Search Results (53)

Clinical trials of drugs specifically targeting the sepsis response have frequently produced negative or inconclusive results. This has largely been due to the broad heterogeneity of enrolled patient populations, particularly when inclusion was based on the presence of the non-specific systemic inflammatory response syndrome (SIRS) criteria. The heterogeneity may have diluted any possible treatment effect: while some patients may have benefited from the intervention under investigation, others will have been harmed, resulting in an overall null-effect. Furthermore, an underlying infection is not always required for immune-modulating interventions to be effective; for example, patients with severe acute pancreatitis, but no infection, may still benefit from such therapies. There is therefore a need for better patient stratification or subphenotyping to identify those most likely to benefit from a particular therapy. Several trials have already adopted this approach using prognostic and/or predictive enrichment strategies. For example, measurements of triggering receptor expressed on myeloid cells (TREM) could be used to identify candidates most likely to respond to anti-TREM therapies, or patients with coagulopathy or specific inflammatory patterns could be selected for treatments like thrombomodulin or activated protein C. However, challenges remain, including the need for more rapid tools that can be used at the bedside to inform real-time treatment decisions given the rapidly evolving nature of the sepsis response. Nevertheless, the era of broad-spectrum “sepsis drugs” is now giving way to more selective, personalized interventions tailored to individual biological profiles, offering a more promising pathway for future therapeutic development—even in the absence of infection. Full article

In this article, we propose a novel nonlinear cross-diffusion framework to model the distribution of susceptible and infected individuals within their habitat using a reduced SIR model that incorporates saturated incidence and treatment rates. The study investigates solution boundedness through the theory of parabolic partial differential equations, thereby validating the proposed spatio-temporal model. Through the implementation of the suggested cross-diffusion mechanism, the model reveals at least one non-constant positive equilibrium state within the susceptible–infected (SI) system. This work demonstrates the potential coexistence of susceptible and infected populations through cross-diffusion and unveils Turing instability within the system. By analyzing codimension-2 Turing–Hopf bifurcation, the study identifies the Turing space within the spatial context. In addition, we explore the results for Turing–Bogdanov–Takens bifurcation. To account for seasonal disease variations, novel perturbations are introduced. Comprehensive numerical simulations illustrate diverse emerging patterns in the Turing space, including holes, strips, and their mixtures. Additionally, the study identifies non-Turing and Turing–Bogdanov–Takens patterns for specific parameter selections. Spatial series and surfaces are graphed to enhance the clarity of the pattern results. This research provides theoretical insights into the implications of cross-diffusion in epidemic modeling, particularly in contexts characterized by localized mobility, clinically evident infections, and community-driven isolation behaviors. Full article

In acute lymphoblastic leukemia (ALL), neutropenia and fever of unknown origin may occur, indicating the use of antimicrobials to control a probable infection. However, in 60–70% of cases there is no obvious infectious focus so treatment is empirical, increasing the risk of developing systemic inflammatory response syndrome (SIRS). The construction of a prognostic model of fever and development of SIRS based on the identification of endogenous molecules, called alarmins or damage-associated molecular patterns (DAMPs) and inflammatory cytokines, can help identify children with ALL and fever or SIRS and who do not have an infection. A cohort of 30 children with recently diagnosed ALL and absence of infectious microorganisms before starting the remission induction phase was studied. Two groups were identified: (1) a group with SIRS (fever, tachycardia, tachypnea, and leukopenia, without focus of infection) and (2) a group without SIRS. The DAMPs, namely HMGB1 and S100A8 proteins, were quantified by ELISA and inflammatory mediators were determined by multiple protein analysis. The medians of DAMPs and inflammatory mediators in children with SIRS were higher than in children who did not have SIRS, and the delta values of the biomarkers studied in patients with and without SIRS showed important differences, with statistically higher medians in patients with SIRS compared to those without SIRS. HMGB1 together with IL-1β, IL-8, IL-10, and MCP-1 can serve as biomarkers to identify children with ALL and fever or SIRS who should not receive antimicrobial treatment because the origin of their fever is not due to an infectious agent. Full article

