Cardiac patch is considered a promising strategy for enhancing stem cell therapy of myocardial infarction (MI). However, the underlying mechanisms for cardiac patch repairing infarcted myocardium ...remain unclear. In this study, we investigated the mechanisms of PCL/gelatin patch loaded with MSCs on activating endogenous cardiac repair. PCL/gelatin patch was fabricated by electrospun. The patch enhanced the survival of the seeded MSCs and their HIF‐1α, Tβ4, VEGF and SDF‐1 expression and decreased CXCL14 expression in hypoxic and serum‐deprived conditions. In murine MI models, the survival and distribution of the engrafted MSCs and the activation of the epicardium were examined, respectively. At 4 weeks after transplantation of the cell patch, the cardiac functions were significantly improved. The engrafted MSCs migrated across the epicardium and into the myocardium. Tendency of HIF‐1α, Tβ4, VEGF, SDF‐1 and CXCL14 expression in the infarcted myocardium was similar with expression in vitro. The epicardium was activated and epicardial‐derived cells (EPDCs) migrated into deep tissue. The EPDCs differentiated into endothelial cells and smooth muscle cells, and some of EPDCs showed to have differentiated into cardiomyocytes. Density of blood and lymphatic capillaries increased significantly. More c‐kit+ cells were recruited into the infarcted myocardium after transplantation of the cell patch. The results suggest that epicardial transplantation of the cell patch promotes repair of the infarcted myocardium and improves cardiac functions by enhancing the survival of the transplanted cells, accelerating locality paracrine, and then activating the epicardium and recruiting endogenous c‐kit+ cells. Epicardial transplantation of the cell patch may be applied as a novel effective MI therapy.
Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the ...effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.
A strategy called ultramicroporous building unit (UBU) is introduced. It allows the creation of hierarchical bi‐porous features that work in tandem to enhance gas uptake capacity and separation. ...Smaller pores from UBUs promote selectivity, while larger inter‐UBU packing pores increase uptake capacity. The effectiveness of this UBU strategy is shown with a cobalt MOF (denoted SNNU‐45) in which octahedral cages with 4.5 Å pore size serve as UBUs. The C2H2 uptake capacity at 1 atm reaches 193.0 cm3 g−1 (8.6 mmol g−1) at 273 K and 134.0 cm3 g−1 (6.0 mmol g−1) at 298 K. Such high uptake capacity is accompanied by a high C2H2/CO2 selectivity of up to 8.5 at 298 K. Dynamic breakthrough studies at room temperature and 1 atm show a C2H2/CO2 breakthrough time up to 79 min g−1, among top‐performing MOFs. Grand canonical Monte Carlo simulations agree that ultrahigh C2H2/CO2 selectivity is mainly from UBU ultramicropores, while packing pores promote C2H2 uptake capacity.
Hole to differentiate, and hole to accommodate. Two types of pores can mingle together using a strategy called UBU (ultramicroporous building unit). This strategy results in a promising gas absorbent for excellent C2H2 storage capacity and top‐level C2H2/CO2 separation ability.
Dormant
Bacillales
and
Clostridiales
spores begin to grow when small molecules (germinants) trigger germination, potentially leading to food spoilage or disease. Germination-specific proteins sense ...germinants, transport small molecules, and hydrolyze specific bonds in cortex peptidoglycan and specific proteins. Major events in germination include (
a
) germinant sensing; (
b
) commitment to germinate; (
c
) release of spores' depot of dipicolinic acid (DPA); (
d
) hydrolysis of spores' peptidoglycan cortex; and (
e
) spore core swelling and water uptake, cell wall peptidoglycan remodeling, and restoration of core protein and inner spore membrane lipid mobility. Germination is similar between
Bacillales
and
Clostridiales
, but some species differ in how germinants are sensed and how cortex hydrolysis and DPA release are triggered. Despite detailed knowledge of the proteins and signal transduction pathways involved in germination, precisely what some germination proteins do and how they do it remain unclear.
