It is significant to investigate the transmission dynamics of vector-borne infection because it has a global impact, can help predict and prevent future outbreaks, is important for understanding the ...impact of climate change on public health, can lead to more effective control strategies, and can improve our comprehension of the dynamics of these infections. Our paper presents a new model for chikungunya virus infection, which considers treatment and vaccination, using the Atangana-Baleanu derivative within the framework of the Caputo definition. First of all, we examine the positivity and uniqueness of the solution for the model. Then, we find out the fundamental results of the suggested model, such as the steady-state without infection and the R0 value that indicates endemicity pertaining to the system. Furthermore, we verify the local asymptotic stability of the infection-free steady-state. Through the application of fixed-point theory, we demonstrate the existence of the solution and propose a numerical methodology to investigate the dynamic behavior of the model. Finally, we performed simulations to demonstrate the effects of vaccination, transmission probability, treatment, and index of memory on the system's solution pathways. The findings of this research suggest the most sensitive parameters of the system responsible for the control and prevention of the infection. Emphasizing the significance of the memory index as an attractive parameter, we propose its consideration to the policy makers as a control parameter for the prevention of the infection.
This study assesses the environmental impact of pine chip-based biorefinery processes, focusing on bioethanol, xylonic acid, and lignin production. A cradle-to-gate Life Cycle Assessment (LCA) is ...employed, comparing a novel biphasic pretreatment method (p-toluenesulfonic acid (TsOH)/pentanol, Sc-1) with conventional sulfuric acid pretreatment (H2SO4, Sc-2). The analysis spans biomass handling, pretreatment, enzymatic hydrolysis, yeast fermentation, and distillation. Sc-1 yielded an environmental impact of 1.45E+01 kPt, predominantly affecting human health (96.55%), followed by ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed 32.61%, 29.28%, and 38.11% to the total environmental burdens, respectively. Sc-2 resulted in an environmental burden of 1.64E+01 kPt, with a primary impact on human health (96.56%) and smaller roles for ecosystems (3.07%) and resources (0.38%). Bioethanol, xylonic acid, and lignin contributed differently at 22.59%, 12.5%, and 64.91%, respectively. Electricity generation was predominant in both scenarios, accounting for 99.05% of the environmental impact, primarily driven by its extensive usage in biomass handling and pretreatment processes. Sc-1 demonstrated a 13.05% lower environmental impact than Sc-2 due to decreased electricity consumption and increased bioethanol and xylonic acid outputs. This study highlights the pivotal role of pretreatment methods in wood-based biorefineries and underscores the urgency of sustainable alternatives like TsOH/pentanol. Additionally, adopting greener electricity generation, advanced technologies, and process optimization are crucial for reducing the environmental footprint of waste-based biorefineries while preserving valuable bioproduct production.
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•Pinewood biorefinery producing bioethanol, xylonic acid, and lignin are environmentally analyzed.•LCA compares the environmental impacts of monophasic and biphasic pretreatment methods.•TsOH/pentanol pretreatment was environmentally superior to H2SO4 (13.05 % reduction).•Lignin production exhibited the highest environmental burden in both scenarios.•Electricity production was a hotspot in both scenarios, with a share of 99.05 %.
Furfural is a versatile chemical building block derived from hemicellulose-rich lignocellulosic biomass. Considering the availability of various process routes and conditions, it is crucial to ...determine the most environmentally sustainable production routes and conditions for furfural production. This study evaluates the life cycle environmental impacts of furfural production in a poplar wood-based biorefinery, considering varying mannitol concentrations (0–15% w/w) and catalyst types (FeCl3, FeCl2, CuCl2, AlCl3, and MgCl2). An attributional cradle-to-gate life cycle assessment (LCA) framework is adopted, with a functional unit of 1 kg of furfural production. The environmental impacts of furfural production are evaluated using the IMPACT World+ method at both midpoint and endpoint levels. Based on the findings, the furfural production process utilizing an AlCl3 catalyst and a 5% w/w concentration of mannitol exhibits superior environmental performance compared to all the other conditions evaluated. Compared to the mannitol-free process, this condition can reduce up to 30.80% of all the environmental impacts of furfural production. By substituting FeCl2, FeCl3, CuCl2, and MgCl2 with AlCl3, significant savings of 64.77%, 45.06%, 78.77%, and 79.30%, respectively, in all the environmental burdens of furfural production can be achieved. The results highlight that choosing an appropriate catalyst can greatly decrease the environmental impact of furfural production. Furthermore, the use of fossil-based electricity is a significant contributor to the environmental impacts of the process. Thus, an eco-friendly approach to producing furfural involves altering the means of electricity generation.
