The concept,theory model and practical studies of public health information searching behaviors in foreign countries were summarized in order to provide the international experiences that should be learned by domestic scholars and the evidence for domestic health education institutions to work out health education-related policies, and open up a novel approach for improving the public health management ability in our country.

Objective To evaluate serum level of pepsinogenⅠ( PGⅠ) ,PGⅡ, and PGⅠ/PGⅡ-ratio ( PGR ) using latex enhanced turbidimetric immunoassay in patients with different gastric mucosal lesions, and to investigate their changes and clinical significance.Methods Case-control study.Two hundred and seventy-five patients who had enteroscopy and pathological examination from the department of gastroenterology and surgery from Beijing Tongren Hospital between January 2015 and January 2016 were enrolled.Endoscopic and histopathological examination confirmed the normal control group (n=20), chronic non-atrophic gastritis group ( n=68 ) , chronic atrophic gastritis group ( n=76 ) , including antral atrophic gastritis ( n=30 ) , gastric body atrophic gastritis ( n=26 ) , and multifocal atrophic gastritis ( n=20 );intestinal metaplasia group ( n=28 ) , intraepithelial neoplasia group ( n=9 ) , benign gastric ulcer group ( n=46) and intestinal gastric cancer group ( n=28).Latex-enhanced immune turbidity method were used to detect the patients fasting serum PGⅠand PGⅡ.Then the PGR was calculated.The normally distributed data of each group were statistically analyzed by ANOVA, the data between groups were nalyzed using the Mann-Whitney U test and Kruskal-Wallis test.Results Serum PGⅠ[ ( 74.23 ±22.36 ) ] ng/ml and PGR (6.92 ±2.16) in chronic atrophic gastritis group were lower than those in normal controls[PGⅠ(98.94 ± 21.00) ng/ml, PGR 8.13 ±2.47],(FPGⅠ =18.297,PPGⅠ <0.01,FPGR =4.713,PPGR <0.01).The serum PGⅠ[(44.46 ±26.72) ng/ml] and PGR (3.09 ±0.83) in the intestinal type of gastric cancer group were lower than those in the chronic atrophic gastritis group[PGⅠ(74.23 ±22.36)ng/ml, PGR 6.92 ±2.16], (ZPGⅠ =-3.921,PPGⅠ <0.01,ZPGR =-6.662,PPGR <0.01).PGⅠ[(129.95 ±43.39) ng/ml].PGⅡ[(21.09 ±6.78) ng/ml]in the gastric benign ulcer group were higher than those in the normal controls[PGⅠ (98.94 ±21.00) ng/ml, PGⅡ(12.64 ±1.84) ng/ml], FPGⅠ =10.803,PPGⅠ <0.01;FPGⅡ =39.130,PPGⅡ <0.01. PGⅠ[(52.44 ±10.37) ng/ml and PGR (5.47 ±1.59) in the multifocal atrophic gastritis group were lower than those in the antral atrophic gastritis[PGⅠ(94.95 ±14.45)ng/ml, PGR 8.39 ±1.48],ZPGⅠ =-5.941,PPGⅠ <0.01,ZPGR =-4.911,PPGR <0.01.The AUC of PGⅠand PGR for diagnosis of chronic atrophic gastritis were 0.752 and 0.683 respectively.The sequence combined detection sensitivity was 72.37%(55/76), and the specificity was 70.85%(141/199).The AUC of PG I and PGR for diagnosis of intestinal type of gastric cancer were 0.852 and 0.895 respectively.The sequence combined detection sensitivity was 71.42% ( 20/28 ) and the specificity was 81.78% ( 202/247 ) . Conclusion The Latex-enhanced immune turbidity method of combined detection of serum PGⅠ, PGⅡlevels and PGR can be used in the clinic to monitor the status and function of gastric mucosa and are informative for gastric cancer and precancerous lesions of gastric mucosa.

It was found that the training targets, offered courses and training levels differed greatly in China and foreign countries by comparing the domestic and foreign education of health information management and the offered courses for undergraduates in 8 foreign and 11 domestic medical universities , indicating thateducation of healthin -formation management should be internationalized and standardized with the IMIA international recommendations as its guidance , its training target defined, and its course system rationalized .

OBJECTIVETo observe the effects of polydatin on dynamic changes of excitatory amino acids in cerebrospinal fluid and water content of brain tissue of cerebral hemorrhage rats. And to discuss the therapeutic action and mechanisms of polydatin on brain hemorrhagic injured rats.

METHODA quantitative determination method of Asp and Glu was established by microdialysis-HPLC. The cerebral hemorrhage model in rats was induced by local injection of type VII collagenase. The dynamic changes of Asp and Glu in cerebrospinal fluid were observed on 0, 6, 12, 24, 36, 48, 60, 72, 84, 96, 108 h of cerebral hemorrhage rats, and then the water content of brain tissue was detected.

