A transition from arbuscular to ectomycorrhizal forests halts soil carbon sequestration during subtropical forest rewilding.

Ecological succession and restoration rapidly promote multiple dimensions of ecosystem functions and mitigate global climate change. However, the factors governing the limited capacity to sequester soil organic carbon (SOC) in old forests are poorly understood. Ecological theory predicts that plants and microorganisms jointly evolve into a more mutualistic relationship to accelerate detritus decomposition and nutrient regeneration in old than young forests, likely explaining the changes in C sinks across forest succession or rewilding. To test this hypothesis, we conducted a field experiment of root-mycorrhizal exclusion in successional subtropical forests to investigate plant-decomposer interactions and their effects on SOC sequestration. Our results showed that SOC accrual rate at the 0-10 cm soil layer was 1.26 mg g-1 yr-1 in early-successional arbuscular mycorrhizal (AM) forests, which was higher than that in the late-successional ectomycorrhizal (EcM) forests with non-significant change. A transition from early-successional AM to late-successional EcM forests increase fungal diversity, especially EcM fungi. In the late-successional forests, the presence of ectomycorrhizal hyphae promotes SOC decomposition and nutrient cycle by increasing soil nitrogen and phosphorus degrading enzyme activity as well as saprotrophic microbial richness. Across early- to late-successional forests, mycorrhizal priming effects on SOC decomposition explain a slow-down in the capacity of older forests to sequester soil C. Our findings suggest that a transition from AM to EcM forests supporting greater C decomposition can halt the capacity of forests to provide nature-based global climate change solutions.

A decentralized federated learning-based cancer survival prediction method with privacy protection.

Background: Survival prediction is one of the crucial goals in precision medicine, as accurate survival assessment can aid physicians in selecting appropriate treatment for individual patients. To achieve this aim, extensive data must be utilized to train the prediction model and prevent overfitting. However, the collection of patient data for disease prediction is challenging due to potential variations in data sources across institutions and concerns regarding privacy and ownership issues in data sharing. To facilitate the integration of cancer data from different institutions without violating privacy laws, we developed a federated learning-based data integration framework called AdFed, which can be used to evaluate patients' survival while considering the privacy protection problem by utilizing the decentralized federated learning technology and regularization method. Results: AdFed was tested on different cancer datasets that contain the patients' information from different institutions. The experimental results show that AdFed using distributed data can achieve better performance in cancer survival prediction (AUC = 0.605) than the compared federated-learning-based methods (average AUC = 0.554). Additionally, to assess the biological interpretability of our method, in the case study we list 10 identified genes related to liver cancer selected by AdFed, among which 5 genes have been proved by literature review. Conclusions: The results indicate that AdFed outperforms better than other federated-learning-based methods, and the interpretable algorithm can select biologically significant genes and pathways while ensuring the confidentiality and integrity of data.

Experimental Research on Shipborne SINS Rapid Mooring Alignment with Variance-Constraint Kalman Filter and GNSS Position Updates.

Analytical coarse alignment and Kalman filter fine alignment based on zero-velocity are typically used to obtain initial attitude for inertial navigation systems (SINS) on a static base. However, in the shipboard mooring state, the static observation condition is corrupted. This paper presents a rapid alignment method for SINS on swaying bases. The proposed method begins with a coarse alignment technique in the inertial frame to obtain an initial rough attitude. Subsequently, a Kalman filter with position updates is employed to estimate the remaining misalignment error. To enhance the filter estimation performance, an appropriate lower boundary is set to the target states' variances according to a carefully designed relative convergence index. The variance-constraint Kalman filter (VCKF) approach is proposed in this paper, and the shipborne experiments validate its effectiveness. The results demonstrate that the VCKF approach significantly reduces the time requirement for fine alignment to achieve the same accuracy on a swaying base, from 90 min in the classic Kalman filter to 30 min. Additionally, the parameter estimation performance in the Kalman filter is also improved, particularly in situations where unpredicted external interference is involved during fine alignment.