Nanozymes, a type of nanomaterials that function similarly to natural enzymes, receive extensive attention in biomedical fields. However, the widespread applications of nanozymes are greatly plagued ...by their unsatisfactory enzyme‐mimicking activity. Localized surface plasmon resonance (LSPR), a nanoscale physical phenomenon described as the collective oscillation of surface free electrons in plasmonic nanoparticles under light irradiation, offers a robust universal paradigm to boost the catalytic performance of nanozymes. Plasmonic nanozymes (PNzymes) with elevated enzyme‐mimicking activity by leveraging LSPR, emerge and provide unprecedented opportunities for biocatalysis. In this review, the physical mechanisms behind PNzymes are thoroughly revealed including near‐field enhancement, hot carriers, and the photothermal effect. The rational design and applications of PNzymes in biosensing, cancer therapy, and bacterial infections elimination are systematically introduced. Current challenges and further perspectives of PNzymes are also summarized and discussed to stimulate their clinical translation. It is hoped that this review can attract more researchers to further advance the promising field of PNzymes and open up a new avenue for optimizing the enzyme‐mimicking activity of nanozymes to create superior nanocatalysts for biomedical applications.
Plasmonic nanozymes (PNzymes), the “beautiful and incredible” encounter between nanozymes and localized surface plasmon resonance effects, and their catalytic mechanisms, bioapplications as well as current challenges and further perspectives are systematically summarized and discussed. It is anticipated that this review will provide new insights into the design of powerful nanocatalysts for biomedical applications.
A robust cluster-based Eu-MOF has been created by a tetrazolyl-carboxyl linker, which shows great chemical and thermal stability and multiple functions of fluorescent sensor for the detection of ...antibiotics (MDZ, DMZ) and pesticides (DCN).
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•A robust cluster-based Eu-MOF as a fluorescence probe.•Resistance to water, organic solvents and wide pH value range.•Selective detection of MDZ, DMZ and DCN with low detection limit, rapid response.•Detected MDZ and DMZ in calf serum and sensed DCN in lake water.•Combining experiments and calculations to study the sensing mechanism.
As a hot issue of global concern, the abuse of organic pollutants, including pesticides and antibiotics poses a great threat to the human health and ecological environment. Effective and accurate detection of these species is of profound significance in many fields. In this work, a novel 3D metal-organic framework (MOF) Eu2(dtztp)(OH)2(DMF)(H2O)2.5·2H2O (1) was solvothermally synthesized. 1 is a three-dimensional framework based on tetranuclear Eu4(μ3-OH)4(μ2-OH2)8+ clusters, and reveals the great chemical stability and excellent tolerance in water and organic solvents. The MOF also shows strong fluorescence that was undisturbed by the pH in aqueous water (pH = 3–12). Importantly, 1 can quickly detect metronidazole (MDZ) and dimetridazole (DMZ) antibiotics as well as 2,6-dichloro-4-nitroaniline (DCN) pesticide in water with good recyclability and low detection limit. MDZ, DMZ and DCN were also successfully detected in calf serum and lake water, respectively. The mechanism of fluorescence quenching was disclosed through the combination of experiments and density functional theory calculations.
Covalent organic frameworks (COFs) are appealing photocatalysts for toxic chemical degradation. Great efforts have been devoted to regulate the photocatalytic performance of COFs by tuning their ...organic building blocks, but the relationship between COF linkage and photochemical properties has rarely been explored. Herein, we report the synthesis and characterisation of a novel aminal‐linked porphyrinic COF, namely Por‐Aminal‐COF. Por‐Aminal‐COF (0.25 mol %) showed excellent photocatalytic activity toward the detoxification of the sulfur mustard simulant with a half‐life (t1/2) of 5 min, which is far lower than that of traditional imine‐linked Por‐COF (t1/2=16 min). Transient absorption spectroscopy indicated that the aminal linkages of Por‐Aminal‐COF facilitated the intersystem crossing process. Thus, Por‐Aminal‐COF showed higher triplet‐state generation efficiency compared with Por‐COF, consequently promoting the activation of oxygen molecular to singlet oxygen.
A novel aminal‐linked porphyrin‐based covalent organic framework (Por‐Aminal‐COF) was synthesized to detoxify the mustard‐gas simulant 2‐chloroethyl ethyl sulfide (CEES). Compared with imine‐linked Por‐COF, Por‐Aminal‐COF demonstrates higher photooxidation of CEES into 2‐chloroethyl ethyl sulfoxide (CEESO). The COF linkages contribute to an intersystem crossing process and enhance the photochemical properties of the catalyst.
