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Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data capable of recognizing infant sepsis at least 4 hours prior to clinical recognition. We performed a retrospective case control study of infants hospitalized ≥48 hours in the Neonatal Intensive Care Unit (NICU) at the Children's Hospital of Philadelphia between September 2014 and November 2017 who received at least one sepsis evaluation before 12 months of age. We considered two evaluation outcomes as cases: culture positive-positive blood culture for a known pathogen (110 evaluations); and clinically positive-negative cultures but antibiotics administered for ≥120 hours (265 evaluations). Case data was taken from the 44-hour window ending 4 hours prior to evaluation. We randomly sampled 1,100 44-hour windows of control data from all times ≥10 days removed from any evaluation. Model inputs consisted of up to 36 features derived from routine EHR data. Using 10-fold nested cross-validation, 8 machine learning models were trained to classify inputs as sepsis positive or negative. When tasked with discriminating culture positive cases from controls, 6 models achieved a mean area under the receiver operating characteristic (AUC) between 0.80-0.82 with no significant differences between them. Including both culture and clinically positive cases, the same 6 models achieved an AUC between 0.85-0.87, again with no significant differences. Machine learning models can identify infants with sepsis in the NICU hours prior to clinical recognition. Learning curves indicate model improvement may be achieved with additional training examples. Additional input features may also improve performance. Further research is warranted to assess potential performance improvements and clinical efficacy in a prospective trial.

Tumor cells display fundamental changes in metabolism and nutrient uptake in order to utilize additional nutrient sources to meet their enhanced bioenergetic requirements. Glutamine (Gln) is one such nutrient that is rapidly taken up by tumor cells to fulfill this increased metabolic demand. A vital step in the catabolism of glutamine is its conversion to glutamate by the mitochondrial enzyme glutaminase (GLS). This study has identified GLS a potential therapeutic target in breast cancer, specifically in the basal subtype that exhibits a deregulated glutaminolysis pathway. Using inducible shRNA mediated gene knockdown, we discovered that loss of GLS function in triple-negative breast cancer (TNBC) cell lines with a deregulated glutaminolysis pathway led to profound tumor growth inhibition in vitro and in vivo. GLS knockdown had no effect on growth and metabolite levels in non-TNBC cell lines. We rescued the anti-tumor effect of GLS knockdown using shRNA resistant cDNAs encoding both GLS isoforms and by addition of an α-ketoglutarate (αKG) analog thus confirming the critical role of GLS in TNBC. Pharmacological inhibition of GLS with the small molecule inhibitor CB-839 reduced cell growth and led to a decrease in mammalian target of rapamycin (mTOR) activity and an increase in the stress response pathway driven by activating transcription factor 4 (ATF4). Finally, we found that GLS inhibition synergizes with mTOR inhibition, which introduces the possibility of a novel therapeutic strategy for TNBC. Our study revealed that GLS is essential for the survival of TNBC with a deregulated glutaminolysis pathway. The synergistic activity of GLS and mTOR inhibitors in TNBC cell lines suggests therapeutic potential of this combination for the treatment of vulnerable subpopulations of TNBC.

Background The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2. Methods We leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8 + T cell responses. Results We first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8 + T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity. Conclusions Using our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4 + and CD8 + T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8 + T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.

Vertical Axis Wind Turbines (VAWTs) have proven to be suitable for changing wind conditions, particularly in urban settings. In this paper, a 2D URANS (Unsteady Reynolds-Averaged Navier Stokes) numerical analysis is employed for an H-Darrieus VAWT. A turbulent domain is created through systemically randomising the inlet velocity to create macro-turbulence in front of the VAWT. The parameters for spatial and temporal randomisation of velocity and its effects on the turbine performance are studied for a mean free stream velocity, U∞ = 10 m/s, and a tip speed ratio (TSR) of 4.1. The mean Coefficient of power (Cp) for randomised fluctuation of 2 m/s and half-cycle randomisation update frequency is 0.411 and for uniform inlet velocity is 0.400. The Cp vs. Tip Speed ratio plot suggests that the optimal tip speed ratio for operation is around 4.1 for this particular wind turbine of diameter 1 m, chord 0.06 m, and NACA 0018 airfoils. The effect of randomisation for tip speed ratio λ = 2.5, 3.3, 4.1, and 5.3 on the performance of the turbine is studied. Turbine wake recovers at a faster rate for macro-turbulent conditions and is symmetric when compared to wake generated by uniform velocity inlet. The maximum velocity deficit for a distance behind the turbine, x/d = 8 at TSR (λ) = 4.1 is 46% for randomised inlet and 64% for uniform inlet. The effect of randomisation for λ = 2.5 to 5.3 on the performance of the turbine is analysed. A time-varying gust based on International Electrotechnical Commission (IEC) Extreme Operating Gust is used to study the effect of fluctuating wind conditions in a turbulent environment. Since real-time conditions often exceed gust factors mentioned by IEC, winds with large gust factors such as 1.50, 1.64, and 1.80 are analysed. With an increase in gust amplitude, Ugust = 6 m/s to Ugust = 12 m/s on a free stream velocity of U∞ = 10 m/s, the mean Cp decreases from 0.41 to 0.35 since the wind turbine operates under tip speed ratios outside optimal range due to large fluctuations in incoming velocity.

