Alzheimer’s disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explore early sensitive markers for AD. A population based nested case-control study design was used. 89 new AD cases with good compliance who were willing to provide urine and blood specimen were selected from the cohort of 2482 community-dwelling elderly aged 60 years and over from 2013 to 2016. For each case, two controls living nearby were identified. Biomarkers for AD in urine and blood, neuropsychological functions and epidemiological parameters were included to analyze potential risk factors of AD. Compared with logistic regression, k-Nearest Neighbor (kNN) and support vector machine (SVM) model, back-propagation neural network of three-layer topology structures was applied to develop the early warning model. The performance of all models were measured by sensitivity, specificity, accuracy, positive prognostic value (PPV), negative prognostic value (NPV), the area under curve (AUC), and were validated using bootstrap resampling. The average age of AD group was about 5 years older than the non-AD controls (P < 0.001). Patients with AD included a significantly larger proportion of subjects with family history of dementia, compared with non-AD group. After adjusting for age and gender, the concentrations of urinary AD7c-NTP and aluminum in blood were significantly higher in AD group than non-AD group (2.01 ± 1.06 vs 1.03 ± 0.43, 1.74 ± 0.62 vs 1.24 ± 0.41 respectively), but the concentration of Selenium in AD group (2.26 ± 0.59) was significantly lower than that in non-AD group (2.61 ± 1.07). All the models were established using 18 variables that were significantly different between AD patients and controls as independent variables. The ANN model outperformed the other classifiers. The AUC for this ANN was 0.897 and the model obtained the accuracy of 92.13%, the sensitivity of 87.28% and the specificity of 94.74% on the average. Increased risk of AD may be associated with higher age among senior citizens in urban communities. Urinary AD7c-NTP is clinically valuable for the early diagnosis. The established ANN model obtained a high accuracy and diagnostic efficiency, which could be a low-cost practicable tool for the screening and diagnosis of AD for citizens.

The purpose of this study was to investigate the general state and characteristics of physical fitness in undergraduates and to improve their physical health through effective measures. Methods: 6691 first-year students (3373 males and 3318 females) in Nanchang University participated in this study. Anthropometric measures and physical fitness tests were performed and assessed in accordance with the National student physical health standard revised in 2014 applied in China. The associations of physical fitness with BMI were examined by partial correlation. Results: More male students than female students were overweight and obese (12.27% vs 5.6%), (3.97% vs 1.51%). 17.37% of the participants failed the physical fitness assessment and female students performed significantly better than male students. Vital capacity, 1000m/800m run and pull-up/sit-up were found relatively poorer, in which the pull-up in male students (mean score was only 15.40) is the most alarming. The results of all physical fitness tests, except for vital capacity and sit-and-reach, were significantly negatively correlated with body mass index (p<0.001). Conclusions: This study shows the fact that freshmen in Nanchang University performed worse in muscular strength, muscular endurance and cardiorespiratory fitness. Being overweight and obese may negatively affect physical fitness of college students. Scientific and specific physical training and education programs must be laid down to promote the development of physical fitness of undergraduates.