Extracorporeal shock wave therapy with imaging examination for early osteonecrosis of the femoral head: a systematic review.

BACKGROUND: Extracorporeal shockwave therapy (ESWT) is a traditional non-invasive therapy to treat osteonecrosis of the femur head (ONFH). This systematic review aims to investigate whether ESWT can improve the clinical function of ONFH and whether differences in improvement can be observed in radiographic outcomes. MATERIALS AND METHODS: Two authors independently searched PubMed, Embase, Cochrane Library, and Web of Science for English articles until October 21, 2023. After screening and reading the literature, the two authors independently used corresponding scales to evaluate the quality of the included articles and extracted data. The key data extracted included the Harris Hip Score (HHS), Visual Analog Scale (VAS), changes in lesion size, the change in the Association Research Circulation Osseous (ARCO) stage, and bone marrow edema stage. RESULTS: Nine articles included 468 males and 248 females. The average age was 43.29 years and the mean follow-up time was 15.19 months. After receiving ESWT, five studies involving 146 hips showed a higher HHS (MD=-33.38; 95%CI, -46.31, -20.45), and the difference was statistically significant (P<0.00001). The average VAS before treatment was above 5, but it dropped to 1.2 after ESWT (MD=4.64; 95%CI, 3.63, 5.64), and the difference was statistically significant (P<0.00001). Three studies found no significant differences in the areas of femoral head necrosis before and after treatment with ESWT(MD=9.66; 95%CI, -0.36, 19.67; P=0.06; I2=84%). Two articles showed that the use of ESWT had no significant effect on the change in the ARCO stage (MD=1.11; 95%CI, 0.76, 1.62; P=0.60; I2=0%). Three studies indicated that using ESWT could improve the bone marrow edema symptom in the early stage of ONFH (MD=4.35; 95%CI, 1.32, 14.37; P=0.02; I2=62%). CONCLUSION: Based on the current evidence, ESWT shows promise as a therapy to enhance hip function and alleviate pain in the early stage of ONFH. With the advancement of more precise imaging techniques, ESWT can potentially reduce the area affected by ONFH. However, such reduction was not found to be statistically significant at the imaging level. Additionally, ESWT could improve symptoms of bone marrow edema in the early stage. However, no significant change in ARCO grade was observed with ESWT treatment.

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. AIM: To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. METHODS: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis. RESULTS: Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927) and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1 and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. CONCLUSION: The proposed MRI-based Resnet50 deep learning model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE).

Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

Mechanisms with network pharmacology approach of Ginsenosides in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory loss, cognitive disorder, language dysfunction, and mental disability. The main neuropathological changes in AD mainly include amyloid plaque deposition, neurofibrillary tangles, synapse loss, and neuron reduction. However, the current anti-AD drugs do not demonstrate a favorable effect in altering the pathological course of AD. Moreover, long-term use of these drugs is usually accompanied with various side effects. Ginsenosides are the major active constituents of ginseng and have protective effects on AD through various mechanisms in both in vivo and in vitro studies. In this review, we focused on discussing the therapeutic potential effects and the mechanisms of pharmacological activities of ginsenosides in AD, to provide new insight for further research and clinical application of ginsenosides in the future. Recent studies on the pharmacological effects and mechanisms of ginsenosides were retrieved from Chinese National Knowledge Infrastructure, National Science and Technology Library, Wanfang Data, Elsevier, ScienceDirect, PubMed, SpringerLink, and the Web of Science database up to April 2023 using relevant keywords. Network pharmacology and bioinformatics analysis were used to predict the therapeutic effects and mechanisms of ginsenosides against AD. Ginsenosides presented a wide range of therapeutic and biological activities, including alleviating Aß deposition, decreasing tau hyperphosphorylation, regulating the cholinergic system, resisting oxidative stress, modulating Ca2+ homeostasis, as well as anti-inflammation and anti-apoptosis in neurons, respectively. For further developing the therapeutic potential as well as clinical applications, the network pharmacology approach was combined with a summary of published studies.

