A Bibliometric and Visual Analysis of the Current Status and Trends of Forensic Mixed Stain Research

doi:10.12116/j.issn.1004-5619.2022.521010

Abstract

Abstract:

Objective To explore the context and hotspot changes of forensic mixed stain research through bibliometric approach. Methods The literature of forensic mixed stain included in the core collection of Web of Science database from 2011 to 2022 were collected as the study object, and the annual publication number, countrie （region）, institution, journal, keywords, etc. were bibliometrically and visually analyzed using the R-based Bibliometrix 1.1.6 package and VOSviewer 1.6.18 software. Results A total of 732 articles on forensic mixed stain were included from 2011 to 2022, with the annual number of articles published and the annual citation frequency showing a steady increase year by year. Among the 59 countries （regions） with the most published articles, the United States ranked first with 246 articles, followed by China with 153 articles. The literature came from 104 journals, and the total number of articles published in the top 10 journals was 633. FORENSIC SCI INT GENET ranked first with 307 articles. Visual analysis using VOSviewer software showed that keywords could be divided into four research clusters, namely the genetic marker development group （blue）, the mixed stain typing analysis theory group （red）, the sequencing analysis group （yellow）, and the case sample research group （green）. It can be divided into four development stages in terms of different time periods: early development （2011—2013）, middle development （2014—2016）, rapid development （2017—2020） and latest development （2021—2022）. Conclusion The number of publications by domestic and foreign scholars in the study of mixed stain in forensic science is showing a relatively stable trend. Machine learning, next generation sequencing and other research have been the hottest topics that have attracted the most attention in recent years, which is expected to further develop the theory of mixed stain typing and sequencing analysis in forensic mixed stain research.

Key words: forensic genetics, bibliometrics, mixed stain, visual analysis, Web of Science

CLC Number:

Qing-wei FAN, Ling LI, Hui-ling YANG, Ting-ting DENG, Dong-dong XU, Yun WANG, Bing DU, Jiang-wei YAN. A Bibliometric and Visual Analysis of the Current Status and Trends of Forensic Mixed Stain Research[J]. Journal of Forensic Medicine, 2024, 40(1): 20-29.

Figures/Tables 9

References 48

1	ARIA M， CUCCURULLO C. Bibliometrix： An R-tool for comprehensive science mapping analysis[J]. J Informetrics，2017，11（4）：959-975. doi：10.1016/j.joi.2017.08.007 .
2	VAN ECK N J， WALTMAN L. Software survey： VOSviewer， a computer program for bibliometric mapping[J]. Scientometrics， 2010，84（2）：523-538. doi：10.1007/s11192-009-0146-3 .
3	PERLIN M W， LEGLER M M， SPENCER C E， et al. Validating TrueAllele^® DNA mixture interpretation[J]. J Forensic Sci，2011，56（6）：1430-1447. doi：10.1111/j.1556-4029.2011.01859.x .
4	TAYLOR D， BRIGHT J A， BUCKLETON J. The interpretation of single source and mixed DNA profiles[J]. Forensic Sci Int Genet，2013，7（5）：516-528. doi：10.1016/j.fsigen.2013.05.011 .
5	BLEKA Ø， STORVIK G， GILL P. EuroForMix： An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts[J]. Forensic Sci Int Genet，2016，21：35-44. doi：10.1016/j.fsigen. 2015.11.008 .
6	GILL P， GUSMÃO L， HANED H， et al. DNA commission of the International Society of Forensic Genetics： Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods[J]. Forensic Sci Int Genet，2012，6（6）：679-688. doi：10.1016/j.fsigen.2012.06.002 .
7	BRIGHT J A， TAYLOR D， CURRAN J M， et al. Developing allelic and stutter peak height models for a continuous method of DNA interpretation[J]. Forensic Sci Int Genet，2013，7（2）：296-304. doi：10.1016/j.fsigen.2012.11.013 .
8	GILL P， HANED H. A new methodological framework to interpret complex DNA profiles using likelihood ratios[J]. Forensic Sci Int Genet，2013，7（2）：251-263. doi：10.1016/j.fsigen.2012.11.002 .
9	BRIGHT J A， TAYLOR D， MCGOVERN C， et al. Developmental validation of STRmix^TM， expert software for the interpretation of forensic DNA profiles[J]. Forensic Sci Int Genet，2016，23：226-239. doi：10.1016/j.fsigen.20 16.05.007 .
10	COWELL R G， GRAVERSEN T， LAURITZEN S L， et al. Analysis of forensic DNA mixtures with artefacts[J]. J R Stat Soc Series B Appl Stat，2015，64（1）：1-48. doi：10.1111/rssc.12071 .
11	BØRSTING C， MORLING N. Next generation sequencing and its applications in forensic genetics[J]. Forensic Sci Int Genet，2015，18：78-89. doi：10.1016/j.fsigen.2015.02.002 .
12	HANED H， SLOOTEN K， GILL P. Exploratory data analysis for the interpretation of low template DNA mixtures[J]. Forensic Sci Int Genet，2012，6（6）：762-774. doi：10.1016/j.fsigen.2012.08.008 .
13	ZHANG J S， ZHANG J Y， TAO R Y， et al. A newly devised multiplex assay of novel polymorphic non-CODIS STRs as a valuable tool for forensic application [J]. Forensic Sci Int Genet， 2020， 48： 8. doi：10.1016/j.fsigen.2020.102341 .
14	WANG Z， ZHOU D， WANG H， et al. Massively parallel sequencing of 32 forensic markers using the Precision ID GlobalFiler^TM NGS STR Panel and the Ion PGM^TM System[J]. Forensic Sci Int Genet，2017，31：126-34. doi：10.1016/j.fsigen.2017. 09.004 .
15	MANABE S， FUJII K， FUKAGAWA T， et al. Evaluation of probability distribution models for stutter ratios in the typing system of GlobalFiler and 3500xL Genetic Analyzer[J]. Legal Med，2021，52：101906. doi：10.1016/j.legalmed.2021.101906 .
16	ALGHAFRI R， GOODWIN W， RALF A， et al. A novel multiplex assay for simultaneously analysing 13 rapidly mutating Y-STRs[J]. Forensic Sci Int Genet，2015，17：91-98. doi：10.1016/j.fsigen.2015. 04.004 .
17	ANDERSEN M M， MOGENSEN H S， ERIKSEN P S， et al. Yfiler^® Plus population samples and dilution series： Stutters， analytic thresholds， and drop-out probabilities[J]. Int J Legal Med， 2017，131（6）：1503-1511. doi：10.1007/s00414-017-1568-8 .
