Ontology highlight
ABSTRACT:
PROVIDER: PRJNA693974 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13513988_1.fastq.gz | Fastqsanger.gz | |||
| SRR13513988_2.fastq.gz | Fastqsanger.gz | |||
| SRR13513989_1.fastq.gz | Fastqsanger.gz | |||
| SRR13513989_2.fastq.gz | Fastqsanger.gz | |||
| SRR13513990_1.fastq.gz | Fastqsanger.gz |
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Project description:Investigation of overall expression level in Plasmodium falciparum male and female mature gametocytes, and detection of any transcriptional differences between male and female gametocytes. The Plasmodium falciparum parasite with green fluorescent protein (GFP) expression under the control of alpha tubulin II promoter facilitated the separation of male and female gametocyte. This engineered parasite strain in this study are further described in Miao J, Fan Q, Parker D, Li X, Li J, et al. (2013) Puf Mediates Translation Repression of Transmission-Blocking Vaccine Candidates in Malaria Parasites. PLoS Pathog 9(4): e1003268. doi: 10.1371/journal.ppat.1003268
2016-01-30 | GSE77398 | GEO
Project description:Investigation of whole genome gene expression level in Plasmodium falciparum male and female mature gametocytes, and detection of any transcriptional differences between male and female gametocytes. The Plasmodium falciparum parasite with green fluorescent protein (GFP) expression under the control of alpha tubulin II promoter facilitated the separation of male and female gametocyte. This engineered parasite strain in this study are further described in Miao J, Fan Q, Parker D, Li X, Li J, et al. (2013) Puf Mediates Translation Repression of Transmission-Blocking Vaccine Candidates in Malaria Parasites. PLoS Pathog 9(4): e1003268. doi: 10.1371/journal.ppat.1003268
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Project description:The TNF superfamily is large, including TNF ligands (n = 19) and TNF receptors (n = 29),as determined following the completion of large-scale sequencing of the human and mouse genomes. These members not only function in immune cells but are also involved in respiratory and intestinal diseases, and some members may act as a double-edged sword. Tumor necrosis factor-like cytokine 1A (TL1A, also known as TNFSF15) is the only known death receptor 3 (DR3, also known as TNFRSF25) ligand (Meylan et al., 2011). The TL1A/DR3 axis plays a role in the regulation of intestinal immunity and fibrosis (Valatas et al., 2019), asthma airway remodeling (Zhang et al., 2022; Herro et al., 2010), and other autoimmune and inflammatory diseases (Herro et al., 2021), exacerbating disease progression. However, some researchers have proposed that the TL1A/DR3 axis has a protective role in some disease models. A novel role for TL1A/DR3 in protection against intestinal injury was reported by Jia et al (Jia et al., 2016). Yang et al. revealed a protective effect of TL1A against intracerebral hemorrhage-induced secondary brain injury and infection (Yang et al., 2021). In addition, TL1A maintains the blood–retinal barrier by modulating SHP-1-Src-VE-cadherin signaling in diabetic retinopathy, as verified by Li et al (Li et al., 2021). However, the role of TL1A/DR3 in ARDS has not been explored.
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Project description:Simple Markov model.
There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal.
The yearly transition probabilities are:
Healthy to Dead: 0.01; Healthy to Sick: 0.2 for Male and 0.1 for Female; Sick to Healthy: 0.1; Sick to Dead: 0.3.
The transition probability now depends on the cohort (Male or Female) and can be expressed as a function of a Boolean covariate Male.
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Project description:Model with functions depending on Age, Male, BP (Blood Pressure).
There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: Age/1000; Healthy to Sick: According to function F1 depending on Age and Male and BP; Sick to Healthy: 0.1; Sick to Dead: according to function F2 depending on Age and Male.
Pre-Transition Rules: Age increased by 1 and BP by Age/10 each simulation cycle. Post-Transition Rules: Treatment = BP>140 , becomes 1 when BP crosses 140 threshold; BP =BP-Treatment*10 , meaning a drop of 10 once treatment is applied; CostThisYear = Age + \Treatment*10 , cost depends on age and if treatment was taken; Cost= Cost + CostThisYear , it accumulates cost over time.
Initial conditions: Healthy = (50 Male, 50 Female with Age =1,2,...,50 for each individual), BP =120, Sick = (0,0) and Dead = (0,0).
Output: Number of men and women in each disease state for years 1-10 and their ages and costs in each state. A stratified report by male and female and young – up to age 30 and old above age 30 is produced.
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