Currently, liquid biopsy has been widely investigated for improving the diagnosis and monitoring of colon cancer. As a potential sample type, platelets are abundant in the circulation and provide multiple types of information closely related to tumors. In this study, we investigated the associations between patients’ platelet TGF-β/Smad2 mRNA levels and colon cancer pathologic type and explored their potential value as biomarkers for diagnosis and grading. We performed a bioinformatics study on the GSE68086 dataset to identify differentially expressed platelet mRNAs, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. For the 104 participants, including colon cancer patients and healthy individuals, the expression of the TGF-β and Smad2 mRNAs in platelets and tissues was quantified. We analyzed the correlations of platelet mRNAs with local tumors and pathological characteristics. We also established cutoff values for platelet TGF-β and Smad2 mRNA levels for colon cancer diagnosis. KEGG analysis revealed high enrichment of platelet-derived TGF-β and SMAD2 in colon cancer. TGF-β and Smad2 mRNAs were upregulated in patient platelets and tumors (P < 0.05). Platelet Smad2 was expressed at higher levels in high-grade tumors than in low-grade tumors (P < 0.05). The diagnostic performance of platelet TGF-β and Smad2 mRNA levels had a sensitivity and area under the curve (AUC) of 86.36

Artificial intelligence (AI)-based digital morphology analyzers are vital for clinical hematology but face critical challenges in identifying immature granulocytes and atypical cells. This study validated the MC-100i AI system against 15 morphologists, with an additional 3 senior experts establishing a gold standard, to assess performance gaps and clinical utility. A total of 104 blood smears containing 19,174 cells (9 abnormal types, including 3154 leukocytes and nucleated erythrocytes) were analyzed. The AI achieved an overall accuracy of 95.97% (ranked second) and 91.38% accuracy for the abnormal subsets (ranked fifth). Critical biases were identified: AI classified ambiguous cells (e.g., promyelocytes) as earlier developmental stages, while humans favored later stages. AI relied on isolated features for atypical cells, unlike experts who integrated smear context. These findings confirm the AI’s robust clinical potential for routine leukocyte classification, and targeted optimizations will further enhance AI-driven peripheral blood cell identification for effective integration into clinical diagnostic workflows.

Point-of-care testing (POCT) of coagulation is rapid, accurate, and portable. However, because it is difficult to standardize the reference intervals due to differences in metrological traceability, normalizing the results across different testing systems is challenging. The International Organization for Standardization 22870:2016 requires POCT to be compared with large automatic analyzers for medical safety. Thus, the aim of this study was to compare the correlations, consistency, and conformity of a mechanical-based coagulation POCT system with a fully automated coagulation analyzer, and to explore the common interfering factors for each test. From September 2023 to December 2023, 551 plasma samples were collected consecutively for coagulation testing by the Laboratory Medicine Department of the First Hospital of Jilin University. Prothrombin time/international normalized ratio, thrombin time (TT), activated partial thromboplastin time (APTT), and fibrinogen concentration were determined by a Sysmex CS-5100 coagulation analyzer to serve as a control group. The coagulation results produced by POCT served as the experimental group. Passing-Bablok, Bland–Altman, and Fourfold tables were used for correlation, consistency, and conformity, respectively. Logistic regression was used for analysis of interference. The correlation coefficients r of PT, INR, TT, APTT, and fibrinogen between the two testing systems were 0.866, 0.863, 0.694, 0.904, and 0.997, respectively ( p  < 0.001). The overall deviations were 1.5, 0.08, 0.2, 0.8, and 0.0, respectively ( p  < 0.001). The conformity were 91.95% (137/149), 93.96% (140/149), 88.44% (130/147), 89.03% (138/155), and 100.00% (100/100), respectively. The kappa values were 0.827, 0.805, 0.570, 0.768, and 1.000, respectively ( p  < 0.001). More than half of the POCT measurements were higher than the analyzer measurements. Logistic regression analysis showed that jaundice (OR = 2.903, p  < 0.05) and white blood cell (WBC) count (OR = 1.012, p  < 0.05) were statistically significant risk factors for outliers between the two measurement systems being compared. We found that a mechanical-based coagulation POCT testing system showed good correlation, consistency, and conformity with automatic analyzers. The factors of hematocrit, platelet count, hemoglobin, WBC count, and jaundice may interfere with the findings of POCT, causing higher results than those produced by large coagulation analyzers.

