In vitro activity of Iclaprim against Methicillin-resistant Staphylococcus aureus nonsusceptible to Daptomycin, Linezolid, or Vancomycin: A pilot study

Iclaprim is a bacterial dihydrofolate reductase inhibitor in Phase 3 clinical development for the treatment of acute bacterial skin and skin structure infections and hospital-acquired bacterial pneumonia caused by Gram-positive bacteria. Daptomycin, linezolid, and vancomycin are commonly used antibiotics for these indications. With increased selective pressure to these antibiotics, outbreaks of bacterial resistance to these antibiotics have been reported. This in vitro pilot study evaluated the activity of iclaprim against methicillin-resistant Staphylococcus aureus (MRSA) isolates, which were also not susceptible to daptomycin, linezolid, or vancomycin. Iclaprim had an MIC ≤ 1 µg/ml to the majority of MRSA isolates that were nonsusceptible to daptomycin (5 of 7 (71.4%)), linezolid (26 of 26 (100%)), or vancomycin (19 of 28 (66.7%)). In the analysis of time-kill curves, iclaprim demonstrated ≥ 3 log10 reduction in CFU/mL at 4–8 hours for tested strains and isolates nonsusceptible to daptomycin, linezolid, or vancomycin. Together, these data support the use of iclaprim in serious infections caused by MRSA nonsusceptible to daptomycin, linezolid, or vancomycin

Five-Year Longitudinal Assessment (2008 to 2012) of E-101 Solution Activity against Clinical Target and Antimicrobial-Resistant Pathogens

This study summarizes the topical E-101 solution susceptibility testing results for 760 Gram-positive and Gram-negative target pathogens collected from 75 U.S. sites between 2008 and 2012 and 103 ESKAPE pathogens. E-101 solution maintained potent activity against all bacterial species studied for each year tested, with MICs ranging from <0.008 to 0.25 μg porcine myeloperoxidase (pMPO)/ml. These results confirm that E-101 solution retains its potent broad-spectrum activity against U.S. clinical isolates and organisms with challenging resistance phenotypes

Prevalence of antimicrobial resistance in bacteria isolated from central nervous system specimens as reported by U.S. hospital laboratories from 2000 to 2002

BACKGROUND: Bacterial infections of the central nervous system, especially acute infections such as bacterial meningitis require immediate, invariably empiric antibiotic therapy. The widespread emergence of resistance among bacterial species is a cause for concern. Current antibacterial susceptibility data among central nervous system (CNS) pathogens is important to define current prevalence of resistance. METHODS: Antimicrobial susceptibility of pathogens isolated from CNS specimens was analyzed using The Surveillance Database (TSN(®)) USA Database which gathers routine antibiotic susceptibility data from >300 US hospital laboratories. A total of 6029 organisms derived from CNS specimen sources during 2000–2002, were isolated and susceptibility tested. RESULTS: Staphylococcus aureus (23.7%) and Streptococcus pneumoniae (11.0%) were the most common gram-positive pathogens. Gram-negative species comprised approximately 25% of isolates. The modal patient age was 1 or <1 year for most organisms. Prevalence of MRSA among S. aureus from cerebrospinal fluid (CSF) and brain abscesses were 29.9–32.9%. Penicillin resistance rates were 16.6% for S. pneumoniae, 5.3% for viridans group streptococci, and 0% for S. agalactiae. For CSF isolates, ceftriaxone resistance was S. pneumoniae (3.5%), E. coli (0.6%), Klebsiella pneumoniae (2.8%), Serratia marcescens (5.6%), Enterobacter cloacae (25.0%), Haemophilus influenzae (0%). Listeria monocytogenes and N. meningitidis are not routinely susceptibility tested. CONCLUSIONS: Resistance is commonly detected, albeit still at relatively low levels for key drugs classes such as third-generation cephalosporins. This data demonstrates the need to consider predominant resistance phenotypes when choosing empiric therapies to treat CNS infections

