BÚSQUEDA DE PRINCIPIOS ACTIVOS ANTIPARASITARIOS EN PLANTAS DE USO TRADICIONAL DE LA AMAZONIA PERUANA. ESPECIAL ENFASIS EN ALCALOIDES INDOLICOS

A fin de evaluar el potencial antimalárico de remedios tradicionales utilizadas en el Perú por las poblaciones indígenas y mestizas del río Nanay en Loreto, fueron entrevistados sobre el uso medicina tradicional para el tratamiento de la malaria. La encuesta se llevó a cabo en seis pueblos y llevaron a la recolección de 59 plantas. 35 extracciones hidro-alcohólico se realizaron en las 21 plantas más citadas. A continuación se ensayaron los extractos para la actividad antiplasmodial in vitro sobre cepa resistente a la cloroquina de Plasmodium falciparum (FCR-3), y también se realizó la prueba de inhibición de ferriprotoporfirina con el fin de asumir propiedades farmacológicas. Los extractos de 9 plantas, en veintiún evaluados, mostraron una actividad antiplasmodial interesante (IC50 <10 µg/ml) y 16 extractos resultaron activos en la prueba de inhibición de la ferriprotoporfirina. Cinco alcaloides oxindólicos y dos alcaloides de tipo plumerano subtipo haplophitina, fueron aislados de plantas medicinales: Aspidosperma rigidum y A. schultesii. Uno de estos compuestos se identificó como un confórmero rotámero transoide de la 18-Oxo-O-metilaspidoalbina que no se describió anteriormente, también fueron determinada la actividad antiparasitaria de los compuestos contra Trypanosoma cruzi y Leishmania infantum

Backsplash galaxies and their impact on galaxy evolution: a three-stage, four-type perspective

We study the population of backsplash galaxies at $z=0$ in the outskirts of massive, isolated clusters of galaxies taken from the MDPL2-SAG semi-analytic catalogue. We consider four types of backsplash galaxies according to whether they are forming stars or passive at three stagesin their lifetimes: before entering the cluster, during their first incursion through the cluster, and after they exit the cluster. We analyse several geometric, dynamic, and astrophysical aspects of the four types at the three stages. Galaxies that form stars at all stages account for the majority of the backsplash population ($58\%$) and have stellar masses typically below $M_\star\sim 3\times 10^{10} h^{-1}{\rm M}_\odot$ that avoid the innermost cluster's regions and are only mildly affected by it. In a similar mass range, galaxies that become passive after exiting the cluster ($26\%$) follow orbits characterised by small pericentric distance and a strong deflection by the cluster potential well while suffering a strong loss of both dark matter and gas content. Only a small fraction of our sample ($4\%$) become passive while orbiting inside the cluster. These galaxies have experienced heavy pre-processing and the cluster's tidal stripping and ram pressure provide the final blow to their star formation. Finally, galaxies that are passive before entering the cluster for the first time ($12\%$) are typically massive and are not affected significantly by the cluster. Using the bulge/total mass ratio as a proxy for morphology, we find that a single incursion through a cluster do not result in significant morphological changes in all four types.Comment: Accepted for publication in MNRAS. Comments are welcom

Copy number variation mapping and genomic variation of autochthonous and commercial turkey populations

This study aims at investigating genomic diversity of several turkey populations using Copy Number Variants (CNVs). A total of 115 individuals from six Italian breeds (Colle Euganei, Bronzato Comune Italiano, Parma e Piacenza, Brianzolo, Nero d\u2019Italia, and Ermellinato di Rovigo), seven Narragansett, 38 commercial hybrids, and 30 Mexican turkeys, were genotyped with the Affymetrix 600K single nucleotide polymorphism (SNP) turkey array. The CNV calling was performed with the Hidden Markov Model of PennCNV software and with the Copy Number Analysis Module of SVS 8.4 by Golden Helix\uae. CNV were summarized into CNV regions (CNVRs) at population level using BEDTools. Variability among populations has been addressed by hierarchical clustering (pvclust R package) and by principal component analysis (PCA). A total of 2,987 CNVs were identified covering 4.65% of the autosomes of the Turkey_5.0/melGal5 assembly. The CNVRs identified in at least two individuals were 362\u2014189 gains, 116 losses, and 57 complexes. Among these regions the 51% contain annotated genes. This study is the first CNV mapping of turkey population using 600K chip. CNVs clustered the individuals according to population and their geographical origin. CNVs are known to be indicators also of adaptation, as some researches in different species are suggesting

