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During the postnatal period in mammals, the cardiac muscle transitions from hyperplasic to hypertrophic growth, the extracellular matrix (ECM) undergoes remodeling, and the heart loses regenerative capacity. While ECM maturation and crosstalk between cardiac fibroblasts (CFs) and cardiomyocytes (CMs) have been implicated in neonatal heart development, not much is known about specialized fibroblast heterogeneity and function in the early postnatal period. In order to better understand CF functions in heart maturation and postnatal cardiomyocyte cell-cycle arrest, we have performed gene expression profiling and ablation of postnatal CF populations. Fibroblast lineages expressing Tcf21 or Periostin were traced in transgenic GFP reporter mice, and their biological functions and transitions during the postnatal period were examined in sorted cells using RNA sequencing. Highly proliferative Periostin (Postn)+ lineage CFs were found from postnatal day 1 (P1) to P11 but were not detected at P30, due to a repression of Postn gene expression. This population was less abundant and transcriptionally different from Tcf21+ resident CFs. The specialized Postn+ population preferentially expresses genes related to cell proliferation and neuronal development, while Tcf21+ CFs differentially express genes related to ECM maturation at P7 and immune crosstalk at P30. Ablation of the Postn+ CFs from P0 to P6 led to altered cardiac sympathetic nerve patterning and a reduction in binucleation and hypertrophic growth with increased fetal troponin (TroponinI1) expression in CM. Thus, postnatal CFs are heterogeneous and include a transient proliferative Postn+ population required for cardiac nerve development and cardiomyocyte maturation soon after birth.

Background Even though human sweat is odorless, bacterial growth and decomposition of specific odor precursors in it is believed to give rise to body odor in humans. While mechanisms of odor generation have been widely studied in adults, little is known for teenagers and pre-pubescent children who have distinct sweat composition from immature apocrine and sebaceous glands, but are arguably more susceptible to the social and psychological impact of malodor. Results We integrated information from whole microbiome analysis of multiple skin sites (underarm, neck, and head) and multiple time points (1 h and 8 h after bath), analyzing 180 samples in total to perform the largest metagenome-wide association study to date on malodor. Significant positive correlations were observed between odor intensity and the relative abundance of Staphylococcus hominis , Staphylococcus epidermidis , and Cutibacterium avidum , as well as negative correlation with Acinetobacter schindleri and Cutibacterium species. Metabolic pathway analysis highlighted the association of isovaleric and acetic acid production (sour odor) from enriched S. epidermidis (teen underarm) and S. hominis (child neck) enzymes and sulfur production from Staphylococcus species (teen underarm) with odor intensity, in good agreement with observed odor characteristics in pre-pubescent children and teenagers. Experiments with cultures on human and artificial sweat confirmed the ability of S. hominis and S. epidermidis to independently produce malodor with distinct odor characteristics. Conclusions These results showcase the power of skin metagenomics to study host-microbial co-metabolic interactions, identifying distinct pathways for odor generation from sweat in pre-pubescent children and teenagers and highlighting key enzymatic targets for intervention.

This position paper summarizes relevant theory and current practice regarding the analysis of longitudinal clinical trials intended to support regulatory approval of medicinal products, and it reviews published research regarding methods for handling missing data. It is one strand of the PhRMA initiative to improve efficiency of late-stage clinical research and gives recommendations from a cross-industry team. We concentrate specifically on continuous response measures analyzed using a linear model, when the goal is to estimate and test treatment differences at a given time point. Traditionally, the primary analysis of such trials handled missing data by simple imputation using the last, or baseline, observation carried forward method (LOCF, BOCF) followed by analysis of (co)variance at the chosen time point. However, the general statistical and scientific community has moved away from these simple methods in favor of joint analysis of data from all time points based on a multivariate model (eg, of a mixed-effects type). One such newer method, a likelihood-based mixed-effects model repeated measures (MMRM) approach, has received considerable attention in the clinical trials literature. We discuss specific concerns raised by regulatory agencies with regard to MMRM and review published evidence comparing LOCF and MMRM in terms of validity, bias, power, and type I error. Our main conclusion is that the mixed model approach is more efficient and reliable as a method of primary analysis, and should be preferred to the inherently biased and statistically invalid simple imputation approaches. We also summarize other methods of handling missing data that are useful as sensitivity analyses for assessing the potential effect of data missing not at random.

