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Hindawi Mediators of Inflammation Volume 2022, Article ID 8760615, 9 pages https://doi.org/10.1155/2022/8760615 Research Article Sexual Effect of Platelet-to-Lymphocyte Ratio in Predicting Cardiovascular Mortality of Peritoneal Dialysis Patients 1,2 1,2 1,2 1,2 1,2 Hui Sheng , Yagui Qiu , Xi Xia , Chunyan Yi , Jianxiong Lin , 1,2 1,2 Xiao Yang , and Fengxian Huang Department of Nephrology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 58th, Zhongshan Road II, Guangzhou 510080, China Key Laboratory of Nephrology, National Health Commission of China and Guangdong Province, Guangzhou 510080, China Correspondence should be addressed to Fengxian Huang; huangfx@mail.sysu.edu.cn Received 9 August 2021; Accepted 9 December 2021; Published 4 January 2022 Academic Editor: Daniela Caccamo Copyright © 2022 Hui Sheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. The study is aimed at exploring the relationship of platelet-to-lymphocyte (PLR), all-cause, and cardiovascular disease (CVD) mortality in peritoneal dialysis (PD) patients based on gender. Methods. A total of 1438 PD patients from January 1,2007 to December 31, 2014 in PD center at The First Affiliated Hospital, Sun Yat-sen University, were included. Patients were followed up until December 31, 2019. The endpoint was all-cause mortality and CVD mortality. Cox proportional hazards regression models were used to evaluate the association of PLR with all-cause and CVD mortality to calculate hazard ratios (HR) and 95% confidence intervals (CI). Results. After a median of 48.9 (interquartile range [IQR]: 23.4-79.3) months of follow-up, 406 (28.2%) patients died based on all-cause death, among which 200 (49.3%) patients died from CVD. In the multivariate Cox regression model, we found that PLR was independently related to an increased risk of CVD mortality only in female PD patients, with HR of 1.003 (95% CI: 1.001-1.006). Interaction test showed that the correlation between PLR level for all-cause and CVD mortality varied with gender (p =0:042 and p =0:012, respectively). Conclusion. Higher PLR was associated with a higher risk of CVD mortality in female PD patients. 1. Introduction is considered an indicator of the systemic inflammatory response when the patient has no significant infection Chronic kidney disease (CKD) is a global problem. One in [8]. Higher PLR can predict poor prognosis of colorectal cancer and clinical outcomes of non-small-cell lung cancer ten adults worldwide has CKD [1]. Peritoneal dialysis (PD) is a recognized method of treatment for patients [8–13]. Moreover, Chen et al. have established a connec- who suffer from end stage renal diseases (ESRD). Inflam- tion between PLR and CVD disease in continuous ambu- latory peritoneal dialysis patients [13]. Recently, Liu et al. mation is an important source of risk for the progress of CKD. It is caused by multiple factors of the toxic uremic reported that high PLR could predict all-cause death in milieu and the dialysis procedure itself [2, 3]. Microin- PD patients [14]. Many researchers had confirmed that flammation is a key component of inflammation and is there were individual differences in PLR. Women had closely related to lack of nutrition and atherosclerosis higher levels of PLR than men [15]. Regrettably, there were few studies reflecting the correlations between PLR [4]. Some studies suggest that limiting inflammation can have important effects on halting CKD progression and and sex difference and the prognostic significance in reducing CVD events [4–7]. patients on PD. The purpose of this scientific research PLR is a test that reflects variations in platelet and was to explore the correlations between PLR and all- lymphocyte levels. In many current clinical studies, PLR cause death and CVD death in patients on PD. 2 Mediators of Inflammation Under 18 years old (n = 21) Accessed for eligibility Malignancy disease (n = 20) (2007.1.1-2014.12.31, n = 1970) Kidney transplant history (n = 10) PD less than 3 months (n = 108) Abdominal catheterization in other hspitals (n = 58) HD more than 3 months (n = 75) Excluded Acute infection within 4 weeks of measurement (n = 88) Hematological disease (n = 4) Autoimmune disease (n = 30) Without data of platelet or lymphocyte (n = 82) Biochemical parameters were obtained more than 3 months aer PD ini ft tiation ( n = 36) Enrollment (n = 1438) Received kidney transplantation (n = 363) Follow up until Transferred to HD (n = 273) December 31,2019 Transferred to other centers (n = 61) Lost to follow-up (n = 41) Remaining on PD therapy (n = 294) Death during follow-up (n = 406) CVD (n = 200) Infection (n = 83) Malignancy (n = 13) Cachexia (n = 24) Other causes (n = 42) Unknown causes (n = 44) Figure 1: Study flow chart. Abbreviation: CVD: cardiovascular disease; PD: peritoneal dialysis; HD: hemadialysis. 2. Materials and Methods from coronary events, arrhythmias, sudden cardiac death, congestive heart failure, arteriosclerosis, or cerebrovascular 2.1. Participants. This was a retrospective cohort study. All events. The history of CVD was defined as congestive heart enrolled patients came from PD center of the First Affiliated failure, ischemic heart disease, cerebrovascular disease, and Hospital, Sun Yat-sen University, and had undergone cathe- arteriosclerosis. Diabetes was defined according to diagnos- terization for PD in the same center from 1 January 2007 to tic criteria from the American Diabetes Association. [20] 31 December 2014. Exclusion criteria were the following: (1) 2.2. Clinical Data. We obtained baseline demographic, bio- age ≤ 18 years old; (2) patients with malignancy history or chemical, and clinical data including age, gender, blood kidney transplant history; (3) the duration of PD treatment pressure, body mass index (BMI), history of diabetes and < 3 months; (4) patients had catheter insertion in other PD CVD, hemoglobin (Hb), serum albumin (Alb), plasma creat- centers; (5) patients transferred from chronic hemodialysis inine, serum uric acid (UA), total cholesterol, platelet, lym- ðHDÞ > 3 months; (6) patients who had acute infection ≤ 4 phocyte, estimated glomerular filtration rate (eGFR), weeks and had hematological disease or autoimmune dis- platelet inhibitors, and β-blockers that were obtained during ease; (7) missing platelet and lymphocyte data at baseline; the first 1-3 month of PD. We measured all biochemical and (8) patients whose biochemical parameters were parameters in the center laboratory of the First Affiliated obtained >3 months after PD initiation. The study was in Hospital of Sun Yat-sen University. Among them, we mea- accordance with the Declaration of Helsinki and approved sured complete blood cell count by using Sysmex XE2100 by the Human Ethics Committees of Sun Yat-sen University. and XE5000 (Sysmex company in Kobe, Japan). We col- When participants started to receive PD treatment, they lected medication usage data according to the patients’ files. signed written informed consent. Platelet inhibitor includes aspirin and clopidogre. We calcu- We followed up all participants until death, transferring lated body mass index (BMI) as follows: BMI = weight ðkgÞ to HD treatment, kidney transplantation or transferring to 2 2 /height ðm Þ. We adopted the modified simplified Modifi- other centers, losing connection, or the deadline of follow- cation of Diet in Renal Disease (MDRD) formula to calculate up on December 31, 2019. Patients were asked to visit our eGFR and standardize the results by using body surface area. PD center quarterly [16–19] for health assessment and con- −1:154 comitant telephone usage. Trained nurses in PD center also We used a formula of eGFR = 186 × ½serum creatinine −0:203 interviewed patients by telephone every month to assess × ½age × ½0:742 if female, and serum creatine was their general health and comprehensive medical assessment. expressed as μmol/L [21]. These quarterly visits and monthly calls were made for clin- ical purposes, not specifically for the study. 2.3. Statistical Analysis. Normal distribution of continuous The outcomes of this research were all-cause mortality variables was presented as means and standard deviations, and CVD mortality. The CVD mortality referred to death and independent-sample t-test was used for the comparison Mediators of Inflammation 3 Table 1: Baseline characteristics of the study cohort. Variable Total (n = 1438) Group 1 (n = 719) Group 2 (n = 719) p value Age (years) 47:4±15:346:4±15:448:4±15:2 0.011 Gender (female, n, %) 565 (39.3) 260 (36.2) 305 (42.4) 0.015 21:6±3:121:6±3:021:6±3:2 BMI (kg/m ) 0.944 SBP (mmHg) 134:2±20:0 133:5±20:2 134:9±19:7 0.203 84:6±23:684:5±15:384:8±29:3 DBP (mmHg) 0.830 History of CVD (n, %) 246 (17.1) 115 (16.0) 131 (18.2) 0.263 Diabetes (n, %) 297 (20.7) 131 (18.2) 166 (23.1) 0.023 HGB (g/L) 101:4±20:7 100:6±20:8 102:3±20:6 0.117 36:5±4:937:2±5:0 ALB (g/L) 36.9 ± 4.9 0.018 739:3 ± 293:3 758:8 ± 314:7 719:9 ± 269:0 Plasma creatinine (μmol/L) 0.012 422:5±94:2 425:0±91:7 419:9±96:7 UA (μmol/L) 0.315 5:1±1:84:9±1:25:2±2:2 Total cholesterol (mmol/L) 0.003 Platelet (×10 /L) 232.0 (183.0, 288.0) 194.0(154.0,243.0) 271.0(222.0,319.0) <0.001 Lymphocyte (×10 /L) 1.5 (1.2, 1.8) 1.7 (1.4, 2.1) 1.2 (1.0, 1.5) <0.001 PLR 156.4 (118.1, 206.4) 118.1 (96.3, 136.1) 206.4 (178.9, 254.6) <0.001 eGFR (mL/min/1.73 m ) 6.8(5.3, 8.8) 6.7 (5.1, 8.8) 7.0(5.5, 8.8) 0.060 Platelet inhibitor (n, %) 85 (5.9) 32 (4.5) 53 (7.4) 0.019 β-Blockers (n, %) 676 (47.0) 325 (45.2) 351 (48.8) 0.170 Abbreviations: BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; CVD: cardiovascular disease; HGB: hemoglobin; ALB: serum albumin; UA: uric acid; PLR: the platelet-to-lymphocyte ratio; eGFR: estimated glomerular filtration rate; PLR: for groups: group 1 (≤156.43) and group 2(>156.43). Table 2: Multiple linear regression analysis on influencing factors of PLR. Unstandardized regression Variable coefficient Standardized regression coefficient Tp value B Standard error Age (y) 0.446 0.173 0.082 2.574 0.010 Gender (female) 10.073 4.619 0.059 2.181 0.029 History of CVD (yes/no) -0.758 6.457 -0.003 -0.117 0.907 Diabetes (yes/no) 16.280 6.229 0.080 2.614 0.009 HGB (g/L) 0.085 0.120 0.021 0.705 0.481 ALB (g/L) 1.098 0.518 0.065 2.121 0.034 eGFR (mL/min/1.73 m ) -0.063 0.568 -0.003 -0.110 0.912 Platelet inhibitor (yes/no) 12.695 9.878 0.036 1.285 0.199 β-Blockers (yes/no) 4.035 4.531 0.024 0.891 0.373 F =3:862, p <0:001, R =0:025. Analysis of factors associated with PLR by multivariable linear regression. Abbreviations: PLR: the platelet-to-lymphocyte ratio; CVD: cardiovascular disease; HGB: hemoglobin; ALB: serum albumin; eGFR: estimated glomerular filtration rate. between groups; skewed distributions of continuous vari- vival curves by using the log-rank test. We used univariate ables were presented as medians and interquartile ranges and multivariate Cox proportional hazards regression (IQR), and the difference between groups was compared by models to analyze the associations between PLR and out- Mann–Whitney U test; we presented categorical variables comes by calculating hazard ratios (HR) and 95% confidence as number and percentages. Variables between two groups intervals (CI). The multivariate Cox proportional hazards regression model included variables which were identified of categorical variables were compared by a χ . Baseline significant association with all-cause and CVD mortality in PLR was evaluated as a continuous variable. We divided all univariate analysis (p <0:1) or conventional confounding patients into two groups by the median of PLR: group 1 factors. The adjusted model included demographic variables (≤156.43) and group 2(>156.43). We used Kaplan-Meier survival analysis to generate sur- (baseline age, sex, history of CVD, diabetic status) and labo- ratory examination (hemoglobin, serum albumin, eGFR) vival curves and examined the differences between the sur- 4 Mediators of Inflammation Table 3: Associations of PLR with all-cause and CVD mortality in univariate and multivariate Cox regression models . All-cause mortality unadjusted Cardiovascular mortality ∗ ∗ Multivariate model Multivariate model Variable model unadjusted model HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value Age (y) 1.063 (1.056-1.071) <0.001 1.056 (1.047-1.065) <0.001 1.065 (1.054-1.076) <0.001 1.054 (1.041-1.068) <0.001 Gender (female, n,%) Female 0.978 (0.804-1.191) 0.826 1.001 (0.817-1.226) 0.995 0.877 (0.661-1.166) 0.367 0.863 (0.640-1.163) 0.333 Male Ref. Ref. Ref. Ref. History of CVD (yes/no) Yes 2.771 (2.242-3.425) <0.001 1.373 (1.089-1.731) 0.007 3.341 (2.496-4.471) <0.001 1.655 (1.200-2.282) 0.002 No Ref. Ref. Ref. Ref. Diabetes (yes/no) Yes 3.299 (2.706-4.021) <0.001 1.650 (1.327-2.051) <0.001 3.911 (2.959-5.171) <0.001 1.999 (1.460-2.737) <0.001 No Ref. Ref. Ref. Ref. HGB (g/L) 0.990 (0.985-0.994) <0.001 0.990 (0.985-0.996) <0.001 0.991 (0.984-0.997) 0.006 0.990 (0.982-0.998) 0.015 ALB (g/L) 0.913 (0.895-0.932) <0.001 0.972 (0.950-0.996) 0.020 0.927 (0.901-0.955) <0.001 0.994 (0.960-1.029) 0.730 eGFR (mL/min/1.73 m ) 1.022 (1.013-1.031) <0.001 1.017 (1.003-1.032) 0.020 1.018 (1.003-1.034) 0.017 1.000 (0.963-1.039) 0.999 PLR 1.001 (1.000-1.003) 0.005 1.000 (0.999-1.001) 0.594 1.002 (1.001-1.004) 0.002 1.001 (0.999-1.002) 0.250 Platelet inhibitor (yes/no) Yes 1.811 (1.301-2.520) <0.001 0.893 (0.628-1.270) 0.529 2.077 (1.333-3.236) 0.001 0.936 (0.583-1.503) 0.784 No Ref. Ref. Ref. Ref. β-Blockers (yes/no) Yes 0.683 (0.560-0.833) <0.001 0.850 (0.692-1.044) 0.121 0.714 (0.538-0.946) 0.019 0.862 (0.644-1.153) 0.316 No Ref. Ref. Ref. Ref. All the models were adjusted for age, gender, history of CVD, diabetic status, hemoglobin, serum albumin, estimated glomerular filtration rate, platelet inhibitor, and β-blockers. Abbreviations: PLR: the platelet- to-lymphocyte ratio; CVD: cardiovascular disease; HGB: hemoglobin; ALB: serum albumin; PLR: the platelet-to-lymphocyte ratio; eGFR: estimated glomerular filtration rate. Mediators of Inflammation 5 Table 4: Interaction tests of PLR and gender and mortality, and the sexual difference in the associations between PLR and mortality . All-cause mortality Cardiovascular mortality Variables HR HR 95% CI p value 95% CI p value For mortality in entire cohort Interaction analysis p for interaction = 0:012 p for interaction = 0:042 PLR × gender Female (n) No:of all − cause deaths = 177 No:of cardiovascular deaths = 79 PLR 1.002 1.000-1.003 0.098 1.003 1.001-1.006 0.008 No:of all − cause deaths = 229 No:of cardiovascular deaths = 121 Male (n) PLR 1.000 0.998-1.001 0.562 1.000 0.998-1.001 0.623 All the models were adjusted for age, history of CVD, diabetic status, hemoglobin, serum albumin, estimated glomerular filtration rate, platelet inhibitor, and β-blockers. Abbreviations: PLR: the platelet-to-lymphocyte ratio. and clinical data (platelet inhibitor, β-blockers). Platelet plantation, 61 (4.2%) cases were referred to other centers, 41 inhibitor includes aspirin, clopidogrel, and sulodexid. We (2.9%) cases had lost follow-up, and 406 (28.2%) cases had evaluated the interactions between PLR and gender and out- died. Among the 406 deaths, 200 (49.3%) died from CVD comes by using the multivariate Cox proportional hazards events, 83 (20.4%) from infection, 13 (3.2%) from malig- regression models. Statistical analysis was performed with nancy, 24 (5.9%) from cachexia, 42(10.3%) from other SPSS software (SPSS version 25.0, SPSS Inc.). p <0:05 was causes, and 44 (10.8%) from unknown causes (Figure 1). considered to be statistically significant. In our study, univariate and multivariate Cox propor- tional hazards regression models used to analyze prognostic factors were listed in Table 3. Multivariate Cox regression 3. Results analysis showed that PLR was not independently linked to 3.1. Baseline Clinical Data. The exclusionary cascade for der- all-cause death (HR: 1.000, 95% CI: 0.999-1.001). PLR was ivation of the cohort was shown in Figure 1. In total, 1438 not independently linked to CVD death (HR: 1.001, 95% PD patients were included in this study. Female accounted CI: 