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Graphical abstract Abstract Gastric floating tablets are a multifunctional dosage form with the merits of long-term gastric retention, sustained release and improved bioavailability though floating time and sustained release are usually not satisfied. Here we designed a novel gastric floating system by combining compressed tablets with 3D printed devices, wherein a riboflavin tablet was filled into a device. The table-filled device can be called a tablet-in-device (TiD) system. Commercial poly(lactic acid) filaments were used for fused deposition modeling (FDM) 3D printing of the body and cap of the device. Four types of TiD systems were prepared. The basic structures of them involved non-net, centrally symmetric double-net (including a peripheral sealed air-filled chamber and a centric net-on-both-sides chamber), single-net (including a sealed air-filled chamber on the top side and a net-on-one-side chamber on the bottom side), and eccentric double-net (including an eccentric net-on-both-sides chamber and an air-filled chamber). They were exquisitely designed after precise calculations of every chamber parameters according to the buoyant principle. All of them showed good floating ability, although only the latter two TiD systems were selected due to appropriate drug release. Compressed riboflavin tablets, consisting of riboflavin, lactose, hydroxypropyl methylcellulose (HPMC) and magnesium stearate, were prepared with the direct compaction method. All the tablets showed rapid drug release though the release was highly hindered by the devices in the TiD systems due to the barrier effect of devices and the tablet slurry formation. The single-net and double-net TiD systems achieved the cumulative release of 41% and 62% at 72 h, respectively, along with simultaneously well floating. In vivo long-term (>72 h) gastric floating function of TiD systems was further demonstrated on the rabbit models by the CT investigation. TiD systems are appropriate for oral administration of drugs with super long-term floating and controlled release in the gastric route. [ABSTRACT FROM AUTHOR]

• Propose a new end-to-end deep model called C2-Net that effectively exploits multi-view information for mammogram soft-tissue lesion detection. • Propose three specific modules to compare ipsilateral features, contrast bilateral features and fuse multi-view features respectively. • Experimental results on both the DDSM dataset and a large multi-center in-house dataset shows our model achieves state-of-the-art performance. The architecture of the proposed C2-Net. The model takes the multiview mammogram images as input and produces the enhanced representation for further detection. The major components are Spatial Context Enhancing (SCE) module, Multi-scale Kernel Pooling (MKP) module and Logic Guided Fusion (LGF) module. SCE module learns to compare corresponding regions of cross-view mammogram images, while MKP module tolerates the geometric deformation for robust contrast of asymmetry. Furthermore, LGF module distills the enhanced representation by fusing multiview features. [Display omitted] Detecting breast soft-tissue lesions including masses, structural distortions and asymmetries is of great importance due to the high risk leading to breast cancer. Most existing deep learning based approaches detect lesions with only unilateral images. However, multi-view mammogram images provide highly related and complementary information which helps to make the clinical analysis more comprehensive and reliable. In this paper, we propose a multi-view network for breast soft-tissue lesion detection called C 2 -Net (Compare and Contrast, C 2) that fuses information across different views. The proposed model contains the following three modules. The spatial context enhancing (SCE) module compares ipsilateral views and extracts complementary features to model lesion inherent 3D structure. The multi-scale kernel pooling (MKP) module contrasts contralateral views with added misalignment tolerance. Finally, the logic guided fusion (LGF) module fuses multi-view features by enhancing logic modeling capacity. Experimental results on both the public DDSM dataset and the in-house multi-center dataset demonstrate that the proposed method has achieved state-of-the-art performance. [ABSTRACT FROM AUTHOR]