Effect of miR-384-targeting LINC00491 on proliferation, migration and invasion of tongue squamous cell carcinoma cells

Purpose: To investigate the effect of long-chain non-coding RNA LINC00491 (LncRNA LINC00491) on the proliferation, migration and invasion of tongue squamous cell carcinoma (TSCC) cells, and the underlying mechanism. Methods: Real-time quantitative polymerase chain reaction (qRT-PCR) was applied to determine the expressions of LINC00491 and micro-RNA-384 (miR-384). Furthermore, LINC00491 and miR-384 were transfected into CAL-27 cells, while cell cycle was analyzed using flow cytometry. Cell proliferation was determined by methyl thiazolyl diphenyl-tetrazolium (MTT) assay. Cell migration and invasion were evaluated using Transwell experiments. The relationship between LINC00491 and miR-384 was confirmed using double luciferase reporting assay, while protein expression levels of P21, Ki67, E- cadherin, N-cadherin, and vimentin were assayed with Western blotting. Results: The expression of LINC00491 increased in TSCC tissues (p < 0.05). The proportion of cells in G1-phase increased, while the proportion of cells in S-phase decreased (p < 0.05). There was decrease in cell survival, cell migration and cell invasion (p < 0.05). The protein expression levels of Ki67, N- cadherin, and vimentin were lowered, while those of P21, E-cadherin protein were increased (p < 0.05). Transfection of LINC00491 and miR- 384 reduced the proportion of cells in G1 phase, but increased the proportion of cells in S-phase (p < 0.05). Moreover, cell survival, migration and invasion were increased. The protein expressions of Ki67, N-cadherin, and vimentin rose, while those of P21 and E-cadherin decreased (p < 0.05). Conclusion: LINC00491 promotes the proliferation, migration and invasion of TSCC cells by inhibiting miR-384. This finding provides a potential target for the treatment of TSCC

Identifying Causal Effects Using Instrumental Variables from the Auxiliary Population

Instrumental variable approaches have gained popularity for estimating causal effects in the presence of unmeasured confounding. However, the availability of instrumental variables in the primary population is often challenged due to stringent and untestable assumptions. This paper presents a novel method to identify and estimate causal effects in the primary population by utilizing instrumental variables from the auxiliary population, incorporating a structural equation model, even in scenarios with nonlinear treatment effects. Our approach involves using two datasets: one from the primary population with joint observations of treatment and outcome, and another from the auxiliary population providing information about the instrument and treatment. Our strategy differs from most existing methods by not depending on the simultaneous measurements of instrument and outcome. The central idea for identifying causal effects is to establish a valid substitute through the auxiliary population, addressing unmeasured confounding. This is achieved by developing a control function and projecting it onto the function space spanned by the treatment variable. We then propose a three-step estimator for estimating causal effects and derive its asymptotic results. We illustrate the proposed estimator through simulation studies, and the results demonstrate favorable performance. We also conduct a real data analysis to evaluate the causal effect between vitamin D status and BMI.Comment: 19 page

Theoretical and Numerical Analysis of mechanical behaviors of a metamaterial-based shape memory polymer stent

Shape memory polymers (SMPs) have gained much attention in biomedical fields due to their good biocompatibility and biodegradability. Researches have validated the feasibility of shape memory polymer stent in treatment of vascular blockage. Nevertheless, the actual application of SMP stents is still in infancy. To improve the mechanical performance of SMP stent, a new geometric model based on metamaterial is proposed in this study. To verify the feasibility and mechanical behavior of this type of stent, buckling analysis, and in vivo expansion performance of SMP stent are simulated. Numerical results exhibit that stent of a smaller radius behaves a higher critical buckling load and smaller buckling displacement. Besides, a smaller contact area with vessel and smaller implanted stress are observed compared with traditional stents. This suggests that this SMP stent attributes to a reduced vascular restenosis. To characterize the radial strength of SMP stent, an analytical solution is derived by the assumption that the deformation of stent is mainly composed of bending and stretch. The radial strength of SMP stent is assessed in form of radial force. Analytical results reveal that radial strength is depended on the radius of stent and periodic numbers of unit cell in circumferential direction

Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most existing MUDA algorithms focus on extracting domain-invariant representations among all domains whereas the task-specific decision boundaries among classes are largely neglected. In this paper, we propose an end-to-end trainable network that exploits domain Consistency Regularization for unsupervised Multi-source domain Adaptive classification (CRMA). CRMA aligns not only the distributions of each pair of source and target domains but also that of all domains. For each pair of source and target domains, we employ an intra-domain consistency to regularize a pair of domain-specific classifiers to achieve intra-domain alignment. In addition, we design an inter-domain consistency that targets joint inter-domain alignment among all domains. To address different similarities between multiple source domains and the target domain, we design an authorization strategy that assigns different authorities to domain-specific classifiers adaptively for optimal pseudo label prediction and self-training. Extensive experiments show that CRMA tackles unsupervised domain adaptation effectively under a multi-source setup and achieves superior adaptation consistently across multiple MUDA datasets

Health economics perspective: Genetic mutation test reports utilize mathematics and computer science to study and analyze cryptographic encryption strategies

Health economics is the focus of current research, and genetic testing has become an emerging and universal means of disease surveillance based on the ever-changing perspective of the global basic medicine in the field of cellular genetics. But genes represent the genetic information of the human physiology, and therefore must be handled in a confidential manner. With the use of current computational and codon knowledge structures, the authors propose and report strategies for problem solving in computer medicine based on genetic properties