Constructing sequence-controlled heterolayered dendritic lanthanide chelates via a one-pot strategy using orthogonal chemistry.

The construction of sequence-controlled heterometallic lanthanide complexes is challenging despite their intriguing physical/chemical properties and enormous potential applications. Here we report a one-pot strategy that exploits orthogonal chemical reactions for modular assembly, which allows for rapid preparation of sequence-controlled heterolayered lanthanide-complex dendritic structures.

A dual-responsive doxorubicin-indoximod conjugate for programmed chemoimmunotherapy.

Herein we report a dual-responsive doxorubicin-indoximod conjugate (DOXIND) for programmed chemoimmunotherapy. This conjugate is able to release doxorubicin and indoximod upon exposure to appropriate stimuli for synergistic chemotherapy and immunotherapy, respectively. We demonstrate its promoting effects on immune response and inhibiting effects on tumor growth through a series of in vitro and in vivo experiments.

Interpretative computer-aided lung cancer diagnosis: From radiology analysis to malignancy evaluation.

BACKGROUND AND OBJECTIVE: Computer-aided diagnosis (CAD) systems promote accurate diagnosis and reduce the burden of radiologists. A CAD system for lung cancer diagnosis includes nodule candidate detection and nodule malignancy evaluation. Recently, deep learning-based pulmonary nodule detection has reached satisfactory performance ready for clinical application. However, deep learning-based nodule malignancy evaluation depends on heuristic inference from low-dose computed tomography (LDCT) volume to malignant probability, and lacks clinical cognition. METHODS: In this paper, we propose a joint radiology analysis and malignancy evaluation network called R2MNet to evaluate pulmonary nodule malignancy via the analysis of radiological characteristics. Radiological features are extracted as channel descriptor to highlight specific regions of the input volume that are critical for nodule malignancy evaluation. In addition, for model explanations, we propose channel-dependent activation mapping (CDAM) to visualize features and shed light on the decision process of deep neural networks (DNNs). RESULTS: Experimental results on the lung image database consortium image collection (LIDC-IDRI) dataset demonstrate that the proposed method achieved an area under curve (AUC) of 96.27% and 97.52% on nodule radiology analysis and nodule malignancy evaluation, respectively. In addition, explanations of CDAM features proved that the shape and density of nodule regions are two critical factors that influence a nodule to be inferred as malignant. This process conforms to the diagnosis cognition of experienced radiologists. CONCLUSION: The network inference process conforms to the diagnostic procedure of radiologists and increases the confidence of evaluation results by incorporating radiology analysis with nodule malignancy evaluation. Besides, model interpretation with CDAM features shed light on the focus regions of DNNs during the estimation of nodule malignancy probabilities.