Congreso Nacional de Ciencia y Tecnología – APANAC : Acta de artículos 2021

El ecosistema de Ciencia, Tecnología e Innovación de Panamá, sin lugar a duda, ha crecido y se fortalecido en los últimos años. Este crecimiento se puede contabilizar y está ligado al crecimiento del número de nuevos investigadores y los proyectos de investigación e innovación que estos realizan a través de las diversas unidades de investigación (Universidades, Institutos Nacionales y Centros de Investigación, entre otros). Por otro lado, se puede decir que este crecimiento es sistemático y reflejado por el grado de los compromisos adquiridos a nivel del Gobierno de la República de Panamá a través del Plan Estratégico de Gobierno 2019-2024 y Los Planes Estratégicos Nacional de Ciencia, Tecnología e Innovación (PENCYT 2019-2024), ambos en vigencia y puestos en práctica actualmente. La Asociación Panameña para el Avance de la Ciencia (APANAC) es una organización sin fines de lucro fundada en 1985, cuya misión es trabajar para la promoción de la ciencia y la tecnología como base del desarrollo nacional. El Congreso Nacional de Ciencia y Tecnología es el evento científico más importante de Panamá y se ha llevado a cabo con frecuencia inter-anual desde 1995. Este año estaremos realizando su XVIII edición de manera virtual algo que ha sido un reto enorme, y que a la vez lo hace muy especial, ya que indica nuestra resiliencia. Este Congreso tiene como objetivo principal servir como plataforma para el intercambio de experiencias entre científicos, tecnólogos, empresarios, la sociedad civil y todos los ciudadanos que buscan mejorar la ciencia y tecnología en nuestro país. Como objetivo secundario es visualizar las labores de investigación e innovación que se realizan a nivel nacional, a nivel regional y a nivel internacional por investigadores, estudiantes y personal de investigación en diversas unidades de Investigación. Esta labor de visualizar es quizás el mayor reto dado por la virtualidad, pero sin lugar a dudas las Tecnologías de Información y Comunicación están allí para ayudarnos a sacar el mejor provecho.Agradecemos especialmente a la SENACYT, por el apoyo constante a la realizacion del Congreso. En esta XVIII version tambien agradecmos al apoy logistico de la Ciudad del Saber y a las gestiones realizadas por la Embajada del Estado de Israel en Panamá asegurar participación de muchos de los prestigiosos expositores invitados

Libro del XVIII Congreso Nacional de Ciencia y Tecnología - APANAC 2021

Desde su fundación en 1985, APANAC ha sido clave en promover un espacio de comunicación y crecimiento para la comunidad científica en Panamá, a través de la promoción de sus logros, así como en la promoción ante el Estado de la necesidad de apoyar el desarrollo de la Ciencia como base para el crecimiento sostenible de nuestra sociedad. Así es como hace cerca de 25 años, APANAC juega un papel fundamental en la generación y promulgación de la Ley 13 de 1997 con la que se crea SENACYT. Igualmente, dentro de esta misión de desarrollo a la comunidad científica se ha logrado consolidar el Congreso que hoy se inaugura en su XVIII versión. Este Congreso en particular ha representado un reto muy importante, sobretodo por darse en medio de una pandemia global, con devastadoras consecuencias económicas, que ha puesto de manifiesto las profundas diferencias que aquejan a la sociedad panameña. Sin embargo, estas circunstancias, han hecho también evidente la importancia de la Ciencia y la Tecnología, reforzando la necesidad de que las políticas públicas, planes de Gobierno o bien las respuestas a las crisis, se hagan no sólo con base en evidencias científicas, sino también con una perspectiva interdisciplinaria. Es así como este Congreso tiene una relevancia única, dado que muestra la existencia en nuestro país de una masa crítica de científicos y académicos comprometidos en sus diferentes áreas de trabajo con el desarrollo de Panamá. La calidad de las conferencias, mesas redondas y simposios que se presentan en este XVIII Congreso es muestra de ello, sobretodo porque en su gran mayoría, son el producto de trabajos nacionales. Agradecemos a la SENACYT, así como a todas las Universidades e Institutos de Investigación Nacional por su apoyo y activa participación en este Congreso, a la Ciudad del Saber por su soporte logístico y a la Embajada del Estado de Israel en Panamá por su gestión en facilitar la participación de muchos de los prestigiosos expositores invitados