This article presents a new evaluation and analysis of the five censuses undertaken at the initiative of philanthropist Sir Moses Montefiore among the Jewish population of Palestine/the Land of Israel between 1839 and 1875. The main purpose of the censuses was to ascertain the composition and needs of a generally poor Jewish population in order to better provide to its welfare. The information collected concerned basic demographic characteristics, countries of origin—namely along the main divide of Ashkenazi and Sephardi/Mizrahi Jews—and periods of immigration, social composition, and religiosity. By combining the different censuses into an integrated database, the authors are able to show changes intervening over time not only regarding the aggregate population, but also concerning individual and household profiles. The data aggregation allows us to better understanding the material conditions of the Jewish population and to outline with greater accuracy the relationship between socio-cultural communities and socio-economic stratification. The analysis unveiled the patterns of Jewish immigration all along the surveyed period and its variations by size and by countries of origin. These data provide important evidence concerning the overall Jewish presence in the Land of Israel and demonstrate that immigration was a significant factor well before the formal beginning of Aliyah in the early 1880s. No such analysis of the whole set of Montefiore censuses had been performed previously. The findings will prove very useful to historians and social scientists in their further investigation of the area and its populations in the 19th century. Full article

This study enhances the classical deterministic SIR model by incorporating soliton-like dynamics and gradient-induced diffusion, effectively capturing the complex spatiotemporal patterns of disease transmission within nomadic populations. The proposed model incorporates an advection–diffusion mechanism that modulates the spatial gradients in infection dynamics, transitioning from highly localized infection peaks to distributed infection fronts. We discussed the role of diffusion coefficients in shaping the spatial distribution of susceptible, infected, and recovered populations, as well as the impact of gradient-induced advection in mitigating epidemic intensity. Numerical simulations demonstrate the effects of varying key parameters such as transmission rates, recovery rates, and advection–diffusion coefficients on the epidemic’s progression. The soliton-like dynamics ensure the stability of infection waves over time, specifying targeted intervention strategies such as localized quarantines and vaccination campaigns. This model underscores the critical importance of spatial heterogeneity and mobility patterns in managing infectious diseases. The applicability of the model has been tested using the AIDS data from the last 25 years. Full article

Plant roots and soil properties play crucial roles in regulating soil hydrological processes, particularly in determining soil water infiltration capacity. However, the infiltration patterns and underlying mechanisms across different forest types in subtropical regions remain poorly understood. In this study, we measured the infiltration characteristics of three typical stands (pure Phyllostachys edulis forest, mixed Phyllostachys edulis-Cunninghamia lanceolata forest, and pure Cunninghamia lanceolata forest) using a double-ring infiltrometer. Stepwise multiple regression and structural equation modeling (SEM) were employed to analyze the effects of root traits and soil physicochemical properties on soil infiltration capacity. The results revealed the following: (1) The initial infiltration rate (IIR), stable infiltration rate (SIR), and average infiltration rate (AIR) followed the order pure Phyllostachys edulis stand > mixed stand > pure Cunninghamia lanceolata stand. (2) Compared to the pure Cunninghamia lanceolata stand, the IIR, SIR, and AIR in the pure Phyllostachys edulis stand increased by 6.66%, 35.63%, and 28.51%, respectively, while those in the mixed stand increased by 28.79%, 28.82%, and 33.51%. (3) Fine root biomass, root length density, non-capillary porosity, and soil bulk density were identified as key factors influencing soil infiltration capacity. (4) Root biomass and root length density affected infiltration capacity through both direct pathways and indirect pathways mediated by alterations in non-capillary porosity and soil bulk density. These findings provide theoretical insights into soil responses to forest types and inform sustainable water–soil management practices in Phyllostachys edulis plantations. Full article

Background: Esculetin, 6,7-dihydroxycoumarin, is a bioactive compound found in various herbal plants, and is known to have health-beneficial properties including anti-obesity effects. However, there is a lack of in vivo studies to clearly determine esculetin’s role in lipid metabolism. Objectives: In this study, we studied esculetin’s effect on lipid accumulation using Caenorhabditis elegans and its underlying mechanisms. Methods: C. elegans were treated with esculetin (100 or 200 μM) for 48 h, and their triglyceride and protein levels were measured. Additionally, behavioral patterns such as pharyngeal pumping rate, body bending rate, body sizes, and locomotive activity were analyzed. Genetic dependencies were examined by utilizing mutant worms and testing relative gene expressions. Results: C. elegans treated with esculetin displayed significantly reduced fat accumulation compared to the controls without effects on the pharyngeal pumping rate, body bending rate, or locomotive activity. Esculetin’s fat-lowering effect was dependent on DAF-2 (insulin/insulin-like growth factor-1 [IGF-1] receptor homolog), DAF-16 (Forkhead box protein O homolog), and AAK-2 (5′-adenosine monophosphate-activated protein kinase [AMPK] catalytic subunit α2) in the mutant experiments. Esculetin also significantly increased the relative expression of downstream targets of DAF-16 (hsp-16.2 and sod-3), AMPK-related genes (aak-1 and aak-2), a sirtuin gene, sir-2.1, and a lipolysis-related gene, atgl-1. Conclusions: These findings suggest that esculetin inhibited fat accumulation in C. elegans and this effect was dependent on the insulin/IGF-1 and 5′-adenosine monophosphate-activated protein kinase signaling pathways. Full article

