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Acuna, M., Eaton, L., & Cifuentes, L. (2004). Genetic variants of the paraoxonases (PON1 and PON2) in the Chilean population. Hum. Biol., 76(2), 299–305.
Abstract: We estimated the frequencies of PON1 and PON2 variants (linked genes) in two hospital samples taken from the northern (San Jose Hospital, SJH) and eastern (Clinica Las Condes, CLC) parts of Santiago, Chile, using the polymerase chain reaction followed by restriction endonuclease digestion. The two hospital samples have different degrees of Amerindian admixture (SJH, 34.5%; CLC, 15.9%), which is reflected in the observed frequencies of the PON1*B allele (SJH, 43.1%; CLC, 33.7%) and the PON2*S allele (SJH, 86.3%; CLC, 77.6%); both allele frequencies are significantly different between samples. The frequencies of the combined PON1-PON2 genotypes *A/*B-*C/*C, *A/*B-*S/*S, and *B/*B-*S/*S and of the haplotypes PON*A,C and PON*B,S were significantly different between the SJH and CLC groups. None of the genotype frequencies deviated significantly from those predicted by the Hardy-Weinberg equation. No linkage disequilibrium was found between the PON1 alleles and any of the PON2 alleles in either group (all p > 0.05). In our samples 38.52% (SJH) and 26.25% (CLC) of chromosomes must have the haplotype PON*B,S, presumed to be related to the risk of coronary artery disease. Twenty-four of 193 (12.4%) SJH individuals and 7 of 122 (5.7%) CLC individuals were homozygotes for this haplotype. Finally, our data indicate ethnic-group-dependent genetic differences in the vulnerability to toxic organophosphorus.
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Altimiras, F., Uszczynska-Ratajczak, B., Camara, F., Vlasova, A., Palumbo, E., Newhouse, S., et al. (2017). Brain Transcriptome Sequencing of a Natural Model of Alzheimer's Disease. Front. Aging Neurosci., 9, 8 pp.
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Blanco, K., Salcidua, S., Orellana, P., Sauma, T., Leon, T., Lopez-Steinmetz, L. C., et al. (2023). Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimers disease. Alzheimer's Res. Ther., Early Access.
Abstract: Mild cognitive impairment ( AQ1 MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80�90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer�s disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for
the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend
towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both crosssectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.
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Cabrera, M., Cordova-Lepe, F., Gutierrez-Jara, J. P. -, & Vogt-Geisse, K. (2021). An SIR-type epidemiological model that integrates social distancing as a dynamic law based on point prevalence and socio-behavioral factors. Sci. Rep., 11(1), 10170.
Abstract: Modeling human behavior within mathematical models of infectious diseases is a key component to understand and control disease spread. We present a mathematical compartmental model of Susceptible-Infectious-Removed to compare the infected curves given by four different functional forms describing the transmission rate. These depend on the distance that individuals keep on average to others in their daily lives. We assume that this distance varies according to the balance between two opposite thrives: the self-protecting reaction of individuals upon the presence of disease to increase social distancing and their necessity to return to a culturally dependent natural social distance that occurs in the absence of disease. We present simulations to compare results for different society types on point prevalence, the peak size of a first epidemic outbreak and the time of occurrence of that peak, for four different transmission rate functional forms and parameters of interest related to distancing behavior, such as: the reaction velocity of a society to change social distance during an epidemic. We observe the vulnerability to disease spread of close contact societies, and also show that certain social distancing behavior may provoke a small peak of a first epidemic outbreak, but at the expense of it occurring early after the epidemic onset, observing differences in this regard between society types. We also discuss the appearance of temporal oscillations of the four different transmission rates, their differences, and how this oscillatory behavior is impacted through social distancing; breaking the unimodality of the actives-curve produced by the classical SIR-model.
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Cáeres, C., Heusser, B., Garnham, A., & Moczko, E. (2023). The Major Hypotheses of Alzheimer's Disease: Related Nanotechnology-Based Approaches for Its Diagnosis and Treatment. Cells, 12(23), 2669.
