Proteins presence and differences of the expression level can clarify the physiological or pathological changes in organisms;so the quantitative detection of proteins is vital for disease mechanism research;diagnosis and prognosis evaluation.Traditional protein quantitation methods at the tissue level reflected the average protein expression in cells;but ignore the differences between individual cells.In contrast;approaches for quantitative detection at single-cell level can better reflect the differences.Recently;a number of approaches for such detec-tion have been proposed;including microfluidics;microwell-based technology;optical fiber nanobiosensor;activity-based probe technology and mass spectrometry.The principles;advantages and drawbacks of these approaches are briefly introduced in this review.

Objective There is no enzyme multiplied immunoassay technique ( EMIT) reagent for therapeutic drug monito-ring ( TDM) in our country.The aim of this study was to compare the properties of self-developed kits and commercially imported kits in TDM, and to evaluate their feasibilities for routine TDM. Methods The sensitivities, accuracies and precisions of self-developed kits, Viva-E kit and AXSYM kits were evaluated by determining the quality control samples of carbamazepine, valproic acid and phe-nobarbital.Self-developed kits, Viva-E kit and AXSYM kits were employed to determine the concentrations of clinical serum samples of 83 cases of carbamazepine, 80 cases of valproic acid and 72 cases of phenobarbital separately, and the results were compared and analyzed. Results Recoveries of carbamazepine, valproic acid and phenobarbital were more than 90% for the three different kits. The recoveries of self-developed kits for the three drugs were in the range of 98.7%-103.9% and the limits of quantification of the self-developed kits for carbamazepine, valproic acid and phenobarbital were 2, 10, 5μg/mL, respectively.Precision was lower than 10%and its average relative deviation was less than 3%after single point correction.There were good correlations (R2>0.985) and no significant statistical difference between self-developed kits and AXSYM kits (P>0.05).Upper and lower 95%limits of agreement in Bland-Altman plots were (-1.32,1.26), (-15.24,15.17) and ( -3.69,3.00) for carbamazepine, valproic acid and phenobar-bital, respectively.About 5%of the points failed in the 95%confidence interval. Conclusion The self-developed kits showed good performance and are suitable for clinical use in TDM.

A method for the real-time polymerase chain reaction ( PCR ) coupled with high specific invader assay to detect single nucleotide polymorphism ( SNP) was established. To reduce the background signal, the amount of flap endonuclease 1 ( FEN1 enzyme ) and wild-type detection probe was optimized. Under the optimum conditions including 0. 05 μmo/L invasive oligonucleotide probe, 0. 125 μmol/L wild-type detection probe, 0. 5 μmol/L mutation detection probe, 0. 25 μmol/L each fluorescence resonance energy transfer (FRET) probe and 1. 5 U FEN1, the background signal of wild-type sample and mutation sample was dramatically decreased and the background interference to the detecting results was thus eliminated. A total of 21 cases of aldehyde dehydrogenase-2*2 ( ALDH2*2 ) , 19 cases of cytochrome p450 2 C19*2 ( CYP2 C19*2 ) and 19 cases of CYP2C19*3 were analyzed with the established method, and the genotypes of ALDH2*2 were 10 cases of GG homozygote, 8 cases of GA heterozygote and 3 cases of AA homozygote; the genotypes of CYP2C19*2 were 9 cases of GG homozygote, 8 cases of GA heterozygote and 2 cases of AA homozygote;and the genotypes of CYP2C19*3 were 18 cases of GG homozygote and 1 case of GA heterozygote. These results were consistent with those by pyrosequencing. The established method was specific, simple, short time-consuming and low cost, and could be used for the detection of SNP genotyping with non-polluting in single closed tube.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.