RESULTThe content of Asp and Glu increased rapidly within 24 h after cerebral hemorrhage, and to the highest in 24 h, then decreased gradually. Compared with the cerebral hemorrhage model group, the content of Asp and Glu increased slowly in polydatin group, and there were significant differences in 12-72 h and 6-84 h (P < 0.01, P < 0.05), but there was no significant difference after 84 h and 96 h. Compared with sham group, water content of brain tissue significantly higher in model group, while significantly lower (P < 0.01) in polydatin group.

CONCLUSIONPolydatin can inhibit increasing content of Asp and Glu in cerebrospinal fluid dynamics, and significantly inhibit cerebral edema of cerebral hemorrhage rats. It shows that the mechanisms of anti-cerebral hemorrhage injury of polydatin may be related to increasing of excitatory amino acids after cerebral hemorrhage.

Animals ; Aspartic Acid ; cerebrospinal fluid ; Cerebral Hemorrhage ; cerebrospinal fluid ; drug therapy ; Disease Models, Animal ; Drugs, Chinese Herbal ; therapeutic use ; Excitatory Amino Acids ; cerebrospinal fluid ; Glucosides ; therapeutic use ; Glutamic Acid ; cerebrospinal fluid ; Humans ; Male ; Rats ; Rats, Sprague-Dawley ; Stilbenes ; therapeutic use

Objective:To investigate the correlation of peripheral blood relative mitochondrial DNA copy number(mtDNAcn)with intrinsic capacity and body composition, and to identify potential biomarkers for healthy aging.Methods:Clinical data of 416 patients admitted to our hospital from September 2019 to June 2021 were consecutively collected.MtDNA was extracted from peripheral blood of these subjects, and mtDNAcn was determined by a real-time fluoresence quantitative reverse transcription-polymerase chain reaction(qRT-PCR). Intrinsic capacity assessment included 5 aspects that were exercise[Morse Fall Scale(MFS), Physiological Frailty Phenotype(PFP), Sarcopenia Questionnaire(SARC-CALF), Short Physical Performance Battery(SPPB), Time Up and Go Test(TUG)]; vitality[Mini Nutritional Assessment(MNA), Multidimensional Prognostic Index(MPI)]; cognition[Mini-Mental State Examination(MMSE)scale]; psychology[Geriatric Depression Scale(GDS), Self-rating Anxiety Scale(SAS)]; sensory capacities[Cumulative Illness Rating Scale-the Comorbidity Index(CIRS-CI)]. To assess body composition, dual-energy X-ray absorptiometry was used to measure body fat, including trunk fat, total body fat, fat in the abdominal region, fat in the buttock region, and then to calculate fat index(FMI)and limb skeletal muscle mass index(ASMI).Results:Spearman correlation analysis showed that mtDNAcn had a negatively correlation with age( r=-0.176, P<0.05). After adjustment for gender and body mass index, partial correlation analysis showed mtDNAcn were still negatively correlated with age( r=-0.144, P<0.05). Furthermore, mtDNAcn was significantly correlated with 4 m gait speed, the scores of SARC-CalF, MFS, MNA, MMSE, MPI and its sub-scale's Activities of Daily Living(ADL)and Short Portable Mental Status Questionnaire(SPMSQ)( r=0.171, -0.207, -0.163, 0.221, 0.184, -0.210, 0.241, -0.269, all P<0.05). After adjustment for age, gender and body mass index, partial correlation analysis showed mtDNAcn still had a significant correlation with gait speed, the scores of MFS, MNA, MPI and SPMSQ( r=0.170, -0.170, 0.148, -0.242, -0.188, all P<0.05). In addition, the Spearman correlation analysis showed that mtDNAcn was positively correlated with FMI, trunk fat, total body fat, abdominal fat and fat in the buttock region( r=0.168, 0.143, 0.175, 0.116, 0.199, all P<0.05). However, after adjustment for age and gender, mtDNAcn was only correlated with FMI, total body fat, fat in the buttock region( r=0.126, 0.131, 0.127, all P<0.05). On the other hand, multiple linear regression analysis showed that mtDNAcn was significantly correlated with age, gait speed, FMI, total body fat, fat in the buttock region, the scores of MFS, PFP, MNA and MPI( β=-0.191, 0.156, 0.126, 0.131, 0.125, -0.119, -0.145, 0.151, -0.171, all P<0.05). Conclusions:MtDNAcn is correlated with physical function, frailty, nutrition, falling, cognition and body composition, and may be considered as a biomarker for the evaluation of the locomotion and vitality of human intrinsic capacity.

