Skip to main content
SpringerLink
Log in

Estimating the time of skeletal muscle contusion based on the spatial distribution of neutrophils: a practical approach to forensic problems

  • Original Article
  • Published:
International Journal of Legal Medicine Aims and scope Submit manuscript

Abstract

The study aimed to explore the neutrophil’s spatial distributions used to estimate the histological age of contused skeletal muscle, and assessed the accuracy of various indicators, such as the proportion of neutrophils, “neutrophil mean distance,” and distribution of neutrophils in areas of “contiguous contour lines.” Fifty-five Sprague–Dawley rats were divided randomly into a control group and contusion groups at 1, 1.5, 2, 3, 4, and 6 h, as well as 1, 3, 5, and 15 days, post-injury (n = 5 per group). Nuclei and neutrophils were detected by hematoxylin and eosin (HE) staining and immunohistochemical (IHC) staining. At 0–24 h after injury, the distribution of neutrophils at distances of 100, 200, 300, 400, 500, and 600 µm from adjacent blood vessels was determined, and the best samples were screened to estimate wound age. To estimate wound age as accurately as possible, Fisher discriminant analysis (FDA) of the proportion of neutrophils, neutrophil mean distance, and distribution of neutrophils was performed, and 100.0% and 95.0% of the original and cross-validated cases were correctly classified, respectively. The spatial distribution of neutrophils at different distances from adjacent blood vessels showed a strong correlation with the histological age of contusion skeletal muscle, and the combination of the proportion of neutrophils, neutrophil mean distance, and distribution of neutrophils could be used to accurately estimate wound age.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Call JA, Nichenko AS (2020) Autophagy: an essential but limited cellular process for timely skeletal muscle recovery from injury. Autophagy 16:1344–1347. https://doi.org/10.1080/15548627.2020.1753000

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Cimmino MA, Ferrone C, Cutolo M (2011) Epidemiology of chronic musculoskeletal pain. Best Pract Res Clin Rheumatol 25:173–183. https://doi.org/10.1016/j.berh.2010.01.012

    Article  PubMed  Google Scholar 

  3. Li N, Du Q, Bai R, Sun J (2020) Vitality and wound-age estimation in forensic pathology: review and future prospects. Forensic Sci Res 5:15–24. https://doi.org/10.1080/20961790.2018.1445441

    Article  CAS  PubMed  Google Scholar 

  4. Yagi Y, Murase T, Kagawa S, Tsuruya S, Nakahara A, Yamamoto T, Umehara T, Ikematsu K (2016) Immunohistochemical detection of CD14 and combined assessment with CD32B and CD68 for wound age estimation. Forensic Sci Int 262:113–120. https://doi.org/10.1016/j.forsciint.2016.02.031

    Article  CAS  PubMed  Google Scholar 

  5. Frederick DW, Loro E, Liu L, Davila A, Chellappa K, Silverman IM, Quinn WJ 3rd, Gosai SJ, Tichy ED, Davis JG, Mourkioti F, Gregory BD, Dellinger RW, Redpath P, Migaud ME, Nakamaru-Ogiso E, Rabinowitz JD, Khurana TS, Baur JA (2016) Loss of NAD homeostasis leads to progressive and reversible degeneration of skeletal muscle. Cell Metab 24:269–282. https://doi.org/10.1016/j.cmet.2016.07.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jiang H, Gebhardt C, Umansky L, Beckhove P, Schulze TJ, Utikal J, Umansky V (2015) Elevated chronic inflammatory factors and myeloid-derived suppressor cells indicate poor prognosis in advanced melanoma patients. Int J Cancer 136:2352–2360. https://doi.org/10.1002/ijc.29297

    Article  CAS  PubMed  Google Scholar 

  7. Ryu YK, Im YS, Moon EY (2010) Cooperation of actin-sequestering protein, thymosin beta-4 and hypoxia inducible factor-1alpha in tumor cell migration. Oncol Rep 24:1389–1394. https://doi.org/10.3892/or_00000997

    Article  CAS  PubMed  Google Scholar 

  8. Wang J (2018) Neutrophils in tissue injury and repair. Cell Tissue Res 371:531–539. https://doi.org/10.1007/s00441-017-2785-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. de Oliveira S, Rosowski EE, Huttenlocher A (2016) Neutrophil migration in infection and wound repair: going forward in reverse. Nat Rev Immunol 16:378–391. https://doi.org/10.1038/nri.2016.49

