Time-dependent expression of skeletal muscle troponin I mRNA in the contused skeletal muscle of rats: a possible marker for wound age estimation

To estimate the age of skeletal muscle contusion, the expression of troponin I mRNA in contused skeletal muscle of rats was detected using real-time polymerase chain reaction (PCR). A total of 51 Sprague–Dawley male rats were divided into control and contusion groups, and another nine rats received contusion injury after death. At 0.5, 1, 6, 12, 18, 24, 30, and 36 h after contusion, the rats were killed with a lethal dose of pentobarbital. Total RNA was isolated from muscle specimens using the SV Total RNA Isolation System and reverse transcribed into first-strand cDNA. Sequence-specific primers were then used to conduct real-time PCR to analyze the expression levels of sTnI mRNA. At 0.5, 1, and 6 h after contusion, the expression levels of sTnI mRNA decreased to 78.17% (P < 0.05), 41.58% (P < 0.05), and 32.13% of that in the control group, respectively. However, there were no significant changes in the expression levels of sTnI mRNA from 6 to 36 h (P > 0.05) after contusion when normalized to RpL32 expression. The expression levels of sTnI mRNA in the normal and contused skeletal muscle of postmortem rats were about 70% of that in the control group (P < 0.05), and no significant changes in the expression levels of sTnI mRNA in the postmortem contusion group were noted among different time points after injury. This result suggests that determination of sTnI mRNA levels by real-time PCR is useful for the estimation of wound age.     

The cannabinoid receptor type 2 is time-dependently expressed during skeletal muscle wound healing in rats

The expression of the cannabinoid receptor type 2 (CB2R) was investigated by immunohistochemistry, Western blotting, and RT-PCR during wound healing of contused skeletal muscle in rats with attempt of its applicability to skeletal muscle wound age estimation. Furthermore, Macrophage Marker (MAC387) was utilized to identify macrophages recruited into injured skeletal muscle tissue. Co-localization of CB2R with Macrophage Marker was detected by confocal laser scanning microscopy. A total of 50 Sprague–Dawley male rats were divided into control and contusion groups (3 h, 6 h, 12 h, 1 day, 3 days, 5 days, 7 days, 10 days, and 14 days post-injury). In the uninjured controls, immunoreactivity of CB2R was detected in the sarcolemma and sarcoplasm of normal myofibers. In the contusion groups, a few polymorphonulcear cells, a large number of macrophages, and spindle-shaped fibroblastic cells showed a positive staining for CB2R in wounded zones. By Western blotting analysis, the average of CB2R to GAPDH ratios in 5–7 days post-injury groups was highest, and all the samples had ratios of >2.60. In the other groups, no samples showed ratios of >2.60 and the CB2R to GAPDH ratios ranged from 1.19 to 2.59. The expression tendency was also confirmed by RT-PCR. From the viewpoint of forensic pathology, these observations suggested that the ratio markedly exceeding 2.60 strongly indicated a wound age of 5–7 days. In conclusion, dynamic distribution and expression of CB2R suggest that CB2R be involved in modulating macrophages in response to inflammatory event in rat skeletal muscle wound healing and CB2R be available as a marker for wound age determination.     

Bioinformatics analysis of sequential gene expression profiling after skin and skeletal muscle wound in mice

It is of great value to use bioinformatics methods to screen the core differentially expressed genes (DEGs) at different times after mouse skin and skeletal muscle wound, and to explore the relationship between them and the wound age. To this end, we downloaded the gene expression profiles of GSE140517 and GSE23006 from the NCBI-GEO gene database, used GEO2R online tools and Venn diagrams to screen out DEGs at different times and common-DEGs. The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) channel analysis were carried out through the DAVID website respectively. Use STRING tool to build a Protein-protein Interaction (PPI) network, and use Cytoscape software to screen out core DEGs. The results showed that 13, 53, 43 and 13 core DEGs were screened out in the 6 h, 12 h, 24 h and common-DEGs group after wound. There were 7 core DEGs (Cxcl2, Cxcl3, Il1b, Ptgs2, Cxcl1, Timp1, Ccl3) in both the different time point and the common DEGs group. Meanwhile, there are 1 core DEGs (Ccl4) specifically expressed in the 6 h, 29 specifically expressed core DEGs (Isg20, Rtp4, Fcgr1, Ifi44, Trim30a, etc.) in the 12 h, and 18 specifically expressed core DEGs (Ccr7, Myd88, Igsf6, Ccr2, Gpsm3, etc.) in the 24 h, there are 6 core DEGs (Ccl4, Ccl7, Saa3, Cxcl5, Ccl2, Lcn2) specifically expressed in the common-DEGs group. The results of GO and KEGG analysis showed that the deterioration and exudation of the inflammatory response were the main cause at 6 h after wound. In addition to inflammation at 12 h and 24 h, the systemic immune response against viral and bacterial infections also gradually increased. In summary, the core DEGs selected in this study have combined characteristics, consistent with the healing function at the corresponding time point, and they are also has specificity and correlation with wound age. Therefore, by detecting the changes in the expression of co-expressed core DEGs at different times after wound, as well as detecting specific expressed DEGs at a specific time point or a specific period of time, it is very promising to provide help for the wound age estimation. However, limited by the GSE140517 gene expression profile in the database, only the difference in gene expression at different times within 24 h after wound was explored, and the research on the late wound age still needs to be further in-depth.     

Measuring temporal expression,systematic response,and post-mortem stability to assess potential markers for estimating wound age: an example of Fosl1 in contused skeletal muscle

To assess whether Fosl1 is a suitable parameter for wound age estimation, a total of 126 male Sprague–Dawley rats were divided into control (n = 6 per group), contusion (including 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h post-severe injury, and 4 or 8 h post-moderate or -mild injury subgroups), and post-mortem (including 6, 12, 18, and 24 h subgroups) groups (n = 6 per subgroup). A contusion was produced in the right limb of the rats using the drop-ball technique (under intraperitoneal chloral hydrate anaesthesia), and the animals were sacrificed using a lethal dose of pentobarbital. The expression of Fosl1 mRNA and protein was determined in the contused and contralateral uninjured muscle and post-mortem specimens of musculi quadriceps femoris using real-time PCR and western blotting, respectively. Compared with the control group, the expression of Fosl1 mRNA was increased in several contused and contralateral uninjured muscles, and decreased in post-mortem groups (P<0.05). The expression of Fosl1 protein was increased in the 4, 8, 32, 36, 40, 44, and 48 h contusion subgroups (P<0.05), but there was no significant change in the contralateral uninjured muscles and post-mortem specimens. The results indicated that Fosl1 protein was more specific and stable than Fosl1 mRNA, which suggests that the temporal expression, systematic response, and post-mortem stability should be comprehensively analysed when exploring other markers for use in wound age estimation in the future.     

Age-related DNA methylation analysis for forensic age estimation using post-mortem blood samples from Japanese individuals

As one of external visible characteristics (EVCs) in forensic phenotyping, age estimation is essential to providing additional information about a sample donor. With the development of epigenetics, age-related DNA methylation may be used as a reliable predictor of age estimation. With the aim of building a feasible age estimation model for Japanese individuals, 53 CpG sites distributed between 11 candidate genes were selected from previous studies. The DNA methylation level of each target CpG site was identified and measured on a massive parallel platform (synthesis by sequencing, Illumina, California, United States) from 60 forensic blood samples during the initial training phase. Multiple linear regression and quantile regression analyses were later performed to build linear and quantile age estimation models, respectively. Four CpG sites on four genes— ASPA, ELOVL2, ITGA2B, and PDE4C —, were found to be highly correlated with chronological age in DNA samples from Japanese individuals (|R| > 0.75). Subsequently, an independent validation dataset (n = 30) was used to verify and evaluate the performance of the two models. Comparison of mean absolute deviation (MAD) with other indicators showed that both models provide accurate age predictions (MAD: linear = 6.493 years; quantile = 6.243 years). The quantile model, however, can provide the changeable prediction intervals that grow wider with increasing age, and this tendency is consistent with the natural aging process in humans. Hence, the quantile model is recommended in this study.     

With or without human interference for precise age estimation based on machine learning?

