Sputum and blood eosinophil counts predict corticosteroid effects in COPD patients. Bacterial infection causes increased airway neutrophilic inflammation. The relationship of eosinophil counts with airway bacterial load in COPD patients is uncertain. We tested the hypothesis that bacterial load and eosinophil counts are inversely related. COPD patients were seen at stable state and exacerbation onset. Sputum was processed for quantitative polymerase chain reaction detection of the potentially pathogenic microorganisms (PPM) H. influenzae, M. catarrhalis and S. pneumoniae. PPM positive was defined as total load ≥1 × 104copies/ml. Sputum and whole blood were analysed for differential cell counts. At baseline, bacterial counts were not related to blood eosinophils, but sputum eosinophil % was significantly lower in patients with PPM positive compared to PPM negative samples (medians: 0.5% vs. 1.25% respectively, p = 0.01). Patients with PPM positive samples during an exacerbation had significantly lower blood eosinophil counts at exacerbation compared to baseline (medians: 0.17 × 109/L vs. 0.23 × 109/L respectively, p = 0.008), while no blood eosinophil change was observed with PPM negative samples. These findings indicate an inverse relationship between bacterial infection and eosinophil counts. Bacterial infection may influence corticosteroid responsiveness by altering the profile of neutrophilic and eosinophilic inflammation.

Meiotic recombination plays a key role in the maintenance of sequence diversity in the human genome. However, little is known about the fine–scale distribution and processes of recombination in human chromosomes, or how these impact on patterns of human diversity. We have therefore developed sperm typing systems that allow human recombination to be analysed at very high resolution. The emerging picture is that human crossovers are far from randomly distributed but instead are targeted into very narrow hot spots that can profoundly influence patterns of haplotype diversity in the human genome. These hot spots provide fundamental information on processes of human crossover and gene conversion, as well as evidence that they can violate basic rules of Mendelian inheritance.

Population diversity data have recently provided profound, albeit inferential, insights into meiotic recombination across the human genome, revealing a landscape dominated by thousands of cross-over hotspots. However, very few of these putative hotspots have been directly analyzed for cross-over activity. We now describe a search for very active hotspots, by using extreme breakdown of marker association as a guide for high-resolution sperm cross-over analysis. This strategy has led to the isolation of the most active cross-over hotspots yet described. Their morphology, sequence attributes, and cross-over processes are very similar to those seen at less active hotspots, but their activity in sperm is poorly predicted from population diversity information. Several of these hotspots showed evidence for biased gene conversion accompanying cross-over, in some cases associated with variation between men in cross-over activity and with two hotspots showing complete presence/absence polymorphism in different men. Hotspot polymorphism is very common at less active hotspots but curiously was not seen at any of the most active hotspots. This contrasts with the prediction that extreme hotspots should be the most vulnerable to attenuation by meiotic drive in favor of mutations that suppress recombination and should therefore show rapid rate evolution and thus variation in activity between men. Finally, these very intense hotspots provide a valuable resource for dissecting meiotic recombination processes and pathways in humans.

The worldwide spread of the coronavirus disease 2019 (COVID-19) pandemic in early 2020 affected all major sectors, including higher education. The measures to contain the spread of this deadly disease led to the closure of colleges and universities across the globe, disrupting the lives of millions of students and subjecting them to a new world of online learning. These sudden disturbances coupled with the demands of a new learning system and the experiences of living through a pandemic have placed additional strains on the mental health of university students. Research on university students’ mental health, conducted during the pandemic, have found high levels of stress, anxiety and depression among students. In this qualitative study, we aimed to understand how pandemic experiences have affected student well-being by conducting in-depth interviews with 34 undergraduate students enrolled in a UK university. All interviews were conducted through Microsoft Teams and recorded with prior permission. Transcripts of recorded interviews were thematically analysed which identified two broad themes: (i) University students’ mental health and well-being experiences during the pandemic; (ii) factors that influenced students’ mental health and well-being. These factors were further distributed across six sub-themes: (a) isolation; (b) health and well-being; (c) bereavement; (d) academic concerns; (e) financial worries and; (f) support, coping, and resilience. Our study identifies the importance of mental health support to university students during pandemics and calls for measures to improve access to support services through these crisis points by universities. Findings can also inform students’ mental health and risk assessments in the aftermath of the pandemic.

Genetic variation databases have become indispensable in many areas of health care. In addition, more and more experts are depositing published and unpublished disease-causing variants of particular genes into locus-specific databases (LSDBs). Some of these databases contain such extensive information that they have become known as knowledge bases. Here, we analyzed 1,188 LSDBs and their content for the presence or absence of 44 content criteria related to database features (general presentation, locus-specific information, database structure) and data content (data collection, summary table of variants, database querying). Our analyses revealed that several elements have helped to advance the field and reduce data heterogeneity, such as the development of specialized database management systems and the creation of data querying tools. We also identified a number of deficiencies, namely, the lack of detailed disease and phenotypic descriptions for each genetic variant and links to relevant patient organizations, which, if addressed, would allow LSDBs to better serve the clinical genetics community. We propose a structure, based on LSDBs and closely related repositories (namely, clinical genetics databases), which would contribute to a federated genetic variation browser and also allow the maintenance of variation data. Hum Mutat 31:1–8, 2010.

To identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). Analysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ≥10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). Among 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. Our method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models.