Biomaterials-Based Technologies in Skeletal Muscle Tissue Engineering.

For many clinically prevalent severe injuries, the inherent regenerative capacity of skeletal muscle remains inadequate. Skeletal muscle tissue engineering (SMTE) seeks to meet this clinical demand. With continuous progress in biomedicine and related technologies including micro/nanotechnology and 3D printing, numerous studies have uncovered various intrinsic mechanisms regulating skeletal muscle regeneration and developed tailored biomaterial systems based on these understandings. Here, the skeletal muscle structure and regeneration process are discussed and the diverse biomaterial systems derived from various technologies are explored in detail. Biomaterials serve not merely as local niches for cell growth, but also as scaffolds endowed with structural or physicochemical properties that provide tissue regenerative cues such as topographical, electrical, and mechanical signals. They can also act as delivery systems for stem cells and bioactive molecules that have been shown as key participants in endogenous repair cascades. To achieve bench-to-bedside translation, the typical effect enabled by biomaterial systems and the potential underlying molecular mechanisms are also summarized. Insights into the roles of biomaterials in SMTE from cellular and molecular perspectives are provided. Finally, perspectives on the advancement of SMTE are provided, for which gene therapy, exosomes, and hybrid biomaterials may hold promise to make important contributions.

Protective effect of FKBP12 on dextran sulfate sodium-induced ulcerative colitis in mice as a tacrolimus receptor.

Ulcerative colitis (UC) is a multifactorial intestinal disease with a high incidence. In recent years, there has been an urgent need for pleiotropic drugs with a clear biosafety profile. Tacrolimus (TAC) is an immunosuppressant with stronger in vivo effects and better gastrointestinal absorption and is considered a potential treatment for UC. FKBP12 is a mediator of TAC immunosuppression; however, it is unclear whether it can participate in the development of UC in combination with TAC. The purpose of this study is to preliminarily validate the function of FKBP12 by establishing dextran sulfate sodium (DSS)-induced UC model and TAC treatment. The results revealed that TAC was effective in alleviating DSS-induced UC symptoms such as body weight and disease activity index (DAI). TAC significantly protects colonic tissue and attenuates DSS-induced histomorphological changes. In addition, FKBP12 is down-regulated in the intestinal tissue of DSS-induced UC mice and in serum samples of UC patients. In conclusion, our study revealed that FKBP12 may act as a TAC receptor to have anti-inflammatory and protective effects on DSS-induced UC in mice, which will provide a new option for the treatment of UC.

Porous Calcium-Silicate-Hydrate as a Low-Cost Nano-Platform for Ultra-High CO2 Capture and Storage.

CO2 capture and storage have been regarded as promising concepts to reduce anthropogenic CO2 emissions. However, the high cost, inferior adsorption capacity, and higher effective activation temperature of traditional sorbents limit their practical application in efficient CO2 capture. Here, a C-S-H@ZIF-8 (C-S-Z) sorbent is fabricated by in situ growth of the ZIF-8 shell on the C-S-H (calcium-silicate-hydrate) surface for ultra-high CO2 adsorption and storage. Among the C-S-Z, the outer ZIF-8 shell acts as a transport channel that promotes CO2 absorption toward the underlying C-S-H substrate for accelerated carbonation while preventing nitrogen and water from reaching the interior C-S-H. As a consequence, C-S-Z possesses the merits of ample pyrrolic nitrogen, porous structure, and ultra-high surface area (577.18 m2  g-1 ), that contribute to an ultra-high CO2 capture capacity, reaching 293.6 mg g-1 . DFT calculations show a high CO2 adsorption energy and the mineral carbonation is dominant by the adsorption process. In particular, the advantages of the outstanding adsorption capacity, low cost, and high CO2 selectivity make this C-S-H-based sorbent hold great potential in the practical application for direct air CO2 capture and storage.

Effects of 30% vs. 60% inspired oxygen fraction during mechanical ventilation on postoperative atelectasis: a randomised controlled trial.

