Determination of Phenolic Acids and Flavonoids in Taraxacum formosanum Kitam by Liquid Chromatography-Tandem Mass Spectrometry Coupled with a Post-Column Derivatization Technique

A liquid chromatography-tandem mass spectrometry method (LC-MS/MS) was developed for the determination of phenolic acids and flavonoids in a medicinal Chinese herb Taraxacum formosanum Kitam. Initially, both phenolic acids and flavonoids were extracted with 50% ethanol in a water-bath at 60 °C for 3 h and eventually separated into acidic fraction and neutral fraction by using a C18 cartridge. A total of 29 compounds were separated within 68 min by employing a Gemini C18 column and a gradient solvent system of 0.1% formic acid and acetonitrile at a flow rate of 1.0 mL/min. Based on the retention behavior as well as absorption and mass spectra, 19 phenolic acids and 10 flavonoids were identified and quantified in T. formosanum, with the former ranging from 14.1 μg/g to 10,870.4 μg/g, and the latter from 9.9 μg/g to 325.8 μg/g. For further identification of flavonoids, a post-column derivatization method involving shift reagents such as sodium acetate or aluminum chloride was used and the absorption spectral characteristics without or with shift reagents were compared. An internal standard syringic acid was used for quantitation of phenolic acids, whereas (±) naringenin was found suitable for quantitation of flavonoids. The developed LC-MS/MS method showed high reproducibility, as evident from the relative standard deviation (RSD) values for intra-day and inter-day variability being 1.0–6.8% and 2.0–7.7% for phenolic acids and 3.7–7.4% and 1.5–8.1% for flavonoids, respectively, and thus may be applied for simultaneous determination of phenolic acids and flavonoids in Chinese herb and nutraceuticals

Integral Kinetic Model for Studying Quercetin Degradation and Oxidation as Affected by Cholesterol During Heating

The degradation and oxidation of quercetin, as affected by cholesterol during heating at 150 °C, was kinetically studied using non-linear regression models. Both TLC and HPLC were used to monitor the changes of quercetin, cholesterol and cholesterol oxidation products (COPs) during heating. The formation of COPs, including triol, 7-keto, 7α-OH and 7β-OH, was completely inhibited during the initial 30 minute heating period in the presence of 0.02% quercetin, accompanied by reduction in cholesterol peroxidation and degradation. However, the quercetin degradation or oxidation proceeded fast, with the rate constants (h−1) in the presence of nitrogen, oxygen and the combination of oxygen and cholesterol being 0.253, 0.868 and 7.17, respectively. When cholesterol and quercetin were heated together, the rate constants (h−1) of cholesterol peroxidation, epoxidation and degradation were 1.8 × 10−4, 0.016 and 0.19, respectively. The correlation coefficients (r2) for all the oxidative and degradation reactions ranged from 0.82–0.99. The kinetic models developed in this study may be used to predict the degradation and oxidation of quercetin as affected by cholesterol during heating

Understanding of colistin usage in food animals and available detection techniques: a review

Progress in the medical profession is determined by the achievements and effectiveness of new antibiotics in the treatment of microbial infections. However, the development of multiple-drug resistance in numerous bacteria, especially Gram-negative bacteria, has limited the treatment options. Due to this resistance, the resurgence of cyclic polypeptide drugs like colistin remains the only option. The drug, colistin, is a well-known growth inhibitor of Gram-negative bacteria like Acinetobacter baumanni, Enterobacter cloacae, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Technological advancements have uncovered the role of the mcr-1(mobilized colistin resistance) gene, which is responsible for the development of resistance in Gram-negative bacteria, which make them distinct from other bacteria without this gene. Additionally, food animals have been determined to be the reservoir for colistin resistance microbes, from which they spread to other hosts. Due to the adverse effects of colistin, many developed countries have prohibited its usage in animal foods, but developing countries are still using colistin in animal food production, thereby imposing a major risk to the public health. Therefore, there is a need for implementation of sustainable measures in livestock farms to prevent microbial infection. This review highlights the negative effects (increased resistance) of colistin consumption and emphasizes the different approaches used for detecting colistin in animal-based foods as well as the challenges associated with its detectio

Large-scale whole-exome sequencing association studies identify rare functional variants influencing serum urate levels

