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Marín-Sánchez O, Pesantes-Grados P, Pérez-Timaná L, Marín-Machuca O, Sánchez-Llatas CJ, Chacón RD. Comparative Epidemiological Assessment of Monkeypox Infections on a Global and Continental Scale Using Logistic and Gompertz Mathematical Models. Vaccines (Basel) 2023; 11:1765. [PMID: 38140170 PMCID: PMC10747842 DOI: 10.3390/vaccines11121765] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 12/24/2023] Open
Abstract
The monkeypox virus (MPXV) has caused an unusual epidemiological scenario-an epidemic within a pandemic (COVID-19). Despite the inherent evolutionary and adaptive capacity of poxviruses, one of the potential triggers for the emergence of this epidemic was the change in the status of orthopoxvirus vaccination and eradication programs. This epidemic outbreak of HMPX spread worldwide, with a notable frequency in Europe, North America, and South America. Due to these particularities, the objective of the present study was to assess and compare cases of HMPX in these geographical regions through logistic and Gompertz mathematical modeling over one year since its inception. We estimated the highest contagion rates (people per day) of 690, 230, 278, and 206 for the world, Europe, North America, and South America, respectively, in the logistic model. The equivalent values for the Gompertz model were 696, 268, 308, and 202 for the highest contagion rates. The Kruskal-Wallis Test indicated different means among the geographical regions affected by HMPX regarding case velocity, and the Wilcoxon pairwise test indicated the absence of significant differences between the case velocity means between Europe and South America. The coefficient of determination (R2) values in the logistic model varied from 0.8720 to 0.9023, and in the Gompertz model, they ranged from 0.9881 to 0.9988, indicating a better fit to the actual data when using the Gompertz model. The estimated basic reproduction numbers (R0) were more consistent in the logistic model, varying from 1.71 to 1.94 in the graphical method and from 1.75 to 1.95 in the analytical method. The comparative assessment of these mathematical modeling approaches permitted the establishment of the Gompertz model as the better-fitting model for the data and the logistic model for the R0. However, both models successfully represented the actual HMPX case data. The present study estimated relevant epidemiological data to understand better the geographic similarities and differences in the dynamics of HMPX.
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Affiliation(s)
- Obert Marín-Sánchez
- Departamento Académico de Microbiología Médica, Facultad de Medicina, Universidad Nacional Mayor de San Marcos, Av. Carlos Germán Amezaga 375, Lima 15081, Peru;
| | - Pedro Pesantes-Grados
- Unidad de Posgrado, Facultad de Ciencias Matemáticas, Universidad Nacional Mayor de San Marcos, Av. Carlos Germán Amezaga 375, Lima 15081, Peru;
| | - Luis Pérez-Timaná
- Escuela Profesional de Genética y Biotecnología, Facultad de Ciencias Biológicas, Universidad Nacional Mayor de San Marcos, Av. Carlos Germán Amezaga 375, Lima 15081, Peru;
| | - Olegario Marín-Machuca
- Departamento Académico de Ciencias Alimentarias, Facultad de Oceanografía, Pesquería, Ciencias Alimentarias y Acuicultura, Universidad Nacional Federico Villarreal, Calle Roma 350, Miraflores 15074, Peru;
| | - Christian J. Sánchez-Llatas
- Department of Genetics, Physiology, and Microbiology, School of Biology, Complutense University of Madrid (U.C.M.), C. de José Antonio Nováis, 12, 28040 Madrid, Spain;
| | - Ruy D. Chacón
- Department of Pathology, School of Veterinary Medicine, University of São Paulo, Av. Prof. Orlando M. Paiva, 87, São Paulo 05508-270, Brazil
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Oliveira MC, Scharan KO, Thomés BI, Bernardelli RS, Reese FB, Kozesinski-Nakatani AC, Martins CC, Lobo SMA, Réa-Neto Á. Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard. BMC Pulm Med 2023; 23:81. [PMID: 36894945 PMCID: PMC9997428 DOI: 10.1186/s12890-023-02369-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND The gold-standard method for establishing a microbiological diagnosis of COVID-19 is reverse-transcriptase polymerase chain reaction (RT-PCR). This study aimed to evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. METHODS Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong versus weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). RESULTS With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5-99.5%) sensitivity, 70% (95% CI 65.8-74.2%) specificity, 85.5% (95% CI 83.4-87.7%) accuracy, PPV of 79.7% (95% CI 76.6-82.7%) and NPV of 97.6% (95% CI 95.9-99.2%). Similar performance was observed when evaluated in the subgroups of patients admitted with mild/moderate respiratory disfunction, and severe respiratory disfunction. CONCLUSION The proposed set of clinical-radiological criteria were accurate in identifying patients with strong versus weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.