18	FOROUZESH M， IRANI S， SOLEIMANI A， et al. Application of Y-STR， DIP-STR and SNP-STR markers in interpretation of forensic genetic profiling： A narrative review[J]. Iran J Public Health， 2022，51（7）：1538-1545. doi：10.18502/ijph.v51i7. 10087 .
19	CASTELLA V， GERVAIX J， HALL D. DIP-STR： Highly sensitive markers for the analysis of unbalanced genomic mixtures[J]. Human Mutation，2013，34（4）：644-654. doi：10.1002/humu.22280 .
20	OLDONI F， CASTELLA V， GROSJEAN F， et al. Sensitive DIP-STR markers for the analysis of unbalanced mixtures from “touch” DNA samples[J]. Forensic Sci Int Genet，2017，28：111-117. doi：10.10 16/j.fsigen.2017.02.004 .
21	TAN Y， WANG L， WANG H， et al. An investigation of a set of DIP-STR markers to detect unbalanced DNA mixtures among the southwest Chinese Han population[J]. Forensic Sci Int Genet，2017，31：34-39. doi：10.1016/j.fsigen.2017.08.014 .
22	JIAN H， WANG L， LV M L， et al. A novel SNP-STR system based on a capillary electrophoresis platform[J]. Front Genet， 2021，12：636821. doi：10.3389/fgene.2021.636821 .
23	LIU J D， LI W Y， WANG J Q， et al. A new set of DIP-SNP markers for detection of unbalanced and degraded DNA mixtures[J]. Electrophoresis， 2019，40（14）：1795-1804. doi：10.1002/elps.201900017 .
24	LIU J D， HAO T， CHENG X J， et al. DIP-microhaplotypes： New markers for detection of unbalanced DNA mixtures[J]. Int J Leg Med，2021，135（1）：13-21. doi：10.1007/s00414-020-02288-y .
25	WARD D， HENRY J， TAYLOR D. Analysis of mixed DNA profiles from the RapidHIT^TM ID platform using probabilistic genotyping software STRmix^TM [J]. Forensic Sci Int Genet，2022，58：102664. doi：10.1016/j.fsigen.2022.102664 .
26	ALOTAIBI H， ALSOLAMI F， ABOZINADAH E， et al. TAWSEEM： A deep-learning-based tool for estimating the number of unknown contributors in DNA profiling[J]. Electronics， 2022，11（4）：548. doi：10.3390/electronics11040548 .
27	BENSCHOP C C G， VAN DER LINDEN J， HOOGENBOOM J， et al. Automated estimation of the number of contributors in autosomal short tandem repeat profiles using a machine learning approach[J]. Forensic Sci Int Genet，2019，43：102150. doi：10.1016/j.fsigen.2019.102150 .
28	MARCIANO M A， ADELMAN J D. PACE： Probabilistic Assessment for Contributor Estimation -- A machine learning-based assessment of the number of contributors in DNA mixtures[J]. Forensic Sci Int Genet，2017，27：82-91. doi：10.1016/j.fsigen.2016.11.006 .
29	VALTL J， MÖNICH U J， LUN D S， et al. A series of developmental validation tests for number of contributors platforms： Exemplars using NOCIt and a neural network[J]. Forensic Sci Int Genet，2021，54：102556. doi：10.1016/j.fsigen.2021.102556 .
30	刘京，何光林，侯一平，等. 大规模平行测序在混合斑检测分析中的应用前景[J].法医学杂志， 2019，35（1）：84-88，94. doi：10.12116/j.issn.1004-5619.2019.01.016 .
	LIU J， HE G L， HOU Y P， et al. Application prospect of massively parallel sequencing in mixed stain detection[J]. Fayixue Zazhi， 2019，35（1）：84-88，94.
31	OLDONI F， KIDD K K， PODINI D. Microhaplotypes in forensic genetics[J]. Forensic Sci Int Genet，2019，38：54-69. doi：10.1016/j.fsigen.2018. 09.009 .
32	PANG J B， RAO M， CHEN Q F， et al. A 124-plex microhaplotype panel based on next-generation sequencing developed for forensic applications[J]. Sci Rep， 2020，10：1945. doi：10.1038/s41598-020-58980-x .
33	CHEN A， XIONG L， QU Y， et al. Opportunity of next-generation sequencing-based short tandem repeat system for tumor source identification[J]. Front Oncol，2022，12：800028. doi：10.3389/fonc. 2022.800028 .
