Hierarchically Porous Polymers from Hyper-cross-linked Block Polymer Precursors

We report synthesis of hierarchically porous polymers (HPPs) consisting of micropores and well-defined 3D continuous mesopores by combination of hyper-cross-linking and block polymer self-assembly. Copolymerization of 4-vinylbenzyl chloride (VBzCl) with divinylbenzene (DVB) in the presence of polylactide (PLA) macro-chain-transfer agent produced a cross-linked block polymer precursor PLA-<i>b</i>-P­(VBzCl-<i>co</i>-DVB) via reversible addition–fragmentation chain transfer polymerization. A nanoscopic bicontinuous morphology containing PLA and P­(VBzCl-<i>co</i>-DVB) microdomains was obtained as a result of polymerization-induced microphase separation. While a basic treatment of the precursor selectively removed PLA to yield a reticulated mesoporous polymer, hyper-cross-linking of the precursor by FeCl₃ generated micropores in the P­(VBzCl-<i>co</i>-DVB) microdomain via Friedel–Crafts alkylation and simultaneously degraded PLA to produce the HPP containing micropores in the mesoporous framework. The mesopore size of the HPP could be precisely controlled from 6 to 15 nm by controlling the molar mass of PLA. We demonstrate acceleration in adsorption rate in the HPP compared to a hyper-cross-linked microporous polymer

Effect of bexarotene on differentiation of glioblastoma multiforme compared with ATRA

Glioblastoma multiforme (GBM) is the most aggressive malignant brain tumor. Since differentiation can attenuate or halt the growth of tumor cells, an image-based phenotypic screening was performed to find out drugs inducing morphological differentiation of GBMs. Bexarotene, a selective retinoid X receptor agonist, showed strong inhibition of neurospheroidal colony formation and migration of cultured primary GBM cells. Bexarotene treatment reduced nestin expression, while significantly increasing glial fibrillary acidic protein (GFAP) expression. The effect of bexarotene on gene expression profile was compared with the activity of all-trans retinoic acid (ATRA), a well-known differentiation inducer. Both drugs largely altered the gene expression pattern into a tumor-ameliorating direction. These drugs increased the gene expression levels of Kruppel-like factor 9 (KLF9), regulator of G-protein signaling 4 (RGS4), growth differentiation factor 15 (GDF15), angiopoietin-like protein 4 (ANGPTL4), and lowered the level of chemokine receptor type 4 (CXCR4). However, transglutaminase 2 (TG2) induction, an adverse effect of ATRA, was much weaker in bexarotene treated primary GBM cells. Consistently, the TG2 enzymatic activity was negligibly affected by bexarotene treatment. It is important to control TG2 overexpression since its upregulation is correlated with tumor transformation and drug resistance. Bexarotene also showed in vivo tumoricidal effects in a GBM xenograft mouse model. Therefore, we suggest bexarotene as a more beneficial differentiation agent than ATRA for GBM.1

Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients

(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods—a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)—focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software

OBJECTIVE: The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. MATERIALS AND METHODS: MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. RESULTS: Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant. CONCLUSION: The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics