RESUMO
ABSTRACT Purpose: Amid rising health awareness, natural products which has milder effects than medical drugs are becoming popular. However, only few systems can quantitatively assess their impact on living organisms. Therefore, we developed a deep-learning system to automate the counting of cells in a gerbil model, aiming to assess a natural product's effectiveness against ischemia. Methods: The image acquired from paraffin blocks containing gerbil brains was analyzed by a deep-learning model (fine-tuned Detectron2). Results: The counting system achieved a 79%-positive predictive value and 85%-sensitivity when visual judgment by an expert was used as ground truth. Conclusions: Our system evaluated hydrogen water's potential against ischemia and found it potentially useful, which is consistent with expert assessment. Due to natural product's milder effects, large data sets are needed for evaluation, making manual measurement labor-intensive. Hence, our system offers a promising new approach for evaluating natural products.
Assuntos
Biomarcadores Tumorais/genética , Transformação Celular Neoplásica/patologia , Evolução Clonal , Leucemia Mieloide Aguda/patologia , Modelos Biológicos , Mutação , Síndromes Mielodisplásicas/patologia , Transformação Celular Neoplásica/genética , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Técnicas In Vitro , Leucemia Mieloide Aguda/genética , Síndromes Mielodisplásicas/genética , Células Tumorais CultivadasRESUMO
We studied the combined influence of noise and constant current stimulations on the Hodgkin-Huxley neuron model through time and frequency analysis of the membrane-potential dynamics. We observed that, in agreement with experimental data (Guttman et al. 1974), at low noise and low constant current stimulation the behavior of the model is well approximated by that of the linearized Hodgkin-Huxley system. Conversely, nonlinearities due to firing dominate at large noise or current stimulations. The transition between the two regimes is abrupt, and takes place in the same range of noise and current intensities as the noise-induced transition characterized by the qualitative change in the stationary distribution of the membrane potential (Tanabe and Pakdaman 2001a). The implications of these results are discussed.