Machine Learning Models for Forward and Inverse Problems in Diffuse Reflectance Spectroscopy

This thesis addresses the challenges encountered in diffuse reflectance spectroscopy (DRS) regarding the accuracy and efficiency of existing forward and inverse models. To overcome these challenges, we propose the application of machine learning methods, utilizing Monte Carlo data and experimental data from lab phantoms, to develop improved models that enhance accuracy, versatility, and time efficiency.Initially, surrogate models are developed using machine learning techniques and compared to Monte Carlo simulations for forward predictions in terms of accuracy and time efficiency. This enables us to evaluate the effectiveness of machine learning approaches in enhancing predictive capabilities.Furthermore, a transfer learning-based model is developed to calibrate the system and improve accuracy. By leveraging pre-trained models, this approach significantly reduces the calibration effort while maintaining high precision.In the context of inverse modeling, we modify the traditional Monte Carlo lookup table approach by replacing the forward model with a trained neural network. This neural network-based inverse model is then validated using DRS phantom datasets, confirming its effectiveness in accurately estimating optical parameters.Finally, our aim is to develop a novel neural network-based inverse model that eliminates the need for generating a lookup table. By directly utilizing the neural network, this model not only enhances accuracy and efficiency but also opens pathways for the development of superior clinical diagnostic tools empowered by machine learning.The outcomes of this thesis contribute to advancing the field of DRS by overcoming existing limitations in accuracy and efficiency. By harnessing the power of machine learning, our proposed models offer improved predictive capabilities, calibration efficiency, and accurate estimation of optical parameters. These advancements lay the foundation for the development of next-generation clinical diagnostic tools with superior performance and reliability.</p

Neural Network-based Inverse Model for Diffuse Reflectance Spectroscopy

In diffuse optical spectroscopy, the retrieval of the optical properties of a target requires the inversion of a measured reflectance spectrum. This is typically achieved through the use of forward models such as diffusion theory or Monte Carlo simulations, which are iteratively applied to optimize the reconstruction of the optical parameters. In this paper, we propose a novel neural network-based approach for solving this inverse problem, and establish its performance using experimentally measured diffuse reflectance data from a previously reported phantom study. Our inverse model was developed from a neural network forward model that was pre-trained with data from Monte Carlo simulations. The neural network forward model then creates a lookup table to invert the diffuse reflectance to the optical coefficients. We describe the construction of the neural network-based inverse model and test its ability to accurately retrieve optical properties from experimentally acquired diffuse reflectance data in liquid optical phantoms. Our results indicate that the developed neural network-based model achieves comparable accuracy to traditional Monte Carlo-based inverse models while offering improved speed and flexibility, potentially providing an alternative for developing faster clinical diagnosis tools. This study highlights the potential of neural networks in solving inverse problems in diffuse optical spectroscopy

Efficacy and safety of fluticasone propionate nasal spray in treatment ofadenoidal hypertrophic snoring in children

Abstract This study investigatedthe efficacy and safety offluticasone propionate nasal spray in treatment of adenoidal hypertrophic snoring in children.Fifty-six children with adenoidal hypertrophic snoring were enrolled. According to adenoidal-nasopharyngeal ratio (ANR) in lateral nasal X-ray examination,the children were assigned in moderategroup (23 cases) and severegroup (33 cases).The fluticasone propionate nasal spray was used for all patientsfor 4 weeks.In 56 patients, after treatment, compared with before treatment, the snoring, sleep apneaand nasal obstruction scores in moderategroupand the nasal obstruction score in severegroupwere significantly decreased,respectively (P </div

Heterogeneous Photodegradation of Pentachlorophenol with Maghemite and Oxalate under UV Illumination

