How Lifestyle Changes during the COVID-19 Global Pandemic Affected the Pattern and Symptoms of the Menstrual Cycle.

This research investigated the implications that the COVID-19 pandemic had on the menstrual cycle and any contributing factors to these changes. A questionnaire was completed by 559 eumenorrheic participants, capturing detail on menstrual cycle symptoms and characteristics prior to and during the COVID-19 pandemic lockdown period. Over half of all participants reported to have experienced lack of motivation (61.5%), focus (54.7%) and concentration (57.8%). 52.8% of participants reported an increase in cycle length. Specifically, there was an increase in the median cycle length reported of 5 days (minimum 2 days, maximum 32 days), with a median decrease of 3 days (minimum 2 days and maximum 17 days). A lack of focus was significantly associated with a change in menstrual cycle length (p = 0.038) reported to have increased by 61% of participants. Changes to eating patterns of white meat (increase p = 0.035, decrease p = 0.003) and processed meat (increase p = 0.002 and decrease p = 0.001) were significantly associated with a change in menstrual cycle length. It is important that females and practitioners become aware of implications of environmental stressors and the possible long-term effects on fertility. Future research should continue to investigate any long-lasting changes in symptoms, as well as providing education and support for females undergoing any life stressors that may implicate their menstrual cycle and/or symptoms.

A Trifecta of New Insights into Ovine Footrot for Infection Drivers, Immune Response, and Host-Pathogen Interactions.

Footrot is a polymicrobial infectious disease in sheep causing severe lameness, leading to one of the industry's largest welfare problems. The complex etiology of footrot makes in situ or in vitro investigations difficult. Computational methods offer a solution to understanding the bacteria involved and how they may interact with the host, ultimately providing a way to identify targets for future hypothesis-driven investigative work. Here, we present the first combined global analysis of bacterial community transcripts together with the host immune response in healthy and diseased ovine feet during a natural polymicrobial infection state using metatranscriptomics. The intratissue and surface bacterial populations and the most abundant bacterial transcriptomes were analyzed, demonstrating that footrot-affected skin has reduced diversity and increased abundances of not only the causative bacterium Dichelobacter nodosus but also other species such as Mycoplasma fermentans and Porphyromonas asaccharolytica. Host transcriptomics reveals the suppression of biological processes related to skin barrier function, vascular functions, and immunosurveillance in unhealthy interdigital skin, supported by histological findings that type I collagen (associated with scar tissue formation) is significantly increased in footrot-affected interdigital skin compared to outwardly healthy skin. Finally, we provide some interesting indications of host and pathogen interactions associated with virulence genes and the host spliceosome, which could lead to the identification of future therapeutic targets.

DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples.

High throughput genomics technologies are applied widely to microbiomes in humans, animals, soil and water, to detect changes in bacterial communities or the genes they carry, between different environments or treatments. We describe a method to test the statistical significance of differences in bacterial population or gene composition, applicable to metagenomic or quantitative polymerase chain reaction data. Our method goes beyond previous published work in being universally most powerful, thus better able to detect statistically significant differences, and through being more reliable for smaller sample sizes. It can also be used for experimental design, to estimate how many samples to use in future experiments, again with the advantage of being universally most powerful. We present three example analyses in the area of antimicrobial resistance. The first is to published data on bacterial communities and antimicrobial resistance genes (ARGs) in the environment; we show that there are significant changes in both ARG and community composition. The second is to new data on seasonality in bacterial communities and ARGs in hooves from four sheep. While the observed differences are not significant, we show that a minimum group size of eight sheep would provide sufficient power to observe significance of similar changes in further experiments. The third is to published data on bacterial communities surrounding rice crops. This is a much larger data set and is used to verify the new method. Our method has broad uses for statistical testing and experimental design in research on changing microbiomes, including studies on antimicrobial resistance.

Infections Up to 76 Days After Stroke Increase Disability and Death.

