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Fish populations subject to heavy exploitation are expected to evolve over time smaller average body sizes. We introduce Stackelberg evolutionary game theory to show how fisheries management should be adjusted to mitigate the potential negative effects of such evolutionary changes. We present the game of a fisheries manager versus a fish population, where the former adjusts the harvesting rate and the net size to maximize profit, while the latter responds by evolving the size at maturation to maximize the fitness. We analyze three strategies: i) ecologically enlightened (leading to a Nash equilibrium in game-theoretic terms); ii) evolutionarily enlightened (leading to a Stackelberg equilibrium) and iii) domestication (leading to team optimum) and the corresponding outcomes for both the fisheries manager and the fish. Domestication results in the largest size for the fish and the highest profit for the manager. With the Nash approach the manager tends to adopt a high harvesting rate and a small net size that eventually leads to smaller fish. With the Stackelberg approach the manager selects a bigger net size and scales back the harvesting rate, which lead to a bigger fish size and a higher profit. Overall, our results encourage managers to take the fish evolutionary dynamics into account. Moreover, we advocate for the use of Stackelberg evolutionary game theory as a tool for providing insights into the eco-evolutionary consequences of exploiting evolving resources.

The increasing number of patients affected by chronic kidney disease makes it necessary to rely on living donors. However, a patient often cannot exploit her potential donor, due to blood or tissue incompatibility. Therefore, crossover transplantation programs have been developed in several countriesin order to increase the number of people receiving a kidney from a living donor. After reviewing the essential medical facts needed for the subsequent results, we quickly introduce two known algorithms for crossover transplantation. Next, we consider a dataset provided by the Nord Italia Transplant program, and we apply the above algorithms in order to highlight the benefits of these efficient procedures.

Prostate-specific antigen (PSA) is the most commonly used serum marker for prostate cancer. It plays a role in cancer detection, treatment monitoring, and more recently, in guiding adaptive therapy protocols, where treatment is alternated based on PSA levels. However, the relationship between PSA levels and tumor volume remains poorly understood. Empirical evidence suggests that different cancer cell types produce varying amounts of PSA. Despite this, current mathematical cancer models often assume either that all cell types contribute equally to PSA levels or that only certain subpopulations produce PSA at fixed rates. In this study, we compare Zhang et al.’s classical adaptive therapy protocol with the standard of care, which involves continuous maximum tolerable dose treatment, under different assumptions regarding PSA production. Specifically, we explore the possibility that testosterone-dependent, testosterone-producing, and testosterone-independent cells contribute to PSA production to varying degrees. We use the time to competitive release as a proxy for the time to disease progression. Our findings indicate that adaptive therapy consistently results in a longer time to competitive release compared to the standard of care, regardless of the assumptions about PSA production. However, when testosterone-independent cells are the sole PSA producers, Zhang et al.’s adaptive therapy protocol becomes inapplicable, as PSA levels never fall to half of their initial value, preventing therapy discontinuation. Additionally, we observe that the number and duration of treatment cycles in adaptive therapy are highly sensitive to assumptions about how much each cell type contributes to PSA production. Overall, our results emphasize the need for a deeper understanding of patient-specific PSA dynamics, which could enhance the effectiveness of adaptive therapy in prostate cancer treatment.

Brown hares have undergone a substantial population decline in Europe during recent decades, caused by, among other factors, the occurrence of European Brown Hare Syndrome (EBHS). To improve our knowledge regarding EBHS epidemiology, we developed a mathematical model that takes into consideration both brown hare biology and the infection dynamics of the EBHS virus (EBHSV). The model consists of eight ordinary differential equations simulating the spread of the virus in a closed hare population. Simulations showed that EBHSV’s transmission has complex dynamics, which are strongly affected by the hare density. In particular, a density threshold of 7 individuals/km2 was identified, determining two opposite epidemiological patterns: the extinction of the EBHSV below the threshold and its endemic stability when the hare population density is above the threshold, with a seroprevalence proportional to the population density. The model was validated using serological data collected in different areas in the province of Brescia (Northern Italy). The results suggested that the maintenance of the endemic circulating viral level through density control mechanisms is the best strategy for reducing EBHS’s impact.