Biofilm infections can induce chronic inflammation and stall the normal orchestrated course of wound-healing cascades. Herein, pH-switchable antimicrobial hydrogel with nanofiber networks for biofilm ...eradication and rescuing stalled healing in chronic wounds is reported on the basis of the self-assembly of a designed octapeptide (IKFQFHFD) at neutral pH. This hydrogel is biocompatible and exhibits an acidic pH (pathological environment of infected chronic wounds)-switchable broad-spectrum antimicrobial effect via a mechanism involving cell wall and membrane disruption. The antimicrobial activity of hydrogel is derived from its acidic pH-dependent nanofiber network destabilization and activated release of IKFQFHFD, which is antimicrobial only at acidic pH due to the antimicrobial peptide-like molecular structure. In addition, supramolecular nanofiber networks loaded with drugs of cypate (photothermal agent) and proline (procollagen component) are further developed. In vitro experiments show that loaded drugs exhibit acidic pH (pH ∼ 5.5)-responsive release profiles, and synergistic biofilm eradication and subsequent healing cascade activation of cells proliferation are achieved on the basis of the supramolecular nanofiber networks. Remarkably, the nanofiber networks of hydrogel enable in vivo complete healing of MRSA biofilm infected wound in diabetic mice within 20 days, showing great potential as promising chronic wound dressings. The proposed synergistic strategy for eradicating biofilm and activating subsequent healing cascades may offer a powerful modality for the management of clinical chronic wounds.
The SARS-CoV-2-infected disease (COVID-19) outbreak is a major threat to human beings. Previous studies mainly focused on Wuhan and typical symptoms. We analysed 74 confirmed COVID-19 cases with GI ...symptoms in the Zhejiang province to determine epidemiological, clinical and virological characteristics.
COVID-19 hospital patients were admitted in the Zhejiang province from 17 January 2020 to 8 February 2020. Epidemiological, demographic, clinical, laboratory, management and outcome data of patients with GI symptoms were analysed using multivariate analysis for risk of severe/critical type. Bioinformatics were used to analyse features of SARS-CoV-2 from Zhejiang province.
Among enrolled 651 patients, 74 (11.4%) presented with at least one GI symptom (nausea, vomiting or diarrhoea), average age of 46.14 years, 4-day incubation period and 10.8% had pre-existing liver disease. Of patients with COVID-19 with GI symptoms, 17 (22.97%) and 23 (31.08%) had severe/critical types and family clustering, respectively, significantly higher than those without GI symptoms, 47 (8.14%) and 118 (20.45%). Of patients with COVID-19 with GI symptoms, 29 (39.19%), 23 (31.08%), 8 (10.81%) and 16 (21.62%) had significantly higher rates of fever >38.5°C, fatigue, shortness of breath and headache, respectively. Low-dose glucocorticoids and antibiotics were administered to 14.86% and 41.89% of patients, respectively. Sputum production and increased lactate dehydrogenase/glucose levels were risk factors for severe/critical type. Bioinformatics showed sequence mutation of SARS-CoV-2 with m
A methylation and changed binding capacity with ACE2.
We report COVID-19 cases with GI symptoms with novel features outside Wuhan. Attention to patients with COVID-19 with non-classic symptoms should increase to protect health providers.
Due to the need to repeatedly call a classifier to evaluate individuals in the population, existing evolutionary feature selection algorithms have the disadvantage of high computational cost. In view ...of it, this paper studies a multi-objective feature selection framework based on sample reduction strategy and evolutionary algorithm, significantly reducing the computational cost of algorithm without affecting optimal results. In the framework, a selection strategy of representative samples, called K-means clustering based differential selection, and a ladder-like sample utilization strategy are proposed to reduce the size of samples used in the evolutionary process. Moreover, a fast multi-objective evolutionary feature selection algorithm, called FMABC-FS, is proposed by embedding an improved artificial bee colony algorithm based on the particle update model into the framework. By applying FMABC-FS to several typical UCI datasets, and comparing with three multi-objective feature selection algorithms, experimental results show that the proposed variable sample size strategy is more suitable to FMABC-FS, and FMABC-FS can obtain better feature subsets with much less running time than those comparison algorithms.
•Establishing a multi-objective evolutionary feature selection framework to reduce the computational cost of algorithm without affecting the result of feature selection.•Developing a K-means clustering based differential selection strategy and a ladder-like utilization strategy of samples to select representative samples for evaluating individuals.•Proposing a fast multi-objective feature selection algorithm, called FMABC-FS, by embedding an improved ABC algorithm into the framework.
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