Producción Científica UPeU

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Strength of Religious Faith in Peruvian Adolescents and Adults: Psychometric Evidence from the Long and Short Version of the Santa Clara Strength of Religious Faith Questionnaire in Spanish
(2021-03-18)
Tomás Caycho‐Rodríguez
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Lindsey W. Vilca
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Thomas G. Plante
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Andrea Vivanco-Vidal
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Daniela Saroli-Araníbar
The aim of this study was to evaluate the psychometric evidence of the original and brief version of the Santa Clara Strength of Religious Faith Questionnaire (SCSRFQ) in Spanish in a sample of 245 Peruvian adolescents and adults (mean age = 21.04 years, SD = 3.07, 47.8% male and 52.2% female), selected by non-probabilistic convenience sampling. Additionally, the Coronavirus Anxiety Scale and the Satisfaction with Life Scale were applied. Confirmatory Factor Analysis, internal consistency reliability methods, hierarchical sequence of variance models and Graded Response Model were used. Results indicate that both versions of the SCSRFQ showed robust psychometric properties: adequate unidimensional structure, adequate difficulty and discrimination parameters, and significant relationships with the measures of fear of COVID-19 and satisfaction with life. The original version of the SCSRFQ showed evidence of strict measurement invariance by gender and age; whereas the short version showed strict invariance by gender and configural invariance by age. Both versions showed acceptable reliability indices. In conclusion, the original and brief versions of the SCSRFQ show evidence of psychometric indicators that support their use to assess the strength of religious faith
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Differential association of SARS-CoV-2 IgG responses with anti-OC43 IgG in a Senegalese cohort
(2025-04-08)
Rokhaya Faye
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Adji Astou Mbow
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Billo Tall
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Aboubacry Gaye
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Amadou Moustapha Ndoye
Abstract Numerous studies elucidated the kinetics of the humoral immune response post-SARS-CoV-2 infection. However, in sub-Saharan Africa, the evolution of SARS-CoV-2 IgG antibody responses and their interaction with pre-existing seasonal human coronavirus (HCoVs: OC43, 229E, NL63, HKU1) immunity remain underexplored. A prospective cohort study was conducted in Senegal during the first year of the COVID-19 pandemic (March to December 2020). A total of 204 patients with laboratory-confirmed COVID-19 were included. Patients were classified as symptomatic (n=157) or asymptomatic (n=47) based on clinical presentation. Plasma samples (n=705) were collected over 6 months from SARS-CoV-2 positive individuals. IgG levels against SARS-CoV-2 and HCoVs were measured using a multiplex bead-based assay. Among the 204 participants included (95 [46.6%] female, median age, 44 [7–95]), SARS-CoV-2 IgG were detectable 6 months post-infection, peaking at 1 month for most antigens, except for Spike (S), which peaked at 3 months. Elderly patients (>60 years) exhibited higher IgG levels against both SARS- CoV-2 and HCoVs. Symptomatic patients had higher IgG levels than asymptomatic individuals, especially for WTS, RBD, S2, and N. Anti-HCoV IgG levels remained stable post-infection, with OC43 peaking at week 3 in symptomatic patients. A positive correlation was found between anti-SARS-CoV- 2 and anti-OC43 IgG in symptomatic patients. The study highlights persistent SARS-CoV-2 IgG antibodies for up to 6 months and suggests a link between pre-existing HCoV-OC43 immunity and COVID-19 outcomes in Senegal. These findings could help shape future vaccine strategies, considering the influence of circulating HCoVs on long- term protection against SARS-CoV-2. Author summary Understanding how our immune system responds to SARS-CoV-2, the virus responsible for COVID- 19, is essential for guiding public health countermeasures and informing vaccine development strategies. In our study, we monitored, in COVID-19 patients, the evolution of IgG antibody responses against SARS-CoV-2 structural proteins over a six-month period. Additionally, we examined how previous exposure to common seasonal coronaviruses might influence immune responses to SARS-CoV-2. Conducting this research in an African context is particularly important, as data on immune responses to SARS-CoV-2 in this region are scarce. Our results provide valuable insights into the complex interplay between immune responses elicited by SARS-CoV-2 and pre-existing immunity from seasonal circulating coronaviruses. These findings enhance our understanding of immune memory and cross-reactivity, two critical factors for assessing long-term protection and optimizing vaccine strategies. By shedding light on the dynamics of antibody responses over time within a sub-Saharan population, our research contributes to the global effort aimed at developing effective interventions against COVID-19 and preparing for future coronavirus outbreaks.
