Comparative Analysis of the Uterine Microbiome in Canine Pyometra: A 16S rRNA Sequencing Study

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Y. Liao, W. Z. Fu, Y. K. Jia, J. C. Cui, Y. Q.H. Li, M. Cao, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7605933/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Canine pyometra is a prevalent reproductive disorder predominantly affecting older, nulliparous bitches, with peak incidence during the diestrus phase. This condition is frequently complicated by systemic infections and carries high mortality risks. Using 16S rRNA sequencing, this study systematically compared the endometrial microbiota between pyometra-affected dogs (n = 11) and healthy controls (n = 12), revealing significant microbial dysbiosis characterized by pathogenic enrichment and metabolic pathway disruption. Alpha diversity indices (Shannon index: 3.21 vs. 1.89, P < 0.01) confirmed reduced microbial richness in pyometra cases. Taxonomic analysis revealed that Pasteurellaceae, Leptotrichiaceae , and Lactobacillaceae were significantly enriched at the family level, whereas Haemophilus , Lactobacillus , Prevotella , and Fusobacterium predominated at the genus level. LDA effect size (LEfSe) analysis identified Haemophilus and Lactobacillus as microbial biomarkers for pyometra. Functional prediction revealed significant downregulation of metabolic pathways related to nutrient absorption, immune modulation, and xenobiotic degradation, alongside upregulation of membrane transport systems and cellular signaling pathways. Network analysis revealed potential disease associations with butyrate metabolism. These findings provide critical insights for the development of microbiota-targeted therapeutic strategies against canine pyometra. Canine pyometra Uterine microbiome 16S rRNA sequencing Microbial dysbiosis Butyrate metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction As a prevalent reproductive disorder affecting 15–24% of intact female dogs over 8 years of age[ 1 ], it poses significant clinical challenges because of its association with systemic inflammatory response syndrome. This life-threatening condition is characterized by the accumulation of purulent exudate in the uterus and is often accompanied by systemic infections, endometrial thickening, tissue congestion, and cystic hyperplasia [ 2 – 4 ]. This condition predominantly affects elderly nulliparous female dogs. [ 5 , 6 ] Pyometra can be classified into open and closed forms [ 7 ]. Open pyometra present with more noticeable symptoms, such as the presence of brown pus in the vaginal area of the affected dog. In contrast, closed pyometra primarily manifests as abdominal distension, with both sides of the abdomen bulging, resembling pregnancy symptoms, making early detection difficult. The causes of this disease are multifaceted and mainly include bacterial infection, hormonal changes, and previous cervical lesions[ 2 ]. During the preestrus period, the secretion of estrogen decreases, whereas progesterone secretion increases, which can lead to ovulation problems [ 8 ]. The sustained effect of estrogen on the reproductive tract of female dogs increases the secretion of uterine glandular epithelium, causing the uterus to become relaxed. During estrus, bacteria can easily enter the uterus, and in the postestrus period, hormonal changes in female dogs decrease uterine resistance, thereby increasing the likelihood of pyometra. The uterine microbiome normally includes commensals (e.g., Lactobacillus ) that maintain ecological balance. However, when pyometra occurs, the abnormal proliferation of specific bacteria disrupts this balance, leading to disease. Some studies suggest that gut microbiota dysbiosis may contribute to mental disorders through the gut‒brain axis[ 9 ]. The pathogens of canine pyometra can include various bacteria, with Escherichia coli being highly associated with severe endometrial lesions[ 10 , 11 ]. Escherichia coli is one of the most prevalent bacteria associated with canine pyometra[ 12 ]. Staphylococcus, Streptococcu s, and Pseudomonas species, as well as Brucella canis , are relatively rare in canine pyometra[ 13 ]. In addition, studies have shown that coinfection occurs in pyometra[ 14 ]. Treatment usually involves the use of antibiotics to control infection, with antibiotic selection on the basis of culture results and sensitivity testing. These findings clearly indicate that natural prostaglandin F2a (PGF2a) or its synthetic analog cloprostenol, dopamine agonists (cabergoline and bromocriptine), or progesterone receptor blockers (aglepristone) can have therapeutic effects on pyometra[ 15 ]. In severe cases, surgical intervention, such as drainage of the uterine abscess or ovariohysterectomy, should be considered [ 16 ]. Timely veterinary consultation is crucial to prevent severe secondary diseases such as pelvic abscess, salpingitis, and sepsis[ 17 ]. 16S ribosomal RNA (16S rRNA) gene sequencing is an essential method for microbial analysis and is commonly used to study microbial community structure and diversity[ 18 ]. To a lesser extent, genera are also recognized by their phylogenetic separation from other such groups and their 16S rRNA gene sequence identities of > 95% [ 19 , 20 ]. While research on 16S rRNA in canine pyometra is limited, it is widely used in studies of the rumen microbiota in cattle. Zheng et al. utilized 16S rRNA sequencing to investigate the uterine microbiota in dogs with pyometra in Jilin city and analyzed potential metabolic pathways involved via untargeted metabolomics[ 21 ]. Despite established links between E. coli and endometrial pathology, the comprehensive microbial landscape and functional implications in canine pyometra remain poorly characterized. 2. Materials and methods 2.1 Animals and samples A total of 23 canine uteruses were collected from six animal medical centers in Hefei, Anhui Province, China, including 12 from healthy dogs and 11 from diseased dogs suspected of pyometra. All animals underwent a standardized anesthetic protocol prior to sample collection. Prior to anesthesia, atropine (0.02–0.04 mg/kg) was administered intramuscularly as an anticholinergic agent. After approximately 15–20 minutes, anesthesia was induced via slow intravenous injection of propofol (2–6 mg/kg) to effect. Following endotracheal intubation, anesthesia was maintained with isoflurane delivered via an anesthesia machine. The concentration of isoflurane was dynamically adjusted according to surgical stimulation and vital signs, typically ranging between 1.5% and 2.5%. Uterine lavage samples were collected aseptically during ovariohysterectomy from 11 bitches diagnosed with closed-cervix pyometra (mean age: 7.78 ± 2.32 years) and 12 age-matched healthy controls. After surgical removal, each intact uterus was placed in a sterile environment, surface blood clots were removed, and the tissue was sterilized. In healthy dogs, the uterine lumen was flushed with saline to collect lavage fluid, which was subsequently centrifuged for bacterial analysis. In pyometra cases, pus was aspirated directly from the uterine lumen. Samples from healthy and diseased dogs were labeled NC01–NC12 and CP01–CP11, respectively. The inclusion criteria for healthy dogs were: no medication within the past six months; normal mental status, body temperature, respiration, heart rate, appetite, and hematological parameters; and being in the anestrus stage at the time of sampling. 2.2 Clinical examination Basic information such as the age, breed, weight, and estrous cycle stage of all the dogs was recorded. The mental state, temperature, respiration, heartbeat, and other vital signs were examined. Ultrasound was used to check the condition of the uterus. The response of a dog to its surrounding environment and various stimuli should be evaluated. When necessary, pupillary light reflexes, corneal reflexes, and limb movements were observed to assess the presence and severity of impaired consciousness. Additionally, the animal was gently restrained. Using the thumbs and index fingers of both hands, the vulvar skin folds were retracted to expose the vulvar mucosa. Examine for signs of oestrus (e.g., swelling, discharge) and check for mucosal abnormalities such as erosions, ulcerations, or necrosis. 