Identificación de incidentes de tráfico en Panamá por medio del análisis de datos de redes sociales

En Panamá, la gran cantidad de carros en las vías y eventos aleatorios de tráfico producen embotellamientos constantes y extensos. Estos problemas no son solventados aun cuando las autoridades planifican y avanzan con la construcción de más carriles para el flujo vehicular. Se propone desarrollar un sistema que permita visualizar información publicada en las redes sociales acerca de incidentes de tráfico. Para esta tarea, diferentes librerías de Python son utilizadas para la extracción de información de redes sociales (Selenium y Tweepy), análisis (sklearn, Pandas, fuzzy wuzzy, etc), visualización de datos (Plotly, Dash y WordCloud) y un método de geocodificación para obtener la localización aproximada de las publicaciones obtenidas. Los resultados muestran que se han obtenido aproximadamente 180 mil tweets desde 2014. Además, se ha iniciado con la preparación de datos para los modelos de clasificación (detección de tweets correspondientes a incidentes de tráfico) y se ha desarrollado la interfaz gráfica. Este sistema presenta ventajas como la agilización y la rapidez de detección y visualización de incidentes de tráfico, que pueden ser de gran ayuda para las autoridades de tránsito del país

Use of Near-Infrared Spectroscopic Analysis of Second Trimester Amniotic Fluid to Assess Preterm Births

This pilot study investigated the possibility that metabolomic differences exist in second trimester of women delivering at term (≥37 weeks, n = 216) and preterm (≤35 weeks, n = 11). For this retrospective study, biobanked AF samples underwent near-infrared (NIR) spectral analysis using wavelengths from 700 to 1050 nm. Spectral data was compressed then optimized by multilinear regression to create a calibration model. The resultant model was able to classify term and preterm births based on differing AF metabolomic profiles with a sensitivity and specificity of 100%. When groups were classified using a prematurity index (PI), there was a statistical difference (P < 0.001) between the predicted preterm group (PI 0.77 ± 0.08) and the term group (PI 1.00 ± 0.02). In conclusion, the 2nd trimester AF samples showed distinct differences in metabolomic profiles between patients delivering preterm as compared to those at term in functional groups related to proteins, carbohydrates, fats, polyols, and water

High infestation of invasive Aedes mosquitoes in used tires along the local transport network of Panama

