Emerging trends and focus of research on the relationship between traumatic brain injury and gut microbiota: a visualized study

BackgroundTraumatic brain injury (TBI) is one of the most serious types of trauma and imposes a heavy social and economic burden on healthcare systems worldwide. The development of emerging biotechnologies is uncovering the relationship between TBI and gut flora, and gut flora as a potential intervention target is of increasing interest to researchers. Nevertheless, there is a paucity of research employing bibliometric methodologies to scrutinize the interrelation between these two. Therefore, this study visualized the relationship between TBI and gut flora based on bibliometric methods to reveal research trends and hotspots in the field. The ultimate objective is to catalyze progress in the preclinical and clinical evolution of strategies for treating and managing TBI.MethodsTerms related to TBI and gut microbiota were combined to search the Scopus database for relevant documents from inception to February 2023. Visual analysis was performed using CiteSpace and VOSviewer.ResultsFrom September 1972 to February 2023, 2,957 documents published from 98 countries or regions were analyzed. The number of published studies on the relationship between TBI and gut flora has risen exponentially, with the United States, China, and the United Kingdom being representative of countries publishing in related fields. Research has formed strong collaborations around highly productive authors, but there is a relative lack of international cooperation. Research in this area is mainly published in high-impact journals in the field of neurology. The “intestinal microbiota and its metabolites,” “interventions,” “mechanism of action” and “other diseases associated with traumatic brain injury” are the most promising and valuable research sites. Targeting the gut flora to elucidate the mechanisms for the development of the course of TBI and to develop precisely targeted interventions and clinical management of TBI comorbidities are of great significant research direction and of interest to researchers.ConclusionThe findings suggest that close attention should be paid to the relationship between gut microbiota and TBI, especially the interaction, potential mechanisms, development of emerging interventions, and treatment of TBI comorbidities. Further investigation is needed to understand the causal relationship between gut flora and TBI and its specific mechanisms, especially the “brain-gut microbial axis.

The Development and Application of a Dot-ELISA Assay for Diagnosis of Southern Rice Black-Streaked Dwarf Disease in the Field

Outbreaks of the southern rice black-streaked dwarf virus (SRBSDV) have caused significant crop losses in southern China in recent years, especially in 2010. There are no effective, quick and practicable methods for the diagnosis of rice dwarf disease that can be used in the field. Traditional reverse transcription-polymerase chain reaction (RT-PCR) methodology is accurate but requires expensive reagents and instruments, as well as complex procedures that limit its applicability for field tests. To develop a sensitive and reliable assay for routine laboratory diagnosis, a rapid dot enzyme-linked immunosorbent assay (dot-ELISA) method was developed for testing rice plants infected by SRBSDV. Based on anti-SRBSDV rabbit antiserum, this new dot-ELISA was highly reliable, sensitive and specific toward SRBSDV. The accuracy of two blotting media, polyvinylidene fluoride membrane (PVDF membrane) and nitrocellulose filter membrane (NC membrane), was compared. In order to facilitate the on-site diagnosis, three county laboratories were established in Shidian (Yunnan province), Jianghua (Hunan Province) and Libo (Guizhou province). Suspected rice cases from Shidian, Yuanjiang and Malipo in Yunnan province were tested and some determined to be positive for SRBSDV by the dot-ELISA and confirmed by the One Step RT-PCR method. To date, hundreds of suspected rice samples collected from 61 districts in southwestern China have been tested, among which 55 districts were found to have rice crops infected by SRBSDV. Furthermore, the test results in the county laboratories showed that Libo, Dehong (suspected samples were sent to Shidian) and Jianghua were experiencing a current SRBSDV outbreak

Increased recruitment of endogenous stem cells and chondrogenic differentiation by a composite scaffold containing bone marrow homing peptide for cartilage regeneration

Even small cartilage defects could finally degenerate to osteoarthritis if left untreated, owing to the poor self-healing ability of articular cartilage. Stem cell transplantation has been well implemented as a common approach in cartilage tissue engineering but has technical complexity and safety concerns. The stem cell homing-based technique emerged as an alternative promising therapy for cartilage repair to overcome traditional limitations. In this study, we constructed a composite hydrogel scaffold by combining an oriented acellular cartilage matrix (ACM) with a bone marrow homing peptide (BMHP)-functionalized self-assembling peptide (SAP). We hypothesized that increased recruitment of endogenous stem cells by the composite scaffold could enhance cartilage regeneration. Methods: To test our hypothesis, in vitro proliferation, attachment and chondrogenic differentiation of rabbit mesenchymal stem cells (MSCs) were tested to confirm the bioactivities of the functionalized peptide hydrogel. The composite scaffold was then implanted into full-thickness cartilage defects on rabbit knee joints for cartilage repair, in comparison with microfracture or other sample groups. Stem cell recruitment was monitored by dual labeling with CD29 and CD90 under confocal microcopy at 1 week after implantation, followed by chondrogenic differentiation examined by qRT-PCR. Repaired tissue of the cartilage defects was evaluated by histological and immunohistochemistry staining, microcomputed tomography (micro-CT) and magnetic resonance imaging (MRI) at 3 and 6 months post-surgery. Macroscopic and histological scoring was done to evaluate the optimal in vivo repair outcomes of this composite scaffold. Results: The functionalized SAP hydrogels could stimulate rabbit MSC proliferation, attachment and chondrogenic differentiation during in vitro culture. At 7 days after implantation, increased recruitment of MSCs based on CD29(+)/CD90(+) double-positive cells was found in vivo in the composite hydrogel scaffold, as well as upregulation of cartilage-associated genes (aggrecan, Sox9 and type II collagen). After 3 and 6 months post-surgery, the articular cartilage defect in the composite scaffold-treated group was fully covered with cartilage-like tissue with a smooth surface, which was similar to the surrounding native cartilage, according to the results of histological and immunohistochemistry staining, micro-CT and MRI analysis. Macroscopic and histological scoring confirmed that the quality of cartilage repair was significantly improved with implantation of the composite scaffold at each timepoint, in comparison with microfracture or other sample groups. Conclusion: Our findings demonstrated that the composite scaffold could enhance endogenous stem cell homing and chondrogenic differentiation and significantly improve the therapeutic outcome of chondral defects. The present study provides a promising approach for in vivo cartilage repair without cell transplantation. Optimization of this strategy may offer great potential and benefits for clinical application in the future

