In situ growth of a cobalt porphyrin-based covalent organic framework on multi-walled carbon nanotubes for ultrasensitive real-time monitoring of living cell-released nitric oxide.

Nitric oxide (NO), as a critical transcellular messenger, participates in a variety of physiological and pathological processes. However, its real-time detection still faces challenges due to its short half-life and trace amounts. Here, MWCNTs@COF-366-Co was prepared by in situ growth of a cobalt porphyrin-based covalent organic framework (COF-366-Co) on multi-walled carbon nanotubes (MWCNTs), and a unique biosensing platform for ultrasensitive real-time NO determination was established. Remarkably, MWCNTs@COF-366-Co contains plenty of atomically arranged M-N4 active sites for electrocatalysis, which provides more efficient electron transfer pathways and resolves the random arrangement issue of active sites. COF-366-Co with a high surface area contains a large number of exposed active M-N4 sites, providing faster NO transport/diffusion and more efficient electron transfer pathways. Due to the synergy of atomic-level periodic structural features of COF-366-Co and high conductivity of MWCNTs, the MWCNTs@COF-366-Co electrochemical biosensor exhibited excellent NO determination performance in a wide range from 0.09 to 400 µM, with high sensitivity (8.9 µA µM-1 cm-2) and a low limit of detection (16 nM). Moreover, the biosensor has been successfully used to sensitively monitor NO molecules released from human umbilical vein endothelial cells (HUVECs). This research not only designed a multifunctional intelligent biosensor platform, but also provided a broad prospect for continuous dynamic monitoring of the activity of living cells and their released metabolites.

Promising natural products against SARS-CoV-2: Structure, function, and clinical trials.

The corona virus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus type 2 (SARS-COV-2) poses a severe threat to human health and still spreads globally. Due to the high mutation ratio and breakthrough infection rate of the virus, vaccines and anti-COVID-19 drugs require continual improvements. Drug screening research has shown that some natural active products can target the critical proteins of SARS-CoV-2, including 3CLpro, ACE2, FURIN, and RdRp, which could produce great inhibitory effects on SARS-COV-2. In addition, some natural products have displayed activities of immunomodulation, antiinflammatory, and antihepatic failure in COVID-19 clinical trials, which may relate to their non-monomeric structures. However, further evaluation and high-quality assessments, including safety verification tests, drug interaction tests, and clinical trials, are needed to substantiate natural products' multi-target and multi-pathway effects on COVID-19. Here, we review the literature on several promising active natural products that may act as vaccine immune enhancers or provide targeted anti-COVID-19 drugs. The structures, mechanisms of action, and research progress of these natural products are analyzed, to hopefully provide effective ideas for the development of targeted drugs that possess better structure, potency, and safety.

Thermal transport properties of graphite carbon nitride.

Graphite carbon nitride (GCN), which can be regarded as a nitrogen heteroatom-substituted graphite framework, has attracted great attention as a new 2D layered structure material with semiconductor electronic characteristics. Using molecular dynamics simulations, the in-plane thermal conductivity and cross-plane thermal resistance of two GCN structures (i.e., triazine-based and heptazine-based) are investigated. Our results show that the in-plane thermal conductivities of the triazine-based and heptazine-based GCN monolayers along the armchair direction are 55.39 and 17.81 W m-1 K-1, respectively. The cross-plane thermal resistance decreases with increasing layer number and reaches asymptotic values of 3.6 × 10-10 and 9.3 × 10-10 m2 K W-1 at 40 layers for triazine-based and heptazine-based GCN, respectively. The in-plane thermal conductivity can be effectively manipulated by changing the temperature and applying strain, while it is insensitive to the number of layers, which is in sharp contrast to that of graphene. Moreover, the cross-plane thermal resistance decreases monotonically with temperature and coupling strength, and can be modulated by external strain. Surprisingly, the cross-plane tensile strain can reduce the thermal resistance of the heptazine-based GCN. Our study serves as a guide to groups interested in the physical properties of GCN.

Effect of strain and defects on the thermal conductance of the graphene/hexagonal boron nitride interface.

In-plane heterojunctions, obtained by seamlessly joining two or more nanoribbon edges of isolated two-dimensional atomic crystals such as graphene and hexagonal boron nitride, are emerging as nanomaterials for the development of future multifunctional devices. The thermal transport behavior at the interface of these heterojunctions plays a pivotal role in determining their functional performance. Using molecular dynamics simulations, the interfacial thermal conductance of graphene/hexagonal boron nitride (GE/BN) in-plane heterojunctions was investigated. The GE/BN heterostructure has a remarkably high interfacial thermal conductance, and thermal rectification occurs at the interface. The results also show that the interfacial thermal conductance is effectively modulated by strain and defect engineering. The atomic defect location can affect the phonon transmission at the interface. Interestingly, compared with the nitrogen doping effect, the boron doping defect can more effectively facilitate vibrational coupling at the interface in the graphene sheet. Stress distribution and vibrational spectral analyses are performed to elucidate the thermal transport mechanism. The results of this study may provide a foundation for future research attempting to manipulate the interfacial thermal conductance in other two-dimensional heterostructures.

Thermal conductivity of two-dimensional BC3: a comparative study with two-dimensional C3N.

