DNA profiles have been increasingly used as the most reliable means to identify remains from war or mass disaster. To establish the identity with such a large set of victims, special care should be taken to correlate remains with correct family references while avoiding coincidental match between non-relatives. Therefore we address here relevant statistical and combinatorial issues in the DNA identification of mass victims. A simple and general formula for the likelihood ratio governing any potential kinship between two DNA profiles was presented, and for that purpose, the probabilities that a given relative and an individual share autosomal identical-bydescent alleles were calculated. In addition, a method dealing with the allele drop-out in kinship analysis and the estimation of a cold hit were discussed.
Alleles ; Cold Temperature ; Disasters ; DNA ; Humans

PURPOSE: This descriptive study was done to identify the academic achievement, self-directed learning (SDL), and critical thinking disposition (CTD) of nursing students according to their learning styles. METHOD: The participants were 240 nursing students. Data were collected using structured questionnaires which included Kolb's Learning Style Inventory, Academic Achievement in Fundamental Nursing and Health Assessment, Self Directed Learning Readiness Scale, and California Critical Thinking Disposition Inventory. Data were analyzed using chi2 test, ANOVA, Pearson' correlation coefficients, and Spearman rank correlation coefficient. RESULTS: One third of respondents were shown to be Convergers in their learning style (33.3%). The Academic Achievement of students who were Convergers was significantly higher than those who were Divergers or Accommodators (F=5.95, p=.001). The SDL and CTD of students who were Convergers were significantly higher than Divergers and Assimilators (F=9.67, p<.001 and F=8.42, p<.001). No significant correlations were found between Academic Achievement and SDL or CTD, but a statistically significant positive correlation was found between SDL and CTD (r=.68, p<.001). CONCLUSION: The findings of this study indicate that learning style influences academic achievement, SDL and CTD.
Achievement ; California ; Surveys and Questionnaires ; Humans ; Learning ; Self-Assessment ; Students, Nursing ; Thinking

Emerging evidence suggests that intestinal dysbiosis contributes to systemic inflammation and cardiovascular diseases in dialysis patients. The purpose of this study was to evaluate the effects of probiotic supplementation on various inflammatory parameters in hemodialysis (HD) patients. Methods: Twenty-two patients with maintenance HD were enrolled. These patients were treated twice a day with 2.0 ×1010 colony forming units of a combination of Bifidobacterium bifidum BGN4 and Bifidobacterium longum BORI for 3 months. The microbiome and fecal short-chain fatty acids (SCFAs) were analyzed. The percentages of CD14+ CD16+ proinflammatory monocytes and CD4+ CD25+ regulatory T-cells (Tregs) before and after probiotic supplementation were determined by flow cytometry. Serum levels of calprotectin and cytokine responses upon lipopolysaccharide (LPS) challenge were compared before and after probiotic supplementation. Results: Fecal SCFAs increased significantly after probiotic supplementation. Serum levels of calprotectin and interleukin 6 upon LPS stimulation significantly decreased. The anti-inflammatory effects of probiotics were associated with a significant increase in the percentage of CD4+ CD25+ Tregs (3.5% vs. 8.6%, p < 0.05) and also with a decrease of CD14+ CD16+ proinflammatory monocytes (310/ mm2 vs. 194/mm2 , p < 0.05). Conclusion: Probiotic supplementation reduced systemic inflammatory responses in HD patients and this effect was associated with an increase in Tregs and a decrease in proinflammatory monocytes. Hence, targeting intestinal dysbiosis might be a novel strategy for decreasing inflammation and cardiovascular risks in HD patients.

Purpose: Hypoxaemia is a significant adverse event during endoscopic retrograde cholangiopancreatography (ERCP) under monitored anaesthesia care (MAC); however, no model has been developed to predict hypoxaemia. We aimed to develop and compare logistic regression (LR) and machine learning (ML) models to predict hypoxaemia during ERCP under MAC. Materials and Methods: We collected patient data from our institutional ERCP database. The study population was randomly divided into training and test sets (7:3). Models were fit to training data and evaluated on unseen test data. The training set was further split into k-fold (k=5) for tuning hyperparameters, such as feature selection and early stopping. Models were trained over k loops; the i-th fold was set aside as a validation set in the i-th loop. Model performance was measured using area under the curve (AUC). Results: We identified 6114 cases of ERCP under MAC, with a total hypoxaemia rate of 5.9%. The LR model was established by combining eight variables and had a test AUC of 0.693. The ML and LR models were evaluated on 30 independent data splits. The average test AUC for LR was 0.7230, which improved to 0.7336 by adding eight more variables with an l 1 regularisation-based selection technique and ensembling the LRs and gradient boosting algorithm (GBM). The high-risk group was discriminated using the GBM ensemble model, with a sensitivity and specificity of 63.6% and 72.2%, respectively. Conclusion We established GBM ensemble model and LR model for risk prediction, which demonstrated good potential for preventing hypoxaemia during ERCP under MAC.