Artificial neural network-assisted thermogravimetric analysis of thermal degradation in combustion reactions: A study across diverse organic samples.

During gasification the kinetic and thermodynamic parameter depend on both the feedstock and the process conditions. As a result, one needs to enhance the understanding of how to model numerically these parameters using thermogravimetric analyzer. Consequently, there exists a pressing need to computationally devise gasification model that can efficiently account to thermodynamic and kinetic parameter from thermogravimetric data. In this study, we numerically model gasification process kinetic and thermodynamic parameters, which vary with feedstock and operational conditions. Our novel approach involves creating an ANN model in MATLAB using a carefully optimized 8-20-20-10-1 architecture. Based on thermogravimetric analyzer (TGA) data, this model uniquely predicts critical kinetic (activation energy, pre-exponential factor) and thermodynamic parameters (entropy, enthalpy, Gibbs free energy, ignition index, boiling temperature). Our ANN model, trained on over 80 diverse samples with the Levenberg-Marquardt algorithm, excels at prediction, with an MSE of 6.185e-6 and an R2 value exceeding 0.9996, ensuring highly accurate estimates. Based on time, temperature, heating rate, and elemental composition, it accurately predicts thermal degradation. The model can predict TGA curves for many materials, demonstrating its versatility. For instance, it accurately estimates the activation energy for pure glycerol at 73.84 kJ/mol, crude glycerol at 67.55 kJ/mol, 12.12 kJ/mol for coal, and 111.3 kJ/mol for wood. These results, particularly for Kissinger-validated glycerol, demonstrate the model's versatility and efficacy in various gasification scenarios, making it a valuable tool for thermochemical conversion studies.

Comparative Analysis of Biomarkers in Type 2 Diabetes Patients With and Without Comorbidities: Insights Into the Role of Hypertension and Cardiovascular Disease.

Background: Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease. Objectives: Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications. Design: In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively. Methods: Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications. Results: MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1ß (IL-1ß), and 8-isoprostane were significant (P < .05) for the transition from prediabetes to t2dm, highlighting importance of mitochondrial involvement. complement component 5a (c5a) is a biomarker associated with disease progression and comorbidities, gsh gssg, monocyte chemoattractant protein-1 (mcp-1), 8-isoprostane being most important biomarkers. Conclusions: Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.

Improving personalized tumor growth predictions using a Bayesian combination of mechanistic modeling and machine learning.

Background: In clinical practice, a plethora of medical examinations are conducted to assess the state of a patient's pathology producing a variety of clinical data. However, investigation of these data faces two major challenges. Firstly, we lack the knowledge of the mechanisms involved in regulating these data variables, and secondly, data collection is sparse in time since it relies on patient's clinical presentation. The former limits the predictive accuracy of clinical outcomes for any mechanistic model. The latter restrains any machine learning algorithm to accurately infer the corresponding disease dynamics. Methods: Here, we propose a novel method, based on the Bayesian coupling of mathematical modeling and machine learning, aiming at improving individualized predictions by addressing the aforementioned challenges. Results: We evaluate the proposed method on a synthetic dataset for brain tumor growth and analyze its performance in predicting two relevant clinical outputs. The method results in improved predictions in almost all simulated patients, especially for those with a late clinical presentation (>95% patients show improvements compared to standard mathematical modeling). In addition, we test the methodology in two additional settings dealing with real patient cohorts. In both cases, namely cancer growth in chronic lymphocytic leukemia and ovarian cancer, predictions show excellent agreement with reported clinical outcomes (around 60% reduction of mean squared error). Conclusions: We show that the combination of machine learning and mathematical modeling approaches can lead to accurate predictions of clinical outputs in the context of data sparsity and limited knowledge of disease mechanisms.

A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia.

Chronic lymphocytic leukemia (CLL) is the most common peripheral blood and bone marrow cancer in the developed world. This manuscript proposes mathematical model equations representing the disease dynamics of B-cell CLL. We interconnect delay differential cell cycle models in each of the tumor-involved disease centers using physiologically relevant cell migration. We further introduce five hypothetical case studies representing CLL heterogeneity commonly seen in clinical practice and demonstrate how the proposed CLL model framework may capture disease pathophysiology across patient types. We conclude by exploring the capacity of the proposed temporally- and spatially distributed model to capture the heterogeneity of CLL disease progression. By using global sensitivity analysis, the critical parameters influencing disease trajectory over space and time are: 1) the initial number of CLL cells in peripheral blood, the number of involved lymph nodes, the presence and degree of splenomegaly; 2) the migratory fraction of nonproliferating as well as proliferating CLL cells from bone marrow into blood and of proliferating CLL cells from blood into lymph nodes; and 3) the parameters inducing nonproliferative cells to proliferate. The proposed model offers a practical platform that may be explored in future personalized patient protocols once validated.