Age estimation can aid in forensic medicine applications, diagnosis, and treatment planning for orthodontics and pediatrics. Existing dental age estimation methods rely heavily on specialized knowledge and are highly subjective, wasting time, and energy, which can be perfectly solved by machine learning techniques. As the key factor affecting the performance of machine learning models, there are usually two methods for feature extraction: human interference and autonomous extraction without human interference. However, previous studies have rarely applied these two methods for feature extraction in the same image analysis task. Herein, we present two types of convolutional neural networks (CNNs) for dental age estimation. One is an automated dental stage evaluation model (ADSE model) based on specified manually defined features, and the other is an automated end-to-end dental age estimation model (ADAE model), which autonomously extracts potential features for dental age estimation. Although the mean absolute error (MAE) of the ADSE model for stage classification is 0.17 stages, its accuracy in dental age estimation is unsatisfactory, with the MAE (1.63 years) being only 0.04 years lower than the manual dental age estimation method (MDAE model). However, the MAE of the ADAE model is 0.83 years, being reduced by half that of the MDAE model. The results show that fully automated feature extraction in a deep learning model without human interference performs better in dental age estimation, prominently increasing the accuracy and objectivity. This indicates that without human interference, machine learning may perform better in the application of medical imaging.

     

Combining current knowledge on DNA methylation-based age estimation towards the development of a superior forensic DNA intelligence tool

The estimation of chronological age from biological fluids has been an important quest for forensic scientists worldwide, with recent approaches exploiting the variability of DNA methylation patterns with age in order to develop the next generation of forensic ‘DNA intelligence’ tools for this application. Drawing from the conclusions of previous work utilising massively parallel sequencing (MPS) for this analysis, this work introduces a DNA methylation-based age estimation method for blood that exhibits the best combination of prediction accuracy and sensitivity reported to date. Statistical evaluation of markers from 51 studies using microarray data from over 4000 individuals, followed by validation using in-house generated MPS data, revealed a final set of 11 markers with the greatest potential for accurate age estimation from minimal DNA material. Utilising an algorithm based on support vector machines, the proposed model achieved an average error (MAE) of 3.3 years, with this level of accuracy retained down to 5 ng of starting DNA input (~ 1 ng PCR input). The accuracy of the model was retained (MAE = 3.8 years) in a separate test set of 88 samples of Spanish origin, while predictions for donors of greater forensic interest (< 55 years of age) displayed even higher accuracy (MAE = 2.6 years). Finally, no sex-related bias was observed for this model, while there were also no signs of variation observed between control and disease-associated populations for schizophrenia, rheumatoid arthritis, frontal temporal dementia and progressive supranuclear palsy in microarray data relating to the 11 markers.     

Immunohistochemical analysis on cyclooxygenase-2 for wound age determination

Immunohistochemical study combined with morphometry was carried out to examine the expression of cyclooxygenase-2 (COX-2) using 60 human skin wounds of different ages: group I, 0–4 h (n = 11); II, 8 h–2 days (n = 21); III, 3–9 days (n = 14); and IV, 12–21 days (n = 14). In wound specimens aged 2 h to 2 days, anti-myeloperoxidase-positive neutrophils observed at the wound site expressed immunopositive reaction to COX-2. In wound specimens of more than 3 days, CD68-positive macrophages as well as neutrophils were positively immunostained with anti-COX-2. In group II, all 21 wound samples had COX-2-positive ratios of >40 %, and 15 out of them showed >50 %. In group III, only three wound samples with the postinfliction intervals of 3 days showed positive ratios of 40–50 % and the remaining 11 cases less than 40 %. In groups I and IV, all 25 wound specimens had COX-2-positive ratio of <40 %. With regard to the practical applicability with forensic safety, these observations suggested that a COX-2-positive ratio of >40 % indicated a wound age of 8 h to 3 days. Moreover, COX-2-positive ratios, considerably exceeding a ratio of 50 %, indicate a wound age of 8 h to 2 days. Collectively, COX-2 would be a useful marker for the determination of early wound age.     

Diagnosis of dementia with Lewy bodies: diagnostic performance of combined 123I-IMP brain perfusion SPECT and 123I-MIBG myocardial scintigraphy

Objective

We assessed the value of combining ¹²³I-IMP brain perfusion SPECT and ¹²³I-MIBG myocardial scintigraphy for the discrimination of dementia with Lewy bodies (DLB) from other types of dementia.

Methods

We subjected 252 consecutive patients with clinically suspected DLB to both ¹²³I-IMP brain perfusion SPECT and ¹²³I-MIBG myocardial scintigraphy. Patients with Parkinson’s disease were included. The 252 patients were randomly assigned to an estimation (n = 152) or a validation group (n = 100). Using univariate analysis, we first analyzed the relationship between various variables and the presence or absence of DLB in estimation group and then proceeded to multivariate analysis to obtain a combined index that predicted the likelihood of DLB. The diagnostic value of the index was assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC) with the cutoff value selected from the ROC curve. We then tested the predictive accuracy of the index in validation group.