0.999-1.002) and ether. It showed that PLR was not inde- for 39.3%. The mean age was 47:4±15:3 years. The median pendently associated with an increased risk of all-cause or CVD death. follow-up time was 48.9 months (IQR: 23.4-79.3). The pri- mary etiology of ESRD was glomerulonephritis (60.9%), and the second cause of ESRD was diabetic nephropathy 3.4. PLR in Mortality Varied with Gender. Interaction tests showed that the correlations between PLR and all-cause (21.5%). The baseline characteristics of patients according to mortality and cardiovascular mortality varied by gender groups of PLR level were summarized in Table 1. Compared (p =0:042 and p =0:012, respectively, Table 4). Subgroup analysis was performed to further assess the with group 1, participants in group 2 had higher levels of PLR, as well as serum albumin, total cholesterol, platelet, correlation of different PLR levels and death risk in gender subgroups. After applying multivariate Cox models and eGFR but a lower serum creatinine and lymphocyte. Participants with a PLR level > 156:43 were older, were more (adjusted for all covariates), higher PLR was associated with often women, and had a higher history of diabetes, platelet increased risk of CVD mortality (HR: 1.003, 95% CI: 1.001- 1.006; p =0:008) only in the female subgroup and not inhibitor. The median of PLR level at baseline for all patients was 156.4 (IQR: 118.1, 206.4) as the median of platelet was among male patients (Table 4). Kaplan-Meier survival showed that higher PLR was associated with a significantly 232:0 × 109/L ðIQR : 183:0, 288:0Þ × 109/L, and the median of lymphocyte was 1:5 × 109/L ðIQR : 1:2, 1:8Þ × 109/L. increased risk for CVD death only in female cases, but not in male cases (Figure 2). 3.2. Factors Associated with PLR. Multivariate linear regres- sion analysis revealed that age (β =0:446, p =0:010), female 4. Discussion gender (β =10:073, p =0:029), diabetes (β =16:280, p = 0:009), and serum albumin (β =1:098, p =0:034) were inde- In this study, CVD mortality in female PD patients with pendently positively associated with PLR, after adjusting for higher PLR level was significantly higher than that in female age, gender, history of CVD and diabetes, hemoglobin, PD patients with lower PLR level. serum albumin, eGFR, platelet inhibitor, and β-blockers PLR may be more valuable than counting platelets or (Table 2). lymphocytes alone due to its reflection of both inflammation and thrombosis [22]. Some studies have reported the associ- 3.3. Associations between PLR and Clinical Outcomes. The ation of PLT and mortality. Chen et al. showed high PLR median overall survival was 48.9 (IQR: 23.4-79.3) months. could predict the risk of CVD events rather than CVD mor- By the end of this study period, 273 (19.0%) cases were tality in continuous ambulatory peritoneal dialysis patients transferred to HD, 363 (25.2%) cases received kidney trans- [13]. Liu et al. reported that PLR was an independent 6 Mediators of Inflammation 1.0 Group 1 0.8 Group 2 0.6 0.4 Log-rank test = 0.432 P = 0.511 0.2 0.0 024 48 72 96 120 144 Months of PD (a) 1.0 Group 1 0.8 Group 2 0.6 0.4 Log-rank test = 4.964 P = 0.026 0.2 0.0 024 48 72 96 120 Months of PD (b) Figure 2: Continued. Cumulative survival Cumulative survival Mediators of Inflammation 7 1.0 Group 1 0.8 Group 2 0.6 0.4 Log-rank test = 0.000 P = 0.993 0.2 0.0 024 48 72 96 120 Months of PD (c) 1.0 Group 1 0.8 Group 2 0.6 0.4 Log-rank test = 6.363 P = 0.012 0.2 0.0 024 48 72 96 120 144 Months of PD (d) Figure 2: PLR and mortality stratified by sex in peritoneal dialysis (PD) patients. Cumulative risk for all-cause mortality in male patients (a) and female patients (b). Cumulative risk for cardiovascular mortality in male patients (c) and female patients (d). PLR for groups: group 1 (≤156.43) and group 2 (>156.43). predictor of all-cause mortality in PD patients [14]. The The potential mechanism for the relation between PLR conclusion was different from our study. It might be and CVD death may be proposed. PLR has been