Development and application of a multiplex PCR system for forensic salivary identification.

In forensics, accurate identification of the origin of body fluids is essential for reconstructing a crime scene or presenting strong evidence in court. Microorganisms have demonstrated great potential in body fluid identification. We developed a multiplex PCR system for forensic salivary identification, which contains five types of bacteria:Streptococcus salivarius, Neisseria subflava, Streptococcus. mutans, Bacteroides thetaiotaomicron, and Bacteroides. uniformis. And the validated studies were carried out following the validation guidelines for DNA analysis methods developed by the Scientific Working Group on DNA Analysis Methods (SWGDAM), which included tests for sensitivity, species specificity, repeatability, stability, and mixed samples, trace samples, case samples, and a population study. Our result depicted that the lowest detection limit of the system was 0.01 ng template DNA. Moreover, the corresponding bacteria can still be detected when the amount of saliva input is low to 0.1 µL for DNA extraction. In addition, the target bacteria were not detected in the DNA of human, seven common animals, and seven bacteria DNA and in nine other body fluid samples (skin, semen, blood, menstrual blood, nasal mucus, sweat, tears, urine, and vaginal secretions). Six common inhibitors such as indigo, EDTA, hemoglobin, calcium ions, alcohol and humic acid were well tolerated by the system. What is more, the salivary identification system recognized the saliva component in all mixed samples and simulated case samples. Among 400 unrelated individuals from the Chinese Han population analyzed by this novel system, the detection rates of N. subflava, S. salivarius, and S. mutans were 97.75%, 70.75%, and 19.75%, respectively, with 100% identification of saliva. In conclusion, the salivary identification system has good sensitivity, specificity, stability, and accuracy, which can be a new effective tool for saliva identification.

Response of salivary microbiome to temporal, environmental, and surface characteristics under in vitro exposure.

The microbiome of saliva stains deposited at crime scenes and in everyday settings is valuable for forensic investigations and environmental ecology. However, the dynamics and applications of microbial communities in these saliva stains have not been fully explored. In this study, we analyzed saliva samples that were exposed to indoor conditions for up to 1 year and to different carriers (cotton, sterile absorbent cotton swab, woolen, dacron) in both indoor and outdoor environments for 1 month using high-throughput sequencing. The analysis of microbial composition and Mfuzz clustering showed that the salivary flora, specifically Streptococcus (cluster7), which was associated with microbial contamination, remained stable over short periods of time. However, prolonged exposure led to significant differences due to the invasion of environmental bacteria such as Pseudomonas and Achromobacter. The growth and colonization of environmental flora were promoted by humidity. The neutral model predictions indicated that the assembly of salivary microbial communities in outdoor environments was significantly influenced by stochastic processes, with environmental characteristics having a greater impact on community change compared to surface characteristics. By incorporating data from previous studies on fecal and vaginal secretion microbiology, we developed RF and XGBoost classification models that achieved high accuracy (>98 %) and AUC (>0.8). Additionally, a RF regression model was created to determine the time since deposition (TsD) of the stains. Time inference models yielded a mean absolute error (MAE) of 7.1 days for stains exposed for 1 year and 14.2 h for stains exposed for 14 days. These findings enhance our understanding of the changes in the microbiome of saliva stains over time, in different environments, and on different surfaces. They also have potential applications in assessing potential microbial contamination, identifying body fluids, and inferring the time of deposition.

Changes in the microbial community of semen exposed to different simulated forensic situations.

Semen is one of the common body fluids in sexual crime cases. The current methods of semen identification have certain limitations, so it is necessary to search for other methods. In addition, there are few reports of microbiome changes in body fluids under simulated crime scenes. It is essential to further reveal the changes in semen microbiomes after exposure to various simulated crime scenes. Semen samples from eight volunteers were exposed in closed plastic bags, soil, indoor, cotton, polyester, and wool fabrics. A total of 68 samples (before and after exposure) were collected, detected by 16S rDNA sequencing, and analyzed for the microbiome signature. Finally, a random forest model was constructed for body fluid identification. After exposure, the relative abundance of Pseudomonas and Rhodococcus changed dramatically in almost all groups. In addition, the treatment with the closed plastic bags or soil groups had a greater impact on the semen microbiome. According to the Shannon indices, the alpha diversity of the closed plastic bags and soil groups was much lower than that of the other groups. Attention should be given to the above two scenes in practical work of forensic medicine. In this study, the accuracy of semen recognition was 100%. The exposed semen can still be correctly identified as semen based on its microbiota characteristics. In summary, semen microbiomes exposed to simulated crime scenes still have good application potential for body fluid identification. IMPORTANCE: In this study, the microbiome changes of semen exposed to different environments were observed, and the exposed semen microbiome still has a good application potential in body fluid identification.

A novel approach for exploring the regional features of vaginal fluids based on microbial relative abundance and alpha diversity.

