Subconjunctival injection of rapamycin-loaded polymeric microparticles for effective suppression of noninfectious uveitis in rats.

Noninfective uveitis is a major cause of vision impairment, and corticosteroid medication is a mainstay clinical strategy that causes severe side effects. Rapamycin (RAPA), a potent immunomodulator, is a promising treatment for noninfective uveitis. However, because high and frequent dosages are required, it is a great challenge to implement its clinical translation for noninfective uveitis therapy owing to its serious toxicity. In the present study, we engineered an injectable microparticulate drug delivery system based on biodegradable block polymers (i.e., polycaprolactone-poly (ethylene glycol)-polycaprolactone, PCEC) for efficient ocular delivery of RAPA via a subconjunctival injection route and investigated its therapeutic efficacy in an experimental autoimmune uveitis (EAU) rat model. RAPA-PCEC microparticles were fabricated using the emulsion-evaporation method and thoroughly characterized using scanning electron microscopy, fourier transform infrared spectroscopy, X-ray diffraction, and differential scanning calorimetry. The formed microparticles exhibited slow in vitro degradation over 28 days, and provided both in vitro and in vivo sustained release of RAPA over 4 weeks. Additionally, a single subconjunctival injection of PCEC microparticles resulted in high ocular tolerance. More importantly, subconjunctival injection of RAPA-PCEC microparticles significantly attenuated the clinical signs of EAU in a dose-dependent manner by reducing inflammatory cell infiltration (i.e., CD45+ cells and Th17 cells) and inhibiting microglial activation. Overall, this injectable microparticulate system may be promising vehicle for intraocular delivery of RAPA for the treatment of noninfective uveitis.

Dissolvable hybrid microneedle patch for efficient delivery of curcumin to reduce intraocular inflammation.

Intraocular inflammation seriously impairs vision, and the effectiveness of intraocular drug delivery is hampered by various physiological barriers, such as the corneal barrier. In this paper, we present a simple approach to fabricating a dissolvable hybrid microneedles (MNs) patch for the efficient delivery of curcumin to treat intraocular inflammatory disorders. Water-insoluble curcumin was first encapsulated into polymeric micelles with high anti-inflammatory capacities, and then were combined with hyaluronic acid (HA) to create a dissolvable hybrid MNs patch using a simple micromolding method. Curcumin was amorphously dispersed within the MNs patch as indicated by Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and X-ray diffraction (XRD) analyses. According to an in vitro drug release study, the proposed MNs patch provided sustainable drug release over 8 h. Following its in vivo topical application, the MNs patch demonstrated an extended pre-corneal retention time over 3.5 h and exhibited great ocular biocompatibility. Additionally, such MNs patch could reversibly penetrate the corneal epithelium, generating an array of microchannels on the corneal surface, thereby increasing ocular bioavailability. Of greater significance, the use of MNs patch demonstrated the improved therapeutic effectiveness in treating endotoxin-induced uveitis (EIU) in a rabbit model compared to curcumin eye drops via a significant reduction in the infiltration of inflammatory cells such as CD45+ leukocytes and CD68+ macrophages. Overall, the topical application of the MNs patch as an efficient ocular drug delivery system could potentially serve as a promising approach for treating different types of intraocular disorders.

Identification of mortality-risk-related missense variant for renal clear cell carcinoma using deep learning.

INTRODUCTION: Kidney renal clear cell carcinoma (KIRCC) is a highly heterogeneous and lethal cancer that can arise in patients with renal disease. DeepSurv combines a deep feed-forward neural network with a Cox proportional hazards function and could provide optimized survival results compared with convenient survival analysis. METHODS: This study used an improved DeepSurv algorithm to identify the candidate genes to be targeted for treatment on the basis of the overall mortality status of KIRCC subjects. All the somatic mutation missense variants of KIRCC subjects were abstracted from TCGA-KIRC database. RESULTS: The improved DeepSurv model (95.1%) achieved greater balanced accuracy compared with the DeepSurv model (75%), and identified 610 high-risk variants associated with overall mortality. The results of gene differential expression analysis also indicated nine KIRCC mortality-risk-related pathways, namely the tRNA charging pathway, the D-myo-inositol-5-phosphate metabolism pathway, the DNA double-strand break repair by nonhomologous end-joining pathway, the superpathway of inositol phosphate compounds, the 3-phosphoinositide degradation pathway, the production of nitric oxide and reactive oxygen species in macrophages pathway, the synaptic long-term depression pathway, the sperm motility pathway, and the role of JAK2 in hormone-like cytokine signaling pathway. The biological findings in this study indicate the KIRCC mortality-risk-related pathways were more likely to be associated with cancer cell growth, cancer cell differentiation, and immune response inhibition. CONCLUSION: The results proved that the improved DeepSurv model effectively classified mortality-related high-risk variants and identified the candidate genes. In the context of KIRCC overall mortality, the proposed model effectively recognized mortality-related high-risk variants for KIRCC.

Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study.

BACKGROUND: Breast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci. OBJECTIVE: This study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients. METHODS: A hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability. RESULTS: The proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes. CONCLUSIONS: We systematically evaluated the interaction effects of 26 SNPs within growth factor-related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets.

PBMDR: A particle swarm optimization-based multifactor dimensionality reduction for the detection of multilocus interactions.

Studies on multilocus interactions have mainly investigated the associations between genetic variations from the related genes and histopathological tumor characteristics in patients. However, currently, the identification and characterization of susceptibility genes for complex diseases remain a great challenge for geneticists. In this study, a particle swarm optimization (PSO)-based multifactor dimensionality reduction (MDR) approach was proposed, denoted by PBMDR. MDR was used to detect multilocus interactions based on the PSO algorithm. A test data set was simulated from the genotype frequencies of 26 SNPs from eight breast-cancer-related gene. In simulated disease models, we demonstrated that PBMDR outperforms existing global optimization algorithms in terms of its ability to explore and power to detect specific SNP-genotype combinations. In addition, the PBMDR algorithm was compared with other algorithms, including PSO and chaotic PSOs, and the results revealed that the PBMDR algorithm yielded higher accuracy and chi-square values than other algorithms did.