Emerging resistance among bacterial pathogens in the intensive care unit – a European and North American Surveillance study (2000–2002)

Background Globally ICUs are encountering emergence and spread of antibiotic-resistant pathogens and for some pathogens there are few therapeutic options available. Methods Antibiotic in vitro susceptibility data of predominant ICU pathogens during 2000–2 were analyzed using data from The Surveillance Network (TSN) Databases in Europe (France, Germany and Italy), Canada, and the United States (US). Results Oxacillin resistance rates among Staphylococcus aureus isolates ranged from 19.7% to 59.4%. Penicillin resistance rates among Streptococcus pneumoniae varied from 2.0% in Germany to as high as 20.2% in the US; however, ceftriaxone resistance rates were comparably lower, ranging from 0% in Germany to 3.4% in Italy. Vancomycin resistance rates among Enterococcus faecalis were ≤ 4.5%; however, among Enterococcus faecium vancomycin resistance rates were more frequent ranging from 0.8% in France to 76.3% in the United States. Putative rates of extended-spectrum β-lactamase (ESBL) production among Enterobacteriaceae were low, \u3c6% among Escherichia coli in the five countries studied. Ceftriaxone resistance rates were generally lower than or similar to piperacillin-tazobactam for most of the Enterobacteriaceae species examined. Fluoroquinolone resistance rates were generally higher for E. coli (6.5% – 13.9%), Proteus mirabilis (0–34.7%), and Morganella morganii (1.6–20.7%) than other Enterobacteriaceae spp (1.5–21.3%). P. aeruginosa demonstrated marked variation in β-lactam resistance rates among countries. Imipenem was the most active compound tested against Acinetobacter spp., based on resistance rates. Conclusion There was a wide distribution in resistance patterns among the five countries. Compared with other countries, Italy showed the highest resistance rates to all the organisms with the exception of Enterococcus spp., which were highest in the US. This data highlights the differences in resistance encountered in intensive care units in Europe and North America and the need to determine current local resistance patterns by which to guide empiric antimicrobial therapy for intensive care infections

Prevalence and antimicrobial susceptibilities of bacteria isolated from blood cultures of hospitalized patients in the United States in 2002

BACKGROUND: Bloodstream infections are associated with significant patient morbidity and mortality. Antimicrobial susceptibility patterns should guide the choice of empiric antimicrobial regimens for patients with bacteremia. METHODS: From January to December of 2002, 82,569 bacterial blood culture isolates were reported to The Surveillance Network (TSN) Database-USA by 268 laboratories. Susceptibility to relevant antibiotic compounds was analyzed using National Committee for Clinical Laboratory Standards guidelines. RESULTS: Coagulase-negative staphylococci (42.0%), Staphylococcus aureus (16.5%), Enterococcus faecalis (8.3%), Escherichia coli (7.2%), Klebsiella pneumoniae (3.6%), and Enterococcus faecium (3.5%) were the most frequently isolated bacteria from blood cultures, collectively accounting for >80% of isolates. In vitro susceptibility to expanded-spectrum β-lactams such as ceftriaxone were high for oxacillin-susceptible coagulase-negative staphylococci (98.7%), oxacillin-susceptible S. aureus (99.8%), E. coli (97.3%), K. pneumoniae (93.3%), and Streptococcus pneumoniae (97.2%). Susceptibilities to fluoroquinolones were variable for K. pneumoniae (90.3–91.4%), E. coli (86.0–86.7%), oxacillin-susceptible S. aureus (84.0–89.4%), oxacillin-susceptible coagulase-negative staphylococci (72.7–82.7%), E. faecalis (52.1%), and E. faecium (11.3%). Combinations of antimicrobials are often prescribed as empiric therapy for bacteremia. Susceptibilities of all blood culture isolates to one or both agents in combinations of ceftriaxone, ceftazdime, cefepime, piperacillin-tazobactam or ciprofloxacin plus gentamicin were consistent (range, 74.8–76.3%) but lower than similar β-lactam or ciprofloxacin combinations with vancomycin (range, 93.5–96.6%). CONCLUSION: Ongoing surveillance for antimicrobial susceptibility remains essential, and will enhance efforts to identify resistance and attempt to limit its spread

