Metabolomics efficiently discriminates monozygotic twins in peripheral blood.

Monozygotic (MZ) twins cannot be distinguished using conventional forensic STR typing because they present identical STR genotypings. However, MZ twins do not always live in the same environment and often have different dietary and other lifestyle habits. Metabolic profiles are deyermined by individual characteristics and are also influenced by the environment in which they live. Therefore, they are potential markers capable of identifying MZ twins. Moreover, the production of proteins varies from organism to organism and is influenced by both the physiological state of the body and the external environment. Hence, we used metabolomics and proteomics to identify metabolites and proteins in peripheral blood to discriminate MZ twins. We identified 1749 known metabolites and 622 proteins in proteomic analysis. The metabolic profiles of four pairs of MZ twins revealed minor differences in intra-MZ twins and major differences in inter-MZ twins. Each pair of MZ twins exhibited distinct characteristics, and four metabolites-methyl picolinate, acesulfame, paraxanthine, and phenylbenzimidazole sulfonic acid-were observed in all four MZ twin pairs. These four differential exogenous metabolites conincidently show that the different external environments and life styles can be well distinguished by metabolites, considering that twins do not all have the same eating habits and living environments. Moreover, MZ twins showed different protein profiles in serum but not in whole blood. Thus, our results indicate that differential metabolites provide potential biomarkers for the personal identification of MZ twins in forensic medicine.

Association of histone modification with the development of schizophrenia.

Schizophrenia, influenced by genetic and environmental factors, may involve epigenetic alterations, notably histone modifications, in its pathogenesis. This review summarizes various histone modifications including acetylation, methylation, phosphorylation, ubiquitination, serotonylation, lactylation, palmitoylation, and dopaminylation, and their implications in schizophrenia. Current research predominantly focuses on histone acetylation and methylation, though other modifications also play significant roles. These modifications are crucial in regulating transcription through chromatin remodeling, which is vital for understanding schizophrenia's development. For instance, histone acetylation enhances transcriptional efficiency by loosening chromatin, while increased histone methyltransferase activity on H3K9 and altered histone phosphorylation, which reduces DNA affinity and destabilizes chromatin structure, are significant markers of schizophrenia.

Rapid detection of human cytomegalovirus by multienzyme isothermal rapid amplification and lateral flow dipsticks.

Introduction: Human cytomegalovirus (HCMV) is the most common viral infection seen in newborns. The major route of transmission for acquired human cytomegalovirus infection is breast milk from mothers who are HCMV seropositive to the infants. Thus, a rapid, economical, and simple method to perform HCMV test in breast milk is crucial and necessary for preventing acquired HCMV infection, especially in underdeveloped regions with limited laboratory resources. Methods: In this study, an effective technique for the detection of HCMV was constructed by combining multienzyme isothermal rapid amplification (MIRA) and lateral flow chromatography strip (LFD). Primers for the conserved HCMV sequence UL83 were utilized for MIRA-LFD testing. Results: Our results showed that the entire MIRA reaction could be completed in 12 minutes at 37°C, and LFD outcomes could be observed visibly after 10 minutes. The detection sensitivity of this method reached 50 copy/µl. Samples of breast milk were examined to compare MIRA-LFD and conventional qPCR. The accuracy of MIRA-LFD was 100%. Discussion: The straightforward, rapid, economic features of the test can provide the significant advantages for the prevention of breast milk-acquired cytomegalovirus infection, particularly in resource-limited locations with high seroprevalence of cytomegalovirus.

Predictive Model of Oxaliplatin-induced Liver Injury Based on Artificial Neural Network and Logistic Regression.

Background and Aims: Identifying potential high-risk groups of oxaliplatin-induced liver injury (OILI) is valuable, but tools are lacking. So artificial neural network (ANN) and logistic regression (LR) models will be developed to predict the risk of OILI. Methods: The medical information of patients treated with oxaliplatin between May and November 2016 at 10 hospitals was collected prospectively. We used the updated Roussel Uclaf causality assessment method (RUCAM) to identify cases of OILI and summarized the patient and medication characteristics. Furthermore, the ANN and LR models for predicting the risk of OILI were developed and evaluated. Results: The incidence of OILI was 3.65%. The median RUCAM score with interquartile range was 6 (4, 9). The ANN model performed similarly to the LR model in sensitivity, specificity, and accuracy. In discrimination, the area under the curve of the ANN model was larger (0.920>0.833, p=0.019). In calibration, the ANN model was slightly improved. The important predictors of both models overlapped partially, including age, chemotherapy regimens and cycles, single and total dose of OXA, glucocorticoid drugs, and antihistamine drugs. Conclusions: When the discriminative and calibration ability was given priority, the ANN model outperformed the LR model in predicting the risk of OILI. Other chemotherapy drugs in oxaliplatin-based chemotherapy regimens could have different degrees of impact on OILI. We suspected that OILI may be idiosyncratic, and chemotherapy dose factors may be weakly correlated. Decision making on prophylactic medications needs to be carefully considered, and the actual preventive effect needed to be supported by more evidence.