ABSTRACT
Directed evolution methods mimic in vitro Darwinian evolution, inducing random mutations and selective pressure in genes to obtain proteins with enhanced characteristics. These techniques are developed using trial-and-error testing at an experimental level with a high degree of uncertainty. Therefore, in silico modeling of directed evolution is required to support experimental assays. Several in silico approaches have reproduced directed evolution, using statistical, thermodynamic, and kinetic models in an attempt to recreate experimental conditions. Likewise, optimization techniques using heuristic models have been used to understand and find the best scenarios of directed evolution. Our study uses an in silico model named HeurIstics DirecteD EvolutioN, which is based on a genetic algorithm designed to generate chimeric libraries from 2 parental genes, cry11Aa and cry11Ba, of Bacillus thuringiensis. These genes encode crystal-shaped δ-endotoxins with 3 conserved domains. Cry11 toxins are of biotechnological interest because they have shown to be effective as biopesticides for disease-spreading vectors. With our heuristic model, we considered experimental parameters such as DNA fragmentation length, number of generations or simulation cycles, and mutation rate, to get characteristics of Cry11 chimeric libraries such as percentage of population identity, truncation of variants obtained from the presence of internal stop codons, percentage of thermodynamic diversity, and stability of variants. Our study allowed us to focus on experimental conditions that may be useful for the design of in vitro and in silico experiments of directed evolution with Cry toxins of 3 conserved domains. Furthermore, we obtained in silico libraries of Cry11 variants, in which structural characteristics of wild Cry families were observed in a review of a sample of in silico sequences. We consider that future studies could use our in silico libraries and heuristic computational models, as the one suggested here, to support in vitro experiments of directed evolution.
ABSTRACT
Introducción: El accidente cerebrovascular (ACV) es la segunda causa de muerte y tercera causa de discapacidad en el mundo. Objetivos: Evaluar la asociación entre variables clínicas, electrocardiográficas, escalas neurológicas en pacientes con ACV como predictoras de mortalidad a 3 meses posteriores al egreso hospitalario. Materiales y métodos: Estudio de cohorte prospectivo con muestreo no probabilístico, en pacientes mayores de 18 años con primer ACV. Se evaluaron variables demográficos, clínicas, escalas neurológicas del Instituto Nacional de Salud (NIHSS) y canadiense (CNS), variabilidad de la frecuencia cardiaca (VFC) y del QT (QTV), dispersión del QT. Se determinó la mortalidad a los tres meses de seguimiento. Se realizó análisis bivariado y de regresión logística múltiple cuyo desenlace fue mortalidad a tres meses post egreso hospitalario, incluyendo variables con baja correlación (r< 0.4) y significancia estadística (p<0.05). Resultados: Se incluyeron 92 pacientes, 13 de los cuales fallecieron en la fase de tratamiento intrahospitalario. Se realizó seguimiento durante tres meses después del egreso hospitalario en 81 pacientes. La mortalidad total en tres meses de seguimiento fue del 21.7%(n=20). Se identificaron cinco variables predictoras de mortalidad en el modelo final: puntaje de escala NIHSS, frecuencia cardiaca media, VLF QT ≥36.311, LF/HF ≤1.019, valores extremos r-MSD (≥7.985o≤2.363) de VFC. La capacidad discriminatoria del modelo mediante el análisis del área bajo la curva fue de 0.95, con valores de sensibilidad y especificidad del 60% y 93% respectivamente. Conclusion: Altos puntajes de escala NIHSS, VLF-QT, frecuencia cardiaca media, así como valores bajos LF/HF y valores extremos r-MSD, fueron factores de riesgo independientes para mortalidad a los 90 días después de un primer ACV.
Introduction: Stroke is the second cause of death and third cause of disability worldwide. Objective: To assess association between clinical and electrocardiographic variables, neurological scales in stroke patients like predictors of mortality at three months after hospital discharge. Subjects and methods: Prospective cohort with nonprobabilistic sampling, in patients over 18 years with first stroke. Demographic and clinical variables, neurological scales (NIHSS, Canadian), heart rate (HRV) and QT variability (QTV), QT dispersion were evaluated. Mortality was determined during the 3 months follow up. Bivariate and multiple logistic regression analysis were performed with mortality at three months after discharge as outcome. Variables were included in the model if they have low correlation (r<0.4) and significant statistically p values (P< 0.05). Results: 92 patients were included in the study, 13 patients died during the intra-hospital stay, 81 were followed at 3 months after their hospital discharge. Total mortality in patients included at three months follow-up was 21.7 % (n=20). We identified five predictors of mortality in the final model: NIHSS score, mean heart rate, VLF QT ≥36,311, LF/HF ≤ 1,019, extreme values of r-MSD (≥ 7,985 or ≤ 2,363) of HRV. The area under the curve (AUC) of the model was 0,95 with sensitivity of 60% and specificity of 93%. Conclusions: High NIHSS scores, VLF-QT, mean heart rate, low values of LF/HF and high extreme values of r-MSD were independent risk factors for mortality at 90 days after a first stroke.
