Bioinspired self-assembly supramolecular hydrogel for ocular drug delivery

Based on a recent report concerning endogenous agents(i.e., pyridoxal phosphate, adenosine triphosphate, adenosine monophosphate, folinic acid) that modulate the oligomerization of apoptosis-associated speck-like protein(ASC) via the peptide epitope of KKFKLKL, we rationally designed and synthesized a nonapeptide(Nap FFKKFKLKL), which can co-assemble with dexamethasone sodium phosphate(Dexp) to generate a Nap FFKKFKLKL/Dexp supramolecular hydrogel for ocular drug delivery.The Nap FFKKFKLKL/Dexp hydrogel formed instantly after the complexation of Nap FFKKFKLKL with Dexp in aqueous solution. The formed supramolecular hydrogels were thoroughly characterized by transmission electron microscopy(TEM), fluorescent spectrum, circular dichroism(CD) spectra and rheology. The peptide concentration significantly affected the in vitro release behavior of Dexp from the supramolecular hydrogel, and the higher peptide concentration resulted in the slower drug release.Following a single intravitreal injection, the proposed Nap FFKKFKLKL/Dexp hydrogel displayed good intraocular biocompatibility without having an adverse impact on the retinal architecture and eyesight functions during one month of follow-up. Using an experimental autoimmune uveitis(EAU) rat model,we demonstrated that the resulting Nap FFKKFKLKL/Dexp hydrogel had potent capacity to alleviate the intraocular inflammation and retain the morphology of retinal architecture. Overall, the resulting Nap FFKKFKLKL/Dexp hydrogel may be a promising drug carrier system to treat various posterior disorders(i.e., uveitis).

Auxiliary generative mutual adversarial networks for class-imbalanced fault diagnosis under small samples

The effect of intelligent fault diagnosis of mechanical equipment based on data-driven is often premised on big data and class-balance.However,due to the limitation of working environment,operating conditions and equipment status,the fault data collected by mechanical equipment are often small and imbalanced with normal samples.Therefore,in order to solve the abovementioned dilemma faced by the fault diagnosis of practical mechanical equipment,an auxiliary generative mutual adversarial network(AGMAN)is proposed.Firstly,the generator combined with the auto-encoder(AE)constructs the decoder reconstruction feature loss to assist it to complete the accurate mapping between noise distribution and real data distribution,generate highquality fake samples,supplement the imbalanced dataset to improve the accuracy of small sample class-imbalanced fault diagnosis.Secondly,the discriminator introduces a structure with unshared dual discriminators.Realize the mutual adversarial between the dual discriminator by setting the scoring criteria that the dual discriminator are completely opposite to the real and fake samples,thus improving the quality and diversity of generated samples to avoid mode collapse.Finally,the auxiliary generator and the dual discriminator are updated alternately.The auxiliary generator can generate fake samples that deceive both discriminators at the same time.Meanwhile,the dual discriminator cannot give correct scores to the real and fake samples according to their respective scoring criteria,so as to achieve Nash equilibrium.Using three different test-bed datasets for verification,the experimental results show that the proposed method can explicitly generate highquality fake samples,which greatly improves the accuracy of class-unbalanced fault diagnosis under small sample,especially when it is extremely imbalanced,after using this method to supplement fake samples,the fault diagnosis accuracy of DCNN and SAE are relatively big improvements.So,the proposed method provides an effective solution for small sample class-unbalanced fault diagnosis.