To avoid the irregularities during the level set evolution, a fractional distance regularized variational model is proposed for image segmentation. We first define a fractional distance regularization term which punishes the deviation of the level set function (LSF) and the signed distance function. Since the fractional derivative of the constant value function outside the starting point is nonzero, the fractional gradient modular of the LSF does not approach infinity where the integer order gradient modular is close to 0. This prevents the sharp reverse diffusion of LSF in flat areas and ensures the smooth evolution of LSF. Then, we use the Grünwald-Letnikov (G-L) fractional derivative to derive the discrete forms of the conjugate of fractional derivatives and fractional divergence. To facilitate the calculation of fractional derivatives and their conjugates, we designed their covering templates. Finally, a numerical solution to the minimization of the energy functional is obtained from these discrete forms and covering templates. Numerical experiments of medical images with different modalities show that the model in this paper can well segment weak images and intensity inhomogeneity images.

A series of quinoline oligoamide foldamers bearing a β-pinene-derived pyridyl group at the N-terminus or the C-terminus were synthesized, and the efficiencies of chiral inductions have been evaluated by H NMR and CD spectra. The chiral inductions were quantitative when chiral pyridyl acid was appended at the N-terminus, but were inferior when chiral pyridyl amine was appended at the C-terminus. Unexpectedly, N-oxidation on the pyridine ring at the C-terminus does not notably enhance the chiral induction efficiency in spite of the presence of three-center hydrogen bonds.

No abstract

When S- or R- oxazolylaniline enantiomers were attached to achiral quinoline oligoamide foldamers (QOFs), a single diastereomerically pure P- or M-handed foldamer was observed and exhibits negative or positive circularly polarized luminescence with the emission dissymmetry factors |g lum| up to 0.038, which is significantly larger than that of QOF with incomplete chiral induction. More importantly, the CPL dissymmetry factors, together with the absorption dissymmetry factors, are enhanced with increases in the lengths of QOFs.

Background/Aims: Osteoarthritis (OA) is the prevalent degenerative disease caused by various factors. MicroRNAs are important regulators in the inflammation and immune response. The aim of this study was to investigate the effect of microRNA-34a (MiR-34a) on the death of chondrocytes, senescence, as well as its role in OA progression. Methods: A series of experiments involving CCK-8, flow cytometry, β-galactosidase staining and wound healing assays were conducted to determine the cellular capabilities of proliferation, cell apoptosis, senescence and the ability of cells to recover from injury, respectively. Binding sites between miR-34a and delta-like protein 1 (DLL1) were identified using a luciferase reporter system, whereas mRNA and protein expression of target genes was determined by RT-PCR and immunoblot, respectively. OA model was generated via surgery. Results: We found that miR-34a expression was increased in the cartilage of OA patients. In rat chondrocytes and chondrosarcoma cells, miR-34a transfections noticeably inhibited the expression of DLL1, triggered cell death and senescence, suppressed proliferation, and prevented scratch assay wound closure. However, transfection of a miR-34a inhibitor displayed adverse effects. Additionally, secretion and expression of factors associated with cartilage degeneration were altered via miR-34a. Moreover, miR-34a directly inhibits DLL1 mRNA. Furthermore, concentrations of DLL1, total PI3K, and p-AKT declined in chondrocytes that overexpress miR-34a. DLL1 overexpression elevated PI3K and p-AKT levels, and eliminated cell death triggered by a miR-34a mimic. In vivo, miR-34a remarkably inhibited miR-34a up-regulation, while enhanced the level of DLL1 expression. In the knee joints of surgery-induced OA rats, articular chondrocyte death and loss of cartilage were attenuated via miR-34a antagomir injection. Conclusions: These findings indicate that miR-34a contributes to chondrocyte death, causing OA progression through DLL1 and modulation of the PI3K/AKT pathway.

Inflammatory cytokines commonly initiate extreme changes in the synovium and cartilage microenvironment of osteoarthritis ( OA ) patients, which subsequently cause cellular dysfunction, especially in chondrocytes. It has been reported that induction of the purinergic P2X7 receptor (P2X7R) can regulate the expression of a variety of inflammatory factors, including interleukin ( IL )‐6 and ‐8, leading to OA pathogenesis. However, knowledge of the mechanism of upregulation of P2X7R in OA is still incomplete, and its role in chondrocyte proliferation is also not clear. It was reported previously that the expression of P2X7R was controlled by certain micro RNA s, and so we tested the expression of several micro RNA s and found that microRNA‐373 (miR‐373) was downregulated in the chondrocytes from OA patients. Regarding the mechanism of action, miR‐373 inhibited chondrocyte proliferation by suppressing the expression of P2X7R, as well as inflammatory factors such as IL ‐6 and IL ‐8. Furthermore, the proliferative and pro‐inflammatory effects of miR‐373 on the chondrocytes could be suppressed by a P2X7R antagonist, further suggesting that miR‐373 mediates chondrocyte proliferation and inflammation by targeting P2X7R. Generally, our results suggest a novel method for OA treatment by targeting the miR‐373–P2X7R pathway.

No abstract

An adaptive regularized level set method for image segmentation is proposed. A weightedp(x)-Dirichlet integral is presented as a geometric regularization on zero level curve, which is used to diminish the influence of image noise on level set evolution while ensuring the active contours not to pass through weak object boundaries. The idea behind the new energy integral is that the amount of regularization on the zero level curve can be adjusted automatically by the variable exponentp(x)to fit the image data. This energy is then incorporated into a level set formulation with an external energy term that drives the motion of the zero level set toward the desired objects boundaries, and a level set function regularization term that is necessary for maintaining stable level set evolution. The proposed model has been applied to a wide range of both real and synthetic images with promising results.