https://www.sciencedirect.com/science/article/pii/S1876034121004263#bib0085
lördag 12 februari 2022
Heikennetty viruskanta sars-2 NIV-2020 on perustana Covaxin rokotteessa
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366528/
Indian J Med Res. 2020 Feb-Mar; 151(2-3): 244–250.
PMCID: PMC7366528
PMID: 32362649
First isolation of SARS-CoV-2 from clinical samples in India
COVAXIN rokote on inaktivoitua Sars-2 virusta, jossa on immunostimulatorinen ingredienssi
Covaxin Vaccine Efficacy, Price, Side Effects, Dose Gap
Here you will discover all information on the Covaxin Vaccine, including its effects, the vaccine’s effective period in the body, vaccination protocols, and much more. Our company has offered all nuances to supply knowledge to those who need it.
Contents
Covaxin Vaccine
Bharat Biotech, an Indian biotechnology business, and the Indian Council of Medical Research produced Covaxin, a COVID-19 vaccine. According to interim phase 3 clinical data, it’s a two-dose vaccination with a 78 percent effectiveness rate.
On January 3, 2021, India’s drug regulatory authorities, the Central Drugs Standard Control Organization, approved the vaccine for emergency use. People aged 18 and above may now receive vaccinations with it. Bahrain, Botswana, Iran, Mexico, Nepal, the Philippines, Vietnam, Paraguay, Zimbabwe, Guyana, Trinidad & Tobago, and Mauritius are among the 12 approved vaccines for emergency use.
Covaxin, also known as BBV152, is an inactivated vaccine, a form of whole-virus vaccination. A modified or dead form of the virus, SARS-CoV-2, is included in an inactivated vaccine, which cannot reproduce and cause sickness.
The virus is an inactivated vaccine that activates the immune system and causes the body to manufacture antibodies, preparing it to fight infection in the future.
Covaxin Efficacy
In India, 25,800 people ranging in age from 18 to 98 took part in the Phase 3 experiment. Two thousand four hundred thirty-three of them were above 60, and 4,500 had pre-existing medical disorders (comorbidities) such as cardiovascular disease, diabetes, or obesity.
Covaxin demonstrated a 93.4 percent effectiveness against severe COVID-19 illness and an overall vaccination efficacy of 77.8 percent against symptomatic infections validated by PCR testing, according to the research. The point against asymptomatic COVID-19 was 63.6 percent. At least two weeks following the second dose, the vaccination provided 65.2 percent protection against symptomatic infection with the Delta variant.
COVAXIN proved to be 77.8% effective against symptomatic COVID-19 illness in a study of 130 confirmed cases, including 24 cases in the vaccination group and 106 in the placebo group. The effectiveness studies show that it protects against asymptomatic COVID-19 by 63.6 percent. 93.4 percent effectiveness against severe symptomatic COVID-19 illness has been shown.
Covaxin is licensed for emergency use in 15 countries outside India, including Iran, Zimbabwe, Mexico, the Philippines, Guatemala, and Botswana. Bharat Biotech has also struck agreements with Ocugen, a biopharmaceutical firm in the United States, to develop the vaccine for the North American market and Precisa Medicamentos, a business based in Brazil, subject to further studies and regulatory clearance.
torsdag 10 februari 2022
COVID-19 ja metabolomin tuotteiden esiintymiä taudin pahetessa
https://www.nature.com/articles/s41598-022-05667-0
Introduction
SARS-COV-2 (severe acute respiratory syndrome coronavirus 2) is extremely infectious and has triggered a global pandemic. Infection of the lungs and human respiratory tract by this coronavirus leads to fever, myalgia and cough, and in some patients to acute respiratory distress syndrome (ARDS). While most patients experience very mild-to-moderate symptoms, around one in five patients develop pneumonia coupled with severe respiratory distress. These patients require treatment in hospital intensive care units (ICU), where infection can lead to multi-organ dysfunction, failure, and sometimes death. The COVID-19 (coronavirus disease 2019) pandemic has led to urgent and intense investigations of this disease, its causative agent, and its interaction with the human host. However, there are still many difficulties for an accurate SARS-CoV-2 patient’s risk categorization, which are consequences of COVID-19 complexity since coronavirus infection reflects a broad spectrum of patient symptoms, and as a result, diverse pathophysiological pathways are perturbed during the disease course. This complexity has taken to many groups to investigate this exciting topic using metabolomics, given that the circulating metabolome provides a snapshot of the physiological state of the organism1,2.