BACKGROUND: There is the ongoing debate over the effect of inspired oxygen fraction (FiO2) during mechanical ventilation on postoperative atelectasis. We aimed to compare the effects of low (30%) and moderate (60%) FiO2 on postoperative atelectasis. The hypothesis of the study was that 30% FiO2 during mechanical ventilation could reduce postoperative atelectasis volume compared with 60% FiO2. METHODS: We performed a randomized controlled trial with 120 patients. Subjects were randomly assigned to receive 30% or 60% FiO2 during mechanical ventilation in a 1:1 ratio. The primary outcome was the percentage of postoperative atelectasis volume in the total lung measured using chest CT within 30 min after extubation. The secondary outcomes included different aeration region volumes, incidence of clinically significant atelectasis, and oxygenation index. RESULTS: In total, 113 subjects completed the trial, including 55 and 58 subjects in the 30% and 60% FiO2 groups, respectively. The percentage of the postoperative atelectasis volume in the 30% FiO2 group did not differ from that in the 60% FiO2 group. Furthermore, there was no significant difference in the atelectasis volume between the two groups after the missing data were imputed by multiple imputation. Additionally, there were no significant differences in the volumes of the over-aeration, normal-aeration, and poor-aeration regions between the groups. No significant differences in the incidence of clinically significant atelectasis or oxygenation index at the end of surgery were observed between the groups. CONCLUSIONS: Compared with 60% FiO2, the use of 30% FiO2 during mechanical ventilation does not reduce the postoperative atelectasis volume. TRIAL REGISTRATION: Chinese Clinical Trial Registry ( http://www.chictr.org.cn ). Identifier: ChiCTR1900021635. Date: 2 March 2019. Principal invetigator: Weidong Gu.

Comparison of 6-min walk test distance vs. estimated maximum oxygen consumption for predicting postoperative pulmonary complications in patients undergoing upper abdominal surgery: a prospective cohort study.

OBJECTIVE: The present study aims to evaluate the predictive ability of estimated maximum oxygen consumption (e[Formula: see text]O2max) and 6-min walk distance (6MWD) for postoperative pulmonary complications (PPCs) in adult surgical patients undergoing major upper abdominal surgery. METHOD: This study was conducted by collecting data prospectively from a single center. The two predictive variables in the study were defined as 6MWD and e[Formula: see text]O2max. Patients scheduled for elective major upper abdominal surgery from March 2019 to May 2021 were included. The 6MWD was measured for all patients before surgery. e[Formula: see text]O2max was calculated using the regression model of Burr, which uses 6MWD, age, gender, weight, and resting heart rate (HR) to predict aerobic fitness. The patients were categorized into PPC and non-PPC group. The sensitivity, specificity, and optimum cutoff values for 6MWD and e[Formula: see text]O2max were calculated to predict PPCs. The area under the receiver operating characteristic curve (AUC) of 6MWD or e[Formula: see text]O2max was constructed and compared using the Z test. The primary outcome measure was the AUC of 6MWD and e[Formula: see text]O2max in predicting PPCs. In addition, the net reclassification index (NRI) was calculated to assess ability of e[Formula: see text]O2max compared with 6MWT in predicting PPCs. RESULTS: A total of 308 patients were included 71/308 developed PPCs. Patients unable to complete the 6-min walk test (6MWT) due to contraindications or restrictions, or those taking beta-blockers, were excluded. The optimum cutoff point for 6MWD in predicting PPCs was 372.5 m with a sensitivity of 63.4% and specificity of 79.3%. The optimum cutoff point for e[Formula: see text]O2max was 30.8 ml/kg/min with a sensitivity of 91.6% and specificity of 79.3%. The AUC for 6MWD in predicting PPCs was 0.758 (95% confidence interval (CI): 0.694-0.822), and the AUC for e[Formula: see text]O2max was 0.912 (95%CI: 0.875-0.949). A significantly increased AUC was observed in e[Formula: see text]O2max compared to 6MWD in predicting PPCs (P < 0.001, Z = 4.713). And compared with 6MWT, the NRI of e[Formula: see text]O2max was 0.272 (95%CI: 0.130, 0.406). CONCLUSION: The results suggested that e[Formula: see text]O2max calculated from the 6MWT is a better predictor of PPCs than 6MWD in patients undergoing upper abdominal surgery and can be used as a tool to screen patients at risk of PPCs.

The Emerging Role of Noncoding RNA Regulation of the Ferroptosis in Cardiovascular Diseases.

Cardiovascular disease (CVD) is a significant public health issue due to its high prevalence and considerable contribution to the global disease burden. Recent studies suggest that genetic factors, including noncoding RNAs, have an important role in the progression of CVD. Noncoding RNA plays a critical role in genetic programming and gene regulation during development. Ferroptosis is a form of iron-dependent regulated cell death (RCD), which is mainly caused by increased lipid hydroperoxide and redox imbalance. Ferroptosis is essentially different from other forms of RCD in morphology and mechanism, such as apoptosis, autophagic cell death, pyroptosis, and necroptosis. Much evidence suggested ferroptosis is involved in the development of various CVDs, especially in cardiac ischemia/reperfusion injury, heart failure, and aortic dissection. Here, we review the latest findings based on noncoding RNA regulation of ferroptosis and its involvement in the pathogenesis of CVD and related treatments, aimed at providing insights into the impact of noncoding RNA regulation of ferroptosis for CVD.