Early infection after stroke is associated with a poor outcome. We aimed to determine whether delayed infections (up to 76 days post-stroke) are associated with poor outcome at 90 days. Data came from the international Efficacy of Nitric Oxide Stroke (ENOS, ISRCTN99414122) trial. Post hoc data on infections were obtained from serious adverse events reports between 1 and 76 days following stroke in this large cohort of patients. Regression models accounting for baseline covariates were used to analyse fatalities and functional outcomes (modified Rankin Scale (mRS), Barthel Index, Euro-Qol-5D) at 90 days, in patients with infection compared to those without infection. Of 4011 patients, 242 (6.0%) developed one or more serious infections. Infections were associated with an increased risk of death (p < 0.001) and an increased likelihood of dependency (measured by mRS) compared to those of all other patients (p < 0.001). This remained when only surviving patients were analysed, indicating that the worsening of functional outcome is not due to mortality (p < 0.001). In addition, the timing of the infection after stroke did not alter its detrimental association with fatality (p = 0.14) or functional outcome (p = 0.47). In conclusion, severe post-stroke infections, whether occurring early or late after stroke, are associated with an increased risk of death and poorer functional outcome, independent of differences in baseline characteristics or treatment. Not only are strategies needed for reducing the risk of infection immediately after stroke, but also during the first 3 months following a stroke. This study is registered: ISRCTN registry, number ISRCTN99414122, ClinicalTrials.gov Identifier, NCT00989716.

Neuroimaging Biomarkers Predict Brain Structural Connectivity Change in a Mouse Model of Vascular Cognitive Impairment.

BACKGROUND AND PURPOSE: Chronic hypoperfusion in the mouse brain has been suggested to mimic aspects of vascular cognitive impairment, such as white matter damage. Although this model has attracted attention, our group has struggled to generate a reliable cognitive and pathological phenotype. This study aimed to identify neuroimaging biomarkers of brain pathology in aged, more severely hypoperfused mice. METHODS: We used magnetic resonance imaging to characterize brain degeneration in mice hypoperfused by refining the surgical procedure to use the smallest reported diameter microcoils (160 µm). RESULTS: Acute cerebral blood flow decreases were observed in the hypoperfused group that recovered over 1 month and coincided with arterial remodeling. Increasing hypoperfusion resulted in a reduction in spatial learning abilities in the water maze that has not been previously reported. We were unable to observe severe white matter damage with histology, but a novel approach to analyze diffusion tensor imaging data, graph theory, revealed substantial reorganization of the hypoperfused brain network. A logistic regression model from the data revealed that 3 network parameters were particularly efficient at predicting group membership (global and local efficiency and degrees), and clustering coefficient was correlated with performance in the water maze. CONCLUSIONS: Overall, these findings suggest that, despite the autoregulatory abilities of the mouse brain to compensate for a sudden decrease in blood flow, there is evidence of change in the brain networks that can be used as neuroimaging biomarkers to predict outcome.

Estimating the within-household infection rate in emerging SIR epidemics among a community of households.

This paper is concerned with estimation of the within-household infection rate γL for a susceptible --> infective --> recovered epidemic among a population of households, from observation of the early, exponentially growing phase of an epidemic. Specifically, it is assumed that an estimate of the exponential growth rate is available from general data on an emerging epidemic and more-detailed, household-level data are available in a sample of households. Estimates of γL obtained using the final size distribution of single-household epidemics are usually biased owing to the emerging nature of the epidemic. A new method, which accounts correctly for the emerging nature of the epidemic, is developed by exploiting the asymptotic theory of supercritical branching processes and proved to yield a strongly consistent estimator of γL as the population and sampled households both tend to infinity in an appropriate fashion. The theory is illustrated by simulations which demonstrate that the new method is feasible for finite populations and numerical studies are used to explore how changes to the parameters governing the spread of an epidemic affect the bias of estimates based on single-household final size distributions.

Menopause, evolution and changing cultures.

The menopause is an isolated event in a much wider process that was probably an evolutionary adaptation essential for survival in the Pliocene. As a reproductive strategy, it is largely vestigial in the 21st century, part of an era that has seen a doubling of the average human longevity compared with that of the past. This process commences as an accelerated decline in female fertility, usually from the fourth decade of life, culminating in a total cessation of reproductive capacity for those surviving. The 20th and 21st century sees a huge increase in the numbers surviving and the duration of that postreproductive life phase extending for decades. This extended period of what is essentially a hormone deficiency state is a recent phenomenon and by no means part of the natural history of the human individual. It is therefore not surprising to see a postmenopausal increase in the incidence of so many disorders above that expected by age alone. Recent reproductive patterns have seen increases in the birth rate and requests for fertility treatments among women in their late 30s and 40s. Many try for pregnancy but are unsuccessful. The genes that permit later reproduction and hence later menopause are therefore being preferentially selected. Slowly over generations we will expect to see the fertility of future 40 year olds increase and the age of menopause to extend much later into our, now, longer lives.