arriving from the same direction. This is due to processing in both time and space dimensions. The performance of these algorithms is assessed by resorting to simulations in multi-path environments using the UMTS-TDD specifications, and also by comparing the rms estimation errors against the Crámer-Rao Bound. The effect of imperfect channel estimation on the performance of RAKE and Hard-Decision Parallel Interference Canceller receivers is also analysed. The results show that a good performance can be achieved with SMET, from low to high values of Eb/n0

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool

Background: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. Objective: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP 3 ) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdge TM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdge TM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

Caco-2 cells cytotoxicity of nifuroxazide derivatives with potential activity against Methicillin-resistant Staphylococcus aureus (MRSA)

It is important to determine the toxicity of compounds and co-solvents that are used in cell monolayer permeability studies to increase confidence in the results obtained from these in vitro experiments. This study was designed to evaluate the cytotoxicity of new nifuroxazide derivatives with potential activity against Methicillin-resistant Staphylococcus aureus (MRSA) in Caco-2 cells to select analogues for further in vitro permeability analyses. In this study, nitrofurantoin and nifuroxazide, in addition to 6 furanic and 6 thiophenic nifuroxazide derivatives were tested at 2, 4, 6, 8 and 10 mu g/mL. In vitro cytotoxicity assays were performed according to the MTT (methyl tetrazolium) assay protocol described in ISO 10993-5. The viability of treated Caco-2 cells was greater than 83% for all tested nitrofurantoin concentrations, while those treated with nifuroxazide at 2, 4 and 6 mu g/mL had viabilities greater than 70%. Treatment with the nifuroxazide analogues resulted in viability values greater than 70% at 2 and 4 mu g/mL with the exception of the thiophenic methyl-substituted derivative, which resulted in cell viabilities below 70% at all tested concentrations. Caco-2 cells demonstrated reasonable viability for all nifuroxazide derivatives, except the thiophenic methyl-substituted compound. The former were selected for further permeability studies using Caco-2 cells. (C) 2012 Elsevier Ltd. All rights reserved.CNPqCNPqFAPESP [2010/07188-7]FAPES

Hydrated electron generation by excitation of copper localized surface plasmon resonance

Hydrated electrons are important in radiation chemistry and chargetransfer reactions, with applications that include chemical damage of DNA, catalysis, and signaling. Conventionally, hydrated electrons are produced by pulsed radiolysis, sonolysis, two-ultraviolet-photon laser excitation of liquid water, or photodetachment of suitable electron donors. Here we report a method for the generation of hydrated electrons via single-visible-photon excitation of localized surface plasmon resonances (LSPRs) of supported sub-3 nm copper nanoparticles in contact with water. Only excitations at the LSPR maximum resulted in the formation of hydrated electrons, suggesting that plasmon excitation plays a crucial role in promoting electron transfer from the nanoparticle into the solution. The reactivity of the hydrated electrons was confirmed via proton reduction and concomitant H2 evolution in the presence of a Ru/ TiO2 catalyst