Antibiotic resistance and molecular characterization of bacteremia Escherichia coli isolates from newborns in the United States.

BACKGROUND: Escherichia coli is a major cause of neonatal sepsis. Contemporary antibiotic resistance data and molecular characterization of neonatal E. coli bacteremia isolates in the US are limited. METHODS: E. coli blood isolates, antibiotic susceptibility data, and clinical characteristics were obtained from prospectively identified newborns from 2006 to 2016. The E. coli isolates were classified using an updated phylogrouping method and multi-locus sequence typing. The presence of several virulence traits was also determined. RESULTS: Forty-three newborns with E. coli bacteremia were identified. Mean gestational age was 32.3 (SD±5.4) weeks. Median age was 7 days (interquartile range 0-10). Mortality (28%) occurred exclusively in preterm newborns. Resistance to ampicillin was 67%, to gentamicin was 14%, and to ceftriaxone was 2%; one isolate produced extended-spectrum beta lactamases. Phylogroup B2 predominated. Sequence type (ST) 95 and ST131 prevailed; ST1193 emerged recently. All isolates carried fimH, nlpI, and ompA, and 46% carried the K1 capsule. E. coli from newborns with bacteremia diagnosed at <72 hours old had more virulence genes compared to E. coli from newborns ≥ 72 hours old. The hek/hra gene was more frequent in isolates from newborns who died than in isolates from survivors. CONCLUSION: Antibiotic resistance in E. coli was prevalent in this large collection of bacteremia isolates from US newborns. Most strains belonged to distinctive extra-intestinal pathogenic E. coil phylogroups and STs. Further characterization of virulence genes in neonatal E. coli bacteremia strains is needed in larger numbers and in more geographically diverse areas.

Route of infection alters virulence of neonatal septicemia Escherichia coli clinical isolates.

Escherichia coli is the leading cause of Gram-negative neonatal septicemia in the United States. Invasion and passage across the neonatal gut after ingestion of maternal E. coli strains produce bacteremia. In this study, we compared the virulence properties of the neonatal E. coli bacteremia clinical isolate SCB34 with the archetypal neonatal E. coli meningitis strain RS218. Whole-genome sequencing data was used to compare the protein coding sequences among these clinical isolates and 33 other representative E. coli strains. Oral inoculation of newborn animals with either strain produced septicemia, whereas intraperitoneal injection caused septicemia only in pups infected with RS218 but not in those injected with SCB34. In addition to being virulent only through the oral route, SCB34 demonstrated significantly greater invasion and transcytosis of polarized intestinal epithelial cells in vitro as compared to RS218. Protein coding sequences comparisons highlighted the presence of known virulence factors that are shared among several of these isolates, and revealed the existence of proteins exclusively encoded in SCB34, many of which remain uncharacterized. Our study demonstrates that oral acquisition is crucial for the virulence properties of the neonatal bacteremia clinical isolate SCB34. This characteristic, along with its enhanced ability to invade and transcytose intestinal epithelium are likely determined by the specific virulence factors that predominate in this strain.

Facilitation of trace metal uptake in cells by inulin coating of metallic nanoparticles.

Trace elements such as zinc and iron are essential for the proper function of biochemical processes, and their uptake and bioavailability are dependent on their chemical form. Supplementation of trace metals through nanostructured materials is a new field, but its application raises concerns regarding their toxicity. Here, we compared the intracellular zinc uptake of different sources of zinc: zinc sulfate, and ZnO and core-shell α-Fe2O3@ZnO nanoparticles, coated or uncoated with inulin, an edible and biocompatible polysaccharide. Using mussel haemocytes, a well-known model system to assess nanomaterial toxicity, we simultaneously assessed zinc accumulation and multiple cellular response endpoints. We found that intracellular zinc uptake was strongly enhanced by inulin coating, in comparison to the uncoated nanoparticles, while no significant effects on cell death, cell viability, mitochondrial membrane integrity, production of reactive oxygen species or lysosome abundance were observed at concentrations up to 20 ppm. Since no significant increments in toxicity were observed, the coated nanomaterials may be useful to increase in vivo zinc uptake for nutritional applications.

