Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: −0.0094; AUPRC: −0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.

In this study, we designed near-infrared (NIR)-responsive Mn²⁺-doped melanin-like poly(L-DOPA) nanoparticles (MNPs), which act as multifunctional nano-platforms for cancer therapy. MNPs, exhibited favorable π-π stacking, drug loading, dual stimuli (NIR and glutathione) responsive drug release, photothermal and photodynamic therapeutic activities, and T₁-positive contrast for magnetic resonance imaging (MRI). First, MNPs were fabricated via KMnO₄ oxidation, where the embedded Mn²⁺ acted as a T₁-weighted contrast agent. MNPs were then modified using a photosensitizer, Pheophorbide A, via a reducible disulfide linker for glutathione-responsive intracellular release, and then loaded with doxorubicin through π-π stacking and hydrogen bonding. The therapeutic potential of MNPs was further explored via targeted design. MNPs were conjugated with folic acid (FA) and loaded with SN38, thereby demonstrating their ability to bind to different anti-cancer drugs and their potential as a versatile platform, integrating targeted cancer therapy and MRI-guided photothermal and chemotherapeutic therapy. The multimodal therapeutic functions of MNPs were investigated in terms of T₁-MR contrast phantom study, photothermal and photodynamic activity, stimuli-responsive drug release, enhanced cellular uptake, and in vivo tumor ablation studies.

Three-dimensional (3D) computational aortic models have been established to reproduce aortic diseases such as aortic aneurysm and dissection; however, no such models have been developed to study diabetes mellitus (DM). To characterize biomechanical properties of the human aorta with DM, reconstructed aortic CT images were converted into DICOM format, and imported into the 3D segmentation using Mimics software. This resulted in a 3D reconstruction of the complete aorta, including three branches. We applied a pulsatile blood pressure waveform for the ascending aorta to provide a biomimetic environment using COMSOL Multiphysics software. Hemodynamics were compared between the control and DM models. We observed that mean blood flow velocity, aortic pressure, and von Mises stress values were lower in the DM model than in the control model. Furthermore, the range of aortic movement was lower in the DM model than in the control model, suggesting that the DM aortic wall is more susceptible to rupture. When comparing biomechanical properties in discrete regions of the aorta, all values were higher in the ascending aorta for both control and DM models, corresponding to the location of most aortic lesions. We have developed a compute based that integrates advanced image processing strategies and computational techniques based on finite element method to perform hemodynamics analysis based on CT images. Our study of image-based CFD analysis hopes to provide a better understanding of the relationship between aortic hemodynamic and developing pathophysiology of aortic diseases.

Physiological loading is essential for the maintenance of articular cartilage through the regulation of tissue remodeling. To correctly understand the behavior of chondrocytes in their native environment, cell stimulating devices and bioreactors have been developed to examine the effect of mechanical stimuli on chondrocytes. This study describes the design and validation of a novel system for analyzing chondrocyte deformation patterns. This involves an in vitro mechanical device for a controlled application of multi-axial-loading regimes to chondrocyte-seeded agarose constructs and in silico models for analyzing chondrocyte deformation patterns. The computer-controlled device precisely applies compressive, tensile, and shear strains to hydrogel constructs using a customizable macro-based program. The synchronization of the displacements is shown to be accurate with a 1.2% error and is highly reproducible. The device design allows housing for up to eight novel designed free-swelling three-dimensional hydrogel constructs. Constructs include mesh ends and are optimized to withstand the application of up to 7% mechanical tensile and 15% shear strains. Constructs were characterized through mapping the strain within as mechanical load was applied and was validated using light microscopy methods, chondrocyte viability using live/dead imaging, and cell deformation strains. Images were then analyzed to determine the complex deformation strain patterns of chondrocytes under a range of dynamic mechanical stimulations. This is one of the first systems that have characterized construct strains to cellular strains. The features in this device make the system ideally suited for a systematic approach for the investigation of the response of chondrocytes to a complex physiologically relevant deformation profile.

We have developed a novel cell stretching device (called Cell Gym) capable of applying physiologically relevant low magnitude strains to tenocytes on a collagen type I coated membrane. We validated our device thoroughly on two levels: (1) substrate strains, (2) cell level strains. Our cell level strain results showed that the applied stretches were transferred to cells accurately (∼90%). Our gene expression data showed that mechanically stimulated tenocytes (4%) expressed a lower level of COL I gene. COX2 gene was increased but did not reach statistical significance. Our device was then tested to see if it could reproduce results from an in vivo study that measured time-dependent changes in collagen synthesis. Our results showed that collagen synthesis peaked at 24 hrs after exercise and then decreased, which matched the results from the in vivo study. Our study demonstrated that it is important to incorporate physiologically relevant low strain magnitudes in in vitro cell mechanical studies and the need to validate the device thoroughly to operate the device at small strains. This device will be used in designing novel tendon tissue engineering scaffolds in the future.

The MOSFET flicker (1/f) noise is reduced by 1 to 2 orders by incorporating fluorine into the oxide-silicon interface. This is attributed to a reduction in interface state density with fluorine as confirmed by charge pumping measurements. Random-Telegraph Signal (RTS) noise measurements on small-size MOSFETs show a considerable increase in the on-off time-constants with fluorine. High-resolution TEM micrographs show the presence of an interface transition layer on samples without fluorine but not with fluorine. It is believed that a transformation of this layer causes a reduction in noise and an increase in oxide thickness.