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn

Dufulin Activates HrBP1 to Produce Antiviral Responses in Tobacco

BACKGROUND: Dufulin is a new antiviral agent that is highly effective against plant viruses and acts by activating systemic acquired resistance (SAR) in plants. In recent years, it has been used widely to prevent and control tobacco and rice viral diseases in China. However, its targets and mechanism of action are still poorly understood. METHODOLOGY/PRINCIPAL FINDINGS: Here, differential in-gel electrophoresis (DIGE) and classical two-dimensional electrophoresis (2-DE) techniques were combined with mass spectrometry (MS) to identify the target of Dufulin. More than 40 proteins were found to be differentially expressed (≥1.5 fold or ≤1.5 fold) upon Dufulin treatment in Nicotiana tabacum K(326). Based on annotations in the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, these proteins were found to be related to disease resistance. Directed acyclic graph (DAG) analysis of the various pathways demonstrated harpin binding protein-1 (HrBP1) as the target of action of Dufulin. Additionally, western blotting, semi-quantitative reverse transcription polymerase chain reaction (RT-PCR), and real time PCR analyses were also conducted to identify the specific mechanism of action of Dufulin. Our results show that activation of HrBP1 triggers the salicylic acid (SA) signaling pathway and thereby produces antiviral responses in the plant host. A protective assay based on lesion counting further confirmed the antiviral activity of Dufulin. CONCLUSION: This study identified HrBP1 as a target protein of Dufulin and that Dufulin can activate the SA signaling pathway to induce host plants to generate antiviral responses

Sensing of Digestive Enzymes—Diagnosis and Monitoring of Pancreatitis

This paper is a comprehensive review of the techniques for the detection of pancreatic enzymes, which are common biochemical indicators of pancreatitis, including amylase, trypsin, chymotrypsin, elastase, and lipase. Pancreatitis is a disease with self-digestion due to the abnormal activation of digestive enzymes in the pancreas. Hospitalization is often required due to the lack of convenient therapeutic agents. The main recent results are reported in this review, especially the techniques that enable portability and Point-of-Care testing (POCT). This is because timely diagnosis at the early stage and avoiding recurrence after recovery are the keys to treatment. It is also important to reduce the rate of misdiagnosis and to avoid overtreatment. Various detection methods are discussed, with particular attention given to the implementation of chemical sensing and probe design. The new sensing technology for digestive enzymes makes it possible to perform early screening for pancreatitis in remote areas or in one’s own home

RUL Prediction for Lithium Batteries Using a Novel Ensemble Learning Method

The remaining useful life (RUL) is the key element of fault diagnosis, prediction and health management (PHM) during the equipment operation service period. The prediction result of RUL is the premise for equipment to adopt preventive maintenance, condition-based maintenance, fault maintenance and other maintenance strategies. Lithium battery is an important energy component of new energy vehicles, mobile phones, etc. Its RUL is related to the state of its equipment system. Many model-based methods have been used to predict the lithium batteries’ RUL, and some studies have begun to use lithium battery monitoring data to predict its remaining service life. With the continuous detection and monitoring capability of equipment throughout its life cycle gradually improved, a large number of monitoring and detection data promote the wide application of data-driven residual life prediction in the field of equipment. At present, the data-driven prediction method of the lithium batteries’ RUL mostly adopts a single time-series forecasting model. The robustness and generalization of the prediction method are insufficient. It needs to be further improved to improve the prediction accuracy and robustness. Preventive maintenance measures shall be taken immediately according to the prediction results to ensure the effective supply of energy at any time. In this paper, an integrated learning algorithm based on monitoring data is proposed to fit the degradation model of lithium batteries and predict their RUL. The ensemble learning method consists of 5 basic learners to achieve better prediction performance, including relevance vector machine (RVM), random forest (RF), elastic net (EN), autoregressive model (AR), and long short-term memory (LSTM) Network. The genetic algorithm (GA) is used in the ensemble learning method to find and determine the optimal weights of the basic learners, and obtain the final prediction result of lithium batteries. Then, the simulation is carried out on the CS2_35 lithium battery data set. The simulation results show that the method proposed in this paper has a smaller Root Mean Square Error (RMSE) than another 5 single methods. The RMSE is respectively 0.00744 for RVM, 0.01097 for RF, 0.01507 for EN, 0.03223 for AR, 0.01541 for LSTM, and 0.00483 for ensemble learning, and the RMSE of ensemble learning is reduced by 0.0274 at the highest and 0.00261 at the lowest, so the ensemble learning algorithm has better robustness and generalization effect