The AIDA randomized clinical trial found no significant difference in clinical failure or survival between colistin monotherapy and colistin-meropenem combination therapy in carbapenem-resistant Gram-negative infections. The aim of this reverse translational study was to integrate all individual preclinical and clinical pharmacokinetic-pharmacodynamic (PKPD) data from the AIDA trial in a pharmacometric framework to explore whether individualized predictions of bacterial burden were associated with the trial outcomes. The compiled dataset included for each of the 207 patients was (i) information on the infecting Acinetobacter baumannii isolate (minimum inhibitory concentration, checkerboard assay data, and fitness in a murine model), (ii) colistin plasma concentrations and colistin and meropenem dosing history, and (iii) disease scores and demographics. The individual information was integrated into PKPD models, and the predicted change in bacterial count at 24 h for each patient, as well as patient characteristics, was correlated with clinical outcomes using logistic regression. The in vivo fitness was the most important factor for change in bacterial count. A model-predicted growth at 24 h of ≥2-log10 (164/207) correlated positively with clinical failure (adjusted odds ratio, aOR = 2.01). The aOR for one unit increase of other significant predictors were 1.24 for SOFA score, 1.19 for Charlson comorbidity index, and 1.01 for age. This study exemplifies how preclinical and clinical anti-infective PKPD data can be integrated through pharmacodynamic modeling and identify patient- and pathogen-specific factors related to clinical outcomes - an approach that may improve understanding of study outcomes.

Group defense in prey and hunting cooperation in predators are two important ecological phenomena and can occur concurrently. In this article, we consider cooperative hunting in generalist predators and group defense in prey under a mathematical framework to comprehend the enormous diversity the model could capture. To do so, we consider a modified Holling-Tanner model where we implement Holling type IV functional response to characterize grazing pattern of predators where prey species exhibit group defense. Additionally, we allow a modification in the attack rate of predators to quantify the hunting cooperation among them. The model admits three boundary equilibria and up to three coexistence equilibrium points. The geometry of the nontrivial prey and predator nullclines and thus the number of coexistence equilibria primarily depends on a specific threshold of the availability of alternative food for predators. We use linear stability analysis to determine the types of hyperbolic equilibrium points and characterize the non-hyperbolic equilibrium points through normal form and center manifold theory. Change in the model parameters leading to the occurrences of a series of local bifurcations from non-hyperbolic equilibrium points, namely, transcritical, saddle-node, Hopf, cusp and Bogdanov-Takens bifurcation; there are also occurrences of global bifurcations such as homoclinic bifurcation and saddle-node bifurcation of limit cycles. We observe two interesting closed \'bubble\' form induced by global bifurcations due to change in the strength of hunting cooperation and the availability of alternative food for predators. A three dimensional bifurcation diagram, concerning the original system parameters, captures how the alternation in model formulation induces gradual changes in the bifurcation scenarios. Our model highlights the stabilizing effects of group or gregarious behaviour in both prey and predator, hence supporting the predator-herbivore regulation hypothesis. Additionally, our model highlights the occurrence of \"saltatory equilibria\" in ecological systems and capture the dynamics observed for lion-herbivore interactions.

Ageing leads to changes in the functionality of the digestive tract but the effect of age on digestion and absorption of nutrients remains unclear. The objective of this study was to investigate in vitro the digestion of two high-protein dairy products similar to cream cheese (24 % w/w proteins, 20 % w/w lipids) with opposite casein to whey protein ratios, 80:20 (WP-20), and 20:80 (WP-80). The new static digestion model adapted to the general older adult population (≥65 y.) proposed by INFOGEST was used, as well as the standard version of the protocol. Kinetics of proteolysis and lipolysis were compared between both models for each product, in the gastric and intestinal phases of digestion. In both cream cheeses, the degree of protein hydrolysis (DH-P) was significantly lower for older adults than for young adults at the end of the gastric phase (-19 % for WP-20, and -44 % for WP-80), and at the end of the intestinal phase (-16 % for WP-20, and -20 % for WP-80). The degree of lipid hydrolysis (DH-L) was also significantly lower for older adults than for young adults at the end of the digestion for WP-20 (-30 %), but interestingly it was not the case for WP-80 (similar DH-L were measured). Free fatty acids were also released faster from WP-80 than from WP-20 in both digestion conditions: after 5 min of intestinal digestion DH-L was already ≈32 % for WP-80 against 14 % for WP-20. This was attributed to the opposite casein to whey protein ratios, leading to the formation of different gel structures resulting in different patterns of deconstruction in the gastrointestinal tract. This study highlights the fact that it is essential to carefully consider the composition, structure, and digestibility of foods to develop products adapted to the specific needs of the older adult population.