Deciphering the impact of urban built environment density on respiratory health using a quasi-cohort analysis of 5495 non-smoking lung cancer cases.

INTRODUCTION: Lung cancer is a major health concern and is influenced by air pollution, which can be affected by the density of urban built environment. The spatiotemporal impact of urban density on lung cancer incidence remains unclear, especially at the sub-city level. We aimed to determine cumulative effect of community-level density attributes of the built environment on lung cancer incidence in high-density urban areas. METHODS: We selected 78 communities in the central city of Shanghai, China as the study site; communities included in the analysis had an averaged population density of 313 residents per hectare. Using data from the city cancer surveillance system, an age-period-cohort analysis of lung cancer incidence was performed over a five-year period (2009-2013), with a total of 5495 non-smoking/non-secondhand smoking exposure lung cancer cases. Community-level density measures included the density of road network, facilities, buildings, green spaces, and land use mixture. RESULTS: In multivariate models, built environment density and the exposure time duration had an interactive effect on lung cancer incidence. Lung cancer incidence of birth cohorts was associated with road density and building coverage across communities, with a relative risk of 1·142 (95 % CI: 1·056-1·234, P = 0·001) and 1·090 (95 % CI: 1·053-1·128, P < 0·001) at the baseline year (2009), respectively. The relative risk increased exponentially with the exposure time duration. As for the change in lung cancer incidence over the five-year period, lung cancer incidence of birth cohorts tended to increase faster in communities with a higher road density and building coverage. CONCLUSION: Urban planning policies that improve road network design and building layout could be important strategies to reduce lung cancer incidence in high-density urban areas.

Non-coding RNAs in necroptosis, pyroptosis, and ferroptosis in cardiovascular diseases.

Accumulating evidence has proved that non-coding RNAs (ncRNAs) play a critical role in the genetic programming and gene regulation of cardiovascular diseases (CVDs). Cardiovascular disease morbidity and mortality are rising and have become a primary public health issue that requires immediate resolution through effective intervention. Numerous studies have revealed that new types of cell death, such as pyroptosis, necroptosis, and ferroptosis, play critical cellular roles in CVD progression. It is worth noting that ncRNAs are critical novel regulators of cardiovascular risk factors and cell functions by mediating pyroptosis, necroptosis, and ferroptosis. Thus, ncRNAs can be regarded as promising therapeutic targets for treating and diagnosing cardiovascular diseases. Recently, there has been a surge of interest in the mediation of ncRNAs on three types of cell death in regulating tissue homeostasis and pathophysiological conditions in CVDs. Although our understanding of ncRNAs remains in its infancy, the studies reviewed here may provide important new insights into how ncRNAs interact with CVDs. This review summarizes what is known about the functions of ncRNAs in modulating cell death-associated CVDs and their role in CVDs, as well as their current limitations and future prospects.

Image Reconstruction Based on Progressive Multistage Distillation Convolution Neural Network.

To address the problem that some current algorithms suffer from the loss of some important features due to rough feature distillation and the loss of key information in some channels due to compressed channel attention in the network, we propose a progressive multistage distillation network that gradually refines the features in stages to obtain the maximum amount of key feature information in them. In addition, to maximize the network performance, we propose a weight-sharing information lossless attention block to enhance the channel characteristics through a weight-sharing auxiliary path and, at the same time, use convolution layers to model the interchannel dependencies without compression, effectively avoiding the previous problem of information loss in channel attention. Extensive experiments on several benchmark data sets show that the algorithm in this paper achieves a good balance between network performance, the number of parameters, and computational complexity and achieves highly competitive performance in both objective metrics and subjective vision, which indicates the advantages of this paper's algorithm for image reconstruction. It can be seen that this gradual feature distillation from coarse to fine is effective in improving network performance. Our code is available at the following link: https://github.com/Cai631/PMDN.

How to organise travel restrictions in the new future: lessons from the COVID-19 response in Hong Kong and Singapore.

