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Xia T, Pan Z, Wan H, Li Y, Mao G, Zhao J, Zhang F, Pan S. Mechanisms of mechanical stimulation in the development of respiratory system diseases. Am J Physiol Lung Cell Mol Physiol 2024; 327:L724-L739. [PMID: 39316681 DOI: 10.1152/ajplung.00122.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 09/06/2024] [Accepted: 09/11/2024] [Indexed: 09/26/2024] Open
Abstract
During respiration, mechanical stress can initiate biological responses that impact the respiratory system. Mechanical stress plays a crucial role in the development of the respiratory system. However, pathological mechanical stress can impact the onset and progression of respiratory diseases by influencing the extracellular matrix and cell transduction processes. In this article, we explore the mechanisms by which mechanical forces communicate with and influence cells. We outline the basic knowledge of respiratory mechanics, elucidating the important role of mechanical stimulation in influencing respiratory system development and differentiation from a microscopic perspective. We also explore the potential mechanisms of mechanical transduction in the pathogenesis and development of respiratory diseases such as asthma, lung injury, pulmonary fibrosis, and lung cancer. Finally, we look forward to new research directions in cellular mechanotransduction, aiming to provide fresh insights for future therapeutic research on respiratory diseases.
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Affiliation(s)
- Tian Xia
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Ziyin Pan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai, People's Republic of China
| | - Haoxin Wan
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Yongsen Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Guocai Mao
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Jun Zhao
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Fangbiao Zhang
- Department of Cardiothoracic Surgery, Lishui Municipal Central Hospital, Lishui, People's Republic of China
| | - Shu Pan
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
- Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
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Salvatti BA, Chagas MA, Fernandes PO, Ladeira YFX, Bozzi AS, Valadares VS, Valente AP, de Miranda AS, Rocha WR, Maltarollo VG, Moraes AH. Understanding the Enzyme ( S)-Norcoclaurine Synthase Promiscuity to Aldehydes and Ketones. J Chem Inf Model 2024; 64:4462-4474. [PMID: 38776464 DOI: 10.1021/acs.jcim.3c01773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The (S)-norcoclaurine synthase from Thalictrum flavum (TfNCS) stereoselectively catalyzes the Pictet-Spengler reaction between dopamine and 4-hydroxyphenylacetaldehyde to give (S)-norcoclaurine. TfNCS can catalyze the Pictet-Spengler reaction with various aldehydes and ketones, leading to diverse tetrahydroisoquinolines. This substrate promiscuity positions TfNCS as a highly promising enzyme for synthesizing fine chemicals. Understanding carbonyl-containing substrates' structural and electronic signatures that influence TfNCS activity can help expand its applications in the synthesis of different compounds and aid in protein optimization strategies. In this study, we investigated the influence of the molecular properties of aldehydes and ketones on their reactivity in the TfNCS-catalyzed Pictet-Spengler reaction. Initially, we compiled a library of reactive and unreactive compounds from previous publications. We also performed enzymatic assays using nuclear magnetic resonance to identify some reactive and unreactive carbonyl compounds, which were then included in the library. Subsequently, we employed QSAR and DFT calculations to establish correlations between substrate-candidate structures and reactivity. Our findings highlight correlations of structural and stereoelectronic features, including the electrophilicity of the carbonyl group, to the reactivity of aldehydes and ketones toward the TfNCS-catalyzed Pictet-Spengler reaction. Interestingly, experimental data of seven compounds out of fifty-three did not correlate with the electrophilicity of the carbonyl group. For these seven compounds, we identified unfavorable interactions between them and the TfNCS. Our results demonstrate the applications of in silico techniques in understanding enzyme promiscuity and specificity, with a particular emphasis on machine learning methodologies, DFT electronic structure calculations, and molecular dynamic (MD) simulations.
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Affiliation(s)
- Brunno A Salvatti
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Marcelo A Chagas
- Departamento de Ciências Exatas, Universidade do Estado de Minas Gerais, João Monlevade, Minas Gerais 35930-314, Brazil
| | - Phillipe O Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Yan F X Ladeira
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Aline S Bozzi
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Veronica S Valadares
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Ana Paula Valente
- Centro Nacional de Ressonância Magnética Nuclear, Instituto de Bioquímica Médica Leopoldo de Meis, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21.941-902, Brazil
| | - Amanda S de Miranda
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Willian R Rocha
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Vinicius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Adolfo H Moraes
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
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Barbosa H, Espinoza GZ, Amaral M, de Castro Levatti EV, Abiuzi MB, Veríssimo GC, Fernandes PDO, Maltarollo VG, Tempone AG, Honorio KM, Lago JHG. Andrographolide: A Diterpenoid from Cymbopogon schoenanthus Identified as a New Hit Compound against Trypanosoma cruzi Using Machine Learning and Experimental Approaches. J Chem Inf Model 2024; 64:2565-2576. [PMID: 38148604 DOI: 10.1021/acs.jcim.3c01410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan Trypanosoma cruzi and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti-T. cruzi potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of T. cruzi. The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from Cymbopogon schoenanthus and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of T. cruzi, showing IC50 values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC50 values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC50 = 92.8 μM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the in silico analyses, we can conclude that andrographolide fulfills many requirements implemented by DNDi to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of T. cruzi.