Photosynthetic efficiency predicts toxic effects of metal nanomaterials in phytoplankton.

High Throughput Screening (HTS) using in vitro assessments at the subcellular level has great promise for screening new chemicals and emerging contaminants to identify high-risk candidates, but their linkage to ecological impacts has seldom been evaluated. We tested whether a battery of subcellular HTS tests could be used to accurately predict population-level effects of engineered metal nanoparticles (ENPs) on marine phytoplankton, important primary producers that support oceanic food webs. To overcome well-known difficulties of estimating ecologically meaningful toxicity parameters, we used novel Dynamic Energy Budget and Toxicodynamic (DEBtox) modeling techniques to evaluate impacts of ENPs on population growth rates. Our results show that population growth was negatively impacted by all four ENPs tested, but the HTS tests assessing many cell/physiological functions lacked predictive power at the population level. However, declining photosynthetic efficiency, a traditional physiological endpoint for photoautotrophs, was a good predictor of population level effects in phytoplankton. DEBtox techniques provided robust estimates of EC10 for population growth rates in exponentially growing batch cultures of phytoplankton, and should be widely useful for ecotoxicological testing. Adoption of HTS approaches for ecotoxicological assessment should carefully evaluate the predictive power of specific assays to minimize the risk that effects at higher levels of biological organization may go undetected.

Scaling Up Endocrine Disruption Effects from Individuals to Populations: Outcomes Depend on How Many Males a Population Needs.

Assessing how endocrine disrupting compounds (EDCs) affect population dynamics requires tracking males and females (and sex-reversed individuals) separately. A key component in any sex-specific model is the "mating function" (the relationship between sex ratio and reproductive success) but this relationship is not known for any fish species. Using a model, we found that EDC effects on fish populations strongly depend upon the shape of the mating function. Additionally, masculinization is generally more detrimental to populations than feminization. We then quantified the mating function for the inland silverside (Menidia beryllina), and used those results and the model to assess the status of wild silverside populations. Contrary to the expectation that a few males can spawn with many females, silversides exhibited a nearly linear mating function. This implies that small changes in the sex ratio will reduce reproductive success. Four out of five wild silverside populations exhibited sex ratios far from 50:50 and thus are predicted to be experiencing population declines. Our results suggest that managers should place more emphasis on mitigating masculinizing rather than feminizing EDC effects. However, for species with a nearly linear mating function, such as Menidia, feminization and masculinization are equally detrimental.

Changes in Menidia beryllina Gene Expression and In Vitro Hormone-Receptor Activation After Exposure to Estuarine Waters Near Treated Wastewater Outfalls.

Fishes in estuarine waters are frequently exposed to treated wastewater effluent, among numerous other sources of contaminants, yet the impacts of these anthropogenic chemicals are not well understood in these dynamic and important waterways. Inland silversides (Menidia beryllina) at an early stage of development [12 days posthatch (dph)] were exposed to waters from two estuarine wastewater-treatment outfall locations in a tidal estuary, the Sacramento/San Joaquin Delta (California, USA) that had varied hydrology and input volumes. The genomic response caused by endocrine-disrupting compounds (EDCs) in these waters was determined using quantitative polymerase chain reaction on a suite of hormonally regulated genes. Relative androgenic and estrogenic activities of the waters were measured using CALUX reporter bioassays. The presence of bifenthrin, a pyrethroid pesticide and known EDC, as well as caffeine and the anti-inflammatory pharmaceutical ibuprofen, which were used as markers of wastewater effluent input, were determined using instrumental analysis. Detectable levels of bifenthrin (2.89 ng L(-1)) were found on one of the sampling dates, and caffeine was found on all sampling dates, in water from the Boynton Slough. Neither compound was detected at the Carquinez Strait site, which has a much smaller effluent discharge input volume relative to the receiving water body size compared with Boynton Slough. Water samples from both sites incubated in the CALUX cell line induced estrogenic and androgenic activity in almost all instances, though the estrogenicity was relatively higher than the androgenicity. Changes in the abundance of mRNA transcripts of endocrine-responsive genes and indicators of general chemical stress were observed after a 96-h exposure to waters from both locations. The relative levels of endocrine response, changes in gene transcript abundance, and contaminant concentrations were greater in water from the Boynton Slough site despite those effluents undergoing a more advanced treatment process. The availability of a widely geographically distributed estuarine model species (M. beryllina) now allows for improved assessment of treated effluent impacts across brackish, estuarine, and marine environments.

Transcriptomic changes underlie altered egg protein production and reduced fecundity in an estuarine model fish exposed to bifenthrin.

Pyrethroid pesticides are a class of insecticides found to have endocrine disrupting properties in vertebrates such as fishes and in human cell lines. Endocrine disrupting chemicals (EDCs) are environmental contaminants that mimic or alter the process of hormone signaling. In particular, EDCs that alter estrogen and androgen signaling pathways are of major concern for fishes because these EDCs may alter reproductive physiology, behavior, and ultimately sex ratio. Bifenthrin, a pyrethroid with escalating usage, is confirmed to disrupt estrogen signaling in several species of fish, including Menidia beryllina (inland silverside), an Atherinid recently established as a euryhaline model. Our main objective was to broadly assess the molecular and physiological responses of M. beryllina to the ng/L concentrations of bifenthrin typically found in the environment, with a focus on endocrine-related effects, and to discern links between different tiers of the biological hierarchy. As such, we evaluated the response of juvenile Menidia to bifenthrin using a Menidia-specific microarray, quantitative real-time polymerase chain reaction (qPCR) on specific endocrine-related genes of interest, and a Menidia-specific ELISA to the egg-coat protein choriogenin, to evaluate a multitude of molecular-level responses that would inform mechanisms of toxicity and any underlying causes of change at higher biological levels of organization. The sublethal nominal concentrations tested (0.5, 5 and 50ng/L) were chosen to represent the range of concentrations observed in the environment and to provide coverage of a variety of potential responses. We then employed a 21-day reproductive assay to evaluate reproductive responses to bifenthrin (at 0.5ng/L) in a separate group of adult M. beryllina. The microarray analysis indicated that bifenthrin influences a diverse suite of molecular pathways, from baseline metabolic processes to carcinogenesis. A more targeted examination of gene expression via qPCR demonstrated that bifenthrin downregulates a number of estrogen-related transcripts, particularly at the lowest exposure level. Choriogenin protein also decreased with exposure to increasing concentrations of bifenthrin, and adult M. beryllina exposed to 0.5ng/L had significantly reduced reproductive output (fertilized eggs per female). This reduction in fecundity is consistent with observed changes in endocrine-related gene expression and choriogenin production. Taken together, our results demonstrate that environmental concentrations of bifenthrin have potential to interfere with metabolic processes, endocrine signaling, and to decrease reproductive output.

Copper oxide and zinc oxide nanomaterials act as inhibitors of multidrug resistance transport in sea urchin embryos: their role as chemosensitizers.

The ability of engineered nanomaterials (NMs) to act as inhibitors of ATP-binding cassette (ABC) efflux transporters in embryos of white sea urchin (Lytechinus pictus) was studied. Nanocopper oxide (nano-CuO), nanozinc oxide (nano-ZnO), and their corresponding metal ions (CuSO4 and ZnSO4) were used as target chemicals. The results showed that nano-CuO, nano-ZnO, CuSO4, and ZnSO4, even at relatively low concentrations (0.5 ppm), significantly increased calcein-AM (CAM, an indicator of ABC transporter activity) accumulation in sea urchin embryos at different stages of development. Exposure to nano-CuO, a very low solubility NM, at increasing times after fertilization (>30 min) decreased CAM accumulation, but nano-ZnO (much more soluble NM) did not, indicating that metal ions could cross the hardened fertilization envelope, but not undissolved metal oxide NMs. Moreover, nontoxic levels (0.5 ppm) of nano-CuO and nano-ZnO significantly increased developmental toxicity of vinblastine (an established ABC transporter substrate) and functioned as chemosensitizers. The multidrug resistance associated protein (MRP, one of ABC transporters) inhibitor MK571 significantly increased copper concentrations in embryos, indicating ABC transporters are important in maintaining low intracellular copper levels. We show that low concentrations of nano-CuO and nano-ZnO can make embryos more susceptible to other contaminants, representing a potent amplification of nanomaterial-related developmental toxicity.

Dynamical learning and tracking of tremor and dyskinesia from wearable sensors.

We have developed and evaluated several dynamical machine-learning algorithms that were designed to track the presence and severity of tremor and dyskinesia with 1-s resolution by analyzing signals collected from Parkinson's disease (PD) patients wearing small numbers of hybrid sensors with both 3-D accelerometeric and surface-electromyographic modalities. We tested the algorithms on a 44-h signal database built from hybrid sensors worn by eight PD patients and four healthy subjects who carried out unscripted and unconstrained activities of daily living in an apartment-like environment. Comparison of the performance of our machine-learning algorithms against independent clinical annotations of disorder presence and severity demonstrates that, despite their differing approaches to dynamic pattern classification, dynamic neural networks, dynamic support vector machines, and hidden Markov models were equally effective in keeping error rates of the dynamic tracking well below 10%. A common set of experimentally derived signal features were used to train the algorithm without the need for subject-specific learning. We also found that error rates below 10% are achievable even when our algorithms are tested on data from a sensor location that is different from those used in algorithm training.

Cost, effectiveness and environmental relevance of multidrug transporters in sea urchin embryos.

ATP-binding cassette transporters protect cells via efflux of xenobiotics and endogenous byproducts of detoxification. While the cost of this ATP-dependent extrusion is known at the molecular level, i.e. the ATP used for each efflux event, the overall cost to a cell or organism of operating this defense is unclear, especially as the cost of efflux changes depending on environmental conditions. During prolonged exposure to xenobiotics, multidrug transporter activity could be costly and ineffective because effluxed substrate molecules are not modified in the process and could thus undergo repeated cycles of efflux and re-entry. Here we use embryos of the purple sea urchin, Strongylocentrotus purpuratus, as a model to determine transport costs and benefits under environmentally relevant xenobiotic concentrations. Strikingly, our results show that efflux transporter activity costs less than 0.2% of total ATP usage, as a proportion of oxygen consumption. The benefits of transport, defined as the reduction in substrate accumulation due to transporter activity, depended largely, but not entirely, on the rate of passive flux of each substrate across the plasma membrane. One of the substrates tested exhibited rapid membrane permeation coupled with high rates of efflux, thus inducing rapid and futile cycles of efflux followed by re-entry of the substrate. This combination significantly reduced transporter effectiveness as a defense and increased costs even at relatively low substrate concentrations. Despite these effects with certain substrates, our results show that efflux transporters are a remarkably effective and low-cost first line of defense against exposure to environmentally relevant concentrations of xenobiotics.

High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.

Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity. The purpose of this study was to advance the current state of the art by (1) introducing hybrid sensor technology to concurrently acquire surface electromyographic (sEMG) and accelerometer data during unconstrained activity and (2) analyzing the data using dynamic neural network algorithms to capture the evolving temporal characteristics of the sensor data and improve motor disorder recognition of tremor and dyskinesia. Algorithms were trained (n=11 patients) and tested (n=8 patients; n=4 controls) to recognize tremor and dyskinesia at 1-second resolution based on sensor data features and expert annotation of video recording during 4-hour monitoring periods of unconstrained daily activity. The algorithms were able to make accurate distinctions between tremor, dyskinesia, and normal movement despite the presence of diverse voluntary activity. Motor disorder severity classifications averaged 94.9% sensitivity and 97.1% specificity based on 1 sensor per symptomatic limb. These initial findings indicate that new sensor technology and software algorithms can be effective in enhancing wearable sensor-based system performance for monitoring PD motor disorders during unconstrained activities.

An approach to detecting estrogenic endocrine disruption via choriogenin expression in an estuarine model fish species.

A large body of work has established a link between endocrine disrupting compounds (EDCs) and a number of abnormalities in fishes. However, most EDC studies use several standard laboratory denizens to assess impacts, so assumptions about sensitivity are primarily based on these few species. Additionally, existing methods rely on obtaining sufficient plasma to measure EDC biomarkers. Our objectives were (a) to establish a new model species for estuarine fishes, (b) to evaluate endocrine impacts with a highly sensitive and specific biomarker, and (c) to develop a method for the analysis of this biomarker in small fish that do not possess sufficient blood plasma for protein measurement. As such, we created a polyclonal antibody (Ab) to the estrogen-responsive proteins chorion (Ch) and choriogenin (Chg) in Menidia beryllina, found throughout coastal North America and already utilized in EPA Whole Effluent Testing. We then validated the Ab by using it to measure the response to aqueous ethinylestradiol (EE2) through the development an ELISA using Menidia whole body homogenate (WBH). Sensitivity of the Ab to Menidia WBH is greater than that of the commercially available option. ELISA sensitivity, with a detection limit of 5 ng/ml and a working range of 22.6-1370.9 ng/ml, is comparable to ELISAs developed to measure plasma Chg. To our knowledge this is the first ELISA method developed for the detection of Chg using WBH. Including additional model species and methods allowing the evaluation of alternative sample matrices will contribute to an enhanced understanding of inter-species differences in EDC response.

Dynamic SVM detection of tremor and dyskinesia during unscripted and unconstrained activities.

In this paper, we report an experimental comparison of dynamic support vector machines (SVMs) to dynamic neural networks (DNNs) in the context of a system for detecting dyskinesia and tremor in Parkinson's disease (PD) patients wearing accelerometer (ACC) and surface electromyographic (sEMG) sensors while performing unscripted and unconstrained activities of daily living. These results indicate that SVMs and DNNs of comparable computational complexities yield approximately identical performance levels when using an identical set of input features.

Resolving signal complexities for ambulatory monitoring of motor function in Parkinson's disease.

Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary activities of daily life. Our approach is the development of multiple dynamic neural network (DNN) classifiers whose application are governed by a rule-based controller within the Integrated Processing and Understanding of Signals (IPUS) framework. Solutions are described for time-varying occurrences of tremor and dyskinesia, classified at 1 s resolution from surface electromyographic (sEMG) and tri-axial accelerometer (ACC) data acquired from patients with PD. The networks were trained and tested on separate datasets, respectively, while subjects performed unscripted and unconstrained activities in a home-like setting. Performance of the classifiers achieved an overall global error rate of less than 10%.

Resolving signal complexities for ambulatory monitoring of motor function in Parkinson's disease.

Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary activities of daily life. Our approach is the development of multiple dynamic neural network (DNN) classifiers whose application are governed by a rule-based controller within the Integrated Processing and Understanding of Signals (IPUS) framework. Solutions are described for time-varying occurrences of tremor and dyskinesia, classified at 1 s resolution from surface electromyographic (sEMG) and tri-axial accelerometer (ACC) data acquired from patients with PD. The networks were trained and tested on separate datasets, respectively, while subjects performed unscripted and unconstrained activities in a home-like setting. Performance of the classifiers achieved an overall global error rate of less than 10%.

What is IPUS and how does it help resolve biosignal complexity?

Integrated Processing and Understanding of Signals (IPUS) combines signal processing and artificial intelligence approaches to develop algorithms for resolving signal complexity. It has also led to development over the last decade and a half of software tools for supporting the algorithm design process. The signals to be analyzed are the superposition of temporally localized and temporally overlapping signal components from broadly defined signal classes pertinent to the given application. Resolving a signal's complexity thus amounts to "decoding" it to reveal details of the specific signal components that are present at each point of a dense temporal grid defined on the signal. IPUS uses artificial intelligence techniques such as rule-based inference in conjunction with parameterized signal processing transformations to combat the combinatorial explosion encountered in any exhaustive search among the possible decoding answers for a given signal. Originally developed in the mid 1990's for auditory scene analysis, the IPUS approach has since been refined and extended in the context of various applications. In this paper, we present an overview of IPUS and discuss why its latest developments significantly impact biosignal analysis in diverse rehabilitation applications.

