Hybrid modeling of an ultracentrifugation process for separation of full and empty adeno-associated virus particles.

Ultracentrifugation is an attractive method for separating full and empty capsids, exploiting their density difference. Changes of the serotype/capsid, density of loading material, or the genetic information contained in the adeno-associated viruses (AAVs) require the adaptation of the harvesting parameters and the density gradient loaded onto the centrifuge. To streamline these adaptations, a mathematical model could support the design and testing of operating conditions.Here, hybrid models, which combine empirical functions with artificial neural networks, are proposed to describe the separation of full and empty capsids as a function of material and operational parameters, i.e., the harvest model. In addition, critical quality attributes are estimated by a quality model which is operating on top of the harvest model. The performance of these models was evaluated using test data and two additional blind runs. Also, a "what-if" analysis was conducted to investigate whether the models' predictions align with expectations.It is concluded that the models are sufficiently accurate to support the design of operating conditions, though the accuracy and applicability of the models can further be increased by training them on more specific data with higher variability.

On the theoretical maximum achievable signal-to-noise ratio (SNR) of piezoelectric microphones.

A theoretical maximum achievable signal to noise ratio (SNR) for piezoelectric microphones is identified as a function of only microphone volume irrespective of architecture and construction details. For a given piezoelectric material, microphone SNR can be reduced to an expression containing only a dimensionless coupling coefficient and microphone volume. For a given material, the coupling coefficient has a theoretical upper bound defined by the most favorable deformation geometry. The ability to identify a theoretical maximum SNR as a function of only microphone size is surprising considering the numerous design variables and infinite design freedom afforded in the microphone design stage.

Immunization of mice with a Mycobacterium tuberculosis genomic expression library results in lower bacterial load in lungs after challenge with BCG.

Tuberculosis is a serious infectious disease in many developing countries. The lack of an effective vaccine for preventing this disease has stimulated the search for new vaccine candidates against Mycobacterium tuberculosis. In the present work, the construction of a genomic expression library of M. tuberculosis in a eukaryotic expression vector was carried out. Immunization of Balb/c mice with a plasmid DNA pool from this library (containing 8360 clones) induced a significant IgG antibody response. Immunized mice were challenged by intratracheal route with 10(5) cfu of non-pathogenic Mycobacterium bovis BCG and were sacrificed 21 days post-challenge. Mice immunized with the genomic expression library showed a significant reduction of viable bacteria in lungs and less pulmonary tissue damage. Granulomas were not observed and the lungs had a more discrete perivascular inflammatory cell infiltrate compared to control mice. Results suggest that the genomic expression library contains genes encoding proteins that are protective against M. tuberculosis infection.

De la secuencia de un genoma bacteriano a la identificación de candidatos vacunales / Identifying Vaccine Candidates from the Sequence of a Bacterial Genome

Cada día hay más secuencias genómicas completas de un gran número de patógenos humanos. La disponibilidad de estas secuencias ha cambiado completamente el panorama para el desarrollo de vacunas, introduciendo una nueva línea de pensamiento en este proceso. Esta metodología comienza por la secuencia genómica y mediante un análisis computacional se predicen aquellos antígenos más probables a ser candidatos vacunales. Por ejemplo, con el uso de herramientas bioinformáticas se puede hacer un pesquisaje in silico de aquellas proteínas expuestas en la superficie bacteriana, con vistas a identificar antígenos candidatos vacunales. La confirmación invitro de estos resultados de localización celular y el uso de modelos animales para evaluar la inmunogenicidad de los candidatos acota finalmente el número de candidatos definidos por la computadora. A este proceso, aplicado por primera vez a Neisseria meningitidisserogrupo B, se le denomina vacunología inversa. La genómica también brinda información sobre la biología y la virulencia de especies patogénicas mediante la genómica comparativa. En este trabajo de revisión, describimos cómo la genómica puede ser usada en la identificación de nuevos candidatos vacunales(AU)