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Abstract: Since there are hydrogen bonds and no exchangeable ions between the layers of kaolinite, the intercalation reaction of organics is difficult. We induct microwave techniques into the preparation of kaolinite/organics intercalation compound. We studied the microwave effect for the inserted piece (dimethylsulfoxide), intercalation rate and intercalation speed and discussed intercalation compound by Fourier-transformed infrared spectroscopy, X-ray diffraction (powder method), thermal analysis (TG/DTA) and transmission electron microscopy. The result shows that microwave radiation can greatly shorten reaction time. XRD shows the intercalation rate can reach above 80% after 40 min reaction. FTIR proves the hydrogen bonds are formed between DMSO molecules and the inner surface hydroxyls of kaolinite and a TEM micrograph shows that microwave radiation can accelerate an exfoliation effect, which makes stacking layers of kaolinite delaminated as well as a reduction of the particle size dimensions. [Copyright &y& Elsevier]

Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is a major cause of mortality. Predicting ER before treatment can guide treatment and follow-up protocols. Deep learning frameworks, known for their superior performance, are widely used in medical imaging. However, they face challenges due to limited annotated data. We propose a multi-task pre-training method using self-supervised learning with medical images for predicting the ER of HCC. This method involves two pretext tasks: phase shuffle, focusing on intra-image feature representation, and case discrimination, focusing on inter-image feature representation. The effectiveness and generalization of the proposed method are validated through two different experiments. In addition to predicting early recurrence, we also apply the proposed method to the classification of focal liver lesions. Both experiments show that the multi-task pre-training model outperforms existing pre-training (transfer learning) methods with natural images, single-task self-supervised pre-training, and DINOv2. [ABSTRACT FROM AUTHOR]

Purpose. The objectives of our study were to assess the association of radiological imaging and gene expression with patient outcomes in non-small cell lung cancer (NSCLC) and construct a nomogram by combining selected radiomic, genomic, and clinical risk factors to improve the performance of the risk model. Methods. A total of 116 cases of NSCLC with CT images, gene expression, and clinical factors were studied, wherein 87 patients were used as the training cohort, and 29 patients were used as an independent testing cohort. Handcrafted radiomic features and deep-learning genomic features were extracted and selected from CT images and gene expression analysis, respectively. Two risk scores were calculated through Cox regression models for each patient based on radiomic features and genomic features to predict overall survival (OS). Finally, a fusion survival model was constructed by incorporating these two risk scores and clinical factors. Results. The fusion model that combined CT images, gene expression data, and clinical factors effectively stratified patients into low- and high-risk groups. The C-indexes for OS prediction were 0.85 and 0.736 in the training and testing cohorts, respectively, which was better than that based on unimodal data. Conclusions. Combining radiomics and genomics can effectively improve OS prediction for NSCLC patients. [ABSTRACT FROM AUTHOR]

Few pieces of evidence have been published on the use of Apatinib Mesylate (AM) against EGFR-TKI resistance in lung adenocarcinoma (LA) patients. Here, we investigate the clinical efficacy and safety of AM in the treatment of advanced progressed epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) resistant LA patients. We conducted a double-blind, randomized controlled trial in 68 patients admitted to 18 hospitals of Anhui province in China. The efficacy and safety of AM treatment were evaluated in terms of progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR), as well as related adverse events (AE). A literature knowledge database analysis and a pathway model reconstruction were performed to decipher the relevant mechanism may be involved. Our results showed that, compared to the control group, AM presented improved efficacy in PFS (P = 0.033), ORR (P < 0.001), and DCR (P < 0.001). No significant difference was observed between case and control group in terms of AE, and no drug-related death occurred. Pathway analysis supports that Apatinib can be repurposed for the treatment of LA. Our results suggested that AM could be a potential option for advanced progressed LA patients to combat EGFR-TKI resistance. [ABSTRACT FROM AUTHOR]