We introduce a model that can be used for the description of the distribution of species when there is scarcity of data, based on our previous work (Ballesteros et al. J Math Biol 85(4):31, 2022). We address challenges in modeling species that are seldom observed in nature, for example species included in The International Union for Conservation of Nature\'s Red List of Threatened Species (IUCN 2023). We introduce a general method and test it using a case study of a near threatened species of amphibians called Plectrohyla Guatemalensis (see IUCN 2023) in a region of the UNESCO natural reserve \"Tacaná Volcano\", in the border between Mexico and Guatemala. Since threatened species are difficult to find in nature, collected data can be extremely reduced. This produces a mathematical problem in the sense that the usual modeling in terms of Markov random fields representing individuals associated to locations in a grid generates artificial clusters around the observations, which are unreasonable. We propose a different approach in which our random variables describe yearly averages of expectation values of the number of individuals instead of individuals (and they take values on a compact interval). Our approach takes advantage of intuitive insights from environmental properties: in nature individuals are attracted or repulsed by specific features (Ballesteros et al. J Math Biol 85(4):31, 2022). Drawing inspiration from quantum mechanics, we incorporate quantum Hamiltonians into classical statistical mechanics (i.e. Gibbs measures or Markov random fields). The equilibrium between spreading and attractive/repulsive forces governs the behavior of the species, expressed through a global control problem involving an energy operator.

Recent advancements in stem cell biology and tissue engineering have revolutionized the field of neurodegeneration research by enabling the development of sophisticated in vitro human brain models. These models, including 2D monolayer cultures, 3D organoids, organ-on-chips, and bioengineered 3D tissue models, aim to recapitulate the cellular diversity, structural organization, and functional properties of the native human brain. This review highlights how these in vitro brain models have been used to investigate the effects of various pathogens, including viruses, bacteria, fungi, and parasites infection, particularly in the human brain cand their subsequent impacts on neurodegenerative diseases. Traditional studies have demonstrated the susceptibility of different 2D brain cell types to infection, elucidated the mechanisms underlying pathogen-induced neuroinflammation, and identified potential therapeutic targets. Therefore, current methodological improvement brought the technology of 3D models to overcome the challenges of 2D cells, such as the limited cellular diversity, incomplete microenvironment, and lack of morphological structures by highlighting the need for further technological advancements. This review underscored the significance of in vitro human brain cell from 2D monolayer to bioengineered 3D tissue model for elucidating the intricate dynamics for pathogen infection modeling. These in vitro human brain cell enabled researchers to unravel human specific mechanisms underlying various pathogen infections such as SARS-CoV-2 to alter blood-brain-barrier function and Toxoplasma gondii impacting neural cell morphology and its function. Ultimately, these in vitro human brain models hold promise as personalized platforms for development of drug compound, gene therapy, and vaccine. Overall, we discussed the recent progress in in vitro human brain models, their applications in studying pathogen infection-related neurodegeneration, and future directions.

Physiologically based pharmacokinetic (PBPK) models were utilized to investigate potential interactions between aflatoxin B1 (AFB1) and efavirenz (EFV), a non-nucleoside reverse transcriptase inhibitor drug and inducer of several CYP enzymes, including CYP3A4. PBPK simulations were conducted in a North European Caucasian and Black South African population, considering different dosing scenarios. The simulations predicted the impact of EFV on AFB1 metabolism via CYP3A4 and CYP1A2. In vitro experiments using human liver microsomes (HLM) were performed to verify the PBPK predictions for both single- and multiple-dose exposures to EFV. Results showed no significant difference in the formation of AFB1 metabolites when combined with EFV (0.15 µM) compared to AFB1 alone. However, exposure to 5 µM of EFV, mimicking chronic exposure, resulted in increased CYP3A4 activity, affecting metabolite formation. While co-incubation with EFV reduced the formation of certain AFB1 metabolites, other outcomes varied and could not be fully attributed to CYP3A4 induction. Overall, this study provides evidence that EFV, and potentially other CYP1A2/CYP3A4 perpetrators, can impact AFB1 metabolism, leading to altered exposure to toxic metabolites. The results emphasize the importance of considering drug interactions when assessing the risks associated with mycotoxin exposure in individuals undergoing HIV therapy in a European and African context.

