In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

Marine microalgae are widespread in the ocean and play a crucial role in the ecosystem. Automatic identification and location of marine microalgae in microscopy images would help establish marine ecological environment monitoring and water quality evaluation system. We proposed a new dataset for the detection of marine microalgae and a range of detection methods, the dataset including images of different genus of algae and the same genus in different states. We set the number of unbalanced classes in the data set and added images of mixed water samples in the test set to simulate the actual situation in the field. Then we trained, validated and tested the, TOOD, YOLOv5, YOLOv8 and variants of RCNN algorithms on this dataset. The results showed both one-stage and two-stage object detection models can achieve high mean average precision, which proves the ability of computer vision in multi-object detection of microalgae, and provides basic data and models for real-time detection of microalgal cells.

An intelligent optoelectrowetting system integrates AI-based detection and tracking to dynamically manipulate and auto-correct droplet movements. This enables precise multivariate independent microalgae cultivation in distinct media, enhancing experimental flexibility and accuracy.

The microbiota in gastrointestinal tracts is recognized to play a pivotal role in the health of their hosts. Music and noise are prevalent environmental factors in human society and animal production and are reported to impact their welfare and physiological conditions; however, the information on the relationship between the microbiota, physiological status, and sound is limited. This study investigated the impact of music and white noise exposure in mice through 16s rRNA gene sequencing, enzyme assay, and qPCR. The results demonstrate that white noise induced oxidative stress in animals by decreasing serum SOD and GSH-PX activity while increasing LDH activity and MDA levels (p < 0.05). Conversely, no oxidative stress was observed in the music treatment group. The relative gene expression of IFN-γ and IL-1β decreased in the white noise group compared to the music and control groups. The 16s rRNA gene amplicon sequencing revealed that Bacteroidetes, Firmicutes, Verrucomicrobia, and Proteobacteria were dominant among all the groups. Furthermore, the proportion of Firmicutes increased in the music treatment group but decreased in the white noise treatment group compared to the control group. In conclusion, white noise has detrimental impacts on the gut microbiota, antioxidant activity, and immunity of mice, while music is potentially beneficial.

Marine microalgae are widely present in the natural environment, exhibiting a significant economic value. However, during the inoculation and cultivation process of microalgae, the introduction of unwanted algae is bound to trigger nutrient competition, leading to a decrease in the growth rate of microalgae and consequently impacting their economic value in production. To address this issue, this study integrates the optically induced dielectrophoresis (ODEP) manipulation technology based on the continuous flow in a microfluidic system. A two-stage cell filter, utilizing two virtual optical spots, is designed and manufactured. Leveraging the size differences between microalgae, continuous separation and purification of mixed samples containing Chlorella vulgaris and Haematococcus pluvialis are achieved within microchannels. Additionally, optimal ODEP manipulation conditions for mixed algal liquid samples, comprising C. vulgaris and H. pluvialis, are demonstrated, including appropriate alternating current voltage (6 V), alternating current frequency (100 kHz), light spot width (40 μm), and sample flow rate (0.9 μl/min). Analysis of mixed liquid samples collected at the chip's outlet reveals a reduction in the proportion of H. pluvialis from 37.5% to 1.2% after separation. In summary, this study proposes an ODEP microfluidic system capable of continuously separating and purifying microalgae with different biological characteristics, showcasing its potential as an alternative to traditional labor-intensive microalgae separation techniques.