Abstract: Alzheimer's disease (AD) is a well-known chronic neurodegenerative disorder that leads to the progressive death of brain cells, resulting in memory loss and the loss of other critical body functions. In March 2019, one of the major pharmaceutical companies and its partners announced that currently, there is no drug to cure AD, and all clinical trials of the new ones have been cancelled, leaving many people without hope. However, despite the clear message and startling reality, the research continued. Finally, in the last two years, the Food and Drug Administration (FDA) approved the first-ever medications to treat Alzheimer's, aducanumab and lecanemab. Despite researchers' support of this decision, there are serious concerns about their effectiveness and safety. The validation of aducanumab by the Centers for Medicare and Medicaid Services is still pending, and lecanemab was authorized without considering data from the phase III trials. Furthermore, numerous reports suggest that patients have died when undergoing extended treatment. While there is evidence that aducanumab and lecanemab may provide some relief to those suffering from AD, their impact remains a topic of ongoing research and debate within the medical community. The fact is that even though there are considerable efforts regarding pharmacological treatment, no definitive cure for AD has been found yet. Nevertheless, it is strongly believed that modern nanotechnology holds promising solutions and effective clinical strategies for the development of diagnostic tools and treatments for AD. This review summarizes the major hallmarks of AD, its etiological mechanisms, and challenges. It explores existing diagnostic and therapeutic methods and the potential of nanotechnology-based approaches for recognizing and monitoring patients at risk of irreversible neuronal degeneration. Overall, it provides a broad overview for those interested in the evolving areas of clinical neuroscience, AD, and related nanotechnology. With further research and development, nanotechnology-based approaches may offer new solutions and hope for millions of people affected by this devastating disease.
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Cardenas, C., Guzman, F., Carmona, M., Munoz, C., Nilo, L., Labra, A., et al. (2020). Synthetic Peptides as a Promising Alternative to Control Viral Infections in Atlantic Salmon. Pathogens, 9(8), 600.
Abstract: Viral infections in salmonids represent an ongoing challenge for the aquaculture industry. Two RNA viruses, the infectious pancreatic necrosis virus (IPNV) and the infectious salmon anemia virus (ISAV), have become a latent risk without healing therapies available for either. In this context, antiviral peptides emerge as effective and relatively safe therapeutic molecules. Based on in silico analysis of VP2 protein from IPNV and the RNA-dependent RNA polymerase from ISAV, a set of peptides was designed and were chemically synthesized to block selected key events in their corresponding infectivity processes. The peptides were tested in fish cell lines in vitro, and four were selected for decreasing the viral load: peptide GIM182 for IPNV, and peptides GIM535, GIM538 and GIM539 for ISAV. In vivo tests with the IPNV GIM 182 peptide were carried out using Salmo salar fish, showing a significant decrease of viral load, and proving the safety of the peptide for fish. The results indicate that the use of peptides as antiviral agents in disease control might be a viable alternative to explore in aquaculture.`
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Garmendia, M. L., Mondschein, S., Montiel, B., & Kusanovic, J. P. (2021). Trends and predictors of birth weight in Chilean children. Public Health, 193, 61–68.
Abstract: Objectives: Birth weight is an important public health indicator that reflects fetal health conditions and predicts future health. Identifying the most important factors related to birth weight would help defining preventive health strategies for both mothers and children. The objectives of this study are i. to describe, using a large birth database from a Chilean hospital, the trend of birth weight during 2002-2015, and ii. to determine factors during prenatal care associated with low and high birth weight.
Study design: This study is a secondary analysis of all single birth records at a Chilean Hospital in the southeast district of Santiago, Chile, during 2002-2015 (N = 78,931).
Methods: Sociodemographic information, clinical and obstetric history, lifestyle, and anthropometric variables were evaluated as potential predictors. Birth weight was categorized into five groups as per percentiles of weight as per gestational age. Data were extracted from clinical records. We used classification and regression tree methodology and logistic regression.