Background Screening of malignant hematological diseases is of great importance for their diagnosis and subsequent treatment. This study constructed an optimal screening model for malignant hematological diseases based on routine blood cell parameters. Methods The venous blood samples of 1751 patients collected from 10 tertiary hospitals in China were divided into a training set (1223 cases) and a validation set (528 cases). In addition to the clinical diagnostic information of the samples in the training set, 26 blood cell parameters including morphological parameters were selected using manual screening and filtering to construct eight machine learning models. These models were used to identify hematological malignancies among the validation set. Results Comparison of the discrimination, calibration and clinical detection performance of the eight machine learning models revealed that the artificial neural network (ANN) model performed the optimal in identifying malignant haematological diseases in the validation set (528 cases), with an area under the receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity of 0.906, 0.857, 0.832 and 0.884, respectively. Conclusion The ANN model constructed can be used for screening of malignant hematological diseases, especially in primary hospitals that lack comprehensive diagnosis, and this ANN model will help patients to get diagnosis and treatment of malignant hematological diseases as early as possible.

BACKGROUND:This study investigated the performance of the MC-100i, a pre-commercial digital morphology analyzer utilizing a convolutional neural network algorithm, in a multicentric setting involving up to 11 tertiary hospitals in China.METHODS:Blood smears were analyzed by MC-100i, verified by morphologists, and manually differentiated. The classification performance on WBCs and RBCs was evaluated by comparing the classification results using different methods. The PLT and PLT clump counting performance was also assessed. The total assay time including hands-on time was evaluated.RESULTS:The agreements between pre- and post-classification were high for normal WBCs (κ > 0.96) and lower for overall abnormal WBCs (κ = 0.90). The post-classification results correlated well with manual differentials for both normal and abnormal WBCs (r > 0.93), except for basophils (r = 0.8480) and atypical lymphocytes (r = 0.8211). The clinical sensitivity and specificity of each RBC abnormality after verification were above 90 % using microscopy reviews as the reference. The PLTs counted by the MC-100i before and after verification correlated well with those measured by the PLT-O mode (r = 0.98). Moreover, PLT clumps were successfully classified by the analyzer in EDTA-dependent pseudothrombocytopenia blood samples.CONCLUSIONS:The MC-100i is an accurate and reliable digital cell morphology analyzer, offering another intelligent option for hematology laboratories.

Hepatitis B virus (HBV) infection has gradually been considered to associate with cancer development and progression. This study aimed to explore the associations of serological indicators of HBV infection with mortality risk among cancer survivors and further validated using a gastric cancer (GC) cohort from China, where HBV infection is endemic. National Center for Health Statistics' National Health and Nutrition Examination Survey (NHANES) data were used in this study. Individuals with positive results of hepatitis B core antigen (anti-HBc) were considered to have current or past HBV infection. Serological indicators were positive only for hepatitis B surface antibodies (anti-HBs), indicating vaccine-induced immunity, whereas negativity for all serologic indicators was considered to indicate the absence of HBV infection and immunity to HBV. The GC cohort included patients from the First Hospital of Jilin University, China. The median follow-up time of the NHANES was 10 years; during the follow-up, 1505 deaths occurred. The results revealed that anti-HBs-positive cancer survivors had a 39% reduced risk of mortality (hazard ratio [HR] 0.61, 95% confidence interval [CI] 0.44-0.85). Men and individuals aged <65 years old with past exposure to HBV had higher mortality risk (HR 1.52, 95% CI 1.09-2.13; HR 2.07, 95% CI 1.13-3.83). In this GC cohort, individuals who were only anti-HBs-positive showed a reduced risk of mortality (HR 0.77, 95% CI 0.62-0.95). Thus, anti-HBs positivity was a significant factor of decreased mortality among cancer survivors. More rigorous surveillance is necessary for cancer survivors with anti-HBc positivity, particularly men, and younger individuals.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

