Deficiencies in the sterile processing of medical instruments contribute to poor outcomes for patients, such as surgical site infections, longer hospital stays, and deaths. In low resources settings, such as some rural and semi-rural areas and secondary and tertiary cities of developing countries, deficiencies in sterile processing are accentuated due to the lack of access to sterilization equipment, improperly maintained and malfunctioning equipment, lack of power to operate equipment, poor protocols, and inadequate quality control over inventory. Inspired by our sterile processing fieldwork at a district hospital in Sierra Leone in 2013, we built an autonomous, shipping-container-based sterile processing unit to address these deficiencies. The sterile processing unit, dubbed “the sterile box,” is a full suite capable of handling instruments from the moment they leave the operating room to the point they are sterile and ready to be reused for the next surgery. The sterile processing unit is self-sufficient in power and water and features an intake for contaminated instruments, decontamination, sterilization via non-electric steam sterilizers, and secure inventory storage. To validate efficacy, we ran tests of decontamination and sterilization performance. Results of 61 trials validate convincingly that our sterile processing unit achieves satisfactory outcomes for decontamination and sterilization and as such holds promise to support healthcare facilities in low resources settings.

Background: Five to ten percent of patients with sickle cell disease (SCD) have a high risk of suffering an ischemic stroke before they are 10 years of age. The clinical consequences of stroke are severe yet our current mechanistic understanding of the causes of stroke in SCD is limited. There is strong evidence that there is genomic contribution to the risk of stroke, other than just the SCD gene mutation. By whole exome sequencing, we have shown that a specific coding variant in the GOLGB1 gene, Y1212C, is significantly associated with decreased risk of stroke. GOLGB1 encodes a conserved Golgi membrane protein that is essential for maintaining Golgi structure and associated protein transport. To elucidate how the GOLGB1 Y1212C variant is protective against stroke risk in SCD, we performed morphological analysis of Golgi in cultured primary macrophages and whole proteome analyses of peripheral immune cells isolated from SCD patients. Methods: For primary macrophage preparations, we collected whole blood from non-SCD controls (n=11) and pediatric SCD patients (n=12). Monocytic CD14 positive cells were isolated and cultured for nine days with granulocyte monocyte stimulating colony factor, fixed and sequentially stained with antibodies against GOLGB1, TGN-46 and GM130. DAPI was used as a nuclear stain. High resolution images were acquired on Zeiss LSM 780 laser scanning confocal microscope with a 63X oil objective. Golgi morphological analysis was performed using a MATLAB script for cell segmentation, estimation of individual cell area, quantification of Golgi fragments per cell, and Golgi volume and organelle spread index. For the proteomic analysis, we isolated peripheral blood mononuclear cells from 7 patients with SCD. These cells were immediately flash-frozen and stored at -80oC until proteomic processing. We used a label-free protocol using dual-pH reverse phase fractionation and peptide sequencing of digested samples on a Thermo Scientific Orbitrap mass spectrometer. Protein abundances in each sample were estimated by using the intensity-based absolute quantification algorithm and total protein normalization was done with a fraction of total score (iFOT). Data were evaluated using GraphPad Prism and statistical analyses for differences between SCD and non-SCD samples or between samples with and without the GOLGB1 variant were performed with unpaired Student's t-test or Kruskal-Wallis test; analyses for multiple group comparisons were performed with the one-way ANOVA method. Statistical significance was set at P < 0.05. Results: In cultured primary macrophages, SCD individuals with the GOLGB1 Y1212C variant had altered Golgi morphologies compared to individuals with wild-type GOLGB1. Overall, macrophages derived from patients with SCD (n=12) were significantly larger (average cell size 11,728 vs. 7,598; p<0.001) yet had smaller Golgi (average Golgi volume 69.6µm vs. 75.4µm, p<0.01) and with more detected Golgi fragments (12.3 vs. 10.2 per cell, p<0.01) compared to non-SCD controls (n=11). For SCD patients with the GOLGB1 variant (n=5 heterozygote and 1 homozygote), their Golgi apparatus were more compact (average spread index 0.087 vs. 0.079; p<0.05) and had less Golgi fragments per cell (average 10.9 vs. 13.9; p<0.05) compared to SCD patients wild-type for GOLGB1 (n=6). From the proteomics analysis of peripheral blood immune cells, we identified 73 proteins that were differentially expressed (p<0.05) between SCD patients with (n=3) and without (n=4) the GOLGB1 variant. Of these proteins, 64 (90%) had lower levels in patients with the GOLGB1 Y1212C variant. Pathway analysis of the identified proteins showed that there strong clustering of proteins involved in platelet activation, regulation of actin cytoskeleton, and COPI-independent Golgi-to-ER retrograde trafficking (Table 1). Conclusions: Our study has shown that a coding variant in GOLGB1, identified as protective against risk of stroke in patients with SCD, has significant effects on Golgi function in SCD samples. We observed that having the GOLGB1 Y1212C variant resulted in more compact and less fragmented Golgi apparatus. Proteomic analysis showed that SCD patients with the GOLGB1 variant also had significantly lower levels of proteins involved in platelet activation and Golgi trafficking. Our findings suggest a novel role for the Golgi apparatus in controlling protein flux that modulates risk of stroke in SCD. Disclosures No relevant conflicts of interest to declare.

