Characteristics of the studies included in the meta-analyses.

<p>ALT, Alanine Aminotransferase; BMI, body mass index; CRP, C-reactive protein; DBP, diastolic blood pressure; FSI, fasting serum insulin; FPG, fasting plasma glucose; r-GT, r-Glutamyl Transpeptidase; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model of assessment for insulin resistence index; 2HPG, 2H postprandial plasma glucose; LDL-C, low-density lipoprotein cholesterol; P, Pearson correlation coefficient; S, Spearman correlation coefficient; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; TBF%, total body fat (%); WC, waist circumference; WHR, waist-hip ratio.</p><p>Characteristics of the studies included in the meta-analyses.</p

Association between Serum Chemerin Concentrations and Clinical Indices in Obesity or Metabolic Syndrome: A Meta-Analysis

<div><p>Objective</p><p>Chemerin is a novel adipokine. Previous research has investigated the association between chemerin and clinical indices in patients with obesity or metabolic syndrome (MS), although the results obtained have been inconsistent. We conducted a meta-analysis to investigate the association between chemerin and clinical indicators of diabetes, MS and obesity with obesity or MS subjects.</p><p>Design and Methods</p><p>Studies were identified by searching the PubMed, the Cochrane Library, EMBASE and CNKI, databases beginning with the original report in July 2007 until the end of May 2013. For each variable, summary correlation coefficients were estimated using random-effects or fixed-effect meta-analysis with 95% confidence interval (CI) performed by STATA software.</p><p>Results</p><p>A total of eight studies with 20 clinical variables (total n = 1787) met the inclusion criteria. The meta-analyse of diabetes markers showed that FSI (<i>r_s</i> = 0.26; 95% CI = 0.21–0.31; P = 0.000), 2HPG (<i>r_s</i> = 0.06; 95% CI = 0.01–0.12; P = 0.030) and HOMA-IR (<i>r_s</i> = 0.178; 95% CI = 0.019–0.337; P = 0.028) were positively correlated with chemerin, however, FPG (<i>r_s</i> = 0.03, 95% CI = −0.02 to 0.08, P = 0.240) and HbA1c (<i>r_s</i> = −0.05; 95% CI = −0.24–0.15; P = 0.641) were not significantly correlated with chemerin. The meta-analyses of MS and obesity markers indicated that TG, TC, CRP BMI, TBF%, WC, WHR and Leptin were positively correlated with chemerin, nevertheless, SBP, DBP, LDL-C, HDL-C, ALT and r-GT were not significantly correlated, adiponectin was negatively correlated. Sensitivity analysis was performed and the summary results did not change significantly.</p><p>Conclusions</p><p>The results suggest that chemerin in patients with obesity or MS may be associated with obesity, imbalances in lipid and diabetes metabolism and insulin resistance. Chemerin played an important role in the pathophysiology of obesity and MS.</p></div

Correlations between serum chemerin concentrations and diabetes markers in Obesity or MS subjects.

<p>Summaries are shown of the correlations between serum chemerin concentrations and (a) fasting plasma glucose concentrations, (b) fasting serum insulin concentrations, (c) 2H postprandial plasma glucose, (d) HOMA_IR and (e) Hemoglobin A1c. 95% confidence intervals (CIs) are represented by the horizontal lines, and diamonds represent the overall estimate and 95% CI.</p

Correlations between serum chemerin concentrations and metabolic syndrome markers in Obesity or MS subjects.

<p>Summaries are shown of the correlations between chemerin concentrations and (a) systolic blood pressure, (b) diastolic blood pressure, (c) low-density lipoprotein cholesterol, (d) high-density lipoprotein cholesterol, (e) triglyceride, (f) total cholesterol, (g) Alanine Aminotransferase, (h) r-Glutamyl Transpeptidase and (I) C-reactive protein. 95% confidence intervals (CIs) are represented by the horizontal lines, and diamonds represent the overall estimate and 95% CI.</p

Correlations between serum chemerin concentrations and obesity markers in Obesity or MS subjects.

<p>Summaries are shown of the correlations between chemerin concentrations and (a) body mass index, (b) total body fat (%), (c) waist circumference, (d) waist-hip ratio, (e) Adiponectin and (f) Leptin. 95% confidence intervals (CIs) are represented by the horizontal lines, and diamonds represent the overall estimate and 95% CI.</p

Frequent amplification of <i>PTP1B</i> is associated with poor survival of gastric cancer patients

