Biochemical aspirin resistance in stroke patients: a cross-sectional single centre study

Background: Aspirin use is known to reduce the recurrence of stroke. However, the clinical response to aspirin has been mixed. The rate of stroke recurrence whilst on aspirin treatment is still unacceptably high. A plausible explanation for this may be resistance to the effects of aspirin. The causes of aspirin resistance are manifold and multi-factorial. We conducted a study to investigate the prevalence rate of biochemical aspirin resistance in a cohort of aspirin-naïve stroke patients. We also sought to determine the inherent factors that may predispose towards the development of aspirin resistance. Method: This was a cross-sectional, observational study conducted on patients admitted to our centre with an acute stroke who were aspirin-naïve. The diagnosis of an acute stroke was confirmed by clinical history and brain imagi ng. Fifty consecutive patients were prospectively enrolled. Socio demographic data were collected and baseline blood investigations were performed. Patients were tested for biochemical aspirin resistance using Multiplate platelet analyser (Dynabyte, Munich, Germany) after 5 doses of aspirin, corresponding to a total dose of 900 mg. Results: The median age of patients was 65.5 years and 54 % of patients were female. There were 11 smokers; of these 10 were male. Twenty-six (52 %) patients were Chinese, 21 (41%) were Malay and 3 (6.0 %) were Indian. Aspirin resistance was present in 14 % of our patients.There was an inverse relationship between the presence of aspirin resistance and plasma HDL levels (r = -0.394; p = 0.005). There was no relationship observed between aspirin resistance and total cholesterol, triglycerides, LDL, HbA1c, ALT, ALP, urea and creatinine levels. There were no significant differences in demographic profiles or smoking status between the aspirin resistant and non-aspirin resistant groups. We did not find any link between ethnicity and aspirin resistance. Conclusions: Our results indicate that a lower HDL leve l is associated with biochemical aspi-rin resistance. This may increase platelet aggregation and consequently increase the risk of a recurrent stroke. The clinical implications for aspirin resistance are far reaching. Any evidence that correctable factors may negatively influence the action of aspirin warrants further investigation. The prevalence rate of biochemical aspirin resistance in our study is comparable to the findings in other studies performed in an Asian population. Further research is required to determine how our findings translate into clinical aspirin resistance and stroke recurrence

An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization

A Survey: To Govern, Protect, and Detect Security Principles on Internet of Medical Things (IoMT)

The integration of medical equipment into the Internet of Things (IoT) led to the introduction of Internet of Medical Things (IoMT). Variation of IoT devices have been equipped in medical facilities. These devices provided convenience to healthcare provider since they can continuously monitor their patients in real-time, while allowing them to have greater physical flexibility and mobility. However, users of healthcare services (such as patients and medical staff) often are less concerned about security issues associated with IoT. These alleviate existing problems and jeopardize the lives of their patients by making them susceptible to attacks. Furthermore, IoMT applications have direct access to healthcare services because it handles sensitive patient information. Therefore, it is extremely important to preserve and establish the security and privacy of IoMT. This further justifies the need to investigate and address the related issues. Despite existing literature on security and privacy mechanisms, the domain still requires more attention. Therefore, this paper aims to discuss the security and privacy principles, as well as challenges associated with IoMT. Besides, a comprehensive analysis of privacy and security solutions for IoMT is also presented. In addition, we introduced a novel taxonomy of IoMT security and privacy based on cyber security principles such as “govern,” “protect,” and “detect”. In conclusion, this paper provides a discussion on existing challenges and future direction for researchers