Polyurethane scaffold-based 3D lung cancer model recapitulates in vivo tumor biological behavior for nanoparticulate drug screening.

Lung cancer is the leading cause of cancer mortality worldwide. Preclinical studies in lung cancer hold the promise of screening for effective antitumor agents, but mechanistic studies and drug discovery based on 2D cell models have a high failure rate in getting to the clinic. Thus, there is an urgent need to explore more reliable and effective in vitro lung cancer models. Here, we prepared a series of three-dimensional (3D) waterborne biodegradable polyurethane (WBPU) scaffolds as substrates to establish biomimetic tumor models in vitro. These 3D WBPU scaffolds were porous and could absorb large amounts of free water, facilitating the exchange of substances (nutrients and metabolic waste) and cell growth. The scaffolds at wet state could simulate the mechanics (elastic modulus ∼1.9 kPa) and morphology (porous structures) of lung tissue and exhibit good biocompatibility. A549 lung cancer cells showed adherent growth pattern and rapidly formed 3D spheroids on WBPU scaffolds. Our results showed that the scaffold-based 3D lung cancer model promoted the expression of anti-apoptotic and epithelial-mesenchymal transition-related genes, giving it a more moderate growth and adhesion pattern compared to 2D cells. In addition, WBPU scaffold-established 3D lung cancer model revealed a closer expression of proteins to in vivo tumor, including tumor stem cell markers, cell proliferation, apoptosis, invasion and tumor resistance proteins. Based on these features, we further demonstrated that the 3D lung cancer model established by the WBPU scaffold was very similar to the in vivo tumor in terms of both resistance and tolerance to nanoparticulate drugs. Taken together, WBPU scaffold-based lung cancer model could better mimic the growth, microenvironment and drug response of tumor in vivo. This emerging 3D culture system holds promise to shorten the formulation cycle of individualized treatments and reduce the use of animals while providing valid research data for clinical trials.

Prediction of Mortality in Geriatric Traumatic Brain Injury Patients Using Machine Learning Algorithms.

Background: The number of geriatric traumatic brain injury (TBI) patients is increasing every year due to the population's aging in most of the developed countries. Unfortunately, there is no widely recognized tool for specifically evaluating the prognosis of geriatric TBI patients. We designed this study to compare the prognostic value of different machine learning algorithm-based predictive models for geriatric TBI. Methods: TBI patients aged ≥65 from the Medical Information Mart for Intensive Care-III (MIMIC-III) database were eligible for this study. To develop and validate machine learning algorithm-based prognostic models, included patients were divided into a training set and a testing set, with a ratio of 7:3. The predictive value of different machine learning based models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy and F score. Results: A total of 1123 geriatric TBI patients were included, with a mortality of 24.8%. Non-survivors had higher age (82.2 vs. 80.7, p = 0.010) and lower Glasgow Coma Scale (14 vs. 7, p < 0.001) than survivors. The rate of mechanical ventilation was significantly higher (67.6% vs. 25.9%, p < 0.001) in non-survivors while the rate of neurosurgical operation did not differ between survivors and non-survivors (24.3% vs. 23.0%, p = 0.735). Among different machine learning algorithms, Adaboost (AUC: 0.799) and Random Forest (AUC: 0.795) performed slightly better than the logistic regression (AUC: 0.792) on predicting mortality in geriatric TBI patients in the testing set. Conclusion: Adaboost, Random Forest and logistic regression all performed well in predicting mortality of geriatric TBI patients. Prognostication tools utilizing these algorithms are helpful for physicians to evaluate the risk of poor outcomes in geriatric TBI patients and adopt personalized therapeutic options for them.

[Three-Dimensional Nonlinear Finite Element Modeling and Analysis of Concomitant Atlanto-Occipital Fusion and Atlantoaxial Joint Dislocation].