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Many topics have been addressed regarding COVID-19 disease using metabolomics, for instance, metabolomics has displayed sex-specific metabolic shifts in non-severe COVID-19 patients during recovery process, showing that the major plasma metabolic changes were fatty acids in men and glycerophosphocholines and carbohydrates in women5. Metabolomics has also shown that it is possible to differentiate plasma metabolite profiles of COVID-19 survivors with abnormal pulmonary function from those of healthy donors or subjects with normal pulmonary function. These alterations mainly involved amino acid and glycerophospholipid metabolic pathways, increased levels of triacylglycerols (TG), phosphatidylcholines (PC), prostaglandin E2, arginine, and decreased levels of betain and adenosine6. Since many issues regarding the immune, both innate and adaptive, response remains unclear, they are subject to ongoing multi-omic investigations1, as well as comprehensive meta-analysis of global metabolomics datasets of COVID-192. Metabolomics also showed that more than 100 lipids including glycerophospholipid, sphingolipids, and fatty acids (FA) were downregulated in COVID-19 patient sera, probably because of damage to the liver, which is also reflected in aberrancy in bilirubin and bile acids7. Significant differences were also determined between COVID-19 patients and healthy controls in terms of purine, glutamine, leukotriene D4 (LTD4), and glutathione metabolisms. Decrease levels were determined in R‐S lactoglutathione and glutamine (Q, gln) , and increase levels were detected for hypoxanthine, inosine, and LTD48).
As mentioned above, there are still many difficulties for an adequate categorization of SARS-COV-2 patients through the use of potential metabolic markers of clinical severity identified at the beginning of the COVID-19 disease, reason why many different works have addressed this challenging topic. Thus, using high-throughput omics, the dynamic changes in the metabolome (and proteome) profile of non/severe to severe disease cohorts were studied, and they could be used to predict the disease development: for example, the simultaneous decline in the levels of malic acid and glycerol 3-phosphate in healthy to mild to fatal groups9. On the other hand, the level of guanosine monophosphate was found to be modulated along with carbamoyl phosphate in mild to severe patients, suggesting the role of immune dysfunction and nucleotide metabolism in the progression of non/severe COVID-19 to severe condition9.
Danlos et al. also reported alterations in the plasma metabolome reflecting the clinical presentation of COVID-19 patients with mild (ambulatory) diseases, moderate disease (radiologically confirmed pneumonitis, hospitalization and oxygen therapy), and critical disease (in intensive care)10; and altered tryptophan (w, trp) metabolism into the kynurenine pathway has been related to inflammation and immunity in critical COVID-19 patients in comparison to mild disease patients10. Increased levels of kynurenine and decreased levels of arginine (R, arg) , sarcosine and LPC were also observed as the top-performing metabolites for identifying COVID-19 positive patients from healthy control subjects11. The role of the tryptophan-nicotinamide pathway, linked to inflammatory signals and microbiota, and the involvement of cytosine were also described as possible markers to discriminate and predict the disease evolution12.
Xiao et al. characterized the globally dysregulated metabolic pathways and cytokine/chemokine levels in COVID-19 patients compared to healthy controls. They identified the escalated correlations between circulating metabolites and cytokines/chemokines from mild to severe patients, and revealed the disturbed metabolic pathways linked to hyper-inflammation in severe COVID-19, demonstrating that arginine (R), tryptophan (W), or purine metabolism modulates the inflammatory cytokine release13.