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Simulation of the energy efficiency auction prices in Brazil
(2016-05-01)
Javier L. L. Gonzales
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Rodrigo Flora Calili
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Reinaldo Castro Souza
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Felipe Leite Coelho da Silva
The electricity consumption behavior in Brazil has been extensively investigated over the years due to financial and social problems. In this context, it is important to simulate the energy prices of the energy efficiency auctions in the Brazilian regulated environment. This paper presents an approach to generate samples of auction energy prices in energy efficiency market, using Markov chain Monte Carlo method, through the Metropolis-Hastings algorithm. The obtained results show that this approach can be used to generate energy price samples.
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Factors associated with knowledge, attitudes and preventive practices towards COVID-19 in health care professionals in Lima, Peru
(2021-10-20)
Oriana Rivera‐Lozada
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César Augusto Gálvez
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Elvis Siprian Castro-Alzate
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César Antonio Bonilla-Asalde
<ns3:p> <ns3:bold>Background</ns3:bold> : Nowadays, we are facing a disease caused by SARS-CoV-2, known globally as COVID-19, which is considered a threat to global health due to its high contagiousness and rapid spread. </ns3:p> <ns3:p> <ns3:bold>Methods</ns3:bold> : Analytical cross-sectional study in 302 health professionals. An online questionnaire consisting of questions about knowledge, attitudes and practices (KAP) towards COVID-19 was applied. Socio-demographic, occupational and comorbidities factors were explored. Simple and multiple logistic regression models were used to identify factors associated with KAP. </ns3:p> <ns3:p> <ns3:bold>Results</ns3:bold> : Of the total, 25.2%, 31.5% and 37.4% had high levels of knowledge, preventive practices and risk perception attitudes respectively. Being married aOR=6.75 IC(1.46-31.2) p=0.014, having a master's degree aOR=0.4, IC(0.21-0.80) p=0.009, having a working day with less than ten hours ORa=0.49 IC(0.25-0.95) p=0.036 and obesity aOR=0.38 IC (0.15-0.95) p=0.039 were associated with a low level of knowledge of COVID-19. The variables associated with preventive practices were being over the age of 50 aOR=0.52 IC(0.27-0.98) p=0.007, working in the hospitalization area aOR=1.86 IC(1.08-3.18) p= 0.018 and having comorbidities such as arterial hypertension aOR=0.28 IC(0.081-0.99) p=0.02 and obesity aOR=0.35 IC(0.14-0.83) p=0.019. In relation to negative attitudes towards COVID-19, it was found that physical contact with patients with a confirmed diagnosis aOR=1.84 IC (1.14-2.97) p=0.006 and having asthma aOR=2.13 IC(1.081-4.22) p=0.029 were associated with these attitudes. </ns3:p> <ns3:p> <ns3:bold>Conclusion</ns3:bold> : Our study revealed that health professionals have an insufficient level of knowledge of COVID-19. This is why we recommend implementing strategies such as health literacy programs among health care workers. Thus, they can help develop positive attitudes towards the acquisition of self-care habits at work that, in turn, improve their confidence so that health care workers can provide adequate care for their patients and protect themselves. </ns3:p>
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Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru
(2021-09-09)
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Manuel Niño Lopez Portocarrero
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Rodrigo Salas
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Romina Torres
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Paulo Canas Rodrigues
Abstract The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.
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A Self-Organizing Topological Multilayer Perceptron for Improving the Prediction of Extreme Values of the PM2.5
(2022-05-12)
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Ana María Gómez Lamus
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Romina Torres
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Rodrigo Salas
Abstract The prediction of air pollutant levels plays an essential role in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter (PM). Even when elevated pollution episodes may be rare, they recirculate within an area where more people may present significant adverse health consequences. Thus, even when they are difficult to predict, pollution peaks prediction of air pollution particulate matter (PM2.5) is a crucial problem to address. Several machine learning (ML) approaches have been used to predict a set of air pollutants using different combinations of predictor parameters. Unfortunately, they are still not enough to generate accurate predictions of extreme values. This paper proposes a new hybrid method that combines the unsupervised learning Self-Organizing Maps with the supervised multilayer perceptron. The proposed method is applied for the prediction of extreme values of PM2.5, using five-year pollution data obtained from nine weather stations located in the metropolitan area of Santiago, Chile. Simulation results show that the hybrid method improves the performance metrics when predicting extreme values of PM2.5.
