Negative impact of anesthesia with midazolam, sufentanil, and propofol used in pediatric flexible bronchoscopy on the tracheal ciliary beat frequency in guinea pigs.

There is no direct evidence for the exact cilia-inhibitory effects of opioids, which are generally used to achieve general anesthesia in combination with other anesthetic drugs. These are the reasons, why we analysed direct concentration-dependent or systemic effects of anesthetics (propofol, sufentanil, and midazolam) at a recommended doses administered individually or simultaneously on the tracheal ciliary beat frequency (CBF) in in vitro experimental conditions. Brush biopsy technique was used to remove the tracheal epithelia of guinea pigs for microscopy evaluation of ciliary beating monitored by high-speed video camera and analysed by Ciliary Analysis software. The tracheal CBF was significantly lower in the presence of sufentanil (10-8 mol/L) than in the control group; similarly for midazolam-sufentanil (10-8 - 10-5 mol/L), as well as for midazolam-propofol (10-5 and 10-3 mol/L) combinations. The fact that concurrent administration of benzodiazepine significantly increased the risk of sufentanil-induced cilia-inhibition was pharmacologically confirmed using GABAA receptor antagonist, bicuculline methiodide. The benefit of propofol on the potent cilia-inhibitory effect achieved by benzodiazepine-opioid combination was non-significant. We highlight the pharmacodynamics interaction between anesthetic drugs mediated via GABAA receptor with negative impact on the CBF in a respiratory epithelium under experimental condition rather than the effect of individual anesthetic.

Real-Time Interference Artifacts Suppression in Array of ToF Sensors.

Time of Flight (ToF) sensors are the source of various errors, including the multi-camera interference artifact caused by the parallel scanning mode of the sensors. This paper presents the novel Importance Map Based Median filtration algorithm for interference artifacts suppression, as the potential 3D filtration method. The approach is based on the processing of multiple depth frames, using the extraction of the interference region and application of the interpolation. Considering the limitations and good functionalities of proposed algorithm, the combination with some standard methods was suggested. Performance of the algorithm was evaluated on the dataset consisting of the real-world objects with different texture and morphology against popular filtering methods based on neural networks and statistics.

Quality inspection of specific electronic boards by deep neural networks.

Reliability and lifetime of specific electronics boards depends on the quality of manufacturing process. Especially soldering splashes in some areas of PCB (printed circuit board) can cause change of selected electrical parameters. Nowadays, the manual inspection is massively replaced by specialized visual systems checking the presence of different defects. The research carried out in this paper can be considered as industrial (industry requested) application of machine learning in automated object detection. Object of interest-solder splash-is characterized by its small area and similar properties (texture, color) as its surroundings. The aim of our research was to apply state-of-the art algorithms based on deep neural networks for detection such objects in relatively complex electronic board. The research compared seven different object detection models based on you-look-only-once (YOLO) and faster region based convolutional neural network architectures. Results show that our custom trained YOLOv8n detection model with 1.9 million parameters can detect solder splashes with low detection speed 90 ms and 96.6% mean average precision. Based on these results, the use of deep neural networks can be useful for early detection of solder splashes and potentially lead to higher productivity and cost savings.

Machine vision application in animal trajectory tracking.

This article was motivated by the doctors' demand to make a technical support in pathologies of gastrointestinal tract research [10], which would be based on machine vision tools. Proposed solution should be less expensive alternative to already existing RF (radio frequency) methods. The objective of whole experiment was to evaluate the amount of animal motion dependent on degree of pathology (gastric ulcer). In the theoretical part of the article, several methods of animal trajectory tracking are presented: two differential methods based on background subtraction, the thresholding methods based on global and local threshold and the last method used for animal tracking was the color matching with a chosen template containing a searched spectrum of colors. The methods were tested offline on five video samples. Each sample contained situation with moving guinea pig locked in a cage under various lighting conditions.