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Affiliation(s)
- Henrique Barbosa
- Center for Natural and Human Sciences, Federal University of ABC, São Paulo 09210-180, Brazil
| | | | - Maiara Amaral
- Laboratory of Pathophysiology, Butantan Institute, São Paulo 05503-900, Brazil
| | | | | | - Gabriel Correa Veríssimo
- Department of Pharmaceutical Products, Federal University of Minas Gerais, Minas Gerais, 31270-901, Brazil
| | | | | | | | - Kathia Maria Honorio
- Center for Natural and Human Sciences, Federal University of ABC, São Paulo 09210-180, Brazil
- School of Arts, Science, and Humanities, University of São Paulo, São Paulo 03828-000, Brazil
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Fernandes PO, Dias ALT, Dos Santos Júnior VS, Sá Magalhães Serafim M, Sousa YV, Monteiro GC, Coutinho ID, Valli M, Verzola MMSA, Ottoni FM, Pádua RMD, Oda FB, Dos Santos AG, Andricopulo AD, da Silva Bolzani V, Mota BEF, Alves RJ, de Oliveira RB, Kronenberger T, Maltarollo VG. Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and Resistant Staphylococcus aureus. J Chem Inf Model 2024; 64:1932-1944. [PMID: 38437501 DOI: 10.1021/acs.jcim.4c00087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
The application of computer-aided drug discovery (CADD) approaches has enabled the discovery of new antimicrobial therapeutic agents in the past. The high prevalence of methicillin-resistantStaphylococcus aureus(MRSA) strains promoted this pathogen to a high-priority pathogen for drug development. In this sense, modern CADD techniques can be valuable tools for the search for new antimicrobial agents. We employed a combination of a series of machine learning (ML) techniques to select and evaluate potential compounds with antibacterial activity against methicillin-susceptible S. aureus (MSSA) and MRSA strains. In the present study, we describe the antibacterial activity of six compounds against MSSA and MRSA reference (American Type Culture Collection (ATCC)) strains as well as two clinical strains of MRSA. These compounds showed minimal inhibitory concentrations (MIC) in the range from 12.5 to 200 μM against the different bacterial strains evaluated. Our results constitute relevant proven ML-workflow models to distinctively screen for novel MRSA antibiotics.
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Affiliation(s)
- Philipe Oliveira Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Anna Letícia Teotonio Dias
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Valtair Severino Dos Santos Júnior
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Yamara Viana Sousa
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Gustavo Claro Monteiro
- Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista (UNESP), Araraquara, São Paulo 14.800-900, Brazil
| | - Isabel Duarte Coutinho
- Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista (UNESP), Araraquara, São Paulo 14.800-900, Brazil
| | - Marilia Valli
- Departamento de Física e Ciência Interdisciplinar, Instituto de Física, Universidade de São Paulo (USP), São Carlos, São Paulo 13.563-120, Brazil
| | - Marina Mol Sena Andrade Verzola
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Flaviano Melo Ottoni
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Rodrigo Maia de Pádua
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Fernando Bombarda Oda
- Departamento de Fármacos e Medicamentos, Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista (UNESP), Araraquara 14.800-903, Brazil
| | - André Gonzaga Dos Santos
- Departamento de Fármacos e Medicamentos, Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista (UNESP), Araraquara 14.800-903, Brazil
| | - Adriano Defini Andricopulo
- Departamento de Física e Ciência Interdisciplinar, Instituto de Física, Universidade de São Paulo (USP), São Carlos, São Paulo 13.563-120, Brazil
| | - Vanderlan da Silva Bolzani
- Departamento de Química Orgânica, Instituto de Química, Universidade Estadual Paulista (UNESP), Araraquara, São Paulo 14.800-900, Brazil
| | - Bruno Eduardo Fernandes Mota
- Departamento de Análises Clínicas e Toxicológicas, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Ricardo José Alves
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Renata Barbosa de Oliveira
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
| | - Thales Kronenberger
- Institute of Pharmacy, Pharmaceutical/Medicinal Chemistry and Tübingen Center for Academic Drug Discovery, Eberhard Karls University Tübingen, 72076 Tübingen, Germany
- School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland, 70211 Kuopio, Finland
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Minas Gerais 31.270-901, Brazil
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Veríssimo GC, Pantaleão SQ, Fernandes PDO, Gertrudes JC, Kronenberger T, Honorio KM, Maltarollo VG. MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling. J Comput Aided Mol Des 2023; 37:735-754. [PMID: 37804393 DOI: 10.1007/s10822-023-00536-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/14/2023] [Indexed: 10/09/2023]
Abstract
QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.
