Journal of Medical Engineering and Technology最新文献

Diabetes remains a critical global health concern that necessitates urgent attention. The contemporary clinical approach to closed-loop care, specifically tailored for insulin-dependent patients, aims to precisely monitor blood glucose levels while mitigating the risks of hyperglycaemia and hypoglycaemia due to erroneous insulin dosing. This study seeks to address this life-threatening issue by assessing and comparing the performance of different controllers to achieve quicker settling and convergence rates with reduced steady-state errors, particularly in scenarios involving meal interruptions. The methodology involves the detection of plasma blood glucose levels, delivery of precise insulin doses to the actuator through a control architecture, and subsequent administration of the calculated insulin dosage to patients based on the control signal. Glucose-insulin dynamics were modelled using kinetics and mass balance equations from the Bergman minimal model. The simulation results revealed that the PID controller exhibited superior performance, maintaining blood glucose concentration around the preferred threshold ∼98.8% of the time, with a standard deviation of 2.50. This was followed by RST with a success rate of 98.5% and standard deviation of 5.00, SPC with a success rate of 58% and standard deviation of 2.99, SFC with a success rate of 55% and standard deviation of 10.08, and finally LCFB with a rate of 10% and significantly higher standard deviation of 64.55.

Access to medical technologies is a critical component of universal access to care; however, the advancement of technologies for children has historically lagged behind those for adults. The small market size, anatomic and physiologic variability, and legal and ethical implications pose unique barriers to developing and commercialising paediatric biomedical innovations. These challenges are magnified in low-resource settings (LRS), which often lack appropriate regulatory oversight, support for service contracts, and supply chain capacity. The COVID-19 pandemic exposed shortcomings in the traditional industry model for medical technologies, while also catalysing open-source approaches to technology development and dissemination. Open-source pathways - where products are freely licenced to be distributed and modified - addressed key shortages in critical equipment. Relatedly, we argue that open-source approaches can accelerate paediatric global health technology development. Open-source approaches can be tailored to clinical challenges independent of economic factors, embrace low-cost manufacturing techniques, and can be highly customisable. Furthermore, diverse stakeholders, including families and patients, are empowered to participate in collaborative communities of practice. How to regulate the development, manufacture, and distribution of open-source technologies remains an ongoing area of exploration. The need for democratised innovation must be carefully balanced against the imperatives of safety and quality for paediatric-specific solutions. This can be achieved, in part, through close coordination between national regulatory agencies and decentralised networks where products can be peer-reviewed and tested. Altogether, there is significant potential for open source to advance more equitable and sustainable medical innovations for all children.

The human factors engineering (HFE) process supports the design and development of medical devices, especially novel devices requiring clinical investigation. The typical culmination of the HFE process prior to market approval is a human factors (HF) validation study, with specific requirements of participant, environment and task representation that carry a financial and temporal burden for medical device manufacturers. Whilst strongly recommended ahead of clinical investigations by regulators (and the authors), the prescribed methodology for HF validation studies required for pre-market approval may be excessive ahead of a clinical investigation during the development process. However, the stringent nature of HF validation studies will support effective clinical investigation design and minimise risks of poor clinical outcome or compliance. This paper provides recommendations in what to consider when determining what type of HF study to conduct ahead of each clinical investigation phase as well as insights into the symbiotic benefits of HFE and clinical investigations.

Climate change has amplified the importance of continuous and precise body core temperature (T_core) monitoring in the everyday life. In this context, assessing T_core through ingestible capsules technology, i.e., gastrointestinal temperature (T_{gastrointestinal}), emerges as a good alternative to prevent heat-related illness. Therefore, we conducted a systematic review to point out values of normal T_{gastrointestinal} measured through ingestible capsules in healthy humans. The study followed PRISMA guidelines and searched the PubMed and Scielo databases from 1971 to 2023. Our search strategy included the descriptors ("gastrointestinal temperature") AND ("measurement"), and eligible studies had to be written in English and measured T_{gastrointestinal} using ingestible capsules or sensors in healthy adults aged 18-59 at rest. Two pairs of researchers independently reviewed titles and abstracts and identified 35 relevant articles out of 1,088 in the initial search. An average value of 37.13 °C with a standard deviation of 0.24 °C was observed, independently of the gender. The values measured ranged from 36.70 °C to 37.69 °C. In conclusion, this systematic review pointed out the mean value of 37.13 ± 0.24 °C measured by ingestible capsules as reference for resting T_{gastrointestinal} in healthy adult individuals.

Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. This disorder can also be correlated to certain diseases like stroke, depression, neurocognitive disorder, non-communicable disease, etc. We implemented machine learning techniques for detecting sleep apnoea to make the diagnosis easier, feasible, convenient, and cost-effective. Electrocardiography signals are the main input used here to detect sleep apnoea. The considered ECG signal undergoes pre-processing to remove noise and other artefacts. Next to pre-processing, extraction of time and frequency domain features is carried out after finding out the R-R intervals from the pre-processed signal. The power spectral density is calculated by using the Welch method for extracting the frequency-domain features. The extracted features are fed to different machine learning classifiers like Support Vector Machine, Decision Tree, k-nearest Neighbour, and Random Forest, for detecting sleep apnoea and performances are analysed. The result shows that the K-NN classifier obtains the highest accuracy of 92.85% compared to other classifiers based on 10 extracted features. The result shows that the proposed method of signal processing and machine learning techniques can be reliable and a promising method for detecting sleep apnoea with a reduced number of features.

Electrophoretic tissue clearing has been a commonly used laboratory method since the early twentieth century. Infrastructure for standard procedures has yet to be formed. In particular, control of the heat produced by electrophoresis, the voltage applied to the electrodes, the resistance, and the speed of liquid circulation create difficulty for researchers. We aimed to develop a compact organ electrophoresis system that enables the researcher to have easy, rapid, and inexpensive working opportunities. The system includes an electronic control unit, a liquid tank, a temperature control unit, and an electrophoresis chamber. The control unit software can keep the system stable by using information on temperature and circulation rate received through the sensors using the feedback principle. Corrosion and particle collection are reduced to a minimum as platinum wires are used for electrophoresis electrodes. A temperature control unit can heat and cool via a liquid tank base. The CORES is an all-in-one, easy-to-use solution for electrophoretic tissue clearing. It assures efficient, rapid, and consistent tissue clearing. The system was stable with 72 h of continuous operation. Patent applications and trial version studies for introducing the system to researchers are still in progress.

It is known that the geometric structures of bones are very complex. This has made researchers unable to model them with the continuum approach and suffice to model them with simulation or experimental tests. Undoubtedly, provide a simple and accurate continuum model for studying bones is always desirable. In this article, as the first serious endeavour, a suggested beam model is investigated to see whether it is suitable for modelling femur bones or not. If this model gives an acceptable answer, it can be a link to the continuum theories for beams. In other words, the approximated beam model can be formulated with continuum approach to study femur bone. For feasibility study of the approximated model for femur bones, both static and dynamic analysis of them are investigated and compared. It is found that in most cases for vibration analysis, the suggested model has acceptable results but in static analysis, the mean difference between the results is about 16%. This research is hoped to be the first serious step in this category.

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.