Manuel Solis-Navarro
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Manuel Solis-Navarro has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Cruz Vargas-De-León
Laboratorio de Modelación Bioestadística, Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Plan de San Luis y Díaz Mirón s/n, Mexico City 11340, Mexico
2 shared publications - Adriana Flores-Miranda
División de Investigación, Hospital Juarez de Mexico. Av. Instituto Politécnico Nacional, Ciudad de México 07760, Mexico
1 shared publication - Claudia Camelia Calzada-Mendoza
Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City 11340, Mexico
1 shared publication - Josselin Carolina Corzo-Gómez
Escuela de Ciencias Químicas, Universidad Autónoma de Chiapas, Ocozocoautla de Espinosa 29140, Chiapas, Mexico
1 shared publication - Karla Aidee Aguayo-Cerón
Laboratorio de Investigación en Genética de Enfermedades Metabólicas, Escuela Superior de Medicina, Instituto Politécnico Nacional, Ciudad de Mexico 11340, Mexico
1 shared publication - María Guzmán-Martínez
Facultad de Matemáticas, Universidad Autónoma de Guerrero, Av. Lázaro Cárdenas S/N, Chilpancingo de los Bravo 39087, Guerrero, Mexico
1 shared publication
Latest Publications
- Open Access
- 25 September 2026
- Published by MDPI AG in Diagnostics
- Vol. 16 (19) , 3116
Analytical Agreement and Diagnostic Performance of a Commercial Point-of-Care Lipid Testing System for Lipid Profile and Atherogenic Ratios
Background: Point-of-care testing (POCT) devices offer a rapid alternative for lipid assessment, but comprehensive analytical validation against reference laboratory methods remains limited. Objective: This study aimed to evaluate the agreement, systematic bias, and diagnostic performance of the Mission® Cholesterol 3-in-1 system for lipid profile measurements and atherogenic ratios. Methods: A prospective cross-sectional agreement study was conducted among 120 participants. Lipid profiles were measured using eleven Mission® Cholesterol 3-in-1 analyzers and compared with those measured using the reference laboratory method. Agreement was assessed using the intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), coefficient of determination (), root mean square error (RMSE), mean absolute error (MAE), percentage bias, Bland–Altman plots, Passing–Bablok regression, and diagnostic accuracy. Results: Triglycerides demonstrated excellent agreement (ICC = CCC = 0.972; bias = 5.19%), whereas total cholesterol, HDL-C, non-HDL-C, and LDL-C showed good agreement (ICC range: 0.817–0.854). Among the atherogenic ratios, TG/HDL-C (ICC = 0.932) and TG/LDL-C (ICC = 0.931) exhibited the highest concordance. Passing–Bablok regression revealed constant bias for all analytes, except for LDL-C estimated using the Sampson equation and the TG/LDL-C ratio, while proportional bias was observed for all analytes, except for triglycerides and the TG/LDL-C ratio. Despite these systematic differences, diagnostic performance remained excellent, with sensitivities of 94.29–98.33% and specificities of 68.33–91.80%. Conclusion: The Mission® Cholesterol 3-in-1 system shows good analytical agreement with laboratory testing, particularly for triglycerides and the HDL-C, TG/HDL-C, and TG/LDL-C ratios. Although systematic bias limits complete interchangeability with reference methods, its excellent diagnostic performance supports its use as a rapid point-of-care screening tool, especially in resource-limited settings.- 25 September 2026
- Published by MDPI AG in Diagnostics
- Vol. 16 (19) , 3116
Open AccessAnalytical Agreement and Diagnostic Performance of a Commercial Point-of-Care Lipid Testing System for Lipid Profile and Atherogenic Ratios
Background: Point-of-care testing (POCT) devices offer a rapid alternative for lipid assessment, but comprehensive analytical validation against reference laboratory methods remains limited. Objective: This study aimed to evaluate the agreement, systematic bias, and diagnostic performance of the Mission® Cholesterol 3-in-1 system for lipid profile measurements and atherogenic ratios. Methods: A prospective cross-sectional agreement study was conducted among 120 participants. Lipid profiles were measured using eleven Mission® Cholesterol 3-in-1 analyzers and compared with those measured using the reference laboratory method. Agreement was assessed using the intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), coefficient of determination (), root mean square error (RMSE), mean absolute error (MAE), percentage bias, Bland–Altman plots, Passing–Bablok regression, and diagnostic accuracy. Results: Triglycerides demonstrated excellent agreement (ICC = CCC = 0.972; bias = 5.19%), whereas total cholesterol, HDL-C, non-HDL-C, and LDL-C showed good agreement (ICC range: 0.817–0.854). Among the atherogenic ratios, TG/HDL-C (ICC = 0.932) and TG/LDL-C (ICC = 0.931) exhibited the highest concordance. Passing–Bablok regression revealed constant bias for all analytes, except for LDL-C estimated using the Sampson equation and the TG/LDL-C ratio, while proportional bias was observed for all analytes, except for triglycerides and the TG/LDL-C ratio. Despite these systematic differences, diagnostic performance remained excellent, with sensitivities of 94.29–98.33% and specificities of 68.33–91.80%. Conclusion: The Mission® Cholesterol 3-in-1 system shows good analytical agreement with laboratory testing, particularly for triglycerides and the HDL-C, TG/HDL-C, and TG/LDL-C ratios. Although systematic bias limits complete interchangeability with reference methods, its excellent diagnostic performance supports its use as a rapid point-of-care screening tool, especially in resource-limited settings. - Open Access
- 25 May 2022
- Published by Wiley in Journal of Tropical Medicine
- Vol. 2022, 1-13
A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico
Dengue is one of the major health problems in the state of Chiapas. Consequently, spatial information on the distribution of the disease can optimize directed control strategies. Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. This is an ecological study that uses data from a range of sources. Dengue cases occurred from January to August 2019. The data analysis used the spatial correlation of dengue cases (DCs), which was calculated with the Moran index statistic, and a generalized linear spatial model (GLSM) within a Bayesian framework, which was considered to model the spatial distribution of DCs in the state of Chiapas. We selected the climatological, geographic, and sociodemographic variables related to the study area. A prediction of the model on Chiapas maps was carried out based on the places where the cases were registered. We find a spatial correlation of 0.115 between neighboring municipalities using the Moran index. The variables that have an effect on the number of confirmed cases of dengue are the maximum temperature ( ; : ), rainfall ( ), and altitude (
- 25 May 2022
- Published by Wiley in Journal of Tropical Medicine
- Vol. 2022, 1-13
Open AccessA Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico
Dengue is one of the major health problems in the state of Chiapas. Consequently, spatial information on the distribution of the disease can optimize directed control strategies. Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. This is an ecological study that uses data from a range of sources. Dengue cases occurred from January to August 2019. The data analysis used the spatial correlation of dengue cases (DCs), which was calculated with the Moran index statistic, and a generalized linear spatial model (GLSM) within a Bayesian framework, which was considered to model the spatial distribution of DCs in the state of Chiapas. We selected the climatological, geographic, and sociodemographic variables related to the study area. A prediction of the model on Chiapas maps was carried out based on the places where the cases were registered. We find a spatial correlation of 0.115 between neighboring municipalities using the Moran index. The variables that have an effect on the number of confirmed cases of dengue are the maximum temperature ( ; : ), rainfall ( ), and altitude (