BackgroundPrevious clinical trials indicate that probiotic consumption may improve blood glucose control, however, results from randomized trials on glycemic control have been inconsistent.ObjectiveTo investigate the effects of probiotics on glycemic control in a systematic review and meta-analysis of randomized controlled trials.Data SourcesPubMed, Embase, Cochrane Library, and Clinicaltrial.gov through October 2014.Data Extraction and SynthesisTwo independent reviewers extracted relevant data and assessed study quality and risk of bias. Data were pooled using a random-effects model and expressed as mean differences (MD) with 95% CI. Heterogeneity was assessed (Cochran Q-statistic) and quantified (I 2).ResultsSeventeen randomized controlled trials were included, in which 17 fasting blood glucose (n = 1105), 11 fasting plasma insulin (n = 788), 8 homeostasis model assessment of insulin resistance (n = 635) comparisons were reported. Probiotic consumption, compared with placebo, significantly reduced fasting glucose (MD = -0.31 mmol/L; 95% CI 0.56, 0.06; p = 0.02), fasting plasma insulin (MD = -1.29 μU/mL; 95% CI -2.17, -0.41; p = 0.004), and HOMA-IR (MD = 0.48; 95% CI -0.83, -0.13; p = 0.007).ConclusionsProbiotic consumption may improve glycemic control modestly. Modification of gut microbiota by probiotic supplementation may be a method for preventing and control hyperglycemia in clinical practice.

We aimed to identify the best diagnostic cutoff value of the atherogenic index of plasma (AIP) for coronary artery disease (CAD) and its correlation with the SYNTAX score (SS). From January 2016 to December 2019, 2253 patients with CAD and 1347 non-CAD patients with complete data were included in the study. Coronary angiography was performed using the Judkins technique, and the SS was calculated using network software. There were differences in age, body mass index, total cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol levels, and AIP between groups (all P < .01). Multivariate logistic regression analysis showed that AIP was an independent risk factor for CAD and a high SS (SS ≥ 23) with adjusted odds ratios of 2.248 (95% CI: 1.666-3.032, P < .01) and 1.623 (95% CI: 1.118-2.358, P < .01) per standard deviation increase. Receiver operating characteristic curve analysis revealed that the best diagnostic cutoff value of AIP for the prediction of CAD and SS ≥ 23 was 2.035 and 2.23, the specificity was 61.8% and 65.7%, the sensitivity was 76.4% and 54.9%, the Youden index was 0.382 and 0.206, and the area under the curve was 0.733 (95% CI: 0.717-0.750, P < .01) and 0.653 (95% CI: 0.630-0.676, P < .01). Atherogenic index of plasma, as a biomarker, may assist in the prevention of CAD in the Chinese population.

• APTw-MRI is applied to predict MGMT promoter methylation status in GBMs. • GBMs with unmethylated MGMT promoter present higher APTw-MRI than methylated GBMs. • Multiple APTw histogram metrics can identify MGMT methylation status. • Mean APTw values showed the highest diagnostic accuracy (AUC = 0.825).

BackgroundThe Glasgow Coma Scale (GCS) is currently the most widely used scoring system for comatose patients. A decade ago, the Full Outline of Unresponsiveness (FOUR) score was devised to better capture four functional aspects of consciousness (eye, motor responses, brainstem reflexes, and respiration). This study aimed to validate the Chinese version of the FOUR score in patients with different levels of consciousness.MethodsThe study had two phases: (1) translation of the FOUR score, and (2) assessment of its reliability and validity. The Chinese version of the FOUR score was developed according to a standardized protocol. One hundred-twenty consecutive patients with acute brain damage, admitted to Nanfang Hospital (Southern Medical University, Guangdong, China) from November 2014 to February 2015, were enrolled. The inter-rater agreement for the FOUR score and GCS was evaluated using intraclass correlation coefficient (ICC). Receiver operating characteristic (ROC) curves were established to determine the scales’ abilities to predict outcome.ResultsThe rater agreement was excellent both for FOUR (ICC = 0.970; p < 0.001) and GCS (ICC = 0.958; p < 0.001). The FOUR score yielded an excellent test-retest reliability (ICC = 0.930; p < 0.001). Spearman’s correlation coefficients between GCS and the FOUR score were high: r = 0.932, first rating; r = 0.887, second rating (all p < 0.001). Areas under the curve (AUC) for mortality were 0.834 (95 % CI, 0.740–0.928) and 0.815 (95 % CI, 0.723–0.908) for the FOUR score and GCS, respectively.ConclusionsThe Chinese version of the FOUR score is a reliable scale for evaluating the level of consciousness in patients with acute brain injury.