Detecting freezing-of-gait during unscripted and unconstrained activity.

We present a dynamic neural network (DNN) solution for detecting instances of freezing-of-gait (FoG) in Parkinson's disease (PD) patients while they perform unconstrained and unscripted activities. The input features to the DNN are derived from the outputs of three triaxial accelerometer (ACC) sensors and one surface electromyographic (EMG) sensor worn by the PD patient. The ACC sensors are placed on the shin and thigh of one leg and on one of the forearms while the EMG sensor is placed on the shin. Our FoG solution is architecturally distinct from the DNN solutions we have previously designed for detecting dyskinesia or tremor. However, all our DNN solutions utilize the same set of input features from each EMG or ACC sensor worn by the patient. When tested on experimental data from PD patients performing unconstrained and unscripted activities, our FoG detector exhibited 83% sensitivity and 97% specificity on a per-second basis.

Dynamic neural network detection of tremor and dyskinesia from wearable sensor data.

We present a dynamic neural network (DNN) solution for detecting time-varying occurrences of tremor and dyskinesia at 1 s resolution from time series data acquired from surface electromyographic (sEMG) sensors and tri-axial accelerometers worn by patients with Parkinson's disease (PD). The networks were trained and tested on separate datasets, each containing approximately equal proportions of tremor, dyskinesia, and disorder-free data from 8 PD and 4 control subjects performing unscripted and unconstrained activities in an apartment-like environment. During DNN testing, tremor was detected with a sensitivity of 93% and a specificity of 95%, while dyskinesia was detected with a sensitivity of 91% and a specificity of 93%. Similar sensitivity and specificity levels were obtained when DNN testing was carried out on subjects who were not included in DNN training.

A comparison of terahertz pulsed imaging with transmission microradiography for depth measurement of enamel demineralisation in vitro.

Terahertz pulsed imaging (TPI) is a relatively new, non-ionising and non-destructive imaging technique for studying hard tissues which does not require tooth section preparation, unlike transmission microradiography (TMR). If TPI can measure the depths of caries/demineralisation lesions accurately the same tooth samples could be reused and remeasured during in vitro and in situ studies on de- and/or re-mineralisation. The aim of this study was to compare TPI and TMR for measuring the depths of a range of artificially induced bovine enamel demineralised lesions in vitro. Bovine slabs with artificial caries, induced to different levels of demineralisation by two different but standard demineralisation techniques ('acid gel' and 'carbopol') were measured by TPI and TMR and the readings compared. The set of TPI/TMR measurements obtained on the gel-demineralised slabs showed an extremely high coefficient of determination (r(2) = 0.995). Detailed analysis of the results and theoretical considerations (involving the relationship between refractive index profiling and mineral loss profile) are used to explain the findings and show that for acid gel lesions TPI is measuring demineralisation in the range of 47% of that of TMR depth plus an intercept of 16 microm, with further calculations allowing the TMR depths to be determined to within 5% using TPI.

The sea urchin embryo as a model for studying efflux transporters: roles and energy cost.

We describe the use of the sea urchin as a model for studying efflux transporters and estimating energy cost for the cytotoxin protective system provided by these transporters. The unfertilized egg has low transport activity, which increases to a new steady state shortly after fertilization. Activity results from p-glycoprotein (p-gp) and MRP type transporters which protect the embryo from cytotoxic drugs that can disrupt cell division or induce apoptosis. The energy cost is estimated from a novel use of calcein-AM as a substrate; keeping 0.25 microM substrate levels out of the cell utilizes only 0.023% of steady state respiration.