[Dynamic changes of reproductive hormone levels and related factors in old and middleaged men in health examination].

OBJECTIVE: To investigate the dynamic changes of serum reproductive hormone levels in old and middleaged males in health examination and their correlation with age and lipid profile. METHODS: This study included 4 333 men in health examination from January 2011 to December 2014. The men were aged from 40 to 85 years old and divided into seven fiveyearspan age groups. We determined the levels of serum testosterone (T), luteinizing hormone (LH), folliclestimulating hormone (FSH), estradiol (E2), progesterone (P), prolactin (PRL), total cholesterol (TC), triglyceride (TG), lowdensity lipoprotein cholesterol (LDLC), highdensity lipoprotein cholesterol (HDLC), and the testosterone secretion index (TSI = T/LH). We analyzed the obtained data using SPSS Pram, KruskalWallis H test, MannWhitney U test, exponential regression, and Spearman correlation analysis. RESULTS: Statistically significant differences were found in LH, FSH, E2 and TSI among the seven age groups (P< 0.05). The levels of serum LH, FSH and E2 were significantly higher (P< 0.05) while TSI remarkably lower (P< 0.05) in the ≥70 yr group than in the other six groups. The serum T and E2 levels and TSI were markedly lower in the 40－44, 45－49 and 50－54 yr groups in 2014 than in the other three years (P< 0.05), and so were the levels of serum T and TSI in the 55－59 yr group (P< 0.05). The levels of serum LH, FSH and E2 were correlated positively while those of P, PRL and TSI negatively with age. The serum T level was correlated positively with HDLC but negatively with TC, TG and LDLC. The levels of serum LH, FSH and E2 showed a yearly average increase of 1.9%, 2.7% and 0.5%, respectively, while TSI an annual mean decline of 2.0% in the 40－85 yr group. CONCLUSIONS: LH, FSH and E2 were increased while TSI decreased with age in the >40 years old males. T and TSI were reduced in the 40－59 years old men from 2011 to 2014, and so was E2 in the 40－54 yr group. Lowlevel testosterone is closely related to dyslipidemia.

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

BACKGROUND: The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. MATERIALS AND METHODS: A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. RESULTS: The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). CONCLUSIONS: The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

Use of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patients.

Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (≥ 22 days, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (≥ 61 year old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors .The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.