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Liyi Li Wenzhou Medical University, Wenzhou 325000, China

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Liming Hu The First People's Hospital of Wenling, Wenling 317500, China

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Bingbao Chen Laboratory Animal Centre of Wenzhou Medical University, Wenzhou 325035, China

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Yanwen Dong Laboratory Animal Centre of Wenzhou Medical University, Wenzhou 325035, China

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Zixia Lin Laboratory Animal Centre of Wenzhou Medical University, Wenzhou 325035, China

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Zhiyi Wang Wenzhou Medical University, Wenzhou 325000, China

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Congcong Wen Laboratory Animal Centre of Wenzhou Medical University, Wenzhou 325035, China

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Xianqin Wang Analytical and Testing Centre of Wenzhou Medical University, Wenzhou 325035, China

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Shuanghu Wang People's Hospital of Lishui City, Lishui 323000, China

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Open access

In this study, we developed a urine metabolomic method by gas chromatography–mass spectrometry (GC–MS) combination with biomedical results to evaluate the effect of activated carbon on methomyl poisoning rats. The rats were divided into four groups, methomyl group, two activated carbon treatment group, and control group. According to the biochemical results, it indicated that activated carbon treated rats could cause liver and kidney function changes. According to the urine metabolomics results, activated carbon treatment group (10 min) and activated carbon treatment group (30 min) could be distinguished from methomyl group, and activated carbon treatment group (10 min) could be separated from activated carbon treatment group (30 min) rats, which indicated that the treatment of rats by activated carbon in different time had a different effect. The results indicate that metabolomic method by GC–MS may be useful to elucidate activated carbon treated on methomyl poisoning rats.

Abstract

In this study, we developed a urine metabolomic method by gas chromatography–mass spectrometry (GC–MS) combination with biomedical results to evaluate the effect of activated carbon on methomyl poisoning rats. The rats were divided into four groups, methomyl group, two activated carbon treatment group, and control group. According to the biochemical results, it indicated that activated carbon treated rats could cause liver and kidney function changes. According to the urine metabolomics results, activated carbon treatment group (10 min) and activated carbon treatment group (30 min) could be distinguished from methomyl group, and activated carbon treatment group (10 min) could be separated from activated carbon treatment group (30 min) rats, which indicated that the treatment of rats by activated carbon in different time had a different effect. The results indicate that metabolomic method by GC–MS may be useful to elucidate activated carbon treated on methomyl poisoning rats.

Introduction

Methomyl is an insecticide belonging to the family of carbamate pesticides. Because of its broad spectrum of activity and efficacy, it is widely used. Generally, the mortality rate for cases of carbamate poisoning is low, but fatalities secondary to methomyl poisoning have been reported [13]. All patients who died with methomyl poisoning experienced cardiac arrest and died from multiple organ dysfunction syndrome (MODS) [4]. Due to lack of effective clinical treatment, the mortality rate of methomyl poisoning was comparable to that of World Health Organization Hazard Class I organophosphate compounds.

Several previous studies had indicated that antioxidant, such as vitamin E [5], vitamin C and selenium [6], afforded protection but not treatment in methomyl-induced toxicity in the animal. Our present study was designed to investigate the effect of administering activated charcoal on rats induced by acute methomyl treatment. Metabolomics is an important science for the understanding of biological systems and the prediction of their behavior, through the profiling of metabolites [711]. In this present study, we adopted a metabolomics approach with gas chromatography–mass spectrometry (GC–MS) to evaluate effect of activated carbon on methomyl poisoning rats.

Experimental

Instrumentation and Conditions

Agilent 6890N-5975B GC/MS with HP-5MS (0.25 mm × 30 m × 0.25 mm) was purchased from Agilent Company (Santa Clara, California, USA). The metabolomics GC–MS conditions were set according to our previous work [12].

