Efficacy and Safety of Systemic Corticosteroids for Urticaria: A systematic review and meta-analysis of randomized clinical trials.

BACKGROUND: Short courses of adjunctive systemic corticosteroids are commonly used to treat acute urticaria and chronic urticaria flares (both with or without mast cell-mediated angioedema), but their benefits and harms are unclear. OBJECTIVE: To evaluate the efficacy and safety of treating acute urticaria or chronic urticaria flares with versus without systemic corticosteroids. METHODS: We searched MEDLINE, EMBASE, CENTRAL, CNKI, VIP, Wanfang, and CBM databases from inception to July 8, 2023 for randomized controlled trials of treating urticaria with versus without systemic corticosteroids. Paired reviewers independently screened records, extracted data, and appraised risk of bias with the Cochrane 2.0 tool. We did random effects meta-analyses of urticaria activity, itch severity and adverse events. We assessed certainty of the evidence using the GRADE approach. RESULTS: We identified 12 randomized trials enrolling 944 patients. For patients with low or moderate probability (17.5% to 64%) to improve with antihistamines alone, add-on systemic corticosteroids likely improve urticaria activity by a 14% to 15% absolute difference (odds ratio [OR] 2.17, 95%CI 1.43-3.31; Number needed to treat [NNT] 7; Moderate certainty). Among patients with a high chance (95.8%) for urticaria to improve with antihistamines alone, add-on systemic corticosteroids likely improved urticaria activity by a 2.2% absolute difference (NNT, 45; Moderate certainty). Corticosteroids may improve itch severity (OR, 2.44; 95%CI 0.87-6.83; Risk difference, 9%; NNT, 11; Low certainty). Systemic corticosteroids also likely increase adverse events (OR, 2.76; 95%CI 1.00-7.62; Risk difference, 15%; number needed to harm [NNH], 9; Moderate certainty). CONCLUSION: Systemic corticosteroids for acute urticaria or chronic urticaria exacerbations likely improve urticaria, depending on antihistamine-responsiveness, but also likely increase adverse effects in approximately 15% more.

Multiplex analysis of inflammatory proteins associated with risk of coronary artery disease in type-1 diabetes patients.

BACKGROUND: Chronic uncontrolled hyperglycemia, a precursor to chronic low-grade inflammation, is a leading cause of coronary artery disease (CAD) due to plaque buildup in type-1 diabetes (T1D) patients. We evaluated levels of 22 inflammatory markers in cross-sectional serum samples from 1222 subjects to evaluate their potential as risk factors for CAD in T1D patients. HYPOTHESIS: Circulating levels of markers of inflammation may be the risk factors for incident CAD. METHODS: The T1D subjects were divided into two groups: those without CAD (n = 1107) and with CAD (n = 115). Serum levels of proteins were assayed using multiplex immunoassays on a Luminex Platform. Differences between the two groups were made by univariate analysis. Multivariate logistic regression was used to ascertain the potential of proteins as risk factors for CAD. Influence of age, duration of diabetes, sex, hypertension, and dyslipidemia was determined in a stepwise manner. Serum levels of 22 proteins were combined into a composite score using Ridge regression for risk-based stratification. RESULTS: Mean levels of CRP, IGFBP1, IGFBP2, insulin-like growth factors binding protein-6 (IGFBP6), MMP1, SAA, sTNFRI, and sTNFRII were elevated in CAD patients (n = 115) compared to T1D patients without CAD (nCAD, n = 1107). After adjusting for age, duration of diabetes, sex, hypertension, and dyslipidemia, higher levels of sTNFRI (odds ratio [OR] = 2.18, 1.1 × 10-3 ), sTNFRII (OR = 1.52, 1 × 10-2 ), and IGFBP6 (OR = 3.62, 1.8 × 10-3 ) were significantly associated with CAD. The composite score based on Ridge regression, was able to stratify CAD patients into low, medium, and high-risk groups. CONCLUSIONS: The results show activation of the TNF pathway in CAD patients. Evaluating these markers in serum can be a potential tool for identifying high-risk T1D patients for intensive anti-inflammatory therapeutic interventions.

