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Rewarming therapies for accidental hypothermia (AH) include extracorporeal membrane oxygenation (ECMO) and non-ECMO related (conventional) therapies. However, there are limited data available to inform the selection of conventional rewarming therapy. The aim of the present study was to explore what patients' factors and which rewarming therapy predicted favorable prognosis. This study is a secondary analysis of the Intensive Care with Extra Corporeal membrane oxygenation Rewarming in Accidentally Severe Hypothermia (ICE-CRASH) study, a multicenter prospective, observational study conducted in Japan. Enrolled in the ICE-CRASH study were patients aged ≥18 years with a core temperature of ≤32 °C who were transported to the emergency departments of 36 tertiary care hospitals in Japan between 1 December 2019 and 31 March 2022, among whom those who were rewarmed with conventional rewarming therapy were included in the present study. Logistic regression analysis was performed with 28-day survival as the objective variable; and seven factors including age, activities of daily living (ADL) independence, sequential organ failure assessment (SOFA) score, and each rewarming technique as explanatory variables. We performed linear regression analysis to identify whether each rewarming technique was associated with rewarming rate. Of the 499 patients enrolled in the ICE-CRASH study, 371 were eligible for this secondary analysis. The median age was 81 years, 50.9% were male, and the median initial body temperature was 28.8 °C. Age (odds ratio [OR]: 0.97, 95% confidence interval [CI]: 0.94–1.00) and SOFA score (OR: 0.73, 95% CI: 0.67–0.81) were associated with lower survival, whereas ADL independence (OR: 2.31, 95% CI: 1.15–4.63) was associated with higher survival. No conventional rewarming therapy was associated with 28-day survival. Hot bath was associated with a high rewarming rate (regression coefficient: 1.14, 95% CI: 0.75–1.53). No conventional rewarming therapy was associated with improved 28-day survival, which suggests that background factors such as age, ADL, and severity of condition contribute more to prognosis than does the selection of rewarming technique. [ABSTRACT FROM AUTHOR]

The association between the gut microbiota and muscle strength has garnered attention in the context of mitigating muscle decline. However, many study subjects have been individuals with existing illnesses or the elderly only. This study aims to elucidate the association between the gut microbiota and muscle strength indicators using grip strength/BMI in a large-scale study of community residents. The mean age of men (n = 442) and women (n = 588) was 50.5 (15.3) and 51.2 (15.9) years, respectively. The muscle strength indicator used was grip/BMI. The association between total read count and genus-level gut microbiota and muscle strength was analyzed. The mean grip/BMI was 1.8 (0.3) for men and 1.2 (0.2) for women. The genus of the gut microbiota that showed an association in both sexes was Eggerthella (men: β = 0.18, CI: 0.04–0.31, p = 0.009; women: β = 0.07, CI: 0.00–0.12, p = 0.028). Blautia, Eggerthella and Faecalibacterium were found to be significantly associated with grip/BMI in both the multiple regression analysis and Spearman's correlation analysis after the multiple comparison adjustment. These results suggest that an increase in Blautia and Eggerthella, coupled with a decrease in Faecalibacterium, may contribute to muscle strengthening or the suppression of muscle weakness. [ABSTRACT FROM AUTHOR]

The bone morphogenetic protein (BMP) pathway promotes differentiation and induces apoptosis in normal colorectal epithelial cells. However, its role in colorectal cancer (CRC) is controversial, where it can act as context‐dependent tumor promoter or tumor suppressor. Here we have found that CRC cells reside in a BMP‐rich environment based on curation of two publicly available RNA‐sequencing databases. Suppression of BMP using a specific BMP inhibitor, LDN193189, suppresses the growth of select CRC organoids. Colorectal cancer organoids treated with LDN193189 showed a decrease in epidermal growth factor receptor, which was mediated by protein degradation induced by leucine‐rich repeats and immunoglobulin‐like domains protein 1 (LRIG1) expression. Among 18 molecularly characterized CRC organoids, suppression of growth by BMP inhibition correlated with induction of LRIG1 gene expression. Notably, knockdown of LRIG1 in organoids diminished the growth‐suppressive effect of LDN193189. Furthermore, in CRC organoids, which are susceptible to growth suppression by LDN193189, simultaneous treatment with LDN193189 and trametinib, an FDA‐approved MEK inhibitor, resulted in cooperative growth inhibition both in vitro and in vivo. Taken together, the simultaneous inhibition of BMP and MEK could be a novel treatment option in CRC cases, and evaluating in vitro growth suppression and LRIG1 induction by BMP inhibition using patient‐derived organoids could offer functional biomarkers for predicting potential responders to this regimen. [ABSTRACT FROM AUTHOR]

