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死后间隔时间估计中积温与昆虫学方法的比较

Comparison of Accumulated Degree-Days and Entomological Approaches in Post Mortem Interval Estimation.

作者信息

Franceschetti Lorenzo, Pradelli Jennifer, Tuccia Fabiola, Giordani Giorgia, Cattaneo Cristina, Vanin Stefano

机构信息

Forensic Medicine Unit, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Piazzale Spedali Civili, 1, 25123 Brescia, Italy.

LABANOF (Laboratorio di Antropologia e Odontologia Forense), Sezione di Medicina Legale Dipartimento di Scienze Biomediche per la Salute, Università degli Studi di Milano, Milano Via Luigi Mangiagalli, 37, 20133 Milano, Italy.

出版信息

Insects. 2021 Mar 21;12(3):264. doi: 10.3390/insects12030264.

Abstract

Establishing the post mortem interval (PMI) is a key component of every medicolegal death investigation. Several methods based on different approaches have been suggested to perform this estimation. Among them, two methods based their evaluation on the effect of the temperature and time on the considered parameters: total body score (TBS)/accumulated degree-days (ADDs) and insect development. In this work, the two methods were compared using the results of minPMI and PMI estimates of 30 forensic cases occurring in northern Italy. Species in the family Calliphoridae (, and were considered in the analyses. The results highlighted the limits of the TBS/ADD method and the importance of the entomological approach, keeping in mind that the minPMI is evaluated. Due to the fact that the majority of the cases occurred in indoor conditions, further research must also be conducted on the different taxa to verify the possibility of increasing the accuracy of the minPIM estimation based on the entomological approach.

摘要

确定死后间隔时间(PMI)是每一次法医学死亡调查的关键组成部分。已经提出了几种基于不同方法的手段来进行这种估计。其中,有两种方法是基于温度和时间对所考虑参数的影响进行评估的:总体评分(TBS)/累积度日数(ADDs)和昆虫发育情况。在这项工作中,利用意大利北部发生的30起法医案件的最短PMI和PMI估计结果对这两种方法进行了比较。分析中考虑了丽蝇科的物种(、和)。研究结果突出了TBS/ADD方法的局限性以及昆虫学方法的重要性,同时要记住最短PMI是经过评估的。由于大多数案件发生在室内环境中,还必须对不同的分类群进行进一步研究,以验证基于昆虫学方法提高最短PMI估计准确性的可能性。

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