Influencing factors for the functional cure of chronic hepatitis B and related mechanism

Yifei GUO; Jiming ZHANG

doi:10.3969/j.issn.1001-5256.2022.08.004

Volume 38 Issue 8

Aug. 2022

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Article Navigation > Journal of Clinical Hepatology > 2022 > 38(8): 1721-1725

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Influencing factors for the functional cure of chronic hepatitis B and related mechanism

DOI: 10.3969/j.issn.1001-5256.2022.08.004

Yifei GUO¹,
Jiming ZHANG^{1, 2
,
,
,}

1.
National Medical Center for Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai 200040, China
2.
Shanghai Institute of Major Infectious Diseases and Biosafety, Shanghai 200032, China

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Corresponding author: ZHANG Jiming, jmzhang@fudan.edu.cn(ORCID: 0000-0001-5820-950X)
Received Date: 2022-06-30
Accepted Date: 2022-07-31
Published Date: 2022-08-20

Abstract

Abstract

Functional cure of chronic hepatitis B (CHB), defined as negative hepatitis B surface antigen (HBsAg) with or without the presence of hepatitis B surface antibody (anti-HBs), is considered the optimal endpoint for CHB treatment at present. Studies have shown that HBsAg clearance can reduce the risk of HBV complications such as liver cirrhosis and hepatocellular carcinoma. However, HBsAg clearance rate remains at a relatively low level, which may be associated with the immune tolerance state caused by HBV infection. HBsAg clearance and/or the presence of anti-HBs indicate the partial recovery of HBV-specific immunity. This article discusses the influencing factors for the functional cure of CHB and the underlying mechanisms.
- Hepatitis B, Chronic,
- Hepatitis B Surface Antigens,
- Hepatitis B e Antigens,
- Antiviral Agents

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持续性炎症-免疫抑制-分解代谢综合征(persistent inflammation, immunosuppression and catabolism syndrome，PICS) 基于“ SIRS-CARS-MARS ”免疫模型基础上进一步发展而来，机体在度过早期多器官功能障碍综合征(multiple organ dysfunction syndrome，MODS)死亡高峰后，全身炎症反应综合征(systemic inflammatory response syndrome，SIRS)及对抗性的代偿性抗炎反应综合征(compensatory anti-inflammatory response syndrome，CARS)势均力敌，机体达到相对平衡稳定状态，最终形成持续低水平炎症和严重免疫抑制^[1]。最早于2012年，由Gentile等^[2]提出，指患者在遭受如脓毒症、创伤、急性胰腺炎等重大疾病后期进入慢性危重症(chronic critical ill，CCI)，表现出的持续炎症反应、免疫失衡与代谢紊乱，需要长期的紧急医疗支持。重症急性胰腺炎(severe acute pancreatitis，SAP)发生PICS后难以有效逆转，不仅增加医疗资源的消耗，也增加患者治疗负担。但目前临床医生对PICS的认识及相对性治疗仍相对缺乏。本研究通过分析SAP继发PICS的影响因素，构建预测模型，以期早期识别并给予针对性的治疗，为改善SAP患者预后提供帮助。

1. 资料与方法

1.1 研究对象

本研究对2012年5月—2022年5月于本院因SAP进入重症医学科、急诊重症监护室的163例患者临床资料进行回顾性分析。纳入标准：(1)SAP符合2012年亚特兰大分类标准：急性胰腺炎伴有持续性器官功能衰竭(＞48 h); (2)临床资料完整。排除标准：(1)年龄≤18岁；(2)合并免疫系统缺陷性疾病；(3)合并恶性肿瘤；(4)胰腺手术术后重症监护患者。

1.2 诊断标准和分组

以SAP患者入住ICU后0~5 d的临床数据进行评估，PICS诊断标准参考相应指南及研究^[2-5]: (1)ICU住院时间＞14 d；(2)炎症反应：C反应蛋白(CRP)＞30 mg/L；(3)免疫抑制: 总淋巴细胞计数(Lym)＜0.8×10⁹/L；(4)蛋白质高分解代谢：白蛋白＜30 g/L或前白蛋白＜100 g/L。将纳入患者分为2组：PICS组(65例，SAP发生PICS患者)和非PICS组(98例，SAP未发生PICS患者)。

