ABSTRACT
BACKGROUND: Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression. METHODS: This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression. RESULTS: In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031. CONCLUSION: This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.
ABSTRACT
A new approach was developed to construct the tetrahydroxanthone by a Knoevenagel condensation/6π-electronic cyclization/aromatization cascade starting from readily available cyclohexane-1,3-diones and unsaturated aldehydes. This strategy provides a new solution for the preparation of monomeric tetrahydroxanthones bearing different functional groups at C-12. As a synthetic application, the asymmetric formal synthesis of rugulotrosin A was achieved.
ABSTRACT
The first total synthesis of the dimeric tetrahydroxanthone ascherxanthone A has been accomplished. This synthetic strategy features (1) enantioselective intramolecular allylic C-H oxidation to construct a core chiral chromane, (2) intramolecular aldol reaction/dehydration to form the enone group, and (3) intermolecular Suzuki-Miyaura coupling to connect two monomeric tetrahydroxanthones. This synthetic work allowed us to determine the axial chirality of the 2,2'-biaryl C-C bond and the absolute configuration of the ascherxanthone A. This approach should facilitate the preparation of derivatives and structurally related natural products for medicinal studies.
Subject(s)
Xanthones/chemistry , Molecular Structure , Oxidation-Reduction , StereoisomerismABSTRACT
The reaction conditions and scope of the photo-Nazarov reaction of aryl vinyl ketones were investigated. In contrast to the conventional acid-catalyzed methods, this photolytic electrocyclization proceeds in the neutral or basic conditions. Irradiating substrates bearing various aromatic rings, acid-sensitive groups, cyclohexenyl, cycloheptenyl, and unsaturated pyran with UV-light (254â nm) smoothly yielded hexahydrofluorenones and related structures. This photo-Nazarov reaction could also be applicable to the substrates carrying ß-alkyl groups on the enone, which gave corresponding polycyclic rings containing quaternary centers. These photo-electrocyclized products may prove useful for synthesizing a variety of natural products and their derivatives. Further application of this mild photo-Nazarov reaction in the synthesis of taiwaniaquinolâ B was achieved.
ABSTRACT
A metal-free, photo-induced C-O bond formation methodology was developed to construct tetrahydroxanthones. This mild and efficient methodology was based on intramolecular oxygen trapping of the reactive species produced by photolytic activation of a C-Cl bond. We believe this method could be used in the synthesis of related xanthone-type natural products.