Long non-coding RNAs (lncRNAs) are emerging as key players in a variety of cellular processes. Deregulation of the lncRNAs has been implicated in prostate and breast cancers. Recently, germline genetic variations associated with cancer risk have been correlated with lncRNA expression and/or function. In addition, single nucleotide polymorphisms (SNPs) at well-characterized cancer-associated lncRNAs have been analyzed for their association with cancer risk. These SNPs may occur within the lncRNA transcripts or spanning regions that may alter the structure, function, and expression of these lncRNA molecules and contribute to cancer progression and may have potential as therapeutic targets for cancer treatment. Additionally, some of these lncRNA have a tissue-specific expression profile, suggesting them as biomarkers for specific cancers. In this review, we highlight some of the cancer risk-associated SNPs that modulated lncRNAs with a potential role in prostate and breast cancers and speculate on how these lncRNAs may contribute to cancer development.

Staphylococcus aureus is a leading cause of severe infections in neonatal intensive care units (NICUs). We present results of precision surveillance of S. aureus carriage and invasive infection in a cohort study of high-risk infants in the NICU with the goal of identifying novel strategies for prevention and control. Using whole genome sequence guided epidemiology over a 3-year timeframe, we identify spatial and temporal links for transmission between babies, suggesting shared spaces, caregivers, and physical proximity as major risks for transmission in the NICU. Moreover, environmental surveillance reveals potential environmental reservoirs of S. aureus . Remarkably, specific clusters of S. aureus strains that are associated with invasive infection are also detected in more infants over time during routine surveillance, suggesting a strong link between the rate of dissemination and disease in this vulnerable population. Overall, our findings demonstrate a strong association amongst colonization, transmission, persistence, and the development of invasive infections, underscoring the importance of targeted measures to prevent S. aureus infections in the NICU setting. In this work, authors use whole genome tracking in a neonatal intensive care unit to reveal a strong link between Staphylococcus aureus colonization and invasive infection, pinpointing critical new targets for infection prevention in high-risk infants.

The majority of extremely low birth weight (ELBW) neonates receive red blood cell (RBC) transfusions; at least 50% receive multiple transfusions. Anemia care bundles could be the most effective approach to reduce transfusion rates. We conducted a quality improvement non-controlled before-and-after retrospective study involving 345 ELBW infants admitted over a 5-year period in two consecutive epochs before and after implementation of an anemia care bundle in January 2017. Bundle components included (a) prophylactic subcutaneous erythropoietin twice each week (600 IU/kg/week) from day 7 through 8 weeks of age and (b) blood sampling stewardship in the first five postnatal weeks. Early postnatal blood sampling losses were significantly reduced following the implementation of the care bundle (21.2 ml/kg vs 25 ml/kg, P < 0.001). We found a 50% reduction in the rate of multiple RBC transfusions (adjusted RR 0.45, 95% CI: 0.34–0.59) and a reduced odds of necrotizing enterocolitis (NEC) (4% vs 10%, adjusted OR 0.38 (95% CI: 0.15–0.78)) among infants that received the anemia care bundle (n = 182 infants). The overall transfusion rate, number and volume of transfusions, and multiple donor exposures were also significantly reduced.Conclusion: The combination of extended subcutaneous erythropoietin administration and reduced early postnatal blood sampling was associated with a significant reduction in the rate of multiple erythrocyte transfusions and NEC in ELBW neonates.What is known:• The majority of extremely low birth weight neonates continue to require blood transfusions despite advances in standardized transfusion practices; at least 50% require multiple transfusions.• Anemia care bundles, employing a combination of anemia prevention strategies, can effectively reduce the RBC transfusion rates in ELBW infants.What is new:• A combination of extended subcutaneous erythropoietin supplementation and blood sampling stewardship practices reduced the rate of multiple RBC transfusions in ELBW neonates by 50%.• Implementation of the anemia care bundle was associated with a significant reduction in the rates of necrotizing enterocolitis.

