Serum Thyrotropin and Triiodothyronine Levels in Levothyroxine-treated Patients.

CONTEXT: Small adjustments in levothyroxine (LT4) dose do not appear to provide clinical benefit despite changes in thyrotropin (TSH) levels within the reference range. We hypothesize that the accompanying changes in serum total triiodothyronine (T3) levels do not reflect the magnitude of the changes in serum TSH. OBJECTIVE: This work aims to characterize the relationships of serum free thyroxine (FT4) vs T3, FT4 vs TSH, and FT4 vs the T3/FT4 ratio. METHODS: This cross-sectional, observational study comprised 9850 participants aged 18 years and older treated with LT4 from a large clinical database from January 1, 2009, to December 31, 2019. Patients had been treated with LT4, subdivided by serum FT4 level. Main outcome measures included model fitting of the relationships between serum FT4 vs TSH, FT4 vs T3, and FT4 vs T3/FT4. Mean and median values of TSH, T3, and T3/FT4 were calculated. RESULTS: The relationships T3 vs FT4 and TSH vs FT4 were both complex and best represented by distinct, segmented regression models. Increasing FT4 levels were linearly associated with T3 levels until an inflection point at an FT4 level of 0.7 ng/dL, after which a flattening of the slope was observed following a convex quadratic curve. In contrast, increasing FT4 levels were associated with steep declines in TSH following 2 negative sigmoid curves. The FT4 vs T3/FT4 relationship was fit to an asymptotic regression curve supporting less T4 to T3 activation at higher FT4 levels. CONCLUSION: In LT4-treated patients, the relationships between serum FT4 vs TSH and FT4 vs T3 across a range of FT4 levels are disproportionate. As a result, dose changes in LT4 that robustly modify serum FT4 and TSH values may only minimally affect serum T3 levels and result in no significant clinical benefit.

Proteomic Analysis of Decellularized Extracellular Matrix: Achieving a Competent Biomaterial for Osteogenesis.

Decellularized ECMs have been used as biological scaffolds for tissue repair due to their tissue-specific biochemical and mechanical composition, poorly simulated by other materials. It is used as patches and powders, and it could be further processed via enzymatic digestion under acidic conditions using pepsin. However, part of the bioactivity is lost during the digestion process due to protein denaturation. Here, stepwise digestion was developed to prepare a competent biomaterial for osteogenesis from three different ECM sources. In addition, three different proteases were compared to evaluate the most effective digestion protocol for specific cellular processes. GAGs and peptide quantification showed that the stepwise method yielded a higher concentration of bioactive residues. Circular dichroism analysis also showed that the stepwise approach preserved the secondary structures better. The protein profiles of the digested ECMs were analyzed, and it was found to be highly diverse and tissue-specific. The digestion of ECM from pericardium produced peptides originated from 94 different proteins, followed by 48 proteins in ECM from tendon and 35 proteins in ECM from bone. In addition, digested products from pericardium ECM yielded increased proliferation and differentiation of bone marrow mesenchymal stem cells to mature osteoblasts.

Use of Statins Among Patients Taking Levothyroxine: an Observational Drug Utilization Study Across Sites.

CONTEXT: Treatment with levothyroxine (LT4) that normalize serum thyrotropin (TSH) is expected to restore lipid metabolism. OBJECTIVE: To assess statin utilization in LT4-treated patients through an observational drug utilization study. METHODS: Three sites were involved: (1) 10 723 outpatients placed on LT4 during 2006-2019 identified from the Clinical Research Data Warehouse of the University of Chicago; (2) ~1.4 million LT4 prescriptions prepared by primary care physicians during January-December 2018, identified from the IQVIA™ database of medical prescriptions in Brazil; (30 ~5.4 million patient interviews during 2009-2019, including ~0.32 million patients on LT4, identified from the Fleury Group database in Brazil. RESULTS: On site 1, initiation of therapy with LT4 increased the frequency of statin utilization (19.1% vs 24.6%), which occurred ~1.5 years later (median 76 weeks) and, among those patients that were on statins, increased intensity of treatment by 33%, despite normalization of serum TSH levels; on site 2, after matching for sex and age, the frequency of statins prescription was higher for those patients using LT4: females, 2.1 vs 3.4% (odds ratio [OR] 1.656 [1.639-1.673]); males, 3.1 vs 4.4% (OR 1.435 [1.409-1.462]); and, on site 3, after matching for sex and age, the frequency of statin utilization was higher in those patients using LT4: females, 10 vs 18% (OR 2.02 [2.00-2.04]); males, 15 vs 25% (OR 1.92 [1.88-1.96]); all P values were <.0001. CONCLUSION: Prescription and utilization of statins were higher in patients taking LT4. The reasons for this association should be addressed in future studies.

Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool.

Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the "signal" of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at http://mdp.sysbio.tools.

webCEMiTool: Co-expression Modular Analysis Made Easy.

Co-expression analysis has been widely used to elucidate the functional architecture of genes under different biological processes. Such analysis, however, requires substantial knowledge about programming languages and/or bioinformatics skills. We present webCEMiTool, a unique online tool that performs comprehensive modular analyses in a fully automated manner. The webCEMiTool not only identifies co-expression gene modules but also performs several functional analyses on them. In addition, webCEMiTool integrates transcriptomic data with interactome information (i.e., protein-protein interactions) and identifies potential hubs on each network. The tool generates user-friendly html reports that allow users to search for specific genes in each module, as well as check if a module contains genes overrepresented in specific pathways or altered in a specific sample phenotype. We used webCEMiTool to perform a modular analysis of single-cell RNA-seq data of human cells infected with either Zika virus or dengue virus.

CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses.

BACKGROUND: The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions. RESULTS: In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease's physiopathology. CONCLUSION: The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.