The thermal conductivities of single-layer BC3 (SLBC) sheets and their responses to environmental temperature, vacancy defects and external strain have been studied and compared with those of single-layer C3N (SLCN) sheets by molecular dynamics (MD) simulations. We found that SLBC and SLCN are isotropic in the basal plane and that their predicted thermal conductivities for infinite length sheets are 488.54 W m-1 K-1 and 799.87 W m-1 K-1, respectively. Despite many similar features in the structures of these materials, SLBC exhibits a lower thermal conductivity than SLCN due to stronger flexural acoustic phonon-defect scattering rates and weaker interatomic bonding stiffnesses. The vibrational density of states (VDOS) are calculated in both structures to elucidate their thermal conductivity differences. SLBC exhibits a more substantial redshift phenomenon in the high- and low-frequency domains than SLCN. In addition, the thermal conductivities of these materials exhibit decreasing trends in response to increases in temperature and defect ratio, and the temperature effect in SLBC is more substantial than that in SLCN, while the defect effect in SLBC is less substantial than that in SLCN. The influences of uniaxial compressive and tensile strains on the thermal conductivities of these materials are analysed separately. These two deformation modes cause different effects on the thermal transport behaviours of SLBC and SLCN: the effect of uniaxial compressive strain is slightly negative, while the effect of uniaxial tensile strain is initially positive and then negative. Moreover, the biaxial strains result in a more severe reduction in thermal conductivity than the uniaxial strains. Remarkably, the impact of uniaxial and biaxial tensile strains on thermal transport was stronger in SLBC than in SLCN. We propose that SLBC nanomembranes are promising candidates for various thermal applications.

Self-standing perylene diimide covalent organic framework membranes for trace TMA sensing at room temperature.

The unprecedented demand for highly selective, real-time monitoring and low-power gas sensors used in food quality control has been driven by the increasing popularity of the Internet of Things (IoT). Herein, the self-standing perylene diimide based covalent organic framework membranes (COFMPDI-THSTZ) were prepared via liquid-liquid interfacial synthesis method. By incorporating the perylene diimide monomer into the COFM through molecular engineering, COFMPDI-THSTZ based sensor demonstrated an outstanding trimethylamine (TMA)-sensing performance at room temperature. Benefited from the TMA-accessible self-standing membrane morphology, π-electron delocalization effect, and extensive surface area with continuous nanochannels, the specific and highly sensitive TMA measurement has been achieved within the range of 0.03-400 ppm, with an exceptional theoretical detection limit as low as 10 ppb. Moreover, the primary internal mechanism of COFMPDI-THSTZ for this efficient TMA detection was investigated through in-situ FT-IR spectra, thereby directly elucidating that the chemisorption interaction of oxygen modulated the depletion layers on sensing material surface, resulting in alterations in sensor resistance upon exposure to the target gas. For practical usage, COFMPDI-THSTZ based sensor exhibited exceptional real-time in-situ sensing capabilities, further confirmed their potential for application in dynamic prediction evaluation of marine fish products and quality monitoring in IoT.

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.

The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis.

Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.

Analysis of coexisting pathogens in nasopharyngeal swabs from COVID-19.

Background: The impact of COVID-19 on the world is still ongoing, and it is currently under regular management. Although most infected people have flu-like symptoms and can cure themselves, coexisting pathogens in COVID-19 patients should not be taken lightly. The present study sought to investigate the coexisting pathogens in SARS-CoV-2 infected patients and identify the variety and abundance of dangerous microbes to guide treatment strategies with a better understanding of the untested factors. Methods: We extracted total DNA and RNA in COVID-19 patient specimens from nasopharyngeal swabs to construct a metagenomic library and utilize Next Generation Sequencing (NGS) to discover chief bacteria, fungi, and viruses in the body of patients. High-throughput sequencing data from Illumina Hiseq 4000 were analyzed using Krona taxonomic methodology for species diversity. Results: We studied 56 samples to detect SARS-CoV-2 and other pathogens and analyzed the species diversity and community composition of these samples after sequencing. Our results showed some threatening pathogens such as Mycoplasma pneumoniae, Klebsiella pneumoniae, Streptococcus pneumoniae, and some previously reported pathogens. SARS-CoV-2 combined with bacterial infection is more common. The results of heat map analysis showed that the abundance of bacteria was mostly more than 1000 and that of viruses was generally less than 500. The pathogens most likely to cause SARS-CoV-2 coinfection or superinfection include Streptococcus pneumoniae, Haemophilus influenzae, Staphylococcus aureus, Klebsiella pneumoniae, and Human gammaherpesvirus 4. Conclusions: The current coinfection and superinfection status is not optimistic. Bacteria are the major threat group that increases the risk of complications and death in COVID-19 patients and attention should be paid to the use and control of antibiotics. Our study investigated the main types of respiratory pathogens prone to coexisting or superinfection in COVID-19 patients, which is valuable for identifying and treating SARS-CoV-2.

Pharmacological mechanism of mylabris in the treatment of leukemia based on bioinformatics and systematic pharmacology.

Leukemia is a common blood cancer, whose treatment usually necessitates chemo/radiotherapy and bone marrow transplant. Hence, safer and more effective options are urgently needed. Mylabris, the dried body of blister beetles, has been used extensively in traditional Chinese medicine. This study applied bioinformatics and systematic pharmacology to investigate the mechanism of action of mylabris in the treatment of leukemia. Five effective components and 35 corresponding target proteins were identified by screening the TCMSP database; whereas 776 genes related to leukemia were selected using OMIM, GeneCards, and the Therapeutic Target Database. Eight genes common to mylabris and leukemia were identified. Protein-protein interaction network analysis and a component-target-pathway diagram identified TP53 and PTEN as key gene targets of mylabris in the treatment of leukemia. GO enrichment analysis pointed to DNA damage and cell cycle disorder caused by p53 signaling as the most significant processes; whereas KEGG enrichment pointed to the p53 signaling pathway. In summary, mylabris may exert a therapeutic effect on leukemia by triggering DNA damage, inducing apoptosis, as well as inhibiting the growth and proliferation of tumor cells through the regulation of TP53 and PTEN. These findings provide a mechanistic rationale for the treatment of leukemia with traditional Chinese medicine.