The degradation of pentachlorophenol (PCP) in a heterogeneous system with maghemite (γ-Fe2O3) and oxalate under UV illumination was investigated in this study. The results of adsorption experiments demonstrated competitive adsorption between PCP and oxalic acid on the surface of γ-Fe2O3. The results of photodegradation experiments showed that the rate of PCP degradation strongly relied on the oxalic acid concentration and that an optimal tested initial concentration of oxalic acid (C0ox) of 0.8 mM was obtained under our experimental conditions. It was observed that a sufficient amount of oxalic acid can be adsorbed on the γ-Fe2O3 to form various Fe(III)-oxalate complexes at C0ox = 0.8 mM. During the photoreaction, Fe(C2O4)2− and Fe(C2O4)33− were found to be the dominant Fe(III)-oxalate complexes at different C0ox, while Fe(C2O4)22− was the dominant Fe(II)-oxalate complex at C0ox ≥ 0.8 mM. The mechanism of H2O2 formation and consumption in the UV-irradiated γ-Fe2O3/oxalate system was proposed and evaluated. Furthermore, six intermediates of PCP degradation were identified by GC/MS, HPLC, and IC analyses, respectively, and a possible pathway of PCP degradation in such a system was proposed

Synthesis, structure, and characterization of two 1-D homometallic coordination polymers based on carboxylate-functionlized salen ligands

<div><p>Two 1-D homometallic coordination polymers <b>1</b> and <b>2</b> have been prepared by one-step hydrothermal reactions of carboxylate-functionalized salen ligands with Co(II) and Mn(II), respectively. Single-crystal X-ray diffraction analyses reveal that both the 1-D chains give 3-D supramolecular structures through intermolecular hydrogen bonds and <i>π</i><i>π</i> packing interactions. Magnetic investigation of <b>2</b> indicates the presence of antiferromagnetic interactions in 1-D chains.</p></div

Supplementary Data from miR-454-3p Is an Exosomal Biomarker and Functions as a Tumor Suppressor in Glioma

Supplementary Table 1. Univariate and multivariate analyses on survival in glioma patients. Supplementary Figure 1: Real-time PCR analysis of exosomal miR-454-3p in the serum of glioma patients and that of healthy controls in the combination group. Supplementary Figure 2: ATG12 is highly expressed in glioma. (A) ATG12 expression in different types of cancer and in their respective normal controls on GEPIA. GBM: glioblastoma, LLG: low grade glioma. (B) ATG12 expression in GBM and non-tumor on Gliovis. GBM: glioblastoma.</p

DataSheet_1_Identifying α-KG-dependent prognostic signature for lower-grade glioma based on transcriptome profiles.pdf

The inhibition of alpha-ketoglutarate (α-KG)-dependent dioxygenases is thought to contribute to isocitrate dehydrogenase (IDH) mutation-derived malignancy. Herein, we aim to thoroughly investigate the expression pattern and prognostic significance of genes encoding α-KG-dependent enzymes for lower-grade glioma (LGG) patients. In this retrospective study, a total of 775 LGG patients were enrolled. The generalized linear model, least absolute shrinkage and selection operator Cox regression, and nomogram were applied to identify the enzyme-based signature. With the use of gene set enrichment analysis and Gene Ontology, the probable molecular abnormalities underlying high-risk patients were investigated. By comprehensively analyzing mRNA data, we observed that 41 genes were differentially expressed between IDHMUT and IDHWT LGG patients. A risk signature comprising 10 genes, which could divide samples into high- and low-risk groups of distinct prognoses, was developed and independently validated. This enzyme-based signature was indicative of a more malignant phenotype. The nomogram model incorporating the risk signature, molecular biomarkers, and clinicopathological parameters proved the incremental utility of the α-KG-dependent signature by achieving a more accurate prediction impact. Our study demonstrates that the α-KG-dependent enzyme-encoding genes were differentially expressed in relation to the IDH phenotype and may serve as a promising indicator for clinical outcomes of LGG patients.</p

Image_5_Identifying α-KG-dependent prognostic signature for lower-grade glioma based on transcriptome profiles.tif

