A computational approach to estimate postmortem interval using postmortem computed tomography of multiple tissues based on animal experiments.

The estimation of postmortem interval (PMI) is a complex and challenging problem in forensic medicine. In recent years, many studies have begun to use machine learning methods to estimate PMI. However, research combining postmortem computed tomography (PMCT) with machine learning models for PMI estimation is still in early stages. This study aims to establish a multi-tissue machine learning model for PMI estimation using PMCT data from various tissues. We collected PMCT data of seven tissues, including brain, eyeballs, myocardium, liver, kidneys, erector spinae, and quadriceps femoris from 10 rabbits after death. CT images were taken every 12 h until 192 h after death, and HU values were extracted from the CT images of each tissue as a dataset. Support vector machine, random forest, and K-nearest neighbors were performed to establish PMI estimation models, and after adjusting the parameters of each model, they were used as first-level classification to build a stacking model to further improve the PMI estimation accuracy. The accuracy and generalized area under the receiver operating characteristic curve of the multi-tissue stacking model were able to reach 93% and 0.96, respectively. Results indicated that PMCT detection could be used to obtain postmortem change of different tissue densities, and the stacking model demonstrated strong predictive and generalization abilities. This approach provides new research methods and ideas for the study of PMI estimation.

CiteSpace-Based Visualization Analysis of Forensic Research in China from 2010 to 2019.

OBJECTIVES: To analyze the research status of forensic medicine in China from 2010 to 2019, obtain the development trend of forensic medicine and explore the hotspots and research frontiers. METHODS: The forensic medical academic papers published on China National Knowledge Infrastructure (CNKI) database from 2010 to 2019 were collected. CiteSpace 5.7.R1, an information visualization analysis software, was used to analyze publication organizations, authors, keywords, and other elements. RESULTS: The majority of the research institutions were universities, provincial and ministerial scientific research and forensic institutions. Forensic pathology was still an important branch of forensic medicine and a popular research direction. The "polymorphism" and "Y chromosome" had been the research hotspots in recent years. "Medical damage" and "standard" were the most novel studies. CONCLUSIONS: In order to provide scientific basis and research direction for forensic research, this paper analyzes the cooperation network, research hotspots and research innovation in forensic research.

Long-term neuroglobin expression of human astrocytes following brain trauma.

Neuroglobin (Ngb), a 17 kDa monomeric protein, was initially described as a vertebrate oxygen-binding heme protein in 2000 and detected in metabolically active organs or cells, like the brain, peripheral nervous system as well as certain endocrine cells. A large array of initial experimental work reported that Ngb displayed a neuron restricted expression pattern in mammalian brains. However, growing evidence indicated astrocytes may also express Ngb under pathological conditions. To address the question whether human astrocytes express Ngb under traumatic insults, we investigated Ngb immuno-reactivity in post-mortem human brain tissues that died of acute, sub-acute and chronic brain trauma, respectively. We observed astrocytic Ngb expression in sub-acute and chronic traumatic brains rather than acute traumatic brains. Strikingly, the Ngb immuno-reactive astrocytes were still strongly detectable in groups that died 12 months after brain trauma. Our findings may imply an unexplored role of Ngb in astrocytes and the involved mechanisms were suggested to be further characterized. Also, therapeutic application of Ngb or Ngb-inducible chemical compounds in neuro-genesis or astrocytic scar forming can be expected.