A National Survey of the Training and Practice Patterns of Practicing Pediatric Advanced Endoscopists in the United States.

BACKGROUND AND AIMS: Pediatric advanced endoscopy consists primarily of endoscopic retrograde cholangiopancreatography (ERCP) and endoscopic ultrasound (EUS) and is becoming more common in pediatrics. This study aims to characterize the current landscape of pediatric advanced endoscopy training and practice by directly surveying independently practicing pediatric advanced endoscopists (PAEs). We also aim to ascertain expert opinion on competency in pediatric ERCP and EUS. METHODS: A 66-question REDCap survey and a 73-question Qualtrics survey were distributed to members of the ERCP Special Interest Group of North American Society of Pediatric Gastroenterology, Hepatology, and Nutrition. Respondents currently performing ERCP or EUS independently in children were included. Statistical analysis was performed using Mann-Whitney U test. RESULTS: Of 41 PAEs surveyed, 38 (92.7%) responded and 27 independent practitioners were included. Thirteen respondents performed EUS. PAEs who completed an advanced endoscopy fellowship (AEF) were more comfortable performing American Society for Gastrointestinal Endoscopy grade 3 or grade 4 ERCPs ( P < 0.0008) and felt more prepared to practice EUS independently than other trainees. Expert opinion of PAEs felt a threshold of 200 procedures was needed to attain competency in either ERCP or EUS. Pediatric duodenoscope exposure improved comfort in performing ERCP in children <10 kg ( P = 0.009). CONCLUSIONS: Training of pediatric gastroenterologists in ERCP and EUS are highly variable, though the skills attained are similar. AEF-trained specialists reported greater training volumes and felt more prepared to practice independently than those who did not. Competency thresholds determined by expert PAEs for ERCP and EUS agree with American Society for Gastrointestinal Endoscopy guidelines for adult advanced endoscopy trainees.

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Use of a glycomics array to establish the anti-carbohydrate antibody repertoire in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease, characterized by the presence of autoantibodies to protein and non-protein antigens. Here we report the identification of specific anti-carbohydrate antibodies (ACAs) that are associated with pathogenesis and progression to T1D. We compare circulatory levels of ACAs against 202 glycans in a cross-sectional cohort of T1D patients (n = 278) and healthy controls (n = 298), as well as in a longitudinal cohort (n = 112). We identify 11 clusters of ACAs associated with glycan function class. Clusters enriched for aminoglycosides, blood group A and B antigens, glycolipids, ganglio-series, and O-linked glycans are associated with progression to T1D. ACAs against gentamicin and its related structures, G418 and sisomicin, are also associated with islet autoimmunity. ACAs improve discrimination of T1D status of individuals over a model with only clinical variables and are potential biomarkers for T1D.

Development of a Single Molecule Counting Assay to Differentiate Chromophobe Renal Cancer and Oncocytoma in Clinics.

Malignant chromophobe renal cancer (chRCC) and benign oncocytoma (RO) are two renal tumor types difficult to differentiate using histology and immunohistochemistry-based methods because of their similarity in appearance. We previously developed a transcriptomics-based classification pipeline with "Chromophobe-Oncocytoma Gene Signature" (COGS) on a single-molecule counting platform. Renal cancer patients (n = 32, chRCC = 17, RO = 15) were recruited from Augusta University Medical Center (AUMC). Formalin-fixed paraffin-embedded (FFPE) blocks from their excised tumors were collected. We created a custom single-molecule counting code set for COGS to assay RNA from FFPE blocks. Utilizing hematoxylin-eosin stain, pathologists were able to correctly classify these tumor types (91.8%). Our unsupervised learning with UMAP (Uniform manifold approximation and projection, accuracy = 0.97) and hierarchical clustering (accuracy = 1.0) identified two clusters congruent with their histology. We next developed and compared four supervised models (random forest, support vector machine, generalized linear model with L2 regularization, and supervised UMAP). Supervised UMAP has shown to classify all the cases correctly (sensitivity = 1, specificity = 1, accuracy = 1) followed by random forest models (sensitivity = 0.84, specificity = 1, accuracy = 1). This pipeline can be used as a clinical tool by pathologists to differentiate chRCC from RO.

Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning.

Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors.

T1DMicro: A Clinical Risk Calculator for Type 1 Diabetes Related Microvascular Complications.

Development of complications in type 1 diabetes patients can be reduced by modifying risk factors. We used a cross-sectional cohort of 1646 patients diagnosed with type 1 diabetes (T1D) to develop a clinical risk score for diabetic peripheral neuropathy (DPN), autonomic neuropathy (AN), retinopathy (DR), and nephropathy (DN). Of these patients, 199 (12.1%) had DPN, 63 (3.8%) had AN, 244 (14.9%) had DR, and 88 (5.4%) had DN. We selected five variables to include in each of the four microvascular complications risk models: age, age of T1D diagnosis, duration of T1D, and average systolic blood pressure and HbA1C over the last three clinic visits. These variables were selected for their strong evidence of association with diabetic complications in the literature and because they are modifiable risk factors. We found the optimism-corrected R2 and Harrell's C statistic were 0.39 and 0.87 for DPN, 0.24 and 0.86 for AN, 0.49 and 0.91 for DR, and 0.22 and 0.83 for DN, respectively. This tool was built to help inform patients of their current risk of microvascular complications and to motivate patients to control their HbA1c and systolic blood pressure in order to reduce their risk of these complications.

Applying Artificial Intelligence to Gynecologic Oncology: A Review.

IMPORTANCE: Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies. OBJECTIVE: The aim of this study was to review the role of AI in gynecologic oncology. EVIDENCE ACQUISITION: Artificial intelligence publications in gynecologic oncology were identified by searching "gynecologic oncology AND artificial intelligence" in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers. RESULTS: A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response. CONCLUSIONS AND RELEVANCE: Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology.

Serum Levels of Inflammatory Proteins Are Associated With Peripheral Neuropathy in a Cross-Sectional Type-1 Diabetes Cohort.

Chronic low-grade inflammation is involved in the pathogenesis of type-1 diabetes (T1D) and its complications. In this cross-section study design, we investigated association between serum levels of soluble cytokine receptors with presence of peripheral neuropathy in 694 type-1 diabetes patients. Sex, age, blood pressure, smoking, alcohol intake, HbA1c and lipid profile, presence of DPN (peripheral and autonomic), retinopathy and nephropathy was obtained from patient's chart. Measurement of soluble cytokine receptors, markers of systemic and vascular inflammation was done using multiplex immunoassays. Serum levels were elevated in in DPN patients, independent of gender, age and duration of diabetes. Crude odds ratios were significantly associated with presence of DPN for 15/22 proteins. The Odds ratio (OR) remained unchanged for sTNFRI (1.72, p=0.00001), sTNFRII (1.45, p=0.0027), sIL2Rα (1.40, p=0.0023), IGFBP6 (1.51, p=0.0032) and CRP (1.47, p=0.0046) after adjusting for confounding variables, HbA1C, hypertension and dyslipidemia. Further we showed risk of DPN is associated with increase in serum levels of sTNFRI (OR=11.2, p<10), sIL2Rα (8.69, p<10-15), sNTFRII (4.8, p<10-8) and MMP2 (4.5, p<10-5). We combined the serum concentration using ridge regression, into a composite score, which can stratify the DPN patients into low, medium and high-risk groups. Our results here show activation of inflammatory pathway in DPN patients, and could be a potential clinical tool to identify T1D patients for therapeutic intervention of anti-inflammatory therapies.

Retrospective Validation of a 168-Gene Expression Signature for Glioma Classification on a Single Molecule Counting Platform.