B cells respond to antigens by engagement of their B-cell antigen receptor (BCR) and of coreceptors through which signals from helper T cells or pathogen-associated molecular patterns are delivered. We show that the proliferative response of B cells to the latter stimuli is controlled by BCR-dependent activation of phosphoinositidyl 3-kinase (PI-3K) signaling. Glycogen synthase kinase 3β and Foxo1 are two PI-3K-regulated targets that play important roles, but to different extents, depending on the specific mitogen. These results suggest a model for integrating signals from the innate and the adaptive immune systems in the control of the B-cell immune response.

Delivery of small-interfering RNA (siRNA) has been of great interest in the past decade for effective gene silencing. To overcome synthetic and regulatory challenges posed by nanoparticle-mediated siRNA delivery, antibody–siRNA conjugate (ARC) platform is emerging as a potential siRNA delivery system suitable for clinical translation. Herein, we have developed a delivery technology based on the ARC platform for stable delivery of siRNA called as Gelatin-Antibody Delivery System (GADS). In GADS, positively charged gelatin acts as a linker between antibody–siRNA and enables the endosomal escape of siRNA for gene silencing postcellular internalization. For proof of concept, we synthesized a scalable GADS conjugate comprising of Cetuximab (CTB), cationized gelatin (cGel) and NSCLC KRASG12C-specific siRNA. CTB was chemically conjugated to cGel through an amide link to form the CTB–cGel complex. Thereafter, siRNA was chemically conjugated to the cGel moiety of the complex through the thioether link to form CTB–cGel–siRNA conjugate. RP-HPLC analysis was used to monitor the reaction while gel retardation assay was used to determine siRNA loading capacity. SPR analysis showed the preservation of ligand binding affinity of antibody conjugates with KD of ∼0.3 nM. Furthermore, cellular internalization study using florescent microscopy revealed receptor-mediated endocytosis. The conjugate targeted EGFR receptor of KRAS mutant NSCLC to specifically knockdown G12C mutation. The oncogene knockdown sensitized the cells toward small molecule inhibitor—Gefitinib causing ∼70% loss in cell viability. Western blot analysis revealed significant downregulation for various RAS downstream proteins postoncogene knockdown. Comparison of the efficiency of GADS vis-à-vis positive siRNA control and CRISPR–Cas9-based knockout of KRAS Exon 2 in the NCI-H23 NSCLC cell line suggests GADS as a potential technology for clinical translation of gene therapy.

Bacterial meningitis is challenging to diagnose in infants, especially in the common setting of antibiotic pre-treatment, which diminishes yield of cerebrospinal fluid (CSF) cultures. Prior studies of diagnostic markers have not demonstrated sufficient accuracy. Interleukin-23 (IL-23), interleukin-18 (IL-18) and soluble receptor for advanced glycation end products (sRAGE) possess biologic plausibility, and may be useful as diagnostic markers in bacterial meningitis. In a prospective cohort study, we measured IL-23, IL-18 and sRAGE levels in CSF. We compared differences between infected and non-infected infants, and conducted receiver operating characteristic (ROC) analyses to identify individual markers and combinations of markers with the best diagnostic accuracy. 189 infants <6 months, including 8 with bacterial meningitis, 30 without meningitis, and 151 with indeterminate diagnosis (due to antibiotic pretreatment) were included. CSF IL-23, IL-18 and sRAGE levels were significantly elevated in infants with culture proven meningitis. Among individual markers, IL-23 possessed the greatest accuracy for diagnosis of bacterial meningitis (area under the curve (AUC) 0.9698). The combination of all three markers had an AUC of 1. IL-23, alone and in combination with IL-18 and sRAGE, identified bacterial meningitis with excellent accuracy. Following validation, these markers could aid clinicians in diagnosis of bacterial meningitis and decision-making regarding prolongation of antibiotic therapy.