目的 分析常见内源性干扰物对凝血试验结果的影响,为临床判断凝血结果异常提供准确的实验室依据.方法 收集表观健康成人血浆50份制成正常混合血浆(NPP),使用干扰物检测试剂盒中的胆红素F、胆红素C、血红蛋白、乳糜模拟内源性干扰物,将干扰物与NPP以不同比例混合配制成含不同浓度干扰物的血浆标本,以加入空白干粉液的NPP作为对照,在Sysmex CS-5100全自动凝血分析仪上检测血浆活化部分凝血活酶时间(APTT)、凝血酶原时间(PT)、凝血酶时间(TT)、纤维蛋白原(Fib)、D-二聚体(DD).结果 当胆红素F血浆浓度在0～207.50 μmol/L时APTT结果不受影响,在0～518.76 μmol/L时PT、TT、FIB、DD检测结果不受影响;胆红素C血浆浓度在0～271.60 μmol/L时Fib检测结果不受影响,在0～543.20 μmol/L时APTT、PT、TT、DD检测结果不受影响;血红蛋白血浆浓度在0～98.88 mg/dL时DD检测结果不受影响,在0～247.20 mg/dL时APTT、PT、TT、Fib检测结果不受影响;乳糜血浆浓度在0～3 382.56 Ftu时APTT检测结果不受影响,在0～3 006.72 Ftu时PT检测结果不受影响,在0～1 127.52 Ftu时TT检测结果不受影响,在0～3 758.40 Ftu时Fib、DD检测结果不受影响.结论 血浆内源性干扰物对常用凝血指标的检测结果存在不同程度的影响,在实际检验工作中,必须明确常见的干扰情况,以保证凝血检测结果的准确性.

目的 探讨新型冠状病毒感染(COVID-19)患者凝血功能的变化情况,为病情转归预测提供依据.方法 收集2021年1月15日至3月15日收治于通化市中心医院的322例COVID-19确诊患者,通过电子病历回顾性分析临床资料及实验室指标检测结果,根据病情分为普通型组(292例)、重型组(30例),以疫情初期该院收治的其他病原体感染患者(351例)作为对照组,分析各组患者凝血相关参数变化,包括血小板计数(PLT)、活化部分凝血活酶时间(APTT)、D-二聚体(DD)、纤维蛋白(原)降解产物(FDP)、凝血酶原时间(PT)、纤维蛋白原(Fib)、凝血酶时间(TT)、抗凝血酶(AT),采用单因素和多因素Logistic回归分析各项参数在重型组的危险因素,绘制ROC曲线,连续监测重症患者住院期间凝血指标变化规律.结果 与普通型组和对照组相比,重型组DD、FDP显著升高,APTT、PT、TT轻度延长(P＜0.05);与对照组比较,重型组Fib、PLT升高,但低于普通型组(P＜0.05);重型组AT明显降低(P＜0.05).单因素和多因素Logistic回归分析显示,DD、APTT、PT、TT是预测COVID-19病情严重程度的危险因素,出现重型的风险比(OR)及其95％CI分别为1.238(1.091～1.546)、1.655(1.449～1.955)、11.133(1.558～79.527)、3.846(1.068～13.852).绘制重型组 COVID-19 患者 DD、APTT、PT、TT 的 ROC 曲线,DD 的AUCROC 最大,AUCROC及其95％CI为0.814(0.732～0.896).结论 凝血指标DD、PT、TT是COVID-19患者病情进展的提示指标,可作为评估病情严重程度的预测依据.动态监测PT和DD水平对COVID-19疗效监测、预后判断具有重要作用.

BackgroundRecent studies have explored the prognostic value of the geriatric nutritional risk index (GNRI) in patients with gastric cancer (GC), but the results are controversial. We aimed to systemically identify the association between the GNRI and prognosis in GC using a meta-analysis. MethodsThe databases of PubMed, Web of Science, Cochrane Library, and Embase were searched until September 25, 2022. Pooled hazard ratios and the corresponding 95% confidence intervals (CIs) were used to estimate the prognostic value of the GNRI in GC. Odds ratios (ORs) and 95% CIs were used to assess the correlation between the GNRI and clinicopathological characteristics of GC. ResultsTen studies including 5,834 patients with GC were included in this meta-analysis. The merged results indicated that a low pretreatment GNRI was associated with inferior overall survival (hazard ratio = 1.21, 95% CI = 1.12-1.30, P < 0.001) and worse cancer-specific survival (hazard ratio = 2.21, 95% CI = 1.75-2.80, P < 0.001) for GC. Moreover, a low GNRI was significantly associated with an advanced pathological stage (OR = 2.27, 95% CI = 1.33-3.85, P = 0.003), presence of adjuvant chemotherapy (OR = 1.25, 95% CI = 1.01-1.55, P = 0.040), and tumor location in the lower stomach (OR = 1.33, 95% CI = 1.06-1.65, P = 0.012) in GC. However, there was no significant association between GNRI and sex, tumor differentiation, or lymph node metastasis in patients with GC. ConclusionOur meta-analysis identified that the pretreatment GNRI level was a significant prognostic factor for patients with GC. A low GNRI is associated with worse overall survival and inferior cancer-specific survival in patients with GC.

