Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital.

BACKGROUND: Early detection of clusters of pathogens is crucial for infection prevention and control (IPC) in hospitals. Conventional manual cluster detection is usually restricted to certain areas of the hospital and multidrug resistant organisms. Automation can increase the comprehensiveness of cluster surveillance without depleting human resources. We aimed to describe the application of an automated cluster alert system (CLAR) in the routine IPC work in a hospital. Additionally, we aimed to provide information on the clusters detected and their properties. METHODS: CLAR was continuously utilized during the year 2019 at Charité university hospital. CLAR analyzed microbiological and patient-related data to calculate a pathogen-baseline for every ward. Daily, this baseline was compared to data of the previous 14 days. If the baseline was exceeded, a cluster alert was generated and sent to the IPC team. From July 2019 onwards, alerts were systematically categorized as relevant or non-relevant at the discretion of the IPC physician in charge. RESULTS: In one year, CLAR detected 1,714 clusters. The median number of isolates per cluster was two. The most common cluster pathogens were Enterococcus faecium (n = 326, 19 %), Escherichia coli (n = 274, 16 %) and Enterococcus faecalis (n = 250, 15 %). The majority of clusters (n = 1,360, 79 %) comprised of susceptible organisms. For 906 alerts relevance assessment was performed, with 317 (35 %) alerts being classified as relevant. CONCLUSIONS: CLAR demonstrated the capability of detecting small clusters and clusters of susceptible organisms. Future improvements must aim to reduce the number of non-relevant alerts without impeding detection of relevant clusters. Digital solutions to IPC represent a considerable potential for improved patient care. Systems such as CLAR could be adapted to other hospitals and healthcare settings, and thereby serve as a means to fulfill these potentials.

Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals.

INTRODUCTION: Outbreaks of communicable diseases in hospitals need to be quickly detected in order to enable immediate control. The increasing digitalization of hospital data processing offers potential solutions for automated outbreak detection systems (AODS). Our goal was to assess a newly developed AODS. METHODS: Our AODS was based on the diagnostic results of routine clinical microbiological examinations. The system prospectively counted detections per bacterial pathogen over time for the years 2016 and 2017. The baseline data covers data from 2013-2015. The comparative analysis was based on six different mathematical algorithms (normal/Poisson and score prediction intervals, the early aberration reporting system, negative binomial CUSUMs, and the Farrington algorithm). The clusters automatically detected were then compared with the results of our manual outbreak detection system. RESULTS: During the analysis period, 14 different hospital outbreaks were detected as a result of conventional manual outbreak detection. Based on the pathogens' overall incidence, outbreaks were divided into two categories: outbreaks with rarely detected pathogens (sporadic) and outbreaks with often detected pathogens (endemic). For outbreaks with sporadic pathogens, the detection rate of our AODS ranged from 83% to 100%. Every algorithm detected 6 of 7 outbreaks with a sporadic pathogen. The AODS identified outbreaks with an endemic pathogen were at a detection rate of 33% to 100%. For endemic pathogens, the results varied based on the epidemiological characteristics of each outbreak and pathogen. CONCLUSION: AODS for hospitals based on routine microbiological data is feasible and can provide relevant benefits for infection control teams. It offers in-time automated notification of suspected pathogen clusters especially for sporadically occurring pathogens. However, outbreaks of endemically detected pathogens need further individual pathogen-specific and setting-specific adjustments.

Lin28 and let-7: ancient milestones on the road from pluripotency to neurogenesis.

Beginning with their discovery in the context of stem cell fate choice in Caenorhabditis elegans, the microRNA (miRNA) let-7 and the RNA-binding protein Lin28 have been recognized as a regulatory pair with far-reaching impact on stem cell behavior in a wide range of organisms and tissues, including the mammalian brain. In this review, we describe molecular interactions between Lin28 and let-7 and the biological role that each plays in implementing stem cell programs that either maintain stem cell self-renewal and plasticity or drive lineage commitment and differentiation. For Lin28, considerable progress has been made in defining let-7-dependent and let-7-independent functions in the maintenance of pluripotency, somatic cell reprogramming, tissue regeneration, and neural stem cell plasticity. For the pro-differentiation activity of let-7, we focus on emerging roles in mammalian neurogenesis and neuronal function. Specific targets and pathways for let-7 have been identified in embryonic and adult neurogenesis, including corticogenesis, retinal specification, and adult neurogenic niches. Special emphasis is given to examples of feedback and feedforward regulation, in particular within the miRNA biogenesis pathway.

Nuclear death receptor TRAIL-R2 inhibits maturation of let-7 and promotes proliferation of pancreatic and other tumor cells.

BACKGROUND & AIMS: Tumor necrosis factor-related apoptosis inducing ligand (TRAIL-R1) (TNFRSF10A) and TRAIL-R2 (TNFRSF10B) on the plasma membrane bind ligands that activate apoptotic and other signaling pathways. Cancer cells also might have TRAIL-R2 in the cytoplasm or nucleus, although little is known about its activities in these locations. We investigated the functions of nuclear TRAIL-R2 in cancer cell lines. METHODS: Proteins that interact with TRAIL-R2 initially were identified in pancreatic cancer cells by immunoprecipitation, mass spectrometry, and immunofluorescence analyses. Findings were validated in colon, renal, lung, and breast cancer cells. Functions of TRAIL-R2 were determined from small interfering RNA knockdown, real-time polymerase chain reaction, Drosha-activity, microRNA array, proliferation, differentiation, and immunoblot experiments. We assessed the effects of TRAIL-R2 overexpression or knockdown in human pancreatic ductal adenocarcinoma (PDAC) cells and their ability to form tumors in mice. We also analyzed levels of TRAIL-R2 in sections of PDACs and non-neoplastic peritumoral ducts from patients. RESULTS: TRAIL-R2 was found to interact with the core microprocessor components Drosha and DGCR8 and the associated regulatory proteins p68, hnRNPA1, NF45, and NF90 in nuclei of PDAC and other tumor cells. Knockdown of TRAIL-R2 increased Drosha-mediated processing of the let-7 microRNA precursor primary let-7 (resulting in increased levels of mature let-7), reduced levels of the let-7 targets (LIN28B and HMGA2), and inhibited cell proliferation. PDAC tissues from patients had higher levels of nuclear TRAIL-R2 than non-neoplastic pancreatic tissue, which correlated with increased nuclear levels of HMGA2 and poor outcomes. Knockdown of TRAIL-R2 in PDAC cells slowed their growth as orthotopic tumors in mice. Reduced nuclear levels of TRAIL-R2 in cultured pancreatic epithelial cells promoted their differentiation. CONCLUSIONS: Nuclear TRAIL-R2 inhibits maturation of the microRNA let-7 in pancreatic cancer cell lines and increases their proliferation. Pancreatic tumor samples have increased levels of nuclear TRAIL-R2, which correlate with poor outcome of patients. These findings indicate that in the nucleus, death receptors can function as tumor promoters and might be therapeutic targets.