Cascaded Nanozyme Based pH-Responsive Oxygenation for Targeted Eradication of Resistant Helicobacter Pylori.

Nanozymes, as substitutes for natural enzymes, are constructed as cascade catalysis systems for biomedical applications due to their inherent catalytic properties, high stability, tunable physicochemical properties, and environmental responsiveness. Herein, a multifunctional nanozyme is reported to initiate cascade enzymatic reactions specific in acidic environments for resistant Helicobacter pylori (H. pylori) targeting eradication. The cobalt-coated Prussian blue analog based FPB-Co-Ch NPs displays oxidase-, superoxide dismutase-, peroxidase-, and catalase- mimicking activities that trigger • O 2 - ${\mathrm{O}}_2^ - {\bm{\ }}$ and H2O2 to supply O2, thereby killing H. pylori in the stomach. To this end, chitosan is modified on the surface to exert bacterial targeted adhesion and improve the biocompatibility of the composite. In the intestinal environment, the cascade enzymatic activities are significantly inhibited, ensuring the biosafety of the treatment. In vitro, sensitive and resistant strains of H. pylori are cultured and the antibacterial activity is evaluated. In vivo, murine infection models are developed and its success is confirmed by gastric mucosal reculturing, Gram staining, H&E staining, and Giemsa staining. Additionally, the antibacterial capacity, anti-inflammation, repair effects, and biosafety of FPB-Co-Ch NPs are comprehensively investigated. This strategy renders a drug-free approach that specifically targets and kills H. pylori, restoring the damaged gastric mucosa while relieving inflammation.

Mucus-Penetrating Nanoassembly as Potential Oral Phototherapeutic Formulation against Multi-Drug Resistant Helicobacter pylori Infection.

Helicobacter pylori (H. pylori) infection presents increasing challenges to antibiotic therapies in limited penetration through gastric mucus, multi-drug resistance (MDR), biofilm formation, and intestinal microflora dysbiosis. To address these problems, herein, a mucus-penetrating phototherapeutic nanomedicine (RLs@T780TG) against MDR H. pylori infection is engineered. The RLs@T780TG is assembled with a near-infrared photosensitizer T780T-Gu and an anionic component rhamnolipids (RLs) for deep mucus penetration and light-induced anti-H. pylori performances. With optimized suitable size, hydrophilicity and weak negative surface, the RLs@T780TG can effectively penetrate through the gastric mucus layer and target the inflammatory site. Subsequently, under irradiation, the structure of RLs@T780TG is disrupted and facilitates the T780T-Gu releasing to target the H. pylori surface and ablate multi-drug resistant (MDR) H. pylori. In vivo, RLs@T780TG phototherapy exhibits impressive eradication against H. pylori. The gastric lesions are significantly alleviated and intestinal bacteria balance is less affected than antibiotic treatment. Summarily, this work provides a potential nanomedicine design to facilitate in vivo phototherapy in treatment of H. pylori infection.

DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.

BACKGROUND: Protein ubiquitination occurs when the ubiquitin protein binds to a target protein residue of lysine (K), and it is an important regulator of many cellular functions, such as signal transduction, cell division, and immune reactions, in eukaryotes. Experimental and clinical studies have shown that ubiquitination plays a key role in several human diseases, and recent advances in proteomic technology have spurred interest in identifying ubiquitination sites. However, most current computing tools for predicting target sites are based on small-scale data and shallow machine learning algorithms. RESULTS: As more experimentally validated ubiquitination sites emerge, we need to design a predictor that can identify lysine ubiquitination sites in large-scale proteome data. In this work, we propose a deep learning predictor, DeepUbi, based on convolutional neural networks. Four different features are adopted from the sequences and physicochemical properties. In a 10-fold cross validation, DeepUbi obtains an AUC (area under the Receiver Operating Characteristic curve) of 0.9, and the accuracy, sensitivity and specificity exceeded 85%. The more comprehensive indicator, MCC, reaches 0.78. We also develop a software package that can be freely downloaded from https://github.com/Sunmile/DeepUbi . CONCLUSION: Our results show that DeepUbi has excellent performance in predicting ubiquitination based on large data.

Serum fat-soluble vitamin levels may be related to sudden sensorineural hearing loss.

BACKGROUND: Few related studies reported yet the association between vitamins and the onset of sudden sensorineural hearing loss (SSNHL). OBJECTIVE: To explore the relationship between the serum levels of fat-soluble vitamins A, D, and E and the risk of SSNHL. METHODS: This retrospective analysis included 310 SSNHL patients and 154 people without risk of hearing loss. The demographic information of all participants like age, gender, body mass index, occupation, cigarette smoking or drinking status, etc. were recorded. The serum levels of vitamins A, D, and E were determined using the electrochemical method. RESULTS: The results indicated that serum vitamin D levels in SSNHL patients were significantly lower. Vitamin D deficiency was only observed in SSNHL group. Similarly, serum vitamin A levels in female SSNHL patients were significantly lower than the control group. Meanwhile, serum vitamin E levels in male SSNHL patients were significantly lower than the control group. CONCLUSION AND SIGNIFICANCE: Our results revealed that the serum levels of fat-soluble vitamins A, D, and E in SSNHL patients were lower than those in the control group with normal hearing, indicating that the decrease of serum fat-soluble vitamins may be related to SSNHL pathogenesis.

Size-Controllable DNA Origami-Stacked Gold Nanoparticles for Deep Tumor-Penetrating Therapy.

With the rapid development of nanotechnology, researchers have designed a variety of intelligent nanodelivery systems to enhance tumor targeting of anticancer drugs. However, increased tumor accumulation does not indicate deeper penetration in the tumor tissue, without which the tumor cells in the core area cannot be sufficiently killed. Herein, we develop a size-controllable nanoparticle system for deep-penetrating cancer therapy, which will be programmably disassembled with the decrease of the pH from the normal tissue to the tumor microenvironment and to the intracellular area. The integrated nanoparticle is composed of a gold nanoparticle (GNP, ∼30 nm) and a tetrahedral DNA nanostructure (TDN, ∼25 nm) loaded with doxorubicin (DOX). Initially, the nanoparticles maintain a larger size (∼100 nm) to accumulate in the tumor through the enhanced permeability and retention effect. At a pH of about 6.5 at the tumor microenvironment, with the linkage of DNA sequences converting into a triplex structure, the TDNs detach from the GNP and penetrate deeply into the tumor interstitium and then are internalized into the cells. Finally, in acidic lysosomes with pH 5.0, the TDNs release DOX by forming an i-motif structure. This nanosmart delivery system thus shows effective deep penetration into the tumor core with good antitumor efficacy and satisfactory biocompatibility and provides new insights into the development of intelligent nanosystems for anti-cancer treatment.

Age Prediction of Human Based on DNA Methylation by Blood Tissues.

In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath's results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.