Background: Delayed esophageal transit or disintegration of oral bisphosphonate tablets before they enter the stomach may be of concern with respect to iatrogenic complications among patients receiving longterm treatment. Different formulations of generic bisphosphonate tablets meeting regulatory requirements may have substantial differences in pharmaceutical attributes from the branded product that may result in different characteristics during esophageal transit. Objective: The primary objective of this study was to evaluate and compare esophageal transit times and in vivo disintegration of 3 bisphosphonate formulations, one branded and the others generic, that are commercially available in Canada and the United Kingdom. Methods: This was a single-center, randomized, singleblind, 6-period crossover study in healthy postmenopausal women aged >50 years. Each subject received a single oral dose of a branded risedronate sodium 35-mg tablet and 2 generic formulations of alendronic acid 70-mg tablets (Novopharm Limited, Toronto, Canada, and Teva UK Limited, Morley, United Kingdom) in both the erect and semisupine (45°) positions. Although the products are labeled to be taken in the erect position, the semisupine position was included to simulate dosing in bedridden patients. Subjects took tablets with 30 mL of water in the morning after an overnight fast. The tablets were radiolabeled with technetium-99m ion-exchange resins to enable visualization and measurement of esophageal transit time and disintegration using a gamma camera. Dynamic scintigraphic images were obtained for a total of 10 minutes: 2 images per second for the first 30 seconds and 1 image every 15 seconds for 9.5 minutes. This was a mechanistic study and tolerability was not assessed. Results: The study was conducted in 20 healthy white female subjects with a mean age of 62 years (range, 51–77 years). The effect of body position was statistically significant ( P = 0.043), with the estimated hazard ratio (HR) of 0.74 indicating longer transit time in the semisupine position relative to the erect position. There was a statistically significant difference in transit time among the 3 types of tablets ( P = 0.007), with the Novopharm tablet (HR = 0.59; P < 0.001) and Teva tablet (HR = 0.71; P = 0.042) having longer transit times compared with the risedronate tablet. In 4 instances, the Novopharm tablet disintegrated and dispersed in the esophagus, once in the erect position and 3 times in the semisupine position. Conclusions: In these healthy female subjects, esophageal transit was delayed when the tablets were given in the semisupine position. The branded risedronate tablet had a significantly faster transit time than the 2 generic formulations of alendronate tested.

Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease. In this Resource article, the authors integrate genomic, bioinformatic and flow cytometric data from human bone marrow to provide an atlas of hematopoietic progenitor cell states in health and disease.

BackgroundVariability in endoscopic assessment necessitates rigorous investigation of descriptors for scoring severity of ulcerative colitis (UC).ObjectiveTo evaluate variation in the overall endoscopic assessment of severity, the intra- and interindividual variation of descriptive terms and to create an Ulcerative Colitis Endoscopic Index of Severity which could be validated.DesignA two-phase study used a library of 670 video sigmoidoscopies from patients with Mayo Clinic scores 0–11, supplemented by 10 videos from five people without UC and five hospitalised patients with acute severe UC. In phase 1, each of 10 investigators viewed 16/24 videos to assess agreement on the Baron score with a central reader and agreed definitions of 10 endoscopic descriptors. In phase 2, each of 30 different investigators rated 25/60 different videos for the descriptors and assessed overall severity on a 0–100 visual analogue scale. κ Statistics tested inter- and intraobserver variability for each descriptor. A general linear mixed regression model based on logit link and β distribution of variance was used to predict overall endoscopic severity from descriptors.ResultsThere was 76% agreement for ‘severe’, but 27% agreement for ‘normal’ appearances between phase I investigators and the central reader. In phase 2, weighted κ values ranged from 0.34 to 0.65 and 0.30 to 0.45 within and between observers for the 10 descriptors. The final model incorporated vascular pattern, (normal/patchy/complete obliteration) bleeding (none/mucosal/luminal mild/luminal moderate or severe), erosions and ulcers (none/erosions/superficial/deep), each with precise definitions, which explained 90% of the variance (pR2, Akaike Information Criterion) in the overall assessment of endoscopic severity, predictions varying from 4 to 93 on a 100-point scale (from normal to worst endoscopic severity).ConclusionThe Ulcerative Colitis Endoscopic Index of Severity accurately predicts overall assessment of endoscopic severity of UC. Validity and responsiveness need further testing before it can be applied as an outcome measure in clinical trials or clinical practice.

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories, and outcomes. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/KANG-BIOINFO/CellDrift * https://github.com/KANG-BIOINFO/CellDrift_analysis