Objective
To explore the value of GALAD model, including gender, age, AFP, AFP-L3 and DCP in diagnosis of primary hepatocellular carcinoma and prediction of microvascular invasion (MVI).
Methods
Using retrospective study method, 5 919 patients with primary hepatocellular carcinoma (HCC) who received radical operation from January 2015 to December 2018 in Eastern Hepatobiliary Surgery Hospital were enrolled into study group. At the same time, 1 745 patients with benign liver diseases (BLDs) were enrolled into control group. The concentration of DCP was detected by Lumipulse G1200 automatic immune analyzer, and the concentration of AFP was detected by Cobas e601 automatic immune analyzer. AFP-L3 was detected by affinity adsorption centrifugation. The non-parametric Mann Whitney test was used to compare the difference between two groups. The chi square test was used to compare the rates. The diagnostic value of single serological marker and GALAD model for primary hepatocellular carcinoma was analyzed. The predictive effect of GALAD model on MVI of primary hepatocellular carcinoma was evaluated.
Results
Compared with single serum marker, the diagnostic value of GALAD model is higher. When the cutoff value is -0.33, the diagnostic sensitivity, specificity and accuracy reach to 91.9% (5 440/5 919), 86.8% (1 515/1 745) and 90.7% (6 955/7 664), respectively. The area under the curve can reach 0.960 [95%CI (0.955-0.964)]. Compared with no MVI (MO) group, the value of GALAD model in MVI low-risk group (M1), MVI high-risk group (M2) and MVI (M1+2) were significantly higher (Z values were-12.517, -22.883, -21.655, P<0.05), Galad model predicts MVI (M2) in high risk group,AUC was 0.717 [95%CI (0.701-0.733)] (M0 ratio M2).
Conclusion
GALAD model has better diagnostic performance in primary hepatocellular carcinoma and has certain predictive value for microvascular invasion.
Key words:
Carcinoma, hepatocellular; Liver neoplasms; Neoplasm invasiveness; Microvessels; alpha-Fetoproteins; Prothrombin
Patients with chronic kidney disease often experience protein-energy wasting.The clinical manifestations performs include the abnormality of nutrient intake,biochemical index,body mass index,and muscle mass.Protein-energy wasting always results from decreased dietary intake,inflammation,metabolic acidosis,hormone level disorder,dialysis related factors,social psychological factors and so on,and seriously affects the prognosis and survival rates of patients with chronic kidney disease.Therefore,early detection,diagnosis and prevention and cure are very important.Nutritional evaluation should be included in daily diagnosis and treatment in patients with chronic kidney diseases.Contrasting diagnostic criteria,prevention and control of protein-energy wasting with early diagnosis and taking various measures will improve the prognosis and survival rate of patients with chronic kidney disease ultimately.Thus,the aim is to improve the patients' understanding of epidemiological characteristics,pathogenesis,assessment,treatment and intervention measures of protein-energy wasting in patients with chronic kidney disease.
Key words:
Protein-energy wasting; Chronic kidney disease
Background and Purpose— Blood pressure (BP) control in the early phase of stroke is controversial to reduce the risk of poststroke cognitive impairment (PSCI). This study was to investigate the impact of BP levels in the early phase of ischemic stroke and stroke subtype on PSCI. Methods— Seven hundred and ninety-six patients with acute ischemic stroke were included. Cognitive function was assessed after stroke onset using the Montreal Cognitive Assessment. Patients were divided into quintiles according to systolic BP and diastolic BP levels in the early phase. Subtype analyses were according to Trial of ORG 10172 in Acute Stroke Treatment classification (infarct cause) and Oxfordshire Community Stroke Project classification (infarct location). Results— After adjusting for multiple variables, the quintiles with the lowest systolic BP (Q1, 102–127 mm Hg) and with the highest systolic BP (Q5, 171–215 mm Hg) were associated with increased PSCI risk (odds ratio, 1.83; 95% confidence interval, 1.64–2.28; P =0.007 in Q1; odds ratio, 2.32; 95% confidence interval, 1.74–2.90; P <0.001 in Q5) at 3 months as compared with the middle quintile (Q3, 143–158 mm Hg). Similar association was found in diastolic BP quintiles. The analysis of cerebral infarction subtype demonstrated that both large artery atherosclerosis and total anterior circulation infarct were associated with increased risk of PSCI at 3 months after adjusting for multiple variables (large artery atherosclerosis: odds ratio, 1.42; 95% confidence interval, 1.06–1.90; P =0.031; total anterior circulation infarct: odds ratio, 1.68; 95% confidence interval, 1.32–2.15; P =0.001). Conclusions— Lower or higher BP in the early phase of ischemic stroke was correlated with increased PSCI risk at 3 months. Maintaining systolic/diastolic BP in the levels of 143 to 158/93 to 102 mm Hg might be beneficial to reduce the occurrence of PSCI. Moreover, large artery atherosclerosis subtype and total anterior circulation infarct subtype were correlated with increased PSCI risk at 3 months. Clinical Trial Registration— URL: https://www.chictr.org . Unique identifier: ChiCTR-TRC-14004804.
Aim: To investigate the association between plasma AAT level and glaucoma. Methods: 163 glaucoma patients and 111 healthy controls were recruited. The plasma AAT levels were measured by ELISA. Results: Plasma AAT level was significantly higher in glaucoma patients than those in healthy controls (p < 0.001). Patients with higher plasma AAT level exhibited severer disease stage (early vs. severe: p < 0.05; H-P-A; early vs. severe: p < 0.05; early vs. end-stage: p < 0.01; AGIS). ROC curves yielded that AAT can distinguish patients with early glaucoma from those with advanced glaucoma (early vs. severe: AUC: 0.616; H-P-A; early vs. severe: AUC: 0.763; early vs. end-stage: AUC: 0.660; AGIS). Conclusion: Plasma AAT is a useful biomarker for the identification of glaucoma severity.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)