BackgroundUnperturbed tumor growth kinetics is one of the more studied cancer topics; however, it is poorly understood. Mathematical modeling is a useful tool to elucidate new mechanisms involved in tumor growth kinetics, which can be relevant to understand cancer genesis and select the most suitable treatment.MethodsThe classical Kolmogorov-Johnson-Mehl-Avrami as well as the modified Kolmogorov-Johnson-Mehl-Avrami models to describe unperturbed fibrosarcoma Sa-37 tumor growth are used and compared with the Gompertz modified and Logistic models. Viable tumor cells (1×105) are inoculated to 28 BALB/c male mice.ResultsModified Gompertz, Logistic, Kolmogorov-Johnson-Mehl-Avrami classical and modified Kolmogorov-Johnson-Mehl-Avrami models fit well to the experimental data and agree with one another. A jump in the time behaviors of the instantaneous slopes of classical and modified Kolmogorov-Johnson-Mehl-Avrami models and high values of these instantaneous slopes at very early stages of tumor growth kinetics are observed.ConclusionsThe modified Kolmogorov-Johnson-Mehl-Avrami equation can be used to describe unperturbed fibrosarcoma Sa-37 tumor growth. It reveals that diffusion-controlled nucleation/growth and impingement mechanisms are involved in tumor growth kinetics. On the other hand, tumor development kinetics reveals dynamical structural transformations rather than a pure growth curve. Tumor fractal property prevails during entire TGK.

We study the plethysmographic signal using principal component analysis (PCA). By decomposing the signal using this method, we are able to regenerate it again, preserving in the process the functional relationships between the components. We have also found the relative contributions of each specific component to the signal. First return maps have been made for the series of residues of the decomposition. Further analysis using spectral methods has shown that the residues have a 1/f -like structure, which confirms the presence and conservation of this component in the signal and its relative independence with respect to the oscillating component (Hernández et al 2000 Rev. Cubana Inform. Medica 1 5). Our conclusions are that: (i) PCA is a good method to decompose the plethysmographic signal since it preserves the functional relationships in the variables, and this could be potentially useful in finding new clinically relevant indices; (ii) the 1/f process of the plethysmographic signal is preserved in the residues of the decomposed signal when PCA is used; (iii) clinically relevant parameters can potentially be obtained from photoplethysmographic signals when PCA is used.

Purpose of review To review and compare the constellations of causes and consequences of the two current pandemics, Covid-19 and climate change. Recent findings There has been a transient counterbalancing, in which the response to Covid-19 has briefly mitigated pollution and greenhouse gasses. This divergence belies multiple commonalities of cause and effect. Summary The convergence of these two pandemics is unprecedented. Although at first glance, they appear to be completely unrelated, they share striking commonalities. Both are caused by human behaviors, and some of those behaviors contribute to both pandemics at the same time. Both illustrate the fact that isolation is not an option; these are global issues that inescapably affect all persons and all nations. Both incur prodigious current and anticipated costs. Both have similar societal impacts, and disproportionately harm those with lesser resources, widening the gap between the ‘haves and the have-nots.’ One can only hope that the devastation caused by these unprecedented pandemics will lead to increased awareness of how human beings have helped to create them and how our responses can and will shape our future.

In the present work, we propose a discrete model to the characterization of heart rate variability (HRV) complexity in the aging process of healthy subjects. We apply the Shannon entropy on the basis of an alternative way to probability calculation and to define a new index taking into consideration the transition probability that is related to the physiological complexity of the system. Our results suggest that in the aging process the capability response decreases according to the reduction of the physiological complexity. In the oldest group, an alternative mechanism emerges to compensate for this lack of capability; however, this effect does not increase the physiological complexity. Concomitantly, we provide some physiological explanation for our results.

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