Private consumers, small businesses, and even large enterprises are all at risk from botnets. These botnets are known for spearheading Distributed Denial-Of-Service (DDoS) attacks, spamming large populations of users, and causing critical harm to major organizations. The development of Internet of Things (IoT) devices led to the use of these devices for cryptocurrency mining, in-transit data interception, and sending logs containing private data to the master botnet. Different techniques were developed to identify these botnet activities, but only a few use Graph Neural Networks (GNNs) to analyze host activity by representing their communications with a directed graph. Although GNNs are intended to extract structural graph properties, they risk causing overfitting, which leads to failure when attempting to do so from an unidentified network. In this study, we test the notion that structural graph patterns might be used for efficient botnet detection. In this study, we also present SIR-GN, a structural iterative representation learning methodology for graph nodes. Our approach is built to work well with untested data, and our model is able to provide a vector representation for every node that captures its structural information. Finally, we demonstrate that, when the collection of node representation vectors is incorporated into a neural network classifier, our model outperforms the state-of-the-art GNN-based algorithms in the detection of bot nodes within unknown networks. Full article

This paper presents a new SIRS model for recurrent childhood diseases under the Caputo fractional difference operator. The existence theory is established using Brouwer’s fixed-point theorem and the Banach contraction principle, providing a comprehensive mathematical foundation for the model. Ulam stability is demonstrated using nonlinear functional analysis. Sensitivity analysis is conducted based on the variation of each parameter, and the basic reproduction number $\left({\mathcal{R}}_0\right)$ is introduced to assess local stability at two equilibrium points. The stability analysis indicates that the disease-free equilibrium point is stable when ${\mathcal{R}}_0<1$, while the endemic equilibrium point is stable when ${\mathcal{R}}_0>1$ and otherwise unstable. Numerical simulations demonstrate the model’s effectiveness in capturing realistic scenarios, particularly the recurrent patterns observed in some childhood diseases. Full article

Silicone rubber (SIR) is a crucial insulating material in cable accessories, but it is also susceptible to faults. In practical applications, mechanical pressure from bending or shrinking can impact the degradation of SIR, necessitating the study of its electrical tree and partial discharge (PD) characteristics under such pressure. This work presents the construction of a test platform for electrical trees under varying pressures to observe their growth process. A high-frequency current transformer is used to measure PD patterns during tree growth, enabling analysis of the effect of PD on tree initiation and propagation under pressure. The experimental results demonstrate a significant decrease in tree inception probability and increase in PD inception voltage under pressure. The pressure also influences the tree structure and PD during the treeing process, where the longest tree with a branch-like structure appears under 800 kPa. The effect of pressure on electrical tree and PD characteristics can be attributed to changes in free volume, alterations in air pressure within the tree channels, and the affected charge accumulation. Full article

The current constitutive model and damage evolution law of crumb rubber concrete (CRC) were obtained by fitting and changing parameters based on the normal concrete model. However, this model does not accurately reflect the characteristics of the material. In this paper, we studied the energy dissipation in the failure process of CRC to derive the constitutive model and damage evolution law of CRC based on the energy method. Four substitution rates of 5%, 10%, 15%, and 20% were selected, and the rubber concrete prism was prepared by replacing the natural fine aggregate with the same volume of crumb rubber aggregate. After that, uniaxial compressive tests were conducted. The energy lost due to the damage was calculated and analyzed, and the energy method was used to establish the damage evolution law and damage model of the crumb rubber concrete. The results demonstrated that the Guo Zhenhai damage model, which is based on the energy method, can more effectively explain the crumb rubber concrete stress–strain full curve, and the energy consumed as a result of the damage exhibits a growing and then reducing pattern with the increase in rubber doses. When the energy-based method is used, the Guo Zhenhai damage evolution model is similar to the damage evolution law calculated using the SIR damage evolution model. During uniaxial compression damage, rubber concrete with various rubber dosages demonstrated varying energy absorption in different deformation phases. When the rubber particle content was 10%, the energy absorption capacity of the specimen was 6.9% higher than that of normal concrete. Full article

In order to investigate the impact of two immunization strategies—vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate—on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system’s steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies. Full article

