Cronobacter Sakazakii Bacteremia in a 76-year-old Woman: A Case Report

Cronobacter sakazakii, commonly found in contaminated infant formula and thereby causes infantile bacteremia, is rarely associated with adult bacteremia. We present the tenth case of C. sakazakii bacteremia in adults. The patient is a 76-year-old woman who resides in a skilled nursing facility and presents with risk factors including bullous pemphigoid, Type II diabetes mellitus, hypertension, hyperlipidemia, chronic kidney disease, and anemia. The therapy was started with intravenous ciprofloxacin and vancomycinempirically. After consultation with an Infectious Diseases specialist, ciprofloxacin and vancomycin was replaced with meropenem based on the patient’s extensive bullous lesions, history of ESBL infections, and possible pneumonia. Later, the therapy was de-escalated to intravenous ceftriaxone and vancomycin after culture and sensitivity testing were available and clinical signs of improvements were evident. She was sent back to her skilled nursing but was re-admitted 10-days later. She was placed on dialysis for altered mental status secondary to acute renal failure. Blood and urine cultures were repeated and had no evidence of bacterial growth

Transmission in double quantum dots in the Kondo regime: Quantum-critical transitions and interference effects

We study the transmission through a double quantum-dot system in the Kondo regime. An exact expression for the transmission coefficient in terms of fully interacting many-body Green's functions is obtained. By mapping the system into an effective Anderson impurity model, one can determine the transmission using numerical renormalization-group methods. The transmission exhibits signatures of the different Kondo regimes of the effective model, including an unusual Kondo phase with split peaks in the spectral function, as well as a pseudogapped regime exhibiting a quantum critical transition between Kondo and unscreened phases.Comment: 4 pages, 3 figures; Submitted to Physica E (EP2DS-17 proceedings, oral presentation), updated Ref

Orexin Receptor Activation Generates Gamma Band Input to Cholinergic and Serotonergic Arousal System Neurons and Drives an Intrinsic Ca(2+)-Dependent Resonance in LDT and PPT Cholinergic Neurons

A hallmark of the waking state is a shift in EEG power to higher frequencies with epochs of synchronized intracortical gamma activity (30-60 Hz) - a process associated with high-level cognitive functions. The ascending arousal system, including cholinergic laterodorsal (LDT) and pedunculopontine (PPT) tegmental neurons and serotonergic dorsal raphe (DR) neurons, promotes this state. Recently, this system has been proposed as a gamma wave generator, in part, because some neurons produce high-threshold, Ca(2+)-dependent oscillations at gamma frequencies. However, it is not known whether arousal-related inputs to these neurons generate such oscillations, or whether such oscillations are ever transmitted to neuronal targets. Since key arousal input arises from hypothalamic orexin (hypocretin) neurons, we investigated whether the unusually noisy, depolarizing orexin current could provide significant gamma input to cholinergic and serotonergic neurons, and whether such input could drive Ca(2+)-dependent oscillations. Whole-cell recordings in brain slices were obtained from mice expressing Cre-induced fluorescence in cholinergic LDT and PPT, and serotonergic DR neurons. After first quantifying reporter expression accuracy in cholinergic and serotonergic neurons, we found that the orexin current produced significant high frequency, including gamma, input to both cholinergic and serotonergic neurons. Then, by using a dynamic clamp, we found that adding a noisy orexin conductance to cholinergic neurons induced a Ca(2+)-dependent resonance that peaked in the theta and alpha frequency range (4-14 Hz) and extended up to 100 Hz. We propose that this orexin current noise and the Ca(2+) dependent resonance work synergistically to boost the encoding of high-frequency synaptic inputs into action potentials and to help ensure cholinergic neurons fire during EEG activation. This activity could reinforce thalamocortical states supporting arousal, REM sleep, and intracortical gamma