Results: The average birth weight for the period was 3316 g (SD 566), with little variation across time. Preterm births increased from 7% in 2002 to 10% in 2015, and births >40 weeks decreased from 10.7% in 2002 to 4.4% in 2015. The percentages of small and large for gestational age changed from 10.9% and 12.7% in 2002 to 9.9% and 13.9% in 2015, respectively. The predictors included in the optimal tree were body mass index, gestational weight gain, pre-eclampsia, and gestational diabetes. We found that women with a pregestational body mass index <28 kg/m(2), gestational weight gain <17 kg, and pre-eclampsia had a probability of 41% of having a small for gestational age neonate. Conversely, women with a body mass index similar to 28 kg/m(2), gestational weight gain similar to 17 kg, and gestational diabetes had a probability of 44% of having a large for gestational age neonate.
Conclusions: This study showed that the most important variables explaining birth weight are those related to maternal nutritional status. Thus, the strategies to promote a normal birth weight should aim for a normal maternal weight at the beginning of pregnancy, gestational weight gain within the recommendations, and prevention of gestational diabetes and pre-eclampsia. (C) 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
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Gutierrez-Jara, J. P., Vogt-Geisse, K., Cabrera, M., Cordova-Lepe, F., & Munoz-Quezada, M. T. (2022). Effects of human mobility and behavior on disease transmission in a COVID-19 mathematical model. Sci. Rep., 12(1), 10840.
Abstract: Human interactions and perceptions about health risk are essential to understand the evolution over the course of a pandemic. We present a Susceptible-Exposed-Asymptomatic-Infectious-Recovered-Susceptible mathematical model with quarantine and social-distance-dependent transmission rates, to study COVID-19 dynamics. Human activities are split across different location settings: home, work, school, and elsewhere. Individuals move from home to the other locations at rates dependent on their epidemiological conditions and maintain a social distancing behavior, which varies with their location. We perform simulations and analyze how distinct social behaviors and restrictive measures affect the dynamic of the disease within a population. The model proposed in this study revealed that the main focus on the transmission of COVID-19 is attributed to the “home” location setting, which is understood as family gatherings including relatives and close friends. Limiting encounters at work, school and other locations will only be effective if COVID-19 restrictions occur simultaneously at all those locations and/or contact tracing or social distancing measures are effectively and strictly implemented, especially at the home setting.
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Kapitanov, G., Alvey, C., Vogt-Geisse, K., & Feng, Z. L. (2015). An Age-Structured Model For The Coupled Dynamics Of Hiv And Hsv-2. Math. Biosci. Eng., 12(4), 803–840.
Abstract: Evidence suggests a strong correlation between the prevalence of HSV-2 (genital herpes) and the perseverance of the HIV epidemic. HSV-2 is an incurable viral infection, characterized by periodic reactivation. We construct a model of the co-infection dynamics between the two diseases by incorporating a time-since-infection variable to track the alternating periods of infectiousness of HSV-2. The model considers only heterosexual relationships and distinguishes three population groups: males, general population females, and female sex workers. We calculate the basic reproduction numbers for each disease that provide threshold conditions, which determine whether a disease dies out or becomes endemic in the absence of the other disease. We also derive the invasion reproduction numbers that determine whether or not a disease can invade into a population in which the other disease is endemic. The calculations of the invasion reproduction numbers suggest a new aspect in their interpretation – the class from which the initial disease carrier arises is important for understanding the invasion dynamics and biological interpretation of the expressions of the reproduction numbers. Sensitivity analysis is conducted to examine the role of model parameters in influencing the model outcomes. The results are discussed in the last section.
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Marin, O., Gonzalez, B., & Poupin, M. J. (2021). From Microbial Dynamics to Functionality in the Rhizosphere: A Systematic Review of the Opportunities With Synthetic Microbial Communities. Front. Plant Sci., 12, 650609.
Abstract: Synthetic microbial communities (SynComs) are a useful tool for a more realistic understanding of the outcomes of multiple biotic interactions where microbes, plants, and the environment are players in time and space of a multidimensional and complex system. Toward a more in-depth overview of the knowledge that has been achieved using SynComs in the rhizosphere, a systematic review of the literature on SynComs was performed to identify the overall rationale, design criteria, experimental procedures, and outcomes of in vitro or in planta tests using this strategy. After an extensive bibliography search and a specific selection process, a total of 30 articles were chosen for further analysis, grouping them by their reported SynCom size. The reported SynComs were constituted with a highly variable number of members, ranging from 3 to 190 strains, with a total of 1,393 bacterial isolates, where the three most represented phyla were Proteobacteria, Actinobacteria, and Firmicutes. Only four articles did not reference experiments with SynCom on plants, as they considered only microbial in vitro studies, whereas the others chose different plant models and plant-growth systems; some of them are described and reviewed in this article. Besides, a discussion on different approaches (bottom-up and top-down) to study the microbiome role in the rhizosphere is provided, highlighting how SynComs are an effective system to connect and fill some knowledge gaps and to have a better understanding of the mechanisms governing these multiple interactions. Although the SynCom approach is already helpful and has a promising future, more systematic and standardized studies are needed to harness its full potential.
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Vicuna, L., Norambuena, T., Miranda, J. P., Pereira, A., Mericq, V., Ongaro, L., et al. (2021). Novel loci and mapuche genetic ancestry are associated with pubertal growth traits in Chilean boys. Hum. Genet., 140(12), 1651–1661.
Abstract: Puberty is a complex developmental process that varies considerably among individuals and populations. Genetic factors explain a large proportion of the variability of several pubertal traits. Recent genome-wide association studies (GWAS) have identified hundreds of variants involved in traits that result from body growth, like adult height. However, they do not capture many genetic loci involved in growth changes over distinct growth phases. Further, such GWAS have been mostly performed in Europeans, but we do not know how these findings relate to other continental populations. In this study, we analyzed the genetic basis of three pubertal traits; namely, peak height velocity (PV), age at PV (APV) and height at APV (HAPV). We analyzed a cohort of 904 admixed Chilean children and adolescents with European and Mapuche Native American ancestries. Height was measured on roughly a 6-month basis from childhood to adolescence between 2006 and 2019. We predict that the difference in HAPV between an European and a Mapuche adolescent is 4.3 cm higher in the European (P = 0.042) and APV is 0.73 years later for the European compared with the Mapuche adolescent on average (P = 0.023). Further, by performing a GWAS on 774, 433 single-nucleotide polymorphisms, we identified a genetic signal harboring 3 linked variants significantly associated with PV in boys (P < 5 x 10(-8)). This signal has never been associated with growth-related traits.
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Vogt-Geisse, K., Ngonghala, C. N., & Feng, Z. L. (2020). The Impact Of Vaccination On Malaria Prevalence: A Vaccine-Age-Structured Modeling Approach. J. Biol. Syst., 28(2), 475–513.
Abstract: A deterministic model for the effects on disease prevalence of the most advanced preerythrocytic vaccine against malaria is proposed and studied. The model includes two vaccinated classes that correspond to initially vaccinated and booster dose vaccinated individuals. These two classes are structured by time-since-initial-vaccination (vaccine-age). This structure is a novelty for vector-host models; it allows us to explore the effects of parameters that describe timed and delayed delivery of a booster dose, and immunity waning on disease prevalence. Incorporating two vaccinated classes can predict more accurately threshold vaccination coverages for disease eradication under multi-dose vaccination programs. We derive a vaccine-age-structured control reproduction number R and establish conditions for the existence and stability of equilibria to the system. The model is bistable when R < 1. In particular, it exhibits a backward (sub-critical) bifurcation, indicating that R = 1 is no longer the threshold value for disease eradication. Thus, to achieve eradication we must identify and implement control measures that will reduce R to a value smaller than unity. Therefore, it is crucial to be cautious when using R to guide public health policy, although it remains a key quantity for decision making. Our results show that if the booster vaccine dose is administered with delay, individuals may not acquire its full protective effect, and that incorporating waning efficacy into the system improves the accuracy of the model outcomes. This study suggests that it is critical to follow vaccination schedules closely, and anticipate the consequences of delays in those schedules.
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