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The use of mice as animal models in biomedical research allows the standardization of genetic background, housing conditions as well as experimental protocols, which all affect phenotypic variability. The phenotypic variability within the experimental unit determines the choice of the group size which is necessary for achieving valid and reproducible results. In this study, the variability of clinical chemical and hematological parameters which represent a comprehensive blood screen of laboratory mice, as well as of immunological parameters and behavioral tests was analyzed in data sets which have been submitted to the Mouse Phenome Database for mouse strains which are predominantly used in biomedical research. Most of the clinical chemical and hematological parameters-except of some parameters being known for their high variability-showed an average coefficient of variation (CV = standard deviation / mean) below 0.25. Most immunological parameters measured in blood samples had a CV between 0.2 and 0.4. The behavioral tests showed a CV between 0.4 and 0.6, or higher. In addition, a large range of the CV was found for most parameters/tests between and within the selected projects. This clearly demonstrates the appearance of unpredictable major interactions between genotype, environment and experiment regarding the variability of the parameters and tests analyzed., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Aigner, Heumann. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A model-based clustering method for compositional data is explored in this article. Most methods for compositional data analysis require some kind of transformation. The proposed method builds a mixture model using Dirichlet distribution which works with the unit sum constraint. The mixture model uses a hard EM algorithm with some modification to overcome the problem of fast convergence with empty clusters. This work includes a rigorous simulation study to evaluate the performance of the proposed method over varied dimensions, number of clusters, and overlap. The performance of the model is also compared with other popular clustering algorithms often used for compositional data analysis (e.g. KMeans, Gaussian mixture model (GMM) Gaussian Mixture Model with Hard EM (Hard GMM), partition around medoids (PAM), Clustering Large Applications based on Randomized Search (CLARANS), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) etc.) for simulated data as well as two real data problems coming from the business and marketing domain and physical science domain, respectively. The study has shown promising results exploiting different distributional patterns of compositional data., Competing Interests: The authors have declared that no competing interests exist.

Multiple Imputation (MI) is always challenging in high dimensional settings. The imputation model with some selected number of predictors can be incompatible with the analysis model leading to inconsistent and biased estimates. Although compatibility in such cases may not be achieved, but one can obtain consistent and unbiased estimates using a semi-compatible imputation model. We propose to relax the lasso penalty for selecting a large set of variables (at most n). The substantive model that also uses some formal variable selection procedure in high-dimensional structures is then expected to be nested in this imputation model. The resulting imputation model will be semi-compatible with high probability. The likelihood estimates can be unstable and can face the convergence issues as the number of variables becomes nearly as large as the sample size. To address these issues, we further propose to use a ridge penalty for obtaining the posterior distribution of the parameters based on the observed data. The proposed technique is compared with the standard MI software and MI techniques available for high-dimensional data in simulation studies and a real life dataset. Our results exhibit the superiority of the proposed approach to the existing MI approaches while addressing the compatibility issue., Competing Interests: The authors have declared that no competing interests exist.

Background: To evaluate the clinical outcomes 12 months after systemic administration of amoxicillin (AMX) and metronidazole (MET) adjunctive to subgingival debridement (SD) in patients with severe chronic periodontitis (sChP)., Material and Methods: 102 patients with sChP were treated randomly as follows: SD within 2 consecutive days and placebo for 7 days (group A), SD+AMX+MET (both 500mg x3 times daily TID) for 3 days (group B), SD+AMX+MET (both 500mg x 3 TID) for 7 days (group C). At baseline, at 3-, 6-, and 12-months post-treatment probing pocket depth (PD), clinical attachment level (CAL), furcation involvement, bleeding on probing (BOP), full-mouth plaque score (FMPS) were determined. The reduction in the number of sites with PD≥6mm was defined as main outcome variable., Results: 75 patients completed the study. At 12 months, all three treatment groups showed statistically significant improvements (p<0.001) of mean PD, CAL, BOP and number of sites with PD≥6mm compared to baseline. Mean residual PD were statistically significantly lower and CAL gain statistically significantly greater in the two antibiotic groups as compared to placebo. While PD reductions (p = 0.012) and CAL gain (p = 0.017) were statistically significantly higher in group C compared to group A, only the 3-day AB group showed statistically significantly fewer sites with PD≥6mm at 12 m (p = 0.003). The reduction in the number of sites with PD≥6 mm (primary outcome) showed no statistical significant differences between the 3 treatment groups. However, in both antibiotic groups significantly more patients compared to the placebo group reached a low risk for disease progression at 12 months (≤4 sites with PD≥5mm)., Conclusion: At 12 months, both adjunctive antibiotic protocols resulted in statistically significantly greater clinical improvements compared to placebo.

Objective: Traditional gold mining is associated with mercury exposure. Especially vulnerable to its neurotoxic effects is the developing nervous system of a child. We aimed to investigate risk factors of mercury exposure among children in a rural mining town in Chile., Methods: Using a validated questionnaire distributed to the parents of the children, a priori mercury risk factors, potential exposure pathways and demographics of the children were obtained. Mercury levels were measured through analyzing fingernail samples. Logistic regression modeling the effect of risk factors on mercury levels above the 75(th) percentile were made, adjusted for potential confounders., Results: The 288 children had a mean age of 9.6 years (SD = 1.9). The mean mercury level in the study population was 0.13 µg/g (SD 0.11, median 0.10, range 0.001-0.86 µg/g). The strongest risk factor for children's odds of high mercury levels (>75(th) percentile, 0.165 µg/g) was to play inside a house where a family member worked with mercury (OR adjusted 3.49 95% CI 1.23-9.89). Additionally, children whose parents worked in industrial gold mining had higher odds of high mercury levels than children whose parents worked in industrial copper mining or outside mining activities., Conclusion: Mercury exposure through small-scale gold mining might affect children in their home environments. These results may further help to convince the local population of banning mercury burning inside the households.