Revista Mexicana de Ingenieria Biomedica

Human Mesenchymal Stem Cells Derived from Adipose Tissue and Umbilical Cord, in Combination with Acellular Human Amniotic Membranes, for Skin Healing Processes in Animal Models: a Systematic Review

2024-08-23T17:23:11+00:00

This systematic review aims to document the available research evidence regarding using mesenchymal stem cells (MSCs) and acellular amniotic membranes (AAM) as scaffolds in the murine model for tissue regeneration. This research was developed by analyzing available information on databases like Google Scholar, Pubmed, Scopus, and Web of Science, using the following key terms ''Human Stem Cells'', ''Amniotic membrane'', ''Wound healing' ' and ''Animal model''. A total of 519 articles published from January 2013 to March 2024 were found, but only 8 studies were included in this review, the inclusion criteria were as follows the use of human-derived stem cells (UCMSCs and ADMSCs) seeded in decellularized hAM, in murine models with induced wounds (incisions or burns); exclusion criteria: stem cells obtained from non-human origin, combination of human stem cells from different tissues, use of a different biological scaffold, and studies that not assess efficacy in skin regeneration. The main outcomes were decreased wound closure time, increased angiogenesis, remodeling and increase in extracellular matrix deposition, increased synthesis of growth factors and anti-inflammatory cytokines, and optimization of biomechanical properties. Moreover, one of the main findings was that combining these methods can improve the healing process in chronic wounds. The main bias was related to the inclusion of more studies that used ADMSC (5 of 8); additionally, there were differences in the animal model used, the induced wound, and the comparison of different variables between the studies. In conclusion, we found that the combination of MSCs and AAM as a bio-scaffold improves general tissue healing and regeneration.

Low-Cost Portable Pupilometer for Circadian Rhythm Studies

2024-09-19T15:37:08+00:00

Given the price tag of commercially available devices, developing a low-cost, portable pupilometer based on the Raspberry Pi platform is significant for advancing clinical and research applications in neurology and circadian rhythm studies. This study aimed to design and characterize a pupilometer capable of assessing pupillary light response (PLR) to different wavelengths and its relationship with circadian cycles. Using a Raspberry Pi, a no-infrared filter (NoIR) camera, and custom software, the device was tested on a healthy 24-year-old female subject over 20 days, measuring responses to 635 nm (red) and 463 nm (blue) light stimuli at two daily intervals (8:00 AM and 8:00 PM) in both eyes. Results showed that blue light induced greater pupillary constriction than red light (F(1)= 284.37, p=6.9e-27), with more pronounced responses in the morning (F(1)=12.02, p=0.001), likely due to higher parasympathetic activity. Significant lateral asymmetry (F(1)=12.36, p=0.0008) was also observed in the pupillary response to blue light, suggesting potential intracranial factors. These findings demonstrate the pupilometer's efficacy in capturing detailed pupillary dynamics, proposing its utility to evaluate pupillary light response in connection with circadian rhythms and lateral asymmetry, providing an affordable solution.

A Real-Valued Kalman Estimation Method for Harmonic Signal Analysis in Biomedical Applications

2024-07-21T17:53:12+00:00

This work presents a methodology for obtaining the harmonic estimation of biomedical signals such as electrocardiogram, cardiorespiratory and blood pressure signals. The proposed methodology is achieved using polynomial approximation and the Kalman filter. As advantage, the technique includes instant estimations of signal harmonics and its derivatives using a real-valued model. Furthermore, a comparison of the results is conducted with the Savitzky-Gola, nonlinear tracking differentiator methods, extended state observer and digital differentiator base on Taylor series. The results suggest that the proposed method has the potential to enhance the quality of signal measurements, especially in the presence of noise.

UMInSe: An Unsupervised Method for Segmentation and Detection of Surgical Instruments based on K-means

2024-07-29T14:25:39+00:00

Surgical instrument segmentation in images is crucial for improving precision and efficiency in surgery, but it currently relies on costly and labor-intensive manual annotations. An unsupervised approach is a promising solution to this challenge. This paper introduces a surgical instrument segmentation method using unsupervised machine learning, based on the K-means algorithm, to identify Regions of Interest (ROI) in images and create the image ground truth for neural network training. The Gamma correction adjusts image brightness and enhances the identification of areas containing surgical instruments. The K-means algorithm clusters similar pixels and detects ROIs despite changes in illumination, yielding an efficient segmentation despite variations in image illumination and obstructing objects. Therefore, the neural network generalizes the image features learning for instrument segmentation in different tasks. Experimental results using the JIGSAWS and EndoVis databases demonstrate the method's effectiveness and robustness, with a minimal error (0.0297) and high accuracy (0.9602). These results underscore the precision of surgical instrument detection and segmentation, which is crucial for automating instrument detection in surgical procedures without pre-labeled datasets. Furthermore, this technique could be applied in surgical applications such as surgeon skills assessment and robot motion planning, where precise instrument detection is indispensable.