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•Environmental impacts of furfural production in a wood-based biorefinery are analyzed.•AlCl3 catalyst and 5% w/w mannitol outperform other process conditions environmentally.•The opted condition reduces 30.80% of environmental impacts than the baseline process.•Substituting other catalysts with AlCl3 can significantly mitigate environmental burdens.•A suitable catalyst can greatly reduce the environmental impacts of furfural production.
Abstract Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. ...Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose–insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S I , the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S I only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients ( N = 42,941 h in total) who received insulin while in the ICU and stayed for ≥72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% IQR 1.18, 6.41%. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7–12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose–insulin models render this article a mini-review of the state of model-based TGC in critical care.
It is well known that viral infections have a high impact on public health in multiple ways, including disease burden, outbreaks and pandemic, economic consequences, emergency response, strain on ...healthcare systems, psychological and social effects, and the importance of vaccination. Mathematical models of viral infections help policymakers and researchers to understand how diseases can spread, predict the potential impact of interventions, and make informed decisions to control and manage outbreaks. In this work, we formulate a mathematical model for the transmission dynamics of COVID-19 in the framework of a fractional derivative. For the analysis of the recommended model, the fundamental concepts and results are presented. For the validity of the model, we have proven that the solutions of the recommended model are positive and bounded. The qualitative and quantitative analyses of the proposed dynamics have been carried out in this research work. To ensure the existence and uniqueness of the proposed COVID-19 dynamics, we employ fixed-point theorems such as Schaefer and Banach. In addition to this, we establish stability results for the system of COVID-19 infection through mathematical skills. To assess the influence of input parameters on the proposed dynamics of the infection, we analyzed the solution pathways using the Laplace Adomian decomposition approach. Moreover, we performed different simulations to conceptualize the role of input parameters on the dynamics of the infection. These simulations provide visualizations of key factors and aid public health officials in implementing effective measures to control the spread of the virus.
Background:
Critically ill ICU patients frequently experience acute insulin resistance and increased endogenous glucose production, manifesting as stress-induced hyperglycemia and hyperinsulinemia. ...STAR (Stochastic TARgeted) is a glycemic control protocol, which directly manages inter- and intra- patient variability using model-based insulin sensitivity (SI). The model behind STAR assumes a population constant for endogenous glucose production (EGP), which is not otherwise identifiable.
Objective:
This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.
Methods:
Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.
Results:
Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.
Conclusions:
Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.
Mathematical models for infectious diseases can help researchers, public health officials, and policymakers to predict the course of an outbreak. We formulate an epidemic model for the transmission ...dynamics of Zika infection with carriers to understand the intricate progression route of the infection. In our study, we focused on the visualization of the transmission patterns of the Zika with asymptomatic carriers, using fractional calculus. For the validity of the model, we have shown that the solutions of the system are positive and bounded. Moreover, we conduct a qualitative analysis and examine the dynamical behavior of Zika dynamics. The existence and uniqueness of the solution of the system have been proved through analytic skills. We establish the necessary conditions to ensure the stability of the recommended system based on the Ulam–Hyers stability concept (UHS). Our research emphasizes the most critical factors, specifically the mosquito biting rate and the existence of asymptomatic carriers, in increasing the complexity of virus control efforts. Furthermore, we predict that the asymptomatic fraction has the ability to spread the infection to non-infected regions. Furthermore, treatment due to medication, the fractional parameter or memory index, and vaccination can serve as effective control measures in combating this viral infection.
