Estimation of parameters for plasma glucose regulation in type-2 diabetics in presence of meal

Efficacy and safety evaluation of acupuncture in the treatment of impaired glucose regulation: A protocol for systematic review and meta-analysis

BACKGROUND

Impaired of glucose regulation belongs to the stage of prediabetes, which is a state of glucose metabolism between diabetes and normal blood glucose. The prevalence of prediabetes in people over 20 years old in China is significantly higher than that in diabetic patients. If no measures are taken to prevent the transition from prediabetes to diabetes, the number of diabetic patients in China will further increase. This study conducted a meta-analysis of the effectiveness of acupuncture in the treatment of impaired glucose regulation by collecting relevant literatures.

METHODS

Nine electronic databases: PubMed, EMBASE, Cochrane library, Web of Science, Google Scholar, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, Chinese Scientific and Journal Database, Wan Fang database, and 2 clinical trials register platforms: Chinese Clinical Trial Registry, ClinicalTrials.gov (www.ClinicalTrials.gov/) will be searched for randomized clinical trails of acupuncture for impaired glucose regulation. The screening process will be developed by 2 independent reviewers, and meta-analysis will be performed with RevMan (V5.3.5) software.

RESULTS

This meta-analysis further confirmed the benefits of acupuncture in the treatment of impaired of glucose regulation.

CONCLUSION

This study will provide a high-quality evidence of the efficacy and safety of acupuncture on patients with impaired glucose regulation.

PROSPERO REGISTRATION NUMBER

INPLASY202170058.

ETHICS AND DISSEMINATION

This systematics review will evaluate the efficacy and safety of acupuncture in the treatment of impaired of glucose regulation. Since all the data included were published, the systematic review did not require ethical approval.

Intelligent automated drug administration and therapy: future of healthcare

In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will have promising opportunities, vast applications, and some challenges. Due to advancements in processing speed, the closed-loop administration of drugs has gained popularity for critically ill patients in intensive care units and routine life such as personalized drug delivery or implantable therapeutic devices. For developing a closed-loop drug delivery system, the control system works with a group of technologies like sensors, micromachining, wireless technologies, and pharmaceuticals. Recently, the integration of artificial intelligence techniques such as fuzzy logic, neural network, and reinforcement learning with the closed-loop drug delivery systems has brought their applications closer to fully intelligent automatic healthcare systems. This review's main objectives are to discuss the current developments, possibilities, and future visions in closed-loop drug delivery systems, for providing treatment to patients suffering from chronic diseases. It summarizes the present insight of closed-loop drug delivery/therapy for diabetes, gastrointestinal tract disease, cancer, anesthesia administration, cardiac ailments, and neurological disorders, from a perspective to show the research in the area of control theory.

Parameter estimation of a meal glucose-insulin model for TIDM patients from therapy historical data

The effect of meal on blood glucose concentration is a key issue in diabetes mellitus because its estimation could be very useful in therapy decisions. In the case of type 1 diabetes mellitus (T1DM), the therapy based on automatic insulin delivery requires a closed-loop control system to maintain euglycaemia even in the postprandial state. Thus, the mathematical modelling of glucose metabolism is relevant to predict the metabolic state of a patient. Moreover, the eating habits are characteristic of each person, so it is of interest that the mathematical models of meal intake allow to personalise the glycaemic state of the patient using therapy historical data, that is, daily measurements of glucose and records of carbohydrate intake and insulin supply. Thus, here, a model of glucose metabolism that includes the effects of meal is analysed in order to establish criteria for data-based personalisation. The analysis includes the sensitivity and identifiability of the parameters, and the parameter estimation problem was resolved via two algorithms: particle swarm optimisation and evonorm. The results show that the mathematical model can be a useful tool to estimate the glycaemic status of a patient and personalise it according to her/his historical data.