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Affiliation(s)
- Gabriel Corrêa Veríssimo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | | | - Philipe de Olveira Fernandes
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | - Jadson Castro Gertrudes
- Department of Computing, Institute of Exact and Biological Sciences, Federal University of Ouro Preto, Ouro Preto, MG, 35400-000, Brazil
| | - Thales Kronenberger
- Department of Pharmaceutical and Medicinal Chemistry, University of Tübingen, Tübingen, BW, 72076, Germany
| | - Kathia Maria Honorio
- Federal University of ABC, Santo André, SP, 09210-170, Brazil
- School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, SP, 03828-000, Brazil
| | - Vinícius Gonçalves Maltarollo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil.
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Lin Y, Zhang Y, Wang D, Yang B, Shen YQ. Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2022; 107:154481. [PMID: 36215788 DOI: 10.1016/j.phymed.2022.154481] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 09/14/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs. PURPOSE This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development. STUDY DESIGN We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research. METHODS The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM. RESULT Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery. CONCLUSIONS The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.
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Affiliation(s)
- Yumeng Lin
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - You Zhang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Dongyang Wang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Bowen Yang
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Ying-Qiang Shen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Chinese Academy of Medical Sciences Research Unit of Oral Carcinogenesis and Management, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
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Xiao J, Li Y. Screening of benzophenone ultraviolet absorbers with high-efficiency light absorption capacity, low-permeability and low-toxicity by 3D-QSAR model. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Ghazalbash S, Zargoush M, Mowbray F, Papaioannou A. Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics. Int J Med Inform 2021; 156:104597. [PMID: 34619571 DOI: 10.1016/j.ijmedinf.2021.104597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 09/19/2021] [Accepted: 09/24/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Patient complexity among older delayed-discharge patients complicates discharge planning, resulting in a higher rate of adverse outcomes, such as readmission and mortality. Early prediction of multimorbidity, as a common indicator of patient complexity, can support proactive discharge planning by prioritizing complex patients and reducing healthcare inefficiencies. OBJECTIVE We set out to accomplish the following two objectives: 1) to examine the predictability of three common multimorbidity indices, including Charlson-Deyo Comorbidity Index (CDCI), the Elixhauser Comorbidity Index (ECI), and the Functional Comorbidity Index (FCI) using machine learning (ML), and 2) to assess the prognostic power of these indices in predicting 30-day readmission and mortality. MATERIALS AND METHODS We used data including 163,983 observations of patients aged 65 and older who experienced discharge delay in Ontario, Canada, during 2004 - 2017. First, we utilized various classification ML algorithms, including classification and regression trees, random forests, bagging trees, extreme gradient boosting, and logistic regression, to predict the multimorbidity status based on CDCI, ECI, and FCI. Second, we used adjusted multinomial logistic regression to assess the association between multimorbidity indices and the patient-important outcomes, including 30-day mortality and readmission. RESULTS For all ML algorithms and regardless of the predictive performance criteria, better predictions were established for the CDCI compared with the ECI and FCI. Remarkably, the most predictable multimorbidity index (i.e., CDCI with Area Under the Receiver Operating Characteristic Curve = 0.80, 95% CI = 0.79 - 0.81) also offered the highest prognostications regarding adverse events (RRRmortality = 3.44, 95% CI = 3.21 - 3.68 and RRRreadmission = 1.36, 95% CI = 1.31 - 1.40). CONCLUSIONS Our findings highlight the feasibility and utility of predicting multimorbidity status using ML algorithms, resulting in the early detection of patients at risk of mortality and readmission. This can support proactive triage and decision-making about staffing and resource allocation, with the goal of optimizing patient outcomes and facilitating an upstream and informed discharge process through prioritizing complex patients for discharge and providing patient-centered care.
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Affiliation(s)
- Somayeh Ghazalbash
- Health Policy and Management, DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada
| | - Manaf Zargoush
- Health Policy and Management, DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada.
| | - Fabrice Mowbray
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Big Data and Geriatric Models of Care (BDG) Cluster, McMaster University, Hamilton, Ontario, Canada
| | - Alexandra Papaioannou
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Division of Geriatric Medicine, Department of Medicine, McMaster University, Hamilton, Ontario, Canada; GERAS Center for Aging Research, Hamilton, Ontario, Canada
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