A National Survey of Pediatric Post-Acute Care Facilities.

OBJECTIVES: To identify and geolocate pediatric post-acute care (PAC) facilities in the United States. DESIGN: Cross-sectional survey using both online resources and telephone inquiry. SETTING: All 50 U.S. states surveyed from June 2022 to May 2023. Care sites identified via state regulatory agencies and the Centers for Medicare & Medicaid Services. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Number, size, and type of facility, scope of practice, and type of care provided. One thousand three hundred fifty-five facilities were surveyed; of these, 18.6% (252/1355) were pediatric-specific units or adult facilities accepting some pediatric patients. There were 109 pediatric-specific facilities identified within 39 U.S. states. Of these, 38 were freestanding with all accepting children with tracheostomies, 97.4% (37/38) accepting those requiring mechanical ventilation via tracheostomy, and 81.6% (31/38) accepting those requiring parenteral nutrition. The remaining 71 facilities were adult facilities with embedded pediatric units or children's hospitals with 88.7% (63/71), 54.9% (39/71), and 54.9% (39/71), accepting tracheostomies, mechanical ventilation via tracheostomy, and parenteral nutrition, respectively. Eleven states lacked any pediatric-specific PAC units or facilities. CONCLUSIONS: The distribution of pediatric PAC is sparse and uneven across the United States. We present an interactive map and database describing these facilities. These data offer a starting point for exploring the consequences of pediatric PAC supply.

Trauma Center Transition From Paper to Electronic Quality Improvement: Enhancing Trauma Performance Efficiency.

BACKGROUND: The American College of Surgeons requires continuous process improvement review to maintain trauma center verification. Paper-based systems to monitor and track performance improvement are commonly used but can be inadequate to monitor concurrent ongoing improvement effectively. OBJECTIVE: To describe the implementation of an electronic process to capture and monitor performance improvement in near real time. METHODS: In 2020, a Midwestern U.S. Level I adult trauma center and a Level II pediatric trauma center's trauma programs transitioned from a paper to an electronic file-sharing system for performance improvement. We converted our primary, secondary, and tertiary review documentation into a single electronic performance improvement file stored on the institution's virtual hard drive, accessible to designated staff, allowing continuous real-time updates. RESULTS: The electronic file-sharing and monitoring process reinvigorated the team and enhanced performance improvement efforts, leading to increased efficiency through documentation and effective loop closure. Real-time monitoring allowed the trauma program to identify opportunities for improvement and enact timely action plans, including targeted performance improvement projects, department education, and ongoing training. CONCLUSION: We found that implementing an electronic file-sharing system enhanced the trauma team's ability to monitor and trend performance improvement in real time.

Pilates to Improve Core Muscle Activation in Chronic Low Back Pain: A Systematic Review.

Low back pain is prevalent in the community and associated with deficits in core muscle strength and activation. Pilates is argued to improve movement and reduce pain, yet there is a limited understanding of the specific effects of Pilates training on core muscle strength or activity. A systematic search of databases (CINAHL, Embase, Ovid MEDLINE) used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methods to evaluate randomised controlled trials (RCTs) on the effects of Pilates to improve core muscle activation. Methodological quality was assessed using the Physiotherapy Evidence Database scale (PEDro). The certainty of findings was determined using the Grading of Recommendations Assessment, Development and Evaluation tool. Of the initial yield of 563 articles, eight RCTs met the inclusion criteria. A diverse range of Pilates interventions and outcome measures were utilised to assess effects on core muscle activation and strength. The main finding was that Pilates is not inferior to equivalently dosed exercises, and can be superior to non-equivalent or no exercise, for improving core muscle strength as indicated by muscle thickness. There was emerging evidence that Pilates positively impacts core muscle strength and can be an effective intervention for people living with chronic low back pain.

Pulmonary osteoclast-like cells in silica induced pulmonary fibrosis.

