In the current study, we aimed to study the effect of miR-146a on proliferation and migration in an in vitro diabetic foot ulcer (DFU) model by targeting A-kinase-anchoring protein 12 (AKAP12). An in ...vitro DFU model was initially established using HaCaT cells derived from human keratinocytes and induced by advanced glycation end products (AGEs). The effects of overexpression of miR-146a on proliferation and migration ability were analysed. The expression levels of miR-146a and AKAP12 were measured by quantitative real-time polymerase chain reaction (qRT-PCR), and AKAP12, hypoxia-inducible factor-1α (HIF-1α), Wnt3a and β-catenin protein levels were measured by western blotting. The cell proliferation ability was measured by MTT, and the migration ability was analysed by a cell scratch assay. The binding between miR-146a and AKAP12 was identified using a luciferase reporter assay. The results demonstrated that AGEs significantly suppressed cell proliferation and migration, while the expression of miR-146a decreased and the expression of AKAP12 increased. A luciferase reporter assay revealed that miR-146a could directly target AKAP12. Overexpression of miR-146a promoted cell proliferation and migration in an in vitro DFU model and also promoted the expression of HIF-1α, Wnt3a and β-catenin but suppressed the expression of AKAP12. Co-overexpression of miR-146a and AKAP12 reversed the effect of miR-146a on cell proliferation and migration. Our findings revealed that miR-146a directly targeted AKAP12 and promoted cell proliferation and migration in an in vitro DFU model. This study provides a new perspective for the study of miR-146a in the treatment of DFU.
2D materials hold great potential for designing novel electronic and optoelectronic devices. However, 2D material can only absorb limited incident light. As a representative 2D semiconductor, ...monolayer MoS2 can only absorb up to 10% of the incident light in the visible, which is not sufficient to achieve a high optical‐to‐electrical conversion efficiency. To overcome this shortcoming, a “gap‐mode” plasmon‐enhanced monolayer MoS2 fluorescent emitter and photodetector is designed by squeezing the light‐field into Ag shell‐isolated nanoparticles–Au film gap, where the confined electromagnetic field can interact with monolayer MoS2. With this gap‐mode plasmon‐enhanced configuration, a 110‐fold enhancement of photoluminescence intensity is achieved, exceeding values reached by other plasmon‐enhanced MoS2 fluorescent emitters. In addition, a gap‐mode plasmon‐enhanced monolayer MoS2 photodetector with an 880% enhancement in photocurrent and a responsivity of 287.5 A W−1 is demonstrated, exceeding previously reported plasmon‐enhanced monolayer MoS2 photodetectors.
By dropping Ag shell‐isolated nanoparticles onto Al2O3‐covered Au film, the gap‐mode plasmonic structure with a gap thickness of 7 nm can form naturally. By integrating monolayer MoS2 into this plasmonic structure, 110‐fold photoluminescence and 880% photocurrent enhancement are achieved. This work shows that the gap‐mode plasmonic structures have huge potential for realizing high‐performance 2D‐material‐based optoelectronic devices.
Controlling toxigenic Fusarium graminearum (FG) is challenging. A bacterial strain (S76-3, identified as Bacillus amyloliquefaciens) that was isolated from diseased wheat spikes in the field ...displayed strong antifungal activity against FG. Reverse-phase high performance liquid chromatography and electrospray ionization mass spectrometry analyses revealed that S76-3 produced three classes of cyclic lipopeptides including iturin, plipastatin and surfactin. Each class consisted of several different molecules. The iturin and plipastatin fractions strongly inhibited FG; the surfactin fractions did not. The most abundant compound that had antagonistic activity from the iturin fraction was iturin A (m/z 1043.35); the most abundant active compound from the plipastatin fraction was plipastatin A (m/z 1463.90). These compounds were analyzed with collision-induced dissociation mass spectrometry. The two purified compounds displayed strong fungicidal activity, completely killing conidial spores at the minimal inhibitory concentration range of 50 µg/ml (iturin A) and 100 µg/ml (plipastatin A). Optical and fluorescence microscopy analyses revealed severe morphological changes in conidia and substantial distortions in FG hyphae treated with iturin A or plipastatin A. Iturin A caused leakage and/or inactivation of FG cellular contents and plipastatin A caused vacuolation. Time-lapse imaging of dynamic antagonistic processes illustrated that iturin A caused distortion and conglobation along hyphae and inhibited branch formation and growth, while plipastatin A caused conglobation in young hyphae and branch tips. Transmission electron microscopy analyses demonstrated that the cell walls of conidia and hyphae of iturin A and plipastatin A treated FG had large gaps and that their plasma membranes were severely damaged and separated from cell walls.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, ...soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its' good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Protein–protein interactions (PPIs) describe the direct physical contact of two proteins that usually results in specific biological functions or regulatory processes. The characterization and study ...of PPIs through the investigation of their pattern and principle have remained a question in biological studies. Various experimental and computational methods have been used for PPI studies, but most of them are based on the sequence similarity with current validated PPI participators or cellular localization patterns. Most methods ignore the fact that PPIs are defined by their specific biological functions. In this study, we constructed a novel rule-based computational method using gene ontology and KEGG pathway annotation of PPI participators that correspond to the complicated biological effects of PPIs. Our newly presented computational method identified a group of biological functions that are tightly associated with PPIs and provided a new function-based tool for PPI studies in a rule manner.
