A neural cell automated analysis system based on pathological specimens in a gerbil brain ischemia model.

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.

A neural cell automated analysis system based on pathological specimens in a gerbil brain ischemia model

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.

Mechanistic evaluation of the effect of sodium-dependent glucose transporter 2 inhibitors on delayed glucose absorption in patients with type 2 diabetes mellitus using a quantitative systems pharmacology model of human systemic glucose dynamics.

Sodium-dependent glucose transporter (SGLT) 2 is specifically expressed in the kidney, while SGLT1 is present in the kidneys and small intestine. SGLT2 inhibitors are a class of oral antidiabetic drugs that lower elevated plasma glucose levels by promoting the urinary excretion of excess glucose through the inhibition of renal glucose reuptake. The inhibition selectivity for SGLT2 over SGLT1 (SGLT2/1 selectivity) of marketed SGLT2 inhibitors is diverse, while SGLT2/1 selectivity of canagliflozin is relatively low. Although canagliflozin suppresses postprandial glucose levels, the degree of contribution for SGLT1 inhibition to this effect remains unproven. To analyze the effect of SGLT2 inhibitors on postprandial glucose level, we constructed a novel quantitative systems pharmacology (QSP) model, called human systemic glucose dynamics (HSGD) model, integrating intestinal absorption, metabolism, and renal reabsorption of glucose. This HSGD model reproduced the postprandial plasma glucose concentration-time profiles during a meal tolerance test under different clinical trial conditions. Simulations after canagliflozin administration showed a dose-dependent delay of time (Tmax,glc ) to reach maximum concentration of glucose (Cmax,glc ), and the delay of Tmax,glc disappeared when inhibition of SGLT1 was negated. In addition, contribution ratio of intestinal SGLT1 inhibition to the decrease in Cmax,glc was estimated to be 23%-28%, when 100 and 300 mg of canagliflozin are administered. This HSGD model enabled us to provide the partial contribution of intestinal SGLT1 inhibition to the improvement of postprandial hyperglycemia as well as to quantitatively describe the plasma glucose dynamics following SGLT2 inhibitors.

The cross-bridge dynamics during ventricular contraction predicted by coupling the cardiac cell model with a circulation model.

The force-velocity (F-V) relationship of filament sliding is traditionally used to define the inotropic condition of striated muscles. A simple circulation model combined with the Laplace heart was developed to get a deeper insight into the relationship between the F-V characteristics and the cardiac ventricular inotropy. The circulation model consists of a preload and an afterload compartments. The linear F-V relationship for filament sliding in the NL model (Negroni and Lascano 1996) was replaced by the exponential F-V relation observed by Piazzesi et al. (2002). We also modified the NL model to a hybrid model to benefit from the Ca(2+) cooperativity described by the Robinson model (Robinson et al. 2002). The model was validated by determining the diastolic ventricular pressure-volume relationship of the Laplace heart and the F-V relation of the new hybrid model. The computed parameters of the cardiac cycle agreed well with the physiological data. Computational results showed that the cross-bridge elongation (h in the NL model) temporally undershot the equilibrium h(c) during the ejection period and overshot it during the rapid refilling phase. Thereby the time course of ejection and refilling was retarded. In a simulation where the velocity of the mobile myosin head (dX/dt) was varied, the systolic peak pressure of the ventricle varied from a minimum value at dX/dt = 0 to a saturating value obtained with a constant h(c), providing in silico evidence for a functional impact of the cross-bridge sliding rate on the ventricular inotropy.

White-noise stimulation of the Hodgkin-Huxley model.

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.