The feasibility of personalized medicine for cancer treatment is largely hampered by costly, labor-intensive and time-consuming models for drug discovery. Herein, establishing new pre-clinical models to tackle these issues for personalized medicine is urgently demanded. Methods: We established a three-dimensional tumor slice culture (3D-TSC) platform incorporating label-free techniques for time-course experiments to predict anti-cancer drug efficacy and validated the 3D-TSC model by multiphoton fluorescence microscopy, RNA sequence analysis, histochemical and histological analysis. Results : Using time-lapse imaging of the apoptotic reporter sensor C3 (C3), we performed cell-based high-throughput drug screening and shortlisted high-efficacy drugs to screen murine and human 3D-TSCs, which validate effective candidates within 7 days of surgery. Histological and RNA sequence analyses demonstrated that 3D-TSCs accurately preserved immune components of the original tumor, which enables the successful achievement of immune checkpoint blockade assays with antibodies against PD-1 and/or PD-L1. Label-free multiphoton fluorescence imaging revealed that 3D-TSCs exhibit lipofuscin autofluorescence features in the time-course monitoring of drug response and efficacy. Conclusion : This technology accelerates precision anti-cancer therapy by providing a cheap, fast, and easy platform for anti-cancer drug discovery.

Background: Immunotherapy for cancer includes chimeric antigen receptor (CAR)-T cells, CAR-natural killer (NK) cells, PD1 and the PD-L1 inhibitor However, the proportion of patients who respond to cancer immunotherapy is not satisfactory. Concurrently, nanotechnology has experienced a revolution in cancer diagnosis and therapy. There are few clinically approved nanoparticles that can selectively bind and target cancer cells and incorporate molecules, although many therapeutic nanocarriers have been approved for clinical use. There are no systematic reviews outlining how nanomedicine and immunotherapy are used in combination to treat cancer. Objective: This review aims to illustrate how nanomedicine and immunotherapy can be used for cancer treatment to overcome the limitations of the low proportion of patients who respond to cancer immunotherapy and the rarity of nanomaterials in clinical use. Methods: A literature review of MEDLINE, PubMed / PubMed Central and Google Scholar was performed. We performed a structured search of literature reviews on nanoparticle drug-delivery systems, which included photodynamic therapy, photothermal therapy, photoacoustic therapy and immunotherapy for cancer. Moreover, we detailed the advantages and disadvantages of the various nanoparticles incorporated with molecules to discuss the challenges and solutions associated with cancer treatment. Conclusion: This review identified the advantages and disadvantages associated with improving health care and outcomes. The findings of this review confirmed the importance of nanomedicine-combined immunotherapy for improving the efficacy of cancer treatment. It may become a new way to develop novel cancer therapeutics using nanomaterials to achieve synergistic anticancer immunity.

Traditional tumor models cannot perfectly simulate the real state of tumors in vivo, resulting in the termination of many clinical trials. 3D tumor models’ technology provides new in vitro models that bridge the gap between in vitro and in vivo findings, and organoids maintain the properties of the original tissue over a long period of culture, which enables extensive research in this area. In addition, they can be used as a substitute for animal and in vitro models, and organoids can be established from patients’ normal and malignant tissues, with unique advantages in clinical drug development and in guiding individualized therapies. 3D tumor models also provide a promising platform for high-throughput research, drug and toxicity testing, disease modeling, and regenerative medicine. This report summarizes the 3D tumor model, including evidence regarding the 3D tumor cell culture model, 3D tumor slice model, and organoid culture model. In addition, it provides evidence regarding the application of 3D tumor organoid models in precision oncology and drug screening. The aim of this report is to elucidate the value of 3D tumor models in cancer research and provide a preclinical reference for the precise treatment of cancer patients.

There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the corresponding labels. Traditional ECG processing approaches rely heavily on prior knowledge, such as those from feature extraction and waveform analysis. The preprocessing for prior knowledge incurs computational overhead. Furthermore, standard deep learning methods do not fully consider the dynamic temporal, spatial and multi-labeling characteristics of ECG data. In clinical ECG waveforms, it is common to see multi-labeling in which a patient is labeled with multiple classes of arrhythmias. However, multiclass approaches in current research mainly solve the multi-label machine learning problem, ignoring the correlation between diseases, resulting in information loss. In this paper, an arrhythmia detection and classification scheme called multi-label fusion deep learning is proposed. The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. First, a multi-label ECG-based feature selection method is combined with a matrix decomposition and sparse learning theory. The optimal feature subset is selected as a preprocessing algorithm for ECG data. A multi-label classifier is then constructed by fusing CNN and RNN networks to fully exploit the interactions and features of the time and space dimensions. The experimental result demonstrates that the proposed method can achieve a state-of-the-art performance compared to other algorithms in multi-label database experiments.

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