Results

The combined index was an arithmetic expression that combined the age, early ¹²³I-MIBG heart-to-mediastinum uptake (E-H/M) ratio, and the parietal lobe hypoperfusion score. Values for the AUC of the combined index, the E-H/M ratio, the parietal lobe hypoperfusion score, and the patient age in validation group were 0.95, 0.90, 0.72, and 0.73, respectively. There was a significant difference in the AUC of the combined index among other indices (p < 0.05). The sensitivity, specificity, and accuracy of the combined index for a diagnosis of probable DLB in validation group were 88, 87, and 87 %, respectively.

Conclusions

The combinational diagnosis based on ¹²³I-IMP brain perfusion SPECT, ¹²³I-MIBG myocardial scintigraphy, and the patient age is a simple and reliable means for predicting probable DLB.     

Wound age estimation based on chronological changes in chitinase 3-like protein 1 expression

In forensic practice, wound age estimation is essential for making assessments of injuries; however, it remains challenging, and markers which correctly indicate wound age are required. Since our previous study showed that chitinase 3-like protein 3 (CHI3L3) expression changed chronologically in murine skin wounds, we hypothesized that other proteins of chitinase and chitinase-like protein (C/CLP) family, which CHI3L3 belongs to, might also have varied expression in wound healing. Therefore, we considered that some proteins of the C/CLP family could be used as markers of wound age estimation, and we aimed to test this hypothesis. Examinations of murine skin wounds revealed that the expression of chitinase 3-like protein 1 (CHI3L1) changed chronologically. CHI3L1 expression in human cadaver skin wounds, which was immunohistochemically analyzed by the average ratio of CHI3L1-expressed cells/total cells in 10 microscopic fields, was weak in wounds from days 0 to 1 after injury (0.11 ± 0.024; mean ± standard error of the mean); however, CHI3L1-positive cells appeared in wounds from days 2 to 3 (1.65 ± 0.19). The number of CHI3L1-expressed cells increased in wounds from days 4 to 6 (5.35 ± 0.35) but dropped from days 7 to 13 (1.53 ± 0.24). Receiver operating characteristic curve analysis indicated that wounds from days 4 to 6 after injury could be clearly distinguished from other wounds based on a cutoff value of 2.75, sensitivity of 92.31%, and specificity of 85.14%. Our findings suggest that CHI3L1 could be a reliable marker for wound age estimation in forensic practice.     

Rib histomorphometry: A reliability and validation study with a critical review of histological techniques for forensic age estimation

Fragmented human remains present a challenge for forensic experts as they attempt to identify individuals using standard forensic methods. Several histological age estimation techniques have been developed during the last fifty years to aid in this process. However, very few validation studies have been conducted in order to test their accuracy and bias, and thus, validation assessment is required as we employ them while testifying in court.Histological variables are assessed from rib thin sections from two Mediterranean samples; Cretans (N = 41) and Greek-Cypriots (N = 47). Intra and inter-observer errors are assessed through TEM analysis and Intra-class Correlation Coefficient by testing observers with different levels of experience as they collected data on osteon counts and area measurements. The relation between the variables and age is determined using correlation coefficients. Histomorphometric data are applied to four widely used age estimation formulas assessing the performance of the methods for the entire sample. Inaccuracy and bias are calculated with age estimations and known age tested for significance and proportional bias assessed.Overall, histological parameters presented acceptable intra- and inter-observer errors. All variables exhibited statistically significant correlation with age (P < 0.01). For three of the techniques, data showed a systematic underestimation of age with an increase in inaccuracy in older individuals. One of the age estimation formulas produced overestimation of young individuals yet, it more accurately estimated the age of older individuals.This validation study explores inter-population variation in bone remodeling dynamics and presents a critical evaluation on methodological issues that can affect the performance of existing histological techniques.     