34	FAN H L， WANG L X， LIU C H， et al. Development and validation of a novel 133-plex forensic STR panel （52 STRs and 81 Y-STRs） using single-end 400 bp massive parallel sequencing[J]. Int J Legal Med，2022，136（2）：447-464. doi：10.1007/s00414-021-027 38-1 .
35	CHENG K， SKILLMAN J， HICKEY S， et al. Variability and additivity of read counts for aSTRs in NGS DNA profiles[J]. Forensic Sci Int Genet，2020，48：102351. doi：10.1016/j.fsigen.2020.102351 .
36	CHAN MUN WEI J， ZHAO Z C， LI S C， et al. NGS-based likelihood ratio for identifying contributors in two- and three-person DNA mixtures[J]. Comput Biol Chem，2018，74：428-433. doi：10.1016/j.compbiolchem.2018.03.010 .
37	CORTES-FIGUEIREDO F， CARVALHO F S， FONSECA A C， et al. From forensics to clinical research： Expanding the variant calling pipeline for the precision ID mtDNA whole genome panel[J]. Int J Mol Sci， 2021，22（21）：12031. doi：10.3390/ijms222112031 .
38	PALENCIA-MADRID L， VINUEZA-ESPINOSA D， BAETA M， et al. Validation of a 52-mtSNP minisequencing panel for haplogroup classification of forensic DNA samples[J]. Int J Legal Med，2020，134（3）：929-936. doi：10.1007/s00414-020-02264-6 .
39	WISNER M， ERLICH H， SHIH S， et al. Resolution of mitochondrial DNA mixtures using a probe capture next generation sequencing system and phylogenetic-based software[J]. Forensic Sci Int Genet，2021，53：102531. doi：10.1016/j.fsigen.2021. 102531 .
40	GROSJEAN F， FAVRE M， CASTELLA V. Comparison between MACSprep^TM forensic sperm microbead kit and Erase Sperm Isolation kit for the enrichment of sperm fractions recovered from sexual assault samples[J]. Int J Legal Med，2023，137（1）：267-278. doi：10.1007/s00414-022-02861-7 .
41	COSTA S， CORREIA-DE-SÁ P， PORTO M J， et al. The use of laser microdissection in forensic sexual assault casework： Pros and cons compared to standard methods[J]. J Forensic Sci，2017，62（4）：998-1006. doi：10.1111/1556-4029.13348 .
42	VANDEWOESTYNE M， VAN NIEUWERBURGH F， VAN HOOFSTAT D， et al. Evaluation of three DNA extraction protocols for forensic STR typing after laser capture microdissection[J]. Forensic Sci Int Genet，2012，6（2）：258-262. doi：10.1016/j.fsigen.2011.06.002 .
43	JÄGER R. New perspectives for whole genome amplification in forensic STR analysis[J]. Int J Mol Sci，2022，23（13）：7090. doi：10.3390/ijms23137090 .
44	HUFFMAN K， HANSON E， BALLANTYNE J. Probabilistic genotyping of single cell replicates from complex DNA mixtures recovers higher contributor LRs than standard analysis[J]. Sci Justice，2022，62（2）：156-163. doi：10.1016/j.scijus.2022. 01.003 .
45	马骏，童奇，高良弼，等. 尼龙膜套管分离技术对混合斑中精子细胞DNA的提取[J].法医学杂志，2018，34（4）：417-419，427. doi：10.12116/j.issn.1004-5619.2018.04.015 .
	MA J， TONG Q， GAO L B， et al. Extraction of DNA from sperm cells in mixed stain by nylon membrane bushing separation technique[J]. Fayixue Zazhi，2018，34（4）：417-419，427.
46	SAUER E， EXTRA A， CACHÉE P， et al. Identification of organ tissue types and skin from forensic samples by microRNA expression analysis[J]. Forensic Sci Int Genet，2017，28：99-110. doi：10.1016/j.fsigen.2017.02.002 .
47	ZHANG X Y， LI J， LIU J D， et al. Identification of the vaginal secretion donor in mixture stains using polymorphic cSNPs on mRNA biomarkers[J]. Forensic Sci Int Genet，2022，59：102703. doi：10.1016/j.fsigen.2022.102703 .
48	LIU J D， CHENG X J， LIU F， et al. Identification of coding region SNPs from specific and sensitive mRNA biomarkers for the deconvolution of the semen donor in a body fluid mixture[J]. Forensic Sci Int Genet，2021，52：102483. doi：10.1016/j.fsigen.2021.102483 .