1.3 数据采集

收集患者年龄、性别、BMI、既往史、SAP病因、入住ICU后的体温、心率、呼吸、平均动脉压、白细胞计数(WBC)、血红蛋白(Hb)、血小板计数、中性粒细胞计数(Neu)、Lym、中性粒细胞与淋巴细胞比值(NLR)、血小板与淋巴细胞比值(PLR)、红细胞压积(HCT)、CRP、血气分析pH值、氧分压(PO₂)、二氧化碳分压(PCO₂)、静脉血血钠(Na)、静脉血血钾(K)、总胆红素(TBil)、直接胆红素(DBil)、前白蛋白、白蛋白、尿素(BUN)、肌酐(SCr)、血淀粉酶、格拉斯哥昏迷评分(GCS)、急性生理与慢性健康评分(APACHE Ⅱ)、序贯器官衰竭估计评分(SOFA)、SAP患者进入ICU时并发症[机械通气、急性呼吸窘迫综合征(ARDS)、急性肾损伤(AKI)、急性肝损伤、低血容量性休克、脓毒症、肺部感染、腹腔高压、腹腔出血、肠瘘、MODS]、ICU住院时间、住院时间、临床结局、总费用等。

1.4 统计学方法

采用SPSS 26.0与R-4.2.1统计软件进行数据分析。符合正态分布的计量资料以x±s表示，2组间比较采用成组t检验；不符合正态分布的计量资料以M(P₂₅~P₇₅)表示，2组间比较采用Mann-Whitney U秩和检验。计数资料2组间比较采用χ²检验或Fisher确切概率法。P＜0.05为差异有统计学意义。计算方差膨胀因子、相关系数矩阵热图评估变量多重共线性，采用Lasso回归法筛选变量，然后采用多因素Logistic回归模型筛选独立危险因素纳入最终模型。采用R-4.2.1统计软件及相关程序包绘制列线图预测模型。采用bootstrap法(抽样1 000次)进行内部验证，采用受试者工作特征曲线(ROC曲线)评估模型的区分度，校准曲线及Hosmer-Lemeshow拟合优度检验评估模型的预测精度，临床决策曲线检验模型的临床实用性。

2. 结果

2.1 一般资料比较

PICS组患者的平均动脉压显著低于非PICS组，差异具有统计学意义(P＜0.05)。预后方面，PICS组的病死率、住院时间、总费用均显著高于非PICS组，差异均有统计学意义(P值均＜0.05)(表 1)。

表 1 2组患者一般资料比较

Table 1. Comparison of general data between the two groups

项目	PICS组(n=65)	非PICS组(n=98)	统计值	P值
年龄(岁)	48.00(39.50~59.00)	47.50(37.00~59.25)	Z=-0.005	0.996
性别[例(%)]			χ²=0.916	0.339
男	52(80.0)	72(73.5)
女	13(20.0)	26(26.5)
BMI(kg/m²)	23.94(22.00~27.55)	24.98(22.49~27.76)	Z=-0.668	0.504
既往史[例(%)]
高血压	14(34.7)	34(20.9)	χ²=3.255	0.071
糖尿病	8(12.3)	15(15.3)	χ²=0.290	0.590
腹部疾病	27(41.5)	52(53.1)	χ²=1.897	0.168
SAP病因[例(%)]
胆源性	5(7.7)	11(1.1)	χ²=0.551	0.458
高脂血症性	17(26.2)	34(34.7)	χ²=1.326	0.250
酒精性	12(18.5)	18(18.4)	χ²＜0.001	0.988
创伤性	2(3.1)	3(3.1)	χ²＜0.001	0.995
病因不明	29(44.6)	32(32.7)	χ²=2.388	0.122
体温(℃)	37.60(37.00~38.00)	37.50(36.78~38.10)	Z=-0.553	0.580
心率(次/min)	119.43±23.02	112.69±21.48	t=1.881	0.062
呼吸(次/min)	24.00(20.00~33.00)	26.00(21.50~32.00)	Z=-1.064	0.287
平均动脉压(mmHg)	98.33(80.67~111.67)	104.50(91.59~113.08)	Z=-2.207	0.027
预后
开放饮食时间(d)	8.00(3.50~13.50)	7.00(3.00~11.00)	Z=-1.288	0.198
病死率[例(%)]	26(40.0)	16(16.3)	χ²=11.450	0.001
住院时间(d)	30.00(22.00~51.00)	22.00(17.00~27.25)	Z=-4.666	＜0.001
总费用(万元)	22.23(12.10~39.31)	8.18(6.06~11.86)	Z=-6.893	＜0.001

