Massilia luteola sp. nov., a novel indole-producing and cellulose-degrading bacterium isolated from soil.

A Gram-stain-negative, rod-shaped, indole-producing, and cellulose-degrading bacterial strain, designated NEAU-G-C5T, was isolated from soil collected from a forest in Dali city, Yunnan province, south China. 16S rRNA gene sequence analysis showed that strain NEAU-G-C5T was assigned to the genus Massilia and showed high sequence similarities to Massilia phosphatilytica 12-OD1T (98.32 %) and Massilia putida 6 NM-7T (98.41 %). Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain NEAU-G-C5T formed a lineage related to M. phosphatilytica 12-OD1T and M. putida 6 NM-7T. The major fatty acids of the strain were C16 : 0, C16 : 1 ω7c, and C17 : 0 cyclo. The respiratory quinone was Q-8. The polar lipid profile of the strain showed the presence of diphosphatidylglycerol, phosphatidylglycerol, and phosphatidylethanolamine. In addition, the average nucleotide identity values between strain NEAU-G-C5T and its reference strains M. phosphatilytica 12-OD1T, M. putida 6 NM-7T, M. norwichensis NS9T, and M. kyonggiensis TSA1T were 89.7, 88.2, 81.3, and 88.0 %, respectively, and the levels of digital DNA-DNA hybridization between them were found to be 58.5 % (54.9-62.0 %), 53.2 % (49.8-56.7 %), 31.9 % (28.6-35.5 %), and 57.7 % (54.1-61.2 %), respectively, which were lower than the accepted threshold values of 95-96 % and 70 %, respectively. The DNA G+C content of strain NEAU-G-C5T was 66.5 mol%. The strain could produce indoleacetic acid and cellulase. On the basis of the phenotypic, genotypic, and chemotaxonomic characteristics, we conclude that strain NEAU-G-C5T represents a novel species of the genus Massilia, for which the name Massilia luteola sp. nov. is proposed. The type strain is NEAU-G-C5T (=MCCC 1K08668T=KCTC 8080T).

Comparison of efficacy and safety of different minimally invasive therapies for thyroid nodules: A network meta-analysis.

OBJECTIVE: This study aimed to compare efficacy and safety of minimally invasive therapies such as radiofrequency ablation (RFA), microwave ablation (MWA), ethanol ablation (EA), and laser ablation (LA) for thyroid nodules through network meta-analysis (NMA). METHODS: This study searched PubMed, Web of Science, Embase, and The Cochrane Library databases to collect randomized controlled trials (RCTs) or cohort studies comparing efficacy and safety of different minimally invasive therapies for thyroid nodules. Newcastle-Ottawa Scale (NOS) was implemented to assess quality of included cohort studies, and Cochrane risk of bias assessment tool was utilized to evaluate quality of included RCTs. Eligible studies contained at least one of the following clinical outcome measures: volume reduction rate (VRR), symptom score, cosmetic score, nodule regrowth rate, and complication rate. STATA software was utilized for NMA. RESULTS: Sixteen eligible studies (4 RCTs, 11 retrospective cohort studies, 1 prospective cohort study) involved 4094 patients. NMA results revealed that RFA group had the highest VRR at 1 months and 12 months. There were no significant differences in symptom scores and cosmetic scores among all treatment methods, with the lowest symptom scores and cosmetic scores in RFA group. LA group had a significantly higher nodule regrowth rate than RFA and MWA groups, with the lowest in RFA group. There were no significant differences in complication rate among all treatment methods. CONCLUSION: RFA had the highest VRR for thyroid nodules, and it excelled in symptom scores, cosmetic scores, and nodule regrowth rates.

Misstatements, misperceptions, and mistakes in controlling for covariates in observational research.

We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.

Correcting calculation and data errors reveals that the original conclusions were incorrect in "The best drug supplement for obesity treatment: a systematic review and network meta-analysis".

The goal of this study was to reproduce and evaluate the reliability of the network meta-analysis performed in the article "The best drug supplement for obesity treatment: A systematic review and network meta-analysis" by Salari et al. In recent years, it has become more common to employ network meta-analysis to assess the relative efficacy of treatments often used in clinical practice. To duplicate Salari et al.'s research, we pulled data directly from the original trials and used Cohen's D to determine the effect size for each treatment. We reanalyzed the data since we discovered significant differences between the data we retrieved and the data given by Salari et al. We present new effect size estimates for each therapy and conclude that the prior findings were somewhat erroneous. Our findings highlight the importance of ensuring the accuracy of network meta-analyses to determine the quality and strength of existing evidence.

Causal models and causal modelling in obesity: foundations, methods and evidence.

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research.

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

Changes in Circulation and Particle Scavenging in the Amerasian Basin of the Arctic Ocean over the Last Three Decades Inferred from the Water Column Distribution of Geochemical Tracers.

Since the 1980-1990s, international research efforts have augmented our knowledge of the physical and chemical properties of the Arctic Ocean water masses, and recent studies have documented changes. Understanding the processes responsible for these changes is necessary to be able to forecast the local and global consequences of these property evolutions on climate. The present work investigates the distributions of geochemical tracers of particle fluxes and circulation in the Amerasian Basin and their temporal evolution over the last three decades (from stations visited between 1983 and 2015). Profiles of 230-thorium (230Th) and 231-protactinium (231Pa) concentrations and neodymium isotopes (expressed as εNd) measured in the Amerasian Basin prior to 2000 are compared to a new, post-2000s data set. The comparison shows a large scale decrease in dissolved 230Th and 231Pa concentrations, suggesting intensification of scavenging by particle flux, especially in coastal areas. Higher productivity and sediment resuspension from the shelves appear responsible for the concentration decrease along the margins. In the basin interior, increased lateral exchanges with the boundary circulation also contribute to the decrease in concentration. This study illustrates how dissolved 230Th and 231Pa, with εNd support, can provide unique insights not only into changes in particle flux but also into the evolution of ocean circulation and mixing.