Antibiotic resistance has become a global issue. The most significant risk is the acquisition of these mechanisms by pathogenic bacteria, which can have a severe clinical impact and pose a public health risk. This problem assumes that bacterial fitness is a constant phenomenon and should be approached from an evolutionary perspective to develop the most appropriate and effective strategies to contain the emergence of strains with pathogenic potential. Resistance mechanisms can be understood as adaptive processes to stressful conditions. This review examines the relevance of homeostatic regulatory mechanisms in antimicrobial resistance mechanisms. We focus on the interactions in the cellular physiology of pathogenic bacteria, particularly Gram-negative bacteria, and specifically Klebsiella pneumoniae. From a clinical research perspective, understanding these interactions is crucial for comprehensively understanding the phenomenon of resistance and developing more effective drugs and treatments to limit or attenuate bacterial sepsis, since the most conserved adjuvant phenomena in bacterial physiology has turned out to be more optimized and, therefore, more susceptible to alterations due to pharmacological action.

Climate change is affecting the life cycle of tight interacting species. Commonly, the seasonal population dynamics of species is analyzed through models with periodic rates; however, assuming periodicity in seasonal phenomena which depend on environmental drivers is very restrictive. In this work, we analyze seasonal commensalism between two species in which the per capita growth rate of each species is affected by a weak Allee effect and the demographic and ecological rates are assumed almost periodic. To do this, we construct and analyze an almost periodic model to describe commensalism using a wide family of functions that describe weak Allee effects and the benefits granted by the interaction. We prove that the model admits a unique almost periodic global attractor for a wide family of functions. Numerical simulations of the solutions of the model shown the result proved in this work. We show that if periodic rates are used when the phenomenon is really almost periodic, underestimation or overestimation of the population size of both species can occur, which can lead to design wrong strategies by the decision makers.

The basic reproduction number, denoted as R0, is a crucial parameter in infectious disease modeling and serves as a key element for designing control strategies. Calculating R0 can be challenging in certain situations due to the complexity of the model. This complexity often hinders the explicit computation of R0 and makes it difficult to