2.3 Hematological examination Blood samples (2 mL each) were collected from the cephalic vein on the medial side of the forelimb or the small saphenous vein on the lateral side of the hind limb in both healthy and diseased dogs. The samples were immediately transferred into sterile tubes containing EDTA or heparin anticoagulant and analyzed for complete blood count (CBC) within 4 hours. Concurrently, an additional 2 mL blood sample was collected and centrifuged at 2500–3500 × g for 10 minutes at 4°C to separate the serum, which was subsequently used for serum biochemical parameter measurements and C-reactive protein (CRP) detection. 2.4 Total bacterial DNA extraction and detection Total bacterial DNA was extracted according to the TIANamp Genomic DNA Kit instructions (catalog no. GDP302; TIANGEN, Beijing, China). The concentration and purity of the DNA were measured via a Nano Photometer® spectrophotometer (IMPLEN, Germany), whereas DNA integrity was assessed via 0.8% agarose gel electrophoresis. 2.5 16S rRNA library construction and sequencing Qualified genomic DNA samples (30 ng) and corresponding fusion primers were used to set up the PCR system. The V3-V4 hypervariable regions of 16S rRNA were amplified via the 341F/806R primers, followed by paired-end sequencing on the Illumina HiSeq 2500 platform (2×250 bp). PCR amplification was performed under set parameters, and the PCR products were purified via Agencourt AMPure XP beads and dissolved in elution buffer. Tags were added, completing library construction. The fragment range and concentration of the library were checked via an Agilent 2100 Bioanalyzer. Qualified libraries were sequenced on the HiSeq platform on the basis of the size of the inserted fragments. All these steps were performed by BGI Biotechnology Co., Ltd. 2.6 Data processing The raw data were filtered, leaving high-quality, clean data for further analysis. Overlapping relationships between reads were used to assemble the reads into tags. Tags were clustered into operational taxonomic units (OTUs) and annotated with species by comparison with a database. Sample species complexity analysis, intergroup species difference analysis, correlation analysis, and model prediction were conducted on the basis of the OTU and annotation results. Software was used to cluster the assembled tags into OTUs, with sequences clustered into OTUs at a 97% similarity threshold. Differences between groups at various taxonomic levels (phylum, class, order, family, and genus) were analyzed via software, and p values were obtained. A t test for species significant differences at each taxonomic level was also performed via R software. Statistical data were analyzed via GraphPad Prism 7.0 and IBM SPSS Statistics 27 software. Data are expressed as case numbers or percentages, and the proportions of breed, age, and concurrent tumors in the total number of dogs with pyometra were analyzed. p < 0.05 was considered statistically significant, and p < 0.01 was considered extremely significant. 3. Results 3.1 Clinical examination Dogs in the CP group exhibited marked lethargy, loss of appetite, vomiting, and elevated body temperature, along with uterine and abdominal distension. Upon opening the abdominal cavity, the uterus of the NC group showed no distension (Fig. 1a). In contrast, ultrasound examinations revealed bilateral uterine wall thickening in the CP group, which presented significant uterine distension with substantial pyometra (pus accumulation) (Fig. 1b and 1c). Dissection further demonstrated cystic hyperplasia within the endometrial lining (Fig. 1d). 3.2 Hematological examination As shown in Table 1, compared with the NC group, the CP group presented significant differences ( P < 0.01) in total white blood cells, granulocytes, total red blood cells, hematocrit, and mean platelet volume. Specifically, white blood cells, granulocytes, and mean platelet volume were significantly elevated, whereas total red blood cells and hematocrit were significantly decreased. Additionally, lymphocyte, monocyte, and hemoglobin levels in the canine pyometra group (CP group) were significantly different from those in the healthy dogs (NC group), with lymphocytes and monocytes significantly elevated and hemoglobin significantly decreased. Table 2 presents the biochemical results of dogs with pyometra, indicating that, compared with the healthy group, the CP group presented elevated levels of total protein, globulin, aspartate aminotransferase (AST), alkaline phosphatase (ALP), lipase (LIP), and blood urea nitrogen (BUN). Table 3 shows that the C-reactive protein level in the dogs was 55.75±4.3 mg/L, which is far above the normal reference range. 3.3 OTU analysis of microbial communities To investigate the uterine endometrial microbial communities in dogs with pyometra and healthy dogs, we performed 16S rRNA gene sequencing on uterine lavage fluid samples from the CP group and NC group. This analysis allowed for a comparative study of the microbial characteristics between the two groups. After OTU clustering, we identified 1,035 OTUs in the CP group and 132 OTUs in the NC group, with a combined total of 1,047 OTUs. Among these, 120 OTUs were shared between the two groups, with 912 OTUs unique to the CP group and 12 OTUs unique to the NC group (Fig. 2a). The bacterial operational taxonomic unit (OTU) count in the CP group was significantly lower than that in the NC group. In the NC group, all samples presented similar numbers of noncore OTUs in addition to the core OTUs, whereas the CP group presented the opposite pattern (Fig. 2b and 2c). 3.4 Complexity Analysis of Samples 3.4.1 Species accumulation curve The species accumulation curve was used to assess the impact of sequencing depth on the diversity of the samples. When the sample size reached 20, the increase in the number of detected OTUs plateaued, and the curve flattened, indicating that the sample size was sufficient and that the data quality was reliable (Fig. 2d). Further increases in sample size did not significantly increase the total number of species observed. 3.4.2 Species rarefaction curve The species rarefaction curve reflects species richness and evenness, where evenness is indicated by the flatness of the curve and richness by the width of the curve. Compared with the NC group, the CP group clustered on one side, with significantly lower species richness and evenness, suggesting the presence of dominant species in the CP group. (Fig. 2e) 3.5 Species Diversity Analysis Phylum-level analysis of enriched bacterial communities in the endometrial tissues of the NC group and CP group. Among the top 10 bacterial phyla, the healthy dog uteri were enriched with Firmicutes (43.76%), Bacteroidetes (13.45%), and Actinobacteria (5.75%). In contrast, the CP group was enriched in Proteobacteria (84.54% vs. NC: 36.03%), Fusobacteria (7.67%), and Tenericutes (2.57%) (Fig. 3a). Furthermore, compared with those in the NC group, the heatmap revealed a noticeable increase in the abundance of Proteobacteria in the CP group at the phylum level, whereas the abundances of Bacteroidetes , Actinobacteria , and Firmicutes were reduced. At the genus level, the clustering of the microbial communities was less pronounced (Fig. 3c). In the comparison between the NC group and the CP group, Proteobacteria and Firmicutes were identified as core microorganisms within the microbial networks, suggesting their potentially significant influence on the structure and function of the microbial community (Fig. 3e). Furthermore, Proteobacteria and Firmicutes were found to belong to the same module, indicating functional or ecological niche similarities between these two phyla. When bacterial correlations were analyzed, a positive correlation was observed between Proteobacteria and Firmicutes , while a negative correlation was detected between Bacteroidetes and Proteobacteria , implying that their abundance trends changed in opposite directions. A comparison of the overall network diagrams between the NC and CP groups revealed that under disease conditions, the associations between bacterial genera almost entirely shifted to positive correlations (Fig. 3f). The genus-level analysis of the top 10 bacterial genera, as shown in Figure 3b, revealed that the healthy dog endometrial tissues were enriched with Escherichia (21.74%), Lactobacillus (8.94%), Prevotella (7.83%), Faecalibacterium (4.30%), Blautia (4.13%), and Bifidobacterium (3.90%). In contrast, the endometrial tissues of dogs with pyometra were enriched with Escherichia (39.39%), Salmonella (23.32%), Haemophilus (10.42%), Brucella (8.18%), Fusobacterium (7.64%), and Mycoplasma (2.57%). In summary, the uteri of dogs with pyometra were enriched in the genera Escherichia, Salmonella, Fusobacterium , and Brucella (Fig. 3b). However, notably, compared with those in the NC group, the abundances of Escherichia , Salmonella , and Bacteroides in the CP group significantly increased and decreased, respectively. These findings highlight the dynamic interactions and shifts in microbial community composition between the NC and CP groups, providing insights into the potential ecological and functional roles of key microbial taxa under different conditions. 