The pathophysiology of silicosis is poorly understood, limiting development of therapies for those who have been exposed to the respirable particle. We explored the mechanisms of silica-induced pulmonary fibrosis in a mouse model using multiple modalities including whole-lung single-nucleus RNA sequencing. These analyses revealed that in addition to pulmonary inflammation and fibrosis, intratracheal silica challenge induced osteoclast-like differentiation of alveolar macrophages and recruited monocytes, driven by induction of the osteoclastogenic cytokine, receptor activator of nuclear factor-κB ligand (RANKL) in pulmonary lymphocytes and alveolar type II cells. Furthermore, anti-RANKL monoclonal antibody treatment suppressed silica-induced osteoclast-like differentiation in the lung and attenuated silica-induced pulmonary fibrosis. We conclude that silica induces osteoclast-like differentiation of distinct recruited and tissue resident monocyte populations, leading to progressive lung injury, likely due to sustained elaboration of bone resorbing proteases and hydrochloric acid. Interrupting osteoclast-like differentiation may therefore constitute a promising avenue for moderating lung damage in silicosis.

Clinical and Molecular Findings After Autologous Stem Cell Transplantation or Cyclophosphamide for Scleroderma: Handling Missing Longitudinal Data.

OBJECTIVE: Among individuals with systemic sclerosis (SSc) randomized to cyclophosphamide (CYC) (n = 34) or hematopoietic stem cell transplantation (HSCT) (n = 33), we examined longitudinal trends of clinical, pulmonary function, and quality of life measures while accounting for the influence of early failures on treatment comparisons. METHODS: Assuming that data were missing at random, mixed-effects regression models were used to estimate longitudinal trends for clinical measures when comparing treatment groups. Results were compared to observed means and to longitudinal trends estimated from shared parameter models, assuming that data were missing not at random. Longitudinal trends for SSc intrinsic molecular subsets defined by baseline gene expression signatures (normal-like, inflammatory, and fibroproliferative signatures) were also studied. RESULTS: Available observed means for pulmonary function tests appeared to improve over time in both arms. However, after accounting for participant loss, forced vital capacity in HSCT recipients increased by 0.77 percentage points/year but worsened by -3.70/year for CYC (P = 0.004). Similar results were found for diffusing capacity for carbon monoxide and quality of life indicators. Results for both analytic models were consistent. HSCT recipients in the inflammatory (n = 20) and fibroproliferative (n = 20) subsets had superior long-term trends compared to CYC for pulmonary and quality of life measures. HSCT was also superior for modified Rodnan skin thickness scores in the fibroproliferative subset. For the normal-like subset (n = 22), superiority of HSCT was less apparent. CONCLUSION: Longitudinal trends estimated from 2 statistical models affirm the efficacy of HSCT over CYC in severe SSc. Failure to account for early loss of participants may distort estimated clinical trends over the long term.

Impact of implementing an evidence-based definition of enteral nutrition intolerance on nutrition delivery: A prospective, cross-sectional cohort study.

BACKGROUND: Enteral nutrition (EN) interruptions because of EN intolerance impede nutrient delivery. We aimed to examine whether revising the EN intolerance definition of an algorithm would decrease EN interruptions and improve nutrient delivery in critically ill children. METHODS: We performed a cross-sectional cohort study including patients who were admitted to our intensive care unit (ICU) for >24 h and received EN. The EN intolerance definition in our nutrition algorithm was modified to include two symptoms of EN intolerance. We compared time to 60% EN adequacy (EN delivered/EN prescribed x 100) and EN interruptions before and after this intervention. RESULTS: We included 150 eligible patients, 78 and 72 patients in the preimplementation and postimplementation cohorts, respectively. There were no significant differences in demographics and clinical characteristics. The preimplementation and postimplementation cohorts achieved 60% EN adequacy 4 (2-5) days and 3 (2-5) days after ICU admission, respectively (P = 0.59). The preimplementation cohort had a median of 1 (1-2) interruption per patient and the postimplementation cohort 2 (1-3; P = 0.08). The frequency of interruptions because of EN intolerance within the first 8 days of ICU admission was 17 in the preimplementation and 10 in the postimplementation cohorts. CONCLUSION: Modifying the EN intolerance definition of a nutrition algorithm did not change the time to 60% EN adequacy or total number of EN interruptions in critically ill children. EN intolerance and interruptions continue to limit nutrient delivery. Research on the best definition for EN intolerance and its effect on nutrition outcomes is needed.

Trends in Disease Severity Among Critically Ill Children With Severe Acute Respiratory Syndrome Coronavirus 2: A Retrospective Multicenter Cohort Study in the United States.