As can be deduced from above and other works14,16,16, the biological mechanisms involved in SARS-CoV-2 infection are only partially understood. Thus in the current work we have explored the plasma metabolome of non-COVID controls as well as 145 COVID patients at diagnose through reverse phase liquid chromatography coupled to quadrupole-time of flight mass spectrometry (RP/HPLC-qTOF MS/MS) analysis. Moreover, patients were stratified based on their clinical evolution in asymptomatic (not requiring hospitalization), patients with mild disease (defined by a total time in hospital lower than 10 days), patients with severe disease (defined by a total time in hospital over 20 days and/or admission at the ICU) and patients with fatal outcome or deceased. In addition, follow up samples between 2 and 3 months after hospital discharge were also obtained from the hospitalized patients with mild prognosis to investigate the disease sequels in the metabolome and how the recovery is reflected in the altered biological pathways. The final goal of the currents work is to find biomarkers that will increase our understanding about how the COVID-19 illness evolves and will improve our prediction about how a patient could progress based on the metabolites profile of plasma obtained at an early stage of the infection.
Results
....
The full list of identified metabolites for each ionization mode
together with the statistical values for the different analyses (ANOVA, U
test and PLS-DA) is shown in Supplementary Tables S1 and S2.
The correlation analysis showed different sets of metabolites with similar abundancy among the analyzed groups. In ESI (+), several acylcarnitines (3-hydroxybutyrylcarnitine, hexanoyl-l-carnitine, decanoyl-l-carnitine, octanoyl-l-carnitine, arachidonoyl-l-carnitine, linoleoylcarnitine, acetyl-l-carnitine, lauroylcarnitine, oleoyl-l-carnitine and palmitoyl-l-carnitine), PC/LPC compounds (2-lysophosphatidylcholine, PC (p-16:0/0:0), LPC (o-16:0), LPC (20:2), PC (18:1/16:0), LPC (16:0), LPC (p-18:0), LPC (17:0), PC (18:2e) PC (18:1e) and PC (20:4e)), and amino acids (tryptophan, L-valine, L-isoleucine, L-methionine and L-tyrosine), were grouped together; whereas in ESI (−), the most relevant sets were composed by LPC, FA derivatives or bile acids (glycodeoxycholic acid, taurodeoxycholic acid, glycocholic acid, glycoursodeoxycholic acid and taurocholic acid). The Pearson correlation (r) values and the respective p-values are presented in Supplementary Tables S3 and S4 for ESI (+), and Supplementary Tables S5 and S6 for ESI (−).
The Mann–Whitney U test between the different COVID-19 positive groups and the non-COVID control group showed 8 metabolites altered in the asymptomatic group (5 of them with increased values and 3 with decreased values). The number of significantly altered metabolites increased to 26 in the mild disease group, 14 and 12 with increased and decreased values, respectively. Some of them were already observed as altered in the asymptomatic group, such as S-methyl-3-thioacetaminophen and nicotinamide riboside cation, which values increased more in the mild disease group; and N-methyl-2-pyrrolidone, trimethoprim and L-methionine, which values continued to decrease in the mild disease group. In the severe disease group, the number of significantly altered metabolites rose to 45 (32 with increased and 13 with decreased values); and for the deceased group, the total number of altered metabolites was 35 (23 with increased values and 12 with decreased values). Many of these metabolites were already observed as significant in the previous PLS-DA and ANOVA analyses.
The further analysis of the fold change ratio patterns obtained in the previous comparisons suggested 8 main clusters to be formed (Fig. 2, the MFuzz membership value and cluster composition are shown in Supplementary Tables S7 and S8).
This analysis provides
a general overview of groups of metabolites with similar alteration patterns between the different groups of samples (after normalization by the non-COVID control group) and allows the combination of ESI (+) and ESI (−) data together since the normalization using the non-COVID control group eliminates the bias derived from the use of different ESI ionization modes. It has to be noted that this analysis does not take into account the statistical differences after a non-parametric Mann–Whitney U test between the COVID-19 positive samples and the control group, as it only clusters the metabolites according to their fold change ratio similarity.
Among the identified clusters, cluster 1 represented those metabolites which abundance continuously increased from the asymptomatic to the deceased group, and included 3-hydroxybutyrylcarnitine, glycocholic acid, LPE (22:6), nervonic acid and palmitic acid, among others.
On the other hand, cluster 7 represented those metabolites which abundance continuously decreased from the asymptomatic to the deceased group, and was composed by three PC (PC (16:0/20:4), PC (20:4e) and PC (20:5e)) and tryptophan (W).