实验诊断学教学创新以培养医学生临床胜任力为目标,搭建"互联互通"体系,通过教学方法、教学内容、教学方式、教学组织和教学评价创新,在"互联互通"体系模式下,多元化手段与信息技术深度融合于教学全程,创设沉浸式、参与式、互动式情境,实现多学科知识整合、多元化能力养成和多维度情感价值构建,最终培养主动学、会应用、有情怀、勤思考的医学人才.

目的 探讨血常规平均红细胞血红蛋白浓度(MCHC)水平异常升高的常见干扰原因及抗干扰处理方法.方法 选取2019年8月至2020年6月于该院检验科行血常规检测且首次MCHC水平异常升高(MCHC>380 g/L)的102例患者为研究对象,分析其MCHC水平异常升高的原因.针对不同干扰因素采用水浴、稀释和(或)血浆置换等方式处理后,经全自动血液分析仪重新检测血常规,记录和比较处理前后白细胞计数(WBC)、红细胞计数(RBC)、血红蛋白(Hb)、血细胞比容(HCT)、平均红细胞体积(MCV)、平均红细胞血红蛋白量(MCH)、MCHC、血小板(PLT)水平,比较采用不同抗干扰方案测得的各指标水平.结果 在102例患者的血常规标本中,脂血标本12份、红细胞冷凝集标本68份、溶血标本20份、高白细胞(WBC>350×109/L)标本2份.与处理前比较,血浆置换处理脂血标本后,WBC、Hb水平差异无统计学意义(P>0.05),RBC、HCT水平升高,MCV、MCH、MCHC和PLT水平降低,差异均有统计学意义(P<0.05).与处理前比较,红细胞凝集标本采用37℃水浴20 min处理后,各指标水平差异无统计学意义(P>0.05).采用37℃水浴30 min处理后,其中9份标本MCHC无法纠正,进一步行稀释+温育(稀释液与标本一同温育)或血浆置换法重新检测,MCHC可准确纠正(P<0.05).与处理前比较,采用41℃水浴10 min处理后,除WBC、Hb外其余各指标水平差异有统计学意义(P<0.05).与处理前比较,溶血标本采用血浆置换法处理后,RBC、HCT、MCV水平升高,MCH、MCHC、PLT水平降低,差异均有统计学意义(P<0.05).结论 对于MCHC水平异常升高的标本,检验人员应先分析可能的干扰因素,针对不同干扰选择快速有效的方法进行纠正,为患者提供准确的检验报告.

Background:Neonatal leukemoid reaction (NLR) is often accompanied by infectious or non-infectious diseases, a low birth weight, sepsis, prematurity, ventricular hemorrhage, and bronchial dysplasia. It has an incidence rate of 1.3-15% and a mortality rate of about 41.4%. Previous studies on NLR have largely focused on its pathogenesis and clinical cases, but little is known about its prognostic laboratory indicators. We found that some of the NLR exhibited obviously elevation in liver function tests like aspartate transaminase (AST) and lactate dehydrogenase (LDH) which were not took by all the LR infants. The necessity for liver function tests for the prognosis of NLR was still unclear.Methods:A total of 39 premature infants with NLR at the First Hospital of Jilin University between March 2016 and March 2017 were included in this retrospective cohort study. The infants were divided into death and cured group based on the clinical outcomes. Premature infants with LR and death were defined as the case group (n=14), while infants without death were defined as the control group (n=25). Confounding factors such as age and gender between the two groups were controlled. Blood routine tests, including the white blood cell (WBC) count and subtypes, and liver function, and clinical features were recorded and analyzed. T tests were used to examine the differences in the laboratory indicators between the NLR and control groups. Receiver operating characteristic curves (ROCs) and areas under the curve (AUCs) were used to examine laboratory indicators for prognosis.Results:For predicting clinical outcomes, the ROC curves showed that the cut-off values for AST and LDH were 279 and 1,412 U/L, respectively. The sensitivity and specificity for AST were 92% and 71.43%, respectively, with an AUC of 0.894, while the sensitivity and specificity for LDH were 88% and 78.57%, respectively, with an AUC of 0.911.Conclusions:This innovative study investigated the NLR prognosis depending on laboratory tests. We found that serum AST and LDH levels had reliable predictive value in determining adverse outcomes of NLR.