Metabolomics Study

Thirty-two male Sprague-Dawley rats (200–220 g) were obtained from Laboratory Animal Center of Wenzhou Medical University (Wenzhou, China). Rats were hosed at Laboratory Animal Center of Wenzhou Medical University at room temperature (25 °C), humidity of 60%–80%, 12–12 hours of light and dark cycles, given free access to conventional food and water during 7 days, and fasted for feed 12 hours before the experiment began. The rats were randomly divided in four groups: control group, methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min), 8 rats for each group. The methomyl group rats were given methomyl by intragastric administration of 10 mg kg−1; activated carbon treatment group (10 min) was given activated carbon 1000 mg kg−1 at 10 min after intragastric administration of 10 mg kg−1 methomyl; activated carbon treatment group (30 min) was given activated carbon 1000 mg kg−1 at 30 min after intragastric administration of 10 mg kg−1 methomyl; control group rats were given saline by intragastric administration. All experimental procedures were approved ethically by the Administration Committee of Experimental Animals of Wenzhou Medical University.

Urine samples were collected from the rats from the methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min) at 8:00 am after 2 days, respectively. The urine was stored at −80 °C until measurement. The sample preparation for GC–MS analysis was according to our previous work [13].

Biochemical Tests

After metabolomics study, the blood was collected from the tail vein for biochemical tests of urine glutamic-pyruvic transaminase, total protein, albumin, globulin, glutamic oxalacetic transaminase, alkaline phosphatase, urea, creatinine, and uric acid (these were used to evaluate the liver and kidney function).

Data Analysis

The resulting data were processed through partial least squares discriminant analysis (PLS-DA) and principal component analysis (PCA) using SIMCA-P 11.5 software (Umetrics, Umea, Sweden) [13].

Statistical Analysis

Statistical analysis was carried out using SPSS software (version 18.0, SPSS). P < 0.05 was considered statistically significant with independent samples T-test.

Results and Discussion

Metabolomics Study

Figure 1 provides the typical metabolic profiles of urine acquired through GC–MS technique. The metabolites in the urine were identified using the NIST 2005.

Figure 1.
Figure 1.

Typical GC–MS total ion chromatogram of rat urine of methomyl group and two activated carbon treatment group

Citation: Acta Chromatographica Acta Chromatographica 30, 1; 10.1556/1326.2017.00146

We compared the GC–MS urine spectrum of PCA of the activated carbon treatment group (10 min) and activated carbon treatment group (30 min) with the rats in the methomyl group (Figure 2A) (the corresponding load diagram was shown in Figure 2B). However, the results of the PCA were not good enough to distinguish between methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min). Then, we used PLS-DA to compare activated carbon treatment group (10 min) and activated carbon treatment group (30 min) with the rats in the methomyl group (Figure 3A) (the corresponding load diagram was shown in Figure 3B, PLS-3D was shown in Figure 3C). As could be seen in Figure 3B and C, activated carbon treatment group (10 min) and activated carbon treatment group (30 min) could be distinguished from methomyl group, and activated carbon treatment group (10 min) could be separated from activated carbon treatment group (30 min) rats, which indicated that the treatment of rats by activated carbon in different time had a different effect.

Figure 2.
Figure 2.

PCA score results of rat urine samples (A) of methomyl group and two activated carbon treatment group. Methomyl group (class 1), activated carbon treatment group (10 min, 1000 mg kg−1) (class 2), activated carbon treatment group (30 min, 1000 mg kg−1) (class 3); the corresponding load diagram (B)

Citation: Acta Chromatographica Acta Chromatographica 30, 1; 10.1556/1326.2017.00146

Figure 3.
Figure 3.

PLS-DA score results of rat urine samples (A) of methomyl group and two activated carbon treatment group. Methomyl group (class 1), activated carbon treatment group (10 min, 1000 mg kg−1) (class 2), activated carbon treatment group (30 min, 1000 mg kg−1) (class 3); the corresponding load diagram (B); PLS-3D score result (C)

Citation: Acta Chromatographica Acta Chromatographica 30, 1; 10.1556/1326.2017.00146

Biochemical Tests

There is no significant difference for glutamic-pyruvic transaminase, total protein, globulin, uric acid among methomyl group, activated carbon treatment group (10 min), activated carbon treatment group (30 min), and control group for biochemical results (Table 1). Albumin decreased in two activated carbon treatment group compared to methomyl group, while there was no significant difference compared to control group.

Table 1.

Biochemical results in rat serum of methomyl group and two activated carbon treatment group

Biochemical index Control Methomyl Activated carbon (10 min, 1000 mg kg−1) Activated carbon (30 min, 1000 mg kg−1)
Glutamic-pyruvic transaminase 38.8 ± 11.9 33.1 ± 4.5 32.4 ± 5.2 46.0 ± 19.2
Total protein 49.0 ± 6.0 53.5 ± 1.4 52.2 ± 1.8 52.0 ± 1.8
Albumin 22.9 ± 3.1 23.3 ± 1.0 21.3 ± 2.3* 22.3 ± 0.8*
Globulin 26.1 ± 3.0 30.2 ± 1.2Ұ 30.9 ± 2.3Ұ 29.7 ± 1.4Ұ
Glutamic oxalacetic transaminase 146.0 ± 46.8 142.3 ± 38.0 158.8 ± 42.3 147.5 ± 73.6
Alkaline phosphatase 440.4 ± 75.7 352.0 ± 106.9 266.2 ± 22.7ҰҰ 239.3 ± 31.9*,ҰҰ
Urea 11.7 ± 2.3 8.2 ± 0.6Ұ 6.3 ± 0.7**,ҰҰ 13.1 ± 7.0
Creatinine 17.6 ± 1.7 19.7 ± 1.0Ұ 23.3 ± 1.8**,ҰҰ 33.5 ± 10.2*,Ұ
Uric acid 51.0 ± 20.6 62.3 ± 18.3 66.7 ± 28.3 51.4 ± 25.6

Compared with control group, ҰP < 0.05 and ҰҰP < 0.01; compared with methomyl group, *P < 0.05 and **P < 0.01; compared with activated carbon treatment group (10 min, 1000 mg kg−1), #P < 0.05, as indicated by the statistical analysis T-test.

Globulin increased in methomyl group and two activated carbon treatment group, and all had significant difference compared to control group, which indicated that methomyl poisoning may stimulate the synthesis of globulin. Alkaline phosphatase was decreased in two activated carbon treatment groups compared to control group, while there was no significant difference between control group and methomyl group. Creatinine increased in methomyl group and two activated carbon treatment group compared to control group, and there was significant difference between the two activated carbon treatment groups and methomyl group. According to the biochemical results, it indicated that activated carbon treated rats could cause liver and kidney function changes.

Changes in Metabolite

Activated charcoal is the most frequently employed method of gastrointestinal decontamination to treat many kinds of poisoning in the developed world. Its tremendous surface area permits the binding of many toxins and drugs in the gastrointestinal lumen, reducing their systemic absorption [14]. In the current study, intragastric administration of activated carbon may also relieve the absorption of methomyl. Metabolomics is a newly emerging omics approach to the investigation of metabolic phenotype changes induced by environmental or endogenous factors [1518].

The identification of endogenous compounds that can be used as metabolic biomarkers would represent an alternative approach of significant importance to detect hidden effects. In this study, the changes of metabolites in methomyl group, activated carbon treatment group (10 min), and activated carbon treatment group (30 min) were shown in Table 2. Compared to the methomyl group, retinoic acid, 2(3H)-furanone, alloxanoic acid, hexadecanoic acid, and citric acid decreased in activated carbon treatment group (10 min), and benzeneacetic acid, d-ribose, and d-xylose decreased in activated carbon treatment group (30 min). Compared to the activated carbon treatment group (10 min), arabitol, retinoic acid, l-leucine, benzeneacetic acid, and alloxanoic acid decreased in activated carbon treatment group (30 min).

Table 2.

Summary of the changes in relative levels of metabolites in rat urine of methomyl group and two activated carbon treatment group

No. Retention time/min Metabolite VIP Methomyl Activated carbon (10 min, 1000 mg kg−1) Activated carbon (30 min, 1000 mg kg−1)
1 13.660 Arabitol 1.587 0.360 0.359 0.230#
2 13.913 Retinoic acid 1.049 0.228 0.304* 0.245#
3 14.971 l-Leucine 1.037 0.031 0.027 0.000#
4 15.084 Benzeneacetic acid 1.722 0.205 0.181 0.090**,##
5 15.192 Glycerol 1.170 0.130 0.194 0.108
6 15.412 d-Ribose 1.218 0.078 0.081 0.018**
7 15.518 d-Xylose 1.154 0.284 0.272 0.156*
8 15.647 Quinoline 1.016 0.019 0.026 0.033#
9 16.089 2(3H)-Furanone 1.347 0.369 0.280* 0.260
10 17.970 Alloxanoic acid 4.265 1.449 2.379* 1.249#
11 18.087 Ribitol 1.393 0.184 0.235 0.231
12 18.839 d-Gluconic acid 1.231 0.394 0.420 0.557
13 19.145 Galactonic acid 1.197 0.188 0.166 0.091
14 19.263 Hexadecanoic acid 1.165 0.533 0.441* 0.470
15 19.990 Inositol 2.382 0.127 0.608 0.270
16 22.685 Citric acid 1.029 0.022 0.054* 0.017

Note: Variable importance in the projection (VIP) was acquired from the PLS-DA model with a threshold of 1.0. Compared with methomyl group, *P < 0.05 and **P < 0.01; compared with activated carbon treatment group (10 min, 1000 mg kg−1), #P < 0.05 and ##P < 0.01, as indicated by the statistical analysis T-test.

Conclusion

We demonstrated that metabolomic methods based on GC–MS could provide a useful tool to evaluate the effect of activated carbon treatment in methomyl poisoning rats, combined with the biochemical results, which indicated that activated carbon treatment could cause liver and kidney function changes.

Disclosure of Conflict of Interest

The authors declare no conflict of interest.

Acknowledgments

This study was supported by grants from WenZhou Science and Technology Bureau (Y20140493 and Y20140688) and Wenling City Science and Technology Project (2015C311029 and 2015C312060).

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    Meng, S. L.; Chen, J. Z.; Xu, P.; Qu, J. H.; Fan, L. M.; Song, C.; Qiu, L. P. Bull. Environ. Contam. Toxicol. 2014 , 92 , 388 392 .

  • 2.

    Meng, S. L.; Chen, J. Z.; Hu, G. D.; Song, C.; Fan, L. M.; Qiu, L. P.; Xu, P. Ecotoxicol. Environ. Saf. 2014 , 101 , 1 6 .

  • 3.

    Lin, C. M. Neurol. Int. 2014 , 6 , 5307 .

  • 4.

    Lee, B. K.; Jeung, K. W.; Lee, H. Y.; Jung, Y. H. Clin. Toxicol. (Phila) 2011 , 49 , 828 833 .

  • 5.

    Garg, D. P.; Kiran, R.; Bansal, A. K.; Malhotra, A.; Dhawan, D. K. Drug Chem. Toxicol. 2008 , 31 , 487 499 .

  • 6.

    Djeffal, A.; Messarah, M.; Boumendjel, A.; Kadeche, L.; Feki, A. E. Toxicol. Ind. Health 2015 , 31 , 31 43 .

  • 7.

    Zaitsu, K.; Hayashi, Y.; Kusano, M.; Tsuchihashi, H.; Ishii, A. Drug Metab. Pharmacokinet. 2016 , 31 , 21 26 .

  • 8.

    Yi, L.; Dong, N.; Yun, Y.; Deng, B.; Ren, D.; Liu, S.; Liang, Y. Anal. Chim. Acta 2016 , 914 , 17 34 .

  • 9.

    Pallares-Mendez, R.; Aguilar-Salinas, C. A.; Cruz-Bautista, I.; Del Bosque-Plata, L. Ann. Med. 2016 , 48 , 89 102 .

  • 10.

    Li, S.; Dunlop, A. L.; Jones, D. P.; Corwin, E. J. Biol. Res. Nurs. 2016 , 18 , 12 22 .

  • 11.

    Guasch-Ferre, M.; Hruby, A.; Toledo, E.; Clish, C. B.; Martinez-Gonzalez, M. A.; Salas-Salvado, J.; Hu, F. B. Diabetes Care 2016 , 39 , 833 846 .

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    • Search Google Scholar
    • Export Citation
  • 12.

    Zhang, M.; Deng, M.; Ma, J.; Wang X. Chem. Pharm. Bull. (Tokyo) 2014 , 62 , 505 507 .

  • 13.

    Zhang, Q.; Wu, H.; Wen, C.; Sun, F.; Yang, X.; Hu, L. Int. J. Clin. Exp. Pathol. 2015 , 8 , 9320 9325 .

  • 14.

    Juurlink, D. N. Br. J. Clin. Pharmacol. 2016 , 81 , 482 487 .

  • 15.

    Patti, G. J.; Yanes, O.; Siuzdak, G. Nat. Rev. Mol. Cell Biol. 2012 , 13 , 263 269 .

  • 16.

    Wang, Z.; Ma, J.; Zhang, M.; Wen, C.; Huang, X.; Sun, F.; Wang, S.; Hu, L.; Lin, G.; Wang, X. Biol. Pharm. Bull. 2015 , 38 , 1049 1053 .

  • 17.

    Wen, C.; Zhang, M.; Zhang, Y.; Sun, F.; Ma, J.; Hu, L.; Lin, G.; Wang, X. Biomed. Chromatogr. 2016 , 30 , 81 84 .

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    Wen, C.; Wang, Z.; Zhang, M.; Wang, S.; Geng, P.; Sun, F.; Chen, M.; Lin, G.; Hu, L.; Ma, J.; Wang, X. Biomed. Chromatogr. 2016 , 30 , 75 80 .

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Senior editors

Editor(s)-in-Chief: Kowalska, Teresa (1946-2023)

Editor(s)-in-Chief: Sajewicz, Mieczyslaw

Editors(s)

  • Danica Agbaba (University of Belgrade, Belgrade, Serbia)
  • Łukasz Komsta (Medical University of Lublin, Lublin, Poland)
  • Ivana Stanimirova-Daszykowska (University of Silesia, Katowice, Poland)
  • Monika Waksmundzka-Hajnos (Medical University of Lublin, Lublin, Poland)

Editorial Board

  • R. Bhushan (The Indian Institute of Technology, Roorkee, India)
  • J. Bojarski (Jagiellonian University, Kraków, Poland)
  • B. Chankvetadze (State University of Tbilisi, Tbilisi, Georgia)
  • M. Daszykowski (University of Silesia, Katowice, Poland)
  • T.H. Dzido (Medical University of Lublin, Lublin, Poland)
  • A. Felinger (University of Pécs, Pécs, Hungary)
  • K. Glowniak (Medical University of Lublin, Lublin, Poland)
  • B. Glód (Siedlce University of Natural Sciences and Humanities, Siedlce, Poland)
  • A. Gumieniczek (Medical University of Lublin, Lublin, Poland)
  • U. Hubicka (Jagiellonian University, Kraków, Poland)
  • K. Kaczmarski (Rzeszow University of Technology, Rzeszów, Poland)
  • H. Kalász (Semmelweis University, Budapest, Hungary)
  • K. Karljiković Rajić (University of Belgrade, Belgrade, Serbia)
  • I. Klebovich (Semmelweis University, Budapest, Hungary)
  • A. Koch (Private Pharmacy, Hamburg, Germany)
  • P. Kus (Univerity of Silesia, Katowice, Poland)
  • D. Mangelings (Free University of Brussels, Brussels, Belgium)
  • E. Mincsovics (Corvinus University of Budapest, Budapest, Hungary)
  • Á. M. Móricz (Centre for Agricultural Research, Budapest, Hungary)
  • G. Morlock (Giessen University, Giessen, Germany)
  • A. Petruczynik (Medical University of Lublin, Lublin, Poland)
  • R. Skibiński (Medical University of Lublin, Lublin, Poland)
  • B. Spangenberg (Offenburg University of Applied Sciences, Germany)
  • T. Tuzimski (Medical University of Lublin, Lublin, Poland)
  • Y. Vander Heyden (Free University of Brussels, Brussels, Belgium)
  • A. Voelkel (Poznań University of Technology, Poznań, Poland)
  • B. Walczak (University of Silesia, Katowice, Poland)
  • W. Wasiak (Adam Mickiewicz University, Poznań, Poland)
  • I.G. Zenkevich (St. Petersburg State University, St. Petersburg, Russian Federation)

 

KOWALSKA, TERESA (1946-2023)
E-mail: kowalska@us.edu.pl

SAJEWICZ, MIECZYSLAW
E-mail:msajewic@us.edu.pl

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2022  
Web of Science  
Total Cites
WoS
647
Journal Impact Factor 1.9
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Chemistry, Analytical (Q3)

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1.9
5 Year
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Scopus  
Scopus
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3.1
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SNIP
0.549

2021  
Web of Science  
Total Cites
WoS
652
Journal Impact Factor 2,011
Rank by Impact Factor Chemistry, Analytical 66/87
Impact Factor
without
Journal Self Cites
1,789
5 Year
Impact Factor
1,350
Journal Citation Indicator 0,40
Rank by Journal Citation Indicator Chemistry, Analytical 72/99
Scimago  
Scimago
H-index
29
Scimago
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0,27
Scimago Quartile Score Chemistry (miscellaneous) (Q3)
Scopus  
Scopus
Cite Score
2,8
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Scopus
SNIP
0,586

2020
 
Total Cites
650
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Journal
Impact Factor
1,639
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Chemistry, Analytical 71/83 (Q4)
Impact Factor
 
Impact Factor
1,412
without
Journal Self Cites
5 Year
1,301
Impact Factor
Journal
0,34
Citation Indicator
 
Rank by Journal
Chemistry, Analytical 75/93 (Q4)
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Citable
45
Items
Total
43
Articles
Total
2
Reviews
Scimago
28
H-index
Scimago
0,316
Journal Rank
Scimago
Chemistry (miscellaneous) Q3
Quartile Score
 
Scopus
393/181=2,2
Scite Score
 
Scopus
General Chemistry 215/398 (Q3)
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Scopus
0,560
SNIP
 
Days from
58
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to acceptance
 
Days from
68
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2019  
Total Cites
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495
Impact Factor 1,418
Impact Factor
without
Journal Self Cites
1,374
5 Year
Impact Factor
0,936
Immediacy
Index
0,460
Citable
Items
50
Total
Articles
50
Total
Reviews
0
Cited
Half-Life
6,2
Citing
Half-Life
8,3
Eigenfactor
Score
0,00048
Article Influence
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0,164
% Articles
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Citable Items
100,00
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Average
IF
Percentile
20,349
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26
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Journal Rank
0,255
Scopus
Scite Score
226/167=1,4
Scopus
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Chemistry (miscellaneous) 240/398 (Q3)
Scopus
SNIP
0,494
Acceptance
Rate
41%

 

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Acta Chromatographica
Language English
Size A4
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1988
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1
Issues
per Year
4
Founder Institute of Chemistry, University of Silesia
Founder's
Address
PL-40-007 Katowice, Poland, Bankowa 12
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2083-5736 (Online)

Monthly Content Usage

Abstract Views Full Text Views PDF Downloads
Sep 2023 0 12 2
Oct 2023 0 25 6
Nov 2023 0 46 4
Dec 2023 0 84 1
Jan 2024 0 73 4
Feb 2024 0 161 8
Mar 2024 0 32 0