3.5 Alpha diversity results Alpha diversity analysis of samples from the CP and NC groups, performed at a 97% similarity threshold using indices (ACE, Chao1, Coverage, Observed species (sobs), Shannon, and Simpson), revealed significant differences. Table 4 shows that ANOVA (SPSS) revealed significant differences in the ACE, Chao1, Sobs, Shannon, and Simpson indices between the groups (P < 0.01), indicating distinct microbial richness profiles. Notably, despite the significantly greater sequencing depth in the CP group, the NC group presented significantly greater values for the ACE, Chao1, Sobs, and Shannon indices. This finding demonstrated that the NC group presented greater species richness, greater overall diversity, and more even species distributions within its microbial community. Conversely, the CP group presented a significantly greater Simpson index, reflecting greater dominance by a few highly abundant species (i.e., higher dominance concentrations) (Fig. 4a and 4b). Collectively, these results indicate that the CP group presented lower species richness, reduced overall diversity, and greater dominance. This pattern strongly suggests microbial community dysbiosis (ecological imbalance) in the CP group. 3.6 Beta diversity results Similarities in microbial community structure (beta diversity) were assessed by UniFrac distances. Multivariate statistical methods, including weighted UniFrac distance-based principal coordinate analysis (PCoA), partial least squares discrimination analysis (PLS-DA), and unweighted pair group method with arithmetic means (UPGMA) clustering analysis, were used to explore the differences between the CP and NC groups. The microbial communities in the uterine endometrial tissues of the NC group clustered together, whereas those of the CP group presented greater dispersion, indicating greater variability within the CP group. The weighted UniFrac PCoA and PLS-DA results revealed that the CP and NC groups clustered separately, with significant differences between the two groups (P<0.01) (Fig. 4c and 4d). 3.8 LDA effect size analysis LDA effect size analysis was utilized to identify biomarkers with significant differences between groups, emphasizing both statistical significance and biological relevance. A threshold LDA score of 4 was set to identify significantly different species. A total of 43 endometrial microbiome members were screened, including six phyla, nine classes, nine orders, ten families and nine genera (Fig. 5a). The phylogenetic tree revealed that the nine bacterial genera with the most significant differences between the NC and CP groups were Haemophilus , Lactobacillus , Prevotella , Fusobacterium , Faecalibacterium , Blautia , Bifidobacterium , Bacteroides , and Mycoplasma (Fig. 5b). Therefore, Haemophilus and Lactobacillus were identified as biomarkers for pyometra. 3.10 Functional difference analysis The functional potential inferred from 16S rRNA gene sequencing revealed no significant differences in the overall COG category distribution at Level 1 between the NC and CP groups ( P > 0.05) (Fig. 6a), with both cohorts exhibiting pronounced enrichment in metabolism, which was consistent with the results of the KEGG Level 1 analysis. The level 1 KEGG analysis identified significant enrichment in environmental information processing and organismal systems ( P < 0.05) (Fig. 6b). Higher-resolution COG analysis (Fig. 6c) demonstrated downregulation in CP subjects for pathways governing carbohydrate degradation/transport, antibiotic resistance biosynthesis, defense mechanisms, cell division/chromosome partitioning, functionally uncharacterized proteins, replication/recombination/repair, and cofactor metabolism; conversely, upregulation was observed for cell motility, transcription, inorganic ion transport, RNA processing/modification, and transcriptional misregulation. Parallel KEGG pathway analysis (Fig. 6d) confirmed the downregulation of metabolic processes, including transcription, lipid metabolism, immune regulation, digestive/endocrine functions, xenobiotic degradation, and cellular maintenance, in the CP group, in contrast with the upregulation of vitamin/cofactor metabolism, membrane transport, transcriptional misregulation, and bacterial pathogenesis pathways. Notably, the xenobiotic biodegradation capacity decreased by 62% ( P = 0.003), whereas bacterial chemotaxis increased 3.8-fold ( P = 0.017) in the CP group compared with the NC group. Notably, butyrate metabolism was not significantly greater in the CP group than in the NC group (P > 0.05; KEGG L3; Fig. 1S.). 4. Discussion With the increasing prevalence of pet ownership, the incidence of clinical diseases in animals, particularly those affecting the reproductive system, has risen accordingly. This study investigated female canine patients admitted to multiple animal hospitals in Hefei city and analyzed the microbial composition of the canine pyometra group. Alpha and beta diversity analyses revealed significant differences in bacterial diversity between the groups. The disease group exhibited a clear predominance of specific bacterial populations, indicating microbial dysbiosis where bacterial homeostasis was disrupted. At the phylum level, Firmicutes and Proteobacteria were the dominant phyla in the NC group. However, in the CP group, the abundance of Firmicutes decreased substantially, whereas the abundance of Proteobacteria increased markedly. This study hypothesizes that the onset of pyometra may be associated with the depletion of Firmicutes . Network analysis revealed a negative correlation (suggesting competition) between Firmicutes and Proteobacteria at the phylum level under healthy conditions. However, this competitive relationship appeared weakened in the diseased state. Within the Proteobacteria phylum, increased abundances of the genera Escherichia , Salmonella , and Haemophilus were observed . The elevated abundance of Escherichia is consistent with previously published findings [22]. Escherichia coli , a common gram-negative bacillus, is a member of the intestinal microbiota and a significant component of the gut microbiome in both humans and animals. While typically beneficial under normal conditions, pathogenic strains such as intestinal pathogenic E. coli (IPEC) can cause intestinal infections, leading to diarrhea and abdominal pain when food or water is contaminated. Furthermore, extraintestinal pathogenic E. coli (ExPEC) strains can cause urinary tract infections, respiratory infections, and other infectious diseases [23]. Research also indicates that uterine E. coli infection can alter the expression of uterine hormone receptors, thereby enhancing factors that promote bacterial growth [24]. Similarly, Salmonella infection typically causes salmonellosis, which is characterized by diarrhea, abdominal pain, fever, nausea, and vomiting [25]. Overgrowth of the genus Haemophilus has been associated with certain inflammatory bowel diseases, intestinal tumors, and metabolic disorders. Their presence can trigger inflammatory responses in the gut, leading to mucosal damage and disease progression [26]. Additionally, interactions exist between Haemophilus and other disease-associated microbial communities. The Firmicutes phylum includes genera identified in this study, such as Blautia wexlerae , Gemmiger formicilis , Megamonas funiformis , Eubacterium coprostanoligenes , Blautia schinkii , and Anaerostipes hadrus . These genera are implicated in anti-inflammatory processes and butyrate metabolism. This hypothesis, however, contradicts the functional