Porphyrin-Based Covalent Organic Frameworks with Donor-Acceptor Structure for Enhanced Peroxidase-like Activity as a Colorimetric Biosensing Platform.

Hydrogen peroxide (H2O2) and glucose play a key role in many cellular signaling pathways. The efficient and accurate in situ detection of H2O2 released from living cells has attracted extensive research interests. Herein, a new porphyrin-based porous covalent organic framework (TAP-COF) was fabricated via one-step condensation of 1,6,7,12-tetrachloroperylene tetracarboxylic acid dianhydride and 5,10,15,20-tetrakis (4-aminophenyl)porphyrin iron(III). The obtained TAP-COF has high surface areas, abundant surface catalytic active sites, and highly effective electron transport due to its precisely controllable donor-acceptor arrangement and 3D porous structure. Then, the new TAP-COF exhibited excellent peroxidase-like catalytic activity, which could effectively catalyze oxidation of the substrate 3,3',5,5'-tetramethylbenzidine by H2O2 to produce a typical blue-colored reaction. On this basis, simple, rapid and selective colorimetric methods for in situ H2O2 detection were developed with the detection limit of 2.6 nM in the wide range of 0.01 to 200 µM. The colorimetric approach also could be used for in situ detection of H2O2 released from living MCF-7 cells. This portable sensor based on a COF nanozyme not only opens a new path for point-of-care testing, but also has potential applications in the field of cell biology and clinical diagnosis.

Risk mapping of lung cancer: a comprehensive appraisal of published meta-analyses incorporating Mendelian randomization studies.

INTRODUCTION: A comprehensive appraisal of published meta-analyses incorporating Mendelian randomization studies was performed to map the different risk factors and assess the causality for lung cancer. METHODS: Systematic reviews and meta-analyses of observational and interventional studies were reviewed based on PubMed, Embase, Web of Science, and Cochrane Library. Mendelian randomization analyses were conducted to validate the causal associations of those various exposures with lung cancer using summary statistics from 10 genome-wide association studies (GWAS) consortia and other GWAS databases in MR-Base platform. RESULTS: In the review of meta-analyses, 105 risk factors associated with lung cancer were identified from 93 articles. It was found that 72 risk factors were nominally significant (P < 0.05) associated with lung cancer. Mendelian randomization analyses were performed to analyze 36 exposures based on 551 SNPs and 4,944,052 individuals, finding that 3 exposures had a consistent risk/protective effect on lung cancer with the results of the meta-analysis. In Mendelian randomization anaylses, smoking (OR 1.44, 95% CI 1.18-1.75; P = 0.001) and blood copper (OR 1.14, 95% CI 1.01-1.29; P = 0.039) significantly associated with increased risk of lung cancer, whereas aspirin use (OR 0.67, 95% CI 0.50-0.89; P = 0.006) showed protective effects. CONCLUSION: This study mapped putative associations of risk factors for lung cancer, revealing the causal hazard effect of smoking, blood copper, and the protective effect of aspirin use in the development of lung cancer. CLINICAL TRIAL REGISTRY: This study is registered with PROSPERO (CRD42020159082).

Nanocage Ferritin Reinforced Polyacrylamide Hydrogel for Wearable Flexible Strain Sensors.

Biocomposite hydrogels are promising for applications in wearable flexible strain sensors. Nevertheless, the existing biocomposite hydrogels are still hard to meet all requirements, which limits the practical application. Here, inspired by the structure and composition of natural ferritin, we design a PAAm-Ferritin hybrid hydrogel through a facile method. Ferritin is uniformly distributed in the cross-linking networks and acts as a nanocage spring model, leading to the enhanced tensile strength of the hydrogel. The fracture stress is 99 kPa at 1400% maximum elongation. As fabricated PAAm-Ferritin hybrid hydrogels exhibit high toughness and low elastic modulus (21 kPa). The PAAm-Ferritin hybrid hydrogels present excellent biocompatibility and increased conductivity compared with PAAm hydrogel. Impressively, as a wearable flexible strain sensor, the PAAm-Ferritin hybrid hydrogels have high sensitivity (gauge factor = 2.06), excellent reliability, and cycling stability. This study indicates the feasibility of utilizing ferritin to synthesize functional materials, which is conducive to expanding the use of protein synthesis of materials technology and application fields.

Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.

PURPOSE: Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positive rate as well as on how to precisely differentiate between benign and malignant nodules. There is a lack of comprehensive examination of the techniques' development which is evolving the pulmonary nodules diagnosis from classical approaches to machine learning-assisted decision support. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over three decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand. CONCLUSION: It is the first literature review of the past 30 years' development in computer-assisted diagnosis of lung nodules. The challenges indentified and the research opportunities highlighted in this survey are significant for bridging current state to future prospect and satisfying future demand. The values of multifaceted driving forces and multidisciplinary researches are acknowledged that will make the computer-assisted diagnosis of pulmonary nodules enter into the main stream of clinical medicine and raise the state-of-the-art clinical applications as well as increase both welfares of physicians and patients. We firmly hold the vision that fair access to the reliable, faithful, and affordable computer-assisted diagnosis for early cancer diagnosis would fight the inequalities in incidence and mortality between populations, and save more lives.

Deep learning aided decision support for pulmonary nodules diagnosing: a review.

Deep learning techniques have recently emerged as promising decision supporting approaches to automatically analyze medical images for different clinical diagnosing purposes. Diagnosing of pulmonary nodules by using computer-assisted diagnosing has received considerable theoretical, computational, and empirical research work, and considerable methods have been developed for detection and classification of pulmonary nodules on different formats of images including chest radiographs, computed tomography (CT), and positron emission tomography in the past five decades. The recent remarkable and significant progress in deep learning for pulmonary nodules achieved in both academia and the industry has demonstrated that deep learning techniques seem to be promising alternative decision support schemes to effectively tackle the central issues in pulmonary nodules diagnosing, including feature extraction, nodule detection, false-positive reduction, and benign-malignant classification for the huge volume of chest scan data. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the deep learning aided decision support for pulmonary nodules diagnosing. As far as the authors know, this is the first time that a review is devoted exclusively to deep learning techniques for pulmonary nodules diagnosing.

Evaluation of the effects of amphiphilic oligomers in PEI based ternary complexes on the improvement of pDNA delivery.

Two kinds of novel oligomers were prepared by reversible addition fragmentation chain transfer (RAFT) polymerization and incorporated into the polyethyleneimine (PEI) gene delivery system through non-electrostatic assembly to improve gene transfection efficiency. The non-electrostatic assembly process was first investigated via probing the interactions of the oligomers with plasmid DNA (pDNA), PEI and AD-293 cells using a quartz crystal microbalance (QCM). The results show that the prepared oligomers almost had no interaction with pDNA while they had much stronger interactions with PEI and AD-293 cells. Meanwhile, we found that the two kinds of oligomers had different interactions with AD-193 cells, which caused different effects on gene transfection. The data of QCM tests combined with the in vitro transfection results can be used to explain what effects the oligomers have on improving gene transfection. The results also indicate that the strategy of detecting the interactions of oligomers with pDNA, polycations and cells will contribute to predetermine whether the prepared oligomer is efficient in improving gene transfection.

4-nitrophenol surface molecularly imprinted polymers based on multiwalled carbon nanotubes for the elimination of paraoxon pollution.

Molecularly imprinted polymers were grafted on the surface of multiwalled carbon nanotubes (MWCNT) using the hydrolysis product of paraoxon 4-nitrophenol as template, 4-vinyl pyridine (4-VPy) as the functional monomer and divinylbenzen (DVB) as the crosslinker. The binding experiments of 4-nitrophenol indicated that the MWCNT based molecularly imprinted polymers (MWCNT-MIP) have much higher adsorption ability than the MWCNT based non-imprinted polymers (MWCNT-NIP). At the same time we found that the adsorption of 4-nitrophenol can help to increase the hydrolytic rate of paraoxon, which indicates that there is an obvious catalyzing effect on the hydrolysis of paraoxon for this kind of materials. Furthermore, the 4-nitrophenol left in the paraoxon hydrolysis medium is only 0.01056 mM for MWCNT-MIP in the catalytic experiment of paraoxon we made (the initial concentration of paraoxon is 0.5 mM and MWCNT-MIP is 4 mg), which indicates that this kind of MWCNT based imprinted polymers can not only catalyze the hydrolysis of paraoxon but also eliminate the poisonous organism product 4-nitrophenol.