Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

Memory deficits and hippocampal cytokine expression in a rat model of ADHD

Attention-deficit/hyperactivity disorder (ADHD) is a complex behavioral disorder characterized by hyperactivity, impulsivity, inattention, and deficits in working memory and time perception. While animal models have advanced our neurobiological understanding of this condition, there are limited and inconsistent data on working and elapsed time memory function. Inflammatory signaling has been identified as a key factor in memory and cognitive impairments, but its role in ADHD remains unclear. Additionally, the disproportionate investigation of male subjects in ADHD research has contributed to a poor understanding of the disorder in females. This study sought to investigate the potential connections between memory, neuroimmunology, and ADHD in both male and female animals. Specifically, we utilized the spontaneously hypertensive rat (SHR), one of the most extensively studied animal models of ADHD. Compared to their control, the Wistar-Kyoto (WKY) rat, male SHR are reported to exhibit several behavioral phenotypes associated with ADHD, including hyperactivity, impulsivity, and poor sustained attention, along with impairments in learning and memory. As the hippocampus is a key brain region for learning and memory, we examined the behavior of male and female SHR and WKY rats in two hippocampal-dependent memory tasks. Our findings revealed that SHR have delay-dependent working memory deficits that were similar to, albeit less severe than, those seen in hippocampal-lesioned rats. We also observed impairments in elapsed time processing in female SHR, particularly in the discrimination of longer time durations. To investigate the impact of inflammatory signaling on memory in these rats, we analyzed the levels of several cytokines in the dorsal and ventral hippocampus of SHR and WKY. Although we found some sex and genotype differences, concentrations were generally similar between groups. Taken together, our results indicate that SHR exhibit deficits in spatial working memory and memory for elapsed time, as well as some differences in hippocampal cytokine concentrations. These findings contribute to a better understanding of the neurobiological basis of ADHD in both sexes and may inform future research aimed at developing effective treatments for the disorder. Nonetheless, the potential mediating role of neuroinflammation in the memory symptomatology of SHR requires further investigation

Prognostic biomarkers for oral tongue squamous cell carcinoma : a systematic review and meta-analysis

Background: Identifying informative prognostic biomarkers for oral tongue squamous cell carcinoma (OTSCC) is of great importance in order to better predict tumour behaviour and to guide treatment planning. Here, we summarise existing evidence regarding immunohistochemical prognostic biomarkers for OTSCC. Methods: A systematic search of the literature was performed using the databases of Scopus, Ovid Medline, Web of Science and Cochrane Library. All studies which had investigated the prognostic significance of immunohistochemical biomarkers in OTSCC during the period from 1985 to 2015 were retrieved. For the five most often evaluated biomarkers a random-effects meta-analysis on overall survival was performed, including those studies that provided the necessary statistical results. Results: A total of 174 studies conducted during the last three decades were found, and in these 184 biomarkers were evaluated for the prognostication of OTSCC. The five biomarkers most frequently assessed were p53, Ki-67, p16, VEGFs and cyclin D1. In the meta-analyses, the most promising results of the prognostic power for OTSCC were obtained for cyclin D1. For studies of VEGF A and C the results were equivocal, but the pooled analysis of VEGF A separately showed it to be a useful prognosticator for OTSCC. There was no sufficient evidence to support p53, Ki-67 and p16 as prognostic biomarkers for OTSCC. Limitations in the quality of the published studies (e.g., small cohorts, lack of compliance with REMARK guidelines) are widespread. Conclusions: Numerous biomarkers have been presented as useful prognosticators for OTSCC, but the quality of the conduct and reporting of original studies is overall unsatisfactory which does not allow reliable conclusions. The value of two biomarkers (VEGFA and cyclin D1) should be validated in a multicentre study setting following REMARK guidelines.Peer reviewe