It has been nearly 2 years since the first case of COVID-19 was reported. Governments worldwide have introduced numerous non-pharmaceutical interventions (NPIs) to combat this disease. Many of these NPIs were designed in response to initial outbreaks but are unsustainable in the long term. Governments are exploring how to adjust their current NPIs to resume normal activities while effectively protecting their population. As one of the most controversial NPIs, the implementation of travel restrictions varies across regions. Some governments have abandoned their previous travel restrictions because of the induced costs to society and on the economy. Other areas, including Hong Kong (Special Administrative Region of China) and Singapore, continue employing these NPIs as a long-term disease prevention tactic. However, the multidimensional impacts of travel restrictions require careful consideration of how to apply restrictions more appropriately. We have proposed an adapted framework to examine Hong Kong and Singapore's travel restrictions. We aimed to study these two regions' experiences in balancing disease control efforts with easing the burden on lives and livelihoods. Based on the experiences of Hong Kong and Singapore, we have outlined six policy recommendations to serve as the cornerstone for future research and policy practices.

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis.

Objectives: The abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms. Methods: Resting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test. Results: We found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus. Conclusion: The decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR. Clinical Trial Registration: : Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.

Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning.

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR. Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).

Multi-Order Brain Functional Connectivity Network-Based Machine Learning Method for Recognition of Delayed Neurocognitive Recovery in Older Adults Undergoing Non-cardiac Surgery.

Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR. Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model. Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients' brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively. Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.

[Expression and Clinical Significance of CTGF in Patients with Multiple Myeloma].

OBJECTIVE: To detect the relationship between CTGF in the bone marrow of MM patients and osteolytic lesion of myeloma, moreover, to investigate the clinical significance of CTGF in MM. METHODS: Fifity-four MM patients treated in our hospital from March 2019 to April 2020 were enrolled, and 28 healthy volunteers were selected as the control group. The plasma in bone marrow of the patients was collected, and the ELISA was used to detect the level of CTGF in bone marrow plasma and the relationship between its and clinical characteristics were statistically analyzed. RESULTS: The CTGF level of MM patients was significantly higher than those in the healthy control group (P<0.001); the CTGF level in male patients was higher than that in female patients (P=0.007); the CTGF level in MM patients with osteolytic lesions was significantly higher than patients without osteolytic lesions and controls (P=0.007, P=0.001). The CTGF level in MM patients was positively correlated with the number of bone lesions (P<0.001, r=0.52). CTGF levels in patients with ≥3 bone lesions were significantly higher than those with <3 bone lesions and without bone lesions (P=0.014, P=0.002). ROC curve result showed that CTGF expression level shows a significant diagnostic value for MM bone disease (P<0.001). CONCLUSION: The abnormally high expression of CTGF level in MM patients is related to the degree of myelomas osteolytic lesions and can reflect the progress of MM.

Reassessment of the Mechanisms of Thermal C-H Bond Activation of Methane by Cationic Magnesium Oxides: A Critical Evaluation of the Suitability of Different Density Functionals.

The mechanisms of the thermal reactions of the two iconic magnesium oxide cations MgO.+ and Mg2 O2.+ with methane have been re-evaluated at the CCSD(T)/CBS//CCSD/def2-TZVP level of theory. For the reaction of MgO.+ with CH4 , only the classical hydrogen-atom transfer (HAT) was found; in contrast, for the Mg2 O2.+ /CH4 couple, both HAT and proton-coupled electron-transfer (PCET) exist as mechanistic variants. In order to evaluate the suitability of density functional theory (DFT) methods, the reactions were computed by using 27 density functionals. The results obtained demonstrate that the various DFT methods often deliver rather different results for both geometric and energetic features. As to the prediction of the apparent barriers, pure functionals give the largest mean absolute errors. BMK, ωB97XD, and the double-hybrid functional mPW2PLYP were confirmed to come closest to the results provided by CCSD(T)/CBS. Thus, mechanistic conclusions based on a single DFT method should be viewed with great caution. In summary, this study may assist in the selection of a suitable quantum chemical method to unravel the mechanistic details of C-H bond activation by charged metal oxides.

Surface-Enhanced Raman Scattering (SERS) Active Gold Nanoparticles Decorated on a Porous Polymer Filter.

