Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images. The study utilized 131 CT scans from the Liver Tumor Segmentation (LiTS) challenge and demonstrated the superior efficiency and accuracy of the proposed Hybrid-ResUNet model with a Dice score of 0.9433 and an AUC of 0.9965 compared to ResNet and EfficientNet models. This FL approach is beneficial for conducting large-scale clinical trials while safeguarding patient privacy across healthcare settings. It facilitates active engagement in problem-solving, data collection, model development, and refinement. The study also addresses data imbalances in the FL context, showing resilience and highlighting local models' robust performance. Future research will concentrate on refining federated learning algorithms and their incorporation into the continuous implementation and deployment (CI/CD) processes in AI system operations, emphasizing the dynamic involvement of clients. We recommend a collaborative human-AI endeavor to enhance feature extraction and knowledge transfer. These improvements are intended to boost equitable and efficient data collaboration across various sectors in practical scenarios, offering a crucial guide for forthcoming research in medical AI.

Synthesis, aggregation induced emission and through space conjugation of triphenylvinylphenyl substituted [2.2]paracyclophane-1,9-diene.

4-Bromo substituted [2.2]paracyclophane-1,9-diene was synthesized from the corresponding dithia[3.3]paracyclophane in three steps through benzyne Steven rearrangement, oxidation, and a thermal elimination reaction. 4-Triphenylvinylphenyl substituted [2.2]paracyclophane-1,9-diene was successfully prepared by the Suzuki-Miyaura cross-coupling reaction of 4-bromo substituted [2.2]paracyclophane-1,9-diene and 4,4,5,5-tetramethyl-2-(4-(1,2,2-triphenylvinyl)phenyl)-1,3,2-dioxaborolane using Pd(OAc)2 as a catalyst, S-Phos as a ligand and K3PO4 as a base. The structures of bromo substituted [2.2] paracyclophane-1,9-diene and triphenylvinylphenyl substituted [2.2]paracyclophane-1,9-diene were fully characterized by 1H NMR spectroscopy and X-ray crystallography. 4-Triphenylvinylphenyl substituted [2.2]paracyclophane-1,9-diene exhibited aggregation-induced emission characteristics when the water fraction was higher than 80% in the THF/water mixtures. 4-Triphenylvinylphenyl substituted [2.2]paracyclophane-1,9-diene displays much higher fluorescence when the water fraction is 90% compared to that of model compounds due to both through bond and through space conjugation. To the best for our knowledge, we are the first to synthesize triphenylvinylphenyl substituted [2.2]paracyclophane-1,9-diene with aggregation-induced emission characteristics.