Background: The long‑distance dispersal of the invasive disease vectors Aedes aegypti and Aedes albopictus has intro‑ duced arthropod‑borne viruses into new geographical regions, causing a significant medical and economic burden. The used‑tire industry is an effective means of Aedes dispersal, yet studies to determine Aedes occurrence and the factors influencing their distribution along local transport networks are lacking. To assess infestation along the primary transport network of Panama we documented all existing garages that trade used tires on the highway and surveyed a subset for Ae. aegypti and Ae. albopictus. We also assess the ability of a mass spectrometry approach to classify mos‑ quito eggs by comparing our findings to those based on traditional larval surveillance. Results: Both Aedes species had a high infestation rate in garages trading used tires along the highways, providing a conduit for rapid dispersal across Panama. However, generalized linear models revealed that the presence of Ae. aegypti is associated with an increase in road density by a log‑odds of 0.44 (0.73 ± 0.16; P = 0.002), while the presence of Ae. albopictus is associated with a decrease in road density by a log‑odds of 0.36 (0.09 ± 0.63; P = 0.008). Identifica‑ tion of mosquito eggs by mass spectrometry depicted similar occurrence patterns for both Aedes species as that obtained with traditional rearing methods. Conclusions: Garages trading used tires along highways should be targeted for the surveillance and control of Aedes‑mosquitoes and the diseases they transmit. The identification of mosquito eggs using mass spectrometry allows for the rapid evaluation of Aedes presence, affording time and cost advantages over traditional vector surveil‑ lance; this is of importance for disease risk assessment.Background: The long‑distance dispersal of the invasive disease vectors Aedes aegypti and Aedes albopictus has intro‑ duced arthropod‑borne viruses into new geographical regions, causing a significant medical and economic burden. The used‑tire industry is an effective means of Aedes dispersal, yet studies to determine Aedes occurrence and the factors influencing their distribution along local transport networks are lacking. To assess infestation along the primary transport network of Panama we documented all existing garages that trade used tires on the highway and surveyed a subset for Ae. aegypti and Ae. albopictus. We also assess the ability of a mass spectrometry approach to classify mos‑ quito eggs by comparing our findings to those based on traditional larval surveillance. Results: Both Aedes species had a high infestation rate in garages trading used tires along the highways, providing a conduit for rapid dispersal across Panama. However, generalized linear models revealed that the presence of Ae. aegypti is associated with an increase in road density by a log‑odds of 0.44 (0.73 ± 0.16; P = 0.002), while the presence of Ae. albopictus is associated with a decrease in road density by a log‑odds of 0.36 (0.09 ± 0.63; P = 0.008). Identifica‑ tion of mosquito eggs by mass spectrometry depicted similar occurrence patterns for both Aedes species as that obtained with traditional rearing methods. Conclusions: Garages trading used tires along highways should be targeted for the surveillance and control of Aedes‑mosquitoes and the diseases they transmit. The identification of mosquito eggs using mass spectrometry allows for the rapid evaluation of Aedes presence, affording time and cost advantages over traditional vector surveil‑ lance; this is of importance for disease risk assessment

Proteomic fingerprint identification of Neotropical hard tick species (Acari: Ixodidae) using a self-curated mass spectra reference library

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used as biomarkers for taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both principal component analysis (PCA) and linear discriminant analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens. Author Summary Hard ticks (Ixodidae) are external parasites that feed on the blood of almost every species of terrestrial vertebrate on earth, including humans. Due to a complete dependency on blood, both sexes and even immature stages, are capable of transmitting disease agents to their hosts, causing distress and sometimes death. Despite the public health significance of ixodid ticks, accurate species identification remains problematic. Vector species identification is core to developing effective vector control schemes. Herein, we provide the first report of MALDI identification of several species of field-collected Neotropical tick specimens preserved in ethanol for up to four years. Our methodology shows that identification does not depend on a commercial reference library of lab-reared samples, but with the help of machine learning it can rely on a self-curated reference library. In addition, our approach offers greater accuracy and lower cost per sample than conventional and modern identification approaches such as morphology and molecular barcoding.Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used as biomarkers for taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both principal component analysis (PCA) and linear discriminant analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens. Author Summary Hard ticks (Ixodidae) are external parasites that feed on the blood of almost every species of terrestrial vertebrate on earth, including humans. Due to a complete dependency on blood, both sexes and even immature stages, are capable of transmitting disease agents to their hosts, causing distress and sometimes death. Despite the public health significance of ixodid ticks, accurate species identification remains problematic. Vector species identification is core to developing effective vector control schemes. Herein, we provide the first report of MALDI identification of several species of field-collected Neotropical tick specimens preserved in ethanol for up to four years. Our methodology shows that identification does not depend on a commercial reference library of lab-reared samples, but with the help of machine learning it can rely on a self-curated reference library. In addition, our approach offers greater accuracy and lower cost per sample than conventional and modern identification approaches such as morphology and molecular barcoding

