A nature-inspired multifunctional adhesive for cartilage tissue-biomaterial integration.

Surgical adhesives play a crucial role in tissue integration and repair, yet their application in wet conditions has been severely limited by inadequate adhesive strength and subpar biocompatibility. Furthermore, tissue adhesives have rarely been reported in cartilage tissue repair. In this study, a three-armed dopamine-modified hyaluronic acid derivative adhesive was prepared to function as a bio-inspired adhesive in moist environments. To meet the clinical requirements for cartilage tissue adhesion, we studied its chemical structure, including microscopic morphology, adhesion properties with materials and tissues, in vivo degradation rules, and biological evaluation. The OGMHA8-DOPA adhesive with the optimal aldehyde substitution degree and dopamine-grafting rate was determined by analyzing the experimental conditions. SEM results revealed that the cartilage tissue adhered to a porous interconnected structure. The excellent biocompatibility of the material not only facilitated chondrocyte adhesion but also supported their proliferation on its surface. Animal experiments have demonstrated that this material has no observable inflammatory response or incidence of fibrous capsule formation. The degradation timeline of the material extends beyond the duration of two weeks. The dopamine-modified adhesive exhibited a tight interfacial binding force between the biomaterial and cartilage tissue and excellent biocompatibility in watery tissue, revealing its potential for application in cartilage tissue repair and minimally invasive surgery.

Identifying prognostic factors for pulmonary embolism patients with hemodynamic decompensation admitted to the intensive care unit.

We aimed to determine prognostic indicators of PE patients with hemodynamic decompensation admitted to the ICU. PE patients with hemodynamic decompensation at ICU admission from Medical Information Mart for Intensive Care IV database were included. Least absolute shrinkage and selection operator with 2 specific lambdas were performed to reduce the dimension of variables after univariate analysis. Then we conducted multivariate logistic regression analysis and 2 models were built. A total of 548 patients were included, among whom 187 died. Lactate, creatine-kinase MB, troponin-T were significantly higher in death group. Eight common factors were screened out from first model statistically mostly in consistent with second model: older age, decreased hemoglobin, elevated anion gap, elevated International Standard Ratio (INR), elevated respiratory rate, decreased temperature, decreased blood oxygen saturation (SpO2) and the onset of cardiac arrest were significantly risk factors for in-Hospital mortality. The nonlinear relationships between these indicators and mortality were showed by the restricted cubic spline and cutoff values were determined. Our study demonstrated that age, hemoglobin levels, anion gap levels, INR, respiratory rate, temperature, SpO2 levels, the onset of cardiac arrest could be applied to predict mortality of PE patients with hemodynamic decompensation at ICU admission.

Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients.

BACKGROUND: Accurate prediction of recurrence-free survival (RFS) is important for the prognosis of cutaneous melanoma patients. The image-based pathological examination remains as the gold standard for diagnosis. It is of clinical interest to account for computer-aided processing of pathology image when performing prognostic analysis. METHODS: We enrolled in this study a total of 152 patients from TCGA-SKCM (The Cancer Genome Atlas Skin Cutaneous Melanoma project) with complete information in recurrence-related survival time, baseline variables (clinicopathologic variables, mutation status of BRAF and NRAS genes), gene expression data, and whole slide image (WSI) features. We preprocessed WSI to segment global or nucleus areas, and extracted 3 types of texture features from each region. We performed cross validation and used multiple evaluation metrics including C-index and time-dependent AUC to determine the best model of predicting recurrence events. We further performed differential gene expression analysis between the higher and lower-risk groups within AJCC pathologic tumor stage III patients to explore the underlying molecular mechanisms driving risk stratification. RESULTS: The model combining baseline variables and WSI features had the best performance among models with any other types of data integration. The prognostic risk score generated by this model could provide a higher-resolution risk stratification within pathologically defined subgroups. We found the selected image features captured important immune-related variations, such as the aberration of expression in T cell activation and proliferation gene sets, and therefore contributed to the improved prediction. CONCLUSIONS: Our study provided a prognostic model based on the combination of baseline variables and computer-processed WSI features. This model provided more accurate prediction than models based on other types of data combination in recurrence-free survival analysis. TRIAL REGISTRATION: This study was based on public open data from TCGA and hence the study objects were retrospectively registered.