磁性纳米颗粒在法医学痕量分析中的应用进展

鉴于法医学痕量分析中生物样本的复杂性以及目标物的痕量性,需要一种简单有效的方法从复杂基质中获得足量的目标物。磁性纳米颗粒(magnetic nanoparticle,MNP)凭借其独特的超顺磁性、稳定的物理化学性质、生物相容性、尺寸小、较高的比表面积等特性,已在生物医药、药物递送和药物分离等多个研究领域显现出广泛的应用价值。为了将MNP应用于法医检材的前处理,最大程度地增加目标物的提取率,尽可能减少干扰因素以满足目标物痕量分析要求,本文综述了近年来MNP在法医毒物分析、环境法医学、痕量物证分析和刑事侦查领域的应用进展,为MNP在法医学痕量分析中的应用提供研究思路。     

脑功能磁共振成像及其法医学应用前景

脑功能磁共振成像是近年来磁共振成像技术的一项新发展。它不仅能清晰、准确地显示脑组织的解剖和病理改变,还能同时观察到脑皮层功能活动时的信息,可无创、实时地对大脑的功能活动进行成像。为法医学领域中所涉及的人体损伤程度鉴定和伤残等级评估以及对法医精神病领域中认知功能的界定,从单一形态学研究到形态与功能相结合的系统研究开辟了一条崭新的道路。本文就人脑的功能活动磁共振成像的概念、原理、优势、临床研究状况及法医学应用前景进行综述。     

Understanding Variations of Soil Mapping Units and Associated Data for Forensic Science

Soil samples have potential to be useful in forensic investigations, but their utility may be limited due to the inherent variability of soil properties, the wide array of analytical methods, and complexity of data analysis. This study examined the differentiation of similar soils based on both gross (texture, color, mineralogy) and explicit soil properties (elemental composition, cation exchange, Fe‐oxyhydroxides). Soils were collected from Fallbrook and adjacent map units from Riverside and San Diego Counties in California. Samples were characterized using multiple techniques, including chemical extracts, X‐ray diffraction (XRD), and Fourier transform infrared spectroscopy. Results were analyzed using multiple analytical approaches to compare counties and land uses. Some analyses (XRD, extractions) were better at distinguishing among samples than others (color, texture). Ratios of rare earth elements were particularly useful for distinguishing samples between counties. This potential to “fingerprint” soils illustrates the usefulness of a comprehensive soil database for criminal investigators.     

In Situ Identification of Semen Stains on Common Substrates via Raman Spectroscopy,,

The spectroscopic identification of body fluids in situ is a major objective in forensic science. This approach offers the confirmatory, nondestructive, rapid, and on‐scene identification of various body fluids. Although Raman spectroscopy has shown tremendous promise toward this goal in prior proof‐of‐concept experiments, a significant challenge which still remains is substrate interference. Here, an approach for detecting semen stains in situ on various substrates using Raman spectroscopy is explored. Simulated semen evidence was prepared on skin, glass, and various fabrics. Raman data were accumulated from stains without any pretreatment using a common confocal mapping spectrometer using 785 nm laser excitation. The results demonstrate that the spectroscopic interferences encountered by substrates can be reduced and eliminated using a combination of existing subtraction techniques and chemometric models. Heterogeneous substrates proved most challenging, however, automatic subtraction treatment, and location of fluid hotspots was able to elucidate a clear spectroscopic signature of semen in every instance.     

Detection of visible and latent fingerprints using micro-X-ray fluorescence elemental imaging

Using micro-X-ray fluorescence (MXRF), a novel means of detecting fingerprints was examined in which the prints were imaged based on their elemental composition. MXRF is a nondestructive technique. Although this method requires a priori knowledge about the approximate location of a print, it offers a new and complementary means for detecting fingerprints that are also left pristine for further analysis (including potential DNA extraction) or archiving purposes. Sebaceous fingerprints and those made after perspiring were detected based on elements such as potassium and chlorine present in the print residue. Unique prints were also detected including those containing lotion, saliva, banana, or sunscreen. This proof-of-concept study demonstrates the potential for visualizing fingerprints by MXRF on surfaces that can be problematic using current methods.     

Elemental analysis of human cremains using ICP-OES to classify legitimate and contaminated cremains

The Tri-State Crematory Incident in Nobel, GA (February 2001) revealed limitations in traditional human cremated remains (cremains) analytical methodology. The goal of this study was to develop a method for effectively classifying questionable sets of cremains as legitimate or contaminated. Eighty-eight samples of known human cremains, concrete, mixtures of the two, and questionable sets of cremains were acid digested and analyzed for 21 elements by Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Variable cluster and principle component analyses identified the seven elements (Sb, B, Li, Mn, Sr, Tl, and V) used to develop discriminant functions to classify questionable sets into two groups: cremains and concrete. The discriminant analysis shows that at the 0.90 probability level, mixtures of 50% or less human content were classified as concrete. Mixtures with 90% human content classified as cremains. Sixty percent and 75% human content mixtures remained in the questionable classification, but as the concentration of human increased in the mixture, the probability of assignment to the known cremains group increased. Most of the questionable human samples classified as cremains. This is a pilot study and cannot yet satisfy Daubert standards for courtroom admissibility, but it indicates that it is possible to determine the legitimacy of cremains using elemental analysis by ICP-OES coupled with multivariate statistical analysis.     