Oral and general health conditions involved in periodontal status during pregnancy: a prospective cohort study

Purpose: Pregnancy is a period in a woman’s life that has important consequences on oral health, particularly for gingival health. Present study aims to identify women at higher risk of developing periodontal disease (gingivitis and periodontitis) during late pregnancy and evaluate how this condition evolves during this period. Methods: Prospective cohort study was designed with pregnant women who were assessed during the first and third trimesters of gestation in a southern Spanish public hospital. Data regarding gingival and periodontal health, oral hygiene, and overall health status (obesity and diabetes mellitus) were collected. Reporting followed STROBE checklist. Results: Significantly higher number of women had the periodontal and gingival disease in the third trimester of gestation compared with in early pregnancy. In the third trimester of gestation, 42 (28.6%) and 63 (42.9%) of women presented symptoms of periodontal disease and gingival disease, respectively. Obesity (OR 2.834; 95%CI 0.919–8.741), worse oral hygiene during the first trimester of gestation (OR: 4.031; 95%CI 2.12–7.65), and periodontal disease during early pregnancy (OR: 15.104; 95%CI 3.60–63.36) most effectively predicted periodontal disease during late pregnancy. Conclusions: Pregnancy is associated with exacerbated periodontal and gingival disease symptoms throughout the different trimesters of gestation. Obesity and oral hygiene during early pregnancy were the risk factors that most contributed to the aforementioned changes in periodontal disease.Ministerio de Ciencia, Innovación y UniversidadesRevisión por pare

Neuro-fuzzy control for artificial pancreas: in silico development and validation