OBJECTIVES: To describe trends in critical illness from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children over the course of the COVID-19 pandemic. We hypothesized that PICU admission rates were higher in the Omicron period compared with the original outbreak but that fewer patients needed endotracheal intubation. DESIGN: Retrospective cohort study. SETTING: This study took place in nine U.S. PICUs over 3 weeks in January 2022 (Omicron period) compared with 3 weeks in March 2020 (original period). PATIENTS: Patients less than or equal to 21 years old who screened positive for SARS-CoV-2 infection by polymerase chain reaction or hospital-based rapid antigen test and were admitted to a PICU or intermediate care unit were included. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 267 patients (239 Omicron and 28 original) were reviewed. Forty-five patients in the Omicron cohort had incidental SARS-CoV-2 and were excluded from analysis. The Omicron cohort patients were younger compared with the original cohort patients (median [interquartile range], 6 yr [1.3-13.3 yr] vs 14 yr [8.3-17.3 yr]; p = 0.001). The Omicron period, compared with the original period, was associated with an average increase in COVID-19-related PICU admissions of 13 patients per institution (95% CI, 6-36; p = 0.008), which represents a seven-fold increase in the absolute number admissions. We failed to identify an association between cohort period (Omicron vs original) and odds of intubation (odds ratio, 0.7; 95% CI, 0.3-1.7). However, we cannot exclude the possibility of up to 70% reduction in intubation. CONCLUSIONS: COVID-19-related PICU admissions were seven times higher in the Omicron wave compared with the original outbreak. We could not exclude the possibility of up to 70% reduction in use of intubation in the Omicron versus original epoch, which may represent differences in PICU/hospital admission policy in the later period, or pattern of disease, or possibly the impact of vaccination.

Machine-learning classification identifies patients with early systemic sclerosis as abatacept responders via CD28 pathway modulation.

Here, the efficacy of abatacept in patients with early diffuse systemic sclerosis (dcSSc) was analyzed to test the hypothesis that patients in the inflammatory intrinsic subset would show the most significant clinical improvement. Eighty-four participants with dcSSc were randomized to receive abatacept or placebo for 12 months. RNA-Seq was performed on 233 skin paired biopsies at baseline and at 3 and 6 months. Improvement was defined as a 5-point or more than 20% change in modified Rodnan skin score (mRSS) between baseline and 12 months. Samples were assigned to intrinsic gene expression subsets (inflammatory, fibroproliferative, or normal-like subsets). In the abatacept arm, change in mRSS was most pronounced for the inflammatory and normal-like subsets relative to the placebo subset. Gene expression for participants on placebo remained in the original molecular subset, whereas inflammatory participants treated with abatacept had gene expression that moved toward the normal-like subset. The Costimulation of the CD28 Family Reactome Pathway decreased in patients who improved on abatacept and was specific to the inflammatory subset. Patients in the inflammatory subset had elevation of the Costimulation of the CD28 Family pathway at baseline relative to that of participants in the fibroproliferative and normal-like subsets. There was a correlation between improved ΔmRSS and baseline expression of the Costimulation of the CD28 Family pathway. This study provides an example of precision medicine in systemic sclerosis clinical trials.

A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosis.

OBJECTIVES: Four intrinsic molecular subsets (inflammatory, fibroproliferative, limited, normal-like) have previously been identified in SSc and are characterized by unique gene expression signatures and pathways. The intrinsic subsets have been linked to improvement with specific therapies. Here, we investigated associations between baseline demographics and intrinsic molecular subsets in a meta-analysis of published datasets. METHODS: Publicly available gene expression data from skin biopsies of 311 SSc patients measured by DNA microarray were classified into the intrinsic molecular subsets. RNA-sequencing data from 84 participants from the ASSET trial were used as a validation cohort. Baseline clinical demographics and intrinsic molecular subsets were tested for statistically significant associations. RESULTS: Males were more likely to be classified in the fibroproliferative subset (P = 0.0046). SSc patients who identified as African American/Black were 2.5 times more likely to be classified as fibroproliferative compared with White/Caucasian patients (P = 0.0378). ASSET participants sera positive for anti-RNA pol I and RNA pol III autoantibodies were enriched in the inflammatory subset (P = 5.8 × 10-5, P = 9.3 × 10-5, respectively), while anti-Scl-70 was enriched in the fibroproliferative subset. Mean modified Rodnan Skin Score (mRSS) was statistically higher in the inflammatory and fibroproliferative subsets compared with normal-like (P = 0.0027). The average disease duration for inflammatory subset was less than fibroproliferative and normal-like intrinsic subsets (P = 8.8 × 10-4). CONCLUSIONS: We identified multiple statistically significant differences in baseline demographics between the intrinsic subsets that may represent underlying features of disease pathogenesis (e.g. chronological stages of fibrosis) and have implications for treatments that are more likely to work in certain SSc populations.

