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This paper develops a second-order explicit predictor-corrector numerical approach for solving a mathematical model on the dynamic of cytokine expressions and human immune cell activation in response to the bacterium staphylococcus aureus (S. aureus). The proposed algorithm is at least zero-stable and second-order accurate. Mathematical modeling works that analyze the human body in response to some antigens have predicted concentrations of a broad range of cells and cytokines. This study deals with a coupled cellular-cytokine model which predicts cytokine expressions in response to gram-positive bacteria S. aureus. Tumor necrosis factor alpha, interleukin 6, interleukin 8 and interleukin 10 are included to assess the relationship between cytokine release from macrophages and the concentration of the S. aureus antigen. Ordinary differential equations are used to model cytokine levels while the cellular responses are modeled by partial differential equations. Interactions between both components provide a more robust and complete systems of immune activation. In the numerical simulations, a low concentration of S. aureus is used to measure cellular activation and cytokine expressions

Quantitative ultrasound (QUS) characterizes the composition of cells to distinguish diseased from healthy tissue. QUS can reflect the complexity of the tumor and detect early lymph node (LN) metastasis ex vivo. The objective in this study was to gather preliminary QUS and cytokine data from dogs undergoing radiation therapy and correlate QUS data with both LN metastasis and tumor response. Spontaneous solid tumors were evaluated with QUS before and up to one year after receiving RT. Additionally, regional LNs were evaluated with QUS in vivo, then excised and examined with histopathology to detect metastasis. Paired t-tests were used to compare QUS data of metastatic and non-metastatic LNs within patients. Furthermore, paired t-tests compared pre- versus post-RT QUS data. Serum was collected at each time point for cytokine profiles. Most statistical tests were underpowered to produce significant $p$ values, but interesting trends were observed. The lowest $p$ values for LN tests were found with the envelope statistics $K$ ($p = 0.142$) and $μ$ ($p = 0.181$), which correspond to cell structure and number of scatterers. For tumor response, the lowest $p$ values were found with $K$ ($p

Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising results in cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta--review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. During evaluation with real--world CRS clinical data, we emphasize the potential of our proposed method of producing interpretable results, in addition to being effective in identifying the onset of cytokine storm.

In this paper, we propose a delayed cytokine-enhanced viral infection model incorporating saturation incidence and immune response. We compute the basic reproduction numbers and introduce a convex cone to discuss the impact of non-negative initial data on solutions. By defining appropriate Lyapunov functionals and employing LaSalle's invariance principle, we investigate the stability of three equilibria: the disease-free equilibrium, the immunity-inactivated equilibrium, and the immunity-activated equilibrium. We establish conditions under which these equilibria are globally asymptotically stable. Numerical analyses not only corroborate the theoretical results but also reveal that intervention in virus infection can be achieved by extending the delay period.

Cytokines are small secreted proteins released by cells have a specific effect on the interactions and communications between cells. Cytokine is a general name; other names include lymphokine (cytokines made by lymphocytes), monokine (cytokines made by monocytes), chemokine (cytokines with chemotactic activities), and interleukin (cytokines made by one leukocyte and acting on other leukocytes). Cytokines may act on the cells that secrete them (autocrine action), on nearby cells (paracrine action), or in some instances on distant cells (endocrine action). There are both pro-inflammatory cytokines and anti-inflammatory cytokines. There is significant evidence showing that certain cytokines/chemokines are involved in not only the initiation but also the persistence of pathologic pain by directly activating nociceptive sensory neurons. Certain inflammatory cytokines are also involved in nerve-injury/inflammation-induced central sensitization, and are related to the development of contralateral hyperalgesia/allodynia. The discussion presented in this chapter describes several key pro-inflammatory cytokines/chemokines and anti-inflammatory cytokines, their relation with pathological pain in animals and human patients, and possible underlying mechanisms.

Patients infected with SARS-CoV-2 show a wide spectrum of clinical manifestations ranging from mild febrile illness and cough up to acute respiratory distress syndrome, multiple organ failure and death. Data from patients with severe clinical manifestations compared to patients with mild symptoms indicate that highly dysregulated exuberant inflammatory responses correlate with severity of disease and lethality. Significantly elevated cytokine levels, i.e. cytokine storm, seem to play a central role in severity and lethality in COVID-19. We have previously shown that excessive cytokine release induced by highly pathogenic avian H5N1 influenza A virus was reduced by application of proteasome inhibitors. In the present study we present experimental data of a central cellular pro-inflammatory signal pathways, NF-kappaB, in the context of published clinical data from COVID-19 patients and develop a hypothesis for a therapeutic approach aiming at the simultaneous inhibition of whole cascades of pro-inflammatory cytokines and chemokines via blocking the nuclear translocation of NF-kappaB by proteasome inhibitors. The simultaneous inhibition of multiple cytokines/chemokines using clinicall