[ES] La Diabetes Mellitus Tipo 1 (DMT1) es una de las enfermedades actuales más dañinas que afectan a personas de cualquier edad incluyendo niños desde el nacimiento. Las inyecciones de insulina exógena siguen siendo el tratamiento más común para estos pacientes, sin embargo, no es el óptimo. La comunidad científica se ha esforzado en optimizar el suministro de insulina usando dispositivos electrónicos y de esta manera mejorar la esperanza de vida de los diabéticos. Existen numerosas limitaciones para que esta evolución biomédica sea realidad tales como la validación de algoritmos controladores, experimentación con dispositivos electrónicos, aplicabilidad en pacientes de diferentes edades, entre otras. Este trabajo presenta el prototipado de un controlador inteligente neuro-fuzzy en la tarjeta LAUNCHXL-F28069M de Texas Instruments para formar un esquema de hardware en el lazo (HIL). Esto es, el controlador embebido manda los datos de la tasa de suministro de insulina al computador donde se capturan por el software Uva/Padova y se integran a la simulación metabólica de pacientes diabéticos virtuales tratados con bomba de insulina. Una tarea principal del algoritmo inteligente embebido es determinar la tasa óptima de infusión insulínica para cada uno de los 30 pacientes virtuales disponibles, los cuales llevan un protocolo de comida. La novedad de este trabajo se centra en superar las limitaciones actuales a través de un primer enfoque de algoritmo de control inteligente aplicable al páncreas artificial (PA) y analizar la factibilidad de esta propuesta en la trascendencia con la edad ya que los resultados corresponden a pruebas in-silico en poblaciones de 10 adultos, 10 adolescentes y 10 niños.[EN] Type 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that aect people of any age, including children from birth. Exogenous insulin injections remain the most common treatment for these patients, however, it is not the optimal one. The scientific community has endeavored to optimize insulin administration using electronic devices and thus improve the diabetics life expectancy. There are numerous limitations for this biomedical evolution to become a reality such as the control algorithms validation, experimentation with electronic devices, and applicability in patients age transcendence, among others. This work presents the prototyping of a neuro-fuzzy intelligent controller on the Texas Instruments LAUNCHXL-F28069M development board to form a hardware in the loop (HIL) scheme. That is, the embedded controller sends the insulin delivery rate data to the computer where it is captured by the Uva/Padova software and integrated into the metabolic simulation of virtual diabetic patients treated with an insulin pump. The main task of the embedded intelligent algorithm is to determine the optimal insulin infusion rate for each of the 30 virtual patients who follow a meal protocol. The novelty of this work focuses on overcoming current limitations through a first intelligent control algorithm approach applicable to artificial pancreas (AP) and analyzing the feasibility of this proposal in age transcendence since the results correspond to in-silico tests in populations of 10 adults, 10 adolescents and 10 children.Rios, Y.; García-Rodríguez, J.; Sánchez, E.; Alanis, A.; Ruiz-Velázquez, E.; Pardo, A. (2020). Control neuro-fuzzy para páncreas artificial: desarrollo y validación in-silico. Revista Iberoamericana de Automática e Informática industrial. 17(4):390-400. https://doi.org/10.4995/riai.2020.13035OJS390400174Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., 2007. Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks. IEEE Transactions on Neural Networks 18 (4), 1185-1195. https://doi.org/10.1109/TNN.2007.899170American Diabetes Association, 2013. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 36 (4), 1033-1046. https://doi.org/10.2337/dc12-2625Brown, J. B., Pedula, K. L., Bakst, A. W., 09 1999. The Progressive Cost of Complications in Type 2 Diabetes Mellitus. JAMA Internal Medicine 159 (16), 1873-1880.https://doi.org/10.1001/archinte.159.16.1873Centers for Disease Control and Prevention, 2017. National Diabetes Statistics Report, 2017. Estimates of Diabetes and Its Burden in the United States. National Center for Chronic Disease Prevention and Health Promotion. USA. 1 (1), 1-20.Chang, F. J., Chiang, Y. M., Chang, L. C., 2010. Multi-step-ahead neural networks for flood forecasting. Hydrological Sciences Journal 52 (1), 114-130. https://doi.org/10.1623/hysj.52.1.114Chen, P. A., Chang, L. C., Chang, F. J., 2013. Reinforced recurrent neural networks for multi-step-ahead flood forecasts. Journal of Hydrology 497 (2013), 71-79. https://doi.org/10.1016/j.jhydrol.2013.05.038Cinar, A., 2018. Artificial Pancreas Systems: An Introduction to the Special Issue. IEEE Control Systems 38 (1), 26-29. https://doi.org/10.1109/MCS.2017.2766321Control, T. D., Group, C. T. R., 1993. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. New England Journal of Medicine 329 (14), 977-986, pMID: 8366922. https://doi.org/10.1056/NEJM199309303291401Dalla Man, C., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C., jan 2014. The UVA/PADOVA Type 1 Diabetes Simulator. Journal of Diabetes Science and Technology 8 (1), 26-34. https://doi.org/10.1177/1932296813514502Freeman, R. A., Kokotovic, P., 2009. Robust Nonlinear Control Design, springer s Edition. Birkhäuser Boston, Boston. https://doi.org/10.1007/978-0-8176-4759-9Geman, O., Chiuchisan, I., Toderean, R., 2017. Application of adaptive neuro-fuzzy inference system for diabetes classification and prediction. In: 2017 E-Health and Bioengineering Conference (EHB). Sinaia, pp. 639-642. https://doi.org/10.1109/EHB.2017.7995505Institute of Medicine, 2005. Summary Tables, Dietary Reference Intakes. In: Press, T. N. A. (Ed.), Dietary Reference Intakes for Energy, the nation Edition. Elsevier, Washington D.C, U.S., Ch. Summary Ta, pp. 1319-1331. https://doi.org/10.17226/10490Karahoca, A., Karahoca, D., Kara, A., sep 