Unraveling host-pathogen dynamics in a murine Model of septic peritonitis induced by vancomycin-resistant Enterococcus faecium.

Vancomycin-resistant Enterococcus faecium (E. faecium) infection is associated with higher mortality rates. Previous studies have emphasized the importance of innate immune cells and signalling pathways in clearing E. faecium, but a comprehensive analysis of host-pathogen interactions is lacking. Here, we investigated the interplay of host and E. faecium in a murine model of septic peritonitis. Following injection with a sublethal dose, we observed significantly increased murine sepsis score and histological score, decreased weight and bacterial burden, neutrophils and macrophages infiltration, and comprehensive activation of cytokine-mediated signalling pathway. In mice receiving a lethal dose, hypothermia significantly improved survival, reduced bacterial burden, cytokines, and CD86 expression of MHC-II+ recruited macrophages compared to the normothermia group. A mathematical model constructed by observational data from 80 animals, recapitulated the host-pathogen interplay, and further verified the benefits of hypothermia. These findings indicate that E. faecium triggers a severe activation of cytokine-mediated signalling pathway, and hypothermia can improve outcomes by reducing bacterial burden and inflammation.

Comparative analysis and evolution of civilian versus combatant mortality ratios in Israel-Gaza conflicts, 2008-2023.

Background: There is a need for statistical methodologies that scrutinize civilian casualties in conflicts, evaluating the degree to which the conduct of war affects civilians and breaches the laws of war. Employing an epidemiological method, this study introduced, developed, and applied a novel approach for investigating mortality of civilians versus combatants in conflicts. Methods: A deterministic mathematical model, structured by age and sex, was developed to describe the process of conflict-related deaths among both combatants and civilians. The model was calibrated using demographic and conflict-related data from different Israel-Gaza conflicts. To quantify the extent of the impact on civilians and determine whether they are the primary focus of a conflict, a statistical metric, the index of killing civilians, along with associated criteria, was devised. Results: The model-estimated proportion of deaths in Gaza categorized as combatants was 62.1% (95% uncertainty interval (UI): 57.6-66.2%), 51.1% (95% UI: 47.1-54.9%), and 12.7% (95% UI: 9.7-15.4%) in the 2008-2009, 2014, and 2023 Israel-Gaza conflicts, respectively. The index of killing civilians was 0.61 (95% UI: 0.51-0.74), 0.96 (95% UI: 0.82-1.12), and 7.01 (95% UI: 5.50-9.29) in the 2008-2009, 2014, and 2023 conflicts, respectively. These index values indicate strong evidence for civilians being an object of war in the 2008-2009 and 2014 conflicts, but combatants were still identified as the primary focus of the conflict. In the 2023 conflict, there is robust evidence for civilians being an object of war, with civilians identified as the primary focus of the conflict. Conclusion: Findings imply a progressive shift in Israel's rules of engagement over time, with a trend towards higher acceptance of casualties among civilians. The 2023 conflict stands apart from preceding Israel-Gaza conflicts, with civilians identified as the primary focus of the conflict.

Fluid shear stress-induced changes in megalin trafficking enhance endocytic capacity in proximal tubule cells.

Proximal tubule (PT) cells maintain a high-capacity apical endocytic pathway to recover essentially all proteins that escape the glomerular filtration barrier. The multi ligand receptors megalin and cubilin play pivotal roles in the endocytic uptake of normally filtered proteins in PT cells but also contribute to the uptake of nephrotoxic drugs, including aminoglycosides. We previously demonstrated that opossum kidney (OK) cells cultured under continuous fluid shear stress (FSS) are superior to cells cultured under static conditions in recapitulating essential functional properties of PT cells in vivo. To identify drivers of the high-capacity, efficient endocytic pathway in the PT, we compared FSS-cultured OK cells with less endocytically active static-cultured OK cells. Megalin and cubilin expression are increased, and endocytic uptake of albumin in FSS-cultured cells is > 5-fold higher compared with cells cultured under static conditions. To understand how differences in receptor expression, distribution, and trafficking rates contribute to increased uptake, we used biochemical, morphological, and mathematical modeling approaches to compare megalin traffic in FSS- versus static-cultured OK cells. Our model predicts that culturing cells under FSS increases the rates of all steps in megalin trafficking. Importantly, the model explains why, despite seemingly counterintuitive observations (a reduced fraction of megalin at the cell surface, higher colocalization with lysosomes, and a shorter half-life of surface-tagged megalin in FSS-cultured cells), uptake of albumin is dramatically increased compared with static-grown cells. We also show that FSS-cultured OK cells more accurately exhibit the mechanisms that mediate uptake of nephrotoxic drugs in vivo compared with static-grown cells. This culture model thus provides a useful platform to understand drug uptake mechanisms, with implications for developing interventions in nephrotoxic injury prevention.