Detection of fibrocytes in human skin wounds and its application for wound age determination

Fibrocytes, a newly identified cell type, are bone marrow-derived mesenchymal progenitors that coexpress hematopoietic cell antigens and fibroblast products. In this study, a double-color immunofluorescence analysis was carried out using anti-CD45 and anti-collagen type I antibodies to examine the time-dependent appearance of fibrocytes, using 53 human skin wounds with different wound ages (group I, 0–3 days; group II, 4–7 days; group III, 9–14 days; and group IV, 17–21 days). In wound specimens with an age of less than 3 days, CD45⁺/collagen type I⁺ fibrocytes were not detected. The fibrocytes were initially observed in wounds aged 4 days, and their number increased in lesions with advances in wound age. In a semiquantitative morphometrical analysis, the average number of fibrocytes was highest in the wounds of group III. These findings imply that human skin wounds containing fibrocytes are at least 4 days old. Moreover, a fibrocyte number of over 10 indicates a wound age between 9 and 14 days (i.e., group III). Based on the average number of fibrocytes in each group, a fibrocyte number of over 15 more strongly suggests a wound age of 9–14 days. Together, our observations indicate the participation of fibrocytes in wound healing of human skin inducing the accumulation of extracellular matrix components, and therefore, detection of fibrocytes could be a useful marker for wound age determination.     

Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation

In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age prediction using the 450 K Human Methylation Beadchip. Various marker selection methods and prediction models have been considered. However, there is a lack of research evaluating different high-dimensional variable selection methods of CpG sites with various models for age prediction. The aim of this study is to evaluate four variable selection methods (forward selection, LASSO, elastic net and SCAD) combined with a classical statistical model and sophisticated machine learning models based on the mean absolute deviation (MAD) and the root-mean-square error (RMSE). We used publicly available 450 K data set containing 991 whole blood samples (age 19–101 years). We found that the multiple linear regression model with 16 markers selected from the forward selection method performed very well in age prediction (MAD = 3.76 years and RMSE = 5.01 years). On the other hand, the highly advanced ultrahigh dimensional variable selection methods and sophisticated machine learning algorithms appeared unnecessary for age prediction based on DNA methylation.     

Time-dependent expression of SNAT2 mRNA in the contused skeletal muscle of rats: a possible marker for wound age estimation

To estimate the age of skeletal muscle contusion, the expression of SNAT2 mRNA in contused skeletal muscle of rats was detected by real-time polymerase chain reaction (PCR). In total, 78 Sprague–Dawley male rats were divided into control and contusion groups. At 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h (n = 6) after contusion, the rats were sacrificed with a lethal dose of pentobarbital. Another 24 rats received contusion injuries at 6, 12, 18, and 24 h (n = 6) after death. Total RNA was isolated from muscle specimens using the TRIzol reagent and reverse-transcribed into first-strand cDNA. Sequence-specific primers and TaqMan fluorogenic probes for SNAT2 mRNA and RPL13 mRNA were designed using the AlleleID 6 software, and the expression levels of SNAT2 mRNA were determined by real-time PCR. At 4, 16, 20, and 24 h after contusion, expression levels of SNAT2 mRNA normalized to RPL13 mRNA increased by 2.07 (P < 0.05), 2.53 (P < 0.05), 2.68 (P < 0.05), and 2.06 fold (P < 0.05) respectively, versus that in the control group. However, there was no significant change in the expression level of SNAT2 mRNA from 24 to 48 h (P > 0.05) after contusion, when normalized to RPL13 mRNA. There was no change in the expression level of SNAT2 mRNA between the normal skeletal muscle from the left limb of the same injured rat and the control. Also, no degradation of SNAT2 mRNA was detected in the postmortem samples (P > 0.05). This result suggests that the determination of SNAT2 mRNA levels by real-time PCR may be useful for estimating wound age.     

Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use

Objectives

With much hype about artificial intelligence (AI) rendering radiologists redundant, a simple radiologist-augmented AI workflow is evaluated; the premise is that inclusion of a radiologist’s opinion into an AI algorithm would make the algorithm achieve better accuracy than an algorithm trained on imaging parameters alone. Open-source BI-RADS data sets were evaluated to see whether inclusion of a radiologist’s opinion (in the form of BI-RADS classification) in addition to image parameters improved the accuracy of prediction of histology using three machine learning algorithms vis-à-vis algorithms using image parameters alone.