β-glucan is part of the cell wall of fungi and yeasts and has been known for decades to have immunomodulating effects on boosting immunity against various infections as a pathogen-associated molecular pattern that is able to modify biological responses. β-glucan has been used in rat models and in vitro studies involving sepsis and SIRS with good results, but this supplement has not been evaluated in the treatment of endotoxemia in horses. This study aims to evaluate the effects of preventive supplementation with β-glucan in horses submitted to endotoxemia by means of inflammatory response modulation. Eight healthy horses, both male and female, aged 18 ± 3 months, weighing 300 ± 100 kg of mixed breed, were randomly assigned to two groups of four animals, both of which were subjected to the induction of endotoxemia via the intravenous administration of E. coli lipopolysaccharides (0.1 µg/kg). For 30 days before the induction of endotoxemia, horses in the β-glucan group (GB) received 10 mg/kg/day of β-glucan orally, and horses in the control group (GC) received 10 mg/kg/day of 0.9% sodium chloride orally. The horses were submitted to physical exams, including a hematological, serum biochemistry, and peritoneal fluid evaluation, and the serum quantification of cytokines TNF-α, IL-6, IL-8, and IL-10. For statistical analysis, the normality of residues and homogeneity of variances were verified; then, the variables were analyzed as repeated measures over time, checking the effect of treatment, time, and the interaction between time and treatment. Finally, the averages were compared using Tukey’s test at a significance level of 5%. Horses from both experimental groups presented clinical signs and hematological changes in endotoxemia, including an increase in heart rate and body temperature, neutrophilic leukopenia, an increase in serum bilirubin, glucose, lactate, and an increase in TNF-α, IL-6, and IL-10. Hepatic and renal function were not compromised by β-glucan supplementation. GB presented higher mean values of the serum total protein, globulins, and IL-8 compared to that observed in GC. In the peritoneal fluid, horses from GB presented a lower mean concentration of neutrophils and a higher mean concentration of macrophages compared to the GC. It was concluded that preventive supplementation of β-glucan for thirty days modulated the immune response, as evidenced by increasing serum total proteins, globulins, IL-8, and changes in the type of peritoneal inflammatory cells, without effectively attenuating clinical signs of endotoxemia in horses. Considering the safety of β-glucan in this study, the results suggest the potential clinical implication of β-glucan for prophylactic use in horse endotoxemia. Full article

The presence of nonignorable missing response variables often leads to complex conditional distribution patterns that cannot be effectively captured through mean regression. In contrast, quantile regression offers valuable insights into the conditional distribution. Consequently, this article places emphasis on the quantile regression approach to address nonrandom missing data. Taking inspiration from fractional imputation, this paper proposes a novel smoothed quantile regression estimation equation based on a sampling importance resampling (SIR) algorithm instead of nonparametric kernel regression methods. Additionally, we present an augmented inverse probability weighting (AIPW) smoothed quantile regression estimation equation to reduce the influence of potential misspecification in a working model. The consistency and asymptotic normality of the empirical likelihood estimators corresponding to the above estimating equations are proven under the assumption of a correctly specified parameter working model. Furthermore, we demonstrate that the AIPW estimation equation converges to an IPW estimation equation when a parameter working model is misspecified, thus illustrating the robustness of the AIPW estimation approach. Through numerical simulations, we examine the finite sample properties of the proposed method when the working models are both correctly specified and misspecified. Furthermore, we apply the proposed method to analyze HIV—CD4 data, thereby exploring variations in treatment effects and the influence of other covariates across different quantiles. Full article

The occurrence of cross-beam interference in the received signal is one of the main problems that limit the possibilities of massive multiple-input–multiple-output technology (massive-MIMO) in fifth-generation (5G) systems. Thus, the evaluation of the level of this interference is one of the most important procedures in the spatial planning of currently wireless networks. We propose a novel modification of simple antenna pattern models, which is based only on changing the directivity of real antenna system patterns. This approach is independent of the antenna system’s type, structure, and analytical description. Based on the developed modification, the original methodology for assessing the signal-to-interference ratio (SIR) from adjacent beams of a common antenna system is presented. The change in the radiation direction and the accompanying change in the complex shape and parameters of the real antenna beam pattern is one of the problems that significantly hinders the evaluation of the analyzed interference. Hence, in the presented methodology, we propose using our modification. In this case, the modification is reduced to a proportional change in the directivity concerning the real antenna system, which results from a change in the beam direction. The simulation studies used a multi-ellipsoidal propagation model and a real massive MIMO antenna pattern description from 3GPP. For the SIR error analysis, the 3GPP pattern is used as a reference. The simulation results show that modifying simple antenna pattern models allows us to obtain an SIR error of no more than 3 dB and 0.1 dB under line-of-sight (LOS) and non-LOS conditions, respectively. Full article