Mathematical analysis of FLASH effect models based on theoretical hypotheses.

OBJECTIVE: Clinical applications of FLASH radiotherapy require formulas to describe how the FLASH radiation features and other related factors determine the FLASH effect. Mathematical analysis of the models can connect the theoretical hypotheses with the radiobiological effect, which provides the foundation for establishing clinical application models. Moreover, experimental and clinical data can be used to explore the key factors through mathematical analysis. Approach: We abstract the complex models of the oxygen depletion hypothesis and radical recombination-antioxidants hypothesis into concise mathematical equations. The equations are solved to analyze how the radiation features and other factors influence the FLASH effect. Then we propose methodologies for determining the parameters in the models and utilizing the models to predict the FLASH effect. Main results: The formulas linking the physical, chemical and biological factors to the FLASH effect are obtained through mathematical derivation of the equation. The analysis indicates that the initial oxygen concentration, radiolytic oxygen consumption and oxygen recovery are key factors for the oxygen depletion hypothesis and that the level of antioxidants is the key factor for the radical recombination-antioxidants hypothesis. According to the model derivations and analysis, the methodologies for determining parameters and predicting the FLASH effect are proposed: the criteria for data filtration; the strategy of hybrid FLASH and conventional dose rate (CONV) irradiation to ensure the acquisition of effective experimental data across a wide dose range; pipelines of fitting parameters and predicting the FLASH effect. Significance: This study establishes the quantitative relationship between the FLASH effect and key factors. The derived formulas can be used to calculate the FLASH effect in future clinical FLASH radiotherapy. The proposed methodologies guide to obtain sufficient high-quality datasets and utilize them to predict FLASH effect. Furthermore, this study indicates the key factors of FLASH effect and offers clues to further explore the FLASH mechanism.

Computational modeling of heart failure in microgravity transitions.

The space tourism industry is growing due to advances in rocket technology. Privatised space travel exposes non-professional astronauts with health profiles comprising underlying conditions to microgravity. Prior research has typically focused on the effects of microgravity on human physiology in healthy astronauts, and little is known how the effects of microgravity may play out in the pathophysiology of underlying medical conditions, such as heart failure. This study used an established, controlled lumped mathematical model of the cardiopulmonary system to simulate the effects of entry into microgravity in the setting of heart failure with both, reduced and preserved ejection fraction. We find that exposure to microgravity eventuates an increased cardiac output, and in patients with heart failure there is an unwanted increase in left atrial pressure, indicating an elevated risk for development of pulmonary oedema. This model gives insight into the risks of space flight for people with heart failure, and the impact this may have on mission success in space tourism.

Modeling hepatitis A epidemiological profiles and estimating the pediatric vaccination threshold in the Russian Federation.

Background: To combat the hesitancy towards implementing a hepatitis A universal mass vaccination (UMV) strategy and to provide healthcare authorities with a comprehensive analysis of the potential outcomes and benefits of the implementation of such a vaccination program, we projected HAV seroprevalence and incidence rates in the total population of the Russian Federation and estimated the pediatric vaccination threshold required to achieve an incidence level of less than 1 case per 100,000 using a new mathematical model. Methods: A dynamic age-structured SEIRV (susceptible-exposed-infectious-recovered-vaccinated) compartmental model was developed and calibrated using demographic, seroprevalence, vaccination, and epidemiological data from different regions of the Russian Federation. This model was used to project various epidemiological measures. Results: The projected national average age at the midpoint of population immunity increases from 40 years old in 2020 to 50 years old in 2036 and is shifted even further to the age of 70 years in some regions of the country. An increase of varying magnitude in the incidence of symptomatic HAV infections is predicted for all study regions and for the Russian Federation as a whole between 2028 and 2032, if the HAV vaccination coverage level remains at the level of 2022. The national average vaccination coverage level required to achieve a symptomatic HAV incidence rate below 1 case per 100,000 by 2032 was calculated to be 69.8% if children aged 1-6 years are vaccinated following the implementation of a UMV program or 34.8% if immunization is expanded to children aged 1-17 years. Conclusion: The developed model provides insights into a further decline of herd immunity to HAV against the background of ongoing viral transmission. The current favorable situation regarding hepatitis A morbidity is projected to be replaced by an increase in incidence rates if vaccination coverage remains at the current levels. The obtained results support the introduction of a hepatitis A UMV strategy in the Russian Federation.

Learning from the COVID-19 pandemic: A systematic review of mathematical vaccine prioritization models.

As the world becomes ever more connected, the chance of pandemics increases as well. The recent COVID-19 pandemic and the concurrent global mass vaccine roll-out provides an ideal setting to learn from and refine our understanding of infectious disease models for better future preparedness. In this review, we systematically analyze and categorize mathematical models that have been developed to design optimal vaccine prioritization strategies of an initially limited vaccine. As older individuals are disproportionately affected by COVID-19, the focus is on models that take age explicitly into account. The lower mobility and activity level of older individuals gives rise to non-trivial trade-offs. Secondary research questions concern the optimal time interval between vaccine doses and spatial vaccine distribution. This review showcases the effect of various modeling assumptions on model outcomes. A solid understanding of these relationships yields better infectious disease models and thus public health decisions during the next pandemic.

An intelligent spraying system for weeds in wheat fields based on a dynamic model of droplets impacting wheat leaves.

Introduction: Targeted herbicide application refers to precise application of herbicides in weed-infested areas according to the location and density of farmland weeds. At present, targeted herbicide application in wheat fields generally faces problems including the low herbicide adhesion rate, leading to omission and excessive loss of herbicides. Methods: To solve these problems, changes in the impact force of herbicide and the weed leaves in the operation process of a spraying system were studied from the interaction between weeds and herbicides applied. A dynamic model of weed leaves was established. On this basis, the research indicated that the herbicide adhesion rate is highest under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s when the spray height is 300 mm. To study the dynamic deformation of weed leaves and the distribution of liquid herbicides in the external flow field under weed-herbicide interaction, a dynamic simulation model of herbicide application was built using the finite element method. Results and Discussion: The results show that when the spray height is 300 mm, the maximum weed leaf deformation index (LDI) is 0.43 and the velocity in the external flow field is 0 m/s under spraying pressure of 0.4 MPa and flow rate of 0.011 kg/s. This finding indicates that the herbicide is not splashed elsewhere and the turbulence intensity in the weed area is 2%, implying steady flow of the herbicide, most of which can be retained on weed leaves. Field test results of application quality of the herbicide show that the maximum LDI is 0.41 and the coverage of the herbicide in the sheltered area below the leaves is 19.02% when the spraying pressure is 0.4 MPa, flow rate is 0.011 kg/s, and spray height is 300 mm. This solves the problem of a low rate of utilization of herbicides because the herbicide passes through weed plants, and achieves the precision herbicide application in wheat fields.

Multiple endemic equilibria in an environmentally-transmitted disease with three disease stages.

We construct, analyze and interpret a mathematical model for an environmental transmitted disease characterized for the existence of three disease stages: acute, severe and asymptomatic. Besides, we consider that severe and asymptomatic cases may present relapse between them. Transmission dynamics driven by the contact rates only occurs when a parameter R∗>1, as normally occur in directly-transmitted or vector-transmitted diseases, but it will not adequately correspond to a basic reproductive number as it depends on environmental parameters. In this case, the forward transcritical bifurcation that exists for R∗<1, becomes a backward bifurcation, producing multiple steady-states, a hysteresis effect and dependence on initial conditions. A threshold parameter for an epidemic outbreak, independent of R∗ is only the ratio of the external contamination inflow shedding rate to the environmental clearance rate. R∗ describes the strength of the transmission to infectious classes other than the I-(acute) type infections. The epidemic outbreak conditions and the structure of R∗ appearing in this model are both responsible for the existence of endemic states.

Optimized microwave-assisted azadirachtin extraction using response surface methodology.

