Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.

Gastric cancer has become a serious worldwide health concern, emphasizing the crucial importance of early diagnosis measures to improve patient outcomes. While traditional histological image analysis is regarded as the clinical gold standard, it is labour intensive and manual. In recognition of this problem, there has been a rise in interest in the use of computer-aided diagnostic tools to help pathologists with their diagnostic efforts.

Integrated Nursing and Psychological Intervention for Tuberculosis Complicated by Lung Cancer: Clinical Efficacy.

Context: The incidence of tuberculosis (TB) complicated by lung cancer has been increasing yearly worldwide. The overlapping effects of these two diseases leads to difficulties in clinical treatment and care. Single-care modalities fail to meet the clinical-care requirements of these complex diseases for both psychological and physical treatment.

Developing ROR1 Targeting CAR-T Cells against Solid Tumors in Preclinical Studies.

Chimeric antigen receptor (CAR)-modified T-cells (CAR-T) have demonstrated promising clinical benefits against B-cell malignancies. Yet, its application for solid tumors is still facing challenges. Unlike haematological cancers, solid tumors often lack good targets, which are ideally expressed on the tumor cells, but not by the normal healthy cells.

Facile Synthesis of Holmium-Based Nanoparticles as a CT and MRI Dual-Modal Imaging for Cancer Diagnosis.

The rapid development of medical imaging has boosted the abilities of modern medicine. As single modality imaging limits complex cancer diagnostics, dual-modal imaging has come into the spotlight in clinical settings. The rare earth element Holmium (Ho) has intrinsic paramagnetism and great X-ray attenuation due to its high atomic number.

A Tumor Microenvironment Responsive Nanotheranostics Agent for Magnetic Resonance Imaging and Synergistic Photodynamic Therapy/Photothermal Therapy of Liver Cancer.

Surgery is the main treatment for liver cancer in clinic owing to its low sensitivity to chemotherapy and radiotherapy, but this results in high mortality, recurrence, and metastasis rates. It is a feasible strategy to construct tumor microenvironments activated by nanotheranostics agents for the diagnosis and therapy of liver cancer. This study reports on a nanotheranostic agent (MONs@PDA-ICG) with manganese oxide nanoflowers (MONs) as core and polydopamine (PDA) as shell loading, with ICG as a photosensitizer and photothermal agent.

On the interplay between physical and content priors in deep learning for computational imaging.

Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two important questions remain largely unanswered: first, how well can the trained neural network generalize to objects very different from the ones in training? This is particularly important in practice, since large-scale annotated examples similar to those of interest are often not available during training. Second, has the trained neural network learnt the underlying (inverse) physics model, or has it merely done something trivial, such as memorizing the examples or point-wise pattern matching? This pertains to the interpretability of machine-learning based algorithms.

Learning to synthesize: robust phase retrieval at low photon counts.

The quality of inverse problem solutions obtained through deep learning is limited by the nature of the priors learned from examples presented during the training phase. Particularly in the case of quantitative phase retrieval, spatial frequencies that are underrepresented in the training database, most often at the high band, tend to be suppressed in the reconstruction. Ad hoc solutions have been proposed, such as pre-amplifying the high spatial frequencies in the examples; however, while that strategy improves the resolution, it also leads to high-frequency artefacts, as well as low-frequency distortions in the reconstructions.

Probing shallower: perceptual loss trained Phase Extraction Neural Network (PLT-PhENN) for artifact-free reconstruction at low photon budget.

Deep neural networks (DNNs) are efficient solvers for ill-posed problems and have been shown to outperform classical optimization techniques in several computational imaging problems. In supervised mode, DNNs are trained by minimizing a measure of the difference between their actual output and their desired output; the choice of measure, referred to as "loss function," severely impacts performance and generalization ability. In a recent paper [A.

DFA7, a new method to distinguish between intron-containing and intronless genes.

Intron-containing and intronless genes have different biological properties and statistical characteristics. Here we propose a new computational method to distinguish between intron-containing and intronless gene sequences. Seven feature parameters α, β, γ, λ, θ, φ and σ based on detrended fluctuation analysis (DFA) are fully used, and thus we can compute a 7-dimensional feature vector for any given gene sequence to be discriminated.

Protein space: a natural method for realizing the nature of protein universe.

Current methods cannot tell us what the nature of the protein universe is concretely. They are based on different models of amino acid substitution and multiple sequence alignment which is an NP-hard problem and requires manual intervention. Protein structural analysis also gives a direction for mapping the protein universe.

A novel method of characterizing genetic sequences: genome space with biological distance and applications.

Background: Most existing methods for phylogenetic analysis involve developing an evolutionary model and then using some type of computational algorithm to perform multiple sequence alignment. There are two problems with this approach: (1) different evolutionary models can lead to different results, and (2) the computation time required for multiple alignments makes it impossible to analyse the phylogeny of a whole genome. This motivates us to create a new approach to characterize genetic sequences.