Based on the previous PLS-DA and ANOVA results, metabolites of cluster 6 were of special interest because their abundance was highest in the severe disease and the deceased groups. This cluster included 2-lysophosphatidylcholine, alpha-linolenic acid, linoleic acid or L-isoleucine (I, ile) , methyl ester.
Finally, cluster 8 was the most crowded (24 metabolites) and was composed by metabolites which abundance mainly increased in the deceased group. Some of these metabolites were adipoyl-l-carnitine, glycodeoxycholic acid and taurodeoxycholic acid.
In order to provide the chemical classes significantly altered in the different group comparisons, a chemical enrichment analysis using ChemRICH was performed. No chemical classes were significantly altered in the comparison between the asymptomatic and the non-COVID control group;
but the chemical class “carnitines” was increased, and the “unsaturated lysophosphatidylcholines” was altered (some species increased, others decreased) in the mild disease group.
In the case of the severe disease group, “androstenols” were significantly decreased (considering 3 metabolites); xanthines were significantly altered with some species increased, others decreased; and 2-pyridinylmethylsulfinylbenzimidazoles, pyridines and unsaturated fatty acids were significantly increased.
This last chemical class was also increased in the deceased group, based on the abundance of nervonic acid, linoleic acid, alpha-linolenic acid, trans-vaccenic acid and palmitoleic acid.
The whole list of chemical classes and the respective p-values obtained for each comparison is presented in Supplementary Tables S9–S11, and the representations of all the annotated metabolites onto biochemical networks, constructed using chemical and biochemical similarities from MetaMapp, is shown in Supplementary Figs. S4–S7.
Finally, metabolite set enrichment analysis using the significantly altered metabolites in each comparison was performed using MBROLE 2.0 (Table 1). It has to be noted that only 5, 17, 28 and 16 altered metabolites in the asymptomatic, mild disease, severe disease and deceased groups, respectively, could be mapped with valid KEGG IDs (many carnitines and omeprazole derivatives could not be mapped).
Table 1 Significantly enriched KEGG human metabolic pathways (in dark shade) from the analysis of significantly altered metabolites (after Mann–Whitney U test with p-value < 0.05) in asymptomatic, mild disease, severe disease and deceased COVID-19 positive groups as compared to non-COVID control patients.
From: Metabolomics study of COVID-19 patients in four different clinical stages
( Kommenttini: Dietisteille tämä on ajattelemisen aihetta).
K1 vitamiinin fyllokinonin ja E-vitamiinin kuljttajalipoproteiinit
https://pubmed.ncbi.nlm.nih.gov/10509895/
. 1999 Sep;287(1-2):45-57.
doi: 10.1016/s0009-8981(99)00117-5.
Interdependence of serum concentrations of vitamin K1, vitamin E, lipids, apolipoprotein A1, and apolipoprotein B: importance in assessing vitamin status
Affiliations
- PMID: 10509895
- DOI: 10.1016/s0009-8981(99)00117-5
Covid-19 sairastuvuuden ja mortaliteetin suhdetta Apolipoproteiineihin
https://www.sciencedirect.com/science/article/pii/S1477893921002416?via%3Dihub
Prognostic value of apolipoproteins in COVID-19 patients: A systematic review and meta-analysis
4. Discussion
The
present systematic review found evidence to recommend low levels of
ApoA1 and Apo B as predictors of disease severity and low levels of
ApoA1 as a predictor of mortality in patients hospitalized for COVID-19.
Exchangeable Apolipoproteins, including Apo As, Apo E, and ApoCs, are constituents of HDL and triglyceride-rich lipoproteins such as VLDL. The best-studied family members are Apo A-I, the most significant HDL protein, in which an anti-atherogenic effect has been documented [32]. In contrast, non-exchangeable Apolipoproteins, such as Apo B, share a similar sequence and structure and can be reversibly associated with lipid surfaces [33]. Apo A is primarily bound to low-density lipoprotein (LDL) in subjects with average triglyceride values. However, Apo A can also bind to APOB100 or triglyceride particles in dyslipidemic states, called very low and intermediate-density lipoproteins [33].