The neem tree (Azadirachta indica A. Juss) is grown mainly for shade, fuel, and numerous non-timber forest products using its leaves, fruit, and bark. It produces an essential oil that is used as a source for obtaining bioinsecticides, with a broad spectrum of action in agricultural production. Its bioinsecticidal activity is due to the presence of triterpenes, such as azadirachtin, a product in continued growth of the global biopesticide market. Optimal conditions for neem oil extraction using response surface methodology (RSM) and microwave-assisted extraction (MAE) methods have been defined. However, the extraction conditions for these methods tend to consume high volumes of organic solvent and long extraction times. The aim of the present study is to determine the optimal conditions for the extraction of azadirachtin from neem seeds in a hydroalcoholic medium using MAE and RSM with a Box-Behnken design (BBD). A BBD was applied to evaluate the effects of the factors, magnetron voltage (X1), extraction time (X2), and pH of the extraction medium (X3), on the yield of the azadirachtin extraction process. The effect of each variable on the extraction yield was studied independently, considering the pure coefficients (linear and quadratic) on the three levels that were studied in the experiments. Moreover, the study experiments were conducted in triplicate, data were presented as mean and standard deviation, homogeneity of variances was estimated using Levene's test, and a two-way ANOVA with Tukey's post hoc analysis was performed to identify the experimental conditions that allowed us to find the highest extraction yield and to analyze whether the response surface model adequately described our data. The most significant effects of the model correspond to quadratic and interaction effects (p < 0.0001); the quadratic terms voltage (X1), extraction time (X2), and pH (X3); and the interaction effects between voltage-pH (X1*X3) and time-pH (X2*X3), which had a significant influence on the model. Moreover, a canonical analysis was performed. The optimal conditions were as follows: 69.22 V, 6.89 min, and a pH value of 4.35, coinciding with the zones shown in the contour plots. Furthermore, the response obtained at the optimal conditions was 37.5 µg of azadirachtin per gram of pretreated seed.

SIR+ models: accounting for interaction-dependent disease susceptibility in the planning of public health interventions.

Avoiding physical contact is regarded as one of the safest and most advisable strategies to follow to reduce pathogen spread. The flip side of this approach is that a lack of social interactions may negatively affect other dimensions of health, like induction of immunosuppressive anxiety and depression or preventing interactions of importance with a diversity of microbes, which may be necessary to train our immune system or to maintain its normal levels of activity. These may in turn negatively affect a population's susceptibility to infection and the incidence of severe disease. We suggest that future pandemic modelling may benefit from relying on 'SIR+ models': epidemiological models extended to account for the benefits of social interactions that affect immune resilience. We develop an SIR+ model and discuss which specific interventions may be more effective in balancing the trade-off between minimizing pathogen spread and maximizing other interaction-dependent health benefits. Our SIR+ model reflects the idea that health is not just the mere absence of disease, but rather a state of physical, mental and social well-being that can also be dependent on the same social connections that allow pathogen spread, and the modelling of public health interventions for future pandemics should account for this multidimensionality.

The role of anaplerotic metabolism of glucose and glutamine in insulin secretion: A model approach.

We propose a detailed computational beta cell model that emphasizes the role of anaplerotic metabolism under glucose and glucose-glutamine stimulation. This model goes beyond the traditional focus on mitochondrial oxidative phosphorylation and ATP-sensitive K+ channels, highlighting the predominant generation of ATP from phosphoenolpyruvate in the vicinity of KATP channels. It also underlines the modulatory role of H2O2 as a signaling molecule in the first phase of glucose-stimulated insulin secretion. In the second phase, the model emphasizes the critical role of anaplerotic pathways, activated by glucose stimulation via pyruvate carboxylase and by glutamine via glutamate dehydrogenase. It particularly focuses on the production of NADPH and glutamate as key enhancers of insulin secretion. The predictions of the model are consistent with empirical data, highlighting the complex interplay of metabolic pathways and emphasizing the primary role of glucose and the facilitating role of glutamine in insulin secretion. By delineating these crucial metabolic pathways, the model provides valuable insights into potential therapeutic targets for diabetes.

Modelling the role of enzymatic pathways in the metabolism of docosahexaenoic acid by monocytes and its association with osteoarthritic pain.