Elevated serum urate levels can cause gout, an excruciating disease with suboptimal treatment. Previous GWAS identified common variants with modest effects on serum urate. Here we report large-scale whole-exome sequencing association studies of serum urate and kidney function among ≤19,517 European ancestry and African-American individuals. We identify aggregate associations of low-frequency damaging variants in the urate transporters SLC22A12 (URAT1; p = 1.3 × 10-56) and SLC2A9 (p = 4.5 × 10-7). Gout risk in rare SLC22A12 variant carriers is halved (OR = 0.5, p = 4.9 × 10-3). Selected rare variants in SLC22A12 are validated in transport studies, confirming three as loss-of-function (R325W, R405C, and T467M) and illustrating the therapeutic potential of the new URAT1-blocker lesinurad. In SLC2A9, mapping of rare variants of large effects onto the predicted protein structure reveals new residues that may affect urate binding. These findings provide new insights into the genetic architecture of serum urate, and highlight molecular targets in SLC22A12 and SLC2A9 for lowering serum urate and preventing gout

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Pigmentation potency of turf Bermuda grass with analysis of carotenoids in egg yolks

Typescript (photocopy).Four experiments were performed to evaluate the potential of using turf Bermuda grass as a pigmenting agent for laying hens. In experiment 1, the carotene and xanthophyll changes during growth of turf Bermuda grass (Cynodon dactylon) were monitored every three days for a total of 18 days. The carotene and xanthophyll concentrations were not proportional to age or length of the grass clippings. Several methods of processing raw turf Bermuda grass to feed-grade materials were also evaluated. Freeze-drying was by far the best method to maintain the maximum carotene and xanthophyll concentrations. Field drying resulted in considerable losses of both carotene and xanthophyll concentrations. In experiment 2, the effect of diets containing various levels of dehydrated turf Bermuda grass on egg production, feed utilization, yolk color and egg weight was studied. Milo-soybean meal diets were formulated into 4 treatments containing 0, 3, 6 and 9% dried turf Bermuda grass. After 4 weeks the average Roche color score was 1.3, 4.9, 7.0 and 8.7 for treatments 1 through 4, respectively. The maximum egg production and minimum feed consumption were observed in diets containing 3% turf Bermuda grass meal. There was no significant difference of egg weight between control and grass-fed treatments. In experiment 3, the deposition of xanthophylls in egg yolks from laying hens fed 9% turf Bermuda grass meal was studied. The various carotenoids present in egg yolks were analyzed by open-column, thin-layer and high-performance liquid chromatography (HPLC). Chromatographic analyses suggest that the yolk color was due mainly to the presence of free lutein and zeaxanthin at a ratio of 79:21 respectively. In experiment 4, a simple, rapid HPLC method was developed to separate and quantify the major carotenoids present in turf Bermuda grasses with minimum isomerization and oxidation. A reversed-phase isocratic solvent system of water-acetonitrile-chloroform (2:83:15) provided a clear separation of neoxanthin, violaxanthin, lutein and β-carotene. Separation occurred within 10 min with detection at 470 nm and a sensitivity at 0.01 a.u.f.s. This method was found to be very reproducible with coefficients of variation less than 3% in 5 sample analyses

Biotype determination and a study of the selective interaction between the Hessian fly, Mayetiola destructor (Say), and wheat, Triticum aestivum L. em Thell

Fifteen collections of Hessian fly from the northern portion of the soft winter wheat region were used to determine the biotype composition and frequency. Biotypes J and L were found to have replaced biotype B as the prevalent biotypes in Indiana since wheat cultivars carrying the H5 and the H6 genes have been grown. Biotype GP, the least virulent of the known biotypes of Hessian fly, was identified in New York indicating that cultivars with no genes for resistance are still being grown there. Experiments were conducted to determine the interaction between wheats having the H6 gene and Hessian fly biotype M. By definition, biotype M should not survive on wheats having the H6 gene. Auburn, Caldwell, Fillmore, and Knox 62, cultivars all having the H6 gene for resistance, were exposed to Hessian fly biotype M. Reactions to biotype M were significantly different among the cultivars. The selection experiments showed that resistance of Auburn and Fillmore could be easily overcome. Selection on wheats having the H6 gene resulted in a biotype derived from biotype M, capable of surviving on wheats having the H6 gene. This new biotype selected on specific cultivars with the H6 gene differed depending on the cultivar used for selection. Results showed that Knox 62 was consistently different from the other three cultivars used in this study because it probably possesses the H6, the H7H8 and Kawvale genes. Therefore, Knox 62 should not be used as the H6 gene source as a differential cultivar. The responses of this new biotype to wheats having the H9, H9H10, H12, and H13 genes were determined. Results showed that this new biotype possesses virulence on wheats having the H9, H12, and H13 genes. The flies have linked virulence for the H6, H9, H12, and H13 genes