Extended kinship analysis of historical remains using SNP capture

DNA-assisted identification of historical remains requires the genetic analysis of highly degraded DNA, along with a comparison to DNA from known relatives. This can be achieved by targeting single nucleotide polymorphisms (SNPs) using a hybridization capture and next-generation sequencing approach suitable for degraded skeletal samples. In the present study, two SNP capture panels were designed to target ~ 25,000 (25 K) and ~ 95,000 (95 K) nuclear SNPs, respectively, to enable distant kinship estimation (up to 4th degree relatives). Low-coverage SNP data were successfully recovered from 14 skeletal elements 75 years postmortem using an Illumina MiSeq benchtop sequencer. All samples contained degraded DNA but were of varying quality with mean fragment lengths ranging from 32 bp to 170 bp across the 14 samples. SNP comparison with DNA from known family references was performed in the Parabon Fx Forensic Analysis Platform, which utilizes a likelihood approach for kinship prediction that was optimized for low-coverage sequencing data with cytosine deamination. The 25 K panel produced 15,000 SNPs on average, which allowed for accurate kinship prediction with strong statistical support in 16 of the 21 pairwise comparisons. The 95 K panel increased the average SNPs to 42,000 and resulted in an additional accurate kinship prediction with strong statistical support (17 of 21 pairwise comparisons). This study demonstrates that SNP capture combined with massively parallel sequencing on a benchtop platform can yield sufficient SNP recovery from compromised samples, enabling accurate, extended kinship predictions.     

A common epigenetic clock from childhood to old age

Forensic age estimation is a DNA intelligence tool that forms an important part of Forensic DNA Phenotyping. Criminal cases with no suspects or with unsuccessful matches in searches on DNA databases; human identification analyses in mass disasters; anthropological studies or legal disputes; all benefit from age estimation to gain investigative leads. Several age prediction models have been developed to date based on DNA methylation. Although different DNA methylation technologies as well as diverse statistical methods have been proposed, most of them are based on blood samples and mainly restricted to adult age ranges. In the current study, we present an extended age prediction model based on 895 evenly distributed Spanish DNA blood samples from 2 to 104 years old. DNA methylation levels were detected using Agena Bioscience EpiTYPER® technology for a total of seven CpG sites located at seven genomic regions: ELOVL2, ASPA, PDE4C, FHL2, CCDC102B, MIR29B2CHG and chr16:85395429 (GRCh38). The accuracy of the age prediction system was tested by comparing three statistical methods: quantile regression (QR), quantile regression neural network (QRNN) and quantile regression support vector machine (QRSVM). The most accurate predictions were obtained when using QRNN or QRSVM (mean absolute prediction error, MAE of ± 3.36 and ± 3.41, respectively). Validation of the models with an independent Spanish testing set (N = 152) provided similar accuracies for both methods (MAE: ± 3.32 and ± 3.45, respectively). The main advantage of using quantile regression statistical tools lies in obtaining age-dependent prediction intervals, fitting the error to the estimated age. An additional analysis of dimensionality reduction shows a direct correlation of increased error and a reduction of correct classifications as the training sample size is reduced. Results indicated that a minimum sample size of six samples per year-of-age covered by the training set is recommended to efficiently capture the most inter-individual variability..     

Simultaneous detection of eight cytokines in human dermal wounds with a multiplex bead-based immunoassay for wound age estimation

We performed quantification of IL 2, IL 4, IL 6, IL 8, IL 10, GM-CSF, IFN γ, and TNF α in human dermal wounds for wound age estimation. The proliferation of dermal cells and infiltration of inflammatory cells were also analyzed. Neutrophils and macrophages were detected from 2 h post-injury, and strong infiltrations were seen at 33–49 h. T and B lymphocytes also infiltrated simultaneously from 71 h. Strong proliferation of fibroblasts were shown from 246 h, and thickening of the epidermis from 71 h. IL 10, GM-CSF, IFNγ, and TNF α increased from the early phase of dermal wound healing, IL 6 exclusively in the middle phase, IL 2, IL 4, and IL 8 from the middle phase to the late phase. Among the cytokines analyzed in the present study, IL 6, IL 8, IFNγ, and TNF α were strongly expressed. Results of the present study suggest that multiplex cytokine analysis at the wound site can be useful for wound age estimation. In addition, multiplex data obtained from the same sample with a single method would demonstrate more accurate interactions of cytokines during dermal wound healing. Although the present study was oriented to practical forensic pathology, the data obtained would be informative for various fields of medicine.