Chronic pain is a major cause of disability and suffering in osteoarthritis (OA) patients. Endogenous specialised pro-resolving molecules (SPMs) curtail pro-inflammatory responses. One of the SPM intermediate oxylipins, 17-hydroxydocasahexaenoic acid (17-HDHA, a metabolite of docosahexaenoic acid (DHA)), is significantly associated with OA pain. The aim of this multidisciplinary work is to develop a mathematical model to describe the contributions of enzymatic pathways (and the genes that encode them) to the metabolism of DHA by monocytes and to the levels of the down-stream metabolites, 17-HDHA and 14-hydroxydocasahexaenoic acid (14-HDHA), motivated by novel clinical data from a study involving 30 participants with OA. The data include measurements of oxylipin levels, mRNA levels, measures of OA severity and self-reported pain scores. We propose a system of ordinary differential equations to characterise associations between the different datasets, in order to determine the homeostatic concentrations of DHA, 17-HDHA and 14-HDHA, dependent upon the gene expression of the associated metabolic enzymes. Using parameter-fitting methods, local sensitivity and uncertainty analysis, the model is shown to fit well qualitatively to experimental data. The model suggests that up-regulation of some ALOX genes may lead to the down-regulation of 17-HDHA and that dosing with 17-HDHA increases the production of resolvins, which helps to down-regulate the inflammatory response. More generally, we explore the challenges and limitations of modelling real data, in particular individual variability, and also discuss the value of gathering additional experimental data motivated by the modelling insights.

A Model for the Coexistence of Competing Mechanisms for Ca 2 + Oscillations in T-lymphocytes.

Ca 2 + is a ubiquitous signaling mechanism across different cell types. In T-cells, it is associated with cytokine production and immune function. Benson et al. have shown the coexistence of competing Ca 2 + oscillations during antigen stimulation of T-cell receptors, depending on the presence of extracellular Ca 2 + influx through the Ca 2 + release-activated Ca 2 + channel (Benson in J Biol Chem 29:105310, 2023). In this paper, we construct a mathematical model consisting of five ordinary differential equations and analyze the relationship between the competing oscillatory mechanisms.. We perform bifurcation analysis on two versions of our model, corresponding to the two oscillatory types, to find the defining characteristics of these two families.

Dynamic analysis of a drug resistance evolution model with nonlinear immune response.

Recent studies have utilized evolutionary mechanisms to impede the emergence of drug-resistant populations. In this paper, we develop a mathematical model that integrates hormonal treatment, immunotherapy, and the interactions among three cell types: drug-sensitive cancer cells, drug-resistant cancer cells and immune effector cells. Dynamical analysis is performed, examining the existence and stability of equilibria, thereby confirming the model's interpretability. Model parameters are calibrated using available prostate cancer data and literature. Through bifurcation analysis for drug sensitivity under different immune effector cells recruitment responses, we find that resistant cancer cells grow rapidly under weak recruitment response, maintain at a low level under strong recruitment response, and both may occur under moderate recruitment response. To quantify the competitiveness of sensitive and resistant cells, we introduce the comprehensive measures R1 and R2, respectively, which determine the outcome of competition. Additionally, we introduce the quantitative indicators CIE1 and CIE2 as comprehensive measures of the immune effects on sensitive and resistant cancer cells, respectively. These two indicators determine whether the corresponding cancer cells can maintain at a low level. Our work shows that the immune system is an important factor affecting the evolution of drug resistance and provides insights into how to enhance immune response to control resistance.

Static and dynamic analysis of cerebral blood flow in fifty-six large arterial vessel networks.

Objective.The cerebral vasculature is formed of an intricate network of blood vessels over many different length scales. Changes in their structure and connection are implicated in multiple cerebrovascular and neurological disorders. In this study, we present a novel approach to the quantitative analysis of the cerebral macrovasculature using computational and mathematical tools in a large dataset.Approach.We analysed a publicly available vessel dataset from a cohort of 56 (32/24F/M) healthy subjects. This dataset includes digital reconstructions of human brain macrovasculatures. We then propose a new mathematical model to compute blood flow dynamics and pressure distributions within these 56-representative cerebral macrovasculatures and quantify the results across this cohort.Main results.Statistical analysis showed that the steady state level of cerebrovascular resistance (CVR) gradually increases with age in both men and women. These age-related changes in CVR are in good agreement with previously reported values. All subjects were found to have only small phase angles (<6°) between blood pressure and blood flow at the cardiac frequency.Significance.These results showed that the dynamic component of blood flow adds very little phase shift at the cardiac frequency, which implies that the cerebral macrocirculation can be regarded as close to steady state in its behaviour, at least in healthy populations, irrespective of age or sex. This implies that the phase shift observed in measurements of blood flow in cerebral vessels is caused by behaviour further down the vascular bed. This behaviour is important for future statistical models of the dynamic maintenance of oxygen and nutrient supply to the brain.

Mathematical model to assess the impact of contact rate and environment factor on transmission dynamics of rabies in humans and dogs.

