Tissue engineering technology has been advanced and applied to various applications in the past few years. The presence of a bioreactor is one key factor to the successful development of advanced tissue engineering products. In this work, we developed a programmable bioreactor with a controlling program that allowed each component to be automatically operated. Moreover, we developed a new pH sensor for non-contact and real-time pH monitoring. We demonstrated that the prototype bioreactor could facilitate automatic cell culture of L929 cells. It showed that the cell viability was greater than 80% and cell proliferation was enhanced compared to that of the control obtained by a conventional cell culture procedure. This result suggests the possibility of a system that could be potentially useful for medical and industrial applications, including cultured meat, drug testing, etc.

We proposed a specially designed sequential injection (SI) amperometric system coupling with a bioreactor for in-line glucose monitoring in cell culture. The system is composed of three main parts which are the bioreactor, SI system, and electrochemical detection unit. The bioreactor accommodates six individual cell culture units which can be operated separately under different conditions. The SI system enables automatic in-line sampling and in-line sample dilution, with a specially designed mixing unit; therefore, it has the benefits of fast analysis time and less contamination risk. The use of 3D-printed microfluidic components, a mixing channel, and a flow cell helped to reduce operational time and sample volume. A disposable screen-printed electrode (SPE), modified with glucose oxidase (GOD), carbon nanotube, and gold nanoparticle, was used for detection. The developed system provided a linear range up to 3.8 mM glucose in cell culture media. In order to work with cell culture in higher glucose media, the in-line sample dilution can be applied. The developed SI system was demonstrated with mouse fibroblast (L929) cell culture. The results show that glucose concentration obtained from the SI system is comparable with that obtained from the conventional colorimetric method. This work can be further developed and applied for in vitro cell-based experiments in biomedical research.

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn’s index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.