Xerostomia prediction in patients with nasopharyngeal carcinoma during radiotherapy using segmental dose distribution in dosiomics and radiomics models.

OBJECTIVES: This study aimed to integrate radiomics and dosiomics features to develop a predictive model for xerostomia (XM) in nasopharyngeal carcinoma after radiotherapy. It explores the influence of distinct feature extraction methods and dose ranges on the performance. MATERIALS AND METHODS: Data from 363 patients with nasopharyngeal carcinoma were retrospectively analyzed. We pioneered a dose-segmentation strategy, where the overall dose distribution (OD) was divided into four segmental dose distributions (SDs) at intervals of 15 Gy. Features were extracted using manual definition and deep learning, applying OD or SD and integrating radiomics and dosiomics, yielding corresponding feature scores (manually defined radiomics, MDR; manually defined dosiomics, MDD; deep learning-based radiomics, DLR; deep learning-based dosiomics, DLD). Subsequently, 18 models were developed by combining features and model types (random forest and support vector machine). RESULTS AND CONCLUSION: Under OD, O(DLR_DLD) demonstrated exceptional performance, with an optimal area under the curve (AUC) of 0.81 and an average AUC of 0.71. Within SD, S(DLR_DLD) surpassed the other models, achieving an optimal AUC of 0.90 and an average AUC of 0.85. Therefore, the integration of dosiomics into radiomics can augment predictive efficacy. The dose-segmentation strategy can facilitate the extraction of more profound information. This indicates that ScoreDLR and ScoreMDR were negatively associated with XM, whereas ScoreDLD, derived from SD exceeding 15 Gy, displayed a positive association with XM. For feature extraction, deep learning was superior to manual definition.

Developments in Atomistic and Nano Structure Evolution Mechanisms of Molten Slag Using Atomistic Simulation Methods.

Molten slag has different properties depending on its composition. The relationship between its composition, structure, and properties has been the focus of attention in industrial manufacturing processes. This review describes the atomistic scale mechanisms by which oxides of different compositions affect the properties and structure of slag, and depicts the current state of research in the atomic simulation of molten slag. At present, the research on the macroscopic properties of molten slag mainly focuses on viscosity, free-running temperature, melting point, and desulphurization capacity. Regulating the composition has become the most direct and effective way to control slag properties. Analysis of the microevolution mechanism is the fundamental way to grasp the macroscopic properties. The microstructural evolution mechanism, especially at the atomic and nanoscale of molten slag, is reviewed from three aspects: basic oxides, acidic oxides, and amphoteric oxides. The evolution of macroscopic properties is analyzed in depth through the evolution of the atomic structure. Resolution of the macroscopic properties of molten slag by the atomic structure plays a crucial role in the development of fundamental theories of physicochemistry.

Acute kidney injury comorbidity analysis based on international classification of diseases-10 codes.

OBJECTIVE: Acute kidney injury (AKI) is a clinical syndrome that occurs as a result of a dramatic decline in kidney function caused by a variety of etiological factors. Its main biomarkers, serum creatinine and urine output, are not effective in diagnosing early AKI. For this reason, this study provides insight into this syndrome by exploring the comorbidities of AKI, which may facilitate the early diagnosis of AKI. In addition, organ crosstalk in AKI was systematically explored based on comorbidities to obtain clinically reliable results. METHODS: We collected data from the Medical Information Mart for Intensive Care-IV database on patients aged [Formula: see text] 18 years in intensive care units (ICU) who were diagnosed with AKI using the criteria proposed by Kidney Disease: Improving Global Outcomes. The Apriori algorithm was used to mine association rules on the diagnoses of 55,486 AKI and non-AKI patients in the ICU. The comorbidities of AKI mined were validated through the Electronic Intensive Care Unit database, the Colombian Open Health Database, and medical literature, after which comorbidity results were visualized using a disease network. Finally, organ diseases were identified and classified from comorbidities to investigate renal crosstalk with other distant organs in AKI. RESULTS: We found 579 AKI comorbidities, and the main ones were disorders of lipoprotein metabolism, essential hypertension, and disorders of fluid, electrolyte, and acid-base balance. Of the 579 comorbidities, 554 were verifiable and 25 were new and not previously reported. In addition, crosstalk between the kidneys and distant non-renal organs including the liver, heart, brain, lungs, and gut was observed in AKI with the strongest heart-kidney crosstalk, followed by lung-kidney crosstalk. CONCLUSION: The comorbidities mined in this study using association rules are scientific and may be used for the early diagnosis of AKI and the construction of AKI predictive models. Furthermore, the organ crosstalk results obtained through comorbidities may provide supporting information for the management of short- and long-term treatment practices for organ dysfunction.

New Insights into the Traditional Charge Compensation Theory: Amphoteric Behavior of TiO2 under the Guidance of Supply-Demand Relationship.

Polymerization degree theory and traditional charge compensation theory are the most fundamental principles to understand the structure and properties of oxide melts. It can well explain the behavior characteristics of acidic oxides and basic oxides in a melt. However, the amphoteric behavior of oxides cannot be explained well by these two theories. Herein, the octahedral connection mode and the behavior of the amphoteric transition of TiO2 are analyzed by molecular dynamics simulation, and then, a calculation model which can quantitatively calculate the amphoteric transition of the oxide is established by analyzing a large number of data. On the basis of the model, a novel theory of supply and demand is put forward, which can explain the amphoteric transition behavior of oxides very well. To a great extent, the supply and demand theory makes up for the deficiency of the atomic structure theory of oxide melts and provides mechanism explanation and model prediction for the oxide amphoteric transformation behavior.

Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes.

BACKGROUND: Autism is a lifelong disability associated with several comorbidities that confound diagnosis and treatment. A better understanding of these comorbidities would facilitate diagnosis and improve treatments. Our aim was to improve the detection of comorbid diseases associated with autism. METHODS: We used an FP-growth algorithm to retrospectively infer disease associations using 1488 patients with autism treated at the Guangzhou Women and Children's Medical Center. The disease network was established using Cytoscape 3.7. The rules were internally validated by 10-fold cross-validation. All rules were further verified using the Columbia Open Health Data (COHD) and by literature search. RESULTS: We found 148 comorbid diseases including intellectual disability, developmental speech disorder, and epilepsy. The network comprised of 76 nodes and 178 directed links. 158 links were confirmed by literature search and 105 links were validated by COHD. Furthermore, we identified 14 links not previously reported. CONCLUSION: We demonstrate that the FP-growth algorithm can detect comorbid disease patterns, including novel ones, in patients with autism.