Diagnostic value of combined detection of three gastric functions and Helicobacter pylori typing in chronic gastritis and gastric cancer

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS
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引用次数: 0

Abstract

This research attempted to clarify the clinical diagnostic value of combined detection of gastric function and Helicobacter pylori (Hp) serotyping in chronic gastritis and gastric cancer (GC). The 80 chronic non atrophic gastritis (CNAG) patients treated in our hospital from October 2021 to October 2022 received selection as the CNAG group. The 96 chronic atrophic gastritis (CAG) patients diagnosed by gastroscopy and pathology in the same period received selection as CAG group. During the same period, 50 patients diagnosed with GC received inclusion in GC group. Pepsin I (PG I), PG II (PG II), gastrin-17 (G-17) and Hp serotyping received detection and comparison in three groups. The diagnostic efficacy of PG Ⅰ, PG Ⅱ, G-17, the ratio of serum PG I to PG II (PGR), and Hp serotyping in chronic gastritis and GC received evaluation by receiver operating characteristic (ROC). Relative to in the CNAG group, PG I and PGR levels in the other two groups exhibited depletion (P < 0.05); no statistical significance was observed in the PG II level among the three groups (P > 0.05); relative to the CNAG group, the G-17 level in the other two groups exhibited elevation (P < 0.05). Total Hp positive rate was 61.06 %, among which GC group exhibited the highest positive rate (72.00 %), and type I Hp positive rate also exhibited the highest in GC group (60.00 %). The type II Hp positive rate exhibited the highest in CNAG group (15.00 %). The PG I and PGR levels in type I Hp positive patients exhibited depletion relative to those in type II Hp positive patients, whereas PG II and G-17 levels exhibited elevation. When testing each indicator alone, the area under the curve (AUC) of PG I exhibited the highest in CNAG group, which was 0.874. When testing each indicator alone, AUC of Hp typing exhibited the highest in CAG group, which was 0.515. When testing each indicator alone, AUC of G-17 exhibited the highest in GC group, which was 0.787. The performance of combined detection was better than that of individual detection, with AUCs greater than 0.9 in three groups. In conclusion, changes in PG I, PG II, PGR and G-17 levels and Hp serotyping can receive application as screening indicators for chronic gastritis and GC, which can reflect relevant status of gastric mucosa to varying degrees. Combined detection of indicators has higher diagnostic performance and can receive application as an auxiliary diagnostic indicator in addition to gastroscopy biopsy, providing a reference basis for the formulation of clinical diagnosis and treatment plans.

联合检测三种胃功能和幽门螺旋杆菌分型对慢性胃炎和胃癌的诊断价值。
本研究试图阐明联合检测胃功能和幽门螺杆菌(Hp)血清型对慢性胃炎和胃癌(GC)的临床诊断价值。选取2021年10月至2022年10月在我院接受治疗的80例慢性非萎缩性胃炎(CNAG)患者作为CNAG组。同期经胃镜检查和病理确诊的 96 例慢性萎缩性胃炎(CAG)患者入选 CAG 组。同期,50 名确诊为 GC 的患者入选 GC 组。胃蛋白酶 I(PG I)、胃蛋白酶 II(PG II)、胃泌素-17(G-17)和 Hp 血清分型在三组中进行检测和比较。PGⅠ、PGⅡ、G-17、血清PGⅠ与PGⅡ的比值(PGR)和Hp血清分型对慢性胃炎和GC的诊断效果由接收者操作特征(ROC)进行评估。与 CNAG 组相比,其他两组的 PG I 和 PGR 水平均下降(P < 0.05);三组的 PG II 水平差异无统计学意义(P > 0.05);与 CNAG 组相比,其他两组的 G-17 水平均升高(P < 0.05)。Hp总阳性率为61.06%,其中GC组阳性率最高(72.00%),I型Hp阳性率也以GC组最高(60.00%)。II 型 Hp 阳性率在 CNAG 组最高(15.00%)。与 II 型 Hp 阳性患者相比,I 型 Hp 阳性患者的 PG I 和 PGR 水平降低,而 PG II 和 G-17 水平升高。在单独检测每个指标时,CNAG 组 PG I 的曲线下面积(AUC)最高,为 0.874。单独检测各项指标时,CAG 组的 Hp 分型曲线下面积(AUC)最高,为 0.515。单独检测每个指标时,GC 组 G-17 的 AUC 最高,为 0.787。联合检测的效果优于单独检测,三组的 AUC 均大于 0.9。总之,PG I、PG II、PGR 和 G-17 水平的变化以及 Hp 血清分型可作为慢性胃炎和 GC 的筛查指标,在不同程度上反映胃黏膜的相关状况。指标联合检测具有更高的诊断性能,可作为胃镜活检以外的辅助诊断指标,为临床诊断和治疗方案的制定提供参考依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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