Association Between Anticholinergic Drug Burden and Adequacy of Enteral Nutrition in Critically Ill, Mechanically Ventilated Pediatric Patients.

OBJECTIVES: Enteral nutrition delivery is limited by intolerance and interruptions in critically ill children. Anticholinergic properties of frequently administered medications may contribute to altered gastric motility and enteral nutrition intolerance in this population. We examined the association between the anticholinergic burden of administered medications using the Anticholinergic Drug Scale and adequacy of enteral nutrition delivery. DESIGN: Secondary analysis of data from a previously characterized PICU cohort. SETTING: Multidisciplinary PICU in a quaternary academic medical center. PATIENTS: Younger than or equal to 18 years, on mechanical ventilation and received enteral nutrition within the first 3 days of PICU admission. MEASUREMENTS AND MAIN RESULTS: Daily Anticholinergic Drug Scale score, demographic data, and clinical data were obtained from the primary study. Percent enteral energy adequacy ([kcal delivered ÷ kcal prescribed] × 100) during the first 3 days of PICU admission was calculated. Forty-two patients received enteral nutrition, with median age (interquartile range) 5 years (1.09-12.54 yr), and 62% were male. Median Anticholinergic Drug Scale score was inversely correlated with energy adequacy, with a median 9% decline in energy adequacy per 1-point increase in Anticholinergic Drug Scale score (coefficient, -9.3; 95% CI, -13.43 to -5.27; R2 = 0.35; p < 0.0001). Median hours of enteral nutrition interruptions directly correlated with Anticholinergic Drug Scale score (coefficient, 1.5; 95% CI, 0.531-2.54; R2 = 0.19; p = 0.004). Severity score was greater in patients with less than or equal to 25% enteral energy adequacy and directly correlated with median Anticholinergic Drug Scale score. CONCLUSIONS: Anticholinergic burden from medications administered in the PICU is a potentially modifiable factor for suboptimal enteral nutrition delivery.

Embryo-scale, single-cell spatial transcriptomics.

Spatial patterns of gene expression manifest at scales ranging from local (e.g., cell-cell interactions) to global (e.g., body axis patterning). However, current spatial transcriptomics methods either average local contexts or are restricted to limited fields of view. Here, we introduce sci-Space, which retains single-cell resolution while resolving spatial heterogeneity at larger scales. Applying sci-Space to developing mouse embryos, we captured approximate spatial coordinates and whole transcriptomes of about 120,000 nuclei. We identify thousands of genes exhibiting anatomically patterned expression, leverage spatial information to annotate cellular subtypes, show that cell types vary substantially in their extent of spatial patterning, and reveal correlations between pseudotime and the migratory patterns of differentiating neurons. Looking forward, we anticipate that sci-Space will facilitate the construction of spatially resolved single-cell atlases of mammalian development.

"I'm not going to be a guinea pig:" Medical mistrust as a barrier to male contraception for Black American men in Los Angeles, CA.