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Mortality from all causes before and during the COVID-19 pandemic in Peru: The role of sociodemographic factors and accessibility
(2022-10-19)
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Lucía Vargas
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Wilter C. Morales-García
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Jorge Sánchez-Garcés
Abstract Peru suffered the highest mortality rates worldwide during the COVID-19 pandemic. In this study, we assessed the Peruvian districts' all causes of mortality-associated sociodemographic factors before and during the COVID-19 pandemic using mixed-effects Poisson regression models. During the pre-pandemic and the first four COVID-19 waves, the Peruvian districts reported mean weekly mortality of 22.3 (standard deviation 40.4), 29.2 (38.7), 32.5 (47.2), 26.8 (38.9), and 24.4 (38.0), respectively. We observed that before the COVID-19 pandemic, the districts' weekly deaths were associated with the human development index ((HDI) adjusted incidence rate ratio (aIRR) 0.11 (95% confidence interval 0.11–0.12)), accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99 (0.99–0.99)). However, during each of the first four COVID-19 waves, the magnitude of association between the districts' weekly deaths and HDI decreased (first, aIRR 0.61 (0.58−0.64); second, aIRR 0.54 (0.52−0.57); third, aIRR 0.20 (0.19−0.22); fourth, aIRR 0.17 (0.15−0.19)), but the association with accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99(0.99–0.99)) remain constant. Before and during the COVID-19 pandemic, a solid association existed between all-cause mortality and the district's sociodemographics, increasing with lower HDI, accessibility, poverty, and anemia rates.
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Optimization, Characterization, and Release Kinetics of the Orange Essential Oil: Β-Cyclodextrin Nanoparticles as a Natural Food Additive
(2023-01-01)
Diana Maria María Nolazco-Cama
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A. Sánchez-Contreras
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Luis Condezo‐Hoyos
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Luis Fernando Vargas Delgado
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Gut microbiota characterization in vegetarian adults from the adventist population living in coastal, highland, and jungle regions of Peru
(2025-11-27)
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Sandra Carvajal Ramirez
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Miguel Otiniano-Trujillo
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Mario Chambi Quispe
Abstract Introduction The gut microbiota plays a crucial role in various physiological processes, and its composition can be influenced by factors such as diet, genetics, age, and health status. Among these, diet is one of the most significant determinants of microbial balance. A well-balanced diet promotes the growth of beneficial bacteria, reducing intestinal inflammation and the risk of chronic diseases. Vegetarian diets have been proposed to confer beneficial effects on gut health; however, many questions remain regarding microbial profiles, their variability, and the potential influence of geographic and cultural factors—particularly within Latin American contexts. This study aims to address this gap by presenting the first characterization of the gut microbiota in vegetarian adults from the Adventist population living in three distinct regions of Peru. Methods Stool samples were used as a proxy to analyze gut microbiota composition through 16S rRNA gene sequencing. Standard descriptive analyses were performed to assess bacterial composition, including diversity and relative abundance across samples. Results The gut microbial communities of Peruvian vegetarians revealed three distinct enterotypes, with distribution varying by region. Enterotype 1 (ET1), predominant in coastal and highland regions, exhibited the highest bacterial richness and diversity. Enterotype 2 (ET2), observed in highland and jungle areas, was characterized by higher levels of Prevotella . Enterotype 3 (ET3), more frequent in the jungle region, showed a greater abundance of Bacteroides and Faecalibacterium . Conclusions Despite all participants adhering to a vegetarian diet, notable diversity in gut microbiota profiles was observed within this population. While three distinct enterotypes were identified, consistent with findings in other populations, the specific profiles differed from those previously reported. This study highlights the importance of incorporating variables that enable greater resolution in future research, allowing better control of within-population variability, such as that observed in Peruvian vegetarians, and ultimately enhancing the accuracy of microbiome-related conclusions.
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Treatment Burden in Peru across Settings and Regions among People with Multiple Long-Term Conditions: A Cross-Sectional Study
(2025-01-01)
J. Jaime Miranda
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Silvana Perez
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Daniel Mendoza‐Quispe
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María Sofía Cuba-Fuentes
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Juan Carlos Bazo-Alvarez

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