prediction results, which indicated a nonsignificant increase (P > 0.05) in butyrate metabolism within the CP group[27,28]. It is important to acknowledge the inherent limitations of the 16S rRNA methodology employed. For example, OTU-based clustering typically achieves resolution only at the genus level, rendering functional difference analyses speculative at this stage and necessitating experimental validation. Given the growing issue of antibiotic dependence, this study proposes a novel therapeutic approach: the early administration of probiotics as a primary intervention. Probiotic microorganisms, which are naturally present in fermented foods, possess antioxidant, antimicrobial, and anti-inflammatory properties. They can improve metabolic parameters, modulate the gut microbiota, and regulate the immune system[29]. The bacterial genera most commonly used as probiotics include Lactobacillus , Bacillus , Bifidobacterium , Streptococcus , and Enterococcus [30]. Microbiota transplantation (MT), such as rumen microbiota transplantation (transferring the rumen microbiota from healthy animals to those with related gastrointestinal issues) or fecal microbiota transplantation (FMT), aims to restore intestinal health and function by reconstructing or adjusting the gut microbial composition. FMT has been demonstrated to alleviate experimental colitis in mice by modulating the gut microbiota [31,32]. Similarly, it remains uncertain whether the uterine microbial state in dogs with pyometra can be modulated by inoculation with specific bacterial consortia. Some studies suggest that butyrate can induce changes in the gut microbial composition; for example, dietary butyrate significantly increases the relative abundance of Firmicutes in mice. Conversely, Lu et al. reported that butyrate did not affect the diversity or abundance (e.g., the Firmicutes -to- Bacteroidetes ratio) of the mouse gut microbiota. Building on disease treatment concepts, exploring adjunctive therapy with dietary butyrate warrants consideration. This approach could offer dual potential benefits: enhancing the animal's anti-inflammatory response, boosting immunity, and regulating glucose levels while simultaneously inducing changes in the gut microbial composition (potentially adjusting the relative abundance of Firmicutes ). Understanding the mechanisms by which butyrate modulates the microbiota to regulate host metabolism could be highly valuable. When investigating different microbial communities, regional variations and dietary differences may contribute to disparities in the uterine microbiota of affected dogs. The uterine mucosal microbial communities observed in the CP group in this study presented slight variations at the class and species levels and in the abundances of Haemophilus and Lactobacillus compared with the results reported by Zheng et al. [10]. Possible reasons include regional climatic differences (e.g., temperature and sunlight) between Jilin Province and Hefei city, which may significantly impact the microbial communities of dogs with pyometra. Furthermore, the surveyed breeds differed, with border collies, golden retrievers, and poodles predominating in this study. Additionally, significant individual variation in microbial carriage exists even within the same habitat. Finally, differences in sequencing samples could also lead to inconsistencies in results. This study concludes that disruption of the intrauterine microbiota can lead to uterine disease and proposes the use of probiotics to mitigate pyometra. Further experiments are essential to refine this approach and provide a reference for pyometra treatment. Declarations Ethics approval and consent to participate Informed written consent was obtained from the owner or legal guardian of all animals involved in the study. The study protocol was reviewed and approved by the Animal Ethics Committee of Laboratory Animals of Anhui Agricultural University. All methods were performed in accordance with the relevant guidelines and regulations. Consent for publication The authors hereby consent to the publication of this article and agree to provide formal written consent and transfer of copyright prior to publication. Data Availability The data and materials in this study are available from the corresponding author upon reasonable request. The data supporting the findings of this study are available in the Bioject repository with the accession number SUB15052484 as of January, 2026. To review BioProject accession PRJNA1218027: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1218027?reviewer=hj30fidgol2kmgt7o35hsi6fu2 Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Science and Technology Major Project of Anhui Province（202103b06020023）；Anhui Key Research and Development Program (2023z04020003). The funder had no role in the study design, data collection/interpretation, or the decision to publish. Author contributions Wanzhen Fu performed the experiments. Jiankun Cui, Yuke Jia, Yanqiuhong Li, Meng Cao assisted with the lab experiments. Hongyan Liao contributed the reagents/materials/ analysis tools, and proofread the manuscript. Fugui Fang, Yunsheng Li, Ya Liu designed the experiment and wrote the manuscript. All authors read and approved the final version of the manuscript. Authors' contributions Ya Liu and FGF designed the experiments. Hongyan Liao, Weina Li and Ziyi Zheng analyzed the data and drafted the manuscript. Wanzhen Fu and Jiankun Cui collected the samples and Yuke Jia, Yanqiuhong Li, Meng Cao performed the experiments. Chunyan Yuan and Ruonan Yuan prepared figures and performed part of the data analysis. Fugui Fang, Yunsheng Li, Hongwei Duan and Ya Liu reviewed the manuscript. All authors have read and approved the final manuscript. Acknowledgements The authors are very grateful to the research technicians for supporting our experiments. References Beaudu-Lange C, Larrat S, Lange E, Lecoq KNguyen F A-O. Prevalence of Reproductive Disorders including Mammary Tumors and Associated Mortality in Female Dogs. LID - 10.3390/vetsci8090184 [doi] LID - 184. （2306-7381 (Electronic)）. 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Tables Table 1 The Blood Routine Test Results of Diseased Dogs with Pyometra and Healthy Dogs Parameters The diseased dogs with pyometra The healthy dogs Reference value Unit Mini Max Mean Min Max Mean White blood cell (WBC) 11 82 38.80 2.9 12 8.29 6.0~17.0 10 9 /L Lymphocyte (Lym) 1.7 22.2 7.90 0.4 4.8 2.63 0.8~5.1 10 9 /L Monocyte (Mon) 0.4 5.64 2.15 0.2 0.7 0.44 0.0~1.8 10 9 /L Granulocyte (Gran) 6.6 67.1 29.45 3 9.2 5.98 4.0~12.6 10 9 /L Red blood cell (RBC) 2.79 7.04 5.04 2.6 7.58 5.67 5.50~8.50 10 12 /L hemoglobin (HGB) 60 173 112.07 75 202 145.89 110~190 g/L Red blood cell specific volume (HCT) 19.00 52.40 35.32 21.2 59.1 42.9 39.0~56.0 % Mean corpusular volume (MCV) 61.8 81.2 69.63 69.4 81.7 75.467 62.0~72.0 fL Mean corpuscular hemoglobin (MCH) 18.3 27.3 21.98 21.3 35.2 26.61 20.0~25.0 Pg Mean corpusular hemoglobin concerntration (MCHC) 282 348 314.93 306 470 355.56 300~380 g/L Red blood cell distribution width~standard deviation (RDW~CV) 12 18 14.55 10.3 14.1 12.13 11.0~15.5 % Platelet count (PLT) 53 1124 367.93 5 378 252.33 117~460 10 9 /L Mean platelet volume (MPV) 8.2 14.2 10.70 8.3 10.4 9.125 7.0~12.9 fL Platelet distribution width (PDW) 15.2 20.30 16.86 15.8 16.5 16.125 Procalcitonin (PCT) 0.05 22.20 3.86 0.27 0.34 0. 