Patterns in complex systems store hidden information of the system which is needed to be explored. We present a simple model of cytokine and T-cells interaction and studied the model within stochastic framework by constructing Master equation of the system and solving it. The solved probability distribution function of the model show classical Poisson pattern in the large population limit $M,Z\rightarrow large$ indicating the system has the tendency to attract a large number small-scale random processes of the cytokine population towards the basin of attraction of the system by segregating from nonrandom processes. Further, in the large $\langle Z\rangle$ limit, the pattern transform to classical Normal pattern, where, uncorrelated small-scale fluctuations are wiped out to form a regular but memoryless spatiotemporal aggregated pattern. The estimated noise using Fano factor shows clearly that the cytokine dynamics is noise induced process driving the system far away from equilibrium.

Background: Community-acquired pneumonia (CAP) is an acute disease condition with a high risk of rapid deteriorations. We analysed the influence of genetics on cytokine regulation to obtain a better understanding of patient's heterogeneity. Methods: For up to N=389 genotyped participants of the PROGRESS study of hospitalised CAP patients, we performed a genome-wide association study of ten cytokines IL-1b, IL-6, IL-8, IL-10, IL-12, MCP-1 (MCAF), MIP-1a (CCL3), VEGF, VCAM-1, and ICAM-1. Consecutive secondary analyses were performed to identify independent hits and corresponding causal variants. Results: 102 SNPs from 14 loci showed genome-wide significant associations with five of the cytokines. The most interesting associations were found at 6p21.1 for VEGF (p=1.58x10E-20), at 17q21.32 (p=1.51x10E-9) and at 10p12.1 (p=2.76x10E-9) for IL-1b, at 10p13 for MIP-1a (CCL3) (p=2.28x10E-9), and at 9q34.12 for IL-10 (p=4.52x10E-8). Functionally plausible genes could be assigned to the majority of loci including genes involved in cytokine secretion, granulocyte function, and cilial kinetics. Conclusions: This is the first context-specific genetic association study of blood cytokine concentra

Major depressive disorder is a widespread mood disorder. One of the most debilitating symptoms patients often experience is cognitive impairment. Recent findings suggest that inflammation is associated with depression and impaired cognition. Pro-inflammatory cytokines are elevated in the blood of depressed patients and impair learning and memory processes, suggesting that an anti-inflammatory approach might be beneficial for both depression and cognition. Utilizing the learned helplessness paradigm, we first established a mouse model of depression in which learning and memory are impaired. We found that learned helplessness (LH) impaired novel object recognition (NOR) and spatial working memory. LH mice also exhibited reduced hippocampal dendritic spine density and increased microglial activation compared to non-shocked (NS) mice or mice that were subjected to the learned helpless paradigm but did not exhibit learned helplessness (non-learned helpless, or NLH). These effects were mediated by microglia, as treatment with PLX5622, which depletes microglia and macrophages, restored learning and memory and hippocampal dendritic spine density in LH mice. However, PLX5622 also impaired l

Complex networks provide us a new view for investigation of immune systems. In this paper we collect data through STRING database and present a model with cooperation network theory. The cytokine-protein network model we consider is constituted by two kinds of nodes, one is immune cytokine types which can act as acts, other one is protein type which can act as actors. From act degree distribution that can be well described by typical SPL -shifted power law functions, we find that HRAS.TNFRSF13C.S100A8.S100A1.MAPK8.S100A7.LIF.CCL4.CXCL13 are highly collaborated with other proteins. It reveals that these mediators are important in cytokine-protein network to regulate immune activity. Dyad act degree distribution is another important property to generalized collaboration network. Dyad is two proteins and they appear in one cytokine collaboration relationship. The dyad act degree distribution can be well described by typical SPL functions. The length of the average shortest path is 1.29. These results show that this model could describe the cytokine-protein collaboration preferably

BACKGROUND: Previous reports have shown that elevated circulating levels of cytokines and/or cytokine receptors predict adverse outcomes in patients with heart failure. However, these studies were limited by small numbers of patients and/or they were performed in a single center. In addition, these studies did not have sufficient size to address the influence of age, race, sex, and cause of heart failure on the circulating levels of these inflammatory mediators in patients with heart failure. METHODS AND RESULTS: We analyzed circulating levels of cytokines (tumor necrosis factor [TNF] and interleukin-6) and their cognate receptors in 1200 consecutive patients who were enrolled in a multicenter clinical trial of patients with advanced heart failure. This analysis constitutes the largest analysis of cytokines and cytokine receptors to date. Analysis of the patients receiving placebo showed that increasing circulating levels of TNF, interleukin-6, and the soluble TNF receptors were associated with increased mortality. In men, there was a linear increase in circulating levels of TNF with advancing age. Women < or = 50 years of age had relatively low levels of TNF, but TNF levels were disproportionately higher in women >50 years of age. No differences existed in cytokines and/or cytokine receptors in whites versus nonwhites, and circulating levels of cytokines and cytokine receptors were significantly greater in patients with ischemic heart disease. CONCLUSIONS: Cytokines and cytokine receptors are independent predictors of mortality in patients with advanced heart failure. Moreover, circulating levels of cytokines are modified by age, sex, and cause of heart failure.