In this work, we designed a process to assemble gold nanoparticles onto a three-dimensional (3D) polymer surface, which can then be monitored using surface-enhanced Raman scattering (SERS). This work is the first demonstration of the assembly of gold nanoparticles on a filter film and in situ measurement with Raman spectroscopy. Herein, a polyhexamethylene adipamide (Nylon66) film embedded in the organic filter film was used as a template to fabricate a tunable SERS-active substrate. A "hotspot"-rich gold-nanoparticle-decorated polymer substrate for SERS was prepared; this substrate exhibited high sensitivity in trace detection of targets. The study was conducted using 4-mercaptobenzoic acid as a probe molecule with the aim of comparing the scattering efficiency and the homogeneity of the Raman signal on selected substrates. In addition, we used the gold-decorated polymer film to detect a biotin-avidin complex. The most powerful advantage of the proposed microanalytical device is the in situ SERS application. The 3D nanoporous structures described in this work hold strong potential for use in various applications such as environmental monitoring and biomolecule detection.

Resistin impairs glucose permeability in EA.hy926 cells by down-regulating GLUT1 expression.

Type 2 diabetes mellitus (T2DM) is a chronic disease which is now affecting the health of more and more people in the world. Resistin, discovered in 2001, is considered to be closely related to metabolic dysfunction and obesity. Previous study showed that hyperglycemia is always accompanied by a high serum resistin concentration. We therefore investigated whether resistin can mediate glucose transfer across the blood-tissue barrier. Here, we employed a transwell system to analyze glucose permeability in EA.hy926 human endothelial cells treated without or with human resistin. In EA.hy926 cells treated with resistin, the permeability to glucose was heavily impaired. This was due to the down-regulation of GLUT1 expression as a result of the treatment, rather than regulation of tight junctions. In addition, overexpression of GLUT1 in EA.hy926 cells was able to recover the blocking effect of resistin on glucose permeability. We further found that resistin could inhibit the expression of peroxisome proliferator-activated receptor gamma (PPARγ) and consequently impede the transcription of GLUT1. The results of the present study suggested that resistin could cause glucose retention in serum and thus result in hyperglycemia. This provides a novel explanation for hyperglycemia and a potential new way of treating type 2 diabetes mellitus.

Nanoscale Ni-B electrocatalyst prepared from nickel ethylenediamine complex for direct ethanol electrocatalytic oxidation.

Ni-B/Cu electrode prepared by the electroless deposition technique of chemical reduction of nickel ethylenediamine complex with BH4- was characterized by SEM, XRD and XPS techniques, respectively, and evaluated using ethanol electrocatalytic oxidation by the method of cyclic voltammetry. The as-prepared Ni-B/Cu electrode exhibited much higher electrocatalytic activity than the Ni-B/Cu electrode obtained via direct reduction of nickel metallic ions. The effects of the nickel ethylenediamine complex can be attributed to both the structural effect and electronic effect. The nickel ethylenediamine complex can not only make an electroless deposition stable and highly dispersed, but also facilitate the formation of more nanoscale microcrystalline Ni on the electrode surface, which increased the electrochemical oxidation activity. Moreover, ethylenediamine influenced the electronic states of Ni and B on the electrode surface, more electrons transfer from the B to Ni that the Ni active sites became more highly unsaturated, which could promote the form of adsorbed active ethanol.

Resistin reduces mitochondria and induces hepatic steatosis in mice by the protein kinase C/protein kinase G/p65/PPAR gamma coactivator 1 alpha pathway.

UNLABELLED: Obesity is associated with many severe chronic diseases and deciphering its development and molecular mechanisms is necessary for promoting treatment. Previous studies have revealed that mitochondrial content is down-regulated in obesity, diabetes, and nonalcoholic fatty liver disease (NAFLD) and proposed that NAFLD and diabetes are mitochondrial diseases. However, the exact mechanisms underlying these processes remain unclear. In this study, we discovered that resistin down-regulated the content and activities of mitochondria, enhanced hepatic steatosis, and induced insulin resistance (IR) in mice. The time course indicated that the change in mitochondrial content was before the change in fat accumulation and development of insulin resistance. When the mitochondrial content was maintained, resistin did not stimulate hepatic fat accumulation. The present mutation study found that the residue Thr464 of the p65 subunit of nuclear factor kappa B was essential for regulating mitochondria. A proximity ligation assay revealed that resistin inactivated peroxisome proliferator activated receptor gamma coactivator 1 alpha (PGC-1α) and diminished the mitochondrial content by promoting the interaction of p65 and PGC-1α. Signaling-transduction analysis demonstrated that resistin down-regulated mitochondria by a novel protein kinase C/protein kinase G/p65/PGC-1α-signaling pathway. CONCLUSION: Resistin induces hepatic steatosis through diminishing mitochondrial content. This reveals a novel pathway for mitochondrial regulation, and suggests that the maintenance of normal mitochondrial content could be a new strategy for treatment of obesity-associated diseases.