2009. Diagnosis of diabetes by using adaptive neuro fuzzy inference systems. In: 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control. Famagusta, pp. 1-4. https://doi.org/10.1109/ICSCCW.2009.5379497Kim, S., 2007. Burden of hospitalizations primarily due to uncontrolled diabetes. Diabetes Care 30 (5), 1281-1282. http://care.diabetesjournals.org/content/30/5/1281 , https://doi.org/10.2337/dc06-2070Kovatchev, B., Raimondo, D., Breton, M., Patek, S., Cobelli, C., jan 2008. In Silico Testing and in Vivo Experiments with Closed-Loop Control of Blood Glucose in Diabetes. IFAC Proceedings Volumes 41 (2), 4234-4239. https://doi.org/10.3182/20080706-5-KR-1001.00712Kovatchev, B. P., Breton, M., Dalla Man, C., Cobelli, C., 2009. In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes. Journal of Diabetes Science and Technology 3 (1), 44-55. https://doi.org/10.1177/193229680900300106Kropff, J., et al., December 2015. 2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial. The Lancet Diabetes & Endocrinology 3 (2), 939-947. https://doi.org/10.1016/S2213-8587(15)00335-6Kux, L., 2012. Guidance for Industry and Food and Drug Administration Staff; The Content of Investigational Device Exemption and Premarket Approval Applications for Artificial Pancreas Device Systems; Availability. Federal Register 77 (226), 1-63.Lekkas, S., Mikhailov, L., 2010. Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases. Artificial Intelligence in Medicine 50 (2), 117-126. https://doi.org/10.1016/j.artmed.2010.05.007Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas-Tellez, F., Ruiz-Velazquez, E., 2013. Neural inverse optimal control applied to type 1 diabetes mellitus patients. Analog Integrated Circuits and Signal Processing 76 (3), 343-352. https://doi.org/10.1007/s10470-013-0109-8Li, W., Todorov, E., Liu, D., 2011. Inverse optimality design for biological movement systems. In: IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 44. Elsevier, Milano, pp. 9662-9667. https://doi.org/10.3182/20110828-6-IT-1002.00877Nath, A., Dey, R., Balas, V. E., 2018. Closed Loop Blood Glucose Regulation of Type 1 Diabetic Patient Using Takagi-Sugeno Fuzzy Logic Control. In: Advances in Intelligent Systems and Computing. Springer, Cham, Switzerland, pp. 286-296. https://doi.org/10.1007/978-3-319-62524-9_23Ornelas, F., Sanchez, E. N., Loukianov, A. G., 2011. Discrete-time nonlinear systems inverse optimal control: A control Lyapunov function approach. In: Proceedings of the IEEE International Conference on Control Applications. IEEE, Denver, pp. 1431-1436. https://doi.org/10.1109/CCA.2011.6044461Ornelas-Tellez, F., Sanchez, E. N., Loukianov, A. G., Navarro-Lopez, E. M., 2011. Speed-gradient inverse optimal control for discrete-time nonlinear systems. In: Proceedings of the IEEE Conference on Decision and Control. IEEE, Orlando, pp. 290-295. https://doi.org/10.1109/CDC.2011.6160374Pesl, P., Herrero, P., Reddy, M., Xenou, M., Oliver, N., Johnston, D., Toumazou, C., Georgiou, P., Jan 2016. An advanced bolus calculator for type 1 diabetes: System architecture and usability results. IEEE Journal of Biomedical and Health Informatics 20 (1), 11-17. https://doi.org/10.1109/JBHI.2015.2464088Rios, Y. Y., Garcia-Rodriguez, J., Sanchez, E. N., Alanis, A. Y., Ruiz-Velazquez, E., 2018a. Rapid Prototyping of Neuro-Fuzzy Inverse Optimal Control as Applied to T1DM Patients. In: 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, Guadalajara, pp. 1-5. https://doi.org/10.1109/LA-CCI.2018.8625241Rios, Y. Y., García-Rodríguez, J. A., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., Durán, C., 2018b. Treatment for T1DM patients using neuro-fuzzy inverse optimal control algorithm: a rapid prototyping implementation. In: Revista Colombiana de Tecnologías de Avanzada. Colombia, pp. 26-33.Rios, Y. Y., García-Rodríguez, J. A., Sánchez, O. D., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., Arana-Daniel, N., 2018c. Inverse Optimal Control Using A Neural Multi-Step Predictor for T1DM Treatment. In: Proceedings of the International Joint Conference on Neural Networks. Rio de Janeiro, pp. 1-8. https://doi.org/10.1109/IJCNN.2018.8489197Rovithakis, G. A., Christodoulou, M. A., 2000. Adaptive Control with Recurrent High-order Neural Networks : Theory and Industrial Applications. Springer London, London, U.K. https://doi.org/10.1007/978-1-4471-0785-9Sanchez, E. N., Ornelas-Tellez, F., 2013. Discrete-time inverse optimal control for nonlinear systems, taylor & f Edition. CRC Press, Boca Raton, Florida, U.S. https://doi.org/10.1201/b14779Takagi, T., Sugeno, M., 1985. Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics SMC-15 (1), 116 - 132. https://doi.org/10.1109/TSMC.1985.6313399Thabit, H., Hovorka, R., Sep. 2016. Coming of age: the artificial pancreas for type 1 diabetes. Diabetologia 59 (9), 1795-1805. https://doi.org/10.1007/s00125-016-4022-4Trevitt, S., Simpson, S., Wood, A., 2016. Artificial Pancreas Device Systems for the Closed-Loop Control of Type 1 Diabetes. Journal of Diabetes Science and Technology 10 (3), 714-723. https://doi.org/10.1177/1932296815617968Turksoy, K., Samadi, S., Feng, J., Littlejohn, E., Quinn, L., Cinar, A., Jan 2016. Meal detection in patients with type 1 diabetes: A new module for the multivariable adaptive artificial pancreas control system. IEEE Journal of Biomedical and Health Informatics 20 (1), 47-54. https://doi.org/10.1109/JBHI.2015.2446413van Bon, A. C., Luijf, Y. M., Koebrugge, R., Koops, R., Hoekstra, J. B. L., DeVries, J. H., 2014. Feasibility of a Portable Bihormonal Closed-Loop System to Control Glucose Excursions at Home Under Free-Living Conditions for 48 Hours. Diabetes Technology & Therapeutics 16 (3), 131-136, pMID: 24224750. https://doi.org/10.1089/dia.2013.0166Yeh, H., et al., 2012. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: A systematic review and meta-analysis. Annals of Internal Medicine 157 (5), 336-347. https://doi.org/10.7326/0003-4819-157-5-201209040-0050