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sultan AS, Elgharib MA, Tavares T, Jessri M, Basile JR (2020) The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. J Oral Pathol Med 49:849–856. https://doi.org/10.1111/jop.13042

    Article  PubMed  Google Scholar 

  11. Farris AB, Vizcarra J, Amgad M, Cooper LAD, Gutman D, Hogan J (2020) Artificial intelligence and algorithmic computational pathology: introduction with renal allograft examples. Histopathology 78:791–804. https://doi.org/10.1111/his.14304

    Article  Google Scholar 

  12. Teshiba R, Kawano S, Wang LL, He L, Naranjo A, London WB, Seeger RC, Gastier-Foster JM, Look AT, Hogarty MD, Cohn SL, Maris JM, Park JR, Shimada H (2014) Age-dependent prognostic effect by Mitosis-Karyorrhexis Index in neuroblastoma: a report from the Children’s Oncology Group. Pediatr Dev Pathol 17:441–449. https://doi.org/10.2350/14-06-1505-OA.1

    Article  PubMed  PubMed Central  Google Scholar 

  13. Niu J, An G, Gu Z, Li P, Liu Q, Bai R, Sun J, Du Q (2020) Analysis of sensitivity and specificity: precise recognition of neutrophils during regeneration of contused skeletal muscle in rats. Forensic Sci Res:1–10. https://doi.org/10.1080/20961790.2020.1713432

  14. Sun JH, Zhu XY, Dong TN, Zhang XH, Liu QQ, Li SQ, Du QX (2017) An “up, no change, or down” system: time-dependent expression of mRNAs in contused skeletal muscle of rats used for wound age estimation. Forensic Sci Int 272:104–110. https://doi.org/10.1016/j.forsciint.2017.01.012

    Article  CAS  PubMed  Google Scholar 

  15. Zhu XY, Du QX, Li SQ, Sun JH (2016) Comparison of the homogeneity of mRNAs encoding SFRP5, FZD4, and Fosl1 in post-injury intervals: Subcellular localization of markers may influence wound age estimation. J Forensic Legal Med 43:90–96. https://doi.org/10.1016/j.jflm.2016.07.013

    Article  Google Scholar 

  16. Lin CH, Wu SH, Lee SS, Lin YN, Kuo YR, Chai CY, Huang SH (2017) Autologous adipose-derived stem cells reduce burn-induced neuropathic pain in a rat model. Int J Mol Sci. 19:34. https://doi.org/10.3390/ijms19010034

    Article  CAS  PubMed Central  Google Scholar 

  17. Du QX, Li N, DangLH DTN, Lu HL, Shi FX, Jin QQ, Jie C, Sun JH (2020) Temporal expression of wound healing-related genes inform wound age estimation in rats after a skeletal muscle contusion: a multivariate statistical model analysis. Int J Legal Med 134:273–282. https://doi.org/10.1007/s00414-018-01990-2

    Article  PubMed  Google Scholar 

  18. Dang LH, Feng N, An GS, Cao J, Du QX, Jin QQ, Huang P, Sun JH (2020) Novel insights into wound age estimation: combined with “up, no change, or down” system and cosine similarity in python environment. Int J Legal Med 134:2177–2186. https://doi.org/10.1007/s00414-020-02411-z

    Article  PubMed  Google Scholar 

  19. Ibrahim SF, Issak M, Bayoumy AA, Abd El-Fatah DS (2016) Cutaneous (tPA) and skeletal (TnI) mRNA as markers of aging in contused wound. J Forensic Sci 61:1007–1010. https://doi.org/10.1111/1556-4029.13072

    Article  CAS  PubMed  Google Scholar 

  20. Sicherer ST, Venkatarama RS, Grasman JM (2020) Recent trends in injury models to study skeletal muscle regeneration and repair. Bioengineering (Basel) 7:76. https://doi.org/10.3390/bioengineering7030076

    Article  CAS  PubMed Central  Google Scholar 

  21. Yoshida M, Matsuzaki T, Date M, Wada K (1997) Skeletal muscle fiber degeneration in mdx mice induced by electrical stimulation. Muscle Nerve 20:1422–1432. https://doi.org/10.1002/(sici)1097-4598(199711)20:11%3c1422::aid-mus10%3e3.0.co;2-3

