Concierge care and patient reviews.

We examine how patient numerical ratings and specific words in written reviews of family physicians and internists in the states of California and Florida differ based upon concierge doctor status. Data are drawn from Healthgrades.com, one of the largest providers of online reviews, and a machine-learning sentiment analysis is used to determine the predictors of concierge status and numerical patient ratings. We find that reviews of concierge doctors are more likely to contain technical words associated with health care, such as "staff" and "office," compared with traditional physicians. In contrast, interpersonal bedside-manner words, like "listen" or "concerns," are most likely in reviews for nonconcierge doctors. We further determine that, whereas interpersonal words exhibit both positive and negative effects on numerical ratings, technical terms seem to primarily correlate negatively with patient scores for all doctors. The present work represents a first step towards understanding the measures of quality of care that relate with the patient experience, and in particular with respect to the growing field of concierge medicine. It is also the first attempt we are aware of that employs sentiment analysis in this context.

The public and private benefit of an impure public good determines the sensitivity of bacteria to population collapse in a snowdrift game.

When cooperation is critical for survival, cheating can lead to population collapse. One mechanism of cooperation that permits the coexistence of cooperators and cheaters is an impure public good, whose public benefits are shared, but with a private benefit retained by the cooperator. It has yet to be determined how the contributions of the public and private benefit affect population survival. Using simulations and experiments with ß-lactamase-expressing bacteria, we found that for a given amount of public and private benefit, the population was most sensitive to collapse when initiated from an intermediate fraction of cooperators due to the near-concurrent collapse of the cooperator and cheater populations. We found that increasing the ratio of public to private benefit increased sensitivity to collapse. A low ratio allowed cooperators to survive on their private benefit after the public benefit could not rescue the cheaters. A high ratio allowed the cheaters to survive to high concentrations of ampicillin due to the high public benefit. However, small increases in ampicillin caused a rapid decline in the entire population as the private benefit was insufficient to allow self-rescue of the cooperators. Our findings have implications in the persistence of populations that rely on cooperation for survival.

Shannon information entropy in the canonical genetic code.

The Shannon entropy measures the expected information value of messages. As with thermodynamic entropy, the Shannon entropy is only defined within a system that identifies at the outset the collections of possible messages, analogous to microstates, that will be considered indistinguishable macrostates. This fundamental insight is applied here for the first time to amino acid alphabets, which group the twenty common amino acids into families based on chemical and physical similarities. To evaluate these schemas objectively, a novel quantitative method is introduced based the inherent redundancy in the canonical genetic code. Each alphabet is taken as a separate system that partitions the 64 possible RNA codons, the microstates, into families, the macrostates. By calculating the normalized mutual information, which measures the reduction in Shannon entropy, conveyed by single nucleotide messages, groupings that best leverage this aspect of fault tolerance in the code are identified. The relative importance of properties related to protein folding - like hydropathy and size - and function, including side-chain acidity, can also be estimated. This approach allows the quantification of the average information value of nucleotide positions, which can shed light on the coevolution of the canonical genetic code with the tRNA-protein translation mechanism.

Experimental evidence that network topology can accelerate the spread of beneficial mutations.

Whether and how the spatial arrangement of a population influences adaptive evolution has puzzled evolutionary biologists. Theoretical models make conflicting predictions about the probability that a beneficial mutation will become fixed in a population for certain topologies like stars, in which "leaf" populations are connected through a central "hub." To date, these predictions have not been evaluated under realistic experimental conditions. Here, we test the prediction that topology can change the dynamics of fixation both in vitro and in silico by tracking the frequency of a beneficial mutant under positive selection as it spreads through networks of different topologies. Our results provide empirical support that meta-population topology can increase the likelihood that a beneficial mutation spreads, broaden the conditions under which this phenomenon is thought to occur, and points the way toward using network topology to amplify the effects of weakly favored mutations under directed evolution in industrial applications.

Discovery of a novel merbecovirus DNA clone contaminating agricultural rice sequencing datasets from Wuhan, China.

HKU4-related coronaviruses are a group of betacoronaviruses belonging to the same merbecovirus subgenus as Middle Eastern Respiratory Syndrome coronavirus (MERS-CoV), which causes severe respiratory illness in humans with a mortality rate of over 30%. The high genetic similarity between HKU4-related coronaviruses and MERS-CoV makes them an attractive subject of research for modeling potential zoonotic spillover scenarios. In this study, we identify a novel coronavirus contaminating agricultural rice RNA sequencing datasets from Wuhan, China. The datasets were generated by the Huazhong Agricultural University in early 2020. We were able to assemble the complete viral genome sequence, which revealed that it is a novel HKU4-related merbecovirus. The assembled genome is 98.38% identical to the closest known full genome sequence, Tylonycteris pachypus bat isolate BtTp-GX2012. Using in silico modeling, we identified that the novel HKU4-related coronavirus spike protein likely binds to human dipeptidyl peptidase 4 (DPP4), the receptor used by MERS-CoV. We further identified that the novel HKU4-related coronavirus genome has been inserted into a bacterial artificial chromosome in a format consistent with previously published coronavirus infectious clones. Additionally, we have found a near complete read coverage of the spike gene of the MERS-CoV reference strain HCoV-EMC/2012, and identify the likely presence of a HKU4-related-MERS chimera in the datasets. Our findings contribute to the knowledge of HKU4-related coronaviruses and document the use of a previously unpublished HKU4 reverse genetics system in apparent MERS-CoV related gain-of-function research. Our study also emphasizes the importance of improved biosafety protocols in sequencing centers and coronavirus research facilities.