•An explainable rule-based machine learning model of protein-protein interaction was built.•Each protein was represented by a binary GO and KEGG annotation vector.•We used the sum and the absolute difference of two protein vectors to represent a protein-protein pair.•The key GO and KEGG function features were identified using feature selection method.•The prediction rules of protein-protein interactions were learned using decision tree.
Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. ...The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression-methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.
Exploiting useful contacts: The exceptional catalytic performance of a photocatalyst composed of Pd nanoparticles and mesoporous carbon nitride for the dehydrogenation of formic acid in water at room ...temperature to produce H2 gas (see picture) is due to enhanced electron enrichment of the Pd nanoparticles through charge transfer at the interface of the Mott–Schottky contact.
Background
The missing asymptomatic COVID‐19 infections have been overlooked because of the imperfect sensitivity of the nucleic acid testing (NAT). Globally understanding the humoral immunity in ...asymptomatic carriers will provide scientific knowledge for developing serological tests, improving early identification, and implementing more rational control strategies against the pandemic.
Measure
Utilizing both NAT and commercial kits for serum IgM and IgG antibodies, we extensively screened 11 766 epidemiologically suspected individuals on enrollment and 63 asymptomatic individuals were detected and recruited. Sixty‐three healthy individuals and 51 mild patients without any preexisting conditions were set as controls. Serum IgM and IgG profiles were further probed using a SARS‐CoV‐2 proteome microarray, and neutralizing antibody was detected by a pseudotyped virus neutralization assay system. The dynamics of antibodies were analyzed with exposure time or symptoms onset.
Results
A combination test of NAT and serological testing for IgM antibody discovered 55.5% of the total of 63 asymptomatic infections, which significantly raises the detection sensitivity when compared with the NAT alone (19%). Serum proteome microarray analysis demonstrated that asymptomatics mainly produced IgM and IgG antibodies against S1 and N proteins out of 20 proteins of SARS‐CoV‐2. Different from strong and persistent N‐specific antibodies, S1‐specific IgM responses, which evolved in asymptomatic individuals as early as the seventh day after exposure, peaked on days from 17 days to 25 days, and then disappeared in two months, might be used as an early diagnostic biomarker. 11.8% (6/51) mild patients and 38.1% (24/63) asymptomatic individuals did not produce neutralizing antibody. In particular, neutralizing antibody in asymptomatics gradually vanished in two months.
Conclusion
Our findings might have important implications for the definition of asymptomatic COVID‐19 infections, diagnosis, serological survey, public health, and immunization strategies.
The combination of NAT and serological testing for IgM antibody significantly improves the detection sensitivity of asymptomatic COVID‐19 infections, compared with NAT alone. S1‐specific IgM antibody response with rapid emergence and disappearance might be helpful to assist NAT for early identification of infectious individuals. A majority of asymptomatics induce very low levels of neutralizing antibody that disappear in two months. Abbreviations: NAT, nucleic acid testing; FI, fluorescence intensity; NT50, half‐maximal neutralizing titer.
Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in ...different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k-mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.
A ultrathin 2D MOF nanosheets decorated with tetra-pyridyl calix4arene was designed and prepared for determination of pesticide through host-guest chemistry.
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•2D MOF nanosheet ...decorated with calix4arene was fabricated by a simple method.•The thickness of MOF nanosheet is only 2.20 nm, better interacting with analytes.•Highly accessible active sites of calix4arene on the surface of 2D MOF nanosheet.•It can selectively sense glyphosate by interaction with the host of calix4arene.
Chemical pesticides are highly toxic and widely spread as environmental pollutants. New strategies are being developed to selectively and sensitively detect pesticides. Herein, we prepared ultrathin two-dimensional (2D) metal-organic framework (MOF) nanosheets, decorated with tetra-pyridyl calix4arene, for determination of pesticide based on host-guest chemistry. The 3D layered MOF {Cd2(5-NO2-BDC)2L(MeOH)∙2MeOH}n (MOF-Calix; 5-NO2-H2BDC = 5-nitro-1,3-benzenedicarboxylic acid; L = 25,26,27,28-tetra-(4-pyridylmethyl)oxycalix4arene) was elaborately constructed, with features of 2D layered structure that are held together by weak π…π interactions. Due to the cup-shaped characteristic of calix4arene ligand and easy-to-departure MeOH molecules in the interspaces of the 2D layers in MOF-Calix, the 3D bulk MOF-Calix can be readily exfoliated into ultrathin single (2.20 nm) or double-layer (3.73 nm) of 2D nanosheets by ultrasound method with high efficiency. We take the intrinsic advantages of calix4arene and the highly accessible active site of calix4arene on the surface of the 2D MOF-Calix nanosheets for selective and sensitive sensing of glyphosate via fluorescence enhancement effect with a detection limit of 2.25 μM. This work demonstrates the simple preparation of a 2D MOF nanosheet as fluorescent sensor for glyphosate detection.
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