Vaginal fluids are one of the most common biological samples in forensic sexual assault cases, and their characterization is vital to narrow the scope of investigation. Presently, approaches for identifying vaginal fluids in different regions are not only rare but also have certain limitations. However, the microbiome has shown the potential to identify the source of body fluids and reveal the characteristics of individuals. In this study, 16S rRNA gene high-throughput sequencing was used to characterize the vaginal microbial community from three regions, Sichuan, Hainan and Hunan. In addition, data on relative abundance and alpha diversity were used to construct a random forest model. The results revealed that the dominant genera in the three regions were Lactobacillus, followed by Gardnerella. In addition, Ureaplasma, Nitrospira, Nocardiodes, Veillonella and g-norank-f-Vicinamibacteraceae were significantly enriched genera in Sichuan, llumatobacter was enriched in Hainan, and Pseudomonas was enriched in Hunan. The random forest classifier based on combined data on relative abundance and alpha diversity had a good ability to distinguish vaginal fluids with similar dominant microbial compositions in the three regions. The study suggests that combining high-throughput sequencing data with machine learning models has good potential for application in the biogeographic inference of vaginal fluids.

Changes in the microbial community after vaginal fluid exposure in different simulated forensic situations.

If vaginal fluid is found on clothing or on the body of the suspect, it may indicate the occurrence of sexual assault. Therefore, it is important to collect the victim's vaginal fluid at different sites from the suspect. Previous studies have revealed that fresh vaginal fluids can be identified based on 16S rRNA gene sequencing data. However, the influence of environmental factors on the stability of microbial markers must be investigated before being used in forensic practice. We collected vaginal fluid from nine unrelated individuals and placed each individual of vaginal swab on five different substrates. A total of 54 vaginal swabs were analyzed using 16S rRNA on the V3-V4 regions. Then, we constructed a random forest model including the samples of all vaginal fluids in this study and the other four types of body fluids in our previous studies. The alpha diversity of vaginal samples increased after exposure to the substrate environment for 30 days. The dominant vaginal bacteria were Lactobacillus and Gardnerella, which remained relatively stable after exposure, with Lactobacillus being the most abundant in all substrates, while Gardnerella was more abundant in other substrates than in the polyester fiber substrate. Except for bed sheets, Bifidobacterium significantly declined when placed on other substrates. Rhodococcus and Delftia from the substrate environment migrated to the vaginal samples. Rhodococcus was abundant in polyester fibers, and Delftia was abundant in wool substrates, while those environmental bacteria were all in low abundance in bed sheets. Overall, the bed sheet substrates showed a good retention capacity for the dominant flora and could reduce the number of taxa migrated by the environment compared with the other substrates. Both fresh and exposed vaginal samples of the same individuals could mostly be clustered and clearly distinguished from different individuals, showing the potential of individual identification, and the confusion matrix value of body fluid identification for vaginal samples was 1. In summary, vaginal samples placed on the surface of different substrates retained their stability and demonstrated good application potential for individual and body fluid identification.

Clinical Accuracy of 6 Intraocular Lens Power Calculation Formulas in Elongated Eyes, According to Anterior Chamber Depth.

PURPOSE: To investigate the influence of anterior chamber depth (ACD) on the accuracy of the Kane, EVO 2.0, Barrett Universal II (BU II), Olsen, SRK/T, and Haigis formulas in patients with elongated eyes. DESIGN: Retrospective case series study. METHODS: A total of 106 patients (106 eyes) diagnosed with high myopia (axial length ≥26 mm) were enrolled and divided into 3 subgroups according to preoperative ACD. Mean refractive error (ME), mean absolute refractive error (MAE), median absolute refractive error (MedAE), and proportions of eyes within ±0.25 D, ±0.50 D, ±0.75 D, and ±1.00 D were calculated. RESULTS: In all patients, the MedAE was lowest for the Kane formula (0.28 D), followed by the BU II (0.34 D). In the shallow ACD subgroup, EVO 2.0 formula produced the lowest MedAE (0.22 D), and the highest proportion of eyes within ±0.25 D (58%); the BU II (0.23 D, 50%) and Kane (0.25 D, 50%) formulas produced similar proportions. In the deep ACD group, the MedAEs of the Haigis and SRK/T formulas (0.68 D and 0.50 D, respectively) were significantly higher than those of the EVO 2.0 (0.37 D), Kane (0.30 D), BU II (0.43 D), and Olsen (0.34 D) formulas (P < 0.05). CONCLUSIONS: Overall, the Kane and EVO 2.0 formulas had the highest accuracy. EVO 2.0 and BU II formulas are recommended for patients with shallow ACD; the Kane formula is recommended for patients with deep ACD (especially patients with extremely elongated eyes). The SRK/T and Haigis formulas should be avoided as much as possible.