RESUMEN
A single guanosine deletion/insertion (4G/5G) polymorphism in the promoter region of plasminogen activator inhibitor-1 (PAI-1) gene encoding PAI-1 protein has been investigated in deep vein thrombosis (DVT) patients. The association between PAI-1 4G/5G polymorphism and increased risk of DVT has been reported in some studies, while others have reported a lack of association. The present study aimed to investigate if the PAI-1 4G/5G polymorphism is associated with an increased risk of DVT in the Indian population and to assess its association with thrombophilic risk factors. Fifty-two adult patients with a history of chronic or recurrent DVT and 52 healthy adult controls were genotyped for PAI-1 4G/5G polymorphism. Plasma levels of PAI-1 and other thrombophilic risk factors were also measured. PAI-1 4G/5G polymorphism was not significantly associated with an increased risk of DVT. Protein C deficiency was significantly associated with the 4G/4G genotype. Patients with the 4G/4G genotype had significantly reduced PAI-1 levels as compared to the controls. PAI-1 4G/5G polymorphism did not significantly contribute to an increased risk of DVT in the Indian population. However, in the presence of thrombophilic risk factor abnormalities, the risk of DVT is increased in individuals with the 4G/4G genotype in the Indian cohort. Supplementary Information: The online version contains supplementary material available at 10.1007/s12288-023-01660-3.
RESUMEN
Depression is a complex psychiatric disorder influenced by various genetic and environmental factors. Strong evidence has established the contribution of genetic factors in depression through twin studies and the heritability rate for depression has been reported to be 37%. Genetic studies have identified genetic variations associated with an increased risk of developing depression. Imaging genetics is an integrated approach where imaging measures are combined with genetic information to explore how specific genetic variants contribute to brain abnormalities. Neuroimaging studies allow us to examine both structural and functional abnormalities in individuals with depression. This review has been designed to study the correlation of the significant genetic variants with different regions of neural activity, connectivity, and structural alteration in the brain as detected by imaging techniques to understand the scope of biomarkers in depression. This might help in developing novel therapeutic interventions targeting specific genetic pathways or brain circuits and the underlying pathophysiology of depression based on this integrated approach can be established at length.
RESUMEN
The healthcare supply chain involves obtaining resources, managing supplies, and delivering goods and services to patients across multiple teams, stakeholders, and geographical boundaries. With such a complex structure, the healthcare supply chain is vulnerable to fraud, inaccurate data, and lack of transparency. These misdeeds cost businesses money and harm health. To address these issues, the health care supply chain needs an end-to-end decentralized track-and-trace system. Most centralized systems risk drug and data safety. This paper presents an Ethereum blockchain-based solution for a health care supply chain track-and-trace mechanism that uses smart contracts and data immutability. Hash functions store data in a public distributed ledger. This protects and discloses data. Smart contracts automate agreement execution so all parties know the outcome instantly, without an intermediary or time loss. It also outlined decentralized healthcare supply chain application architecture and algorithms. This paper proposes a system to address the lack of transparency and tracking in traditional supply chains. The blockchain-based method proposed in this paper runs on Solidity smart contracts. The system's algorithms and methods are tested against a variety of inputs, and the results are presented as an average gas cost for specific functionality. The proposed system tracks goods' histories (medicine). The average gas cost for all accounts is 18,027.2. Overall, log gas costs 48,118.6 to buy medicine, gas costs 229,607.5, and to log out 14,275.The results of the proposed system are compared to state-of-the-art methods. Thus, the presented work allows a seamless flow of medicines via blockchain and smart contracts without intermediaries. Finally, it addresses building a secure pharma supply chain application for blockchain 4.0.