considered attributed to sample size, timing of follow-up, and the dif- as an indicator of systemic inflammation [9–12]. As we all ference of multivariate Cox covariates. However, the asso- know, systemic inflammation was closely related to cardio- ciation of PLR and gender and CVD mortality was not vascular mortality [4–7]. Some studies have shown that high studied in these studies. In our study, we proved that an PLR is associated with increased mortality in various disease increased in PLR may increase the risk of CVD death in states [23–25]. A multitude of proinflammatory cytokines is female patients on PD. released by the activation of platelets and mediates the Cumulative survival Cumulative survival 8 Mediators of Inflammation interaction with leukocytes, which lead to the exacerbation should pay close attention to PLR in PD patients, especially of inflammation [26, 27]. Inflammation becomes ubiquitous in female patients. Regular evaluating and monitoring are among PD patients [4, 14]. Both acute peritonitis and micro- essential. inflammation are important constituents of systemic inflam- mation responses [28–30]. In PD patients, Data Availability microinflammation may be associated with the accumula- The clinical data used to support the findings of the study tion of uremic wastes, PD catheterization, bioincompatible are available from the corresponding author or the PD cen- dialysate, and periodontal problems [28, 31]. Microinflam- ter at the First Affiliated Hospital, Sun Yat-sen University mation is also an important part of systemic inflammatory upon request. response. As we known, the association among inflamma- tion, malnutrition, and atherosclerosis has been described Conflicts of Interest as malnutrition, inflammation, and atherosclerosis (MIA) syndrome. These three factors influence one another and All authors have declared no conflicts of interest. eventually leading to increased CVD death [29–32]. Furthermore, interaction analysis suggested that gender Acknowledgments was the most important effect modifier in this study. In sub- group analysis, PLR was independently associated with The present study was supported by the Key Laboratory of higher mortality of cardiovascular diseases only in female Nephrology, Guangdong Province, Guangzhou, China patients. Some scholars have reported the sex difference in (grant no. 2020B1212060028), Key Laboratory of National PLR. Lee et al. found that PLR was higher in women than Health Commission, National Key R&D Program of China that in men according to their study of more than 10,000 (grant nos. 2016YFC0906100, 2016YFC0906101), and Sci- patients from a single racial group [15]. However, another ence and Technology Project of Guangdong Province (grant research from Central China reported that there was no dif- no. 2013B022200003). The research would not have been ference between male and female in PLR [33]. A study possible without the help of nephrologist and nurses at the showed that high baseline PLR could predict poor renal sur- PD Center; thank you all. vival in patients with IgAN, especially in female cases [22]. In our study, we found that the sex difference in PLR may References influence the effects of PLR on mortality in PD patients. Several potential mechanisms for the association between [1] P. K. Li, G. C. Chan, J. Chen et al., “Tackling dialysis Burden PLR and sex difference may be proposed. Firstly, platelets around the world: a global challenge,” Kidney Disease, vol. 7, and lymphocytes arise from the same hematopoietic stem no. 3, pp. 167–175, 2021. cells; so, PLR remains constant to keep the balance in vivo [2] O. M. Akchurin and F. Kaskel, “Update on inflammation in [34, 35]. An elevated level of PLR may represent relatively high chronic kidney Disease,” Blood Purification, vol. 39, no. 1-3, platelets and low lymphocytes. 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Mediators of Inflammation – Hindawi Publishing Corporation
Published: Jan 4, 2022
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