Gene expression profiling has been shown to be comparable to other molecular methods for glioma classification. We sought to validate a gene-expression based glioma classification method. Formalin-fixed paraffin embedded tissue and flash frozen tissue collected at the Augusta University (AU) Pathology Department between 2000-2019 were identified and 2 mm cores were taken. The RNA was extracted from these cores after deparaffinization and bead homogenization. One hundred sixty-eight genes were evaluated in the RNA samples on the nCounter instrument. Forty-eight gliomas were classified using a supervised learning algorithm trained by using data from The Cancer Genome Atlas. An ensemble of 1000 linear support vector models classified 30 glioma samples into TP1 with classification confidence of 0.99. Glioma patients in TP1 group have a poorer survival (HR (95% CI) = 4.5 (1.3-15.4), p = 0.005) with median survival time of 12.1 months, compared to non-TP1 groups. Network analysis revealed that cell cycle genes play an important role in distinguishing TP1 from non-TP1 cases and that these genes may play an important role in glioma survival. This could be a good clinical pipeline for molecular classification of gliomas.

The 3p21.31 genetic locus promotes progression to type 1 diabetes through the CCR2/CCL2 pathway.

Multiple cross-sectional and longitudinal studies have shown that serum levels of the chemokine ligand 2 (CCL-2) are associated with type 1 diabetes (T1D), although the direction of effect differs. We assessed CCL-2 serum levels in a longitudinal cohort to clarify this association, combined with genetic data to elucidate the regulatory role of CCL-2 in T1D pathogenesis. The Diabetes Autoimmunity Study in the Young (DAISY) followed 310 subjects with high risk of developing T1D. Of these, 42 became persistently seropositive for islet autoantibodies but did not develop T1D (non-progressors); 48 did develop T1D (progressors). CCL-2 serum levels among the three study groups were compared using linear mixed models adjusting for age, sex, HLA genotype, and family history of T1D. Summary statistics were obtained from the CCL-2 protein quantitative trait loci (pQTL) and CCR2 expression QTL (eQTL) studies. The T1D fine mapping association data were provided by the Type 1 Diabetes Genetics Consortium (T1DGC). Serum CCL-2 levels were significantly lower in both progressors (p = 0.004) and non-progressors (p = 0.005), compared to controls. Two SNPs (rs1799988 and rs746492) in the 3p21.31 genetic locus, which includes the CCL-2 receptor, CCR2, were associated with increased CCR2 expression (p = 8.2e-5 and 5.2e-5, respectively), decreased CCL-2 serum level (p = 2.41e-9 and 6.21e-9, respectively), and increased risk of T1D (p = 7.9e-5 and 7.9e-5, respectively). The 3p21.31 genetic region is associated with developing T1D through regulatory control of the CCR2/CCL2 immune pathway.

Are There Survival Differences Between Women with Equivalent Residual Disease After Interval Cytoreductive Surgery Compared with Primary Cytoreductive Surgery for Advanced Ovarian and Peritoneal Cancer?

OBJECTIVE: The aim of this study was to investigate survival differences between equivalent residual disease [complete gross resection (CGR), minimal residual disease (MRD), suboptimal] at the time of primary debulking surgery (PDS) and interval debulking surgery (IDS). METHODS: The National Cancer Database was used to identify patients from 2010 to 2015 with stage IIIC/IV primary peritoneal or ovarian cancer who had residual disease recorded. Propensity score matching (PSM) was used to correct for differences in characteristics between the PDS and IDS groups. RESULTS: Of 8683 patients with advanced ovarian cancer, 4493 (52%), 2546 (29%), and 1644 (19%) had CGR, MRD, or suboptimal resection, respectively. From 2010 to 2015, the number of patients undergoing IDS increased 27% (ptrend < 0.001), and there was an 18% increase in CGRs (ptrend = 0.005). The increased use of IDS from 2010 to 2015 was associated with increased CGRs (ptrend = 0.02) and decreased MRD (ptrend = 0.001), but not with decreased suboptimal resections (ptrend = 0.18). IDS, even after PSM, was associated with inferior overall survival [OS; hazard ratio (HR) 1.12, 95% confidence interval (CI) 1.03-1.22, p = 0.008]. A CGR at PDS had prolonged median OS compared with a CGR at IDS (51 vs. 44 months, p < 0.001). Additionally, MRD at PDS had worse median OS compared with a CGR at IDS (41 vs. 44 months, p = 0.03), but improved median OS compared with MRD at IDS (median OS 35 months, p = 0.05). CONCLUSION: The use of IDS continues to rise in the US, and is associated with improved surgical outcomes but not necessarily similar oncologic outcomes. There should be continued efforts to improve cytoreductive outcomes in women with advanced ovarian and peritoneal malignancies.