Here, employing computer simulation tools, we present a study on the development of a bacterial biofilm from a single starter cell on a flat inert surface overlaid by an aqueous solution containing nutrients. In our simulations, surface colonization involves an initial stage of two-dimensional cell proliferation to eventually transition to three-dimensional growth leading to the formation of biofilm colonies with characteristic three-dimensional semi-ellipsoids shapes. Thus, we have introduced the influence of the nutrient concentration on bacterial growth, and calculated the cell growth rate as a function of nutrient uptake, which in turn depends on local nutrient concentration in the vicinity of each bacterial cell. Our results show that the combination of cell growth and nutrient uptake and diffusion leads to the formation of stratified colonies containing an inner core in which nutrients are depleted and cells cannot grow or divide, surrounded by an outer, shallow crust in which cells have access to nutrients from the bulk medium and continue growing. This phenomenon is more apparent at high uptake rates that enable fast nutrient depletion. Our simulations also predict that the shape and internal structure of the biofilm are largely conditioned by the balance between nutrient diffusion and uptake.

Understanding the role of mechanical force on tissue nutrient transport is essential, as sustained force may affect nutrient levels within the disc and initiate disc degeneration. This study aims to evaluate the time-dependent effects of different compressive force amplitudes as well as tensile force on glucose concentration and cell viability within the disc. Based on the mechano-electrochemical mixture theory, a multiphasic finite element model of the lumbar intervertebral disc was developed. The minimum glucose concentration and minimum cell density in both normal and degenerated discs were predicted for different compressive force amplitudes, tensile force, and corresponding creep time. Under high compressive force, the minimum glucose concentration exhibited an increasing and then decreasing trend with creep time in the normal disc, whereas that of the degenerated disc increased, then decreased, and finally increased again. At steady state, a higher compressive force was accompanied by a lower glucose concentration distribution. In the degenerated disc, the minimum cell density was negatively correlated with creep time, with a greater range of affected tissue under a higher compressive force. For tensile force, the minimum glucose concentration of the degenerated disc raised over time. This study highlighted the importance of creep time, force magnitude, and force type in affecting nutrient concentration and cell viability. Sustained weight-bearing activities could deteriorate the nutrient environment of the degenerated disc, while tensile force might have a nonnegligible role in effectively improving nutrient levels within the degenerated disc.

The liver is one of the most important organs in the human body. It performs many important functions, including being responsible for the metabolism of most drugs, which is often associated with its drug-induced damage. Currently, there are no ideal pharmacological models that would allow the evaluation of the effect of newly tested drugs on the liver in preclinical studies. Moreover, the influence of hepatic metabolism on the effectiveness of the tested drugs is rarely evaluated. Therefore, in this work we present an advanced model of the liver, which reflects most of the morphologically and metabolically important features of the liver in vivo, namely: three-dimensionality, cellular composition, presence of extracellular matrix, distribution of individual cell types in the structure of the liver model, high urea and albumin synthesis efficiency, high cytochrome p450 activity. In addition, the work, based on the example of commonly used anticancer drugs, shows how important it is to take into account hepatic metabolism in the effective assessment of their impact on the target organ, in this case cancer. In our research, we have shown that the most similar to liver in vivo are 3D cellular aggregates composed of three important liver cells, namely hepatocytes (HepG2), hepatic stellate cells (HSCs), and hepatic sinusoidal endothelial cells (HSECs). Moreover, we showed that the cells in 3D aggregate structure need time (cell-cell interactions) to improve proper liver characteristic. The triculture model additionally showed the greatest ability to metabolize selected anticancer drugs.

Dissection of the matter into its constituents leads us to the smallest particles that we know. These particles form a material structure that is determined by the electromagnetic field generated and carried by those particles. Changes in any of the two major constituents leads to changes in that material system, be it a living organism or a lifeless object. The latter statement carries the mystery of life that is born from a continuous and programmed series of system changes fuelled by an energy source with a yet unknown functioning mechanism. The present work is a theoretical approach towards the understanding and potential discovery of the aforementioned, not-yet-known cellular energetic mechanism. Understanding the energetic basis of intracellular biochemistry is equally important in human and animal therapeutics. Additionally, as all such discoveries offer novel solutions in various fields of the global industry, the final outcome of this theoretical work also brings about the idea of a new discovery in electronics industry.