Effects of photoperiod and temperature on the developmental duration and diapause in Dolycoris baccarum (Heteroptera: Pentatomidae) from Hohhot, Inner Mongolia, China.

The shield bug, Dolycoris baccarum (L.) (Heteroptera: Pentatomidae), is widely distributed across Asia and Europe. At high latitudes, it overwinters, as adult in diapause, which then becomes the insect source for the following year. To fully understand the developmental duration and diapause characteristics of D. baccarum, the effects of photoperiod and temperature were studied in a population from Hohhot, Inner Mongolia, China. The results indicated that the developmental duration was significantly prolonged at temperatures of 20 or 25 °C, with a prolonged light period; however, when the light period was prolonged to 16L:8D and 18L:6D, the developmental duration was shortened significantly. Furthermore, the developmental duration was also shortened significantly with increasing temperature, when the photoperiod was 12L:12D for short days and 16L:8D for long days. All individuals entered diapause under short-day conditions of 10L:14D and 12L:12D at a temperature of 20 °C; however, the diapause rate decreased significantly under 14L:10D and 16L:8D photoperiods, and the diapause rate decreased significantly at a temperature of 25 °C with prolonged photoperiod. Interestingly, when the photoperiod was fixed at 12L:12D, the diapause rates at different temperatures (20, 25, 28, and 30 °C) exceeded 95%; while the effect of temperature on diapauses was nonsignificant under this photoperiod, it was still sensitive to the photoperiod; at a photoperiod of 16L:8D, the effect of temperature on the diapause rate was noticeable, and the diapause rate decreased significantly with increasing temperature.

High-risk event prone driver identification considering driving behavior temporal covariate shift.

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.

BACKGROUND: Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information. RESULTS: The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study. CONCLUSIONS: Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .

Mycorrhizal mediation of soil carbon in permafrost regions depends on soil nutrient stoichiometry and physical protection.

Mycorrhizal associations are considered as one of the key drivers for soil carbon (C) accumulation and stability. However, how mycorrhizal associations influence soil organic C (SOC) and its fractions (i.e., particulate organic C [POC] and mineral-associated organic C [MAOC]) remain unclear. In this study, we examined effects of plant mycorrhizal associations with arbuscular mycorrhiza (AM), ectomycorrhiza (ECM), and their mixture (Mixed) on SOC and its fractions as well as soil stoichiometric ratios across 800-km transect in permafrost regions. Our results showed that soil with only ECM-associated trees had significantly higher SOC and POC compared to only AM-associated tree species, while soil in Mixed plots with both AM- and ECM- associated trees tend to be somewhat in the middle. Using structural equation models, we found that mycorrhizal association significantly influenced SOC and its fraction (i.e., POC, MAOC) indirectly through soil stoichiometric ratios (C:N, C:P, and N:P). These results suggest that selecting ECM tree species, characterized by a "slow cycling" nutrient uptake strategy, can effectively enhance accumulation of SOC and its fractions in permafrost forest ecosystems. Our findings provide novel insights for quantitatively assessing the influence of mycorrhiza-associated tree species on the management of soil C pool and biogeochemical cycling.

The management of non-culprit vessel(s) in patients with unstable angina/non-ST elevation myocardial infarction and chronic kidney dysfunction.