Comparative analysis of transcriptomic profile, histology, and IDH mutation for classification of gliomas.

Gliomas are currently classified through integration of histology and mutation information, with new developments in DNA methylation classification. However, discrepancies exist amongst the major classification methods. This study sought to compare transcriptome-based classification to the established methods. RNAseq and microarray data were obtained for 1032 gliomas from the TCGA and 395 gliomas from REMBRANDT. Data were analyzed using unsupervised and supervised learning and other statistical methods. Global transcriptomic profiles defined four transcriptomic glioma subgroups with 91.4% concordance with the WHO-defined mutation subtypes. Using these subgroups, 168 genes were selected for the development of 1000 linear support vector classifiers (LSVC). Based on plurality voting of 1000 LSVC, the final ensemble classifier confidently classified all but 17 TCGA gliomas to one of the four transcriptomic profile (TP) groups. The classifier was validated using a gene expression microarray dataset. TP1 cases include IDHwt, glioblastoma high immune infiltration and cellular proliferation and poor survival prognosis. TP2a is characterized as IDHmut-codel, oligodendrogliomas with high tumor purity. TP2b tissue is mostly composed of neurons and few infiltrating malignant cells. TP3 exhibit increased NOTCH signaling, are astrocytoma and IDHmut-non-codel. TP groups are highly concordant with both WHO integrated histology and mutation classification as well as methylation-based classification of gliomas. Transcriptomic profiling provides a robust and objective method to classify gliomas with high agreement to the current WHO guidelines and may provide additional survival prediction to the current methods.

Senescence-Associated Secretory Phenotype Determines Survival and Therapeutic Response in Cervical Cancer.

Molecular biomarkers that can predict survival and therapeutic outcome are still lacking for cervical cancer. Here we measured a panel of 19 serum proteins in sera from 565 patients with stage II or III cervical cancer and identified 10 proteins that have an impact on disease specific survival (DSS) (Hazzard's ratio; HR = 1.51-2.1). Surprisingly, all ten proteins are implicated in senescence-associated secreted phenotype (SASP), a hallmark of cellular senescence. Machine learning using Ridge regression of these SASP proteins can robustly stratify patients with high SASP, which is associated with poor survival, and patients with low SASP associated with good survival (HR = 3.09-4.52). Furthermore, brachytherapy, an effective therapy for cervical cancer, greatly improves survival in SASP-high patients (HR = 3.3, p < 5 × 10-5) but has little impact on survival of SASP-low patients (HR = 1.5, p = 0.31). These results demonstrate that cellular senescence is a major determining factor for survival and therapeutic response in cervical cancer and suggest that senescence reduction therapy may be an efficacious strategy to improve the therapeutic outcome of cervical cancer.

Identification of a transcriptomic signature with excellent survival prediction for squamous cell carcinoma of the cervix.