Youden index is widely utilized in studies evaluating accuracy of diagnostic tests and performance of predictive, prognostic, or risk models. However, both one and two independent sample tests on Youden index have been derived ignoring the dependence (association) between sensitivity and specificity, resulting in potentially misleading findings. Besides, paired sample test on Youden index is currently unavailable. This article develops efficient statistical inference procedures for one sample, independent, and paired sample tests on Youden index by accounting for contingency correlation, namely associations between sensitivity and specificity and paired samples typically represented in contingency tables. For one and two independent sample tests, the variances are estimated by Delta method, and the statistical inference is based on the central limit theory, which are then verified by bootstrap estimates. For paired samples test, we show that the estimated covariance of the two sensitivities and specificities can be represented as a function of kappa statistic so the test can be readily carried out. We then show the remarkable accuracy of the estimated variance using a constrained optimization approach. Simulation is performed to evaluate the statistical properties of the derived tests. The proposed approaches yield more stable type I errors at the nominal level and substantially higher power (efficiency) than does the original Youden's approach. Therefore, the simple explicit large sample solution performs very well. Because we can readily implement the asymptotic and exact bootstrap computation with common software like R, the method is broadly applicable to the evaluation of diagnostic tests and model performance.

Birth weight and related outcomes have profound influences on life cycle health, but the effect of maternal hemoglobin concentration during pregnancy on birth weight is still unclear. This study aims to reveal the associations between maternal hemoglobin concentrations in different trimesters of pregnancy and neonatal birth weight, LBW, and SGA. This was a prospective study based on a cluster-randomized controlled trial conducted from July 2015 to December 2019 in rural areas of Northwest China. Information on maternal socio-demographic status, health-related factors, antenatal visits, and neonatal birth outcomes were collected. A total of 3748 women and their babies were included in the final analysis. A total of 65.1% and 46.3% of the participants had anemia or hemoglobin ≥ 130 g/L during pregnancy. In the third trimester, maternal hemoglobin concentration was associated with birth weight in an inverted U-shaped curve and with the risks of LBW and SGA in extended U-shaped curves. The relatively higher birth weight and lower risks for LBW and SGA were observed when hemoglobin concentration was 100–110 g/L. When maternal hemoglobin was <70 g/L or >130 g/L, the neonatal birth weight was more than 100 g lower than that when the maternal hemoglobin was 100 g/L. In conclusion, both low and high hemoglobin concentrations in the third trimester could be adverse to fetal weight growth and increase the risks of LBW and SGA, respectively. In addition to severe anemia, maternal hemoglobin >130 g/L in the third trimester should be paid great attention to in the practice of maternal and child health care.

Background Numerous evidence has suggested that long non-coding RNA (lncRNA) acts an important role in tumor biology. This study focuses on the identification of novel prognostic lncRNA biomarkers predicting tumor recurrence in human colon adenocarcinoma. Methods We obtained the research data from The Cancer Genome Atlas (TCGA) database. The interaction among different expressed lncRNA, miRNA and mRNA markers between colon adenocarcinoma patients with and without tumor recurrence were verified with miRcode, starBase and miRTarBase databases. We established the lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) network based on the verified association between the selected markers. We performed the functional enrichment analysis to obtain better understanding of the selected lncRNAs. Then we use multivariate logistic regression to identify the prognostic lncRNA markers with covariates. We also generated a nomogram predicting tumor recurrence risk based on the identified lncRNA biomarkers and clinical covariates. Results We included 12,727 lncRNA, 1881 miRNA and 47,761 mRNA profiling and clinical features for 113 colon adenocarcinoma patients obtained from the TCGA database. After filtration, we used 37 specific lncRNAs, 60 miRNAs and 148 mRNAs in the ceRNA network analysis. We identified five lncRNAs as prognostic lncRNA markers predicting tumor recurrence in colon adenocarcinoma, in which four of them were identified for the first time. Finally, we generated a nomogram illustrating the association between the identified lncRNAs and the tumor recurrence risk in colon adenocarcinoma. Conclusions The four newly identified lncRNA biomarkers might be potential prognostic biomarkers predicting tumor recurrence in colon adenocarcinoma. We recommend that further clinical and fundamental researches be conducted on the identified lncRNA markers.

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