Rheological, chemical and DSC thermal characteristics of different types of palm oil/palm stearin-based shortenings

This study was carried out to evaluate the physical and chemical properties of different types of shortenings, formulated by mixing refined, bleached, and deodorized palm oil and palm stearin (PO:PS) in the following ratios: 100:0, 80:20, 60:40, 50:50, 40:60 and 20:80 and 0:100. The properties of experimental and commercial shortenings were investigated using four different analytical techniques, namely high performance liquid chromatography (HPLC), gas chromatography (GC), differential scanning calorimetry (DSC) and controlled stress rheometer. In addition, iodine value (IV) analysis was carried out. The results revealed that the prominent fatty acids in the products were palmitic (44.88-61.91%), oleic (26.24-39.14%) and linoleic (6.13-11.68%). At the same time, triacyglycerols (TAG), such as OOO, OOP and OOS, were found to decrease, while PPO increased due to the increase in the palm stearin content of the shortenings. Higher viscosity and more storage (G′) or loss (G″) modulus properties were noted in the experimental and commercial shortenings containing higher and lower concentrations of palm stearin and palm oil, respectively. Certain parameters such as the onset, peak and endset temperatures (ºC) were detected for both the melting and cooling data. However, increasing the palm stearin concentrations in the samples was shown to have caused increases in the endset temperature and peak height, and vice versa. Thus, chemical and physical properties of the formulated shortenings may influence the quality of baked products

Production of erythromycin antibiotic by saccharoplyspora erythraea fermentation in shake flasks and bioreactor

Recently success of erythromycin in antibiotic market over the other antibiotics was due to that erythromycin has high quality and it is cheap in price. Erythromycin received much attention because of the increasing applications of its semi-synthetic modified derivatives to infection diseases, such as azithromycin, roxithromycin and clarithromycin. It is produced by the strain Saccharoplyspora erythraea (formerly known as Streptomyces erythraea). In this research, the aims were to optimize medium components for high erythromycin antibiotic production by the strain S. erythae via submerged fermentation using statistical technique known as response surface methodology. Glucose and yeast extract were found to have significant effect to erythromycin production using Placket-Burman experimental design for media screening. The Box-Benkhen experimental design was adopted for optimization studied. Finally, the optimal concentration of glucose, yeast extract, sodium nitrate, dipotasium hydrogen phosphate, sodium chloride and magnesium sulphate obtained using statistical media optimization is approximately 45;8; 4; 2.5;1.0; 0.5 (g L-1), respectively. Result showed that the maximal erythromycin concentration and CDW obtained in shake flasks of optimize medium were 412.5 mg L-1 and 4.9 g L-1, respectively. Production of erythromycin antibiotic reached 30.43% under the optimize medium. Furthermore, the batch culture using new medium formulation for erythromycin production was implemented using controlled and un-controlled pH conditions. Compared with the un-controlled pH bioreactor, the controlled bioreactor was increased erythromycin concentration by 12.9 % up to 567.5 mg L-1. This present work demonstrated that great potential production of erythromycin antibiotic at industrial scale

Impact of the COVID-19 Pandemic on Acute Stroke Care, Time Metrics, Outcomes, and Racial Disparities in a Southeast Michigan Health System

BACKGROUND: COVID-19 has impacted acute stroke care with several reports showing worldwide drops in stroke caseload during the pandemic. We studied the impact of COVID-19 on acute stroke care in our health system serving Southeast Michigan as we rolled out a policy to limit admissions and transfers. METHODS: in this retrospective study conducted at two stroke centers, we included consecutive patients presenting to the ED for whom a stroke alert was activated during the period extending from 3/20/20 to 5/20/20 and a similar period in 2019. We compared demographics, time metrics, and discharge outcomes between the two groups. RESULTS: of 385 patients presented to the ED during the two time periods, 58% were African American. There was a significant decrease in the number of stroke patients presenting to the ED and admitted to the hospital between the two periods (p \u3c0.001). In 2020, patients had higher presenting NIHSS (median: 2 vs 5, p = 0.012), discharge NIHSS (median: 2 vs 3, p = 0.004), and longer times from LKW to ED arrival (4.8 vs 9.4 h, p = 0.031) and stroke team activation (median: 10 vs 15 min, p = 0.006). In 2020, stroke mimics rates were lower among African Americans. There were fewer hospitalizations (p \u3c0.001), and transfers from outside facilities (p = 0.015). CONCLUSION: a trend toward faster stroke care in the ED was observed during the pandemic along with dramatically reduced numbers of ED visits, hospitalizations and stroke mimics. Delayed ED presentations and higher stroke severity characterized the African American population, highlighting deepening of racial disparities during the pandemic

Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants

BACKGROUND: One of the global targets for non-communicable diseases is to halt, by 2025, the rise in the age-standardised adult prevalence of diabetes at its 2010 levels. We aimed to estimate worldwide trends in diabetes, how likely it is for countries to achieve the global target, and how changes in prevalence, together with population growth and ageing, are affecting the number of adults with diabetes. METHODS: We pooled data from population-based studies that had collected data on diabetes through measurement of its biomarkers. We used a Bayesian hierarchical model to estimate trends in diabetes prevalence—defined as fasting plasma glucose of 7·0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs—in 200 countries and territories in 21 regions, by sex and from 1980 to 2014. We also calculated the posterior probability of meeting the global diabetes target if post-2000 trends continue. FINDINGS: We used data from 751 studies including 4 372 000 adults from 146 of the 200 countries we make estimates for. Global age-standardised diabetes prevalence increased from 4·3% (95% credible interval 2·4–7·0) in 1980 to 9·0% (7·2–11·1) in 2014 in men, and from 5·0% (2·9–7·9) to 7·9% (6·4–9·7) in women. The number of adults with diabetes in the world increased from 108 million in 1980 to 422 million in 2014 (28·5% due to the rise in prevalence, 39·7% due to population growth and ageing, and 31·8% due to interaction of these two factors). Age-standardised adult diabetes prevalence in 2014 was lowest in northwestern Europe, and highest in Polynesia and Micronesia, at nearly 25%, followed by Melanesia and the Middle East and north Africa. Between 1980 and 2014 there was little change in age-standardised diabetes prevalence in adult women in continental western Europe, although crude prevalence rose because of ageing of the population. By contrast, age-standardised adult prevalence rose by 15 percentage points in men and women in Polynesia and Micronesia. In 2014, American Samoa had the highest national prevalence of diabetes (>30% in both sexes), with age-standardised adult prevalence also higher than 25% in some other islands in Polynesia and Micronesia. If post-2000 trends continue, the probability of meeting the global target of halting the rise in the prevalence of diabetes by 2025 at the 2010 level worldwide is lower than 1% for men and is 1% for women. Only nine countries for men and 29 countries for women, mostly in western Europe, have a 50% or higher probability of meeting the global target. INTERPRETATION: Since 1980, age-standardised diabetes prevalence in adults has increased, or at best remained unchanged, in every country. Together with population growth and ageing, this rise has led to a near quadrupling of the number of adults with diabetes worldwide. The burden of diabetes, both in terms of prevalence and number of adults affected, has increased faster in low-income and middle-income countries than in high-income countries. FUNDING: Wellcome Trust

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap

BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). DISCUSSION: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression