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The inception of forensic DNA elimination database represents a pivotal advancement in forensic science, aiming to streamline the process of distinguishing between DNA found at crime scenes and that of individuals involved in the investigation process, such as law enforcement personnel and forensic lab staff. In subsequent phases, once familiarity with the database is achieved by its administrators and other stakeholders, and they have accrued sufficient experience, the possibility of expanding the database to encompass first responders—including firefighters, paramedics, emergency medical technicians, and other emergency services personnel—can be contemplated. Key challenges in managing these databases encompass the grounds for collecting samples, ensuring the integrity of both samples and profiles, along with the duration of retention, access to the database, and the protocols to follow when a match is found in the database. This paper outlines the conceptual and detailed legislative framework in Hungary, where the forensic DNA elimination database was introduced in 2022. •Elimination DNA databases help exclude individuals with legitimate reasons for being at a crime scene by compiling their DNA profiles.•The utilization of the elimination database was introduced in Hungary on January 1, 2022; however, a systematic analysis of the regulation suggests improvements to the regulation.

Background Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Results Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. Conclusions Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies.

Phenotypic trait prediction in ancient DNA analysis can provide information about the external appearance of individuals from past human populations. Some studies predicting eye and hair color in ancient adult skeletons have been published, but not for ancient subadult skeletons, which are more prone to decay. In this study, eye and hair color were predicted for an early medieval adult skeleton and a subadult skeleton that was anthropologically characterized as a middle-aged man and a subadult of unknown sex about 6 years old. When processing the petrous bones, precautions were taken to prevent contamination with modern DNA. The MillMix tissue homogenizer was used for grinding, 0.5 g of bone powder was decalcified, and DNA was purified in Biorobot EZ1. The PowerQuant System was used for quantification and a customized version of the HIrisPlex panel for massive parallel sequencing (MPS) analysis. Library preparation and templating were performed on the HID Ion Chef Instrument and sequencing on the Ion GeneStudio S5 System. Up to 21 ng DNA/g of powder was obtained from ancient petrous bones. Clean negative controls and no matches with elimination database profiles confirmed no contamination issue. Brown eyes and dark brown or black hair were predicted for the adult skeleton and blue eyes and brown or dark brown hair for the subadult skeleton. The MPS analysis results obtained proved that it is possible to predict hair and eye color not only for an adult from the Early Middle Ages, but also for a subadult skeleton dating to this period.

This paper describes molecular genetic identification of one third of the skeletal remains of 88 victims of postwar (June 1945) killings found in the Konfin I mass grave in Slovenia. Living relatives were traced for 36 victims. We analyzed 84 right femurs and compared their genetic profiles to the genetic material of living relatives. We cleaned the bones, removed surface contamination, and ground the bones into powder. Prior to DNA isolation using Biorobot EZ1 (Qiagen), the powder was decalcified. The nuclear DNA of the samples was quantified using the real-time polymerase chain reaction method. We extracted 0.8 to 100 ng DNA/g of bone powder from 82 bones. Autosomal genetic profiles and Y-chromosome haplotypes were obtained from 98% of the bones, and mitochondrial DNA (mtDNA) haplotypes from 95% of the bones for the HVI region and from 98% of the bones for the HVII region. Genetic profiles of the nuclear and mtDNA were determined for reference persons. For traceability in the event of contamination, we created an elimination database including genetic profiles of the nuclear and mtDNA of all persons that had been in contact with the skeletal remains. When comparing genetic profiles, we matched 28 of the 84 bones analyzed with living relatives (brothers, sisters, sons, daughters, nephews, or cousins). The statistical analyses showed a high confidence of correct identification for all 28 victims in the Konfin I mass grave (posterior probability ranged from 99.9% to more than 99.999999%).

Background Elimination dietary trials for the diagnosis of adverse food reactions (food allergies) in dogs and cats are often conducted with commercial pet foods while relying on their label to select those not containing previously-eaten ingredients. There are concerns that industrial pet foods might contain unlisted food sources that could negate the usefulness of performing food trials. Furthermore, unidentified ingredients might cause clinical reactions in patients hypersensitive to such items. Results We searched two article databases on July 7, 2017 and January 12, 2018 for relevant articles, and we screened abstracts from the leading international veterinary dermatology congresses for suitable material. Additional citations were found in the selected papers. In all, we extracted data from 17 articles and one abstract. The studies varied both in the number of pet foods tested (median: 15; range: 1 to 210) and that of ingredients specifically evaluated (median: 4; range: 1 to 11). Studies most often employed either PCR to detect DNA or ELISA to identify proteins from one or more vegetal or animal species; two studies used mass spectrometry to increase the number of detectable proteins. The various methods found ingredients that were not on the label in 0 to 83% (median: 45%) of tested diets; this percentage varied between 33 and 83% in pet foods with “novel/limited” ingredients proposed for elimination diets. Similarly, ingredients were found to be missing from the label in 0 to 38% (median: 1%) of tested foods. Finally, six studies evaluated, among others, several hydrolysate-containing pet foods: mislabeling with unlabeled or missing ingredients was found only in one diet. Conclusions The mislabeling of pet foods appears rather common, even in those with “novel” or “limited” ingredients proposed for elimination diets. Unexpected added ingredients are more frequently detected than those missing from the label. There is insufficient information to determine if the presence of a contaminating component will lead to a clinical reaction in a patient allergic to it, as challenges with the mislabeled foods were not performed in dogs or cats allergic to such ingredients. The testing of hydrolysate-containing pet foods found only one instance of possible mislabeling.