期刊全称	期刊缩写	发文量/篇	影响因子	分区	国家	被引频次
Forensic Science International-Genetics	FORENSIC SCI INT GENET	306	4.453	Q1	爱尔兰	7 699
International Journal of Legal Medicine	INT J LEGAL MED	87	2.791	Q1	德国	846
Journal of Forensic Sciences	J FORENSIC SCI	45	1.717	Q3	美国	703
Electrophoresis	ELECTROPHORESIS	39	3.595	Q2	德国	463
Legal Medicine	LEGAL MED-TOKYO	26	2.017	Q3	美国	144
Forensic Science International	FORENSIC SCI INT	23	2.676	Q2	爱尔兰	151
Science & Justice	SCI JUSTICE	23	1.993	Q3	英国	439
Genes	GENES	16	4.141	Q2	瑞士	94
PLoS One	PLOS ONE	14	3.752	Q2	美国	294
Australian Journal of Forensic Sciences	AUS J FORENSIC SCI	13	1.210	Q4	英国	29

期刊全称	期刊缩写	发文量/篇	影响因子	分区	国家	被引频次
Forensic Science International-Genetics	FORENSIC SCI INT GENET	306	4.453	Q1	爱尔兰	7 699
International Journal of Legal Medicine	INT J LEGAL MED	87	2.791	Q1	德国	846
Journal of Forensic Sciences	J FORENSIC SCI	45	1.717	Q3	美国	703
Electrophoresis	ELECTROPHORESIS	39	3.595	Q2	德国	463
Legal Medicine	LEGAL MED-TOKYO	26	2.017	Q3	美国	144
Forensic Science International	FORENSIC SCI INT	23	2.676	Q2	爱尔兰	151
Science & Justice	SCI JUSTICE	23	1.993	Q3	英国	439
Genes	GENES	16	4.141	Q2	瑞士	94
PLoS One	PLOS ONE	14	3.752	Q2	美国	294
Australian Journal of Forensic Sciences	AUS J FORENSIC SCI	13	1.210	Q4	英国	29

序号	作者	期刊	文题	年份	LC/次	GC/次	LC/GC
1	Perlin MW	J FORENSIC SCI	Validating TrueAllele^® DNA Mixture Interpretation^[3]	2011	98	152	0.644 7
2	Taylor D	FORENSIC SCI INT GENET	The interpretation of single source and mixed DNA profiles^[4]	2013	88	170	0.517 6
3	Bleka O	FORENSIC SCI INT GENET	EuroForMix： An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts^[5]	2016	80	142	0.563 4
4	Gill P	FORENSIC SCI INT GENET	DNA commission of the International Society of Forensic Genetics： Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods^[6]	2012	58	121	0.479 3
5	Bright JA	FORENSIC SCI INT GENET	Developing allelic and stutter peak height models for a continuous method of DNA interpretation^[7]	2013	56	102	0.549 0
6	Gill P	FORENSIC SCI INT GENET	A new methodological framework to interpret complex DNA profiles using likelihood ratios^[8]	2013	51	87	0.586 2
7	Bright JA	FORENSIC SCI INT GENET	Developmental validation of STRmix^TM， expert software for the interpretation of forensic DNA profiles^[9]	2016	50	73	0.684 9
8	Cowell RG	J R STAT SOC C-APPL	Analysis of forensic DNA mixtures with artefacts^[10]	2015	41	69	0.594 2
9	Børsting C	FORENSIC SCI INT GENET	Next generation sequencing and its applications in forensic genetics^[11]	2015	41	230	0.178 3
10	Haned H	FORENSIC SCI INT GENET	Exploratory data analysis for the interpretation of low template DNA mixtures^[12]	2012	40	67	0.597 0

序号	作者	期刊	文题	年份	LC/次	GC/次	LC/GC
1	Perlin MW	J FORENSIC SCI	Validating TrueAllele^® DNA Mixture Interpretation^[3]	2011	98	152	0.644 7
2	Taylor D	FORENSIC SCI INT GENET	The interpretation of single source and mixed DNA profiles^[4]	2013	88	170	0.517 6
3	Bleka O	FORENSIC SCI INT GENET	EuroForMix： An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts^[5]	2016	80	142	0.563 4
4	Gill P	FORENSIC SCI INT GENET	DNA commission of the International Society of Forensic Genetics： Recommendations on the evaluation of STR typing results that may include drop-out and/or drop-in using probabilistic methods^[6]	2012	58	121	0.479 3
5	Bright JA	FORENSIC SCI INT GENET	Developing allelic and stutter peak height models for a continuous method of DNA interpretation^[7]	2013	56	102	0.549 0
6	Gill P	FORENSIC SCI INT GENET	A new methodological framework to interpret complex DNA profiles using likelihood ratios^[8]	2013	51	87	0.586 2
7	Bright JA	FORENSIC SCI INT GENET	Developmental validation of STRmix^TM， expert software for the interpretation of forensic DNA profiles^[9]	2016	50	73	0.684 9
8	Cowell RG	J R STAT SOC C-APPL	Analysis of forensic DNA mixtures with artefacts^[10]	2015	41	69	0.594 2
9	Børsting C	FORENSIC SCI INT GENET	Next generation sequencing and its applications in forensic genetics^[11]	2015	41	230	0.178 3
10	Haned H	FORENSIC SCI INT GENET	Exploratory data analysis for the interpretation of low template DNA mixtures^[12]	2012	40	67	0.597 0

[1]	Yan-ru YAO, Jing JIN, Ying-jie WANG, Jin-zhuan ZHANG, Ying-zhe LI, Yong-xin XU. Research Progress on Biological Evidence Identification in Fire Scenes [J]. Journal of Forensic Medicine, 2024, 40(1): 64-69.
[2]	Wen-jing HU, Ting-ting YANG, Ya-ya WANG, Jiang-wei YAN. The Latest Research Progress on Cell-Free DNA and Prospects of Its Forensic Application [J]. Journal of Forensic Medicine, 2024, 40(1): 70-76.
[3]	Yi-ming DONG, Chun-mei ZHAO, Nian-nian CHEN, Li LUO, Zhan-peng LI, Li-kai WANG, Xiao-qian LI, Ting-gan REN, Cai-rong GAO, Xiang-jie GUO. Visualization Analysis of Artificial Intelligence Literature in Forensic Research [J]. Journal of Forensic Medicine, 2024, 40(1): 1-14.
[4]	Peng YUN, An-qi CHEN, Li-qin CHEN, Cheng-tao LI. Establishment and Application of a 42-plex Microhaplotype Assay in Forensic Genetics [J]. Journal of Forensic Medicine, 2024, 40(1): 50-58.
[5]	Xin-yu DONG, Ru-xin ZHU, Yin-lei LEI, Rui-yang TAO, Cheng-tao LI. Traceability of Geographic Origin Using Human Skin and Oral Microbiota [J]. Journal of Forensic Medicine, 2023, 39(6): 557-563.
[6]	Yi-hang HUANG, Wei-bo LIANG, Hui JIAN, Sheng-qiu QU. Modeling Methods and Influencing Factors for Age Estimation Based on DNA Methylation [J]. Journal of Forensic Medicine, 2023, 39(6): 601-607.
[7]	Lin-lin GAO, Wei XIE, Su-juan ZHU, Da LI, Qin WANG, Liang HONG, You-ying LI. Forensic Validation and Application Evaluation of IDentifier DNA Typing Kit （Yan-Huang34） [J]. Journal of Forensic Medicine, 2023, 39(6): 579-585.
[8]	Rui-yang TAO, Shou-yu WANG, Chun-yan YUAN, Ruo-cheng XIA, Cheng-tao LI. Application of SNaPshot Technology in Semen-Specific cSNP Genetic Marker [J]. Journal of Forensic Medicine, 2023, 39(5): 465-470.
[9]	Zhong-hua WANG, Shu-jin LI. Research Progress on Molecular Biology of Human Height Estimation [J]. Journal of Forensic Medicine, 2023, 39(5): 487-492.
[10]	Qi ZHANG, He-miao ZHAO, Kang YANG, Jing CHEN, Rui-qin YANG, Chong WANG. Construction of an Analysis Model of mRNA Markers in Menstrual Blood Based on Naïve Bayes and Multivariate Logistic Regression Methods [J]. Journal of Forensic Medicine, 2023, 39(5): 447-451.
[11]	Lu CHEN, Zhe ZHOU, Sheng-qi WANG. Process of Forensic Medicine in DNA Identification of Aged Human Remains [J]. Journal of Forensic Medicine, 2023, 39(5): 478-486.
[12]	Chao XIAO, Dai-xin HUANG. General Formulas for Calculating Commonly Used Kinship Index [J]. Journal of Forensic Medicine, 2023, 39(3): 276-282.
[13]	Zheng TAN, Guan-ju MA, Li-hong FU, Xiao-jing ZHANG, Qian WANG, Guang-ping FU, Qing-qing DU, Shu-jin LI. Identification Strategy of Biological Half Sibling Relationship [J]. Journal of Forensic Medicine, 2023, 39(3): 262-270.
[14]	Yu-ting WANG, Qiang ZHU, Yu-han HU, Yi-fan WEI, Ting-yun HOU, Ji ZHANG. Calculation of the Paternity Index for the Alleged Father Related to the Child’s Mother [J]. Journal of Forensic Medicine, 2023, 39(3): 271-275.
[15]	Hui CHEN, Ran LI, Yu ZANG, Jing-yi YANG, Ri-ga WU, Hong-yu SUN. Evaluation of Detection Efficiency for Trio Full Sibling Testing [J]. Journal of Forensic Medicine, 2023, 39(3): 247-253.