[1] J. Zhao, R. Wang, Y. Wu, C. Zhao, Y. Qi, S. Li, H. Jiang, B. Cao. From characteristics to practical applications of skin temperature in thermal comfort research – a comprehensive review. Build. Environ., 262, 111820(2024).

[2] N. Karimi. Approaches in line with human physiology to prevent skin aging. Front. Physiol., 14, 1279371(2023).

[3] O. B. Osman, Z. B. Harris, M. E. Khani, J. W. Zhou, A. Chen, A. J. Singer, M. Hassan Arbab. Deep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging. Biomed. Opt. Express, 13, 1855-1868(2022).

[4] L. Rittié, G. J. Fisher. Natural and sun-induced aging of human skin. Cold Spring Harb. Perspect. Med., 5, a015370(2015).

[5] Y. Wang, J. Beekman, J. Hew, S. Jackson, A. C. Issler-Fisher, R. Parungao, S. S. Lajevardi, Z. Li, P. K. M. Maitz. Burn injury: Challenges and advances in burn wound healing, infection, pain and scarring. Adv. Drug Deliver. Rev., 123, 3-17(2018).

[6] D. Pantalone, C. Bergamini, J. Martellucci, G. Alemanno, A. Bruscino, G. Maltinti, M. Sheiterle, R. Viligiardi, R. Panconesi, T. Guagni, P. Prosperi. The role of DAMPS in burns and hemorrhagic shock immune response: Pathophysiology and clinical issues. Review. Int. J. Mol. Sci., 22, 7020(2021).

[7] M. Abazari, A. Ghaffari, H. Rashidzadeh, S. M. Badeleh, Y. Maleki. A systematic review on classification, identification, and healing process of burn wound healing. Int. J. Low. Extr. Wounds, 21, 18-30(2020).

[8] H. A. Phelan, J. H. Holmes, W. L. Hickerson, C. J. Cockerell, J. W. Shupp, J. E. Carter. Use of 816 consecutive burn wound biopsies to inform a histologic algorithm for burn depth categorization. J. Burn Care Res., 42, 1162-1167(2021).

[9] Y. Zuo, X. Yu, S. Lu. Dermal fibroblasts from different layers of pig skin exhibit different profibrotic and morphological characteristics. Anat. Rec., 299, 1585-1599(2016).

[10] B. Zheng, C. Shen, J. Sun, W. Guo, Y. Jin, Y. Niu. Developing a simple burn model in rats of different ages. J. Burn Care Res., 40, 639-647(2019).

[11] B. Wan, C. Ganier, X. Du-Harpur, N. Harun, F. M. Watt, R. Patalay, M. D. Lynch. Applications and future directions for optical coherence tomography in dermatology. Brit. J. Dermatol., 184, 1014-1022(2021).

[12] Y. J. Wang, J. Y. Wang, Y. H. Wu. Application of cellular resolution full-field optical coherence tomography in vivo for the diagnosis of skin tumours and inflammatory skin diseases: A pilot study. Dermatology, 238, 121-131(2021).

[13] J. Olsen, J. Holmes, G. B. E. Jemec. Advances in optical coherence tomography in dermatology — a review. J. Biomed. Opt., 23, 040901(2018).

[14] W. Gao, V. P. Zakharov, O. O. Myakinin, I. A. Bratchenko, D. N. Artemyev, D. V. Kornilin. Medical images classification for skin cancer using quantitative image features with optical coherence tomography. J. Innov. Opt. Heal. Sci., 09, 1650003(2015).

[15] X. Liang, V. Crecea, S. A. Boppart. Dynamic optical coherence elastography: A review. J. Innov. Opt. Heal. Sci., 03, 221-233(2010).

[16] V. Y. Zaitsev, A. L. Matveyev, L. A. Matveev, E. V. Gubarkova, A. A. Sovetsky, M. A. Sirotkina, G. V. Gelikonov, E. V. Zagaynova, N. D. Gladkova, A. Vitkin. Practical obstacles and their mitigation strategies in compressional optical coherence elastography of biological tissues. J. Innov. Opt. Heal. Sci., 10, 1742006(2017).