Survival for patients with newly diagnosed cervical cancer has not significantly improved over the past several decades. We sought to identify a clinically relevant set of prognostic genes for squamous cell carcinoma of the cervix (SCCC), the most common cervical cancer subtype. Using RNA-sequencing data and survival data from 203 patients in The Cancer Genome Atlas (TCGA), we conducted a series of analyses using different decile cutoffs for gene expression to identify genes that could indicate large and consistent survival differences across different decile cutoffs of gene expression. Those analyses identified 42 high-risk genes. A patient's survivability could be estimated by simply counting the number of high-risk genes with extremely high expression (above the 90th percentile) or estimating a transcriptomic risk score (TRS) using a machine learning algorithm with 9 of the 42 genes. On multivariate analysis, the significant predictors of mortality included high TRS (HR = 44.8), stage IV (HR = 28.1), intermediate TRS (HR = 4.75), and positive lymph node status (HR = 2.92). Approximately 18% of earlier-stage patients were identified as a poor-prognosis subgroup with high TRS. In patients with SCCC, transcriptomic risk appears to better predict survival than clinical prognostic factors, including stage.

Two Blood Cultures With Age-Appropriate Volume Enhance Suspected Sepsis Decision-Making.

BACKGROUND: Multiple blood cultures have been shown to improve pathogen yield and antimicrobial stewardship for adult patients with suspected serious bacterial infection (SBI). For children, the use of multiple blood cultures is less common and volume recommendations are more complicated, often resulting in single cultures with low volume. METHODS: In 2010, Children's Hospital Colorado instituted electronic medical record (EMR) decision support to recommend collection of 2 blood cultures before administration of antibiotics for suspected SBI. Recommended blood culture volumes were calculated by age rather than weight. We evaluated all children admitted to inpatient units between 2008 and 2009 (pre-intervention) and 2011 and 2013 (postintervention) who received antibiotics in the hospital after having blood cultures drawn in the emergency department, excluding those with a length of stay >8 days. We compared blood culture yield, isolate classification (pathogen vs contaminant), and antimicrobial modifications before and after the interventions. RESULTS: A total of 3948 children were included in the study. EMR guidelines were associated with a significantly higher number of children with multiple blood cultures drawn before antibiotic administration (88.0% vs 12.3%; P < .001) and an increased percentage of blood cultures with the recommended volume (74.3% vs 15.2%; P < .001), resulting in a significantly higher pathogen isolation rate and improved antimicrobial decisions. Multiple cultures helped define the role of common contaminants in the clinical decision process. CONCLUSIONS: Multiple blood cultures with age-based volumes taken before starting antibiotics increase pathogen isolation rates and appropriate modification of antimicrobial treatment in children.

A 73-gene proliferative transcriptomic signature predicts uterine serous carcinoma patient survival and response to primary therapy.

OBJECTIVES: To develop a transcriptomic signature capable of predicting overall survival (OS) for uterine serous carcinoma (USC). METHODS: RNAseq data for 58 USC patients were obtained from TCGA. Expression of 73 candidate genes was measured for 67 Augusta University (AU) samples using NanoString technology. RESULTS: Analysis of the TCGA RNAseq data identified 73 genes that individually predict prognosis for USC patients and an elastic net model with all 73 genes (USC73) distinguishes a good OS group with low USC73 score from a poor OS group with high USC73 score (5-year OS = 83.3% and 13.3% respectively, HR = 40.1; p = 3 × 10-8). This finding was validated in the independent AU cohort (HR = 4.3; p = 0.0004). The poor prognosis group with high USC73 score consists of 37.9% and 32.8% of patients in the TCGA and AU cohort respectively. USC73 score and pathologic stage independently contribute to OS and together provide the best prognostic value. Early stage, low USC73 patients have the best prognosis (5-year OS = 85.1% in the combined dataset), while advanced stage, high USC73 patients have the worst prognosis (5-year OS = 6.4%, HR = 30.5, p = 1.2 × 10-12). Consistent with the observed poor survival, primary cell cultures from high USC73 patients had higher proliferation rate and cell cycle progression; and high USC73 patients had lower rates of complete response to standard therapy. CONCLUSIONS: The USC73 transcriptomic signature and stage independently predict OS of USC patients and the best prediction is achieved using USC73 and stage. USC73 may also serve as a therapeutic biomarker to guide patient care.