Colorectal carcinoma (CRC) ranks as the third most common malignancy. Long non-coding RNA DLGAP1-AS1 was reported to be dysregulated and to play a pivotal role in hepatocellular carcinoma (HCC). This work aims to analyze the functions and molecular basis of DLGAP1-AS1 in CRC progression and 5-fluorouracil resistance. Cell Counting Kit-8 (CCK-8) assay, Transwell assay, flow cytometry, and western blot were utilized to measure the CRC cell activity, invasiveness, and apoptosis. RNA immunoprecipitation (RIP) and dual-luciferase reporter gene assay were adopted to verify the direct mutual action between DLGAP1-AS1 and miR-149-5p. The effect of DLGAP1-AS1 knockdown on tumor growth and chemosensitivity of 5-fluorouracil (5-FU) were investigated in the mouse CRC xenograft models. Functional assays showed that silencing DLGAP1-AS1 expression remarkably inhibited cell proliferation and aggressiveness ability and enhanced apoptosis rate and cell chemosensitivity to 5-FU. In addition, miR-149-5p was identified as a tumor suppressor and a direct downstream target of DLGAP1-AS1 in CRC. Furthermore, miR-149-5p was confirmed to directly bind to TGFB2 and DLGAP1-AS1 could regulate the expression of TGFB2 signaling pathway via miR-149-5p in CRC. These new findings indicate that DLGAP1-AS1 knockdown inhibited the progression of CRC and enhanced the 5-FU sensitivity of CRC cells through miR-149-5p/TGFB2 regulatory axis, suggesting that DLGAP1-AS1 may be a promising therapeutic target for CRC.

The rapid development of point-of-care testing (POCT) in clinical laboratories has brought challenges to the unified management in the hospital. There are many problems, such as how to ensure the ability and qualification of POCT operators, how to improve the quality management awareness of human, machines, materials, methods and environment in the process of POCT in clinical laboratories, how to help the clinical laboratories in the hospital to carry out POCT comparison, and how to strengthen the information construction of POCT in the hospital. Thus, this article reviews the practice and experience of POCT management in our hospital on POCT quality assurance and the problems existing in POCT in clinical departments, proposes suggestions and solutions to strengthen the unified management of POCT in clinical laboratories and establish POCT quality management documents and to improve quality awareness. We hope to provide references for hospital administrators, medical departments, nursing departments, quality control departments and other functional departments on the quality management of POCT in the hospital, and find helpful answers to the puzzles of clinical laboratory in POCT, so as to make joint efforts to standardize the quality management of POCT in the hospital to ensure the accuracy of testing results.

卫生行业标准《血浆凝固实验血液标本的采集及处理指南WS/T 359—2011》对凝血试验血液标本采集器具、抗凝剂、信息确认、标本采集、运送、接收及处理等方面做了规范，近十年来有效提高了凝血检验前的质量。为了进一步保证凝血试验结果的准确性，推动该标准在国内尤其是市、县及基层医院进一步推广、理解与应用，本文对该标准的条款进行了解读，包括采血器具选择及抗凝剂浓度、检验前标本申请单与标签信息确认、标本采集程序与注意事项、标本的运送、接收、处理与保存等，并结合国际其他相关指南以及笔者在检验实践中应用标准的体会进行阐述。

