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The craft of family therapy : challenging certainties
\"Family therapy trainees are inundated with a multitude of family therapy theories. They also have difficulty shifting from an individualistic view to one of seeing interactions and systems. How do therapists hone their own methods with all of these choices? And how do they learn how to best treat families with all of the focus being taken away from their clients and redirected instead on processes? Perhaps most importantly, how can they learn through an inductive process of exploring what has occurred during the therapeutic session? Veteran therapist and founder of Structural Family Therapy, Salvador Minuchin, goes back to basics with his two co-authors Michael D. Reiter and Charmaine Borda in The Craft of Family Therapy. In this book they teach readers basic communication and family therapy skills using some of Dr. Minuchin's most interesting and illuminating cases. Not only do readers re-learn basic techniques, such as reframing and joining, but they are treated to an in-depth commentary on each case, with Dr. Minuchin emphasizing the techniques he uses that allow him to refocus attention from the Identifying Patient to the family as a whole. The book ends with three supervision transcripts from Dr. Minuchin's students, whose commentary illuminates the struggles, fears, and insecurities that new family therapists face and how they can overcome them. Each of these chapters ends with a consultation interview that Dr. Minuchin conducted with each supervisees case family.\"-- Provided by publisher.
Book
The challenge of antimicrobial resistance
The accelerating tide of antimicrobial resistance (AMR) is a major worldwide policy concern. Like climate change, the incentives for individual decision-makers do not take into account the costs to society at large. AMR represents an impending “tragedy of the commons,” and there is an immediate need for collective action to prevent future harm. Roope et al. review the issues associated with AMR from an economics perspective and draw parallels with climate change. A major stumbling block for both challenges is to build consensus about the best way forward when faced with many uncertainties and inequities. Science , this issue p. eaau4679 As antibiotic consumption grows, bacteria are becoming increasingly resistant to treatment. Antibiotic resistance undermines much of modern health care, which relies on access to effective antibiotics to prevent and treat infections associated with routine medical procedures. The resulting challenges have much in common with those posed by climate change, which economists have responded to with research that has informed and shaped public policy. Drawing on economic concepts such as externalities and the principal–agent relationship, we suggest how economics can help to solve the challenges arising from increasing resistance to antibiotics. We discuss solutions to the key economic issues, from incentivizing the development of effective new antibiotics to improving antibiotic stewardship through financial mechanisms and regulation.
Journal Article
Effect of Delta variant on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK
The effectiveness of the BNT162b2 and ChAdOx1 vaccines against new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections requires continuous re-evaluation, given the increasingly dominant B.1.617.2 (Delta) variant. In this study, we investigated the effectiveness of these vaccines in a large, community-based survey of randomly selected households across the United Kingdom. We found that the effectiveness of BNT162b2 and ChAdOx1 against infections (new polymerase chain reaction (PCR)-positive cases) with symptoms or high viral burden is reduced with the B.1.617.2 variant (absolute difference of 10–13% for BNT162b2 and 16% for ChAdOx1) compared to the B.1.1.7 (Alpha) variant. The effectiveness of two doses remains at least as great as protection afforded by prior natural infection. The dynamics of immunity after second doses differed significantly between BNT162b2 and ChAdOx1, with greater initial effectiveness against new PCR-positive cases but faster declines in protection against high viral burden and symptomatic infection with BNT162b2. There was no evidence that effectiveness varied by dosing interval, but protection was higher in vaccinated individuals after a prior infection and in younger adults. With B.1.617.2, infections occurring after two vaccinations had similar peak viral burden as those in unvaccinated individuals. SARS-CoV-2 vaccination still reduces new infections, but effectiveness and attenuation of peak viral burden are reduced with B.1.617.2.
A large, community-based study in the United Kingdom indicates that the effectiveness of BNT162b2 and ChAdOx1 vaccines against SARS-CoV-2 infections with symptoms or high viral burden is reduced with the Delta variant compared to the Alpha variant.
Journal Article
Effect of Covid-19 Vaccination on Transmission of Alpha and Delta Variants
In this study, 37% of 146,000 PCR-tested contacts of infected persons in England were positive for SARS-CoV-2. Transmission of the alpha variant from twice-vaccinated index patients was rarer than that from unvaccinated index patients (adjusted rate ratio with BNT162b2, 0.32). Vaccine protection waned over time and was more effective against the alpha strain than against the delta strain.
Journal Article
Trajectory of long covid symptoms after covid-19 vaccination: community based cohort study
AbstractObjectiveTo estimate associations between covid-19 vaccination and long covid symptoms in adults with SARS-CoV-2 infection before vaccination.DesignObservational cohort study.SettingCommunity dwelling population, UK.Participants28 356 participants in the Office for National Statistics COVID-19 Infection Survey aged 18-69 years who received at least one dose of an adenovirus vector or mRNA covid-19 vaccine after testing positive for SARS-CoV-2 infection.Main outcome measurePresence of long covid symptoms at least 12 weeks after infection over the follow-up period 3 February to 5 September 2021.ResultsMean age of participants was 46 years, 55.6% (n=15 760) were women, and 88.7% (n=25 141) were of white ethnicity. Median follow-up was 141 days from first vaccination (among all participants) and 67 days from second vaccination (83.8% of participants). 6729 participants (23.7%) reported long covid symptoms of any severity at least once during follow-up. A first vaccine dose was associated with an initial 12.8% decrease (95% confidence interval −18.6% to −6.6%, P<0.001) in the odds of long covid, with subsequent data compatible with both increases and decreases in the trajectory (0.3% per week, 95% confidence interval −0.6% to 1.2% per week, P=0.51). A second dose was associated with an initial 8.8% decrease (95% confidence interval −14.1% to −3.1%, P=0.003) in the odds of long covid, with a subsequent decrease by 0.8% per week (−1.2% to −0.4% per week, P<0.001). Heterogeneity was not found in associations between vaccination and long covid by sociodemographic characteristics, health status, hospital admission with acute covid-19, vaccine type (adenovirus vector or mRNA), or duration from SARS-CoV-2 infection to vaccination.ConclusionsThe likelihood of long covid symptoms was observed to decrease after covid-19 vaccination and evidence suggested sustained improvement after a second dose, at least over the median follow-up of 67 days. Vaccination may contribute to a reduction in the population health burden of long covid, although longer follow-up is needed.