OBJECTIVES: to describe the first wave of the COVID-19 pandemic with a focus on undetected cases and to evaluate different post-lockdown scenarios. DESIGN: the study introduces a SEIR compartmental model, taking into account the region-specific fraction of undetected cases, the effects of mobility restrictions, and the personal protective measures adopted, such as wearing a mask and washing hands frequently. SETTING AND PARTICIPANTS: the model is experimentally validated with data of all the Italian regions, some European countries, and the US. MAIN OUTCOME MEASURES: the accuracy of the model results is measured through the mean absolute percentage error (MAPE) and Lewis criteria; fitting parameters are in good agreement with previous literature. RESULTS: the epidemic curves for different countries and the amount of undetected and asymptomatic cases are estimated, which are likely to represent the main source of infections in the near future. The model is applied to the Hubei case study, which is the first place to relax mobility restrictions. Results show different possible scenarios. Mobility and the adoption of personal protective measures greatly influence the dynamics of the infection, determining either a huge and rapid secondary epidemic peak or a more delayed and manageable one. CONCLUSIONS: mathematical models can provide useful insights for healthcare decision makers to determine the best strategy in case of future outbreaks.

Prostate-specific antigen (PSA) is the most common serum marker for prostate cancer. It is used to detect prostate cancer, to assess responses to treatment and recently even to determine when to switch treatment on and off in adaptive therapy protocols. However, the correlation between PSA and tumor volume is poorly understood. Moreover, even though there is empirical evidence that some cancer cell types produce more PSA than others, recent mathematical cancer models assume that all cell types contribute equally to the PSA level. Here, we compare time to competitive release of the PSA-based adaptive therapy protocol to that of the standard of care with continuous maximum tolerable dose under different assumptions on PSA production. In particular, we assume that androgen dependent, androgen producing, and androgen independent may contribute to the PSA production to different extents. Our results show that, regardless the assumption on how much each type contributes to PSA, adaptive therapy is always at least as good as standard of care in the sense that it prolongs the time of competitive release when resistant androgen independent cells outcompete the other types. The time to competitive release under adaptive therapy and standard of care coincides if the PSA dynamics are influenced only by the resistant cells. Furthermore, we observe that in the adaptive therapy protocol, the number of treatment cycles and their length strongly depend on the assumptions about the PSA contribution of the three types. Thus, our results suggest that investigating the patient-specific PSA dynamics is crucial to designing adaptive therapy protocols. Competing Interest Statement The authors have declared no competing interest. Footnotes * Textual changes

In this paper, a large dataset of 590 Non-Small Cell Lung Patients treated with either chemotherapy or immunotherapy was used to determine whether a game-theoretic model including both evolution of therapy resistance and cost of resistance provides a better fit than classical mathematical models of population growth (exponential, logistic, classic Bertalanffy, general Bertalanffy, Gompertz, general Gompertz). This is the first time a large clinical patient cohort (as opposed to only in-vitro data) has been used to apply a game-theoretic cancer model. The game-theoretic model provides a better fit to the tumor dynamics of the 590 Non-Small Cell Lung Cancer patients than any of the non-evolutionary population growth models. This is not simply due to having more parameters in the game-theoretic model. The game-theoretic model is able to fit accurately patients whose tumor burden exhibit a U-shaped trajectory over time. We then demonstrate how this game-theoretic model provides predictions of tumor growth based on just a few initial measurements. Assuming that treatment-specific parameters define the treatment impact completely, we then explore alternative treatment protocols and their impact on the tumor growth. As such, the model can be used to suggest patient-specific optimal treatment regimens with the goal of minimizing final tumor burden. Therapeutic protocols based on game-theoretic modeling can predict tumor growth, and improve patient outcome. The model invites evolutionary therapies that anticipate and steer the evolution of therapy resistance. Competing Interest Statement JNK declares consulting services for Owkin, France and Panakeia, UK. No other potential conflicts of interest are reported by any of the authors. Footnotes * - small adjustments to title - small adjustments to text - few relevant references added * https://github.com/yo9299/NSCLC