Background and Aims: Currently, there is a lack of real-time metric with high sensitivity and specificity to diagnose sepsis. Insulin sensitivity (SI) may be determined in real-time using ...mathematical glucose-insulin models; however, its effectiveness as a diagnostic test of sepsis is unknown. Our aims were to determine the levels and diagnostic value of model-based SI for identification of sepsis in critically ill patients. Materials and Methods: In this retrospective, cohort study, we analyzed SI levels in septic (n = 18) and nonseptic (n = 20) patients at 1 (baseline), 4, 8, 12, 16, 20, and 24 h of their Intensive Care Unit admission. Patients with diabetes mellitus Type I or Type II were excluded from the study. The SI levels were derived by fitting the blood glucose levels, insulin infusion and glucose input rates into the Intensive Control of Insulin-Nutrition-Glucose model. Results: The median SI levels were significantly lower in the sepsis than in the nonsepsis at all follow-up time points. The areas under the receiver operating characteristic curve of the model-based SI at baseline for discriminating sepsis from nonsepsis was 0.814 (95% confidence interval, 0.675-0.953). The optimal cutoff point of the SI test was 1.573 × 10−4 L/mu/min. At this cutoff point, the sensitivity was 77.8%, specificity was 75%, positive predictive value was 73.7%, and negative predictive value was 78.9%. Conclusions: Model-based SI ruled in and ruled out sepsis with fairly high sensitivity and specificity in our critically ill nondiabetic patients. These findings can be used as a foundation for further, prospective investigation in this area.
Respiratory dysfunction and failure are common in the intensive care unit (ICU); they are often the primary reasons for ICU admission and affect length of stay, mortality, and cost. However, ...diagnosing respiratory dysfunction requires arterial blood gas values to calculate the partial pressure of arterial oxygen (PaO2) to a fraction of inspired oxygen (FiO2) or P/F ratio. These intermittent blood gas values may be difficult to obtain in some patients or where financial resources are limited. Its varying etiologies and lack of other specific biomarkers make diagnosing difficult without this measurement. Thus, in this study, we investigate commonly available parameters in the ICU for the classification of respiratory dysfunction without arterial blood gas values using a Bayesian network, an unsupervised structural learning method. Clinical data from selected patients in the Medical Information Mart for Intensive Care (MIMIC) III v1.4 database is used to create and validate these models. Bayesian network generated using the taboo order algorithm showed a satisfying performance in the classification of respiratory dysfunction. Results are compared to standard diagnosis with P/F ratio. The predictor variables selected could stratify respiratory dysfunction with 80% accuracy and 94% sensitivity. Hence, without using arterial blood gas values, these parameters could identify respiratory dysfunction in 90% of cases using Bayesian networks.
Blood glucose variability is common in healthcare and it is not related or influenced by diabetes mellitus. To minimise the risk of high blood glucose in critically ill patients, Stochastic Targeted ...Blood Glucose Control Protocol is used in intensive care unit at hospitals worldwide. Thus, this study focuses on the performance of stochastic modelling protocol in comparison to the current blood glucose management protocols in the Malaysian intensive care unit. Also, this study is to assess the effectiveness of Stochastic Targeted Blood Glucose Control Protocol when it is applied to a cohort of diabetic patients.
Retrospective data from 210 patients were obtained from a general hospital in Malaysia from May 2014 until June 2015, where 123 patients were having comorbid diabetes mellitus. The comparison of blood glucose control protocol performance between both protocol simulations was conducted through blood glucose fitted with physiological modelling on top of virtual trial simulations, mean calculation of simulation error and several graphical comparisons using stochastic modelling.
Stochastic Targeted Blood Glucose Control Protocol reduces hyperglycaemia by 16% in diabetic and 9% in nondiabetic cohorts. The protocol helps to control blood glucose level in the targeted range of 4.0–10.0 mmol/L for 71.8% in diabetic and 82.7% in nondiabetic cohorts, besides minimising the treatment hour up to 71 h for 123 diabetic patients and 39 h for 87 nondiabetic patients.
It is concluded that Stochastic Targeted Blood Glucose Control Protocol is good in reducing hyperglycaemia as compared to the current blood glucose management protocol in the Malaysian intensive care unit. Hence, the current Malaysian intensive care unit protocols need to be modified to enhance their performance, especially in the integration of insulin and nutrition intervention in decreasing the hyperglycaemia incidences. Improvement in Stochastic Targeted Blood Glucose Control Protocol in terms of uen model is also a must to adapt with the diabetic cohort.
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