BACKGROUND: Racial disparities in unintended pregnancy and contraceptive use in the United States are not mediated by access to family planning services alone. Rather, a history of medical mistrust underlies Black Americans' adoption of new medical technologies, inclusive of contraception. Efforts to develop hormonal male contraceptives need to incorporate Black Americans' experiences and perspectives so that new contraceptives enable their reproductive goals and promote gender equity. STUDY DESIGN: Working with our community-based partner, Healthy African American Families in Los Angeles, California, we conducted six 60-minute focus group discussions with 39 Black men over age 18, in ongoing heterosexual relationships, to explore attitudes towards and willingness to use hormonal male contraceptives. RESULTS: Just over one-third (35%) of respondents reported willingness to use or rely on hormonal male contraceptives. The majority held negative attitudes about hormonal male contraceptives, citing concerns about side effects and safety. Several respondents expressed mistrust of the medical community and medical research, noting that hormonal male contraceptives could be used against Black communities; several expressed unwillingness to trial hormonal male contraceptives without years of testing. However, all groups described scenarios where they would use them despite stated concerns. CONCLUSIONS: Black men's hypothetical willingness to use hormonal male contraceptives is limited by medical mistrust, which may be overcome by their concerns about the unreliability of current options or the contraceptive behaviors of female partners. Nevertheless, addressing Black Americans' history of medical mistreatment and exploitation will be essential for hormonal male contraceptives to positively contribute to Black men's reproductive options and agency. IMPLICATIONS: While the development of reversible, hormonal male contraception intends to fulfill unmet global needs for contraception, the utility of these hormonal male contraceptive methods among Black men living on low incomes in Los Angeles, California cannot be fully realized until developers address and overcome historical and ongoing medical mistrust.

Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvement.

OBJECTIVE: We sought to determine histologic and gene expression features of clinical improvement in early diffuse cutaneous systemic sclerosis (dcSSc; scleroderma). METHODS: Fifty-eight forearm biopsies were evaluated from 26 individuals with dcSSc in two clinical trials. Histologic/immunophenotypic assessments of global severity, alpha-smooth muscle actin (aSMA), CD34, collagen, inflammatory infiltrate, follicles and thickness were compared with gene expression and clinical data. Support vector machine learning was performed using scleroderma gene expression subset (normal-like, fibroproliferative, inflammatory) as classifiers and histology scores as inputs. Comparison of w-vector mean absolute weights was used to identify histologic features most predictive of gene expression subset. We then tested for differential gene expression according to histologic severity and compared those with clinical improvement (according to the Combined Response Index in Systemic Sclerosis). RESULTS: aSMA was highest and CD34 lowest in samples with highest local Modified Rodnan Skin Score. CD34 and aSMA changed significantly from baseline to 52 weeks in clinical improvers. CD34 and aSMA were the strongest predictors of gene expression subset, with highest CD34 staining in the normal-like subset (p<0.001) and highest aSMA staining in the inflammatory subset (p=0.016). Analysis of gene expression according to CD34 and aSMA binarised scores identified a 47-gene fibroblast polarisation signature that decreases over time only in improvers (vs non-improvers). Pathway analysis of these genes identified gene expression signatures of inflammatory fibroblasts. CONCLUSION: CD34 and aSMA stains describe distinct fibroblast polarisation states, are associated with gene expression subsets and clinical assessments, and may be useful biomarkers of clinical severity and improvement in dcSSc.

In silico APC/C substrate discovery reveals cell cycle-dependent degradation of UHRF1 and other chromatin regulators.

The anaphase-promoting complex/cyclosome (APC/C) is an E3 ubiquitin ligase and critical regulator of cell cycle progression. Despite its vital role, it has remained challenging to globally map APC/C substrates. By combining orthogonal features of known substrates, we predicted APC/C substrates in silico. This analysis identified many known substrates and suggested numerous candidates. Unexpectedly, chromatin regulatory proteins are enriched among putative substrates, and we show experimentally that several chromatin proteins bind APC/C, oscillate during the cell cycle, and are degraded following APC/C activation, consistent with being direct APC/C substrates. Additional analysis revealed detailed mechanisms of ubiquitylation for UHRF1, a key chromatin regulator involved in histone ubiquitylation and DNA methylation maintenance. Disrupting UHRF1 degradation at mitotic exit accelerates G1-phase cell cycle progression and perturbs global DNA methylation patterning in the genome. We conclude that APC/C coordinates crosstalk between cell cycle and chromatin regulatory proteins. This has potential consequences in normal cell physiology, where the chromatin environment changes depending on proliferative state, as well as in disease.

Machine learning predicts stem cell transplant response in severe scleroderma.

