Search

Abstract Background Although the COVID-19 pandemic has persisted for over 3 years, reinfections with SARS-CoV-2 are not well understood. We aim to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. Methods We use an electronic health record study cohort of over 3 million patients from the National COVID Cohort Collaborative as part of the NIH Researching COVID to Enhance Recovery Initiative. We calculate summary statistics, effect sizes, and Kaplan–Meier curves to better understand COVID-19 reinfections. Results Here we validate previous findings of reinfection incidence (6.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present findings that the proportion of Long COVID diagnoses is higher following initial infection than reinfection for infections in the same epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between initial infection and reinfection (chi-squared value: 25,697, p-value:

Abstract Background Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for “Post COVID-19 condition, unspecified.” Methods We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.

Abstract Previous work has demonstrated some benefit from alternative breeds in low-input dairying, although there has been no systematic analysis of the simultaneous effect of Jersey crossbreeding on productivity, health, fertility parameters or milk nutritional quality. This work aimed to understand the effects of, and interactions/interrelations between, dairy cow genotypes (Holstein-Friesian (HF), Holstein-Friesian × Jersey crossbreds (HF × J)) and season (spring, summer, autumn) on milk yield; basic composition; feed efficiency, health, and fertility parameters; and milk fatty acid (FA) profiles. Milk samples (n = 219) and breed/diet data were collected from 74 cows in four UK low-input dairy farms between March and October 2012. HF × J cows produced milk with more fat (+ 3.2 g/kg milk), protein (+ 2.9 g/kg milk) and casein (+ 2.7 g/kg milk); and showed higher feed, fat, and protein efficiency (expressed as milk, fat and protein outputs per kg DMI) than HF cows. Milk from HF × J cows contained more C4:0 (+ 2.6 g/kg FA), C6:0 (+ 1.9 g/kg FA), C8:0 (+ 1.3 g/kg FA), C10:0 (+ 3.0 g/kg FA), C12:0 (+ 3.7 g/kg FA), C14:0 (+ 4.6 g/kg FA) and saturated FA (SFA; + 27.3 g/kg milk) and less monounsaturated FA (MUFA; -23.7 g/kg milk) and polyunsaturated FA (− 22.3 g/kg milk). There was no significant difference for most health and fertility parameters, but HF × J cows had shorter calving interval (by 39 days). The superior feed, fat and protein efficiency of HF × J cows, as well as shorter calving interval can be considered beneficial for the financial sustainability of low-input dairy farms; and using such alternative breeds in crossbreeding schemes may be recommended. Although statistically significant, it is difficult to determine if differences observed between HF and HF × J cows in fat composition are likely to impact human health, considering average population dairy fat intakes and the relatively small difference. Thus, the HF × J cow could be used in low-input dairying to improve efficiency and productivity without impacting milk nutritional properties.

Summary: Background: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. Methods: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. Findings: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. Funding: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Abstract Although there are many studies on the importance of fatty acids (FA) in our diet and on the influence of dairy diets on FA metabolism, only a few investigate their predictive capacity to discriminate the type, amount and conservation method of farm forages. This research quantifies differences in milk FA concentrations and, using a supervised factorial discriminant analysis, assesses potential biomarkers when replacing maize with other silages, grass/lucerne hays or fresh grass. The statistical modelling identified three main clusters of milk FA profiles associated with silages, hays and fresh grass as dominant roughages. The main implication of a dairy cow feeding system based on poliphytic forages from permanent meadows is enhancing milk’s nutritional quality due to an increase in beneficial omega-3 polyunsaturated FA, conjugated linoleic acids and odd chain FA, compared to feeding maize silage. The study also identified a small but powerful and reliable pool of milk FA that can act as biomarkers to authenticate feeding systems: C16:1 c-9, C17:0, C18:0, C18:3 c-9, c-12, c-15, C18:1 c-9, C18:1 t-11 and C20:0.

The sustainability of dairying has been questioned, yet cattle exploit non-food resources (especially forages) and provide key nutrients for consumers’ health. This study, using different forage types, considered milk’s nutritional quality, focusing on fatty acid profiles alongside methane emissions—investigating whether methane can be predicted from milk fatty acids (FAs). Compared with grass/clover/maize silage, cows grazing grass/clover pasture produced milk 70% higher in beneficial omega-3 FAs, which increased by an additional 15% when grazing more diverse pasture. Milk from grazing also had less omega-6 FAs (compared with silage diets), and their ratio with omega-3 FAs fell from 2.5:1 on silage to 1.2:1 when grazing grass/clover and 1.1:1 on diverse pasture. Measured methane emissions (at 8.7 g/kg energy-corrected milk) were lower than published values, and existing models for estimating methane from lactating cows were poor predictors for this dataset. The multiple regression of methane against milk FAs in this study provided predictions with an R2 of 0.56 for daily emissions and 0.65 relative to milk output. Grazing quality and a diverse pasture with productive cows were potentially beneficial to milk nutritional quality, and our results reinforce the theory that milk fat composition could be an accessible tool for methane prediction; however, they also suggest that more work is needed for alternative production systems.

Many try to eat sustainably but reliable information is lacking, and environmental impact often prioritises over nutritional quality. Here we consider beneficial fatty acid profiles in steaks from 4 UK systems: non-organic, organic, certified pasture-fed and conservation cattle.Most individual fatty acids and ratios considered beneficial for health (individual and total omega 3, vaccenic acid and CLA) followed a gradient, against assumed intensity of production. Ratios for linoleic acid: α-linolenic acid, omega-6:omega-3 and SFA:PUFA in pasture-fed sirloins were only 27%, 55% and 70% (respectively) of those in non-organic beef. Intramuscular fat from pasture-fed meat had twice as much omega-3 and 1.9x the long chain omega-3 concentrations compared with non-organic meat, with a ratio of omega-6:omega-3 only 38% of that in non-organic meat.Meat from 100% pasture-fed and conservation grazing offer public good, matching dietary guidelines better than meat from mainstream systems and qualifying as a source of long chain omega-3 fatty acids.

Low-input (LI) dairy farming, relying heavily on grazing, is increasing in popularity for perceived sustainability, welfare, and milk nutritional quality benefits. However, there is little research into the breed suitability for these systems. The popular Holstein–Friesians are not well-suited to LI production as, to achieve their potential high yields, they require high levels of concentrate intakes and veterinary inputs. Holstein–Friesians were traditionally bred for high milk yields, which often correlate negatively with functional traits, such as fertility and health. This drives the need for alternative breed choices, and UK dairy farmers use several crossbreeding practices. Additionally, classic measures of production efficiency (kilogram feed per liter of milk) are not the sole priority in LI systems, which also aim for improved health, fertility, forage conversion, and milk quality. This study aimed to explore the effect of breeding strategy on LI and organic production in dairy systems, collecting data from 17 farms throughout England and Wales: 7 organic and 10 low-input conventional systems with both purebred and crossbred cows from different breeds. Records from 1,070 cows were collected, including background data, health, fertility, breeding, and parity. Additionally, milk was analyzed on four occasions (autumn 2011 and winter, spring, and summer 2012). Principal components analysis was used to visualize the effect of management, Farm ID, and stage of lactation on LI production. The analysis clustered cows by Farm ID, showing that individual management practice on each farm had the greatest impact on various production traits. Cows were allocated a composite score based on their yield, health records, and milk fatty acid profile, and a linear mixed-effects model indicated (p < 0.01) that crossbred New Zealand Friesian cows scored highest, whereas Dairy Shorthorn cows scored the lowest. This paper highlights weaknesses in current breeding programs for LI and organic farms in the UK, in terms of the alignment of breeds with husbandry practices. Additional research is needed to explore any gene by environment interactions to meet the true potential of individual cows and certain breeds under LI and organic management.

Livestock production is under increasing scrutiny as a component of the food supply chain with a large impact on greenhouse gas emissions. Amidst growing calls to reduce industrial ruminant production, there is room to consider differences in meat quality and nutritional benefits of organic and/or pasture-based management systems. Access to forage, whether fresh or conserved, is a key influencing factor for meat fatty acid profile, and there is increasing evidence that pasture access is particularly beneficial for meat’s nutritional quality. These composition differences ultimately impact nutrient supply to consumers of conventional, organic and grass-fed meat. For this review, predicted fatty acid supply from three consumption scenarios were modelled: i. average UK population National Diet and Nutrition Survey (NDNS) (

The environmental and biodiversity benefits of organic farming are widely recognized, but there is still controversy about the effects of organic production methods on the nutritional composition of food and human health. In the first part of this article therefore, we critically review the evidence that organic farming methods improve the nutritional quality of food crops. Moreover, we summarize our current understanding of how quality gains are linked to the implementation of the “innovations” introduced into conventional crop production during the intensification or “green revolution” of agriculture over the last 100 years. In the second part of the article, we critically review the evidence for the range of health benefits related to organic food consumption. Specifically, we describe and discuss the results from: (i) dietary intervention studies which have found that organic food consumption substantially reduces pesticide exposure in humans and affects feed intake, growth, hormone balances and immune system responsiveness in animal models; (ii) human cohort/epidemiological studies which have reported significant positive associations between organic food consumption and the lower incidence of a range of diseases including obesity, metabolic syndrome, cancer, hypospadias, pre-eclampsia, eczema and middle ear infections in infants; (iii) interactions and trade-offs between diet (e.g., whole-grain, fruit and vegetables and reduced red-meat consumption) and food types (organic versus conventional) concerning public health and future food security. The article also identifies knowledge gaps and highlights the need for (i) long-term, factorial field experiments to understand the relative effects of agronomic and pedoclimatic drivers on crop quality and safety, and (ii) clinical trials and additional human cohort studies to confirm the positive health outcomes linked to organic food consumption. The main conclusions from our review are that there is growing evidence that (i) agricultural intensification has resulted in a reduction in the nutritional quality of food and the sustainability of food production, and (ii) organic farming practices not only improve food quality and human health, but also food security. This is particularly true where current nutritional guidelines (increasing whole-grain, fruit and vegetable products, while reducing red-meat consumption) are implemented.

The Microgastrinae (Hymenoptera: Braconidae) from ten islands of the Canadian Arctic Archipelago (CAA) and Greenland were studied based on 2,183 specimens deposited in collections. We report a total of 33 species in six genera, more than doubling the totals previously known. Most of the species (75.7%) have a distribution restricted to the Nearctic, with nine of those (27.3%) confirmed to be High Arctic endemics and another 10 species considered very likely to be High Arctic endemics as well – accounting for all of those, more than half of all species found are endemic to the region. The most diverse genera were Cotesia (10 species), Glyptapanteles (9 species), and Microplitis (7 species), representing 78.8% of the overall species diversity in the region. The six most frequently collected species comprised 84.7% of all examined specimens. The flight period for Microgastrinae in the High Arctic encompasses only two months, with activity peaking during the first half of July, when almost 40% of all available specimens were collected, and then plummeting in the first half to the end of August. Microgastrinae wasps from the High Arctic are currently known to parasitize eight species within four families of Lepidoptera: three species of Noctuidae, two each of Lymantridae and Nymphalidae, and one species of Pterophoridae. However, that information is very preliminary, as only six of the 33 species of microgastrines currently have associated host data. An annotated checklist, including photographs for 24 of the 33 species, is provided, as well as a key to all Microgastrinae genera present in the region.