Pneumococcal Colonisation Rates in Patients Admitted to a UK Hospital with Lower Respiratory Tract Infection - a prospective case-control study

BACKGROUND Current diagnostic tests are ineffective at identifying the aetiological pathogen in hospitalised adults with lower respiratory tract infection (LRTI). The association of pneumococcal colonisation with disease has been suggested as a means to increase diagnostic precision. We compared pneumococcal colonisation rate and density of nasal pneumococcal colonisation by a) classical culture and b) quantitative real time lytA Polymerase Chain Reaction (qPCR) in patients admitted to hospital in the UK with LRTI compared to control patients. METHODS 826 patients were screened for inclusion in this prospective case-control study. 38 patients were recruited, 19 with confirmed LRTI and 19 controls with another diagnosis. Nasal wash (NW) was collected at the time of recruitment. RESULTS Pneumococcal colonisation was detected in 1 LRTI patient and 3 controls (p=0.6) by classical culture. Using qPCR pneumococcal colonisation was detected in 10 LRTI patients and 8 controls (p=0.5). Antibiotic usage prior to sampling was significantly higher in the LRTI than control group 19 v. 3 (p8000 copies/ml on qPCR pneumococcal colonisation was found in 3 LRTI patients and 4 controls (p > 0.05). CONCLUSIONS We conclude that neither prevalence nor density of nasal pneumococcal colonisation (by culture and qPCR) can be used as a method of microbiological diagnosis in hospitalised adults with LRTI in the UK. A community based study recruiting patients prior to antibiotic therapy may be a useful future step

Adaptations to Climate in Candidate Genes for Common Metabolic Disorders

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders.</p

Adaptations to Climate-Mediated Selective Pressures in Humans

Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans.</p

Adaptations to Climate in Candidate Genes for Common Metabolic Disorders

Evolutionary pressures due to variation in climate play an important role in shaping phenotypic variation among and within species and have been shown to influence variation in phenotypes such as body shape and size among humans. Genes involved in energy metabolism are likely to be central to heat and cold tolerance. To test the hypothesis that climate shaped variation in metabolism genes in humans, we used a bioinformatics approach based on network theory to select 82 candidate genes for common metabolic disorders. We genotyped 873 tag SNPs in these genes in 54 worldwide populations (including the 52 in the Human Genome Diversity Project panel) and found correlations with climate variables using rank correlation analysis and a newly developed method termed Bayesian geographic analysis. In addition, we genotyped 210 carefully matched control SNPs to provide an empirical null distribution for spatial patterns of allele frequency due to population history alone. For nearly all climate variables, we found an excess of genic SNPs in the tail of the distributions of the test statistics compared to the control SNPs, implying that metabolic genes as a group show signals of spatially varying selection. Among our strongest signals were several SNPs (e.g., LEPR R109K, FABP2 A54T) that had previously been associated with phenotypes directly related to cold tolerance. Since variation in climate may be correlated with other aspects of environmental variation, it is possible that some of the signals that we detected reflect selective pressures other than climate. Nevertheless, our results are consistent with the idea that climate has been an important selective pressure acting on candidate genes for common metabolic disorders

Not just bricks and mortar: planning hospital cancer services for Aboriginal people

<p>Abstract</p> <p>Background</p> <p>Aboriginal people in Australia experience higher mortality from cancer compared with non-Aboriginal Australians, despite an overall lower incidence. A notable contributor to this disparity is that many Aboriginal people do not take up or continue with cancer treatment which almost always occurs within major hospitals.</p> <p>Thirty in-depth interviews with urban, rural and remote Aboriginal people affected by cancer were conducted between March 2006 and September 2007. Interviews explored participants' beliefs about cancer and experiences of cancer care and were audio-recorded, transcribed verbatim and coded independently by two researchers. NVivo7 software was used to assist data management and analysis. Information from interviews relevant to hospital services including and building design was extracted.</p> <p>Findings</p> <p>Relationships and respect emerged as crucial considerations of participants although many aspects of the hospital environment were seen as influencing the delivery of care. Five themes describing concerns about the hospital environment emerged: (i) being alone and lost in a big, alien and inflexible system; (ii) failure of open communication, delays and inefficiency in the system; (iii) practicalities: costs, transportation, community and family responsibilities; (iv) the need for Aboriginal support persons; and (v) connection to the community.</p> <p>Conclusions</p> <p>Design considerations and were identified but more important than the building itself was the critical need to build trust in health services. Promotion of cultural safety, support for Aboriginal family structures and respecting the importance of place and community to Aboriginal patients are crucial in improving cancer outcomes.</p

Combinations of β-lactam or aminoglycoside antibiotics with plectasin are synergistic against methicillin-sensitive and methicillin-resistant Staphylococcus aureus.

