Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes

The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic cycling [2,3]. Part of the problem may be that those efforts have implemented different classes of unrelated antibiotics, and relied on removal of resistance by random loss of resistance genes from bacterial populations (drift). Here, we show that alternating structurally similar antibiotics can restore susceptibility to antibiotics after resistance has evolved. We found that the resistance phenotypes conferred by variant alleles of the resistance gene encoding the TEM {\beta}-lactamase (blaTEM) varied greatly among 15 different {\beta}-lactam antibiotics. We captured those differences by characterizing complete adaptive landscapes for the resistance alleles blaTEM-50 and blaTEM-85, each of which differs from its ancestor blaTEM-1 by four mutations. We identified pathways through those landscapes where selection for increased resistance moved in a repeating cycle among a limited set of alleles as antibiotics were alternated. Our results showed that susceptibility to antibiotics can be sustainably renewed by cycling structurally similar antibiotics. We anticipate that these results may provide a conceptual framework for managing antibiotic resistance. This approach may also guide sustainable cycling of the drugs used to treat malaria and HIV

Analysis of auditory functions in grades one, two, and three.

Thesis (Ed.M.)--Boston Universit

Seroprevalence of hantaviruses and Leptospira in muskrat and coypu trappers in the Netherlands, 2016.

Aims: Seoul orthohantavirus (SEOV) and Leptospira spp. are zoonotic pathogens with rats as main reservoir. Recently, the presence of SEOV in brown rats was reported in one region in the Netherlands. Brown rats are a frequent bycatch in traps placed to catch muskrats (Ondatra zibethicus) and coypus (Myocastor coypus), and thus are a potential health risk for trappers. It was our aim to determine the seroprevalence of orthohantavirus, specifically SEOV, and Leptospira spp in Dutch trappers. Methods and results: Participating trappers provided serum samples and completed an online questionnaire. The serum was tested for the presence of antibodies against six orthohantaviruses and eight Leptospira serovars. Two hundred-sixty trappers completed the online questionnaire (65%), and 246 (61%) and 162 (40%) serum samples were tested for relevant orthohantaviruses and Leptospira spp., respectively. The seroprevalence of Puumala orthohantavirus in Dutch trappers was 0.4% (95% CI: 0.1-2.3%). None of the participants tested positive for SEOV. The seroprevalence of leptospirosis was 1.2% (95% CI: 0.3-4.4%), although Leptospira spp. are present in brown rats in the Netherlands.Significance of study: The results indicate that the infections with orthohantaviruses and leptospires is low for muskrat and coypu trappers

Do People Vote on the Basis of Minimax Regret?

Rational choice theory has yet to provide a satisfactory explanation of voter turnout. One such account, minimax regret, is analyzed using data from a survey involving students at two Canadian universities during the 1993 Canadian federal election campaign. While the minimax regret hypothesis is supported at the bivariate level, it fails to pass a multivariate test in which other components of the calculus of voting are included. Minimax regret appears to be little more than a rationalization on the part of those having a strong sense of duty to vote.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68661/2/10.1177_106591299504800408.pd

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.