Growth Differentiation Factor 15 (GDF15) is a mitochondrial-stress-responsive molecule whose expression strongly increases with aging and age-related diseases. However, its role in neurodegenerative diseases, including Alzheimer's disease (AD), is still debated. We have characterized the expression of GDF15 in brain samples from AD patients and non-demented subjects (controls) of different ages. Although no difference in CSF levels of GDF15 was found between AD patients and controls, GDF15 was expressed in different brain areas and seems to be predominantly localized in neurons. The ratio between its mature and precursor form was higher in the frontal cortex of AD patients compared to age-matched controls (  < 0.05). Moreover, this ratio was even higher for centenarians (  < 0.01), indicating that aging also affects GDF15 expression and maturation. A lower expression of OXPHOS complexes I, III, and V in AD patients compared to controls was also noticed, and a positive correlation between and mRNA levels was observed. Finally, when GDF15 was silenced in dermal fibroblasts, a decrease in OXPHOS complexes transcript levels and an increase in levels were observed. Although GDF15 seems not to be a reliable CSF marker for AD, it is highly expressed in aging and AD brains, likely as a part of stress response aimed at counteracting mitochondrial dysfunction and neuroinflammation.

Perilipin 2 (PLIN2) is a lipid droplet (LD)‐coating protein playing important roles in lipid homeostasis and suppression of lipotoxicity in different tissues and cell types. Recently, a role for PLIN2 in supporting mitochondrial function has emerged. PLIN2 dysregulation is involved in many metabolic disorders and age‐related diseases. However, the exact consequences of PLIN2 dysregulation are not yet completely understood. In this study, we knocked down (KD) PLIN2 in primary human dermal fibroblasts (hDFs) from young (mean age 29 years) and old (mean age 71 years) healthy donors. We have found that PLIN2 KD caused a decline of mitochondrial function only in hDFs from young donors, while mitochondria of hDFs from old donors (that are already partially impaired) did not significantly worsen upon PLIN2 KD. This mitochondrial impairment is associated with the increased expression of the stress‐related mitokine growth differentiation factor 15 (GDF15) and the induction of cell senescence. Interestingly, the simultaneous KD of PLIN2 and GDF15 abrogated the induction of cell senescence, suggesting that the increase in GDF15 is the mediator of this phenomenon. Moreover, GDF15 KD caused a profound alteration of gene expression, as observed by RNA‐Seq analysis. After a more stringent analysis, this alteration remained statistically significant only in hDFs from young subjects, further supporting the idea that cells from old and young donors react differently when undergoing manipulation of either PLIN2 or GDF15 genes, with the latter being likely a downstream mediator of the former. Perilipin 2 (PLIN2) plays a role in supporting a normal mitochondrial functionality. We showed that PLIN2 knockdown (KD) in human dermal fibroblasts caused a decline of mitochondrial function, an increased expression of the stress‐related mitokine GDF15 and the induction of cell senescence. Interestingly, the GDF15 KD abrogated the induction of PLIN2 KD‐associated cell senescence, suggesting that GDF15 could be the mediator of this phenomenon.

Epidemiological studies have demonstrated a positive relationship between dietary fat intake and the onset of several metabolic diseases. This association is particularly evident in a diet rich in saturated fatty acids, typical of animal foods, such as dairy products. However, these foods are the main source of fatty acids with a proven nutraceutical effect, such as the ω-3 fatty acid α-linolenic acid (ALA) and the conjugated linoleic acid (CLA), which have demonstrated important roles in the prevention of various diseases. In the present study, the effect of a supplementation with cheese enriched with ω-3 fatty acids and CLA on the metabolism and lipid profiles of C57bl/6 mice was evaluated. In particular, the analyses were conducted on different tissues, such as liver, muscle, adipose tissue and brain, known for their susceptibility to the effects of dietary fats. Supplementing cheese enriched in CLA and ω-3 fats reduced the level of saturated fat and increased the content of CLA and ALA in all tissues considered, except for the brain. Furthermore, the consumption of this cheese resulted in a tissue-specific response in the expression levels of genes involved in lipid and mitochondrial metabolism. As regards genes involved in the inflammatory response, the consumption of enriched cheese resulted in a reduction in the expression of inflammatory genes in all tissues analyzed. Considering the effects that chronic inflammation associated with a high-calorie and high-fat diet (meta-inflammation) or aging (inflammaging) has on the onset of chronic degenerative diseases, these data could be of great interest as they indicate the feasibility of modulating inflammation (thus avoiding/delaying these pathologies) with a nutritional and non-pharmacological intervention.