Bacterial infections remain the leading killer worldwide which is worsened by the continuous emergence of antibiotic resistance. In particular, methicillin-sensitive (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) are prevalent and the latter can be difficult to treat. The traditional strategy of novel therapeutic drug development inevitably leads to emergence of resistant strains, rendering the new drugs ineffective. Therefore, rejuvenating the therapeutic potentials of existing antibiotics offers an attractive novel strategy. Plectasin, a defensin antimicrobial peptide, potentiates the activities of other antibiotics such as β-lactams, aminoglycosides and glycopeptides against MSSA and MRSA. We performed in vitro and in vivo investigations to test against genetically diverse clinical isolates of MSSA (n = 101) and MRSA (n = 115). Minimum inhibitory concentrations (MIC) were determined by the broth microdilution method. The effects of combining plectasin with β-lactams, aminoglycosides and glycopeptides were examined using the chequerboard method and time kill curves. A murine neutropenic thigh model and a murine peritoneal infection model were used to test the effect of combination in vivo. Determined by factional inhibitory concentration index (FICI), plectasin in combination with aminoglycosides (gentamicin, neomycin or amikacin) displayed synergistic effects in 76-78% of MSSA and MRSA. A similar synergistic response was observed when plectasin was combined with β-lactams (penicillin, amoxicillin or flucloxacillin) in 87-89% of MSSA and MRSA. Interestingly, no such interaction was observed when plectasin was paired with vancomycin. Time kill analysis also demonstrated significant synergistic activities when plectasin was combined with amoxicillin, gentamicin or neomycin. In the murine models, plectasin at doses as low as 8 mg/kg augmented the activities of amoxicillin and gentamicin in successful treatment of MSSA and MRSA infections. We demonstrated that plectasin strongly rejuvenates the therapeutic potencies of existing antibiotics in vitro and in vivo. This is a novel strategy that can have major clinical implications in our fight against bacterial infections

Adaptations to Climate-Mediated Selective Pressures in Humans

Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans

Adjuvant Sorafenib for Renal Cell Carcinoma at Intermediate or High Risk of Relapse: Results From the SORCE Randomized Phase III Intergroup Trial.

PURPOSE: SORCE is an international, randomized, double-blind, three-arm trial of sorafenib after surgical excision of primary renal cell carcinoma (RCC) found to be at intermediate or high risk of recurrence. PATIENTS AND METHODS: We randomly assigned participants (2:3:3) to 3 years of placebo (arm A), 1 year of sorafenib followed by 2 years of placebo (arm B), or 3 years of sorafenib (arm C). The initial sorafenib dose was 400 mg twice per day orally, amended to 400 mg daily. The primary outcome analysis, which was revised as a result of external results, was investigator-reported disease-free survival (DFS) comparing 3 years of sorafenib versus placebo. RESULTS: Between July 2007 and April 2013, we randomly assigned 1,711 participants (430, 642, and 639 participants in arms A, B, and C, respectively). Median age was 58 years, 71% of patients were men, 84% had clear cell histology, 53% were at intermediate risk of recurrence, and 47% were at high risk of recurrence. We observed no differences in DFS or overall survival in all randomly assigned patients, patients with high risk of recurrence, or patients with clear cell RCC only. Median DFS was not reached for 3 years of sorafenib or for placebo (hazard ratio, 1.01; 95% CI, 0.83 to 1.23; P = .95). We observed nonproportional hazards; the restricted mean survival time (RMST) was 6.81 years for 3 years of sorafenib and 6.82 years for placebo (RMST difference, 0.01 year; 95% CI, -0.49 to 0.48 year; P = .99). Despite offering treatment adaptations, more than half of participants stopped treatment by 12 months. Grade 3 hand-foot skin reaction was reported in 24% of participants on sorafenib. CONCLUSION: Sorafenib should not be used as adjuvant therapy for RCC. Active surveillance remains the standard of care for patients at intermediate or high risk of recurrence after nephrectomy and is the appropriate control of our current international adjuvant RCC trial, RAMPART.CRU

A two-step adaptive walk rewires nutrient transport in a challenging edaphic environment

Most well-characterized cases of adaptation involve single genetic loci. Theory suggests that multilocus adaptive walks should be common, but these are challenging to identify in natural populations. Here, we combine trait mapping with population genetic modeling to show that a two-step process rewired nutrient homeostasis in a population of Arabidopsis as it colonized the base of an active stratovolcano characterized by extremely low soil manganese (Mn). First, a variant that disrupted the primary iron (Fe) uptake transporter gene (IRT1) swept quickly to fixation in a hard selective sweep, increasing Mn but limiting Fe in the leaves. Second, multiple independent tandem duplications occurred at NRAMP1 and together rose to near fixation in the island population, compensating the loss of IRT1 by improving Fe homeostasis. This study provides a clear case of a multilocus adaptive walk and reveals how genetic variants reshaped a phenotype and spread over space and time

The impact of land use/land cover scale on modelling urban ecosystem services

Context Urbanisation places increasing stress on ecosystem services; however existing methods and data for testing relationships between service delivery and urban landscapes remain imprecise and uncertain. Unknown impacts of scale are among several factors that complicate research. This study models ecosystem services in the urban area comprising the towns of Milton Keynes, Bedford and Luton which together represent a wide range of the urban forms present in the UK. Objectives The objectives of this study were to test (1) the sensitivity of ecosystem service model outputs to the spatial resolution of input data, and (2) whether any resultant scale dependency is constant across different ecosystem services and model approaches (e.g. stock- versus flow-based). Methods Carbon storage, sediment erosion, and pollination were modelled with the InVEST framework using input data representative of common coarse (25 m) and fine (5 m) spatial resolutions. Results Fine scale analysis generated higher estimates of total carbon storage (9.32 vs. 7.17 kg m−2) and much lower potential sediment erosion estimates (6.4 vs. 18.1 Mg km−2 year−1) than analyses conducted at coarser resolutions; however coarse-scale analysis estimated more abundant pollination service provision. Conclusions Scale sensitivities depend on the type of service being modelled; stock estimates (e.g. carbon storage) are most sensitive to aggregation across scales, dynamic flow models (e.g. sediment erosion) are most sensitive to spatial resolution, and ecological process models involving both stocks and dynamics (e.g. pollination) are sensitive to both. Care must be taken to select model data appropriate to the scale of inquiry