HLA-B*51:01 is strongly associated with clindamycin-related cutaneous adverse drug reactions.

Clindamycin causes cutaneous adverse drug reactions (cADRs), sometimes with the mechanisms of pathogenicity or risk factors unknown. This study aims to assess whether HLA alleles are associated with clindamycin-related cADRs in the Han Chinese population. We performed an association study of 12 subjects with clindamycin-related cADRs, 279 controls and 26 clindamycin-tolerant subjects. Subjects who received clindamycin through intravenous drip were analyzed separately. Unbiased, in silico docking was conducted. We found 6 out of 12 clindamycin-induced cADR patients carried HLA-B*51:01, and all of them received clindamycin via intravenous drip (6/9). The carrier frequency of HLA-B*51:01 is significantly higher compared with the control group (P=0.0006; OR=9.731, 95% CI: 2.927-32.353) and the clindamycin-tolerant group (OR=24.000, 95% CI: 3.247-177.405). In silico docking showed clindamycin is potentially more stable inside HLA-B*51:01 protein. Our results suggested, for the first time, that HLA-B*51:01 is a risk allele for clindamycin-related cADRs in Han Chinese, especially when clindamycin is administered via intravenous drip.

Prevalence of halitosis in children considering oral hygiene, gender and age.

BACKGROUND: To date, few studies have addressed halitosis in the paediatric population. As such, the aim of the present study was to investigate symptoms, signs and risk factors associated with halitosis in healthy children and to present a model based on the clinical data that predicts the presence of halitosis. METHODS: A total of 101 individuals were included. All patients received a questionnaire that queried on sociodemographic characteristics, self-reported halitosis and dental treatment history. Individuals received a thorough intra-oral examination, and the volatile sulphur compounds (VSC) were measured to test the presence of halitosis with a portable sulphide monitor (Halimeter(®); Interscan Co., Chatsworth, CA, USA). The distribution of the sociodemographic characteristics, self-reported halitosis, dental treatment history and other oral features was evaluated. Finally, a statistical model was constructed with the best set of features to predict halitosis in children. RESULTS: The median age was 12.0 years (mean: 11.7 ± SD 2.7) with 54.5% males. Halitosis (VSC > 100 parts per billion, or ppb) was objectively measured in 37.6% of patients. For comparison purposes, Bayesian network was obtained using clinical and demographic data. The model consisted of four variables (sex, age, oral hygiene status and self-reported halitosis) directly related to the presence of halitosis (VSC > 100 ppb). This model achieved 76.4% area under receiver operating characteristics curve (AUROC). Overall, female patients or individuals with dental plaque on more than 25% of the dental surfaces or patients older than 13 year old were more prone to present with halitosis. CONCLUSIONS: The results suggest that halitosis in the paediatric population is related to poor oral hygiene and may be more common in females and older individuals. This specific predictive model may be useful to identify subgroups to target for intervention to treat oral halitosis.

SNP-based Bayesian networks can predict oral mucositis risk in autologous stem cell transplant recipients.

OBJECTIVE: Approximately 40% of patients receiving conditioning chemotherapy prior to autologous hematopoietic stem cell transplants (aHSCT) develop severe oral mucositis (SOM). Aside from disabling pain, ulcerative lesions associated with SOM predispose to poor health and economic outcomes. Our objective was to develop a probabilistic graphical model in which a cluster of single-nucleotide polymorphisms (SNPs) derived from salivary DNA could be used as a tool to predict SOM risk. METHODS: Salivary DNA was extracted from 153 HSCT patients and applied to Illumina BeadChips. Using sequential data analysis, we filtered extraneous SNPs, selected loci, and identified a predictive SNP network for OM risk. We then tested the predictive validity of the network using SNP array outputs from an independent HSCT cohort. RESULTS: We identified an 82-SNP Bayesian network (BN) that was related to SOM risk with a 10-fold cross-validation accuracy of 99.3% and an area under the ROC curve of 99.7%. Using samples from a small independent patient cohort (n = 16), we demonstrated the network's predictive validity with an accuracy of 81.2% in the absence of any false positives. CONCLUSIONS: Our results suggest that SNP-based BN developed from saliva-sourced DNA can predict SOM risk in patients prior to aHSCT.

Genomic studies of GVHD-lessons learned thus far.

GVHD remains the most significant complication of hematopoietic SCT, despite advances in HLA matching and the identification of risk various factors. To account for the variation in the incidence and severity of this disease, many genetic association studies have been performed in order to explore the role of immunoregulatory gene polymorphisms. These genes include those that encode cytokines, chemokines, and costimulatory molecules. Polymorphisms in other classes of genes such as those involved in drug metabolism, protein folding, and DNA replication have also been studied. In this review, we address the current knowledge of the role of genetic polymorphisms in GVHD. We also discuss the potential pitfalls inherent in genetic association testing and alternative strategies to address these problems.