[17] W. J. Moy, E. Su, J. J. Chen, C. Oh, J. C. Jing, Y. Qu, Y. He, Z. Chen, B. J. F. Wong. Association of electrochemical therapy with optical, mechanical, and acoustic impedance properties of porcine skin. JAMA Facial Plast. Surg., 19, 502(2017).

[18] X. Feng, G.-Y. Li, A. Ramier, A. M. Eltony, S.-H. Yun. In vivo stiffness measurement of epidermis, dermis, and hypodermis using broadband Rayleigh-wave optical coherence elastography. Acta Biomater., 146, 295-305(2022).

[19] C. H. Liu, S. Assassi, S. Theodore, C. Smith, A. Schill, M. Singh, S. Aglyamov, C. Mohan, K. V. Larin. Translational optical coherence elastography for assessment of systemic sclerosis. J. Biophotonics, 12, e201900236(2019).

[20] M. A. Kirby, P. Tang, H. C. Liou, M. Kuriakose, J. J. P., T. N. Pham, R. E. Ettinger, R. K. Wang, M. O’Donnell, I. Pelivanov. Probing elastic anisotropy of human skin in vivo with light using non-contact acoustic micro-tapping OCE and polarization sensitive OCT. Sci. Rep., 12, 3963(2022).

[21] H. Liu, D. Yang, R. Jia, W. Wang, J. Shang, Q. Liu, Y. Liang. Dynamic optical coherence elastography for skin burn assessment: A preliminary study on mice model. J. Biophotonics, 17, e202400028(2024).

[22] N. C. Rouze, M. H. Wang, M. L. Palmeri, K. R. Nightingale. Finite element modeling of impulsive excitation and shear wave propagation in an incompressible, transversely isotropic medium. J. Biomech., 46, 2761-2768(2013).

[23] A. Rodriguez, A. Laio. Clustering by fast search and find of density peaks. Science, 344, 1492-1496(2014).

[24] A. Wardhana, R. F. M. Lumbuun, D. Kurniasari. How to create burn porcine models: A systematic review. Ann. Burns Fire Disasters, 31, 65-72(2018).

[25] L. Liu, X. Hao, J. Zhang, S. Li, S. Han, P. Qian, Y. Zhang, H. Yu, Y. Kang, Y. Yin, W. Zhang, J. Chen, Y. Yu, H. Jiang, J. Chai, H. Yin, W. Chai. The wound healing of deep partial-thickness burn in Bama miniature pigs is accelerated by a higher dose of hUCMSCs. Stem Cell Res. Ther., 15, 437(2024).

[26] D. M. Burmeister, D. M. Supp, R. A. Clark, E. E. Tredget, H. M. Powell, P. Enkhbaatar, J. K. Bohannon, L. C. Cancio, D. M. Hill, R. M. Nygaard. Advantages and disadvantages of using small and large animals in burn research: Proceedings of the 2021 research special interest group. J. Burn Care Res., 43, 1032-1041(2022).

[27] M. Seaton, A. Hocking, N. S. Gibran. Porcine models of cutaneous wound healing. ILAR J., 56, 127-138(2015).

[28] T. M. Cannon, N. Uribe-Patarroyo, M. Villiger, B. E. Bouma. Measuring collagen injury depth for burn severity determination using polarization sensitive optical coherence tomography. Sci. Rep., 12, 10479(2022).

[29] M. Gan, C. Wang, T. Yang, N. Yang, M. Zhang, W. Yuan, X. D. Li, L. Wang. Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights. Biomed. Opt. Express, 9, 4481-4495(2018).

[30] Z. Yang, J. Shang, C. Liu, J. Zhang, Y. Liang. Identification of oral cancer in OCT images based on an optical attenuation model. Lasers Med. Sci., 35, 1999-2007(2020).

[31] Y.-Y. Dang, Q.-S. Ren, H.-X. Liu, J.-B. Ma, J.-S. Zhang. Comparison of histologic, biochemical, and mechanical properties of murine skin treated with the 1064-nm and 1320-nm Nd:YAG lasers. Exp. Dermatol., 14, 876-882(2005).