Metastable polymer substrate for transient electronics

Transient electronics are designed to operate for programmed life times and then degrade leaving little to no trace behind. The initial work on transient electronics has been focused on biomedical applications in which the electronics are implanted, but eventually degrade and resorb into the body after the treatment is complete [1]. Biomedical transient electronics typically use a water soluble substrate, such as silk that slowly degrades as water diffuses into the substrate. However, the ability to more precisely program the lifetime of electronics and utilize other degradation stimuli would enable new applications in a variety of industries. Essential to this effort is the development of a new class of degradable substrates that can be triggered to degrade by exposure to a variety of environmental stimuli (e.g., mechanical stress, UV light, pH). In this study, we present a photodegradable transient substrate made of cyclic poly(phthalaldehyde) (PPA) doped with a photo-acid generator (PAG). Exposing the substrate to UV light generates acid through reaction of the PAG which then promotes the cleavage of the acetal backbone of PPA, leading to rapid film degradation. We monitored the degradation of the film using dynamic mechanical analysis and Fourier transform infrared spectroscopy. Results demonstrate that the polymer degrades into monomer and that the degradation rate is controlled by varying the concentration of PAG and the intensity of the UV source. In addition, electronic transistors, diodes, and resistors were fabricated from magnesium and silicon nanomembranes using our newly designed substrate. A combination of transfer-printing and electron-beam evaporation were used to demonstrate lithographic compatibility. We demonstrate electronic transience of a Mg resistor in as fast as 20 minutes with substantial physical degradation in 72 hours. REFERENCE [1] Hwang, S.-W., et. al. Science. 2012, 337, 1640

COVID-19 and the Impact on Rural and Black Church Congregants: Results of the C-M-C Project

The COVID-19 pandemic has had devastating effects on Black and rural populations with a mortality rate among Blacks three times that of Whites and both rural and Black populations experiencing limited access to COVID-19 resources. The primary purpose of this study was to explore the health, financial, and psychological impact of COVID-19 among rural White Appalachian and Black nonrural central Kentucky church congregants. Secondarily we sought to examine the association between sociodemographics and behaviors, attitudes, and beliefs regarding COVID-19 and intent to vaccinate. We used a cross sectional survey design developed with the constructs of the Health Belief and Theory of Planned Behavior models. The majority of the 942 respondents were ≥ 36 years. A total of 54% were from central Kentucky, while 47.5% were from Appalachia. Among all participants, the pandemic worsened anxiety and depression and delayed access to medical care. There were no associations between sociodemographics and practicing COVID-19 prevention behaviors. Appalachian region was associated with financial burden and delay in medical care (p = 0.03). Appalachian respondents had lower perceived benefit and attitude for COVID-19 prevention behaviors (p = 0.004 and \u3c 0.001, respectively). Among all respondents, the perceived risk of contracting COVID was high (54%), yet 33.2% indicated unlikeliness to receive the COVID-19 vaccine if offered. The COVID-19 pandemic had a differential impact on White rural and Black nonrural populations. Nurses and public health officials should assess knowledge and explore patient\u27s attitudes regarding COVID-19 prevention behaviors, as well as advocate for public health resources to reduce the differential impact of COVID-19 on these at-risk populations

Principal components ancestry adjustment for Genetic Analysis Workshop 17 data

Statistical tests on rare variant data may well have type I error rates that differ from their nominal levels. Here, we use the Genetic Analysis Workshop 17 data to estimate type I error rates and powers of three models for identifying rare variants associated with a phenotype: (1) by using the number of minor alleles, age, and smoking status as predictor variables; (2) by using the number of minor alleles, age, smoking status, and the identity of the population of the subject as predictor variables; and (3) by using the number of minor alleles, age, smoking status, and ancestry adjustment using 10 principal component scores. We studied both quantitative phenotype and a dichotomized phenotype. The model with principal component adjustment has type I error rates that are closer to the nominal level of significance of 0.05 for single-nucleotide polymorphisms (SNPs) in noncausal genes for the selected phenotype than the model directly adjusting for population. The principal component adjustment model type I error rates are also closer to the nominal level of 0.05 for noncausal SNPs located in causal genes for the phenotype. The power for causal SNPs with the principal component adjustment model is comparable to the power of the other methods. The power using the underlying quantitative phenotype is greater than the power using the dichotomized phenotype

A review of mathematical models of human trust in automation

Understanding how people trust autonomous systems is crucial to achieving better performance and safety in human-autonomy teaming. Trust in automation is a rich and complex process that has given rise to numerous measures and approaches aimed at comprehending and examining it. Although researchers have been developing models for understanding the dynamics of trust in automation for several decades, these models are primarily conceptual and often involve components that are difficult to measure. Mathematical models have emerged as powerful tools for gaining insightful knowledge about the dynamic processes of trust in automation. This paper provides an overview of various mathematical modeling approaches, their limitations, feasibility, and generalizability for trust dynamics in human-automation interaction contexts. Furthermore, this study proposes a novel and dynamic approach to model trust in automation, emphasizing the importance of incorporating different timescales into measurable components. Due to the complex nature of trust in automation, it is also suggested to combine machine learning and dynamic modeling approaches, as well as incorporating physiological data