Due to their potential effects and prominence in different pathologies, apolipoproteins have been studied as predictors of clinical outcomes in some diseases. For example, various systematic reviews associated the Apo E with ischemic and hemorrhagic stroke and a higher risk of worse outcomes in patients with traumatic brain disease [[34], [35], [36], [37], [38]]. Similarly, Apo C was associated with the risk of ischemic stroke, although a systematic review found no evidence of this association [39].
In the case of Apo A1, probably due to its anti-atherogenic effect, some systematic reviews and meta-analyses sought its association with cardiovascular outcomes. Haji Aghajani M et al., in a review of seventeen case-control studies, found an association between Apo A 1 levels and premature coronary artery disease. However, the authors note the lack of good quality prospective cohort studies [40]. Erqou S et al., in a systematic review of thirty-six studies, found that people with smaller Apo A isoforms have an approximately 2-fold higher risk of coronary heart disease or ischemic stroke than those with larger proteins [41]. As with cardiovascular outcomes, other systematic reviews found evidence of Apo A1 as a diagnostic marker for bladder cancer [42], a poor prognosis of multiple cancers [43,44], and it was found at lower levels in patients with Alzheimer's disease [45].
To the best of our knowledge, no systematic reviews have been published on the association between Apolipoprotein values as a prognostic factor in patients with COVID-19 or some other infectious disease; however, our results are not surprising. Apo-I's presence characterizes High-density lipoproteins (HDL), and their ability to transport cholesterol from peripheral tissues back to the liver gives it a cardioprotective function [46]. Similarly, it has antioxidant, anti-apoptotic, anti-thrombotic, anti-inflammatory or anti-infectious functions and decreases rapidly in patients with sepsis, which could explain our findings [46]. A study in pediatric patients in intensive care for sepsis found that Apo A5 serum levels were significantly lower in patients who died than survivors. Similarly, Apo A5 serum levels were significantly correlated with multiple organ failure, shock, acute kidney injury, acute liver injury, and gastrointestinal dysfunction, although not respiratory failure [47]. In adults, an association was also found between low levels of Apolipoproteins and a poor prognosis in patients with sepsis. Although the mechanisms are not well understood, it is suggested that the association is explained due to increased platelet activation and monocyte activation [48,49]. In addition, the low levels of Apo A are related to high levels of inflammation [50], and this being a prognostic marker in patients infected by COVID-19 [51], its role in the binding and neutralization of lipopolysaccharides in bacterial infections is known [52].
In patients with virus infections, changes in plasma HDL-C levels were reported during infections, where the viruses would take advantage of the HDL lipid transfer activity in host cells [53]. Although the best evidence is in patients with hepatitis C virus and acquired immunodeficiency virus, in the case of patients with COVID-19 infection, a similar theory is suggested [54]. Therefore, the HDL lipid transfer activity mechanism could explain our results as the relationship between viral load and worse prognosis in patients with COVID-19 is known [55]. Similarly, the hypothesis of the relationship between lipoproteins and inflammation and thrombosis was raised. In this way, our findings could explain since the association between thrombosis and the prognosis are known [56].
Finally, due to its known association with brain and cardiovascular disease, it is possible that in patients with COVID-19, the prognostic value of ApoA1 is mediated by the occurrence of these diseases. Indeed, complications including myocarditis, acute myocardial infarction, heart failure, arrhythmias and venous thromboembolic events are described in these patients [57,58]. Similarly, concerning cerebrovascular complications, episodes of stroke, necrotizing hemorrhagic encephalitis, among others, were reported [59,60].
Our study is the first systematic review to evaluate the prognostic value of Apo A1, ApoB and the ratio of both in patients with an infectious disease. In addition, our study used the NOS to assess the risk of bias of the included articles, which allowed sensitivity analyses when the association between Apo A1 and Apo B with the severity of the disease of patients hospitalized for COVID-19 was analyzed. Our findings allow us to suggest a potential low-cost prognostic marker in patients hospitalized for COVID-19 that will allow health personnel to prioritize or individualize management strategies in patients with low values of these markers.
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