6 research outputs found
The effect of Rosmarinus officinalis and Zataria multiflora extracts on the stability of poly unsaturated fatty acids in frozen silver carp minced
The purpose of this study, Evaluation the effect of Rosmarinus officinalis and Thymus
vulgaris extracts on the stability of poly unsaturated fatty acids in frozen Silver carp
minced. Treatments include:
Treatment 1 - Control: frozen meat packaged in conventional
Treatment 2: Frozen Silver carp minced+Thyme 300 mg/kg in normal packaging
Treatment 3: Frozen Silver carp minced+Rosemary 200 mg/kg in normal packaging
Treatment 4: Frozen Silver carp minced+Rosemary compound (100 mg/kg) and Thyme
(100 mg/kg) in normal packaging
After rapid freezing of samples in the spiral freezer by individual quick freezing method, to
maintain the cold temperature (-18) °C were transferred. Sampling and measurements to
determine the fatty acid profile of the zero phase beginning in the first month and then
every ten days, and 15 days in the second month of the third month after the monthly test.
Identifying, defining and measuring the fatty acid profile by gas chromatography was
performed. In this study, levels of both saturated and unsaturated fatty acids in three
experimental and one control were identified as follows:
A: saturated fatty acids: Meristic C14: 0/Palmitic C16: 0/Hepta decaenoic C17: 0/Stearic C18: 0/Arashidic C20: 0/B:Mono unsaturated fatty acids: palmitoleic C16: 1-W7/Oleic C18: 1-W9/Gadoleic C20:
1-W9
C:Poly unsaturated fatty acids: Linoleic C18: 2-W6/α-Linolenic C18: 3-W3
D:High unsaturated fatty acids: Arachidonic C20: 4-W6 Eicosapentaenoic acid C20: 5-EPA/W3 Docosahexaenoic C22: 6-DHA/W3
Results of this study was to determine, Thyme and rosemary extracts containing silver carp
minced stored in freezing conditions, Stability of different types of fatty acids, monounsaturated
fatty acids, poly-unsaturated fatty acids, omega-3 and omega-6 fatty acids are.
So that none of the fatty acids measured were not significant 100% increase or decrease,
While changes in the fatty acid oxidation during storage time is minimized. The results
obtained from the fatty acid profiles and indicators of their and statistical tests show that
treatment with rosemary extract More stable during storage (-18) ° C In comparison with
the control and other treatments are shown; And at relatively low compared to other
treatments and control samples oleic acid and linoleic acid, palmitic more.
According to studies,in Silver carp minced that containing rosemary extract, end of the
storage period of six months. Were usable, so even rosemary extract the shelf-life
examples to increase more than six months
The study of stability and changes poly unsaturated fatty acid (PUFA), coefficient of the (Polyen Index) in burger production of Kilka (Clupeonella cultriventris) and Silver carp (Hypophthalmichthys molitrix) during storage at - 18 °C
Berger is one of the most significant products as fish paste, now produced of red meat and red meat waste with different qualities, does not have much nutritional value .Using fish as an alternative to red meat, diversify and enhance the quality of nutritional value and the ability to use for all ages in the community of the aims of this study were , in addition the combined use of a cultured marine species is effective and to raise the index of unsaturated fatty acids and amino acids in the final product .In this study the effects of different replacement of kilka meat instead of Silver carp in the preparation of the consolidated burger on the acceptance, as well as changes unsaturated fatty acids and poly factor that it considered during 5 months of the best treatment choice for industrial production to be introduced industry Therefore 4 treatments Burger compilation as follows: Treatment (control), combination of burger with %100 Silver carp meat , treatment 1, combination of burger with %100 Kilka meat, treatment 2, combination of burger with %75 + 25 Kilka and Silver carp meat , treatment 3, combination of burger with %50 + 50 Kilka and Silver carp meat , after adding the %5.6 flour and breadcrumbs, %6 onions, %5soy, %3.25tomato paste,% 0.15 lemon juice,%2.1 garlic and egg powder and spices and %1.2salt to treatments , after preparation and packaging of the kept at cold room(-18o c )was evaluated . According to treatment 3 test (50% Kilka meat and 50% Silver carp meat) with the other treatments had significant difference (p <0.05) and had higher levels of unsaturated fatty acid. Also the treatment2, control and 1 respectively are secondary priorities. According to the goals of this project that combine Kilka and Silver crap meat on preparation of fusion burger is to economies the final product , results within 5 months of product storage at - 18 ° C reflects the fact that product quality in the long term preservation of unsaturated fatty acids has been accepted
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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
Ectopic expression of TWIST1 upregulates the stemness marker OCT4 in the esophageal squamous cell carcinoma cell line KYSE30
Abstract Background The transcription factor TWIST1 plays an important role in the epithelial–mesenchymal transition (EMT) process and in the migration, invasion and metastasis of cancer cells. OCT4, which is a homeobox transcription factor, has an important role in the self-renewal potential of cancer cells. Our aim here is to elucidate impact of ectopic expression of TWIST1 on OCT4 gene expression in esophageal squamous cell carcinoma (ESCC). Methods The ESCC line was KYSE30. GP293T cells were transfected with purf-IRES-GFP and pGP plasmids to produce recombinant viral particles. A semi-confluent KYSE30 culture was transduced with the prepared retroviral particles. mRNA extraction and cDNA synthesis were performed from normal KYSE30 cells and those ectopically expressing TWIST1. Expressional analysis of TWIST1 and OCT4 were performed with relative comparative real-time PCR. Results Ectopic expression of TWIST1 in KYSE30 cells was related to its significant overexpression: nearly nine-fold higher in GFP-hTWIST1 KYSE-30 cells than in control GFP cells. This induced expression of TWIST1 caused significant upregulation of OCT4 in GFP-hTWIST1 KYSE-30 cells: nearly eight-fold higher. In silico analysis predicted the correlation of TWIST1 and OCT4 through ETS2. Conclusions Overexpressed TWIST1 can be correlated with upregulation of the cancer stem cell marker OCT4 and the protein may play critical regulatory role in OCT4 gene expression. Since OCT4 is involved in the self-renewal process, the results may suggest a new linkage between TWIST1 and OCT4 in the cell biology of ESCC, highlighting the probable role of TWIST1 in inducing self-renewal
Ectopic expression of TWIST1 upregulates the stemness marker OCT4 in the esophageal squamous cell carcinoma cell line KYSE30
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
BackgroundRegular, 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.MethodsThe 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.FindingsThe 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.InterpretationLong-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