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2.2 PICS因素比较

在PICS因素中，2组患者的ICU住院时间、Lym、前白蛋白、白蛋白差异均有统计学意义(P值均＜0.05)(表 2)。

表 2 2组患者PICS指标比较

Table 2. Comparison of PICS factors between the two groups

指标	PICS组(n=65)	非PICS组(n=98)	Z值	P值
ICU住院时间(d)	22.00(16.00~42.50)	7.00(4.00~11.00)	-8.862	＜0.001
CRP(mg/L)	180.39(120.84~192.00)	190.67(109.04~192.00)	-0.265	0.791
Lym(×10⁹/L)	0.60(0.49~0.75)	1.07(0.72~1.39)	-6.547	＜0.001
前白蛋白(g/L)	83.30(52.25~104.15)	110.90(76.73~155.35)	-3.488	＜0.001
白蛋白(g/L)	28.30(26.10~31.25)	30.80(27.58~34.78)	-3.517	＜0.001

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2.3 实验室指标和并发症比较

PICS组Hb、HCT、GCS评分显著低于非PICS组，NLR、PLR、BUN、SCr、APACHE Ⅱ评分、SOFA评分均高于非PICS组，差异均有统计学意义(P值均＜0.05)。并发症方面，PICS组合并机械通气、ARDS、AKI、急性肝损伤、低血容量性休克、脓毒症、腹腔高压、腹腔出血、MODS均高于非PICS组，差异均有统计学意义(P值均＜0.05)(表 3)。

表 3 2组患者实验室指标和并发症比较

Table 3. Comparison of laboratory data and complication between the two groups

指标	PICS组(n=65)	非PICS组(n=98)	统计值	P值
WBC(×10⁹/L)	14.46(8.94~18.69)	14.72(10.45~20.09)	Z=-0.857	0.391
Hb(g/L)	87.70(75.05~112.70)	106.60(83.68~126.13)	Z=-2.532	0.011
血小板计数(×10⁹/L)	176.00(117.15~237.20)	196.35(141.93~272.40)	Z=-1.366	0.172
Neu(×10⁹/L)	13.22(8.00~17.00)	12.18(8.38~17.45)	Z=-0.095	0.924
NLR	21.69(11.47~30.30)	12.35(7.41~23.13)	Z=-3.383	0.001
PLR	261.85(198.42~455.46)	207.28(118.19~315.79)	Z=-3.210	0.001
HCT	0.26(0.22~0.36)	0.33(0.26~0.39)	Z=-2.649	0.008
PH	7.44(7.36~7.48)	7.43(7.38~7.48)	Z=-0.061	0.951
PO₂(mmHg)	102.00(78.05~136.05)	88.50(71.00~126.03)	Z=-1.161	0.246
PCO₂(mmHg)	34.10(30.40~39.40)	35.55(30.00~39.00)	Z=-0.278	0.781
氧合指数	253.00(198.00~332.90)	234.50(184.25~326.50)	Z=-0.552	0.581
血Na(mmol/L)	138.10(133.95~143.20)	137.00(133.80~140.95)	Z=-0.727	0.467
血K(mmol/L)	4.10(3.80~4.52)	4.00(3.59~4.31)	Z=-1.466	0.143
TBil(μmol/L)	26.30(13.30~47.30)	20.55(12.48~35.03)	Z=-1.469	0.142
DBil(μmol/L)	13.30(6.25~28.20)	9.30(5.00~18.75)	Z=-1.737	0.082
BUN(mmol/L)	10.36(6.32~17.51)	6.43(4.15~10.50)	Z=-3.250	0.001
SCr(μmol/L)	152.00(71.50~293.50)	73.50(55.75~141.00)	Z=-2.915	0.004
血淀粉酶(U/L)	76.00(44.00~306.50)	103.00(53.75~389.75)	Z=-0.935	0.350
GCS评分	15.00(13.00~15.00)	15.00(15.00~15.00)	Z=-2.973	0.003
APACHE Ⅱ	13.00(10.00~16.00)	11.00(7.75~14.00)	Z=-3.173	0.002
SOFA	6.00(4.00~10.00)	4.00(2.00~6.00)	Z=-4.309	＜0.001
并发症[例(%)]
机械通气	54(83.1)	34(34.7)	χ²=17.785	＜0.001
ARDS	54(83.1)	49(50.0)	χ²=17.785	＜0.001
AKI	40(61.5)	23(23.5)	χ²=44.521	＜0.001
急性肝损伤	16(24.6)	12(12.2)	χ²=105.631	＜0.001
低血容量性休克	15(23.1)	3(3.1)	χ²=108.741	＜0.001
脓毒症	37(56.9)	27(27.6)	χ²=50.971	＜0.001
肺部感染	63(96.9)	90(91.8)	χ²=3.053	0.158
腹腔高压	34(52.3)	22(22.4)	χ²=57.715	＜0.001
腹腔出血	12(18.5)	6(6.1)	χ²=118.409	＜0.001
肠瘘	2(3.1)	2(2.0)	χ²=0.175	0.524
MODS	49(75.4)	36(36.7)	χ²=26.749	＜0.001

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2.4 多重共线性检验

计算P＜0.05变量的方差膨胀因子，结果显示Hb及HCT的方差膨胀因子≥10。所有因素的相关系数矩阵热图提示Hb与HCT(r=0.99)、BUN与SCr(r=0.84)、BUN与AKI(r=0.62)、SCr与AKI(r=0.68)、机械通气与ARDS(r=0.67)、AKI与MODS(r=0.66)间均存在强相关(图 1)。

图 1 多重共线性检验

Figure 1. Test for multicollinearity

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2.5 SAP发生PICS的Lasso回归分析

Lasso回归可通过添加惩罚系数，将非特征变量的系数压缩为0，从而限制多重共线性带来的影响，筛选高质量变量。将P＜0.05的19个变量(包括平均动脉压、Hb、NLR、PLR、HCT、BUN、SCr、GCS评分、APACHE Ⅱ评分、SOFA评分、机械通气、ARDS、AKI、急性肝损伤、低血容量性休克、脓毒症、腹腔高压、腹腔出血、MODS)进行Lasso回归分析，图 2a结果提示随着惩罚系数的对数logλ增大，模型纳入的变量也在逐渐减少，图 2b结果显示了10倍交叉验证确定最优λ值的过程，二项式偏差越小说明方程的拟合效果越好，最终选择最小λ右侧的1个标准差(0.050)作为模型的最优值，此时筛选的预测变量包括PLR、HCT、APACHE Ⅱ、SOFA、机械通气、AKI、低血容量性休克、腹腔高压。

图 2 Lasso回归进行临床特征筛选

注：a，19个临床特征的系数曲线；b，Lasso回归10倍交叉验证选择临床特征。

Figure 2. Selection of potential predictors of PICS in patients with SAP by the Lasso regression

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2.6 SAP发生PICS的多因素Logistic回归分析

将Lasso回归筛选出来的8个变量纳入多因素Logistic分析中，结果显示PLR、机械通气、AKI、低血容量性休克是SAP发生PICS的独立危险因素(P值均＜0.05)(表 4)。

表 4 SAP患者发生PICS多因素Logistic回归分析

Table 4. Multivariate logistic regression analysis of PICS in patients with SAP

变量	β值	P值	OR	95%CI
PLR	0.006	＜0.001	1.006	1.003~1.009
HCT	-2.810	0.256	0.060	0.000~7.768
APACHE Ⅱ	0.076	0.124	1.079	0.982~1.195
SOFA	0.053	0.458	1.055	0.913~1.216
机械通气	1.464	0.002	4.324	1.723~11.423
AKI	1.233	0.018	3.432	1.253~9.854
低血容量性休克	1.933	0.013	6.910	1.685~39.068
腹腔高压	0.743	0.107	2.102	0.852~5.245

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2.7 列线图预测模型构建

将多因素Logistic回归分析结果筛选出的变量PLR、机械通气、AKI、低血容量性休克纳入列线图预测模型，结局指标选取SAP患者PICS的发病风险，绘制列线图(图 3)，根据每个风险因素所对应列线图上方的标尺，从而得到该因素的单项评分，所有风险因素评分相加得到总分，便可得到对应SAP患者PICS发生率，总分越高，SAP患者发生PICS风险的可能性越大。

图 3 SAP患者发生PICS风险预测模型列线图

Figure 3. Nomogram of the risk predictive model for PICS in SAP patients

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2.8 预测模型的内部验证与评价

运用bootstrap法(抽样1 000次)对该预测模型进行内部验证，其ROC曲线下面积为0.874，95%CI：0.822~0.925，最佳截断值为-1.197，在该截断值下，预测模型的敏感度为0.922，特异度为0.673。表明该预测模型具有良好的区分度(图 4)。绘制模型的校准曲线，可见校准曲线接近参考曲线(图 5)，Hosmer-Lemeshow拟合优度检验(χ²=8.895，P=0.351)表明该模型具有良好的拟合度。临床决策曲线分析发现，在SAP患者的阈值概率为0~0.8时，预测模型的决策曲线均高于2条极端曲线，即预测模型的净收益更高。说明该预测模型具有良好的临床实用性(图 6)。

图 4 SAP患者发生PICS风险预测模型的ROC曲线

Figure 4. ROC curve of the risk predictive model for PICS in SAP patients

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图 5 SAP患者发生PICS风险预测模型的校准曲线

Figure 5. Calibration curve of the risk predictive model for PICS in SAP patients

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图 6 SAP患者发生PICS风险预测模型的决策曲线

Figure 6. Decision curve of the risk predictive model for PICS in SAP patients

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3. 讨论

SAP患者约43.1%后期可合并PICS^[6]，这与本中心约39.9% SAP患者发生PICS基本相符，SAP发生时，大量胰蛋白酶激活可刺激胰腺内单核巨噬细胞及损伤的胰腺腺泡产生炎症介质，引起白细胞过度激活-炎症介质级联“瀑布效应”，导致PICS。杨蓉等^[7]的研究发现重症患者发生PICS组CD3⁺、CD4⁺、CD4⁺/CD8⁺均低于非PICS组，而CD4⁺T淋巴细胞比例减少可引起免疫麻痹，CD4⁺/CD8⁺比例改变可出现Th1/Th2漂移，Th2细胞极化进一步表现为固有免疫与适应性免疫失衡，导致免疫抑制。SAP发生时大量细胞因子及应激相关激素释放，引起机体高分解代谢，大量能量消耗及肌肉蛋白质分解，同时促炎细胞因子等可作用于线粒体致呼吸链酶活性下降，导致能量代谢不足，加重能量供需失衡，介导代谢紊乱。

SAP胰酶释放引起局部炎症，导致血管通透性增加，凝血系统被激活，促进血小板黏附聚集，同时炎症因子作用于骨髓细胞，引起血小板产生和释放增加^[8]。PLR为血小板与淋巴细胞比值，是一个能综合反映机体凝血和炎症的指标，Zhou等^[9]对406例急性胰腺炎患者进行回顾性分析，结果提示PLR在预测急性胰腺炎严重程度及预后方面均有意义，本研究结果也提示PLR越高，SAP患者发生PICS风险越大。Hawkins等^[10]研究发现SAP发生PICS时骨髓来源抑制性细胞(MDSC)持续增殖，同时释放大量炎症因子，炎症因子可进一步收缩肾血管，加重肾缺血缺氧，同时MDSC可浸润于肾脏，引起肾脏氧化应激及免疫抑制，直接损伤肾组织。此研究也发现AKI患者进展为慢性肾脏病是CCI发生的主要原因，本研究同样发现SAP患者入住ICU时出现AKI，发生PICS的风险增大。一项Meta分析^[11]提示采用机械通气是SAP患者并发AKI的危险因素，这可能与机械通气通过调节神经体液、改变肾脏血流动力学等机制影响肾脏灌注和滤过功能有关。SAP时由于胰腺及胰周大量渗出而导致循环血容量不足，严重者可出现低血压乃至低血容量性休克，而由于血流重分布，极易发生选择性肠道缺血，若未及时进行充分的液体复苏，会引起小肠组织灌注不足、肠黏膜机械屏障受损。Efron等^[5]研究表明肠道屏障受损与PICS中全身炎症及免疫抑制发生有关，同时低血容量性休克时肾脏供血不足、肾血管收缩，AKI风险增大，CCI发生风险增高。

目前PICS治疗包括以下几方面：(1)改善炎症反应。Wang等^[12]研究结果表明在SAP大鼠模型中乌司他丁治疗可有效减少血清中TNFα、IL-6水平。王燕等^[13]研究发现厚朴酚能够改善肠屏障功能，通过功能性阻断肠淋巴循环，降低炎症因子水平，减轻SAP肺损伤。王雪等^[14]研究发现血必净注射液治疗组CD4⁺T淋巴细胞、CD8⁺T淋巴细胞、CD4⁺/CD8⁺及NKT淋巴细胞水平较对照组显著增加。此外血液净化治疗可清除炎性因子，改善单核巨噬细胞功能，提高淋巴细胞数量、维持机体免疫系统内环境稳定^[15]。(2)免疫调节治疗。胸腺肽α1可诱导T淋巴细胞分化、成熟，增强单核巨噬细胞的吞噬，抑制中性粒细胞凋亡。苏和毅等^[16]对68例脓毒症后发生PICS的老年患者进行研究，结果提示接受胸腺肽α1治疗组CD4⁺/CD8⁺、HLADR/CD14⁺水平显著高于对照组。(3)营养支持治疗。Lubbers等^[17]一项双盲、随机对照研究发现富含脂肪酸、蛋白质的肠内营养剂可抑制早期炎症反应，患者体内的促炎因子TNFα、IL-6、IL-8水平显著降低，同时增强抗炎作用，免疫功能得到调节。精氨酸是正常T淋巴细胞合成所必需的氨基酸，而PICS发生后，表达精氨酸酶的MDSC增殖，精氨酸减少导致免疫抑制，补充精氨酸有助于促进淋巴细胞的增殖和成熟^[18]。此外，早期锻炼康复、调节肠道菌群等在PICS患者治疗方面均有报道。

本研究基于PLR、机械通气、AKI、低血容量性休克等指标，构建了预测SAP患者发生PICS的列线图模型，该模型具有良好的区分度、校准度和临床实用性，便于入住ICU的SAP患者早期识别和预测发生PICS风险并及时干预，以期减少SAP患者发生PICS可能、降低SAP合并PICS患者的住院时间及病死率。本研究局限性在于为单中心回顾性研究，样本量相对较少，一定程度上存在选择偏倚，影响预测模型的准确性，同时未进行外部验证评估模型的泛化能力，仍需开展多中心、大样本的对照研究进一步证实本研究结果。

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沈阳化工大学材料科学与工程学院沈阳 110142

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Influencing factors for the functional cure of chronic hepatitis B and related mechanism

DOI: 10.3969/j.issn.1001-5256.2022.08.004