Differences of semen microbiota among breeding boars with different reproductive ages

Abstract

In this study, we investigated 18 healthy and fertile Duroc boars, dividing them into two groups based on their reproductive age: 9 boars aged 18 mo and 9 boars aged 36 mo. Prior to semen sampling, all boars were raised together under identical management conditions for a period of 3 mo. Our findings revealed that older boars exhibited lower sperm motility and a higher proportion of abnormal sperm morphology compared to younger boars. Furthermore, older boars demonstrated lower anti-oxidant capacity in their semen, as indicated by elevated levels of malondialdehyde and decreased levels of superoxide dismutase and glutathione peroxidase. Microbiota analysis utilizing the 16S rRNA technique showed that the semen microbiota of older boars had reduced alpha-diversity and beta-diversity in comparison to younger boars. We identified the Streptococcus genus and Streptococcus gallolyticus subsp macedonicus species served as biomarkers for semen from younger breeding boars, while the Bacteroides pyogenes species as a biomarker for semen from older breeding boars. Additionally, the semen from older boars exhibited a higher abundance of Aerococcus, Gallicola, Ulvibacter, and Proteiniphilum compared to younger boars. Spearman correlation analysis showed that these four bacteria were negatively correlated with semen quality. The abundance of Gallicola and Proteiniphilum were negatively correlated with semen anti-oxidant capacity. Additionally, the reduction of semen anti-oxidant capacity was correlated to the decrease of semen quality. Based on these findings, we concluded that the semen of older boars contains a higher abundance of harmful bacteria, which contributes to the observed reduction in semen anti-oxidant capacity and overall semen quality in this group.

The decline in semen quality is commonly observed with advancing age. Advances in microbiota characterization techniques have revealed that semen is not sterile. In this study, we discovered an increased abundance of harmful bacteria in the semen of older boars. This elevated presence of harmful bacteria is believed to contribute to lower semen anti-oxidant capacity and overall semen quality.

Introduction

Breeding boars play a critical role as semen providers in genetic improvement, maintaining genetic diversity, ensuring successful reproduction, and supporting the long-term sustainability of pig farming operations. Breeding boars have a productive lifespan, and their performance may decline with age. Depending on the breeding program, boars may be replaced after reaching a certain age to ensure optimal reproductive performance and genetic progress (Knox, 2016). Semen quality serves as a crucial indicator of boar fertility and reproductive potential. Therefore, investigating the factors that influence boar semen quality is of utmost importance in the field of swine reproduction. Age is widely recognized as a significant factor that influences semen quality, as it is generally acknowledged that semen quality tends to decline with advancing age (Jung et al., 2002).

Advancements in techniques for characterizing microbiota communities have revealed that previously believed sterile secretions, including urine and semen, actually contain a wide range of diverse microbes (Lundy et al., 2020). Similarly, the semen of boars has been found to harbor a rich microbiota (Gòdia et al., 2020). Even under strict aseptic conditions, various bacteria can be detected in semen (Ciornei et al., 2012). The presence of a diverse microbiota in semen and its potential impact on boar fertility have become active areas of research in recent years.

Bacteria present in semen have the ability to induce damage to sperm lipids, proteins, and DNA by promoting excessive production of reactive oxygen species (ROS; Fraczek et al., 2007; Ďuračka et al., 2021). This process can lead to compromised sperm viability (Althouse et al., 2003), reduced sperm motility (Baud et al., 2018), altered sperm structure (Dalmutt et al., 2020), and impaired sperm fertilizing capacity (Emokpae and Chima, 2018).

Hence, investigating the variations in semen quality, anti-oxidant capacity, and microbiota among boars with different reproductive ages is essential for understanding the potential age-related changes that could affect boar fertility. In this study, we hypothesized that there would be differences in semen quality and anti-oxidant capacity among breeding boars with varying reproductive ages, and these differences would be correlated with variations in semen microbiota. Therefore, the main objective of this study was to examine and compare the differences in semen quality, semen anti-oxidant capacity, and semen microbiota among boars with different reproductive ages.

Materials and Methods

Experimental design

In this study, a total of 30 Duroc breeding boars were initially included, with 15 boars aged 18 mo and 15 boars aged 36 mo. All boars were selected from the same breeding herd to ensure they had the same genetic background. The younger boars were born in December 2020, while the older boars were born in June 2019. Although they were born in different seasons, they underwent the same production management after birth. Therefore, we consider the influence of seasons to be negligible as they were not exposed to the external environment. Once they reached 8 mo of age, all boars were raised in an environment-controlled room and provided with the same feed.

To ensure the health and fertility of the breeding boars included in this study, a total of 30 boars, as mentioned above, were incorporated into the breeding plan. In order to assess their fertility, 90 sows with similar parity, age, and body weight were selected. Each boar was paired with three sows. After a period of 30 d of breeding, the pregnancy status of the sows was determined through pregnancy examinations. Based on the pregnancy performance of the sows, the body mass index of the boars (Zhou et al., 2016), and the levels of inflammatory cytokines in the semen (determined using ELISA kits from Cusabio, Wuhan, China, to assess signs of urogenital infection), 9 boars aged 18 mo and 9 boars aged 36 mo were selected for further experimentation. All selected boars had consistent semen collection history, collection frequency, and collection intervals throughout the experiment. Additionally, under the guidance of a veterinarian, all boars were experienced in using a dummy sow. The grouping of boars was based on their age. The experimental protocol (S20210506L) and procedures were reviewed and approved by the Animal Care and Use Committee of Jinzhou Medical University (Jinzhou, China) to ensure ethical standards were followed.

Semen collection process

The semen collection procedure was performed by a skilled technician using the gloved-hand technique. The collection process took place in a meticulously clean and disinfected area to prevent surface contamination that could come into direct contact with the boar, such as the dummy sow. All equipment utilized during the collection process underwent proper sterilization through autoclaving.

During the semen collection process, the boars were leaned on the dummy sows, and experienced collectors wore disposable gloves to ensure hygiene. A sterile water-based lubricant was applied to the gloved hand. The folds of skin around the preputial orifice were carefully separated to expose the penis. A clean paper towel dampened with a mild antiseptic solution was used to cleanse the preputial area, removing any debris or excess dirt. Once the prepuce was empty and the area was clean, the index and middle fingers were inserted cautiously into the preputial orifice to exert gentle pressure on the sigmoid flexure, straightening the penis. Using a stroking motion, the penis was massaged firmly yet gently in a downward direction to facilitate ejaculation.

After the initiation of ejaculation, a waiting period of 2 min allows for the expulsion of the initial portion of the ejaculate. Following this, the middle part of the semen is collected. A sterile collection tube is positioned below the penis to capture the ejaculate. Once ejaculation is complete, the hand is carefully withdrawn from the boar’s preputial cavity. The collected semen is then stored in an isothermal vessel maintained at a temperature of 37 °C. To maintain hygiene and prevent cross-contamination, gloves were changed between each boar.

Sampling and measurements

Semen quality analysis

The volume of semen was estimated by weighing it and using the conversion of 1 gram of semen equals 1 mL. The concentration of semen was evaluated using a self-calibrating photometer called SpermaCue from Minitube of America, Verona, WI. Sperm motility and morphology were measured using the Sperm Vision software, which involved capturing images with a digital camera attached to phase contrast microscopes. For sperm motility analysis, 500 μL of semen samples were diluted with 500 μL of Androhep extender and incubated at 37 °C for 30 min. Each sample was analyzed in duplicate, with five microscopic fields examined. For sperm morphology analysis, 50 μL of semen samples were fixed with 5 μL of buffered formalin (10%). All intact sperm and cellular particles within the triple lines of the hemocytometer were analyzed.

Anti-oxidant capacity analysis

After semen collection, the samples were subjected to centrifugation to separate the supernatant. The levels of malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPX) were measured using specific Assay kits provided by the Institute of Biological Engineering of Nanjing Jianchen (Nanjing, China). To ensure the accuracy of the measurements, the intra-assay co-efficient of variation (CV) was less than 10%, indicating minimal variation within the same assay. The interassay CV was 12%, indicating acceptable variation between different assays.

Semen microbiota analysis

In this study, a total of 18 semen samples were initially collected, with 9 samples from boars aged at 18 mo and 9 samples from boars aged at 36 mo. However, during transportation, one semen sample from the older group was accidentally leaked. To maintain balanced sample sizes between the two age groups, a total of 16 semen samples (8 from the younger group and 8 from the older group) were used for further analysis. The total DNA from the 16 semen samples was extracted using a magnetic Soil and Stool DNA kit (cat# DP712, TIANGEN Biotech Co., Ltd, Beijing, China). The concentration and purity of the extracted DNA were assessed using a Qubit 2.0 spectrophotometer (Invitrogen, Carlsbad, CA) and 1% (w/v) agarose gel electrophoresis. The DNA samples were diluted to a concentration of 1 ng/μL with sterile water and stored at −20 °C until analysis. Next, the V3–V4 hypervariable regions of the bacterial 16S rRNA gene were amplified using specific full-length universal forward (5'-ACTCCTACGGGAGGCAGCAG-3') and reverse (5'-GGACTACHVGGGTWTCTAAT-3') primers. The PCR reactions were performed in triplicate, and the resulting PCR products were further purified using a Qiagen Gel Extraction Kit (cat# 28706, Qiagen, Germany). The purity of the PCR mixture was assessed using a Qubit 2.0 dsDNA HS Assay Kit (cat# Q32854, Invitrogen). The microbial community structures were analyzed through 16S rRNA gene sequencing using the NovaSeq 6000 platform (Illumina, San Diego, CA) at Novogene Bioinformatics Co., Ltd. (Tianjin, China). Raw data were processed by removing low-quality reads using Cutadapt software version 1.9.1, and chimeric sequences were trimmed through alignment and detection. High-quality reads were clustered into operational taxonomic units (OTUs) at 97% sequence identity using Uparse v7.0.1001. The taxonomic assignment of representative sequences was performed using QIIME v1.9.1. To assess the microbial diversity, rarefaction curves were plotted for each sample using R software (version 1.9.1) to determine the appropriate sequencing depth that captures the extent of microbial diversity. Alpha-diversity metrics, including observed species, Chao1, Ace, Shannon, and Simpson diversity indices, as well as beta-diversity analysis using Weighted UniFrac, were calculated based on the number of observed OTUs. Furthermore, the LEfSe analysis was conducted using LEfSe software, with a screening value of 3.5 for the linear discriminant analysis (LDA) score (Dang et al., 2022).

Statistical analysis

All data collected in this study were assessed for normality using the Shapiro–Wilk test and quantile–quantile plots. Each individual boar served as an experimental unit. Student’s t-test was employed to analyze the data related to semen quality, semen anti-oxidant capacity, as well as the statistical significance of alpha-diversity, beta-diversity, and differences in semen microbiota at the Genus level using SPSS software (Version 21.0). Spearman analysis was used to evaluate the correlations among semen differential bacteria and semen quality and semen anti-oxidant capacity as well as the correlations among semen quality and semen anti-oxidant capacity. The results were presented as means ± standard deviation. A probability value below 0.05 was considered statistically significant.

Results

No statistical differences in ejaculate volume and semen concentration were observed among boars with different reproductive ages. However, older boars exhibited lower sperm motility (P = 0.015) and a lower ratio of sperm normal morphology (P = 0.002), while having a higher ratio of sperm abnormal morphology (P = 0.006) compared to younger boars (Table 1).

Table 1.

Differences in semen quality among breeding boars of different reproductive ages

Items	18M1	36M2	P-value
Volume, mL	183.50 ± 11.59	170.90 ± 12.26	0.185
Concentration, 1 × 106 sperm/mL	294.90 ± 24.15	270.60 ± 17.93	0.158
Motility3, %	84.02 ± 1.69	79.76 ± 1.89	0.015
Normal morphology4, %	89.99 ± 0.91	83.51 ± 2.20	0.002
Abnormal morphology4, %	10.08 ± 0.82	15.74 ± 2.60	0.006

Results were presented as mean ± standard deviation.

¹Semen samples were collected from 18-mo-old boars.

²Semen samples were collected from 36-mo-old boars.

³Values were assessed from 500 μL ejaculates.

⁴Values were assessed from 50 μL ejaculates.

Older boars demonstrated lower semen anti-oxidant capacity compared to younger boars, as evidenced by higher levels of MDA (P = 0.002) and lower levels of SOD (P = 0.008) and GPX (P = 0.042; Table 2).

Table 2.

Differences in semen anti-oxidant capacity among breeding boars of different reproductive ages¹

Items	18M2	36M3	P-value
MDA, nmol/mL	26.78 ± 0.65	30.09 ± 1.15	0.002
SOD, U/mL	423.80 ± 24.15	330.90 ± 40.68	0.008
GPX, μmol/L	13.55 ± 5.19	4.71 ± 0.49	0.042

Results were presented as mean ± standard deviation.

¹Values were assessed from 5 mL ejaculates.

²Semen samples were collected from boars with 18-mo-old.

³Semen samples were collected from boars with 36-mo-old.

There were no significant differences observed in the indexes of observed species, Simpson, Chao1, and Ace among the groups. However, the Shannon index (P = 0.030) in semen samples from older boars was lower than that in younger boars (Table 3).

Table 3.

Differences in semen microbiota diversity and abundance indexes among breeding boars of different reproductive ages

Alpha diversity indexes	18M1	36M2	P-value
Observed species	655.70 ± 36.68	645.30 ± 85.71	0.129
Shannon	6.70 ± 0.09	6.19 ± 0.49	0.030
Simpson	0.98 ± 0.01	0.95 ± 0.03	0.052
Chao1	784.40 ± 127.10	691.80 ± 122.00	0.135
ACE	724.50 ± 46.15	697.30 ± 127.60	0.543

Results were presented as mean ± standard deviation.

¹Semen samples were collected from 18-mo-old boars.

²Semen samples were collected from 36-mo-old boars.

Spearman correlation analysis among semen differential bacteria and semen quality parameters revealed that the abundance of Aerococcus negatively correlated with sperm normal morphology ratio (P = 0.047) whereas positively correlated with sperm abnormal morphology ratio (P = 0.021); the abundance of Gallicola negatively correlated with sperm motility (P = 0.015) and sperm normal morphology ratio (P = 0.002); the abundance of Ulvibacter positively correlated with sperm abnormal morphology ratio (P = 0.037); the abundance of Proteiniphilum positively correlated with sperm abnormal morphology ratio (P = 0.021; Table 4).

Table 4.

Spearman correlations analysis among semen microbiota and semen quality parameters

Variables	Aerococcus	Gallicola	Ulvibacter	Proteiniphilum
Volume, mL	−0.214	−0.619	−0.310	−0.333
Concentration, 1 × 106 sperm/mL	−0.667	−0.524	−0.667	−0.595
Motility, %	−0.619	−0.810*	−0.595	−0.619
Normal morphology, %	−0.714*	−0.905**	−0.691	−0.548
Abnormal morphology, %	0.786*	0.595	0.738*	0.786*

*P < 0.05, ^**P < 0.01.

In addition, we observed that the MDA levels of semen (P = 0.028) positively correlated with the abundance of Proteiniphilum; the SOD (P = 0.007) and GSH (P = 0.021) levels of semen negatively correlated with the abundance of Gallicola (Table 5).

Table 5.

Spearman correlations analysis among semen microbiota and semen anti-oxidant capacity parameters

Variables	Aerococcus	Gallicola	Ulvibacter	Proteiniphilum
MDA, nmol/mL	0.595	0.691	0.571	0.762*
SOD, U/mL	−0.667	−0.857**	−0.667	−0.595
GSH, μmol/L	−0.595	−0.786*	−0.619	−0.619

*P < 0.05, ^**P < 0.01.

As shown in Table 6, the levels of semen MDA negatively correlated to the sperm motility (P = 0.015) and sperm normal morphology ratio (P = 0.037), whereas positively correlated to the sperm abnormal morphology ratio (P = 0.010). Additionally, the SOD and GSH levels in semen positively correlated to the sperm motility (P = 0.010, P = 0.007) and sperm normal morphology ratio (P < 0.001, P = 0.001), whereas negatively correlated to the sperm abnormal morphology ratio (P = 0.015, P = 0.010).

Table 6.

Spearman correlations analysis among semen quality parameters and semen anti-oxidant capacity parameters

Variables	MDA, nmol/mL	SOD, U/mL	GSH, μmol/L
Volume, mL	−0.595	0.452	0.524
Concentration, 1 × 106 sperm/mL	−0.071	0.191	0.071
Motility, %	−0.810*	0.833*	0.857**
Normal morphology, %	−0.738*	0.976***	0.929***
Abnormal morphology, %	0.833*	−0.810*	−0.833*

*P < 0.05; ^**P < 0.01; ^***P < 0.001.

The Venn diagram revealed that a total of 723 unique OTUs were shared among the groups. Additionally, the group of younger boars had a total of 218 unique OTUs, while the group of older boars had a total of 79 unique OTUs (Figure 1).

Figure 1.

Venn diagram depicting the composition of semen microbiota among breeding boars at different reproductive ages. Group D was defined as the semen sample collected from 18-mo-old boars. Group Y was defined as the semen sample collected from 36-mo-old boars.

Beta-diversity analysis revealed that the Weighted UniFrac indices in the group of older boars were lower than those in younger boars (P = 0.036; Figure 2).

Figure 2.

Weighted UniFrac examining the differences in community structure of semen microbiota among breeding boars at different reproductive ages. Group D was defined as the semen sample collected from 18-mo-old boars. Group Y was defined as the semen sample collected from 36-mo-old boars.

The 10 most abundant bacteria in the semen microbiota of breeding boars with different reproductive ages were ­Streptococcus (0.159% for younger, 0.045% for older), Lactococcus (0.061% for younger, 0.003% for older), Atopostipes (0.062% for younger, 0.150% for older), unidentified_Corynebacteriaceae (0.075% for younger, 0.021% for older), unidentified_Clostridiales (0.097% for younger, 0.130% for older), Porphyromonas (0.025% for younger, 0.054% for older), Lactobacillus (0.022% for younger, 0.029% for older), Terrisporobacter (0.023% for younger, 0.045% for older), Campylobacter (0.019% for younger, 0.023% for older), Jeotgalicoccus (0.022% for younger, 0.025% for older; Figure 3).

Figure 3.

The ten most abundant bacteria found in the semen microbiota of breeding boars with different reproductive ages. The horizontal axis represents the groups, while the vertical axis represents the relative abundance of each bacterial species. The category “others” represents the combined relative abundance of bacteria not included in the top 10 listed in the figure. Group D was defined as the semen sample collected from 18-mo-old boars. Group Y was defined as the semen sample collected from 36-mo-old boars.

Species taxons with high LDA scores in a given group may serve as potential biomarkers for that group. Four species assemblages with LDA scores < 3.5 were identified. We identified the Streptococcaceae family, Streptococcus genus, and Streptococcus gallolyticus subsp macedonicus species served as biomarkers for semen from younger breeding boars, while the unidentified Clostridiales family, Clostridia class, and Bacteroides pyogenes species as a biomarker for semen from older breeding boars (Figure 4).

Figure 4.

LDA effect size analysis illustrating the phylogenetic distribution of lineages within the semen microbiota of breeding boars with different reproductive ages. The cladogram highlights the lineages with values greater than 3.5 from the least discriminant analysis. Group D was defined as the semen sample collected from 18-mo-old boars. Group Y was defined as the semen sample collected from 36-mo-old boars.

Statistical differences among semen microbiota at the genus level revealed that the abundance of Aerococcus (P = 0.032), Gallicola (P = 0.038), Ulvibacter (P = 0.040), and Proteiniphilum (P = 0.010) in older boars were higher than those in younger boars (Figure 5).

Figure 5.

Statistical differences between groups on the Genus level were analyzed using a t-test for the semen microbiota of breeding boars with different reproductive ages. Group D was defined as the semen sample collected from 18-mo-old boars. Group Y was defined as the semen sample collected from 36-mo-old boars.

Discussion

Using 16S rRNA sequencing, the study examined the semen microbiota of breeding boars of different ages and identified significant differences in alpha- and beta-diversity. Older boars had lower levels of alpha- and beta-diversity compared to younger boars. Furthermore, the analysis identified the presence of the Streptococcus genus as a biomarker in semen samples from younger breeding boars. It is noteworthy that Streptococcus is commonly found in semen and has been observed to be more abundant in samples obtained from healthy men in previous studies (Hou et al., 2013; Weng et al., 2014).

Differential analysis of bacteria revealed variations in semen composition among boars of different ages. The abundance of Aerococcus, Gallicola, Ulvibacter, and Proteiniphilum was found to be higher in the semen of older boars compared to younger ones. Aerococcus is known for its association with urinary tract infections and its potential to cause invasive infections, particularly in older individuals, leading to more severe disease manifestations (Rasmussen, 2016). A study investigating the relationship between semen microbiota and male infertility reported a significant presence of Aerococcus in semen samples from infertile men (Lundy et al., 2021). Gallicola and Ulvibacter are potential pathogens associated with infections (Bernardet and Nakagawa, 2006; Könönen et al., 2007). The function of Proteiniphilum remains unknown, however, its existence was observed exclusively in the urethra of older patients and not in younger individuals (Lewis et al., 2013). Therefore, the microbial composition of semen differs between older and younger breeding boars.

Correlation analysis between semen microbiota and semen quality revealed that a decrease in semen quality is associated with an increase in the abundance of Aerococcus, Gallicola, Ulvibacter, and Proteiniphilum. This finding is consistent with the study conducted by Weng et al. (2014), which also reported a significant association between bacterial community types in semen and semen health. Furthermore, Baud et al. (2019) compared semen samples from men with normal and abnormal spermiograms and found that the differential abundance of specific bacterial genera, such as Prevotella, Staphylococcus, and Lactobacillus, correlated with deficiencies in sperm motility and morphology. In a study by Koziol et al. (2022) involving beef bulls, the unsatisfactory semen exhibited the co-occurrence of opportunistic pathogens such as Campylobacter and Fusobacterium. Based on these findings, it can be proposed that the observed differences in semen microbiota may contribute to the variations in semen quality among boars of different age groups.

The potential mechanism by which variations in semen microbiota affect semen quality may involve changes in semen anti-oxidant capacity. Epithelial cells respond to microbial signals by generating ROS (Jones et al., 2012). Recent research by Zeng et al. (2023) showed that the intestinal microbiota, in the context of a high-fat diet, can increase ROS levels, leading to damage to the integrity of the gut barrier. Furthermore, Kumar et al. (2007) demonstrated that Lactobacillus can induce the generation of epithelial ROS and cause oxidative stress. In the present study, the correlation analysis revealed that the genera Gallicola and Proteiniphilum are associated with a reduction in semen anti-oxidant capacity. However, specific information on the oxidative responses induced by Gallicola and Proteiniphilum bacteria is currently limited. Considering the ability of bacteria to interact with host cells and induce oxidative responses, it is speculated that the variation in the abundance of Gallicola and Proteiniphilum may be responsible for the differences in semen anti-oxidant capacity.

Indeed, the overproduction of ROS induced by bacteria plays a critical role in the decline of semen quality (Ďuračka et al., 2021). Sperm cells are particularly vulnerable to oxidative stress (Paulenz et al., 2000), and the presence of ROS can impair sperm DNA and plasma membrane integrity (Henkel, 2011). Mitochondria, which lack DNA repair mechanisms, are highly susceptible to DNA damage, leading to compromised sperm motility (Lundy et al., 2020). Oxidative chain reactions initiated by ROS can alter membrane properties and disrupt the internal environment of sperm cells, resulting in changes in sperm morphology (Fraczek et al., 2007; Rui et al., 2017; Ďuračka et al., 2021; Lenický et al., 2021). Seminal oxidative stress has been identified as a significant biochemical mechanism associated with male infertility (Tremellen, 2008), and studies have reported lower total anti-oxidant capacity in semen from infertile patients compared to healthy individuals (Du Plessis et al., 2015; Emokpae and Chima, 2018). Therefore, the reduction in semen quality observed in older boars can be attributed to variations in semen anti-oxidant capacity, which is supported by the correlation analysis conducted in this study.

In conclusion, our study identified notable variations in the diversity and composition of semen microbiota among breeding boars of varying ages by employing the 16S rRNA sequencing technique. Specifically, older boars displayed lower microbiota diversity and higher abundance of Aerococcus, Gallicola, Ulvibacter, and Proteiniphilum compared to younger boars. These alterations in semen microbiota were associated with a reduction in semen anti-oxidant capacity, ultimately leading to a decline in semen quality.

Glossary

Abbreviations:

CV

co-efficient of variation

GPX

glutathione peroxidase

LDA

linear discriminant analysis;

MDA

malondialdehyde

OTUs

operational taxonomic units

ROS

reactive oxygen species

SOD

superoxide dismutase

Ethics statement

Experimental protocol and the process were approved and supervised by the Animal Care and Use Committee of Jinzhou Medical University (Jinzhou, China).