    Article  CAS  PubMed  Google Scholar 

  22. Tidball JG (2011) Mechanisms of muscle injury, repair, and regeneration. Compr Physiol 1:2029–2062. https://doi.org/10.1002/cphy.c100092

    Article  PubMed  Google Scholar 

  23. Ng LG, Qin JS, Roediger B, Wang Y, Jain R, Cavanagh LL, Smith AL, Jones CA, de Veer M, Grimbaldeston MA, Meeusen EN, Weninger W (2011) Visualizing the neutrophil response to sterile tissue injury in mouse dermis reveals a three-phase cascade of events. J Invest Dermatol. 131:2058–2068. https://doi.org/10.1038/jid.2011.179

    Article  CAS  PubMed  Google Scholar 

  24. Acs B, Hartman J (2020) Next generation pathology: artificial intelligence enhances histopathology practice. J Pathol 250:7–8. https://doi.org/10.1002/path.5343

    Article  PubMed  Google Scholar 

  25. Cheng J, Zhang J, Han Y, Wang X, Ye X, Meng Y, Parwani A, Han Z, Feng Q, Huang K (2017) Integrative analysis of histopathological images and genomic data predicts clear cell renal cell carcinoma prognosis. Cancer Res 77:e91–e100. https://doi.org/10.1158/0008-5472.CAN-17-0313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang M, Zhang MZ, Wen SH, Sun YF, Jiang PH, Wang LL, Zhao R, Wang CL, Jiang SK, Guan DW (2019) The distribution and time-dependent expression of HIPK2 during the repair of contused skeletal muscle in mice. Histol Histopathol. 34:745–753. https://doi.org/10.14670/HH-18-072

    Article  CAS  PubMed  Google Scholar 

  27. Naqvi A, Raynor E, Freemont AJ (2019) Histological ageing of fractures in infants: a practical algorithm for assessing infants suspected of accidental or non-accidental injury. Histopathology. 75:74–80. https://doi.org/10.1111/his.13850

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zha S, Wei X, Fang R, Wang Q, Lin H, Zhang K, Zhang H, Liu R, Li Z, Huang P, Wang Z (2020) Estimation of the age of human semen stains by attenuated total reflection Fourier transform infrared spectroscopy: a preliminary study. Forensic Sci Res 5:119–125. https://doi.org/10.1080/20961790.2019.1642567

    Article  PubMed  Google Scholar 

  29. Jin L, Yang J, Kuang K, Ni B, Gao Y, Sun Y, Gao P, Ma W, Tan M, Kang H, Chen J, Li M (2020) Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet. EBioMedicine 62:103106. https://doi.org/10.1016/j.ebiom.2020.103106

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kim T, Heo J, Jang DK, Sunwoo L, Kim J, Lee KJ, Kang SH, Park SJ, Kwon OK, Oh CW (2019) Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network. EBioMedicine 40:636–642. https://doi.org/10.1016/j.ebiom.2018.12.043

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by the Natural Science Foundation for Excellent Young Scientists of Shanxi Province (grant number 20191D211351) and the National Natural Science Foundation of China (grant number 81971795).

Author information

Authors and Affiliations

  1. School of Forensic Medicine, Shanxi Province, Shanxi Medical University, No. 98, University Street, Wujinshan Town, Yuci District, Jinzhong City, 030604, People’s Republic of China

    Qiu-xiang Du, Liang Wang, Dan Li, Jia-jia Niu, Xu-dong Zhang & Jun-hong Sun

Authors
  1. Qiu-xiang Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jia-jia Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xu-dong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jun-hong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

QD conceived the idea and drafted the manuscript; LW and DL established animal model and immunohistochemical staining; JN interpreted the results and statistical analysis; XZ performed data collection; JS conceived the study and performed some interpretation of the results. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Jun-hong Sun.

Ethics declarations

Ethical approval

This article does not contain any studies with human participants performed by any of the authors. The principles of the Guide for the Care and Use of Laboratory Animals of the Ministry of the People’s Republic of China were followed.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Du, Qx., Wang, L., Li, D. et al. Estimating the time of skeletal muscle contusion based on the spatial distribution of neutrophils: a practical approach to forensic problems. Int J Legal Med 136, 149–158 (2022). https://doi.org/10.1007/s00414-021-02690-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00414-021-02690-0

Keywords

Search

Navigation