Baseline-Covariate Adjusted Confidence Interval for Proportional Difference Between Two Treatment Groups in Clinical Trials

The treatment effect of a therapeutic product on a binary endpoint is often expressed as the difference in proportions of subjects with the outcome of interest between the treated and control groups of a clinical trial. Analysis of the proportional difference and construction of the associated confidence interval (CI) is often complicated due to the baseline covariate(s) being associated with the primary endpoint. Analysis adjusting for such baseline covariate(s) generally improves efficiency of hypothesis testing and precision of treatment effect estimation, and avoids possible bias caused by baseline covariate imbalances. Most existing literatures focus on constructing unadjusted or categorical covariate(s) adjusted only CI, which provides very limited advice on how different statistical methods perform and which method is optimal in terms of constructing both categorical and continuous baseline covariate(s) adjusted CI for proportional difference. We review and compare the performance of three commonly used model-based methods as well as the traditional nonparametric weighted-difference methods for the construction of covariate-adjusted CI for proportional difference via a real data application and simulations. The coverage of 95% CI, Type I error control, and power are examined. We also examine the factors leading to the model convergence failure in different scenarios via simulations.</p

Synthesis of Oxazolidines and Dihydroxazines via Cyclization of α‑Aminated Ketones

A new approach to oxazolidines and dihydroxazines was developed by regioselective cyclization of α-aminated ketones under transition metal-free conditions. Oxazolidine derivatives were generated in the presence of chloro benziodoxole and TFA, while dihydroxazines were formed without a hypervalent iodine reagent. The reaction was performed under room temperature and gave the products in good to excellent yields

Postulated N balance in the UREA soil with and without biochar addition.

<p>Total amounts of urea N in the soil were assumed to be lost through N_<b>2</b>O emission, NH_<b>3</b> volatilization and leaching and be remained in the soil as organic N and mineral N (NH_<b>4</b>^<b>+</b> + NO_<b>3</b>^<b>-</b>). The amount of NH_<b>3</b> volatilization, plant biomass N and soil organic N were assumed to be the same between NO CHAR and CHAR treatments and not shown in the graph. Different letters beside the bars indicate significant differences between the NO CHAR and CHAR treatments at a 5% probability level.</p

The N₂O emissions represented as a) the temporal changes from the soils with biochar and amendments and b) the average of the interactive effects between biochar and amendments.

<p>Four solid arrows show the urea and compost application events and two thick open arrows indicate the timing for biochar application. Error bars in b) represent the standard errors among the average data of the sampling dates.</p

Interactive effect between biochar and amendments on a) microbial activity and b) microbial biomass C in the soil.

<p>Bars with different letters indicate significant differences in the average values of sampling dates among treatments at a 5% probably level.</p

Effect of biochar and fertilization on soil water holding capacity (WHC).Bars with different letters indicate significant differences among treatments at a 5% probability level.

<p>Effect of biochar and fertilization on soil water holding capacity (WHC).Bars with different letters indicate significant differences among treatments at a 5% probability level.</p

Physicochemical properties of the soil and biochar.

<p>^*HWC stands for hot water extractable C</p><p>Physicochemical properties of the soil and biochar.</p

Temporal change in total C contents influenced by biochar and amendments.

<p>Bars with different letters indicate significant differences among treatments at a 5% probability level.</p

Effects of biochar and amendments on the average amounts of NH₄⁺ + NO₃^-.Bars with different letters indicate significant differences among treatments at a 5% probability level.

<p>Effects of biochar and amendments on the average amounts of NH₄⁺ + NO₃^-.Bars with different letters indicate significant differences among treatments at a 5% probability level.</p

Image1_Association Among the Gut Microbiome, the Serum Metabolomic Profile and RNA m6A Methylation in Sepsis-Associated Encephalopathy.jpg

Objective: To investigate the relationship among the gut microbiome, serum metabolomic profile and RNA m6A methylation in patients with sepsis-associated encephalopathy (SAE), 16S rDNA technology, metabolomics and gene expression validation were applied.Methods: Serum and feces were collected from patients with and without (SAE group and non-SAE group, respectively, n = 20). The expression of serum markers and IL-6 was detected by enzyme-linked immunosorbent assay (ELISA), and blood clinical indicators were detected using a double antibody sandwich immunochemiluminescence method. The expression of RNA m6A regulator were checked by Q-RTPCR. The gut microbiome was analyzed by 16S rDNA sequencing and the metabolite profile was revealed by liquid chromatography-mass spectrometry (LC-MS/MS).Results: In the SAE group, the IL-6, ICAM-5 and METTL3 levels were significantly more than those in the non-SAE group, while the FTO levels were significantly decreased in the SAE group. The diversity was decreased in the SAE gut microbiome, as characterized by a profound increase in commensals of the Acinetobacter, Methanobrevibacter, and Syner-01 genera, a decrease in [Eubacterium]_hallii_group, while depletion of opportunistic organisms of the Anaerofilum, Catenibacterium, and Senegalimassilia genera were observed in both groups. The abundance of Acinetobacter was positively correlated with the expression of METTL3. The changes between the intestinal flora and the metabolite profile showed a significant correlation. Sphingorhabdus was negatively correlated with 2-ketobutyric acid, 9-decenoic acid, and l-leucine, and positively correlated with Glycyl-Valine [Eubacterium]_hallii_group was positively correlated with 2-methoxy-3-methylpyazine, acetaminophen, and synephrine acetonide.Conclusion: The gut microbiota diversity was decreased. The serum metabolites and expression of RNA m6A regulators in PBMC were significantly changed in the SAE group compared to the non-SAE group. The results revealed that serum and fecal biomarkers could be used for SAE screening.</p