Robot-assisted cell microinjection, which is precise and can enable a high throughput, is attracting interest from researchers. Conventional probe-type cell microforce sensors have some real-time injection force measurement limitations, which prevent their integration in a cell microinjection robot. In this paper, a novel supported-beam based cell micro-force sensor with a piezoelectric polyvinylidine fluoride film used as the sensing element is described, which was designed to solve the real-time force-sensing problem during a robotic microinjection manipulation, and theoretical mechanical and electrical models of the sensor function are derived. Furthermore, an array based cell-holding device with a trapezoidal microstructure is micro-fabricated, which serves to improve the force sensing speed and cell manipulation rates. Tests confirmed that the sensor showed good repeatability and a linearity of 1.82%. Finally, robot-assisted zebrafish embryo microinjection experiments were conducted. These results demonstrated the effectiveness of the sensor working with the robotic cell manipulation system. Moreover, the sensing structure, theoretical model, and fabrication method established in this study are not scale dependent. Smaller cells, e.g., mouse oocytes, could also be manipulated with this approach.

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IntroductionDrug monotherapy was inadequate in controlling blood glucose levels and other comorbidities. An agent that selectively tunes multiple targets was regarded as a new therapeutic strategy for type 2 diabetes. Acanthopanax trifoliatus (L.) Merr polysaccharide (ATMP) is a bio-macromolecule isolated from Acanthopanax trifoliatus (L.) Merr and has therapeutic potential for diabetes management due to its anti-hyperglycemia activity.MethodsType 2 diabetes mellitus was induced in mice using streptozotocin, and 40 and 80 mg/kg ATMP was administered daily via the intragastric route for 8 weeks. Food intake, water intake, and body weight were recorded. The fasting blood glucose (FBG), fasting insulin (FINS) and an oral glucose tolerance test (OGTT) were performed. Histological changes in the liver and pancreas were analyzed by H&E staining. The mRNA and the protein levels of key factors involved in glycogen synthesis, glycogenolysis, and gluconeogenesis were measured by quantitative real time PCR and Western blotting.ResultsIn this study, we found that ATMP could effectively improve glucose tolerance and alleviate insulin resistance by promoting insulin secretion and inhibiting glucagon secretion. In addition, ATMP decreases glycogen synthesis by inhibiting PI3K/Akt/GSK3β signaling, reduces glycogenolysis via suppressing cAMP/PKA signaling, and suppresses liver gluconeogenesis by activating AMPK signaling.ConclusionTogether, ATMP has the potential to be developed as a new multitargets therapeutics for type 2 diabetes.

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There is a limitation in the process of acoustic signal detection, which lies in serious background noise interference and the complex correlation between acoustic signal characteristics and operation states. Integrating the denoising model and feature classification model, a method of transformer acoustic signal anti-interference detection and operation state detection based on deep learning is proposed in this paper. Through tests in anechoic rooms, acoustic signals of transformers in the normal state or under harmonic load are acquired. Combining these signals with the background noise, a dataset containing 12000 samples of acoustic signals is constructed. To implement anti-interference detection, Conv-TasNet is utilized to get the transformer acoustic signal and environmental noise separated; then, ResNet is utilized to classify the operation states of the transformer accurately. Results show that compared with the blind source separation method through RNN and FastICA, the denoising model established in this paper improves Si-SDRi parameters by 37.4dB and 17.53dB respectively, and the transformer operation state classification model established in this paper classifies the test dataset with an accuracy of 97.7%, thus providing an effective method for the extraction of transformer acoustic signal and diagnosis of transformer operation states in complex environments.