OBJECTIVE: To establish, with finite element technology, a three-dimensional nonlinear finite element model of the normal occipital bone, atlas and axis and a three-dimensional nonlinear finite element model of concomitant atlanto-occipital fusion and atlantoaxial dislocation, providing a biomechanical method for clinical research on the upper cervical spine. METHODS: Finite element analysis was conducted with the CT data of a 27-year-old male volunteer, and a three-dimensional nonlinear finite element model, i.e., the normal model, of the normal occipital bone, atlas and axis was established accordingly. Finite element analysis was conducted with the CT data of a 35-year-old male patient with concomitant atlanto-occipital fusion and atlantoaxial dislocation. Then, the ideal state of a simple ligament rupture under high load was generated by computer simulation, and a three-dimensional nonlinear finite element model of concomitant atlanto-occipital fusion and atlantoaxial dislocation was established, i.e., the atlanto-occipital fusion with atlantoaxial dislocation model. For both models, a vertical upward torque of 1.5 N·m was applied on the upper surface of the occipital bone. Through comparative analysis of the two models under stress, the data of the range of motion (ROM) for flexion, extension, lateral bending, and rotation were examined. In addition, stress and deformation analysis with 1.5 N·m torque load was conducted to validate the effectiveness of the two three-dimensional nonlinear finite element models established in the study. RESULTS: When the normal model established in the study was under 1.5 N·m torque load, it exhibited a maximum ROM for each unit of flexion, extension, and the ROM approximated the experimental measurement results of human mechanics, confirming the validity of the simulation. The stress and deformation results of the model were consistent with the basic principles of mechanics. The moment-angular displacement of the model showed obvious nonlinear characteristics. Compared with the normal model, the atlanto-occipital fusion with atlantoaxial dislocation model showed reduced ROM of the atlanto-occipital joint under a torque of 1.5 N·m, while the ROM of the C1-C2 joint for the four conditions of flexion, posterior extention, lateral bending, and rotation under load, with the exception of rotating motion, was greatly increased compared with that of the normal model, which was in line with the actual clinical performance of the patient. CONCLUSION: The atlanto-occipital fusion with atlantoaxial dislocation model and the three-dimensional nonlinear finite element model of the normal occipital bone, atlas and axis were successfully established by finite element technology. The models had valid simulation and reliable kinematic characteristics, and could be used as a reliable tool to simulate clinical diseases.

Circular Ribonucleic Acid Expression Alteration in the Spinal Cord Tissue after Spinal Cord Injury in Rats.

BACKGROUND AND PURPOSE: Increased researches focus into pathophysiological mechanisms of spinal cord injury (SCI), particularly toward the relationship between relevant biomarkers and the degree of SCI and prognosis. Circular ribonucleic acids (circRNAs) possess microRNA (miRNA) binding sites that can play the role of miRNA sponges and thus participate in the expression of parental gene modification. This study focused on rat SCI models and explore the relationship between circRNAs and SCI at a genomic level. METHODS: We first established a rat SCI model and extracted the target spinal cord tissue according to 4 time points. Then investigated the alterations in the circRNA expression by high-throughput whole transcriptome sequencing, analyzed data by gene ontology and the Kyoto Encyclopedia of Genes and Genomes, and constructed the circRNA-miRNA network. RESULTS: A total of 178 circRNAs were dysregulated (89 upregulated/89 downregulated). Differential circRNAs were found to be mainly involved in the composition of specific organelles in the cytoplasm and are mainly involved in the energy transfer process associated with electron transfer (and similar activities). In all the signaling pathways identified in this study, the MAPK, Wnt, and mTOR signaling pathways are intimately associated with the pathophysiological process of rats post-SCI. In this study, 10 circRNAs with obvious dysregulation were selected for prediction, 26 miRNAs with additional interactions were obtained, and a network diagram of circRNAs-miRNAs was constructed. In this manner, one can understand in further detail the pathogenesis of SCI and to provide new strategies for the prevention, diagnosis, and treatment of SCI-related injuries at the genetic level.