A Literature Review of Similarities Between and Among Patients With Autism Spectrum Disorder and Epilepsy.

Autism spectrum disorder (ASD) has been shown to be associated with various other conditions, and most commonly, ASD has been demonstrated to be linked to epilepsy. ASD and epilepsy have been observed to exhibit high rates of comorbidity, even when compared to the co-occurrence of other disorders with similar pathologies. At present, nearly one-half of the individuals diagnosed with ASD also have been diagnosed with comorbid epilepsy. Research suggests that both conditions likely share similarities in their underlying disease pathophysiology, possibly associated with disturbances in the central nervous system (CNS), and may be linked to an imbalance between excitation and inhibition in the brain. Meanwhile, it remains unclear whether one condition is the consequence of the other, as the pathologies of both disorders are commonly linked to many different underlying signal transduction mechanisms. In this review, we aim to investigate the co-occurrence of ASD and epilepsy, with the intent of gaining insights into the similarities in pathophysiology that both conditions present with. Elucidating the underlying disease pathophysiology as a result of both disorders could lead to a better understanding of the underlying mechanism of disease activity that drives co-occurrence, as well as provide insight into the underlying mechanisms of each condition individually.

Critical and Ictal Phases in Simulated EEG Signals on a Small-World Network.

Healthy brain function is marked by neuronal network dynamics at or near the critical phase, which separates regimes of instability and stasis. A failure to remain at this critical point can lead to neurological disorders such as epilepsy, which is associated with pathological synchronization of neuronal oscillations. Using full Hodgkin-Huxley (HH) simulations on a Small-World Network, we are able to generate synthetic electroencephalogram (EEG) signals with intervals corresponding to seizure (ictal) or non-seizure (interictal) states that can occur based on the hyperexcitability of the artificial neurons and the strength and topology of the synaptic connections between them. These interictal simulations can be further classified into scale-free critical phases and disjoint subcritical exponential phases. By changing the HH parameters, we can model seizures due to a variety of causes, including traumatic brain injury (TBI), congenital channelopathies, and idiopathic etiologies, as well as the effects of anticonvulsant drugs. The results of this work may be used to help identify parameters from actual patient EEG or electrocorticographic (ECoG) data associated with ictogenesis, as well as generating simulated data for training machine-learning seizure prediction algorithms.

A binary representation of the genetic code.

This article introduces a novel binary representation of the canonical genetic code based on both the structural similarities of the nucleotides, as well as the physicochemical properties of the encoded amino acids. Each of the four mRNA bases is assigned a unique 2-bit identifier, so that the 64 triplet codons are each indexed by a 6-bit label. The ordering of the bits reflects the hierarchical organization manifested by the DNA replication/repair and tRNA translation systems. In this system, transition and transversion mutations are naturally expressed as binary operations, and the severities of the different point mutations can be analyzed. Using a principal component analysis, it is shown that the physicochemical properties of amino acids related to protein folding also correlate with certain bit positions of their respective labels. Thus, the likelihood for a point mutation to be conservative, and less likely to cause a change in protein functionality, can be estimated.

A model of band-pass phenotypic resistance in a modified Beverton-Holt framework.

A new mathematical model of phenotypic bacterial resistance to a periodically applied antibiotic is presented. Using a modified Beverton-Holt framework, and without appealing to any explicit internal timescale or heritable mutations, we map the parameter-space regions corresponding to bacterial colony survival or extinction. It is demonstrated that band-pass behavior, in which colony survival occurs at intermediate - but not short or long - antibiotic application periods, is a possible regime for some initial population values if the bactericidal activity is sufficient. However, below this threshold value of antibiotic efficacy, a "fixed-point catastrophe" occurs, and colony extinction does not occur when the bacteria are challenged with long application periods. These results suggest that the dosing schedule of antibiotics within a clinical setting merits additional scrutiny, since even seemingly unimportant modifications to the frequency of administration may lead to widely diverging patient outcomes.

Locus of control and obesity.

In the developed world, the hazards associated with obesity have largely outstripped the risk of starvation. Obesity remains a difficult public health issue to address, due in large part to the many disciplines involved. A full understanding requires knowledge in the fields of genetics, endocrinology, psychology, sociology, economics, and public policy - among others. In this short review, which serves as an introduction to the Frontiers in Endocrinology research topic, we address one cross-disciplinary relationship: the interaction between the hunger/satiation neural circuitry, an individual's perceived locus of control, and the risk for obesity. Mammals have evolved a complex system for modulating energy intake. Overlaid on this, in humans, there exists a wide variation in "perceived locus of control" - that is, the extent to which an individual believes to be in charge of the events that affect them. Whether one has primarily an internal or external locus of control itself affects, and is affected by, external and physiological factors and has been correlated with the risk for obesity. Thus, the path from hunger and satiation to an individual's actual behavior may often be moderated by psychological factors, included among which is locus of control.