The aim of the present study was to identify risk genes in myocardial infarction. Microarray data GSE34198, containing data from the peripheral blood of 49 myocardial infarction samples and 48 corresponding control samples, were downloaded from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). The DEGs were used to construct a protein‑protein interaction (PPI) network of patient samples, from which the feature genes were identified using the neighboring score method. The recursive feature elimination (RFE) algorithm was employed to select the risk genes among feature genes, which were subsequently applied to perform a support vector machine (SVM) classifier to identify the specific signature in myocardial infarction samples. Another dataset, GSE61144, was also downloaded to verify the efficacy of the classifier. A total of 724 downregulated and 483 upregulated DEGs were screened in patient samples compared with control samples in the GSE34198 dataset. The PPI network of myocardial infarction was comprised of 1,083 nodes (genes) and 46,363 lines (connections). Using the neighborhood scoring method, the top 100 feature genes in myocardial infarction samples were identified as the disease feature genes, which distinguish the myocardial infarction samples from the control samples. The RFE algorithm screened 15 risk genes, which were employed to construct a SVM classifier with an average precision of 88% to the patient sample following visualization by a confusion matrix. The predictive precision of the classifier on another microarray dataset, GSE61144, was 0.92, with an average true positive of 0.9278 and an average false positive of 0.2361. A‑kinase‑anchoring protein 12 (AKAP12) and glycine receptor α2 (GLRA2) were two risk genes in the SVM classifier. Therefore, AKAP12 and GLRA2 exert potential roles in the development of myocardial infarction, potentially by influencing cardiac contractility and protecting against ischemia‑reperfusion injury, which may provide clues in developing potential diagnostic biomarkers or therapeutic targets for myocardial infarction.

The present study aimed to identify potential biomarkers for atherosclerosis via analysis of gene expression profiles. The microarray dataset no. GSE20129 was downloaded from the Gene Expression Omnibus database. A total of 118 samples from the peripheral blood of female patients was used, including 47 atherosclerotic and 71 non-atherosclerotic patients. The differentially expressed genes (DEGs) in the atherosclerosis samples were identified using the Limma package. Gene ontology term and Kyoto Encyclopedia of Genes and Genomes pathway analyses for DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. The recursive feature elimination (RFE) algorithm was applied for feature selection via iterative classification, and support vector machine classifier was used for the validation of prediction accuracy. A total of 430 DEGs in the atherosclerosis samples were identified, including 149 up- and 281 downregulated genes. Subsequently, the RFE algorithm was used to identify 11 biomarkers, whose receiver operating characteristic curves had an area under curve of 0.92, indicating that the identified 11 biomarkers were representative. The present study indicated that APH1B, JAM3, FBLN2, CSAD and PSTPIP2 may have important roles in the progression of atherosclerosis in females and may be potential biomarkers for early diagnosis and prognosis as well as treatment targets for this disease.

Methylation of CpG sites in the genome, which is generally conserved during cell replication, is considered to play important roles in cell differentiation and carcinogenesis. However, investigations on changes in methylation status have been limited to known genes. To make a genome-wide search for differentially methylated genes, we developed a methylation-sensitive-representational difference analysis (MS-RDA) method. The representation of the genome was prepared using the methylation-sensitive restriction enzyme HpaII, and the mixture ratio of tester and driver DNAs was optimized to detect differences in methylation status of a single copy per diploid mammalian genome. By performing comparative MS-RDA of one hepatocellular carcinoma and of background liver tissue of one mouse treated with a food carcinogen (2-amino-3,4-dimethylimidazo[4,5-f] quinoline), we were able to identify (i) extensive hypomethylation of long interspersed nuclear element repetitive sequences in a number of hepatocellular carcinomas, (ii) reduction of the gene dosage of their mitochondrial DNA, and (iii) a hypermethylated DNA fragment of unknown origin. Furthermore, by adding the clones obtained in the first MS-RDA to the driver DNA [MS-RDA with elimination of excessive clones (MS-RDA-WEEC)], nine DNA fragments that could not be detected at the first MS-RDA were isolated as differentially methylated DNA fragments. MS-RDA, combined with MS-RDA-WEEC, is thus a promising approach to identify DNA fragments differentially methylated in two DNA sources.

This chapter focuses on the National DNA Database (NDNAD) of England and Wales, the oldest and largest national DNA intelligence database, the daily operation of which is managed by FSS Ltd., a former executive agency of the Home Office. The DNA profiles at the NDNAD are from three different sources: casework profiles from unknown persons, criminal justice profiles, and elimination or volunteer profiles.