This paper presents a mathematical model to understand how rabies spreads among humans, free-range, and domestic dogs. By analyzing the model, we discovered that there are equilibrium points representing both disease-free and endemic states. We calculated the basic reproduction number, R 0 using the next generation matrix method. When R 0 < 1 , the disease-free equilibrium is globally stable, whereas when R 0 ≥ 1 , the endemic equilibrium is globally stable. To identify the most influential parameters in disease transmission, we used the normalized forward sensitivity index. The simulations revealed that the contact rates between the infectious agent and humans, free-range dogs, and domestic dogs, have the most significant impact on rabies transmission. The study also examines how periodic changes in transmission rates affect the disease dynamics, emphasizing the importance of transmission frequency and amplitude on the patterns observed in rabies spread. To reduce disease sensitivity, one should prioritize effective disease control measures that focus on keeping both free-range and domestic dogs indoors. This is a crucial factor in preventing the spread of disease and should be implemented as a primary disease control measure.

Establishment and Application of a Radiation Dose Rate Model for Nuclear Medicine Examinees.

Introduction: Traditional methods for determining radiation dose in nuclear medicine include the Monte Carlo method, the discrete ordinate method, and the point kernel integration method. This study presents a new mathematical model for predicting the radiation dose rate in the vicinity of nuclear medicine patients. Methods: A new algorithm was created by combining the physical model of "cylinder superposition" of the human body with integral analysis to assess the radiation dose rate in the vicinity of nuclear medicine patients. Results: The model accurately predicted radiation dose rates within distances of 0.1-3.0 m, with a deviation of less than 11% compared to observed rates. The model demonstrated greater accuracy at shorter distances from the radiation source, with a deviation of only 1.55% from observed values at 0.1 m. Discussion: The model proposed in this study effectively represents the spatial and temporal distribution of the radiation field around nuclear medicine patients and demonstrates good agreement with actual measurements. This model has the potential to serve as a radiation dose rate alert system in hospital environments.

A system dynamics modelling and analytical framework for imported dengue outbreak surveillance and risk mapping.

System Dynamics (SD) models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. Spain is an at-risk country of imported dengue outbreaks, but only qualitative assessments are available to guide public health action and control. We propose a modular SD model combining temperature-dependent vector population, transmission parameters, and epidemiological interactions to simulate outbreaks from imported cases accounting for heterogeneous local climate-related transmission patterns. Under our assumptions, 15 provinces sustain vector populations capable of generating outbreaks from imported cases, with heterogeneous risk profiles regarding seasonality, magnitude and risk window shifting from late Spring to early Autum. Results being relative to given vector-to-human populations allow flexibility when translating outcomes between geographic scales. The model and the framework are meant to serve public health by incorporating transmission dynamics and quantitative-qualitative input to the evidence-based decision-making chain. It is a flexible tool that can easily adapt to changing contexts, parametrizations and epidemiological settings thanks to the modular approach.

Inhibition of somatostatin enhances the long-term metabolic outcomes of sleeve gastrectomy in mice.

OBJECTIVE: Bariatric surgery is an effective treatment to obesity, leading to weight loss and improvement in glycemia, that is characterized by hypersecretion of gastrointestinal hormones. However, weight regain and relapse of hyperglycemia are not uncommon. We set to identify mechanisms that can enhance gastrointestinal hormonal secretion following surgery to sustain weight loss. METHODS: We investigated the effect of somatostatin (Sst) inhibition on the outcomes of bariatric surgery using a mouse model of sleeve gastrectomy (SG). RESULTS: Sst knockout (sst-ko) mice fed with a calorie-rich diet gained weight normally and had a mild favorable metabolic phenotype compared to heterozygous sibling controls, including elevated plasma levels of GLP-1. Mathematical modeling of the feedback inhibition between Sst and GLP-1 showed that Sst exerts its maximal effect on GLP-1 under conditions of high hormonal stimulation, such as following SG. Obese sst-ko mice that underwent SG had higher levels of GLP-1 compared with heterozygous SG-operated controls. The SG-sst-ko mice regained less weight than controls and maintained lower glycemia months after surgery. Obese wild-type mice that underwent SG and were treated daily with a Sst receptor inhibitor for two months had higher GLP-1 levels, regained less weight, and improved metabolic profile compared to saline-treated SG-operated controls, and compared to inhibitor or saline-treated sham-operated obese mice. CONCLUSIONS: Our results suggest that inhibition of Sst signaling enhances the long-term favorable metabolic outcomes of bariatric surgery.