Manuel Solis Navarro

Prof. Manuel Solis Navarro

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Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 May 2022 in Journal of Tropical Medicine
Verified
Journal Article
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MNManuel Solis Navarro
Manuel Solis Navarro
CVCruz Vargas-De-León
Cruz Vargas-De-León
MGMaría Guzmán-Martínez
María Guzmán-Martínez
JCJosselin Carolina Corzo-Gómez
Josselin Carolina Corzo-Gómez
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 September 2026 in Diagnostics
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Journal Article
Open access
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SASusana Guadalupe Guzmán Aquino
Susana Guadalupe Guzmán Aquino
RRRoberto Rivera-Sánchez
Roberto Rivera-Sánchez
KCKARLA AIDEE AGUAYO CERON
KARLA AIDEE AGUAYO CERON
MNManuel Solis Navarro
Manuel Solis Navarro
CCClaudia Calzada
Claudia Calzada
CVCruz Vargas-De-León
Cruz Vargas-De-León
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 (R2), 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.