30 % Eosinophil (Eos) 5.50 1.30 2.94 1.40 5.70 3.54 % Table 2 The Blood Biochemistry Test Results of Diseased Dogs with Pyometra and Healthy Dogs Parameters The diseased dogs with pyometra The healthy dogs Reference value Unit Min Max Mean Min Max Mean Alanine transaminase (ALT) 11 55.9 31.53 21.8 55.05 55.05 10.0~100.0 U/L Aspartate aminotransferase (AST) 12 81.8 42.79 36 19.8 27.6 ≤50.0 U/L Alkaline phosphatase (ALP) 11.1 303 128.75 19 181 54.22 23.0~212.0 U/L Albumin (ALB) 0 3.8 1.91 22.9 41.5 34.08 27.0-38.0 g/mL r~GGT 59.7 103.8 79.17 2.7 9 5.31 ≤7.0 U/L Total Protein (TP) 13.4 29.5 20.23 58.78 79 68.83 52.0~82.0 g/L GLB 5.40 85.90 52.46 33.48 47.7 41.28 24.0~45.0 g/L A/G 0.20 0.50 0.33 0.5 1.2 0.92 % Total bilirubin (TBIL) 0.009 15.18 4.73 0.28 6.18 3.80 ≤15.00 μmol/L Blood Urea Nitrogen (BUN) 2.45 58.8 15.71 3.15 12.57 6.86 2.50~9.60 mmol/L Creatinine (CRE) 2.60 105 54.57 38.1 111 65.11 44.0~159.0 μmol/L Amylase (AMY) 67.00 1278.7 784.45 291.3 3343 864.18 500.0~1500 U/L Lipase (LIP) 6.81 257 84.08 15.6 100.3 44.90 20.0-180.0 mmol/L Glutamic acid (GLU) 2.18 12.83 7.18 4.67 7.36 5.94 4.28~6.94 mmol/L Calcium (Ca) 1.58 9.4 3.32 2.20 2.69 2.46 1.98~3.00 mmol/L Phosphorus (P) 0.54 2.37 1.43 3.79 6.70 1.54 0.81~2.19 mmol/L TC 4.27 9.43 6.33 3.79 6.70 5.40 2.84~8.27 mmol/L TG 0.45 1.13 0.74 0.37 0.85 0.60 0.11~1.13 mmol/L LDH 36.00 327 154.31 27.20 247.90 114.87 40-400 U/L FUN 357.1 200.9 266.83 121.60 245.9 207.41 μmol/L CK 44.30 526.6 177.60 68.00 198.00 127.92 20-200 U/L Table 3 The C-Reactive Protein (CRP) Test of Diseased Dogs with Pyometra Parameters results reference units CRP 55.75±4.3 0.0~10.0 mg/L Table 4 The Comparison of Alpha Diversity of Pyometra Dogs and Health Dogs Note: Values are expressed as mean counts ±standard error. Statistically significant differences (P < 0.05) is indicated by *，Statistically dramatically significant differences (P < 0.01) is indicated by **. Groups Alpha diversity Sobs Chao1 Ace Shannon Simpson Coverage NC group 16.970 22.872 25.890 0.382 0.808 1.000 CP group 18.667 24.507 27.232 0.386 0.801 1.000 P value ＜0.01** ＜0.01** ＜0.01** ＜0.01** ＜0.01** ＜0.01** Additional Declarations No competing interests reported. Supplementary Files floatimage7.png Fig. 1S. Comparison of predicted pathway abundance in Level 3 KEGG classifications in dogs with pyometra compared with healthy dogs: Bar graphs display the differential enrichment of specific metabolic pathway subclasses within Level 2 categories. 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16:27:16","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62193,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/81088ddcd62c10edca5e1433.png"},{"id":93612438,"identity":"dc0d79e7-b245-467e-8b93-f51092a4232e","added_by":"auto","created_at":"2025-10-15 16:19:16","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35602,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/6f380f716feaf5df10e28ec5.png"},{"id":93610717,"identity":"83457665-37f0-471f-b5a7-ebfe46bceb30","added_by":"auto","created_at":"2025-10-15 16:11:16","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74153,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/b992b994c1753e58e314213f.png"},{"id":93612439,"identity":"910deec2-b0d3-4d8e-ae95-656ba71dd611","added_by":"auto","created_at":"2025-10-15 16:19:16","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144026,"visible":true,"origin":"","legend":"","description":"","filename":"02441f6896c74bd682e948f7fba4cdc01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/79c404f01a824ca6ad69c2ce.xml"},{"id":93610727,"identity":"332dc982-c693-4972-b51f-03894ef6b8cf","added_by":"auto","created_at":"2025-10-15 16:11:16","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158852,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/9f84c5dce5061982ee4dbb5f.html"},{"id":93610703,"identity":"045b4fae-4a69-498a-ad78-73d0dd78584d","added_by":"auto","created_at":"2025-10-15 16:11:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":742930,"visible":true,"origin":"","legend":"\u003cp\u003eGross Pathological Comparison of Uteri Between Healthy Dogs (NC) and Dogs with Pyometra (CP).\u003cbr\u003e\n(a) Uterus from healthy control (NC) group: Normal morphology with smooth serosal surface; absence of inflammatory exudates or hyperplastic lesions. (b, c) Surgical specimens of closed-cervix pyometra (CP group): (b) and (c) Markedly distended uterine horns (arrows) showing tense cystic dilation with serosal vascular congestion. (d) Endometrium exhibiting diffuse cystic hyperplasia (arrows), covered with necrotic tissue and purulent exudate.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/94965e4b30df2af039227d73.png"},{"id":93612424,"identity":"09d3293f-a972-4ff8-8f65-2b1d73c96c22","added_by":"auto","created_at":"2025-10-15 16:19:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":151986,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Canine Endometrial Microbial Community Structure\u003cbr\u003e\n(a) Venn diagram of OTU distribution between NC and CP groups Overlapping region: shared OTUs (120); Non-overlapping regions: group-specific OTUs (NC: 915; CP: 12).\u003cbr\u003e\n(b) Flower plot of OTU sharing within NC group: Petal width corresponds to number of shared OTUs among samples; Central value: core OTUs shared by all samples (129).\u003cbr\u003e\n(c) Flower plot of OTU sharing between individual NC-4 and CP group: Shared OTUs between individual NC-4 and CP group accounted for 0.35%, reflecting microbial similarity between the individual and disease group.\u003cbr\u003e\n(d) Sample coverage analysis: Species accumulation curve, X-axis: Sequencing depth; Y-axis: Observed OTU count. Curve plateau indicates sufficient sampling (n = 23 groups).\u003cbr\u003e\n(e) Rarefaction curve: X-axis: Randomly selected sequence count; Y-axis: OTU richness.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/3641149daf31758c98571cfc.png"},{"id":93610707,"identity":"3f400f15-7c38-4f4e-b5ab-fe9413ba881e","added_by":"auto","created_at":"2025-10-15 16:11:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":186588,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis of Endometrial Microbiota Between Pyometra-Affected (CP) and Healthy Dogs (NC) (a) Microbial relative abundance at the phylum level: Firmicutes dominated in the NC group, while Proteobacteria was significantly enriched in the CP group. (b) Microbial relative abundance at the genus level: Abundances of \u003cem\u003eEscherichia\u003c/em\u003e and \u003cem\u003eHaemophilus\u003c/em\u003e increased in the CP group; Abundances of \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e decreased. (c) Heatmap analysis at the phylum level (Z-score normalized): Hierarchical clustering based on Bray-Curtis dissimilarity reveals microbial profile differences between groups (red: high abundance; blue: low abundance). (d) Differential analysis of key genera: Significantly different genera: \u003cem\u003eEscherichia\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e were enriched in the CP group. (e) Microbial co-occurrence network at the phylum level (NC group): Node size represents relative abundance; edges denote strong correlations (P \u0026lt; 0.01). (f) Microbial interaction network between CP and NC groups: Red edges: positive correlations; blue edges: negative correlations, reflecting altered community interactions under disease conditions.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/21b885638ab60be6add6944a.png"},{"id":93610700,"identity":"c7bc5287-2454-4241-9b13-5c408b1c0dd7","added_by":"auto","created_at":"2025-10-15 16:11:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82774,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Alpha and Beta Diversity\u003cbr\u003e\n(a) Alpha diversity (ACE index): The endometrial microbial richness of the CP group was significantly lower than that of the NC group (P \u0026lt; 0.01, Kruskal-Wallis test). (b) Alpha diversity (Chao1 index): The estimated species richness of the CP group was decreased compared to the NC group. (P \u0026lt; 0.01, Kruskal-Wallis test). (c) Beta diversity principal coordinates analysis (PCoA): Based on Bray-Curtis distance, PCo1 (64.04%) and PCo2 (19.65%) revealed significant separation between groups. (d) Partial least squares-discriminant analysis (PLS-DA): Statistically significant difference (P \u0026lt; 0.01) is indicated by ∗∗.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/d9ee468860df5491ac47b054.png"},{"id":93612426,"identity":"d621e840-cb67-420b-91df-b4b671ccf29d","added_by":"auto","created_at":"2025-10-15 16:19:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":272495,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of endometrial bacterial biomarkers in dogs with pyometra compared with healthy dogs. (a). LEfSe (linear discriminant analysis and effect size) dendrogram, where in LEfSe was used to analyze the classification tree of sample species and find the marker species with significant differences in CP and NC groups; (b). Bacterial taxa that best characterize each group were identified by using linear discriminant analysis of effect size on OTU tables.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/501ade3f8b665c94ef548adb.png"},{"id":93610705,"identity":"27d84b93-4001-4b5c-b0f2-45702f3c7399","added_by":"auto","created_at":"2025-10-15 16:11:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":250211,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Functional Enrichment Differences Between NC and CP Groups Based on OTU Analysis\u003cbr\u003e\n(a) Comparison of predicted functional abundance in Level 1 COG categories:\u003cbr\u003e\nBar graphs depict the relative abundance or enrichment scores of significantly enriched functional categories in the NC and CP groups. (b) Comparison of predicted pathway abundance in Level 1 KEGG classifications: Bar graphs show the relative abundance or enrichment scores of significantly enriched major metabolic pathway categories between groups. (c) Comparison of predicted functional abundance in Level 2 COG categories:\u003cbr\u003e\nBar graphs illustrate the differential enrichment of specific functional subclasses under Level 1 categories. (d) Comparison of predicted pathway abundance in Level 2 KEGG classifications:\u003cbr\u003e\nBar graphs display the differential enrichment of specific metabolic pathway subclasses within Level 1 categories.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/d43d02b7b07f84354c9843bf.png"},{"id":93681981,"identity":"11642760-5d06-4d69-90d1-4ccbd415c8f0","added_by":"auto","created_at":"2025-10-16 12:30:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2657119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/f762ea4f-4980-4525-97b1-6d7717bdce7e.pdf"},{"id":93613398,"identity":"8d453aca-87bb-4b32-9e25-fb83352a1260","added_by":"auto","created_at":"2025-10-15 16:27:16","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":375500,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 1S. Comparison of predicted pathway abundance in Level 3 KEGG classifications in dogs with pyometra compared with healthy dogs: Bar graphs display the differential enrichment of specific metabolic pathway subclasses within Level 2 categories.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7605933/v1/39022a73657fea66bd7e3256.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of the Uterine Microbiome in Canine Pyometra: A 16S rRNA Sequencing Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs a prevalent reproductive disorder affecting 15\u0026ndash;24% of intact female dogs over 8 years of age[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], it poses significant clinical challenges because of its association with systemic inflammatory response syndrome. This life-threatening condition is characterized by the accumulation of purulent exudate in the uterus and is often accompanied by systemic infections, endometrial thickening, tissue congestion, and cystic hyperplasia [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This condition predominantly affects elderly nulliparous female dogs. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003ePyometra can be classified into open and closed forms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Open pyometra present with more noticeable symptoms, such as the presence of brown pus in the vaginal area of the affected dog. In contrast, closed pyometra primarily manifests as abdominal distension, with both sides of the abdomen bulging, resembling pregnancy symptoms, making early detection difficult.\u003c/p\u003e\u003cp\u003eThe causes of this disease are multifaceted and mainly include bacterial infection, hormonal changes, and previous cervical lesions[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. During the preestrus period, the secretion of estrogen decreases, whereas progesterone secretion increases, which can lead to ovulation problems [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The sustained effect of estrogen on the reproductive tract of female dogs increases the secretion of uterine glandular epithelium, causing the uterus to become relaxed. During estrus, bacteria can easily enter the uterus, and in the postestrus period, hormonal changes in female dogs decrease uterine resistance, thereby increasing the likelihood of pyometra.\u003c/p\u003e\u003cp\u003eThe uterine microbiome normally includes commensals (e.g., \u003cem\u003eLactobacillus\u003c/em\u003e) that maintain ecological balance. However, when pyometra occurs, the abnormal proliferation of specific bacteria disrupts this balance, leading to disease. Some studies suggest that gut microbiota dysbiosis may contribute to mental disorders through the gut‒brain axis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The pathogens of canine pyometra can include various bacteria, with \u003cem\u003eEscherichia coli\u003c/em\u003e being highly associated with severe endometrial lesions[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. \u003cem\u003eEscherichia coli\u003c/em\u003e is one of the most prevalent bacteria associated with canine pyometra[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \u003cem\u003eStaphylococcus, Streptococcu\u003c/em\u003es, and \u003cem\u003ePseudomonas\u003c/em\u003e species, as well as \u003cem\u003eBrucella canis\u003c/em\u003e, are relatively rare in canine pyometra[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition, studies have shown that coinfection occurs in pyometra[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTreatment usually involves the use of antibiotics to control infection, with antibiotic selection on the basis of culture results and sensitivity testing. These findings clearly indicate that natural prostaglandin F2a (PGF2a) or its synthetic analog cloprostenol, dopamine agonists (cabergoline and bromocriptine), or progesterone receptor blockers (aglepristone) can have therapeutic effects on pyometra[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In severe cases, surgical intervention, such as drainage of the uterine abscess or ovariohysterectomy, should be considered [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Timely veterinary consultation is crucial to prevent severe secondary diseases such as pelvic abscess, salpingitis, and sepsis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e16S ribosomal RNA (16S rRNA) gene sequencing is an essential method for microbial analysis and is commonly used to study microbial community structure and diversity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To a lesser extent, genera are also recognized by their phylogenetic separation from other such groups and their 16S rRNA gene sequence identities of \u0026gt;\u0026thinsp;95% [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. While research on 16S rRNA in canine pyometra is limited, it is widely used in studies of the rumen microbiota in cattle. Zheng et al. utilized 16S rRNA sequencing to investigate the uterine microbiota in dogs with pyometra in Jilin city and analyzed potential metabolic pathways involved via untargeted metabolomics[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Despite established links between \u003cem\u003eE. coli\u003c/em\u003e and endometrial pathology, the comprehensive microbial landscape and functional implications in canine pyometra remain poorly characterized.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Animals and samples\u003c/h2\u003e\u003cp\u003eA total of 23 canine uteruses were collected from six animal medical centers in Hefei, Anhui Province, China, including 12 from healthy dogs and 11 from diseased dogs suspected of pyometra. All animals underwent a standardized anesthetic protocol prior to sample collection. Prior to anesthesia, atropine (0.02\u0026ndash;0.04 mg/kg) was administered intramuscularly as an anticholinergic agent. After approximately 15\u0026ndash;20 minutes, anesthesia was induced via slow intravenous injection of propofol (2\u0026ndash;6 mg/kg) to effect. Following endotracheal intubation, anesthesia was maintained with isoflurane delivered via an anesthesia machine. The concentration of isoflurane was dynamically adjusted according to surgical stimulation and vital signs, typically ranging between 1.5% and 2.5%.\u003c/p\u003e\u003cp\u003eUterine lavage samples were collected aseptically during ovariohysterectomy from 11 bitches diagnosed with closed-cervix pyometra (mean age: 7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32 years) and 12 age-matched healthy controls. After surgical removal, each intact uterus was placed in a sterile environment, surface blood clots were removed, and the tissue was sterilized. In healthy dogs, the uterine lumen was flushed with saline to collect lavage fluid, which was subsequently centrifuged for bacterial analysis. In pyometra cases, pus was aspirated directly from the uterine lumen. Samples from healthy and diseased dogs were labeled NC01\u0026ndash;NC12 and CP01\u0026ndash;CP11, respectively.\u003c/p\u003e\u003cp\u003eThe inclusion criteria for healthy dogs were: no medication within the past six months; normal mental status, body temperature, respiration, heart rate, appetite, and hematological parameters; and being in the anestrus stage at the time of sampling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Clinical examination\u003c/h2\u003e\u003cp\u003eBasic information such as the age, breed, weight, and estrous cycle stage of all the dogs was recorded. The mental state, temperature, respiration, heartbeat, and other vital signs were examined. Ultrasound was used to check the condition of the uterus. The response of a dog to its surrounding environment and various stimuli should be evaluated. When necessary, pupillary light reflexes, corneal reflexes, and limb movements were observed to assess the presence and severity of impaired consciousness. Additionally, the animal was gently restrained. Using the thumbs and index fingers of both hands, the vulvar skin folds were retracted to expose the vulvar mucosa. Examine for signs of oestrus (e.g., swelling, discharge) and check for mucosal abnormalities such as erosions, ulcerations, or necrosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Hematological examination\u003c/h2\u003e\u003cp\u003eBlood samples (2 mL each) were collected from the cephalic vein on the medial side of the forelimb or the small saphenous vein on the lateral side of the hind limb in both healthy and diseased dogs. The samples were immediately transferred into sterile tubes containing EDTA or heparin anticoagulant and analyzed for complete blood count (CBC) within 4 hours. Concurrently, an additional 2 mL blood sample was collected and centrifuged at 2500\u0026ndash;3500 \u0026times; g for 10 minutes at 4\u0026deg;C to separate the serum, which was subsequently used for serum biochemical parameter measurements and C-reactive protein (CRP) detection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Total bacterial DNA extraction and detection\u003c/h2\u003e\u003cp\u003eTotal bacterial DNA was extracted according to the TIANamp Genomic DNA Kit instructions (catalog no. GDP302; TIANGEN, Beijing, China). The concentration and purity of the DNA were measured via a Nano Photometer\u0026reg; spectrophotometer (IMPLEN, Germany), whereas DNA integrity was assessed via 0.8% agarose gel electrophoresis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 16S rRNA library construction and sequencing\u003c/h2\u003e\u003cp\u003eQualified genomic DNA samples (30 ng) and corresponding fusion primers were used to set up the PCR system. The V3-V4 hypervariable regions of 16S rRNA were amplified via the 341F/806R primers, followed by paired-end sequencing on the Illumina HiSeq 2500 platform (2\u0026times;250 bp). PCR amplification was performed under set parameters, and the PCR products were purified via Agencourt AMPure XP beads and dissolved in elution buffer. Tags were added, completing library construction. The fragment range and concentration of the library were checked via an Agilent 2100 Bioanalyzer. Qualified libraries were sequenced on the HiSeq platform on the basis of the size of the inserted fragments. All these steps were performed by BGI Biotechnology Co., Ltd.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Data processing\u003c/h2\u003e\u003cp\u003eThe raw data were filtered, leaving high-quality, clean data for further analysis. Overlapping relationships between reads were used to assemble the reads into tags. Tags were clustered into operational taxonomic units (OTUs) and annotated with species by comparison with a database. Sample species complexity analysis, intergroup species difference analysis, correlation analysis, and model prediction were conducted on the basis of the OTU and annotation results. Software was used to cluster the assembled tags into OTUs, with sequences clustered into OTUs at a 97% similarity threshold. Differences between groups at various taxonomic levels (phylum, class, order, family, and genus) were analyzed via software, and p values were obtained. A t test for species significant differences at each taxonomic level was also performed via R software.\u003c/p\u003e\u003cp\u003eStatistical data were analyzed via GraphPad Prism 7.0 and IBM SPSS Statistics 27 software. Data are expressed as case numbers or percentages, and the proportions of breed, age, and concurrent tumors in the total number of dogs with pyometra were analyzed. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was considered extremely significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1 Clinical examination\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDogs in the CP group exhibited marked lethargy, loss of appetite, vomiting, and elevated body temperature, along with uterine and abdominal distension. Upon opening the abdominal cavity, the uterus of the NC group showed no distension (Fig. 1a). In contrast, ultrasound examinations revealed bilateral uterine wall thickening in the CP group, which presented significant uterine distension with substantial pyometra (pus accumulation) (Fig. 1b and 1c). Dissection further demonstrated cystic hyperplasia within the endometrial lining (Fig. 1d).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2 Hematological examination\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1, compared with the NC group, the CP group presented significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) in total white blood cells, granulocytes, total red blood cells, hematocrit, and mean platelet volume. Specifically, white blood cells, granulocytes, and mean platelet volume were significantly elevated, whereas total red blood cells and hematocrit were significantly decreased. Additionally, lymphocyte, monocyte, and hemoglobin levels in the canine pyometra group (CP group) were significantly different from those in the healthy dogs (NC group), with lymphocytes and monocytes significantly elevated and hemoglobin significantly decreased. Table 2 presents the biochemical results of dogs with pyometra, indicating that, compared with the healthy group, the CP group presented elevated levels of total protein, globulin, aspartate aminotransferase (AST), alkaline phosphatase (ALP), lipase (LIP), and blood urea nitrogen (BUN). Table 3 shows that the C-reactive protein level in the dogs was 55.75\u0026plusmn;4.3 mg/L, which is far above the normal reference range.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3 OTU analysis of microbial communities\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the uterine endometrial microbial communities in dogs with pyometra and healthy dogs, we performed 16S rRNA gene sequencing on uterine lavage fluid samples from the CP group and NC group. This analysis allowed for a comparative study of the microbial characteristics between the two groups. After OTU clustering, we identified 1,035 OTUs in the CP group and 132 OTUs in the NC group, with a combined total of 1,047 OTUs. Among these, 120 OTUs were shared between the two groups, with 912 OTUs unique to the CP group and 12 OTUs unique to the NC group (Fig. 2a).\u0026nbsp;The bacterial operational taxonomic unit (OTU) count in the CP group was significantly lower than that in the NC group. In the NC group, all samples presented similar numbers of noncore OTUs in addition to the core OTUs, whereas the CP group presented the opposite pattern (Fig. 2b and 2c).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4 Complexity Analysis of Samples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4.1 Species accumulation curve\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe species accumulation curve was used to assess the impact of sequencing depth on the diversity of the samples. When the sample size reached 20, the increase in the number of detected OTUs plateaued, and the curve flattened, indicating that the sample size was sufficient and that the data quality was reliable (Fig. 2d). Further increases in sample size did not significantly increase the total number of species observed.