Daily dietary habits directly or indirectly influence the intestinal microbiota, and the resulting changes in its composition and metabolic activity alter the health conditions of the host. Although many studies have analyzed the association between individual nutrients/food items and intestinal microbiota, the assessment of the diet and intestinal microbiota from a macroscopic perspective has not yet been performed in Japan. Therefore, we focused on vegetables and fruits and aimed to identify dietary patterns of high intake of these foods and to examine their relationship with the intestinal microbiota. This cross-sectional study included 1019 healthy individuals aged ≥20 years in a rural area in northern Japan. Six dietary patterns were detected by factor analysis using the brief-type self-administered diet history questionnaire (BDHQ) data to identify the "vege pattern", which was the dietary pattern rich in vegetables and fruits. Permutational multivariate analysis of variance revealed changes in β-diversity according to dietary patterns. In multivariable-adjusted models, the adherence to the vege pattern was positively correlated with α-diversity. This is the first study to reveal a correlation between intestinal microbiota and dietary habits rich in vegetables and fruits in a rural area of Japan. [ABSTRACT FROM AUTHOR]

The gut microbiota has become a significant factor associated with health and disease. Although many studies have reported the implications of changes in the gut microbiota on cardiovascular diseases, there are no reports on the relationship between heart rate variability (HRV) and the gut microbiota. Therefore, we investigated the association between gut microbiota abundance and HRV parameters in this cross-sectional study of the general Japanese population. This study included 950 participants of the Iwaki Health Promotion Project who underwent a medical examination in 2019 that included HRV and gut microbiota measurements. At the genus level, multivariate regression analysis showed that higher gut microbial diversity was associated with a higher standard deviation of RR intervals (SDNN). Moreover, a higher SDNN was associated with a higher relative count of Lachnospiraceae incertae sedis. L. incertae sedis abundance was associated with higher HRV parameters such as SDNN, coefficient of variation of RR intervals, low-frequency component power (LF)/high-frequency component power, and LF. In the general Japanese population, higher gut microbial diversity and L. incertae sedis abundance were associated with higher HRV parameters. [ABSTRACT FROM AUTHOR]

Although many studies have reported factors associated with reduced heart rate variability (HRV) in Western populations, evidence is limited among Asian populations. Therefore, we investigated the factors associated with reduced HRV values in a general Japanese population by measuring HRV among the participants of the Iwaki Health Promotion Project who underwent medical examination in 2019. We performed 90-s HRV measurements in 1065 participants. Of these, we evaluated the coefficient of variation in R–R intervals (CVRR) and standard deviation in R–R intervals (SDNN). Blood was collected under a fasting condition, and investigations of glucose metabolism, lipid metabolism, renal function, liver function, advanced glycation end products, and blood pressure were performed. A multivariate regression analysis of the association between CVRR or SDNN and blood test parameters and blood pressure in 987 participants with adequately completed HRV assessments showed that reduced CVRR or SDNN was associated with higher levels of glycated hemoglobin (HbA1c), glycoalbumin, blood glucose, triglycerides, creatinine, plasma pentosidine, and diastolic blood pressure. In the general Japanese population, higher levels of HbA1c, glycoalbumin, blood glucose, triglycerides, creatinine, plasma pentosidine, and diastolic blood pressure are associated with reduced CVRR or SDNN, which are typical HRV parameters. [ABSTRACT FROM AUTHOR]

Abstract Background Apoptosis is a critical process in endothelial cell (EC) biology and pathology, which has been extensively studied at protein level. Numerous gene expression studies of EC apoptosis have also been performed, however few attempts have been made to use gene expression data to identify the molecular relationships and master regulators that underlie EC apoptosis. Therefore, we sought to understand these relationships by generating a Bayesian gene regulatory network (GRN) model. Results ECs were induced to undergo apoptosis using serum withdrawal and followed over a time course in triplicate, using microarrays. When generating the GRN, this EC time course data was supplemented by a library of microarray data from EC treated with siRNAs targeting over 350 signalling molecules. The GRN model proposed Vasohibin-1 (VASH1) as one of the candidate master-regulators of EC apoptosis with numerous downstream mRNAs. To evaluate the role played by VASH1 in EC, we used siRNA to reduce the expression of VASH1. Of 10 mRNAs downstream of VASH1 in the GRN that were examined, 7 were significantly up- or down-regulated in the direction predicted by the GRN.Further supporting an important biological role of VASH1 in EC, targeted reduction of VASH1 mRNA abundance conferred resistance to serum withdrawal-induced EC death. Conclusion We have utilised Bayesian GRN modelling to identify a novel candidate master regulator of EC apoptosis. This study demonstrates how GRN technology can complement traditional methods to hypothesise the regulatory relationships that underlie important biological processes.