Tracking the implementation of NCCLS M100-S12 expanded-spectrum cephalosporin MIC breakpoints for nonmeningeal isolates of Streptococcus pneumoniae by clinical laboratories in the United States during 2002 and 2003

BACKGROUND: The Performance Standards for Antimicrobial Susceptibility Testing, Twelfth Informational Supplement, M100-S12, published by the National Committee for Clinical Laboratory Standards (NCCLS) in January 2002 introduced distinct minimum inhibitory concentration (MIC) interpretative breakpoints for ceftriaxone, cefotaxime, and cefepime for nonmeningeal isolates of Streptococcus pneumoniae. Previously, a single set of interpretative breakpoints was used for both meningeal and nonmeningeal isolates. METHODS: To estimate the rate of adoption of the M100-S12 interpretive breakpoints by clinical laboratories, antimicrobial susceptibility test results for ceftriaxone and cefotaxime from nonmeningeal S. pneumoniae isolates were studied using data collected from January 2002 to June 2003 by The Surveillance Network(® )Database – USA (TSN(®)), an electronic surveillance database. RESULTS: Of the 262 laboratories that provided data that could be evaluated, 67.6% had adopted the M100-S12 breakpoints one and one-half years after they were published. CONCLUSIONS: The NCCLS M100-S12 recommendation to interpret MICs to expanded-spectrum cephalosporins using two distinct sets of breakpoints for meningeal and nonmeningeal isolates of S. pneumoniae was steadily implemented by clinical microbiology laboratories in the United States following their initial publication in January 2002. The use of these new breakpoints more accurately reflects the clinical activities of expanded-spectrum cephalosporins than did the single set of interpretative breakpoints previously used for both meningeal and nonmeningeal isolates

Use of Binary Cumulative Sums and Moving Averages in Nosocomial Infection Cluster Detection1

Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications

DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management

Abstract Background Variability in standard-of-care classifications precludes accurate predictions of early tumor recurrence for individual patients with meningioma, limiting the appropriate selection of patients who would benefit from adjuvant radiotherapy to delay recurrence. We aimed to develop an individualized prediction model of early recurrence risk combining clinical and molecular factors in meningioma. Methods DNA methylation profiles of clinically annotated tumor samples across multiple institutions were used to develop a methylome model of 5-year recurrence-free survival (RFS). Subsequently, a 5-year meningioma recurrence score was generated using a nomogram that integrated the methylome model with established prognostic clinical factors. Performance of both models was evaluated and compared with standard-of-care models using multiple independent cohorts. Results The methylome-based predictor of 5-year RFS performed favorably compared with a grade-based predictor when tested using the 3 validation cohorts (ΔAUC = 0.10, 95% CI: 0.03–0.018) and was independently associated with RFS after adjusting for histopathologic grade, extent of resection, and burden of copy number alterations (hazard ratio 3.6, 95% CI: 1.8–7.2, P &lt; 0.001). A nomogram combining the methylome predictor with clinical factors demonstrated greater discrimination than a nomogram using clinical factors alone in 2 independent validation cohorts (ΔAUC = 0.25, 95% CI: 0.22–0.27) and resulted in 2 groups with distinct recurrence patterns (hazard ratio 7.7, 95% CI: 5.3–11.1, P &lt; 0.001) with clinical implications. Conclusions The models developed and validated in this study provide important prognostic information not captured by previously established clinical and molecular factors which could be used to individualize decisions regarding postoperative therapeutic interventions, in particular whether to treat patients with adjuvant radiotherapy versus observation alone. </jats:sec

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing