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Immune-Modulatory Effects of Ethanolic Plant Extracts: A Systematic Review and Meta-Analysis of Specific Plant-Parts Responses Cover

Immune-Modulatory Effects of Ethanolic Plant Extracts: A Systematic Review and Meta-Analysis of Specific Plant-Parts Responses

Open Access
|Feb 2026

Figures & Tables

Figure 1.

PRISMA flow diagram summarizing the systematic literature search and study selection process. The initial database search conducted on July 1, 2023, using Scopus and Web of Science yielded 1,252 records. An additional 13 records were identified through other sources such as Google Scholar. After removing duplicates and screening titles, abstracts, and full texts according to PRISMA guidelines (Liberati et al., 2009), 30 studies were included in the final meta-analysis. Inclusion criteria required that studies (1) used ethanol-extracted plant parts, (2) measured immune or antioxidant biomarkers such as lysozyme activity, white blood cell counts, superoxide dismutase (SOD), catalase (CAT), or malondialdehyde (MDA), and (3) were conducted on fish, crustacean or fish cell lines
PRISMA flow diagram summarizing the systematic literature search and study selection process. The initial database search conducted on July 1, 2023, using Scopus and Web of Science yielded 1,252 records. An additional 13 records were identified through other sources such as Google Scholar. After removing duplicates and screening titles, abstracts, and full texts according to PRISMA guidelines (Liberati et al., 2009), 30 studies were included in the final meta-analysis. Inclusion criteria required that studies (1) used ethanol-extracted plant parts, (2) measured immune or antioxidant biomarkers such as lysozyme activity, white blood cell counts, superoxide dismutase (SOD), catalase (CAT), or malondialdehyde (MDA), and (3) were conducted on fish, crustacean or fish cell lines

Figure 2.

Funnel plot assessing publication bias in studies evaluating the effects of ethanolic extracts from different medicinal plant parts on white blood cell (WBC) counts in fish. The x-axis represents the mean difference (effect size), and the y-axis denotes the standard error. Dotted lines indicate pseudo 95% confidence limits. Asymmetry in the distribution of studies, with several outliers beyond the funnel boundaries, suggests potential publication bias and high heterogeneity
Funnel plot assessing publication bias in studies evaluating the effects of ethanolic extracts from different medicinal plant parts on white blood cell (WBC) counts in fish. The x-axis represents the mean difference (effect size), and the y-axis denotes the standard error. Dotted lines indicate pseudo 95% confidence limits. Asymmetry in the distribution of studies, with several outliers beyond the funnel boundaries, suggests potential publication bias and high heterogeneity

Figure 3.

Forest plot of subgroup meta-analysis evaluating the effects of different medicinal plant parts and their ethanolic extracts on white blood cell (WBC) counts in fish. Subgroups included root, leaves, whole plant, flower, fruits, seeds, aerial parts, and rind. The plot shows mean differences (MD) with 95% confidence intervals. Significant heterogeneity was observed (I2 = 99.6%), and subgroup analysis revealed that plant part type significantly influenced the immunomodulatory effect (p < 0.001)
Forest plot of subgroup meta-analysis evaluating the effects of different medicinal plant parts and their ethanolic extracts on white blood cell (WBC) counts in fish. Subgroups included root, leaves, whole plant, flower, fruits, seeds, aerial parts, and rind. The plot shows mean differences (MD) with 95% confidence intervals. Significant heterogeneity was observed (I2 = 99.6%), and subgroup analysis revealed that plant part type significantly influenced the immunomodulatory effect (p < 0.001)

Figure 4.

Funnel plot assessing publication bias in studies investigating the effects of ethanolic extracts from various medicinal plant parts on lysozyme activity in fish. The x-axis represents the standardized mean difference (SMD), and the y-axis shows the standard error. Each circle denotes an individual study. Dashed lines represent pseudo 95% confidence limits. While the majority of studies are symmetrically distributed, a few outliers with high effect sizes and larger standard errors suggest potential small-study effects and possible publication bias
Funnel plot assessing publication bias in studies investigating the effects of ethanolic extracts from various medicinal plant parts on lysozyme activity in fish. The x-axis represents the standardized mean difference (SMD), and the y-axis shows the standard error. Each circle denotes an individual study. Dashed lines represent pseudo 95% confidence limits. While the majority of studies are symmetrically distributed, a few outliers with high effect sizes and larger standard errors suggest potential small-study effects and possible publication bias

Figure 5.

Forest plot of meta-analysis evaluating the effects of medicinal plants ethanolic extract on lysozyme activity (LYZ) in fish. The plot shows standardized mean differences (SMD) with 95% confidence intervals for each study comparing experimental groups to control groups. Each square represents the effect size of an individual study, scaled by its weight, while horizontal lines indicate 95% confidence intervals. Pooled effect estimates under both the common effect and random effects models are shown as diamonds. High heterogeneity was observed across studies (I2 = 99.2%), and the wide prediction interval under the random effects model reflects substantial variability in lysozyme responses among studies
Forest plot of meta-analysis evaluating the effects of medicinal plants ethanolic extract on lysozyme activity (LYZ) in fish. The plot shows standardized mean differences (SMD) with 95% confidence intervals for each study comparing experimental groups to control groups. Each square represents the effect size of an individual study, scaled by its weight, while horizontal lines indicate 95% confidence intervals. Pooled effect estimates under both the common effect and random effects models are shown as diamonds. High heterogeneity was observed across studies (I2 = 99.2%), and the wide prediction interval under the random effects model reflects substantial variability in lysozyme responses among studies

Figure 6.

Funnel plot evaluating publication bias among studies examining the impact of ethanolic plant extract supplementation on superoxide dismutase (SOD) activity in fish. The x-axis denotes the mean difference, while the y-axis represents the standard error. Each circle corresponds to an individual study. Dashed lines indicate pseudo 95% confidence intervals. The asymmetrical distribution, particularly the extreme right-hand outlier, suggests potential small-study effects and possible publication bias
Funnel plot evaluating publication bias among studies examining the impact of ethanolic plant extract supplementation on superoxide dismutase (SOD) activity in fish. The x-axis denotes the mean difference, while the y-axis represents the standard error. Each circle corresponds to an individual study. Dashed lines indicate pseudo 95% confidence intervals. The asymmetrical distribution, particularly the extreme right-hand outlier, suggests potential small-study effects and possible publication bias

Figure 7.

Forest plot of meta-analysis evaluating the effects of plant-derived ethanolic extracts across different plant parts on the super oxide dismutase activities (SOD) of fish. The plot shows mean differences (MD) with 95% confidence intervals for each study comparing experimental groups to control groups, stratified by plant part (roots, leaves, flowers, fruits, stems, and whole plants with seeds). Each square represents the effect size of an individual study, scaled by its weight, while horizontal lines indicate 95% confidence intervals. Pooled effect estimates for subgroups and overall analyses are shown as diamonds under both common effect and random effects models. Extreme heterogeneity was observed across studies (I2 = 100%), and the wide prediction interval under the random effects model reflects substantial variability in treatment effects among different plant parts. Subgroup analyses confirmed significant differences in responses based on plant part (P < 0.001 for both common and random effects models)
Forest plot of meta-analysis evaluating the effects of plant-derived ethanolic extracts across different plant parts on the super oxide dismutase activities (SOD) of fish. The plot shows mean differences (MD) with 95% confidence intervals for each study comparing experimental groups to control groups, stratified by plant part (roots, leaves, flowers, fruits, stems, and whole plants with seeds). Each square represents the effect size of an individual study, scaled by its weight, while horizontal lines indicate 95% confidence intervals. Pooled effect estimates for subgroups and overall analyses are shown as diamonds under both common effect and random effects models. Extreme heterogeneity was observed across studies (I2 = 100%), and the wide prediction interval under the random effects model reflects substantial variability in treatment effects among different plant parts. Subgroup analyses confirmed significant differences in responses based on plant part (P < 0.001 for both common and random effects models)

Figure 8.

Funnel plot assessing publication bias for the effect of ethanol-extracted plant parts on MDA levels. The x-axis represents the mean difference in MDA, and the y-axis shows the standard error. Symmetry around the zero line suggests minimal bias, while asymmetry may indicate potential bias or heterogeneity among included studies
Funnel plot assessing publication bias for the effect of ethanol-extracted plant parts on MDA levels. The x-axis represents the mean difference in MDA, and the y-axis shows the standard error. Symmetry around the zero line suggests minimal bias, while asymmetry may indicate potential bias or heterogeneity among included studies

Figure 9.

Forest plot of meta-analysis of malondialdehyde (MDA) levels following treatment with ethanol-extracted plant parts. The plot displays mean differences (MD) in MDA levels with 95% confidence intervals comparing experimental (ethanol-extracted) and control groups, stratified by plant part (roots, leaves, fruits). Squares represent individual study effect sizes (proportional to study weight), horizontal lines show 95% CIs, and diamonds indicate pooled subgroup and overall estimates. Extreme heterogeneity (I2 = 100%) and significant subgroup differences (P < 0.001) reflect the variable impact of ethanol-extracted plant parts on oxidative stress markers. The wide prediction interval under random effects highlights substantial variability in MDA responses across different plant parts
Forest plot of meta-analysis of malondialdehyde (MDA) levels following treatment with ethanol-extracted plant parts. The plot displays mean differences (MD) in MDA levels with 95% confidence intervals comparing experimental (ethanol-extracted) and control groups, stratified by plant part (roots, leaves, fruits). Squares represent individual study effect sizes (proportional to study weight), horizontal lines show 95% CIs, and diamonds indicate pooled subgroup and overall estimates. Extreme heterogeneity (I2 = 100%) and significant subgroup differences (P < 0.001) reflect the variable impact of ethanol-extracted plant parts on oxidative stress markers. The wide prediction interval under random effects highlights substantial variability in MDA responses across different plant parts

Figure 10.

Funnel plot evaluating publication bias and heterogeneity in studies analysing the effect of ethanol-extracted plant parts on catalase activity. The x-axis represents the mean difference in catalase activity, and the y-axis shows the standard error. Asymmetry in the distribution of points may indicate selective reporting of positive outcomes or variability in study design
Funnel plot evaluating publication bias and heterogeneity in studies analysing the effect of ethanol-extracted plant parts on catalase activity. The x-axis represents the mean difference in catalase activity, and the y-axis shows the standard error. Asymmetry in the distribution of points may indicate selective reporting of positive outcomes or variability in study design

Figure 11.

Forest plot of meta-analysis of catalase activity following treatment with ethanol-extracted plant parts. The plot displays mean differences (MD) in catalase activity with 95% confidence intervals comparing experimental (ethanol-extracted) and control groups, stratified by plant part. Squares represent study effect sizes (proportional to weight), horizontal lines show 95% CIs, and diamonds indicate pooled estimates. Extreme heterogeneity (I2 = 99.8%) and significant subgroup differences (P < 0.001) reflect plant-part-specific effects on catalase activity. The wide prediction interval under random effects highlights substantial variability in enzymatic responses
Forest plot of meta-analysis of catalase activity following treatment with ethanol-extracted plant parts. The plot displays mean differences (MD) in catalase activity with 95% confidence intervals comparing experimental (ethanol-extracted) and control groups, stratified by plant part. Squares represent study effect sizes (proportional to weight), horizontal lines show 95% CIs, and diamonds indicate pooled estimates. Extreme heterogeneity (I2 = 99.8%) and significant subgroup differences (P < 0.001) reflect plant-part-specific effects on catalase activity. The wide prediction interval under random effects highlights substantial variability in enzymatic responses

Summary of the key studies included in the meta-analysis

CodePlant speciesPlant partFish speciesReferences
1C. rotundusRootNile tilapiaWigraiboon et al. (2024)
2Ocimum basilicumLeavesNile tilapiaMansour et al. (2023)
3Paulownia tomentosa cimum basilicum,LeavesNile tilapiaEl-Refiae et al. (2024)
4Cinnamomum zeylanicum, Juglans regia and Mentha piperitaWhole plantC. carpioAbasali and Mohamad (2010)
5Clove basil, Ocimum gratissimumleavesAfrican catfish, Clarias gariepinusAbdel-Tawwab et al. (2018)
6clove, Eugenia caryophyllataflowerAfrican catfish, Clarias gariepinusAdeshina et al. (2019)
7basil (Ocimum basilicum)leavesC. carpioAmirkhani and Firouzbakhsh (2015)
8Persian oakfruitsrainbow troutBohlouli et al. (2016)
9Panax ginsengRootrainbow troutBulfon et al. (2017)
10Garcinia kolaSeedsClarias gariepinusDada and Ikuerowo (2009)
11Psidium guajavaLeavesOreochromis mossambicus rainbwo troutGobi et al. (2016)
12Aloe veraLeavesOncorhynchus mykissHaghighi et al. (2017)
13Azadirachta indica (neem)LeavesC. carpioHarikrishnan et al. (2005)
14A. indica, O. sanctum and C. longaLeavesCarassius auratus gold fishHarikrishnan et al. (2009)
15green teaLeavesThe black rockfish, Sebastes schlegeliHwang et al. (2013)
16Cynodon dactylonWhole plantIndian major carp, Catla catlaKaleeswaran et al. (2011)
17Cynodon dactylonWhole plantIndian major carp, Catla catla white shrimp,Kaleeswaran et al. (2012)
18Panax ginsengRootLitopenaeus vannameiLiu et al. (2011)
19Camellia sinensisLeavesOncorhynchus mykissNootash et al. (2013)
20Pedalium murexSeedsLabeo rohitaOjha, M.L. et al. (2014)
21Mucuna pruriensSeedsLabeo rohitaOjha, M. et al. (2014)
22Epilobium hirsutumAerial partsCyprinus carpioPakravan et al. (2012)
23Cotinus coggygriaLeavesCyprinus carpioBilen et al. (2013)
24Garcinia gummi-guttaRindPangasianodon hypophthlmusPrasad and Priyanka (2011)
25DillAll plant +seedsRainbow TroutZeilab Sendijani et al. (2020)
26Rubus coreanusFruitsPenaeus vannameiSubramanian et al. (2013)
27Zingiber officinaleStemLabeo rohitaGobi et al. (2016)
28Apium graveolensLeavesLabeo chrysophekadionSutthi et al. (2020)
29ginkgo bilobaLeavesgrouperTan et al. (2018)
30Sophora flavescensRootGIFT Oreochromis niloticusWu et al. (2013)
DOI: https://doi.org/10.2478/aoas-2025-0129 | Journal eISSN: 2300-8733 | Journal ISSN: 1642-3402
Language: English
Submitted on: Aug 6, 2025
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Accepted on: Nov 12, 2025
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Published on: Feb 17, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2026 Mohammed A.E. Naiel, Samar S. Negm, Shrouq Eladawy, Safa Elnahass, Sally Hosny, Rasha Abd El-Hady Naiel, Xiaolin Meng, Abeer El Shahawy, published by National Research Institute of Animal Production
This work is licensed under the Creative Commons Attribution 3.0 License.

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