Gut microbiota-derived metabolite Trimethylamine-N-oxide (TMAO) and multiple health outcomes:an umbrella review and updated meta-analysis

BACKGROUND: Trimethylamine-N-oxide (TMAO) is a gut microbiota-derived metabolite produced from dietary nutrients. Many studies have discovered that circulating TMAO levels are linked to a wide range of health outcomes. OBJECTIVES: This study aimed to summarize health outcomes related to circulating TMAO levels. METHODS: We searched Embase, Medline, Web of Science and Scopus databases from inception to 15 February 2022 to identify and update meta-analyses examining the associations between TMAO and multiple health outcomes. For each health outcome, we estimated the summary effect size, 95% prediction confidence interval (CI), between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. These metrics were used to evaluate the evidence credibility of the identified associations. RESULTS: This umbrella review identified 24 meta-analyses that investigated the association between circulating TMAO levels and health outcomes including all-cause mortality, cardiovascular diseases, diabetes mellitus, cancer, and renal function. We updated these meta-analyses by including a total of 82 individual studies in 18 unique health outcomes. Among them, 14 associations were nominally significant. After evidence credibility assessment, we found six (33%) associations (i.e., all-cause mortality, cardiovascular disease mortality, major adverse cardiovascular events, hypertension, diabetes mellitus, and glomerular filtration rate) to present highly suggestive evidence. CONCLUSIONS: TMAO might be a novel biomarker related to human health conditions including all-cause mortality, hypertension, cardiovascular disease, diabetes, cancer and kidney function. Further studies are needed to investigate whether circulating TMAO levels could be an intervention target for chronic disease

Modeling and Prediction of Momentum Wheel Speed Data

To solve the problems of data loss and unequal interval of momentum wheel (MW) speed during a satellite stable operation, this paper presents a multidimensional AR model. A Lagrange interpolation method is used to convert measurements to equal interval data, and the FFT algorithm is adopted to calculate the period of MW speed variation. The long data sequence is converted into multidimensional time series, based on the equal interval data and the period. A multidimensional AR model is established, and the least square method is used to estimate the model parameters. The future data trend is predicted by the proposed model. Simulation results show that the prediction algorithm can achieve the across cycle prediction of the MW speed data

Long non-coding RNA HOTAIR inhibits miR-17-5p to regulate osteogenic differentiation and proliferation in non-traumatic osteonecrosis of femoral head.

BACKGROUND AND AIM:The biological functions of non-coding RNAs (ncRNAs) have been widely identified in many human diseases. In the present study, the relationship between long non-coding RNA HOTAIR and microRNA-17-5p (miR-17-5p) and their roles in osteogenic differentiation and proliferation in non-traumatic osteonecrosis of femoral head (ONFH) were investigated. METHODS:The expression levels of HOTAIR and miR-17-5p in the mesenchymal stem cells (MSCs) derived from patients with non-traumatic ONFH and osteoarthritis (OA) were examined by real-time PCR. BMP-2 induced human MSCs from bone marrow (hMSC-BM) were used for osteogenic differentiation. RESULTS:It was observed that the expression level of miR-17-5p was lower and the level of HOTAIR was higher in samples of non-traumatic ONFH compared with OA. HOTAIR downregulation induced by si-HOTAIR led to the increase of miR-17-5p expression and the decrease of miR-17-5p target gene SMAD7 expression. The values of osteogenic differentiation markers, including RUNX2 and COL1A1 mRNA expression and ALP activity, were also elevated by si-HOTAIR. However, the increase of these values was canceled by miR-17-5p inhibitor or SMAD7 upregulation. CONCLUSION:HOTAIR played a role in regulating osteogenic differentiation and proliferation through modulating miR-17-5p and its target gene SMAD7 in non-traumatic ONFH

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit

Abstract Purpose This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying a machine learning algorithm. Methods Adult patients who were diagnosed with sepsis during admission to ICU were extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used for model development and internal validation. The other three databases were used for external validation. Our proposed model was developed based on the Extreme Gradient Boosting (XGBoost) algorithm. The generalizability, discrimination, and validation of our model were evaluated. The Shapley Additive Explanation values were used to interpret our model and analyze the contribution of individual features. Results A total of 16,741, 15,532, 22,617, and 1,198 sepsis patients were extracted from the MIMIC-III, MIMIC-IV, eICU, and Zigong databases, respectively. The proposed model had an area under the receiver operating characteristic curve (AUROC) of 0.84 in the internal validation, which outperformed all the traditional scoring systems. In the external validations, the AUROC was 0.87 in the MIMIC-IV database, better than all the traditional scoring systems; the AUROC was 0.83 in the eICU database, higher than the Simplified Acute Physiology Score III and Sequential Organ Failure Assessment (SOFA),equal to 0.83 of the Acute Physiology and Chronic Health Evaluation IV (APACHE-IV), and the AUROC was 0.68 in the Zigong database, higher than those from the systemic inflammatory response syndrome and SOFA. Furthermore, the proposed model showed the best discriminatory and calibrated capabilities and had the best net benefit in each validation. Conclusions The proposed algorithm based on XGBoost and SHAP-value feature selection had high performance in predicting the mortality of sepsis patients within 24 h of ICU admission

Identification and functional analysis of two alternatively spliced transcripts of ABSCISIC ACID INSENSITIVE3 (ABI3) in linseed flax (Linum usitatissimum L.).

Alternative splicing is a popular phenomenon in different types of plants. It can produce alternative spliced transcripts that encode proteins with altered functions. Previous studies have shown that one transcription factor, ABSCISIC ACID INSENSITIVE3 (ABI3), which encodes an important component in abscisic acid (ABA) signaling, is subjected to alternative splicing in both mono- and dicotyledons. In the current study, we identified two homologs of ABI3 in the genome of linseed flax. We screened two alternatively spliced flax LuABI3 transcripts, LuABI3-2 and LuABI3-3, and one normal flax LuABI3 transcript, LuABI3-1. Sequence analysis revealed that one of the alternatively spliced transcripts, LuABI3-3, retained a 6 bp intron. RNA accumulation analysis showed that all three transcripts were expressed during seed development, while subcellular localization and transgene experiments showed that LuABI3-3 had no biological function. The two normal transcripts, LuABI3-1 and LuABI3-2, are the important functional isoforms in flax and play significant roles in the ABA regulatory pathway during seed development, germination, and maturation

IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust

It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user’s in-group membership. The local implicit trust relationship and the user’s in-group membership were adjusted by the adaptive weight to determine the degree of each part’s influence. Then, the author integrated the user’s score of items and the user’s implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations

Consensus genetic linkage map construction and QTL mapping for plant height-related traits in linseed flax (Linum usitatissimum L.)

Abstract Background Flax is an important field crop that can be used for either oilseed or fiber production. Plant height and technical length are important characters for flax. For linseed flax, plants usually have a short technical length and plant height than those for fiber flax. As an important agronomical character for fiber and linseed flax, plant height is usually a selection target for breeding. However, because of limited technologies and methods available, there has been little research focused on discovering the molecular mechanism controlling plant height. Results In this study, two related recombinant inbred line (RIL) populations developed from crosses of linseed and fiber parents were developed and phenotyped for plant height and technical length in four environments. A consensus linkage map based on two RIL populations was constructed using SNP markers generated by genotyping by sequencing (GBS) technology. A total of 4497 single nucleotide polymorphism (SNP) markers were included on 15 linkage groups with an average marker density of one marker every 2.71 cM. Quantitative trait locus (QTL) mapping analysis was performed for plant height and technical length using the two populations. A total of 19 QTLs were identified for plant height and technical length. For the MH population, eight plant height QTLs and seven technical length QTLs were identified, five of which were common QTLs for both traits. For the PH population, six plant height and three technical length QTLs were identified. By comparing the QTLs and candidate gene information in the two population, two common QTLs and three candidate genes were discovered. Conclusions This study provides a foundation for map-based cloning of QTLs and marker-assisted selection for plant height-related traits in linseed and fiber flax

IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust

It is well-known that data sparsity and cold start are two of the open problems in recommendation system research. Numerous studies have been dedicated to dealing with those two problems. Among these, a method of introducing user context information could effectively solve the problem of data sparsity and improve the accuracy of recommendation algorithms. This study proposed a novel approach called IT-PMF (Implicit Trust-Probabilistic Matrix Factorization) based on implicit trust, which consists of local implicit trust relationships and in-group membership. The study started from generating the user commodity rating matrix based on the cumulative purchases for items according to their historical purchase records to find the similarity of purchase behaviors and the number of successful interactions between users, which represent the local implicit trust relationship between users. The user group attribute value was calculated through a fuzzy c-means clustering algorithm to obtain the user’s in-group membership. The local implicit trust relationship and the user’s in-group membership were adjusted by the adaptive weight to determine the degree of each part’s influence. Then, the author integrated the user’s score of items and the user’s implicit trust relationship into the probabilistic matrix factorization algorithm to form a trusted recommendation model based on implicit trust relationships and in-group membership. The extensive experiments were conducted using a real dataset collected from a community E-commerce platform, and the IT-PMF method had a better performance in both MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) indices compared with well-known existing algorithms, such as PMF (Probabilistic Matrix Factorization) and SVD (Single Value Decomposition). The results of the experiments indicated that the introduction of implicit trust into PMF could improve the quality of recommendations

The expression levels of miR-17-5p and HOTAIR in MSCs of patients with non-traumatic necrosis of femoral head and osteoarthritis.

<p>The MSCs were isolated from patients with non-traumatic necrosis of femoral head (ONFH, n = 15), osteoarthritis (OA, n = 13) and healthy donor (n = 10), the expression levels of miR-17-5p (A) and HOTAIR (B) were detected by real-time PCR. Each sample was repeated three times. ** <i>P</i><0.01.</p