Lithium arylborates have reacted with primary and secondary alkyl halides to give the corresponding coupling products in the presence of novel iron(II) chloride-diphosphine complexes and magnesium bromide. High functional group compatibility is shown in the reactions of substrates possessing reactive substituents, such as alkoxycarbonyl, cyano, and carbonyl groups.

A 15-electron iron complex with a formal Fe(I) center, [FeBr(BPEP)] (BPEP = 2,6-bis(1-phenyl-2-phosphaethenyl)pyridine), is prepared by one-electron reduction of the dibromide precursor [Fe[Br.sub.2](BPEP)]. The effective d[pi]-p[pi] interaction between iron and BPEP is shown to be responsible for the highly distorted structure of [FeBr(BPEP)], with its uncommon trigonal monopyramidal configuration.

We present a novel parallel algorithm that solves the optimal directed acyclic graph search problem using less memory as compared to existing algorithms. Using a dynamic programming approach, the time and space complexity of the problem is found to be O ( n ⋅ 2 n ) , where n represents the number of vertices. The previous algorithm uses adjacent communication to efficiently exchange data between computation nodes. However, it consumes too much memory, and thus is capable of solving up to n = 36 . Our novel algorithm, ParaOS-DC, employs direct communication to reduce the memory consumption; this is the main cause of the inefficiency of the previous algorithm. Through computational experiments, we confirmed that our proposed algorithm is much faster than the previous algorithm when memory is insufficient. We also succeeded in solving a problem for n = 37 without any constraints, which is the largest problem size solved in literature till date. [ABSTRACT FROM AUTHOR]

Summary When investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot , which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot , including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets. Availability and implementation The library, documentation and web server are available at https://github.com/noriakis/CBNplot. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

We present a parallel algorithm for the score-based optimal structure search of Bayesian networks. This algorithm is based on a dynamic programming (DP) algorithm having O(n ⋅ 2n) time and space complexity, which is known to be the fastest algorithm for the optimal structure search of networks with n nodes. The bottleneck of the problem is the memory requirement, and therefore, the algorithm is currently applicable for up to a few tens of nodes. While the recently proposed algorithm overcomes this limitation by a space-time trade-off, our proposed algorithm realizes direct parallelization of the original DP algorithm with O(nσ) time and space overhead calculations, where σ > 0 controls the communication-space trade-off. The overall time and space complexity is O(nσ+12n). This algorithm splits the search space so that the required communication between independent calculations is minimal. Because of this advantage, our algorithm can run on distributed memory supercomputers. Through computational experiments, we confirmed that our algorithm can run in parallel using up to 256 processors with a parallelization efficiency of 0.74, compared to the original DP algorithm with a single processor. We also demonstrate optimal structure search for a 32-node network without any constraints, which is the largest network search presented in literature. [ABSTRACT FROM AUTHOR]

Simple Summary: Intestinal microflora has been associated with obesity. While cardiovascular disorders are more strongly associated with visceral fat than the body mass index (BMI), the link between visceral fat area (VFA) and intestinal microflora has been little studied. In this study, we investigated the association between intestinal microflora and VFA and BMI using a longitudinal study (N = 767). We found that the intestinal microflora composition is significantly associated with VFA or BMI; however, the associated gut microbes differ. Furthermore, two gut species—Blautia hansenii and Blautia producta—were significantly and negatively associated with VFA accumulation. Intestinal microflora has been associated with obesity. While visceral fat is more strongly associated with cardiovascular disorder, a complication linked to obesity, than the body mass index (BMI), the association between intestinal microflora and obesity (as defined in terms of BMI) has been studied widely. However, the link between visceral fat area (VFA) and intestinal microflora has been little studied. In this study, we investigate the association between intestinal microflora and VFA and BMI using a longitudinal study on Japanese subjects with different VFA statuses (N = 767). Principal component analysis of the changes in intestinal microflora composition over the one-year study period revealed the different associations between intestinal microflora and VFA and BMI. As determined by 16S rRNA amplicon sequencing, changes in the abundance ratio of two microbial genera—Blautia and Flavonifractor—were significantly associated with VFA changes and changes in the abundance ratio of four different microbial genera were significantly associated with BMI changes, suggesting that the associated intestinal microbes are different. Furthermore, as determined by metagenomic shotgun sequences, changes in the abundance ratios of two Blautia species—Blautia hansenii and Blautia producta—were significantly and negatively associated with VFA changes. Our findings might be used to develop a new treatment for visceral fat. [ABSTRACT FROM AUTHOR]

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at . [ABSTRACT FROM AUTHOR]