BACKGROUND AND AIMS: The clinical effects of multivessel interventions in patients with unstable angina/non-ST-segment elevation myocardial infarction (UA/NSTEMI), multivessel disease (MVD) and chronic kidney disease (CKD) remain uncertain. This study aimed to investigate the safety and effectiveness of intervention in non-culprit lession(s) among this cohort. METHODS: We consecutively included patients diagnosed with UA/NSTEMI, MVD and CKD between January 2008 and December 2018 at our centre. After successful percutaneous coronary intervention (PCI), we compared 48-month overall mortality between those undergoing multivessel PCI (MV-PCI) through a single-procedure or staged-procedure approach and culprit vessel-only PCI (CV-PCI) after 1:1 propensity score matching. We conducted stratified analyses and tests for interaction to investigate the modifying effects of critical covariates. Additionally, we recorded the incidence of contrast-induced nephropathy (CIN) to assess the perioperative safety of the two treatment strategies. RESULTS: Of the 749 eligible patients, 271 pairs were successfully matched. Those undergoing MV-PCI had reduced all-cause mortality (hazard ratio (HR): 0.67, 95% confidence interval (CI): 0.48-0.67). Subgroup analysis showed that those with advanced CKD (estimated glomerular filtration rate (eGFR) ≤ 30 mL/min/1.73 m2 ) could not benefit from MV-PCI (P = 0.250), and the survival advantage also tended to diminish in diabetes (P interaction < 0.01; HR = 0.95, 95% CI = 0.65-1.45). Although the staged-procedure approach (N = 157) failed to bring additional survival benefits compared to single-procedure MV-PCI (N = 290) (P = 0.460), it showed a tendency to decrease the death risk. CIN risks in MV-PCI and CV-PCI groups were not significantly different (risk ratio = 1.60, 95% CI = 0.94-2.73). CONCLUSION: Among patients with UA/NSTEMI and non-diabetic CKD and an eGFR > 30 mL/min/1.73 m2 , MV-PCI was associated with a reduced risk of long-term death but did not increase the incidence of CIN during the management of MVD compared to CV-PCI. And staged procedures might be a preferable option over single-procedure MV-PCI.

Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas.

Alfalfa (Medicago sativa L.) is known as the "king of forages". The aim of the current study is to determine the optimum planting density as the key cultivation technique for high yield of alfalfa seed. Alfalfa variety (Longmu 801) was planted in experimental fields from 2014 to 2017. In the planting density test, the row spacing was 65, 80, and 95 cm, and the plant spacing was 30, 45, 60, 75, and 90 cm. The seed yield and yield components in the row spacing and plant spacing tests were measured. On the basis of 3 years average of the experimental data, the highest seed yield of 225.49 kg ha-1 was obtained with row spacing vs plant spacing of 65 and 60 cm, respectively. Correlation analysis showed a significant positive correlation between the racemes per stem, pods per raceme, pods per stem, seeds per pod, and the seed yield. These results suggested that Longmu 801 should be cultivated with 65 cm row spacing and 60 cm plant spacing to maximize seed yields in western Heilongjiang areas.

Ultrasensitive electrochemical detection and inhibition evaluation of DNA methyltransferase based on cascade strand displacement amplification.

An electrochemical sensing approach for ultrasensitive DNA methyltransferase (MTase) activity assay is proposed. After specific cleavage reaction in the presence of a methylated state, strand displacement polymerization (SDP) is initiated in the solution. The product of upstream SDP further triggers downstream SDP, which enriches abundant electrochemical species at the electrode. The whole process is quite convenient with shared enzymes. Due to the cascade signal amplification, ultrahigh sensitivity is promised. Inhibitor screening results are also demonstrated to be good. Besides, target MTase can be accurately determined in human serum samples, confirming excellent practical utility. This work provides a reliable approach for the analysis of MTase activity, which is of vital importance for related biological studies and clinical applications.

Critical safety management driver identification based upon temporal variation characteristics of driving behavior.

Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Previous studies commonly evaluated driver-level risks based upon aggregated statistical characteristics (e.g., driving exposure and driving behavior), which were obtained from long-period driving monitoring data. However, given the great advancements of the connected vehicle and in-vehicle data instrumentation technologies, there has been a notable increase in the collection of short-period driving data, which has emerged as a prominent data source for analysis. In this data environment, traditionally employed aggregated behavior characteristics are unstable due to the time-varying feature of driving behavior coupled with insufficient data sampling periods. Thus, traditional modeling methods based upon aggregated statistical characteristics are no longer feasible. Instead of utilizing such unreliable statistical information to represent driver-level risks, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers in the short-period driving data environment. Specifically, the relationships between driving behavior temporal variation characteristics and individual crash occurrence probability were developed. To eliminate the impacts of drivers' driving behavior heterogeneity on model performance, "traffic entropy" index that could quantify the abnormal degrees of driving behavior was proposed. Deep learning models including convolutional neural network (CNN) and long short-term memory (LSTM) were employed to conduct the temporal variation feature mining. Empirical analyses were conducted using data obtained from online ride-hailing services. Experiment results showed that temporal variation characteristics based models outperformed traditional aggregated statistical characteristics based models. The area under the curve (AUC) index was improved by 4.1%. And the proposed traffic entropy index further enhanced the model performance by 5.3%. The best model achieved an AUC of 0.754, comparable to existing approaches utilizing long-period driving data. Finally, applications of the proposed method in driver management program development and its further investigations have been discussed.

AFEI: adaptive optimized vertical federated learning for heterogeneous multi-omics data integration.

Vertical federated learning has gained popularity as a means of enabling collaboration and information sharing between different entities while maintaining data privacy and security. This approach has potential applications in disease healthcare, cancer prognosis prediction, and other industries where data privacy is a major concern. Although using multi-omics data for cancer prognosis prediction provides more information for treatment selection, collecting different types of omics data can be challenging due to their production in various medical institutions. Data owners must comply with strict data protection regulations such as European Union (EU) General Data Protection Regulation. To share patient data across multiple institutions, privacy and security issues must be addressed. Therefore, we propose an adaptive optimized vertical federated-learning-based framework adaptive optimized vertical federated learning for heterogeneous multi-omics data integration (AFEI) to integrate multi-omics data collected from multiple institutions for cancer prognosis prediction. AFEI enables participating parties to build an accurate joint evaluation model for learning more information related to cancer patients from different perspectives, based on the distributed and encrypted multi-omics features shared by multiple institutions. The experimental results demonstrate that AFEI achieves higher prediction accuracy (6.5% on average) than using single omics data by utilizing the encrypted multi-omics data from different institutions, and it performs almost as well as prognosis prediction by directly integrating multi-omics data. Overall, AFEI can be seen as an efficient solution for breaking down barriers to multi-institutional collaboration and promoting the development of cancer prognosis prediction.

Counterfactual safety benefits quantification method for en-route driving behavior interventions.

Driving behavior intervention is a dominant traffic safety countermeasure being implemented that has substantially reduced crash occurrence. However, during implementation, the intervention strategy faces the curse of dimensionality as there are multiple candidate intervention locations with various intervention measures and options. Quantifying the interventions' safety benefits and further implementing the most effective ones could avoid too frequent interventions which may lead to counterproductive safety impacts. Traditional intervention effects quantification approaches rely on observational data, thus failing to control confounding variables and leading to biased results. In this study, a counterfactual safety benefits quantification method for en-route driving behavior interventions was proposed. Empirical data from online ride-hailing services were employed to quantify the safety benefits of en-route safety broadcasting to speed maintenance behavior. Specifically, to effectively control the impacts of confounding variables on the quantification results of interventions, the "if without intervention" case of the intervention case is inferred based on the structural causality model according to the Theory of Planned Behavior (TPB). Then, a safety benefits quantification method based on Extreme Value Theory (EVT) was developed to connect changes of speed maintenance behavior with crash occurrence probabilities. Furthermore, a closed-loop evaluation and optimization framework for the various behavior interventions was established and applied to a subset of Didi's online ride-hailing service drivers (more than 1.35 million). Analyses results indicated safety broadcasting could effectively reduce driving speed by approximately 6.30 km/h and contribute to an approximate 40% reduction in speeding-related crashes. Besides, empirical application results showed that the whole framework contributed to a remarkable reduction in the fatality rate per 100 million km, from an average of 0.368 to 0.225. Finally, directions for future research in terms of data, counterfactual inference methodology, and research subjects have been discussed.

Assembly of DNA triangular pyramid frustum for ultrasensitive quantification of exosomal miRNA.

