The effect of bone and analytical methods on the assessment of bone mineralization response to dietary phosphorus, phytase, and vitamin D in nursery pigs.

A total of 360 pigs (DNA 600 × 241, DNA; initially 11.9 ±â€…0.56 kg) were used in a 28-d trial to evaluate the effects of different bones and analytical methods on the assessment of bone mineralization response to dietary P, vitamin D, and phytase in nursery pigs. Pens of pigs (six pigs per pen) were randomized to six dietary treatments in a randomized complete block design with 10 pens per treatment. Dietary treatments were designed to create differences in bone mineralization and included: (1) 0.19% standardized total tract digestibility (STTD) P (deficient), (2) 0.33% STTD P (NRC [2012] requirement) using monocalcium phosphate, (3) 0.33% STTD P including 0.14% release from phytase (Ronozyme HiPhos 2700, DSM Nutritional Products, Parsippany, NJ), (4) 0.44% STTD P using monocalcium phosphate, phytase, and no vitamin D, (5) diet 4 with vitamin D (1,653 IU/kg), and (6) diet 5 with an additional 50 µg/kg of 25(OH)D3 (HyD, DSM Nutritional Products, Parsippany, NJ) estimated to provide an additional 2,000 IU/kg of vitamin D3. After 28 d on feed, eight pigs per treatment were euthanized for bone (metacarpal, 2nd rib, 10th rib, and fibula), blood, and urine analysis. The response to treatment for bone density and ash was dependent upon the bone analyzed (treatment × bone interaction for bone density, P = 0.044; non-defatted bone ash, P = 0.060; defatted bone ash, P = 0.068). Thus, the response related to dietary treatment differed depending on which bone (metacarpal, fibula, 2nd rib, or 10th rib) was measured. Pigs fed 0.19% STTD P had decreased (P < 0.05) bone density and ash (non-defatted and defatted) for all bones compared to 0.44% STTD P, with 0.33% STTD P generally intermediate or similar to 0.44% STTD P. Pigs fed 0.44% STTD P with no vitamin D had greater (P < 0.05) non-defatted fibula ash compared to all treatments other than 0.44% STTD P with added 25(OH)D3. Pigs fed diets with 0.44% STTD P had greater (P < 0.05) defatted second rib ash compared to pigs fed 0.19% STTD P or 0.33% STTD P with no phytase. In summary, bone density and ash responses varied depending on bone analyzed. Differences in bone density and ash in response to P and vitamin D were most apparent with fibulas and second ribs. There were apparent differences in the bone ash percentage between defatted and non-defatted bone. However, differences between the treatments remain consistent regardless of the analytic procedure. For histopathology, 10th ribs were more sensitive than 2nd ribs or fibulas for the detection of lesions.

Lameness is defined as impaired movement or deviation from normal gait. There are many factors that can contribute to lameness, including but not limited to: infectious disease, genetic and conformational anomaly, and toxicity that affects the bone, muscle, and nervous systems. Metabolic bone disease is another cause of lameness in swine production and can be caused by inappropriate levels of essential vitamins or minerals. To understand and evaluate bone mineralization, it is important to understand the differences in diagnostic results between different bones and analytical techniques. Historically, percentage bone ash has been used as one of the procedures to assess metabolic bone disease as it measures the level of bone mineralization; however, procedures and results vary depending on the methodology and type of bone measured. Differences in bone density and ash in response to dietary P and vitamin D were most apparent in the fibulas and second ribs. There were apparent differences in the percentage of bone ash between defatted and non-defatted bone; however, the differences between the treatments remain consistent regardless of the analytic procedure. For histopathology, 10th ribs were more sensitive than 2nd ribs or fibulas for detection of lesions associated with metabolic bone disease.

Genomics, social media and mobile phone data enable mapping of SARS-CoV-2 lineages to inform health policy in Bangladesh.

Genomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook 'Data for Good' and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.

U.S. county-level characteristics to inform equitable COVID-19 response.

Background: The spread of Coronavirus Disease 2019 (COVID-19) across the United States confirms that not all Americans are equally at risk of infection, severe disease, or mortality. A range of intersecting biological, demographic, and socioeconomic factors are likely to determine an individual's susceptibility to COVID-19. These factors vary significantly across counties in the United States, and often reflect the structural inequities in our society. Recognizing this vast inter-county variation in risks will be critical to mounting an adequate response strategy. Methods and Findings: Using publicly available county-specific data we identified key biological, demographic, and socioeconomic factors influencing susceptibility to COVID-19, guided by international experiences and consideration of epidemiological parameters of importance. We created bivariate county-level maps to summarize examples of key relationships across these categories, grouping age and poverty; comorbidities and lack of health insurance; proximity, density and bed capacity; and race and ethnicity, and premature death. We have also made available an interactive online tool that allows public health officials to query risk factors most relevant to their local context.Our data demonstrate significant inter-county variation in key epidemiological risk factors, with a clustering of counties in certain states, which will result in an increased demand on their public health system. While the East and West coast cities are particularly vulnerable owing to their densities (and travel routes), a large number of counties in the Southeastern states have a high proportion of at-risk populations, with high levels of poverty, comorbidities, and premature death at baseline, and low levels of health insurance coverage.The list of variables we have examined is by no means comprehensive, and several of them are interrelated and magnify underlying vulnerabilities. The online tool allows readers to explore additional combinations of risk factors, set categorical thresholds for each covariate, and filter counties above different population thresholds. Conclusion: COVID-19 responses and decision making in the United States remain decentralized. Both the federal and state governments will benefit from recognizing high intra-state, inter-county variation in population risks and response capacity. Many of the factors that are likely to exacerbate the burden of COVID-19 and the demand on healthcare systems are the compounded result of long-standing structural inequalities in US society. Strategies to protect those in the most vulnerable counties will require urgent measures to better support communities' attempts at social distancing and to accelerate cooperation across jurisdictions to supply personnel and equipment to counties that will experience high demand.