目的 探讨互渗式整合培养在检验住院医师能力培养的做法及有效性,提高检验医师整体素质.方法 将理论、实践、前沿、持续改进整合互渗,即按照《细则》要求制定轮转方案、轮转计划、小讲课计划、及技能培训计划,整理疑难病例供讨论学习,指导住院医师签发检验报告及形态学诊断报告,发放调查问卷进行住院医师与带教教师互评,鼓励住院医师参加省市级继续教育.设计能力评估量表,包含德、勤、能、技四方面共25项,对住院医师个人整体素质进行评分.结果 住院医师在检验科22个月内完成血液、体液、生化、免疫、微生物、分子诊断、细胞遗传7个亚专业共24个岗位的培训,参加:1)小讲课85次:内容涵盖检验原理、临床应用、质量控制、仪器操作、形态学识别、结果 报告等,并接受培训后考核;2)技能培训103次:血涂片制备、尿沉渣镜检等;3)疑难病例讨论75次;4)出科考核8次:7次专业组考核和1次年度考核;5)签发报告1000余份,其中形态学报告50余份;6)能力验证及性能验证多次;7)质量管理体系相关工作;8)临床沟通6次;9)省市级学术活动3次.能力评估均能达到合格,丢分主要为科研和论文.结论 根据培养目标并建立整合互渗的系统培养方案,更有利于检验住培人员的人才培养和检验学科建设.

新型冠状病毒(简称"新冠病毒")肺炎疫情发生以来,党中央、国务院多次对提高病毒核酸检测能力做出明确指示和战略部署.2021年1月21日,国务院联防联控机制综合组吉林工作组和吉林省卫生健康委员会紧急抽调经验丰富的检验人员,赴通化地区协助检测基地60名检验人员开展大规模人群筛查.本文总结了大规模新冠病毒核酸检测工作中,城市检测基地的仪器配置、人员配置及工作流程设计思路,为国内其他城市检测基地在可能出现的突发公共卫生事件下进行大规模核酸筛查提供参考.

Objective:To conduct periodic revalidation of the 15 items and 43 terms autoverification rules of blood analysis after 1 year of application, analyze the application suitability and make the rules improved.Methods:Track the results of 528 010 blood analysis samples of our hospital from August 1, 2019 to January 31, 2020, and analyze the pass rate and interception rate of autoverification; 600 specimens in total were selected randomly for microscope examination, including 300 specimens which touched autoverification rules (1 012 items of autoverification rules) and were intercepted by autoverification and 300 specimens which untouched autoverification rules and were released by autoverification. The abnormal characteristics and unacceptable Delta check of the specimens also need to be concerned at the same time.The false negative rate and false positive rate, true negative rate, true positive rate and pass correct rate of autoverification were verified and compared with the rate of the second phase verification when the autoverification rule was established. The false negative rate, false positive rate, true negative rate and true positive rate of the Delta check rule which 54 716 specimens touched were calculated and compared with the second phase verification rate when the autoverification rule was established.The results of microscopic examination were used as the gold standard for the calculation of the rates, and P<0.05 was considered as a significant difference. The false positive and true positive of 1 012 autoverification rules were analyzed item by item.The false positive and true positive of 108 specimens which touched blast cell autoverification rule were analyzed terms by terms. The mean TAT and median TAT of 528 010 specimens and 193 750 outpatient specimens were calculated respectively, and the report percentages of 528 010 samples that TAT<30, 30-60　and>60 min were calculated respectively. Analyze and evaluate the application suitability of autoverification rules to juge whether they meet the needs of doctors and laboratory. The design process and the rules and application process of autoverification were optimized and improved.Results:The autoverification pass rate was 63.06% (332 971/528 010), the interception rate was 36.94% (195 039/528 010). The false negative rate was 1.00% (1/600), the false positive rate was 12.67% (76/600), the true negative rate was 49% (294/600), the true positive rate was 37.33% (224/600), and the correct rate was 98% (294/300). The pass rate, true negative rate, true positive rate and correct rate of the periodic reverification group were higher than the second phase verification group, the false negative rate and false positive rate were lower than that the second phase verification group. The false negative rate and true positive rate of the Delta check of periodic verification group were lower than that the second phase verification group, the false positive rate and true negative rate were higher than the second phase verification group, there were significant differences in the comparition results. The mean TAT of 528 010 specimens was25 min, and the median TAT was 22 min. The mean TAT of 193 750 outpatient specimens was 23 min, and the median TAT was 20 min. The report percentages of 528 010 samples that TAT<30 min, 30 min-60 min and>60 min were 83.30% (439 819/528 010), 8.00% (42 250/528 010) and 8.70% (45 941/528 010), respectively.Conclusion:The results of periodic revalidation of autoverification after 1 years application show that the 15 items and 43 terms autoverification rules of blood analysis could meet requirements about the accuracy and efficiency of the laboratory, and have a good suitability for application.