A Novel Technique for Imaging and Analysis of Hair Cells in the Organ of Corti Using Modified Sca/eS and Machine Learning

Machine learning (ML) techniques as effective methods for evaluating hair and skin assessments: A systematic review

Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Intelligence (AI) techniques in order to evaluate hair and skin assessments. PubMed, Web of Science, IEEE Xplore, and Science Direct were searched in order to retrieve research publications between 1 January 2010 and 31 March 2020 using appropriate keywords such as "hair and skin analysis." Following accurate screening, 20 peer-reviewed publications were selected for inclusion in this systematic review. The analysis demonstrated that prevalent Machine Learning (ML) methods comprised of Support Vector Machine (SVM), k-nearest Neighbor, and Artificial Neural Networks (ANN). ANN's were observed to yield the highest accuracy of 95% followed by SVM generating 90%. These techniques were most commonly applied for drafting framework assessments such as that of Melanoma. Values of parameters such as Sensitivity, Specificity, and Area under the Curve (AUC) were extracted from the studies and with the help of comparisons, relevant inferences were also made. ANN's were observed to yield the highest sensitivity of 82.30% as well as a 96.90% specificity. Hence, with this systematic review, a summarization of the studies was drafted that encapsulated how Machine Learning (ML) techniques have been employed for the analysis and evaluation of hair and skin assessments.

Tissue clearing and three-dimensional imaging of the whole cochlea and vestibular system from multiple large-animal models

The inner ear of humans and large animals is embedded in a thick and dense bone that makes dissection challenging. Here, we present a protocol that enables three-dimensional (3D) characterization of intact inner ears from large-animal models. We describe steps for decalcifying bone, using solvents to remove color and lipids, and imaging tissues in 3D using confocal and light sheet microscopy. We then detail a pipeline to count hair cells in antibody-stained and 3D imaged cochleae using open-source software. For complete details on the use and execution of this protocol, please refer to (Moatti et al., 2022).¹.

Three-dimensional mouse cochlea imaging based on the modified Sca/eS using confocal microscopy

The three-dimensional stria vascularis (SV) and cochlear blood vessel structure is essential for inner ear function. Here, modified Sca/eS, a sorbitol-based optical-clearing method, was reported to visualize SV and vascular structure in the intact mouse cochlea. Cochlear macrophages as well as perivascular-resident macrophage-like melanocytes were detected as GFP-positive cells of the CX3CR1^+/GFP mice. This study's method was effective in elucidating inner ear function under both physiological and pathological conditions.