Journal Article
Impact of vaccination on new SARS-CoV-2 infections in the United Kingdom
The effectiveness of COVID-19 vaccination in preventing new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general community is still unclear. Here, we used the Office for National Statistics COVID-19 Infection Survey—a large community-based survey of individuals living in randomly selected private households across the United Kingdom—to assess the effectiveness of the BNT162b2 (Pfizer–BioNTech) and ChAdOx1 nCoV-19 (Oxford–AstraZeneca; ChAdOx1) vaccines against any new SARS-CoV-2 PCR-positive tests, split according to self-reported symptoms, cycle threshold value (<30 versus ≥30; as a surrogate for viral load) and gene positivity pattern (compatible with B.1.1.7 or not). Using 1,945,071 real-time PCR results from nose and throat swabs taken from 383,812 participants between 1 December 2020 and 8 May 2021, we found that vaccination with the ChAdOx1 or BNT162b2 vaccines already reduced SARS-CoV-2 infections ≥21 d after the first dose (61% (95% confidence interval (CI) = 54–68%) versus 66% (95% CI = 60–71%), respectively), with greater reductions observed after a second dose (79% (95% CI = 65–88%) versus 80% (95% CI = 73–85%), respectively). The largest reductions were observed for symptomatic infections and/or infections with a higher viral burden. Overall, COVID-19 vaccination reduced the number of new SARS-CoV-2 infections, with the largest benefit received after two vaccinations and against symptomatic and high viral burden infections, and with no evidence of a difference between the BNT162b2 and ChAdOx1 vaccines.
Results from the Office of National Statistics COVID-19 Infection Survey in the United Kingdom demonstrate that the ChAdOx1 nCoV-19 and BNT162b2 vaccines reduce the incidence of new SARS-CoV-2 infections by up to 65% with a single dose and up to 80% after two doses, with no significant differences in efficacy observed between the two vaccines.
Journal Article
Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers
In a longitudinal study of seropositive and seronegative health care workers undergoing asymptomatic and symptomatic SARS-CoV-2 testing, the presence of anti-spike or anti-nucleocapsid IgG antibodies was associated with a substantially reduced risk of SARS-CoV-2 reinfection in the ensuing 6 months.
Journal Article
Within-host evolution of bacterial pathogens
Key Points
Whole-genome sequencing of several isolates from single hosts has revealed previously unsuspected within-host diversity of many bacterial pathogens.
Within-host bacterial populations are subject to multifarious evolutionary forces including mutation, genetic drift, natural selection and fluctuating population size.
Within-host evolution limits the utility of sampling a single genome per host for reconstructing transmission relationships, conferring a benefit to sequencing several genomes per host.
Resistance to some antimicrobials frequently evolves independently in individual hosts, revealing the substantial potential of bacteria to adapt in the human body.
Within-host adaptation has a major role in the evolution of opportunistic infections in immunocompromised patients by otherwise free-living bacteria.
The study of within-host genomic evolution promises to shed light on whether pathogens tend to become more or less virulent within the host, and the selective pressures underlying this evolution.
Advances in whole-genome sequencing have enabled within-host genome evolution to be studied with unprecedented detail. In this Review article, Didelot, Wilson and colleagues discuss how these studies have altered our view of host adaptation and antibiotic resistance during bacterial infection.
Whole-genome sequencing has opened the way for investigating the dynamics and genomic evolution of bacterial pathogens during the colonization and infection of humans. The application of this technology to the longitudinal study of adaptation in an infected host — in particular, the evolution of drug resistance and host adaptation in patients who are chronically infected with opportunistic pathogens — has revealed remarkable patterns of convergent evolution, suggestive of an inherent repeatability of evolution. In this Review, we describe how these studies have advanced our understanding of the mechanisms and principles of within-host genome evolution, and we consider the consequences of findings such as a potent adaptive potential for pathogenicity. Finally, we discuss the possibility that genomics may be used in the future to predict the clinical progression of bacterial infections and to suggest the best option for treatment.
Journal Article
Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study
Diagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drug-susceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistance-determining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all first-line and second-line drugs for tuberculosis.
Between Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identified from the drug-resistance scientific literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance.
We characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7–93·7) and 98·4% specificity (98·1–98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identified among mutations under selection pressure in non-candidate genes.
A broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workflows, phasing out phenotypic drug-susceptibility testing while reporting drug resistance early.
Wellcome Trust, National Institute of Health Research, Medical Research Council, and the European Union.
Journal Article
Modelling microbiome recovery after antibiotics using a stability landscape framework
Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiome’s diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a ‘stability landscape’: the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to data from a previous study of the year-long effects of short courses of four common antibiotics on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes, and further validate our model using data from another study looking at the impact of a combination of last-resort antibiotics on the gut microbiome. Using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of certain antibiotics in both the gut and oral microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.
Journal Article