Control neuro-fuzzy para páncreas artificial: Desarrollo y validación in-silico

Type 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that affect people of any age, including children from birth. Exogenous insulin injections remain the most common treatment for these patients, however, it is not the optimal one. The scientific community has endeavored to optimize i nsulin administration using electronic devices and thus improve the diabetics life expectancy. There are numerous limitations for this biomedical evolution to become a reality such as the control algorithms validation, experimentation with electronic devices, and applicability in patients age transcendence, among others. This work presents the prototyping of a neuro-fuzzy intelligent controller on the Texas Instruments LAUNCHXL-F28069M development board to form a hardware in the loop (HIL) scheme. That is, the embedded controller sends the insulin delivery rate data to the computer where it is captured by the Uva/Padova software and integrated into the metabolic simulation of virtual diabetic patients treated with an insulin pump. The main task of the embedded intelligent algorithm is to determine the optimal insulin infusion rate for each of the 30 virtual patients who follow a meal protocol. The novelty of this work focuses on overcoming current limitations through a first intelligent control algorithm a pproach applicable to artificial pancreas (A P) and an alyzing the feasibility of this proposal in age transcendence since the results correspond to in-silico tests in populations of 10 adults, 10 adolescents and 10 children. © 2020 Universitat Politecnica de Valencia. All rights reserved