Microbial metagenome profiling using amplicon length heterogeneity-polymerase chain reaction proves more effective than elemental analysis in discriminating soil specimens

The combination of soil's ubiquity and its intrinsic abiotic and biotic information can contribute greatly to the forensic field. Although there are physical and chemical characterization methods of soil comparison for forensic purposes, these require a level of expertise not always encountered in crime laboratories. We hypothesized that soil microbial community profiling could be used to discriminate between soil types by providing biological fingerprints that confer uniqueness. Three of the six Miami-Dade soil types were randomly selected and sampled. We compared the microbial metagenome profiles generated using amplicon length heterogeneity-polymerase chain reaction analysis of the 16S rRNA genes with inductively coupled plasma optical emission spectroscopy analysis of 13 elements (Al, B, Ca, Cu, Fe, K, Mg, Mn, Na, P, S, Si, and Zn) that are commonly encountered in soils. Bray-Curtis similarity index and analysis of similarity were performed on all data to establish differences within sites, among sites, and across two seasons. These data matrices were used to group samples that shared similar community patterns using nonmetric multidimensional scaling analysis. We concluded that while chemical characterization could provide some differentiation between soils, microbial metagenome profiling was better able to discriminate between the soil types and had a high degree of reproducibility, therefore proving to be a potential tool for forensic soil comparisons.     

Morphological and spectroscopic measurements of plastic bags for the purpose of discrimination

The discrimination of noncolored transparent polyethylene bags was studied by several nondestructive and semidestructive analytical methods. X-ray diffraction, infrared spectroscopy, and optical microscopy (differential interference contrast microscopy and phase contrast microscopy) were applied to polyethylene films. X-ray diffraction was used to distinguish variations in the crystalline phase, infrared spectroscopy was used to distinguish variations in the molecular components, and optical microscopy was used to distinguish the different surface morphologies. The results show that X-ray diffraction classifies the crystalline phase of the film depending on whether it is made from low-density polyethylene, linear low-density polyethylene, or high-density polyethylene; that infrared spectroscopy is useful to distinguish the molecular components and it is the most discriminating technique; and that optical microscopy discriminate films easily by their morphological differences.     

The changes of fourier transform infrared spectrum in rat brain

Estimation of the time since death (postmortem interval [PMI]) is one of the most difficult problems in forensic investigations, and many methods currently are utilized to estimate the PMI. The goal of this study was to investigate the changes of attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectra of rat brain from postmortem time 0-144 h. The intensity ratios of major absorbance bands were examined (I(1066)/I(1392), I(1168)/I(1392), I(1234)/I(1454), I(1301)/I(1392), I(1647)/I(2956), I(2921)/I(2850), and I(1647)/I(1539)). The spectra of rat brain displayed prominent changes with increasing PMI. The band at 2871 and 1737 per cm became weak with the time increasing and even disappeared at postmortem 96 and 72 h, respectively. A close linear correlation was shown between the relative absorption intensity and the PMI, and the I(1234) /I(1454) offered a stronger correlation (r = 0.973). Our results indicate that ATR-FTIR spectroscopy may be a useful technique for estimating the PMI.     

Comparison of feature selection and data fusion of Fourier transform infrared and Raman spectroscopy for identifying watercolor ink

The identification of different kinds of watercolor inks is an important work in the field of forensic science. Four different kinds of watercolor ink Spectroscopy data fusion strategies (Fourier Transform Infrared spectroscopy and Raman spectroscopy) combined with a non-linear classification model (Extreme Learning Machine) were used to identify the brand of watercolor inks. The study chose Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), Variable Combination Population Analysis-Genetic Algorithm (VCPA-GA), and Variable Combination Population Analysis-Iteratively Retains Informative Variables (VCPA-IRIV) to extract characteristic variables for mid-level data fusion. The Cuckoo Search (CS) algorithm is used to optimize the extreme learning machine classification model. The results showed that the classification capacity of the mid-level fusion spectra model was more satisfactory than that of single Infrared spectroscopy or Raman spectroscopy. The CS-ELM models based on infrared spectroscopy used to recognize the watercolor ink according to brands (ZHENCAI, DELI, CHENGUANG, and STAEDTLER) obtained an accuracy of 66.67% in the test set using all spectral datasets. The accuracy of CS-ELM models based on Raman spectroscopy was 67.39%. The characteristic wavelength selection algorithms effectively improved the accuracy of the CS-ELM models. The classification accuracy of the mid-level spectroscopy fusion model combined with the VCPA-IRIV algorithm was 100%. The data fusion method increased effectively spectral information. The method could satisfactorily identify different brands of watercolor inks and support the preservation of artifacts, paintings, and forensic document examination.