OBJECTIVE: The Scleroderma: Cyclophosphamide or Transplantation (SCOT) trial demonstrated clinical benefit of haematopoietic stem cell transplant (HSCT) compared with cyclophosphamide (CYC). We mapped PBC (peripheral blood cell) samples from the SCOT clinical trial to scleroderma intrinsic subsets and tested the hypothesis that they predict long-term response to HSCT. METHODS: We analysed gene expression from PBCs of SCOT participants to identify differential treatment response. PBC gene expression data were generated from 63 SCOT participants at baseline and follow-up timepoints. Participants who completed treatment protocol were stratified by intrinsic gene expression subsets at baseline, evaluated for event-free survival (EFS) and analysed for differentially expressed genes (DEGs). RESULTS: Participants from the fibroproliferative subset on HSCT experienced significant improvement in EFS compared with fibroproliferative participants on CYC (p=0.0091). In contrast, EFS did not significantly differ between CYC and HSCT arms for the participants from the normal-like subset (p=0.77) or the inflammatory subset (p=0.1). At each timepoint, we observed considerably more DEGs in HSCT arm compared with CYC arm with HSCT arm showing significant changes in immune response pathways. CONCLUSIONS: Participants from the fibroproliferative subset showed the most significant long-term benefit from HSCT compared with CYC. This study suggests that intrinsic subset stratification of patients may be used to identify patients with SSc who receive significant benefit from HSCT.

Regulator combinations identify systemic sclerosis patients with more severe disease.

Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 "intrinsic" subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We found that regulator activity scores can reproduce the intrinsic molecular subsets, with distinct sets of regulators identified for inflammatory, fibroproliferative, limited, and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identified subgroups of patients with fibroproliferative and inflammatory SSc with more severe pathophenotypes, such as higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent cohort, we show that the group with more severe ILD was more likely to show forced vital capacity decline over a period of 36-54 months. Our results demonstrate an association among the activation of regulators, gene expression subsets, and clinical variables that can identify patients with SSc with more severe disease.

Abatacept in Early Diffuse Cutaneous Systemic Sclerosis: Results of a Phase II Investigator-Initiated, Multicenter, Double-Blind, Randomized, Placebo-Controlled Trial.

OBJECTIVE: T cells play a key role in the pathogenesis of early systemic sclerosis. This study was undertaken to assess the safety and efficacy of abatacept in patients with diffuse cutaneous systemic sclerosis (dcSSc). METHODS: In this 12-month, randomized, double-blind, placebo-controlled trial, participants were randomized 1:1 to receive either subcutaneous abatacept 125 mg or matching placebo, stratified by duration of dcSSc. Escape therapy was allowed at 6 months for worsening disease. The coprimary end points were change in the modified Rodnan skin thickness score (MRSS) compared to baseline and safety over 12 months. Differences in longitudinal outcomes were assessed according to treatment using linear mixed models, with outcomes censored after initiation of escape therapy. Skin tissue obtained from participants at baseline was classified into intrinsic gene expression subsets. RESULTS: Among 88 participants, the adjusted mean change in the MRSS at 12 months was -6.24 units for those receiving abatacept and -4.49 units for those receiving placebo, with an adjusted mean treatment difference of -1.75 units (P = 0.28). Outcomes for 2 secondary measures (Health Assessment Questionnaire disability index and a composite measure) were clinically and statistically significantly better with abatacept. The proportion of subjects in whom escape therapy was needed was higher in the placebo group relative to the abatacept group (36% versus 16%). In the inflammatory and normal-like skin gene expression subsets, decline in the MRSS over 12 months was clinically and significantly greater in the abatacept group versus the placebo group (P < 0.001 and P = 0.03, respectively). In the abatacept group, adverse events occurred in 35 participants versus 40 participants in the placebo group, including 2 deaths and 1 death, respectively. CONCLUSION: In this phase II trial, abatacept was well-tolerated, but change in the MRSS was not statistically significant. Secondary outcome measures, including gene expression subsets, showed evidence in support of abatacept. These data should be confirmed in a phase III trial.

Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile.