Early screening of biomarkers benefits therapy and prognosis of cancers. MiRNAs encapsulated in tumor-derived exosomes are emerging biomarkers for early diagnosis of cancers. Nevertheless, traditional methods suffer certain drawbacks, which hamper their wide applications. In this contribution, we have developed a convenient electrochemical approach for quantification of exosomal miRNA based on the assembly of DNA triangular pyramid frustum (TPF) and strand displacement amplification. Four single-stranded DNA helps the formation of primary DNA triangle with three thiols for gold electrode immobilization at the bottom and three amino groups on overhangs for the capture of silver nanoparticles. On the other hand, target miRNA induced strand displacement reaction produces abundant specific DNA strands, which help the DNA structural transition from triangle to TPF. Amino groups are thus hidden and the declined silver stripping current can be used for the evaluation of target miRNA concentration. This biosensor exhibits excellent analytical performances and successfully achieves analysis of exosomal miRNAs from cells and clinical serum samples.

Irregular DNA Triangular Prism/Triplex Assembly for Duplicate MiRNA Analysis with Nicking Endonuclease-Mediated Amplification.

Highly sensitive and selective detection of microRNA (miRNA) is becoming more and more important in the discovery, diagnosis, and prognosis of various diseases. Herein, we develop a three-dimensional DNA nanostructure based electrochemical platform for duplicate detection of miRNA amplified by nicking endonuclease. Target miRNA first helps construction of three-way junction structures on the surfaces of gold nanoparticles. After nicking endonuclease-powered cleavage reactions, single-stranded DNAs labeled with electrochemical species are released. These strands can be facilely immobilized at four edges of the irregular triangular prism DNA (iTPDNA) nanostructure via triplex assembly. By evaluating the electrochemical response, target miRNA levels can be determined. In addition, the triplexes can be disassociated by simply changing pH conditions, and the iTPDNA biointerface can be regenerated for duplicate analyses. The developed electrochemical method not only exhibits an excellent prospect in the detection of miRNA but also may inspire the engineering of recyclable biointerfaces for biosensing platforms.

Global Trends in Research of Mitochondrial Biogenesis over past 20 Years: A Bibliometric Analysis.

Background: Mitochondrial biogenesis-related studies have increased rapidly within the last 20 years, whereas there has been no bibliometric analysis on this topic to reveal relevant progress and development trends. Objectives: In this study, a bibliometric approach was adopted to summarize and analyze the published literature in this field of mitochondrial biogenesis over the past 20 years to reveal the major countries/regions, institutions and authors, core literature and journal, research hotspots and frontiers in this field. Methods: The Web of Science Core Collection database was used for literature retrieval and dataset export. The CiteSpace and VOSviewer visual mapping software were used to explore research collaboration between countries/regions, institutions and authors, distribution of subject categories, core journals, research hotspots, and frontiers in this field. Results: In the last 20 years, the annual number of publications has shown an increasing trend yearly. The USA, China, and South Korea have achieved fruitful research results in this field, among which Duke University and Chinese Academy of Sciences are the main research institutions. Rick G Schnellmann, Claude A Piantadosi, and Hagir B Suliman are the top three authors in terms of number of publications, while RC Scarpulla, ZD Wu, and P Puigserver are the top three authors in terms of cocitation frequency. PLOS One, Biochemical and Biophysical Research Communications, and Journal of Biological Chemistry are the top three journals in terms of number of articles published. Three papers published by Richard C Scarpulla have advanced this field and are important literature for understanding the field. Mechanistic studies on mitochondrial biosynthesis have been a long-standing hot topic; the main keywords include skeletal muscle, oxidative stress, gene expression, activation, and nitric oxide, and autophagy and apoptosis have been important research directions in recent years. Conclusion: These results summarize the major research findings in the field of mitochondrial biogenesis over the past 20 years in various aspects, highlighting the major research hotspots and possible future research directions and helping researchers to quickly grasp the overview of the developments in this field.