Statistical analysis of topological indices in linear phenylenes for predicting physicochemical properties using algorithms.

QSPR mathematically links physicochemical properties with the structure of a molecule. The physicochemical properties of chemical molecules can be predicted using topological indices. It is an effective method for eliminating costly and time-consuming laboratory tests. We established a QSPR between mev-degree and mve-degree-based indices and the physical properties of benzenoid hydrocarbons. To compute these indices, we designed a program using Maple software and the correlation between indices and physical properties was developed using the SPSS software. Our study reveals that the mve-degree-based sum-connectivity ( χ mve ) and atom bond connectivity ( A B C mve ) index, mev-degree-based Randic ( R mev ) and Zagreb ( M mev ) index are the three most significant parameters and have good prediction ability for the physicochemical properties. We examined that R mev predicts the molar refractivity and boiling point, χ mve predicts the LogP and enthalpy, A B C mve predicts the molecular weight, M mev predicts the Gibb's energy, Pie-electron energy and Henry's law. Moreover, we computed the indices for the linear [n]-phenylen.

On physical analysis of topological indices for iron disulfide network via curve fitting model.

In this study, we conduct a comprehensive physical analysis of topological indices for the Iron Disulfide (FeS 2 ) network using a curve-fitting model. Iron Disulfide is a cubic compound. In metamorphic rock, sedimentary rock, and quartz veins, it is typically found in combination with other sulfides or oxides. The numerical properties of molecular structures are referred to as topological indices. There are several different kinds of topological indices, including those that are based on distance, degree, or counting, among other factors. The real process of creating a topological index involves turning a chemical structure into a numerical value. In this paper, we calculate the iron disulfide network topological indices using the degrees of vertices in a chemical network of Iron Disulfide (FeS 2 ). Thereafter, we discovered the physical parameters of FeS 2 production, such as heat of formation. We then fitted curves between the thermodynamic properties and several indices. Several techniques based on rationality, linearity, and nonlinearity were used to fit curves in MATLAB. These quantitative results imply that a variety of thermodynamic characteristics of semiconducting materials may be accurately predicted by topological indices. These findings have significant ramifications as they provide the groundwork for the application of topological indices in semiconducting network design and optimization, which might result in more effective and economical material creation.

On curve fitting between topological indices and Gibb's energy for semiconducting carbon nitrides network.

Quantitative structure relationships linked to a chemical structure that shed light on its properties and chemical reactions are called topological indices. This structure is upset by the addition of silicon (Si) doping, which changes the electrical and optical characteristics. In this article, we examine the connection between a chemical structure's Gibbs energy (GE) and K-Banhatti indices. In this article, we compute the K-Banhatti indices and then show the correlation between the indices and Gibb's energy of the molecule using curve fitting. Through the curve fitting, we see that there is a strong correlation between indices and Gibb's energy of a molecule. We use the polynomial curve fitting approach to see the correlation between indices and Gibb's energy.

Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach.

BACKGROUND: Cushing's Disease (CD) is a rare clinical syndrome characterized by excessive secretion of adrenocorticotrophic hormone, leading to significant functional and structural brain alterations as observed in Magnetic Resonance Imaging (MRI). While traditional statistical analysis has been widely employed to investigate these MRI changes in CD, it has lacked the ability to predict individual-level outcomes. PURPOSE: To address this problem, this paper has proposed an interpretable machine learning (ML) framework, including model-level assessment, feature-level assessment, and biology-level assessment to ensure a comprehensive analysis based on structural MRI of CD. METHODS: The ML framework has effectively identified the changes in brain regions in the stage of model-level assessment, verified the effectiveness of these altered brain regions to predict CD from normal controls in the stage of feature-level assessment, and carried out a correlation analysis between altered brain regions and clinical symptoms in the stage of biology-level assessment. RESULTS: The experimental results of this study have demonstrated that the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and the opercular portion of the Inferior frontal gyrus of CD showed significant alterations in brain regions. Furthermore, our study has revealed significant correlations between clinical symptoms and the frontotemporal lobes, insulin, and olfactory cortex, which also have been confirmed by previous studies. CONCLUSIONS: The ML framework proposed in this study exhibits exceptional potential in uncovering the intricate pathophysiological mechanisms underlying CD, with potential applicability in diagnosing other diseases.

Proteomic Profiling of Messenger Ribonucleoproteins in Mouse Tissues Based on Formaldehyde Cross-Linking.

Messenger ribonucleoprotein particles (mRNPs) are vital for tissue-specific gene expression via mediating posttranscriptional regulations. However, proteomic profiling of proteins in mRNPs, i.e., mRNA-associated proteins (mRAPs), has been challenging at the tissue level. Herein, we report the development of formaldehyde cross-linking-based mRNA-associated protein profiling (FAXRAP), a chemical strategy that enables the identification of mRAPs in both cultured cells and intact mouse organs. Applying FAXRAP, tissue-specific mRAPs were systematically profiled in the mouse liver, kidney, heart, and brain. Furthermore, brain mRAPs in Parkinson's disease (PD) mouse model were investigated, which revealed a global decrease of mRNP assembly in the brain of mice with PD. We envision that FAXRAP will facilitate uncovering the posttranscriptional regulation networks in various biological systems.