OBJECTIVES: Determine global skin transcriptome patterns of early diffuse systemic sclerosis (SSc) and how they differ from later disease. METHODS: Skin biopsy RNA from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by next-generation RNA sequencing. Data were analysed for cell type-specific signatures and compared with similarly obtained data from 55 previously biopsied patients in Genetics versus Environment in Scleroderma Outcomes Study cohort with longer disease duration (mean 7.4 years) and their matched controls. Correlations with histological features and clinical course were also evaluated. RESULTS: SSc patients in PRESS had a high prevalence of M2 (96%) and M1 (94%) macrophage and CD8 T cell (65%), CD4 T cell (60%) and B cell (69%) signatures. Immunohistochemical staining of immune cell markers correlated with the gene expression-based immune cell signatures. The prevalence of immune cell signatures in early diffuse SSc patients was higher than in patients with longer disease duration. In the multivariable model, adaptive immune cell signatures were significantly associated with shorter disease duration, while fibroblast and macrophage cell type signatures were associated with higher modified Rodnan Skin Score (mRSS). Immune cell signatures also correlated with skin thickness progression rate prior to biopsy, but did not predict subsequent mRSS progression. CONCLUSIONS: Skin in early diffuse SSc has prominent innate and adaptive immune cell signatures. As a prominently affected end organ, these signatures reflect the preceding rate of disease progression. These findings could have implications in understanding SSc pathogenesis and clinical trial design.

A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.

OBJECTIVE: High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene expression subsets: inflammatory, fibroproliferative, normal-like, and limited. Prior methods required agglomerative clustering of many samples. In order to classify individual patients in clinical trials or for diagnostic purposes, supervised methods that can assign single samples to molecular subsets are required. We undertook this study to introduce a novel machine learning classifier as a robust accurate intrinsic subset predictor. METHODS: Three independent gene expression cohorts were curated and merged to create a data set covering 297 skin biopsy samples from 102 unique patients and controls, which was used to train a machine learning algorithm. We performed external validation using 3 independent SSc cohorts, including a gene expression data set generated by an independent laboratory on a different microarray platform. In total, 413 skin biopsy samples from 213 individuals were analyzed in the training and testing cohorts. RESULTS: Repeated cross-fold validation identified consistent and discriminative markers using multinomial elastic net, performing with an average classification accuracy of 87.1% with high sensitivity and specificity. In external validation, the classifier achieved an average accuracy of 85.4%. Reanalyzing data from a previous study, we identified subsets of patients that represent the canonical inflammatory, fibroproliferative, and normal-like subsets. CONCLUSION: We developed a highly accurate classifier for SSc molecular subsets for individual patient samples. The method can be used in SSc clinical trials to identify an intrinsic subset on individual samples. Our method provides a robust data-driven approach to aid clinical decision-making and interpretation of heterogeneous molecular information in SSc patients.

BioMethyl: an R package for biological interpretation of DNA methylation data.

MOTIVATION: The accumulation of publicly available DNA methylation datasets has resulted in the need for tools to interpret the specific cellular phenotypes in bulk tissue data. Current approaches use either single differentially methylated CpG sites or differentially methylated regions that map to genes. However, these approaches may introduce biases in downstream analyses of biological interpretation, because of the variability in gene length. There is a lack of approaches to interpret DNA methylation effectively. Therefore, we have developed computational models to provide biological interpretation of relevant gene sets using DNA methylation data in the context of The Cancer Genome Atlas. RESULTS: We illustrate that Biological interpretation of DNA Methylation (BioMethyl) utilizes the complete DNA methylation data for a given cancer type to reflect corresponding gene expression profiles and performs pathway enrichment analyses, providing unique biological insight. Using breast cancer as an example, BioMethyl shows high consistency in the identification of enriched biological pathways from DNA methylation data compared to the results calculated from RNA sequencing data. We find that 12 out of 14 pathways identified by BioMethyl are shared with those by using RNA-seq data, with a Jaccard score 0.8 for estrogen receptor (ER) positive samples. For ER negative samples, three pathways are shared in the two enrichments with a slight lower similarity (Jaccard score = 0.6). Using BioMethyl, we can successfully identify those hidden biological pathways in DNA methylation data when gene expression profile is lacking. AVAILABILITY AND IMPLEMENTATION: BioMethyl R package is freely available in the GitHub repository (https://github.com/yuewangpanda/BioMethyl). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.