BIORREMEDIACIÓN: ACTUALIDAD DE CONCEPTOS Y APLICACIONES

Vivimos una época que experimenta un crecimiento acelerado de la población y una fuerte industrialización. La humanidad, en el afán de satisfacer sus múltiples necesidades, se ha supeditado tanto a tecnologías que dañan el medio ambiente como a la dependencia de compuestos xenobióticos. En consecuencia, serios problemas de contaminación que amenazan tanto la salud de los seres vivos como del ambiente se han suscitado. Como respuesta, la biotecnología ambiental a través de la biorremediación como una de sus aplicaciones, desempeña un rol clave en la remoción de contaminantes. Diferentes sistemas biológicos de remediación, que incluyen el uso de plantas, algas, bacterias y hongos, se han empleado con éxito para tratar ambientes contaminados de metales pesados, hidrocarburos, compuestos xenobióticos, y elementos radioactivos. Aunque la biorremediación no es una tecnología nueva, esta ha ido evolucionando y se ha posicionado como un factor sustancial, tanto en términos de eficiencia como en aspectos económicos, para abatir la contaminación. Esta revisión analiza diferentes problemáticas de contaminación ambiental, describe las principales estrategias de biorremediación y detalla mecanismos moleculares empleados por algunos microorganismos para degradar compuestos tóxicos y recalcitrantes

Proteasome Dysfunction Associated to Oxidative Stress and Proteotoxicity in Adipocytes Compromises Insulin Sensitivity in Human Obesity

AIMS: Obesity is characterized by a low-grade systemic inflammatory state and adipose tissue (AT) dysfunction, which predispose individuals to the development of insulin resistance (IR) and metabolic disease. However, a subset of obese individuals, referred to as metabolically healthy obese (MHO) individuals, are protected from obesity-associated metabolic abnormalities. Here, we aim at identifying molecular factors and pathways in adipocytes that are responsible for the progression from the insulin-sensitive to the insulin-resistant, metabolically unhealthy obese (MUHO) phenotype. RESULTS: Proteomic analysis of paired samples of adipocytes from subcutaneous (SC) and omental (OM) human AT revealed that both types of cells are altered in the MUHO state. Specifically, the glutathione redox cycle and other antioxidant defense systems as well as the protein-folding machinery were dysregulated and endoplasmic reticulum stress was increased in adipocytes from IR subjects. Moreover, proteasome activity was also compromised in adipocytes of MUHO individuals, which was associated with enhanced accumulation of oxidized and ubiquitinated proteins in these cells. Proteasome activity was also impaired in adipocytes of diet-induced obese mice and in 3T3-L1 adipocytes exposed to palmitate. In line with these data, proteasome inhibition significantly impaired insulin signaling in 3T3-L1 adipocytes. INNOVATION: This study provides the first evidence of the occurrence of protein homeostasis deregulation in adipocytes in human obesity, which, together with oxidative damage, interferes with insulin signaling in these cells. CONCLUSION: Our results suggest that proteasomal dysfunction and impaired proteostasis in adipocytes, resulting from protein oxidation and/or misfolding, constitute major pathogenic mechanisms in the development of IR in obesity.IMIBIC/Universidad de Córdoba-SCAI (ProteoRed, PRB2-ISCIII)MINECO/FEDERJunta de Andalucía/FEDERCIBERobn(Instituto de Salud Carlos III

Surgical management of a diabetic calcaneal ulceration and osteomyelitis with a partial calcanectomy and a sural neurofasciocutaneous flap

The treatment of calcaneal osteomyelitis in diabetic patients poses a great challenge to the treating physician and surgeon. The use of a distally based sural neurofasciocutaneous flap after an aggressive debridement of non-viable and poorly vascularized tissue and bone that is combined with a thorough antibiotic regimen provides a great technique for adequate soft tissue coverage of the heel. In this case report, the authors describe the aforementioned flap as a versatile alternative to the use of local or distant muscle flaps for diabetic patients with calcaneal osteomyelitis and concomitant large wounds

EFECTO DEL ÁCIDO CÍTRICO COMO PRETRATAMIENTO SOBRE LA ACTIVIDAD DE AGUA Y COMPORTAMIENTO DE SORCIÓN EN CALLO DE ALMEJA SECO (Nodipecten subnodosus)

En presente estudio se aplicó un tratamiento de inmersión en ácido cítrico a callo de almeja, con el objetivo de reducir el tiempo de secado de la almeja mano de león. Las muestras se sometieron a inmersión en ácido cítrico 0.1 M, durante cero (control), una y tres h. Se evaluó el efecto del pre-tratamiento de inmersión en solución ácida y la temperatura de secado (50, 60 y 70 °C), sobre la aw y el fenómeno de sorción en el callo seco. Ambas variables mostraron un efecto significativo (