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. 2023 Feb 28;9(3):e14178. doi: 10.1016/j.heliyon.2023.e14178

Meta-analysis of Saccharomyces cerevisiae on enhancement of growth performance, rumen fermentation and haemato-biochemical characteristics of growing goats

Ifeanyi Princewill Ogbuewu a,b,∗, Christian Anayo Mbajiorgu b
PMCID: PMC10009197  PMID: 36923902

Abstract

The use of Saccharomyces cerevisiae (SC) feed additives to improve animal performance are on the increase; however, the results of the action of SC supplementation on goats performance indices are conflicting. Thus, the thrust of this meta-analysis was to examine the influence of dietary SC intervention on the growth performance, haemato-biochemical indices and ruminal fermentation characteristics of growing goats fed total mixed ration (TMR). The search conducted in Google Scholar, PubMed and Scopus databases using several keywords yielded 500 studies of which 16 full-text articles were utilised for study. Response variables were aggregated via a random-effects model. The results showed that goats fed SC experienced higher average daily gain (ADG) than the controls (as standardized mean difference, SMD = 2.14; 95% confidence interval, CI: 1.40 to 2.89). In converse, dietary SC intervention had a small impact on dry matter intake (DMI) and feed conversion ratio (FCR). Subgroup analysis demonstrated that SC type (active vs inactive) improved FCR and ADG in growing goats. Results suggested that SC preparation increased blood glucose, white blood cell (WBC), ruminal propionate and total volatile fatty acid levels. There is heterogeneity among the articles used in the study, and aspects of studied covariates explained the variation. In conclusion, this study indicated that dietary yeast can positively influence growth performance, haemato-biochemical indices, and rumen fermentation parameters of growing goats.

Keywords: Goats, Saccharomyces cerevisiae, Performance, Blood characteristics, Ruminal parameters, Meta-analysis

Abbreviations: ADG, average daily gain; CI, confidence interval; DMI, dry matter intake; ES, effect size; FCR, feed conversion ratio; Hb, haemoglobin; Nfs, fail-safe number; NH3N, ammonia nitrogen; OpenMEE, open meta-analyst for ecology and evolution; PCV, packed cell volume; PICO, population intervention comparison outcome; PRISMA, preferred reporting items for systematic reviews andmeta-analyses; RBC, red blood cell; SC, saccharomyces cerevisiae; SMD, standardised mean difference; TMR, total mixed ration; TVFA, total volatile fatty acid; VFA, volatile fatty acid; WBC, white blood cell; YC, yeast culture

1. Introduction

Goats constitute the majority of the small ruminant population in developing nations. They are source of livelihood for resource-poor farmers [1]. Previous studies indicated feed additives prevent health disorders, enhance digestibility and improve performance in farm animals [2,3]. Saccharomyces cerevisiae (SC), an eco-friendly feed additive is rich in crude protein (40–45%) and moderate in essential nutrients [4]. Studies have shown that SC supplementation changes the type and proportions of ruminal volatile fatty acids (VFAs), stabilises rumen pH, decreased lactate production and enhance the absorption of essential nutrients in the small intestine [5,6]. Some of the commercial SC products used in ruminant nutrition are active live and inactive (dead) SC. According to Amin and Mao [6], active SC contains a pure culture of metabolically active live cells (>15 billion live cells per gram). Inactive SC is cultivated basically to produce specific nutrients. In ruminal fluid, active SC facilitates the multiplication of cellulolytic bacteria by scavenging oxygen [7]. It does this by upregulating the expression of genes associated with oxidative phosphorylation [8]. In addition, active SC generates metabolites that are utilised as food by the cellulolytic bacteria that inhabit the rumen [6,9].

The effects of dietary SC intervention on the productivity of growing goats have been evaluated and varying results have been observed. Some studies indicated that SC supplementation decreased growth performance indices in growing goats [[10], [11], [12]]. However, others found that SC increase growth performance in young goats [[13], [14], [15], [16]]. The impact of dietary SC intervention on rumen fermentation traits and haemato-biochemical parameters of ruminants differs as well [5,6,17,18]. The observed differences in performance parameters of growing goats on dietary SC supplementation could be linked to the breeds used, type of SC, supplementation dose, diet composition, feeding duration, sex among other factors reported to affect small ruminant [19,20]. Based on the literature available to date, no study has examined the effect of these explanatory moderators on productivity of young goats fed SC using published data. Meta-analysis is one method for combining published results in order to increase statistical power and determine heterogeneity [21,22]. Meta-analysis is a mathematical procedure that averages results across articles with the same objectives.

Several investigators have studied the benefit of SC additive on growth performance in goats [10,11,[23], [24], [25]]. Nevertheless, none have studied meta-analytic influence of SC additive on health and productive indices of growing goats. To bridge the identified research gap, this data synthesis aimed to determine the benefits of SC on growth performance, rumen fermentation and haemato-biochemical variables of growing goats offered total mixed rations.

2. Materials and methods

2.1. Search strategy

Studies that evaluated performance of growing goats fed SC diets were identified and selected through a systematic search done in Google Scholar, PubMed, and Scopus databases using a combination of key words (growing goats, SC, growth performance, blood, and rumen fermentation variables) and search queries (AND/OR). The study followed the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) guidelines. Identified studies were screened for new references. The PICO method was used to determine the conditions for an article to be included in the study, as presented in Table 1.

Table 1.

PICO criteria.

Search strategy Exclusion criteria
Participant Growing goats Not in growing goats
Intervention SC supplementation Not SC treatment
Comparison Control group (without SC supplementation)
Outcomes Growth indices, blood variables and rumen fermentation
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PICO - population, intervention, comparison and outcomes.

2.2. Study selection guidelines

Controlled studies that assessed the impact of TMR with and without SC supplementation on all, or any of the response variables of interest; growth performance (DMI, FCR or ADG), haemato-biochemical indices [red blood cell (RBC), haemoglobin (Hb) concentration, packed cell volume (PCV), WBC, total protein, albumin, glucose, creatinine, urea and cholesterol) and ruminal fermentation parameters (acetate, propionate, acetate/propionate ratio, pH, ammonia nitrogen (NH3N) and total volatile fatty acids (TVFA)] in growing goats were used for the data-synthesis. Reviews and controlled experiments not in growing goats were excluded. Interventions in growing goats not in any of the response variables of interest were excluded. Studies without controlled groups were not used for the data synthesis. Six authors were contacted to supply missing information on SC type, goat breeds, and goat's age at the beginning of the study; however, none of the authors responded to our email. However, the missing data is not a problem as the Open Meta-analyst for Ecology and Evolution (OpenMEE) software used for the meta-analysis has provision for missing data. Disagreements on whether to add an article or not were settled through consensus. Five hundred (500) studies were identified from the database searches. Out of these 500 studies, 442 duplicates were removes after scooping the topics and abstracts. Following the screening of the remaining 58 full-texts, 42 were further removed. Sixteen controlled articles were suitable for the investigation. The details of study selection protocol are described in Fig. 1.

Fig. 1.

Fig. 1

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Flow chart showing article selection process.

2.3. Data extraction and statistical analyses

Data on the surname of the first author, year of publication, and moderator variables [trial country (Sudan, Jordan, India, Egypt, China, Turkey, Hungary, Nigeria, and Pakistan), sex (male or female), SC type (active and inactive), goat breeds (Nubian Shami, Damascus, Taihang black, Saanen x Sami, Saanen, Surti, Taihang black, West Africa Dwarf, Surti, Balady goats Pateri crossbreed and Macheng black × Boer crossbred), supplementation level (0–4.5%), goat's age at the beginning of the study (2–6 months) and duration of supplementation (0–180 days)] were retrieved from the 16 articles used for the analysis as illustrated in Table 2. Data presented in graphical formats were retrieved using WebPlotDigitizer designed by Rohatgi [33]. All analyses were done in OpenMEE software. Effect sizes (ES) were pooled and expressed as SMD at 95% CI. SMD values of 0.2, 0.5, and 0.8 are taken small or low, moderate or medium, and large respectively [34]. Standardised mean difference was said to be significant when zero is not included in 95% CI. Heterogeneity was calculated using Q-statistic and I2 - statistic [35]. Heterogeneity was considered significant at 5%. The quantity of I2 accounted by the moderators was determined through meta-regression analysis. The effect of SC type on growth traits in growing goats was determined via subgroup analysis. Subgroup analyses of the impact of SC type on ruminal fermentation and blood parameters were not performed in this study because of insufficient data. Sensitivity analysis was done using a standard method [36]. In addition, publication bias was evaluated via funnel plot and fail-safe number (Nfs) methods. However, Jennions et al. [37], revealed that outcomes of meta-analysis is deemed robust, notwithstanding existence of publication bias when Nfs higher than [5*(n) + 10], where n = number of comparisons.

Table 2.

Characteristics of studies included in the meta-analysis.

s/n Authors Covariates
TC SC type Breed SL (%) DOS (day) Age (month) Sex
1 Fadel Elseed and Abusamra [10] Sudan nr Nubian 0–0.5 72 3 1
2 Titi et al. [11] Jordan Active Shami 0–1.26 84 4 1
3 Jinturkar et al. [23] India nr nr 0–0.2 21 6 3
4 Hafez et al. [24] Egypt Active Damascus 0–0.5 135 4 3
5 Kamdev et al. [25] India Inactive nr 0–0.3 180 3 1
6 Shi et al. [26] China Inactive Taihang black 0–0.3 90 3 1
7 Ozsoy et al. [27] Turkey Active Saanen × Sami 0–4.5 70 4 1
8 Kamal et al. [13] India Active nr 0–1.5 120 3 3
9 Oguz et al. [14] Hungary Active Saanen 0–1.0 77 2 1
10 Shankhpal et al. [15] India nr Surti 0–2.0 60 – 3
11 Shi et al. [28] China Inactive Taihang black 0–4.0 120 4 1
12 Adelusi et al. [29] Nigeria nr WAD 0–1.5 70 – 3
13 Pradhan et al. [30] India Active Surti 0–2.0 80 4 1
14 Abd-Elkader et al. [31] Egypt Active Balady goats 0–0.3 120 5 1
15 Solangi et al. [32] Pakistan Inactive Pateri cross breed 0–0.3 100 4 2
16 Cai et al. [16] China Active * 0–1.6 20 6 2
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1 - Male; 2 – Female; 3 – mixed; *- Macheng black × Boer crossbred; TC - trial country; SC - Saccharomyces cerevisiae; SOD - Duration of supplementation; SL - supplementation level; WAD - West Africa Dwarf; nr - not reported.

3. Results and discussion

3.1. Overview of studies included in the meta-analysis

Nutrition plays an important part in productivity of fattening goats. The importance of SC in improving health status and growth characteristics of animals has been reported [19]. The present study assessed the effect of dietary SC intervention on the growth performance, blood parameters, and ruminal characteristics of growing goats using 16 peer-reviewed controlled studies passed the selection condition for the present meta-analysis (Table 2). The flow diagram of study selection protocol and characteristics of the selected studies are presented in Fig. 1 and Table 2. The included articles were published between 2007 and 2021, and conducted in nine countries. Most of the studies were carried out in India and China.

3.2. Growth performance

There are indications that SC increased small ruminant growth and productivity by stimulating rumen bacteria activity leading to an improvement in DMI, nutrient digestibility, total VFAs, and microbial protein production [38]. In comparison with the controls, our results as shown in Fig. 2, Fig. 3 demonstrated that SC have no impact on dry matter intake (SMD = 0.18; 95% CI: −0.16 to 0.52) and FCR (SMD = −0.39; 95% CI: −1.10 to 0.32), but increased ADG (SMD = 2.14, 95% CI: 1.40 to 2.89; Fig. 4). The observation disagreed with the findings of others [39,40], who found enhanced DMI in dairy goats fed SC-supplemented diets at early lactation stage. This disparity could be associated with the variation in diet composition, physiological status or age. However, our result found that SC significantly increased ADG in growing goats. Studies have shown that dietary YC supplementation increased ADG in farm animals [9,41,42]. This may suggest the potential of SC feed additive to increase the diversity and abundance of bacteria that degrade cellulose in the rumen resulting to improvement in fibre digestibility and nutrient uptake [38]. In addition, YC contain metabolites that enhance animal productivity [43]. Conversely, other investigators reported that YC did not improve ADG in goats [44,45], which may be ascribed to yeast type, diet composition, and among other variables [43,45].

Fig. 2.

Fig. 2

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Effect of SC supplementation on DMI in growing goats.

Fig. 3.

Fig. 3

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Effect of SC supplementation on FCR in growing goats.

Fig. 4.

Fig. 4

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Effect of SC supplementation on ADG in growing goats.

Subgroup analysis results as illustrated in Table 3 suggested that SC type (active versus inactive) did not enhance DMI in growing goats. Feed conversion ratio in goats fed active SC supplemented diets was significantly different from controls (SMD = −0.62, 95% CI: −1.23 to −0.01; Table 3). This indicates that active SC promotes the activities of fibre-degrading bacteria in ruminal fluids [7], which in turn improves nutrient digestibility, changes the patterns of ruminal VFAs production, stabilise rumen pH and decreased ruminal lactate concentrations [5]. In converse, our subgroup analysis showed no significant effect of inactive SC on FCR (SMD = 0.78, 95% CI: −1.04 to 2.53). This probably means that inactive SC did not enhance FCR in growing goats. Results demonstrated that SC type had beneficial effect on ADG (active SC: SMD = 1.14, 95% CI: 0.42 to 1.86 vs inactive SC: SMD = 2.91, 95% CI: 1.33 to 4.49). Consistent results were also reported in ruminants [11,26,28].

Table 3.

Effect of SC type on DMI, ADG and FCR in growing goats.

Outcomes SC type SMD 95% CI
 SE p-val Heterogeneity
Lower Upper I2 p-val
DMI Active 0.21 −0.07 0.48 0.14 0.145 0 0.937
Inactive 0.42 −0.69 1.53 0.57 0.460 89 0.001
ADG Active 1.14 0.42 1.86 0.37 0.002 80 0.001
Inactive 2.91 1.33 4.49 0.80 <0.001 91 0.001
FCR Active −0.62 −1.23 −0.01 0.31 0.045 76 0.001
Inactive 0.78 −1.04 2.53 0.91 0.411 94 0.001
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DMI – dry matter intake; ADG – average daily gain; FCR - feed conversion ratio; SMD – standardised mean difference; SE – standard error; CI - confidence interval; I2 – Inconsistency index.

3.3. Haemato-biochemical characteristics

There are variable findings on the actions of SC supplementation on blood traits of farm animals [17,19,46]. Table 4 demonstrated that SC had no effect on the haemoglobin (Hb) concentration, packed cell volume (PCV) and red blood cell (RBC) of growing goats. In addition, our pooled analyses showed that blood total proteins, albumin, urea, and cholesterol levels in growing goats were not affected by SC supplementation. This observation agrees with the results of other investigators [27,47], who reported that SC preparation did not increase blood cholesterol concentrations in goats and cows. In the current study, dietary YC increased WBC i (SMD = 0.88, 95% CI: 0.18 to 0.58), which agreed with the observations of others [42,43,48] that YC increased immunity in animals. This finding is consistent with the results of others in lambs [17] and young goats [18]. Although the mechanisms by which SC improve the immune status of goats are not clear; however, SC may enhance the immune status of goats through one or blend of the following modes of action: (i) increased activity of beneficial rumen microbes; (ii) production of antigens by dead organisms [49]; (iii) modulation of intestinal microbiota and protection of mucosal wall against inflammation [50,51]; (v) exclusion of destructive gut microbes [52]; (vi) enhancement of the absorptive capacity of the villi [53]; (vii) stabilises of immunopotent cells.

Table 4.

Effect of SC supplementation on haemato-biochemical indices of growing goats.

Parameters SMD 95% CI
 SE p-Val Heterogeneity
Lower Upper I2 (%) p-Val
Haematology
Heamoglobin 0.29 −0.07 0.66 0.19 0.116 0 0.993
Packed cell volume −0.14 −0.83 0.54 0.35 0.685 65 0.006
Red blood cell 0.26 −0.12 0.64 0.20 0.184 0 0.995
White blood cell 0.88 0.18 0.58 0.36 0.013 59 0.022
Biochemistry
Total protein 0.19 −0.28 0.66 0.24 0.435 58 0.006
Albumin 0.11 −0.17 0.38 0.14 0.441 0 0.794
Glucose 0.63 0.29 0.98 0.18 <0.001 3 0.413
Creatinine 1.83 −0.04 3.71 0.96 0.056 85 <0.001
Urea −0.44 −0.90 0.02 0.23 0.061 33 0.151
Cholesterol 0.41 −0.37 1.19 0.40 0.303 83 <0.001
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CI - confidence interval; Hb - haemoglobin; PCV – packed cell volume; RBC – red blood cell; WBC- white blood cell; SE – standard error; I2 – Inconsistency index.

Blood metabolites are used in feeding experiment to determine the quality of feedstuffs and feed additives [45,54]. Several factors affect blood glucose contents in farm animals, including diet composition, sex, breeds, disease, physiological status, and sample time [55]. A number of feeding experiments have indicated that SC feed additive improves blood glucose levels in small ruminants [45,56]. These findings corroborated with the results of the meta-analysis which indicated that growing goats fed SC-supplemented diets had higher blood glucose levels (SMD = 0.63, 95% CI: 0.29 to 0.98) than the controls. Noeck et al. [57] found similar blood glucose levels in ruminants fed SC-based diets, which contrast with the findings of this meta-analysis. The observed disparity could be linked to diet consistency, SC strains, the amount of SC added to the diet, and physiological state. The results of this study suggested no significant differences in blood creatinine of goats fed diets with and without SC (SMD = 1.83, 95% CI: −0.04 to 3.71). This suggests that these goats are not in negative energy balance [58]. This observation agrees with Hafez et al. [24] (2011), who observed that YC did not affect blood creatinine levels in Damascus kids. In comparison with controls, dietary SC did not influence blood urea levels in young goats (SMD = −0.44, 95% CI: −0.90 to 0.02), which corroborates Geng et al. [59] and Adelusi et al. [29], who observed similar serum urea values in ruminants fed SC supplemented diets.

3.4. Rumen fermentation characteristics

Direct-fed microbes have beneficial impact on the rumen by improving the proliferation of lactate utilising bacteria and other beneficial microns in the rumen, resulting in a shift in the type and amount of VFA produced in the rumen [6,60]. Pooled analysis as shown in Table 5 indicated that SC had small negative effect on acetate/propionate ratio and small positive effects on the concentrations of rumen acetate, pH, and ammonia nitrogen (NH3N) in growing goats. In converse, young goats on dietary SC supplementation had statistically higher concentrations of ruminal propionate (SMD = 0.80, 95% CI: 0.31 to 1.29) and total VFAs (SMD = 0.95, 95% CI: 0.19 to 1.71) when compared to the controls. Some studies have demonstrated that SC can improve total VFAs in ruminants [56,61,62]. The observation is in agreement with the finding this investigation. In converse, Ding et al. [63] and Reséndiz-Hernández et al. [64] observed that dietary SC did not significantly increase the concentrations of total VFA in the rumen of growing lambs. This difference may be attributed to animal species and SC strains used in the feeding experiment [9,65,66]. The significantly higher ruminal propionate in SC-fed goats could be connected to the capability of SC to enhance the activity of Megasphaera elsdenii [67] discovered to convert lactate to propionate [68]. Propionate is the main substrate for glucose in ruminants [69] and gluconeogenesis [70]. The significantly higher propionate in the rumen of growing goats fed SC supplemented diets implies less production of carbon dioxide and methane, in contrast to increased acetate production, which results in increased carbon dioxide and methane production [71]. The significantly higher blood glucose concentrations observed in the SC-fed goats in this investigation could be linked to higher ruminal propionate concentrations [69].

Table 5.

Effect of diet with and without SC on mean rumen fermentation parameters in growing goats.

Parameters SMD 95% CI
 SE p-Val Heterogeneity
Lower Upper I2 (%) p-Val
Acetate 0.38 −0.28 1.03 0.33 0.256 56 0.025
Propionate 0.80 0.31 1.29 0.25 0.001 25 0.230
Acetate/Propionate ratio −0.01 −0.83 0.82 0.42 0.984 63 0.042
pH 0.16 −0.19 0.52 0.18 0.364 0 1.000
NH3N 0.34 −0.67 1.34 0.51 0.509 85 <0.001
Total VFAs 0.95 0.19 1.71 0.39 0.015 68 0.004
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CI - confidence interval; VFA – total volatile fatty acid; NH3N – ammonia nitrogen; SE – standard error; I2 – Inconsistency index.

3.5. Moderators and bias analysis

Meta-regression as shown in Table 6 revealed that SC supplementation level (P = 0.0003; R2 = 96%) and duration of SC supplementation (P = 0.0042; R2 = 76%) predicted the impact of SC intervention on DMI in growing goats. This indicates that the variable results of dietary SC intervention on DMI were explained by supplementation level and duration of supplementation. Meta-regression analyses showed that trial country, SC type, breed, sex and goat's age had no effect on DMI of growing goats fed SC-based diets. Outside SC type, all the studied moderators predicted the effect of SC on ADG, and explained most of the variations. Aspects of studied moderators had no significant impact on FCR in growing goats. The influence of SC on growth indices of growing goats as shown in Supplementary F. 1, 2 and 3 revealed minimal evidence of publication bias. The Nfs for the database (430) was 4 times higher than the threshold required to declare the SMD of outcome measures robust.

Table 6.

Relationships between moderators and aspects of response variables (DMI, FCR and ADG).

Outcomes Covariates QM p-val R2 -index (%)
DMI Trial country 4.57 0.713 0
SC type 0.13 0.721 0
Breed 3.63 0.889 0
Supplementation level 34.8 0.0003 96
Duration of supplementation 25.7 0.0042 76
Goat's age at the beginning of the study 6.80 0.147 18
Sex 1.69 0.193 15
ADG
Trial country 33.3 2.35e − 05 73
SC type 3.62 0.057 22
Breed 40.8 2.23e − 06 80
Supplementation level 52.8 4.48e − 07 85
Duration of supplementation 98.6 3.33e − 16 97
Goat's age at the beginning of the study 11.7 0.0201 30
Sex 9.42 0.00215 57
FCR
Trial country 96.7 0.000 100
SC type 3.71 0.0539 19
Breed 83 8.88e − 16 100
Supplementation level 8.81 0.551 0
Duration of supplementation 102 0.000 100
Goat's age at the beginning of the study 6.51 0.0893 15
Sex 3.1 0.0783 23
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DMI - dry matter intake; ADG – average daily gain; FCR – feed conversion ratio; R2 - amount of heterogeneity accounted for by covariate; QM - Coefficient of moderators; p-val - probability value.

3.6. Limitations and strengths of the analysis

This data synthesis was focused in growing goats and outcomes may not be generalised to other animals. There are disparities in the amount of SC added to the ration, goats' age, the duration of SC feeding, and the season of the year the studies included in the meta-analysis was done and this may affect the validity of this study. Individual articles utilised for the study may have used different laboratory procedures and analytical techniques, which may be a limitation. Insufficient data hindered subgroup analyses of the effect of explanatory moderator variables (i.e., trial country, breeds, supplementation level, duration of supplementation, goat's age at the beginning of the study, and sex) on the measured outcomes in growing goats fed SC-supplemented diets in this meta-analysis. Thus, more research is needed in this area. Despite these constraints, the outcomes of this meta-analysis have shown that SC had positive effects on growth traits, rumen fermentation and haemato-biochemical characteristics of growing goats.

4. Conclusion

The pooled estimation demonstrated that incorporation of Saccharomyces cerevisiae into growing goat diets improved average daily gain. In addition, subgroup analysis indicated that Saccharomyces cerevisiae type (active and inactive) had no effect on dry matter intake in growing goats, but significantly enhanced feed conversion ratio and average daily gain. Pooled results revealed that addition of Saccharomyces cerevisiae to growing goat diets enhanced blood glucose and white blood cell counts. Supplementation with Saccharomyces cerevisiae also improves the concentrations of propionate and total volatile fatty acids in the rumen of growing goats. This suggests that addition of inactive and active Saccharomyces cerevisiae to the diets of growing goats is desirable, because of its positive effect on feed conversion ratio, average daily gain, blood glucose and white blood cell counts There is evidence of low to substantial heterogeneity among the studies included in the meta-analysis, and meta-regression analysis found that studied moderators accounted for most of the sources of heterogeneity.

Author contribution statement

Ifeanyichukwu Princewill Ogbuewu: Conceived and designed the experiments; Analysed and interpreted the data; Wrote the paper.

Christain Anayo Mbajiorgu: Performed the experiments; Analysed and interpreted the data; Wrote the paper.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Data included in article/supplementary material/referenced in article.

Additional information

Supplementary content related to this article has been published online at [URL].

Declaration of interest's statement

The authors declare no conflict of interest.

Footnotes

Appendix A

Supplementary data related to this article can be found at https://doi.org/10.1016/j.heliyon.2023.e14178.

Appendix A. Supplementary data

The following is the supplementary data related to this article:

Supplementary Figure
mmc1.docx (47.7KB, docx)

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