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Affiliation(s)
- Mirella Cristine Oliveira
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná, 81050-000, Brazil
| | - Karoleen Oswald Scharan
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil
| | - Bruna Isadora Thomés
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil
| | - Rafaella Stradiotto Bernardelli
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,School of Medicine and Life Sciences, Pontifical Catholic University of Paraná, Imaculada Conceição Street, 1155, Curitiba, Paraná, 80215-901, Brazil
| | - Fernanda Baeumle Reese
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná, 81050-000, Brazil
| | - Amanda Christina Kozesinski-Nakatani
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Hospital Santa Casa de Curitiba, Praça Rui Barbosa, 694, Curitiba, Paraná, 80010-030, Brazil
| | - Cintia Cristina Martins
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil.,Complexo Hospitalar do Trabalhador (CHT), República Argentina Street, 4406, Curitiba, Paraná, 81050-000, Brazil
| | - Suzana Margareth Ajeje Lobo
- Departament of Medicine, São José do Rio Preto Medical School, Brigadeiro Faria Lima avenue, 5416, São José do Rio Preto, São Paulo, 15090-000, Brazil
| | - Álvaro Réa-Neto
- Center for Studies and Research in Intensive Care Medicine - CEPETI, Monte Castelo Street, 366, Curitiba, Paraná, 82590-300, Brazil. .,Internal Medicine Department, Hospital de Clínicas, Federal University of Paraná, General Carneiro Street, 181, Curitiba, Paraná, 80060-900, Brazil.
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Liu CW, Wu FH, Hu YL, Pan RH, Lin CH, Chen YF, Tseng GS, Chan YK, Wang CL. Left ventricular hypertrophy detection using electrocardiographic signal. Sci Rep 2023; 13:2556. [PMID: 36781924 PMCID: PMC9924839 DOI: 10.1038/s41598-023-28325-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 01/17/2023] [Indexed: 02/15/2023] Open
Abstract
Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.
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Affiliation(s)
- Cheng-Wei Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei, Taiwan
| | - Fu-Hsing Wu
- Bachelor Degree Program of Artificial Intelligence, National Taichung University of Science and Technology, Taichung, Taiwan
| | - Yu-Lun Hu
- Department of Management Information Systems, National Chung-Hsing University, Taichung, Taiwan
| | - Ren-Hao Pan
- La Vida Tec. Co. Ltd., Taichung, Taiwan
- Preventive Medicine Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Information Management, Tunghai University, Taichung, Taiwan
| | - Chuen-Horng Lin
- Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan
| | - Yung-Fu Chen
- Department of Dental Technology and Materials Science, Central Taiwan University of Science and Technology, Taichung, Taiwan
| | - Guo-Shiang Tseng
- Division of Cardiology, Department of Internal Medicine, Taoyuan Armed Force General Hospital Hsinchu Branch, Hsinchu, Taiwan
| | - Yung-Kuan Chan
- Department of Management Information Systems, National Chung-Hsing University, Taichung, Taiwan.
| | - Ching-Lin Wang
- Department of Information Management, National Chin-Yi University of Technology, Taichung, Taiwan.
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