Background: Clinical features have been previously associated with the development of chronic immune thrombocytopenia (ITP) in children, but biomarkers to distinguish acute, self-resolving ITP from chronic disease remain elusive. Identifying biomarkers specific for chronic ITP would guide treatment decisions and also inform disease pathogenesis, which remains poorly understood. Prior studies have shown that inflammatory cytokines are elevated in chronic ITP patients as compared to acute patients, and DNA and RNA microarray data stratified chronic and acute ITP patients. This study leverages RNA sequencing data to characterize acute and chronic ITP cohorts. RNA sequencing has the advantage of examining RNA expression differences in a systematic and unbiased manner, as opposed to prior studies which were limited by candidate approach or by array technology. Methods: We collected biologic materials for sequencing and clinical data from 274 pediatric ITP Texas Children's Hospital patients from July 2015 to July 2018 under an approved IRB protocol. Patients were followed prospectively and classified as (1) chronic with disease persisting >12 months or (2) acute with spontaneous resolution in <12 months. Whole blood samples were collected at two time points over disease course. For acute patients, the initial sample was collected within 1 month of initial diagnosis and the second sample was drawn at the time of disease resolution. Two samples, 3-6 months apart, were taken from known chronic ITP patients with active disease at both time points. Acute patients were excluded if they had received any ITP directed therapy. Chronic patients were excluded if they had any therapy within the six months prior to the first sample or had ever received rituximab. Peripheral blood mononuclear cells were isolated using a ficoll separation from whole blood, and total RNA was extracted and purified using an RNeasy Mini Kit (QIAGEN). Those sample pairs which met standards for amount and purity of RNA for both samples were sequenced. RNA-Seq libraries were generated using Ovation® Universal RNA-Seq System (NuGen), sequenced on an Illumina NextSeq 500 instrument, and generated 40 million paired end reads. In total, 9 acute pairs, 12 chronic pairs and 9 healthy controls were sequenced. All protein coding transcripts were collapsed by gene, then rank statistics were applied and scaled by z-score. Cohorts were refined such that samples met uniform quality standards. Cluster analysis was performed using t-distributed stochastic neighbor embedding (t-SNE), a nonlinear method to reduce dimensionality and identify similar structures between samples. Results: Over 20,000 RNA transcripts were identified in approximately 2,000 genes. This preliminary analysis focused on initial samples only, when all ITP patients had active disease. T-SNE analysis showed that healthy controls, acute, and chronic ITP patients stratify into three separate clusters (Figure 1). A hierarchical clustering heatmap demonstrated a stratification between samples from acute patients at presentation, samples from chronic patients, and samples from healthy pediatric controls. Transcripts which stratified patients via hierarchical clustering were analyzed using Gene Set Enrichment Analysis (GSEA) to provide functional annotations. Applying transcripts with high expression in active acute ITP, mid-range expression in chronic ITP, and low expression in healthy controls identified pathways which are also important in other autoimmune diseases such as systemic lupus erythematosus, rheumatoid arthritis, and inflammatory bowel disease. Individual transcript expression profiles were not specifically compared in this analysis, but transcripts with immune function including Fc-Gamma Receptor IIIb, CXCL8, BMP7, DEFA1, DEFA1B, HLA-DRB4, and HLA-DRB5 were differentially expressed between ITP patient samples and controls. Conclusions: This study is the first to use RNA sequencing to show that active, acute ITP is distinct from chronic ITP. This study confirmed that ITP patient RNA expression patterns differ from that of healthy controls. Both transcripts with differential expression between cohorts and functional annotation pathways identified patterns with relevance to autoimmunity. Disclosures Grace: Agios Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Agios Pharmaceuticals: Research Funding; Agios Pharmaceuticals: Consultancy. Lambert:Summus: Consultancy; Shionogi: Consultancy; Amgen: Membership on an entity's Board of Directors or advisory committees; Educational Concepts in Medicine: Consultancy; Rigel: Consultancy; Sysmex: Consultancy; CSL: Consultancy; Novartis: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees. Despotovic:Sanofi: Consultancy; Novartis: Research Funding; AmGen: Research Funding.

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