The inhibition of alpha-ketoglutarate (α-KG)-dependent dioxygenases is thought to contribute to isocitrate dehydrogenase (IDH) mutation-derived malignancy. Herein, we aim to thoroughly investigate the expression pattern and prognostic significance of genes encoding α-KG-dependent enzymes for lower-grade glioma (LGG) patients. In this retrospective study, a total of 775 LGG patients were enrolled. The generalized linear model, least absolute shrinkage and selection operator Cox regression, and nomogram were applied to identify the enzyme-based signature. With the use of gene set enrichment analysis and Gene Ontology, the probable molecular abnormalities underlying high-risk patients were investigated. By comprehensively analyzing mRNA data, we observed that 41 genes were differentially expressed between IDHMUT and IDHWT LGG patients. A risk signature comprising 10 genes, which could divide samples into high- and low-risk groups of distinct prognoses, was developed and independently validated. This enzyme-based signature was indicative of a more malignant phenotype. The nomogram model incorporating the risk signature, molecular biomarkers, and clinicopathological parameters proved the incremental utility of the α-KG-dependent signature by achieving a more accurate prediction impact. Our study demonstrates that the α-KG-dependent enzyme-encoding genes were differentially expressed in relation to the IDH phenotype and may serve as a promising indicator for clinical outcomes of LGG patients.</p

Image_6_Identifying α-KG-dependent prognostic signature for lower-grade glioma based on transcriptome profiles.tif

The inhibition of alpha-ketoglutarate (α-KG)-dependent dioxygenases is thought to contribute to isocitrate dehydrogenase (IDH) mutation-derived malignancy. Herein, we aim to thoroughly investigate the expression pattern and prognostic significance of genes encoding α-KG-dependent enzymes for lower-grade glioma (LGG) patients. In this retrospective study, a total of 775 LGG patients were enrolled. The generalized linear model, least absolute shrinkage and selection operator Cox regression, and nomogram were applied to identify the enzyme-based signature. With the use of gene set enrichment analysis and Gene Ontology, the probable molecular abnormalities underlying high-risk patients were investigated. By comprehensively analyzing mRNA data, we observed that 41 genes were differentially expressed between IDHMUT and IDHWT LGG patients. A risk signature comprising 10 genes, which could divide samples into high- and low-risk groups of distinct prognoses, was developed and independently validated. This enzyme-based signature was indicative of a more malignant phenotype. The nomogram model incorporating the risk signature, molecular biomarkers, and clinicopathological parameters proved the incremental utility of the α-KG-dependent signature by achieving a more accurate prediction impact. Our study demonstrates that the α-KG-dependent enzyme-encoding genes were differentially expressed in relation to the IDH phenotype and may serve as a promising indicator for clinical outcomes of LGG patients.</p

Image_4_Identifying α-KG-dependent prognostic signature for lower-grade glioma based on transcriptome profiles.tif

The inhibition of alpha-ketoglutarate (α-KG)-dependent dioxygenases is thought to contribute to isocitrate dehydrogenase (IDH) mutation-derived malignancy. Herein, we aim to thoroughly investigate the expression pattern and prognostic significance of genes encoding α-KG-dependent enzymes for lower-grade glioma (LGG) patients. In this retrospective study, a total of 775 LGG patients were enrolled. The generalized linear model, least absolute shrinkage and selection operator Cox regression, and nomogram were applied to identify the enzyme-based signature. With the use of gene set enrichment analysis and Gene Ontology, the probable molecular abnormalities underlying high-risk patients were investigated. By comprehensively analyzing mRNA data, we observed that 41 genes were differentially expressed between IDHMUT and IDHWT LGG patients. A risk signature comprising 10 genes, which could divide samples into high- and low-risk groups of distinct prognoses, was developed and independently validated. This enzyme-based signature was indicative of a more malignant phenotype. The nomogram model incorporating the risk signature, molecular biomarkers, and clinicopathological parameters proved the incremental utility of the α-KG-dependent signature by achieving a more accurate prediction impact. Our study demonstrates that the α-KG-dependent enzyme-encoding genes were differentially expressed in relation to the IDH phenotype and may serve as a promising indicator for clinical outcomes of LGG patients.</p