Objective:To investigate the value of number of negative lymph nodes (NLNs) in predicting the prognosis of patients with esophageal cancer after neoadjuvant therapy and the construction of nomogram prodiction model.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 924 patients with esophageal cancer after neoadjuvant therapy uploaded to the Surveillance, Epidemiology, and End Results Database of the National Cancer Institute from 2004 to 2015 were collected. There were 1 624 males and 300 females, aged 63 (range, 23?85)years. All 1 924 patients were randomly divided into the training dataset of 1 348 cases and the validation dataset of 576 cases with a ratio of 7:3 based on random number method in the R software (3.6.2 version). The training dataset was used to constructed the nomogram predic-tion model, and the validation dataset was used to validate the performance of the nomogrram prediction model. The optimal cutoff values of number of NLNs and number of examined lymph nodes (ELNs) were 8, 14 and 10, 14, respectively, determined by the X-tile software (3.6.1 version), and then data of NLNs and ELNs were converted into classification variables. Observation indicators: (1) clinicopathological characteristics of patients in the training dataset and the validation dataset; (2) survival of patients in the training dataset and the validation dataset; (3) prognostic factors analysis of patients in the training dataset; (4) survival of patients in subgroup of the training dataset; (5) prognostic factors analysis in subgroup of the training dataset; (6) construction of nomogram prediction model and calibration curve. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. The Kaplan-Meier method was used to draw survival curve and Log-Rank test was used for survival analysis. The COX proportional hazard model was used for univariate and multivariate analyses. Based on the results of multivariate analysis, the nomogram prediction model was constructed. The prediction efficacy of nomogram prediction model was evaluated using the area under curve (AUC) of the receiver operating characteristic curve and the Harrell′s c index. Errors of the nomogram prediction model in predicting survival of patients for the training dataset and the validation dataset were evaluated using the calibration curve. Results:(1) Clinicopathological characteristics of patients in the training dataset and the validation dataset. There was no significant difference in clinicopatholo-gical characteristics between the 1 348 patients of the training dataset and the 576 patients of the validation dataset ( P>0.05). (2) Survival of patients in the training dataset and the validation dataset. All 1 924 patients were followed up for 50(range, 3?140)months, with 3-year and 5-year cumulative survival rate as 59.4% and 49.5%, respectively. The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the training dataset was 46.7%, 62.0% and 66.0%, respectively, and the 5-year cumulative survival rate was 38.1%, 52.1% and 59.7%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=33.70, P<0.05). The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the validation dataset was 51.1%, 54.9% and 71.2%, respectively, and the 5-year cumulative survival rate was 39.3%, 42.5% and 55.7%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=14.49, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the training dataset was 53.9%, 60.0% and 62.7%, respectively, and the 5-year cumulative survival rate was 44.7%, 49.1% and 56.9%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=9.88, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the validation dataset was 56.2%, 47.9% and 69.3%, respectively, and the 5-year cumula-tive survival rate was 44.9%, 38.4% and 51.9%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=9.30, P<0.05). (3) Prognostic factors analysis of patients in the training dataset. Results of multivariate analysis showed that gender, neoadjuvant pathological (yp) T staging, ypN staging (stage N1, stage N2, stage N3) and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=0.65, 1.44, 1.96, 2.41, 4.12, 0.69, 0.56, 95% confidence interval as 0.49?0.87, 1.17?1.78, 1.59?2.42, 1.84?3.14, 2.89?5.88, 0.56?0.86, 0.45?0.70, P<0.05). (4) Survival of patients in subgroup of the training dataset. Of the patients with NLNs in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 61.1%, 71.6% and 76.8%, respectively, and the 5-year cumulative survival rate was 50.7%, 59.9% and 70.1%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=12.66, P<0.05). Of the patients with positive lymph nodes in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 26.1%, 42.9% and 44.7%, respectively, and the 5-year cumulative survival rate was 20.0%, 36.5% and 39.3%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=20.39, P<0.05). (5) Prognostic factors analysis in subgroup of the training dataset. Results of multivariate analysis in patients with NLNs in the training dataset showed that gender, ypT staging and number of NLNs (>14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadju-vant therapy ( hazard ratio=0.67, 1.44, 0.56, 95% confidence interval as 0.47?0.96, 1.09?1.90, 0.41?0.77, P<0.05). Results of multi-variate analysis in patients with positive lymph nodes in the training dataset showed that race as others, histological grade as G2, ypN staging as stage N3 and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=2.73, 0.70, 2.08, 0.63, 0.59, 95% confidence interval as 1.43?5.21, 0.54?0.91, 1.44?3.02, 0.46?0.87, 0.44?0.78, P<0.05). (6) Construction of nomogram prediction model and calibration curve. Based on the multivariate analysis of prognosis in patients of the training dataset ,the nomogram prediction model for the prognosis of patients with esophageal cancer after neoadju-vant treatment was constructed based on the indicators of gender, ypT staging, ypN staging and number of NLNs. The AUC of nomogram prediction model in predicting the 3-, 5-year cumulative survival rate of patients in the training dataset and the validation dataset was 0.70, 0. 70 and 0.71, 0.71, respectively. The Harrell′s c index of nomogram prediction model of patients in the training dataset and the validation dataset was 0.66 and 0.63, respectively. Results of calibration curve showed that the predicted value of the nomogram prediction model of patients in the training dataset and the validation dataset was in good agreement with the actual observed value. Conclusion:The number of NLNs is an independent influencing factor for the prognosis of esophageal cancer patients after neoadjuvant therapy, and the nomogram prediction model based on number of NLNs can predict the prognosis of esophageal cancer patients after neoadjuvant therapy.