The role of neuro-inflammation in diverse, acute and chronic brain pathologies is being increasingly recognized. Neuro-inflammation is accompanied by increased levels of both pro- and anti-inflammatory cytokines; these have deleterious as well as protective/reparative effects. Inflammation has varying effects on neurogenesis and is a subject of intense contemporary interest. We show that TNF-alpha and IFN-gamma, used concomitantly, cause apoptosis of adult rat hippocampal progenitor/stem cells in vitro as detected by the TUNEL and MTT assays on time scales of several hours. We have coupled Raman spectroscopy to an optical trap to probe early changes of apoptosis in single, live neural stem cells that have been treated with pro-inflammatory cytokines, TNF-alpha and IFN-gamma. Changes caused by inflammation-induced denaturation of DNA are observed in the Raman spectra that correspond to very early stages of apoptosis, occurring on very fast time scales: as short as 10 minutes. Addition of the anti-inflammatory cytokine IL-10 either 10-30 min before or 10-30 min after treatment with TNF-alpha and IFN-gamma reverses the changes substantially. Our findings imply that inflammation can in

Sepsis is a life-threatening condition affecting one million people per year in the US in which dysregulation of the body's own immune system causes damage to its tissues, resulting in a 28 - 50% mortality rate. Clinical trials for sepsis treatment over the last 20 years have failed to produce a single currently FDA approved drug treatment. In this study, we attempt to discover an effective cytokine mediation treatment strategy for sepsis using a previously developed agent-based model that simulates the innate immune response to infection: the Innate Immune Response agent-based model (IIRABM). Previous attempts at reducing mortality with multi-cytokine mediation using the IIRABM have failed to reduce mortality across all patient parameterizations and motivated us to investigate whether adaptive, personalized multi-cytokine mediation can control the trajectory of sepsis and lower patient mortality. We used the IIRABM to compute a treatment policy in which systemic patient measurements are used in a feedback loop to inform future treatment. Using deep reinforcement learning, we identified a policy that achieves 0% mortality on the patient parameterization on which it was trained. Mor

The evolution of macrophages has made them primordial for both development and immunity. Their functions range from the shaping of body plans to the ingestion and elimination of apoptotic cells and pathogens. Cytokines are small soluble proteins that confer instructions and mediate communication among immune and non-immune cells. A portfolio of cytokines is central to the role of macrophages as sentries of the innate immune system that mediate the transition from innate to adaptive immunity. In concert with other mediators, cytokines bias the fate of macrophages into a spectrum of inflammation-promoting "classically activated," to anti-inflammatory or "alternatively activated" macrophages. Deregulated cytokine secretion is implicated in several disease states ranging from chronic inflammation to allergy. Macrophages release cytokines via a series of beautifully orchestrated pathways that are spatiotemporally regulated. At the molecular level, these exocytic cytokine secretion pathways are coordinated by multi-protein complexes that guide cytokines from their point of synthesis to their ports of exit into the extracellular milieu. These trafficking proteins, many of which were discovered in yeast and commemorated in the 2013 Nobel Prize in Physiology or Medicine, coordinate the organelle fusion steps that are responsible for cytokine release. This review discusses the functions of cytokines secreted by macrophages, and summarizes what is known about their release mechanisms. This information will be used to delve into how selected pathogens subvert cytokine release for their own survival.

The cytokine storm has captured the attention of the public and the scientific community alike, and while the general notion of an excessive or uncontrolled release of proinflammatory cytokines is well known, the concept of a cytokine storm and the biological consequences of cytokine overproduction are not clearly defined. Cytokine storms are associated with a wide variety of infectious and noninfectious diseases. The term was popularized largely in the context of avian H5N1 influenza virus infection, bringing the term into popular media. In this review, we focus on the cytokine storm in the context of virus infection, and we highlight how high-throughput genomic methods are revealing the importance of the kinetics of cytokine gene expression and the remarkable degree of redundancy and overlap in cytokine signaling. We also address evidence for and against the role of the cytokine storm in the pathology of clinical and infectious disease and discuss why it has been so difficult to use knowledge of the cytokine storm and immunomodulatory therapies to improve the clinical outcomes for patients with severe acute infections.