Developing Hypoxia-Sensitive System via Designing Tumor-Targeted Fullerene-Based Photosensitizer for Multimodal Therapy of Deep Tumor.

Photodynamic therapy (PDT) has been approved for clinic. However, powerless efficiency for deep hypoxic tumor therapy remains an enormous challenge for PDT. Herein, a hypoxia-sensitive nanotherapeutic system (FTCD-SRGD) based on fullerene (C70) and anoxic activating chemical prodrug tirapazamine (TPZ) is rationally designed for multimodal therapy of deep hypoxic tumors. To enhance the accumulation and achieve specific drug release in tumor, the FTCD-SRGD is modified with cyclo(Arg-Gly-Asp-d-Phe-Lys) (cRGDfK) peptide and disulfide bonds. With the exacerbated hypoxic microenvironment created by C70 consuming O2 for generating reactive oxygen species (ROS), TPZ is activated to produce toxic radical species to ablate deep tumors, which achieves a synergistic treatment of C70-mediated PDT and hypoxia-enhanced chemotherapy. Additionally, given this hypoxia-sensitive system-induced immunogenic cell death (ICD) activating anticancer cytotoxic T lymphocyte to result in more susceptible tumor to immunotherapy, FTCD-SRGD plus immune checkpoint inhibitor (anti-PD-L1) fully inhibit deep hypoxic tumors by promoting infiltration of effector T cells in tumors. Collectively, it is the first time to develop a multimodal therapy system with fullerene-based hypoxia-sensitive PS for deep tumors. The powerful multimodal nanotherapeutic system for combining hypoxia-enhanced PDT and immunotherapy to massacre deep hypoxic tumors can provide a paradigm to combat the present bottleneck of tumor therapy.

Strength and Deformation Characteristics of Fiber and Cement-Modified Waste Slurry.

Using fiber and cement to modify waste slurry and apply it to roads is an effective way to recycle waste slurry. A new type of road material, fiber-cement-modified waste slurry (FRCS), was prepared in this study. The static and dynamic characteristics of the cement soil were studied using an unconfined compressive strength test and dynamic triaxial test. The results show that the optimum fiber content of FRCS is 0.75%. In the unconfined compressive strength test, under this fiber content, the unconfined compressive strength (UCS) of the FRCS is the largest, and the elastic modulus and modulus strength ratio are both the smallest, indicating that the tensile properties of the cement slurry have been enhanced. In the dynamic triaxial test, the hysteretic curve of the FRCS tends to be stable with the increase in the number of cycles, the dynamic elastic modulus of the FRCS decreases first and then increases with the increase in the dosage, while the damping ratio becomes stable after a rapid decline, and the fiber incorporation increases the cumulative strain of the soil-cement under low-stress cycles, indicating that the ductility of the FRCS is improved. In addition, a cumulative strain prediction model of the FRCS is established in this paper, which can provide a reference for the resource application of waste slurry in road engineering.

Risk factors associated with contrast-associated acute kidney injury in ST-segment elevation myocardial infarction patients: a systematic review and meta-analysis.

OBJECTIVE: The objective of this systematic review and meta-analysis was to evaluate the risk factors for contrast-associated acute kidney injury (CA-AKI) in ST-elevation myocardial infarction patients treated with primary percutaneous coronary intervention. DESIGN: Systematic review and meta-analysis. DATA SOURCES: We searched the databases of PubMed, Embase and Ovid, up to February 2022, for observational studies that investigated the association between risk factors and CA-AKI. RESULTS: A total of 21 studies were included in the meta-analysis. Of the total 22 015 participants, 2728 developed CA-AKI. Pooled incidence was 11.91% (95% CI 9.69%, 14.14%). Patients with CA-AKI were more likely to be older, female, also had comorbidities (hypertension, diabetes, previous heart failure). Smoking (OR: 0.60; 95% CI 0.52, 0.69) and family history of CAD (coronary artery disease) (OR: 0.76; 95% CI 0.60, 0.95) were associated with lower risk of CA-AKI. Left anterior descending (LAD) artery occlusion (OR: 1.39; 95% CI 1.21, 1.59), left main disease (OR: 4.62; 95% CI 2.24, 9.53) and multivessel coronary disease (OR: 1.33; 95% CI 1.11, 1.60) were risk factors for CA-AKI. Contrast volume (weighted mean difference: 20.40; 95% CI 11.02, 29.79) was associated with increased risk in patients receiving iso-osmolar or low-osmolar non-ionic contrast. CONCLUSIONS: In addition to the known risk factors, LAD artery infarction, left main disease and multivessel disease are risk factors for CA-AKI. The unexpected favourable association between smoking, as well as family history of CAD, and CA-AKI requires further investigation. PROSPERO REGISTRATION NUMBER: CRD42021289868.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery.

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

DeepEBV: a deep learning model to predict Epstein-Barr virus (EBV) integration sites.

MOTIVATION: Epstein-Barr virus (EBV) is one of the most prevalent DNA oncogenic viruses. The integration of EBV into the host genome has been reported to play an important role in cancer development. The preference of EBV integration showed strong dependence on the local genomic environment, which enables the prediction of EBV integration sites. RESULTS: An attention-based deep learning model, DeepEBV, was developed to predict EBV integration sites by learning local genomic features automatically. First, DeepEBV was trained and tested using the data from the dsVIS database. The results showed that DeepEBV with EBV integration sequences plus Repeat peaks and 2-fold data augmentation performed the best on the training dataset. Furthermore, the performance of the model was validated in an independent dataset. In addition, the motifs of DNA-binding proteins could influence the selection preference of viral insertional mutagenesis. Furthermore, the results showed that DeepEBV can predict EBV integration hotspot genes accurately. In summary, DeepEBV is a robust, accurate and explainable deep learning model, providing novel insights into EBV integration preferences and mechanisms. AVAILABILITYAND IMPLEMENTATION: DeepEBV is available as open-source software and can be downloaded from https://github.com/JiuxingLiang/DeepEBV.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interventions for Treating 3- or 4-part proximal humeral fractures in elderly patient: A network meta-analysis of randomized controlled trials.

BACKGROUND: The fractures of proximal humerus have a high incidence in elderly patients, especially 3- or 4-part fracture. There are 4 main treatments including nonoperation, open reduction and internal fixation(ORIF), hemiarthroplasty(HA) and reverse shoulder arthroplasty(RSA). However, which one is the optimal choice remains a controversial topic. METHODS: Through the search of three electronic databases (PubMed, Embase, and Cochrane), the randomized controlled trials of 3- or 4-part proximal humeral fracture, until the end of July 2017, which is concerned with the elderly patients, were identified. The quality evaluation of each study was evaluated, Constant score and reoperation rates were extracted and analyzed. We used R(R i386 3.3.2) and the package of gemtc to perform our network meta-analysis. RESULTS: Seven randomized controlled experiments with a total number of 347 patients were brought into our network meta-analysis. The rank probability plot of Constant score showed that the RSA had significantly the highest Constant score and lower reoperation than other treatments. The other way around, the efficacy of ORIF was the poorest. The rank for the Constant score was: RSA, HA, nonoperation and ORIF. The rank for the reduction in total reoperation rates was: RSA, nonoperation, HA and ORIF. CONCLUSIONS: The statistical result suggested that RSA has become a beneficial choice to treat displaced 3-or 4-part fracture in elderly patients, that might result in more favorable clinical outcomes and reduction of reoperation rates than other methods performed for the same indication. But the ORIF is the worst.