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Deep metric learning for the classification of MALDI-TOF spectral signatures from multiple species of neotropical disease vectors

Deep Learning techniques have significant advantages for mass spectral classification, such as parallelized signal correction and feature extraction. Deep Metric Learning models combine Metric Learning to determine the degree of similarity or difference between a set of mass spectra with the generalization power of Deep Learning to improve feature extraction even further. The two most popular of these models combine multiple neural networks with identical architectures and are commonly called Siamese (SNN) and Triplet Neural Networks (TNN). Herein, using both SNNs and TNNs, we intended to taxonomically categorize two sets of previously-validated mass spectra that corresponded to 30 species of Neotropical arthropods in the Culicidae and Ixodidae families, some of which are disease vectors. The effectiveness of SNNs and TNNs to correctly classify 826 spectra from 12 mosquito species and 310 spectra from 18 species of hard ticks was highly effective, with both algorithms performing with minimal average loss during cross-validation. SNNs produced accuracy rates for ticks and mosquitoes of 91.22% and 94.46%, respectively, while accuracy rates of 93% and 99% were obtained with TNNs. Our results indicate that Deep Metric Learning is a practical machine learning tool for quickly and precisely classifying MALDI-TOF-generated mass spectra of Neotropical and public-health-relevant arthropod species

Using a Statistical Crop Model to Predict Maize Yield by the End-Of-Century for the Azuero Region in Panama

In this article, we evaluate the impact of temperature and precipitation at the end of the 21st century (2075&ndash;2099) on the yield of maize in the Azuero Region in Panama. Using projected data from an atmospheric climate model, MRI-ACGM 3.2S, the study variables are related to maize yield (t ha&minus;1) under four different sea surface Temperature (SST) Ensembles (C0, C1, C2, and C3) and in three different planting dates (21 August, 23 September, and 23 October). In terms climate, results confirm the increase in temperatures and precipitation intensity that has been projected for the region at the end of the century. Moreover, differences are found in the average precipitation patterns of each SST-ensemble, which leads to difference in maize yield. SST-Ensembles C0, C1, and C3 predict a doubling of the yield observed from baseline period (1990&ndash;2003), while in C1, the yield is reduced around 5%. Yield doubling is attributed to the increase in rainfall, while yield decrease is related to the selection of a later planting date, which is indistinct to the SST-ensembles used for the calculation. Moreover, lower yields are related to years in which El Ni&ntilde;o Southerm Oscilation (ENSO) are projected to occur at the end of century. The results are important as they provide a mitigation strategy for maize producers under rainfed model on the Azuero region, which is responsible for over 95% of the production of the country

Using compartmental models and Particle Swarm Optimization to assess Dengue basic reproduction number R0 for the Republic of Panama in the 1999-2022 period

Nowadays, the ability to make data-driven decisions in public health is of utmost importance. To achieve this, it is necessary for modelers to comprehend the impact of models on the future state of healthcare systems. Compartmental models are a valuable tool for making informed epidemiological decisions, and the proper parameterization of these models is crucial for analyzing epidemiological events. This work evaluated the use of compartmental models in conjunction with Particle Swarm Optimization (PSO) to determine optimal solutions and understand the dynamics of Dengue epidemics. The focus was on calculating and evaluating the rate of case reproduction, R0, for the Republic of Panama. Three compartmental models were compared: Susceptible-Infected-Recovered (SIR), Susceptible-Exposed-Infected-Recovered (SEIR), and Susceptible-Infected-Recovered Human-Susceptible-Infected Vector (SIR Human-SI Vector, SIR-SI). The models were informed by demographic data and Dengue incidence in the Republic of Panama between 1999 and 2022, and the susceptible population was analyzed. The SIR, SEIR, and SIR-SI models successfully provided R0 estimates ranging from 1.09 to 1.74. This study provides, to the best of our understanding, the first calculation of R0 for Dengue outbreaks in the Republic of Panama