<p>The protein tyrosine phosphatase 1B (<i>PTP1B</i>), a non-transmembrane protein tyrosine phosphatase, has been implicated in gastric pathogenesis. Several lines of recent evidences have shown that <i>PTP1B</i> is highly amplified in breast and prostate cancers. The aim of this study was to investigate <i>PTP1B</i> amplification in gastric cancer and its association with poor prognosis of gastric cancer patients, and further determine the role of <i>PTP1B</i> in gastric tumorigenesis. Our data demonstrated that <i>PTP1B</i> was significantly up-regulated in gastric cancer tissues as compared with matched normal gastric tissues by using quantitative RT-PCR (qRT-PCR) assay. In addition, copy number analysis showed that <i>PTP1B</i> was amplified in 68/131 (51.9%) gastric cancer cases, whereas no amplification was found in the control subjects. Notably, <i>PTP1B</i> amplification was positively associated with its protein expression, and was significantly related to poor survival of gastric cancer patients. Knocking down <i>PTP1B</i> expression in gastric cancer cells significantly inhibited cell proliferation, colony formation, migration and invasion, and induced cell cycle arrested and apoptosis. Mechanically, <i>PTP1B</i> promotes gastric cancer cell proliferation, survival and invasiveness through modulating Src-related signaling pathways, such as Src/Ras/MAPK and Src/phosphatidylinositol-3-kinase (PI3K)/Akt pathways. Collectively, our data demonstrated frequent overexpression and amplification <i>PTP1B</i> in gastric cancer, and further determined the oncogenic role of <i>PTP1B</i> in gastric carcinogenesis. Importantly, <i>PTP1B</i> amplification predicts poor survival of gastric cancer patients.</p

Epithelial cell-derived periostin functions as a tumor suppressor in gastric cancer through stabilizing p53 and E-cadherin proteins via the Rb/E2F1/p14ARF/Mdm2 signaling pathway

<div><p>Periostin is usually considered as an oncogene in diverse human cancers, including breast, prostate, colon, esophagus, and pancreas cancers, whereas it acts as a tumor suppressor in bladder cancer. In gastric cancer, it has been demonstrated that periglandular periostin expression is decreased whereas stromal periostin expression is significantly increased as compared with normal gastric tissues. Moreover, periostin produced by stromal myofibroblasts markedly promotes gastric cancer cell growth. These observations suggest that periostin derived from different types of cells may play distinct biological roles in gastric tumorigenesis. The aim of this study was to explore the biological functions and related molecular mechanisms of epithelial cell-derived periostin in gastric cancer. Our data showed that periglandular periostin was significantly down-regulated in gastric cancer tissues as compared with matched normal gastric mucosa. In addition, its expression in metastatic lymph nodes was significantly lower than that in their primary cancer tissues. Our data also demonstrated that periglandular periostin expression was negatively associated with tumor stage. More importantly, restoration of periostin expression in gastric cancer cells dramatically suppressed cell growth and invasiveness. Elucidation of the mechanisms involved revealed that periostin restoration enhanced Rb phosphorylation and sequentially activated the transcription of E2F1 target gene <i>p14^ARF</i>, leading to Mdm2 inactivation and the stabilization of p53 and E-cadherin proteins. Strikingly, these effects of periostin were abolished upon Rb deletion. Collectively, we have for the first time demonstrated that epithelial cell-derived periostin exerts tumor-suppressor activities in gastric cancer through stabilizing p53 and E-cadherin proteins via the Rb/E2F1/p14^ARF/Mdm2 signaling pathway.</p></div

Performance of Physical Examination Skills in Medical Students during Diagnostic Medicine Course in a University Hospital of Northwest China

<div><p>This study was conducted to evaluate the performance of physical examination (PE) skills during our diagnostic medicine course and analyze the characteristics of the data collected to provide information for practical guidance to improve the quality of teaching. Seventy-two fourth-year medical students were enrolled in the study. All received an assessment of PE skills after receiving a 17-week formal training course and systematic teaching. Their performance was evaluated and recorded in detail using a checklist, which included 5 aspects of PE skills: examination techniques, communication and care skills, content items, appropriateness of examination sequence, and time taken. Error frequency and type were designated as the assessment parameters in the survey. The results showed that the distribution and the percentage in examination errors between male and female students and among the different body parts examined were significantly different (<i>p</i><0.001). The average error frequency per student in females (0.875) was lower than in males (1.375) although the difference was not statistically significant (<i>p</i> = 0.167). The average error frequency per student in cardiac (1.267) and pulmonary (1.389) examinations was higher than in abdominal (0.867) and head, neck and nervous system examinations (0.917). Female students had a lower average error frequency than males in cardiac examinations (<i>p</i> = 0.041). Additionally, error in examination techniques was the highest type of error among the 5 aspects of PE skills irrespective of participant gender and assessment content (<i>p</i><0.001). These data suggest that PE skills in cardiac and pulmonary examinations and examination techniques may be included in the main focus of improving the teaching of diagnostics in these medical students.</p></div

Error frequency of five error types in the students.

<p>Average error frequency per student: Pearson chi square, <i>p</i> = 0.167; Error frequency of physical examination skills: Wilcoxon rank sum test, <i>p</i><0.001.</p><p>Error frequency of five error types in the students.</p

Error types in the students and error types according to body parts examined.

<p>Data are calculated as error frequency/total error frequency (n). Comparison in gender: Pearson chi square, <i>p</i> = 0.405. Comparison of techniques with communication and care skills, items, appropriateness of physical examination sequence and time taken in pairwise: Pearson chi square, <i>p</i> = 0.001, <0.001, <0.001 and <0.001, respectively. Comparison among assessment content (body parts), Pearson chi square <i>p</i> = 0.367.</p><p>Error types in the students and error types according to body parts examined.</p