Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results. Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancy-classifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists. Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914–0.979 vs. 0.951; 0.919–0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960–1.000), metastatic tumors (0.998; 0.989–1.000), and other primary malignancies (0.963; 0.896–1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deep-neural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists. Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.

Stimuli-responsive nano-immunochemotherapy modulates the tumor microenvironment to enhance antitumor immunity.

Objectives: To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC). Methods: Seven hundred six eligible RC patients (T1/2 = 287, T3/4 = 419) were retrospectively enrolled in this study and chronologically allocated into a training cohort (n = 565) and a validation cohort (n = 141). We conducted a deep-transfer-learning network on high-resolution (HR) T2-weighted imaging (T2WI) to enhance the z-resolution of the images and acquired the preoperative SRT2WI. The radiomics models named model_HRT2 and model_SRT2 were respectively constructed with high-dimensional quantitative features extracted from manually segmented volume of interests of HRT2WI and SRT2WI through the Least Absolute Shrinkage and Selection Operator method. The performances of the models were evaluated by ROC, calibration, and decision curves. Results: Model_SRT2 outperformed model_HRT2 (AUC 0.869, sensitivity 71.1%, specificity 93.1%, and accuracy 83.3% vs. AUC 0.810, sensitivity 89.5%, specificity 70.1%, and accuracy 77.3%) in distinguishing T1/2 and T3/4 RC with significant difference (p < 0.05). Both radiomics models achieved higher AUCs than the expert radiologists (0.685, 95% confidence interval 0.595–0.775, p < 0.05). The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. Conclusions: Model_SRT2 yielded superior predictive performance in preoperative RC T-staging by comparison with model_HRT2 and expert radiologists’ visual assessments. Key Points: • For the first time, DL-based 3D SR images were applied in radiomics analysis for clinical utility. • Compared with the visual assessment of expert radiologists and the conventional radiomics model based on HRT2WI, the SR radiomics model showed a more favorable capability in helping clinicians assess the invasion depth of RC preoperatively. • This is the largest radiomics study for T-staging prediction in RC.

Airway mucus dysfunction and impaired immunological defenses are hallmarks of several lung diseases, including asthma, cystic fibrosis, and chronic obstructive pulmonary diseases, and are mostly causative factors in bacterial-biofilm-associated respiratory tract infections. Bacteria residing within the biofilm architecture pose a complex challenge in clinical settings due to their increased tolerance to currently available antibiotics and host immune responses, resulting in chronic infections with high recalcitrance and high rates of morbidity and mortality. To address these unmet clinical needs, potential anti-biofilm therapeutic strategies are being developed to effectively control bacterial biofilm. This review focuses on recent advances in the development and application of nanoparticulate drug delivery systems for the treatment of biofilm-associated respiratory tract infections, especially addressing the respiratory barriers of concern for biofilm accessibility and the various types of nanoparticles used to combat biofilms. Understanding the obstacles facing pulmonary drug delivery to bacterial biofilms and nanoparticle-based approaches to combatting biofilm may encourage researchers to explore promising treatment modalities for bacterial-biofilm-associated chronic lung infections.

Breast cancer is the leading cause of cancer-related death in women worldwide. Metastatic disease remains the primary cause of death in patients with breast cancer. Basal-like breast cancer (BLBC) is associated with aggressive behavior, stem-like phenotype, high histological grade, poor clinical features, and high rates of recurrences and/or metastasis. However, the mechanism of BLBC phenotype shaping remains obscure. Seventeen normal breast/breast cancer cell lines were used for evaluating the breast cancer subtype-markers, WNT targets and constitutive components, and epithelial mesenchymal transition (EMT) markers analysis by western blot. One hundred and twenty formalin-fixed breast cancer tissues were used for immunohistochemistry (IHC) staining. Nine online platforms (cBioPortal, CCLE, GEPIA, etc.) were used for related analyses. We identified Wnt5b as a key regulatory factor that governs the phenotype of BLBC by activating canonical and non-canonical WNT signaling. Wnt5b exhibited basal-like specificity in cells and clinical samples both at the mRNA and protein levels and also showed good correlation with basal-like phenotype at the mRNA level. Besides, Wnt5b was also a promising therapeutic target for LGK-974 treatment. In addition, we identified that CK1α was expressed at low levels in BLBC and that the activation of CK1α by pyrvinium was an alternative strategy for BLBC treatment. Wnt5b is not only a diagnostic biomarker but also a potential therapeutic target of BLBC.