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4.2 Species rarefaction curve\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe species rarefaction curve reflects species richness and evenness, where evenness is indicated by the flatness of the curve and richness by the width of the curve. Compared with the NC group, the CP group clustered on one side, with significantly lower species richness and evenness, suggesting the presence of dominant species in the CP group. (Fig. 2e)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5 Species Diversity Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePhylum-level analysis of enriched bacterial communities in the endometrial tissues of the NC group and CP group. Among the top 10 bacterial phyla, the healthy dog uteri were enriched with \u003cem\u003eFirmicutes\u003c/em\u003e (43.76%), \u003cem\u003eBacteroidetes\u0026nbsp;\u003c/em\u003e(13.45%), and \u003cem\u003eActinobacteria\u003c/em\u003e (5.75%).\u0026nbsp;In contrast, the CP group was enriched in \u003cem\u003eProteobacteria\u003c/em\u003e (84.54% vs. NC: 36.03%), \u003cem\u003eFusobacteria\u0026nbsp;\u003c/em\u003e(7.67%), and \u003cem\u003eTenericutes\u003c/em\u003e (2.57%) (Fig. 3a). Furthermore, compared with those in the NC group, the heatmap revealed a noticeable increase in the abundance of \u003cem\u003eProteobacteria\u003c/em\u003e in the CP group at the phylum level, whereas the abundances of \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;Firmicutes\u003c/em\u003e were reduced. At the genus level, the clustering of the microbial communities was less pronounced (Fig. 3c).\u003c/p\u003e\n\u003cp\u003eIn the comparison between the NC group and the CP group, \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003ewere identified as core microorganisms within the microbial networks, suggesting their potentially significant influence on the structure and function of the microbial community (Fig. 3e). Furthermore, \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003ewere found to belong to the same module, indicating functional or ecological niche similarities between these two phyla. When bacterial correlations were analyzed, a positive correlation was observed between\u003cem\u003e\u0026nbsp;Proteobacteria\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFirmicutes\u003c/em\u003e, while a negative correlation was detected between \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e, implying that their abundance trends changed in opposite directions. A comparison of the overall network diagrams between the NC and CP groups revealed that under disease conditions, the associations between bacterial genera almost entirely shifted to positive correlations (Fig. 3f).\u003c/p\u003e\n\u003cp\u003eThe genus-level analysis of the top 10 bacterial genera, as shown in Figure 3b, revealed that the healthy dog endometrial tissues were enriched with \u003cem\u003eEscherichia\u003c/em\u003e (21.74%),\u003cem\u003e\u0026nbsp;Lactobacillus\u003c/em\u003e (8.94%), \u003cem\u003ePrevotella\u003c/em\u003e (7.83%), \u003cem\u003eFaecalibacterium\u003c/em\u003e (4.30%), \u003cem\u003eBlautia\u003c/em\u003e (4.13%), and \u003cem\u003eBifidobacterium\u003c/em\u003e (3.90%). In contrast, the endometrial tissues of dogs with pyometra were enriched with \u003cem\u003eEscherichia\u0026nbsp;\u003c/em\u003e(39.39%), \u003cem\u003eSalmonella\u003c/em\u003e (23.32%), \u003cem\u003eHaemophilus\u003c/em\u003e (10.42%), \u003cem\u003eBrucella\u003c/em\u003e (8.18%), \u003cem\u003eFusobacterium\u003c/em\u003e (7.64%), and \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003e(2.57%). In summary, the uteri of dogs with pyometra were enriched in the genera \u003cem\u003eEscherichia, Salmonella, Fusobacterium\u003c/em\u003e, and \u003cem\u003eBrucella\u0026nbsp;\u003c/em\u003e(Fig. 3b).\u003c/p\u003e\n\u003cp\u003eHowever, notably, compared with those in the NC group, the abundances of \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e in the CP group significantly increased and decreased, respectively. These findings highlight the dynamic interactions and shifts in microbial community composition between the NC and CP groups, providing insights into the potential ecological and functional roles of key microbial taxa under different conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5 Alpha diversity results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlpha diversity analysis of samples from the CP and NC groups, performed at a 97% similarity threshold using indices (ACE, Chao1, Coverage, Observed species\u0026nbsp;(sobs), Shannon, and Simpson), revealed significant differences. Table 4 shows that ANOVA (SPSS) revealed significant differences in the ACE, Chao1, Sobs, Shannon, and Simpson indices between the groups (P \u0026lt; 0.01), indicating distinct microbial richness profiles.\u003c/p\u003e\n\u003cp\u003eNotably, despite the significantly greater sequencing depth in the CP group, the NC group presented significantly greater values for the ACE, Chao1, Sobs, and Shannon indices. This finding demonstrated that the NC group presented greater species richness, greater overall diversity, and more even species distributions within its microbial community. Conversely, the CP group presented a significantly greater Simpson index, reflecting greater dominance by a few highly abundant species (i.e., higher dominance concentrations) (Fig. 4a and 4b). Collectively, these results indicate that the CP group presented lower species richness, reduced overall diversity, and greater dominance. This pattern strongly suggests microbial community dysbiosis (ecological imbalance) in the CP group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.6 Beta diversity results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSimilarities in microbial community structure (beta diversity) were assessed by UniFrac distances. Multivariate statistical methods, including weighted UniFrac distance-based principal coordinate analysis (PCoA), partial least squares discrimination analysis (PLS-DA), and unweighted pair group method with arithmetic means (UPGMA) clustering analysis, were used to explore the differences between the CP and NC groups. The microbial communities in the uterine endometrial tissues of the NC group clustered together, whereas those of the CP group presented greater dispersion, indicating greater variability within the CP group. The weighted UniFrac PCoA and PLS-DA results revealed that the CP and NC groups clustered separately, with significant differences between the two groups (P\u0026lt;0.01) (Fig. 4c and 4d).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.8 LDA effect size analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLDA effect size analysis was utilized to identify biomarkers with significant differences between groups, emphasizing both statistical significance and biological relevance. A threshold LDA score of 4 was set to identify significantly different species. A total of 43 endometrial microbiome members were screened, including six phyla,\u0026nbsp;nine classes, nine orders, ten families and nine genera (Fig. 5a).\u0026nbsp;The phylogenetic tree revealed that the nine bacterial genera with the most significant differences between the NC and CP groups were \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eMycoplasma\u0026nbsp;\u003c/em\u003e(Fig. 5b). Therefore, \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e were identified as biomarkers for pyometra.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.10 Functional difference analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe functional potential inferred from 16S rRNA gene sequencing revealed no significant differences in the overall COG category distribution at Level 1 between the NC and CP groups (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05) (Fig. 6a), with both cohorts exhibiting pronounced enrichment in metabolism, which was consistent with the results of the KEGG Level 1 analysis. The level 1 KEGG analysis identified significant enrichment in environmental information processing and organismal systems (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Fig. 6b). Higher-resolution COG analysis (Fig. 6c) demonstrated downregulation in CP subjects for pathways governing carbohydrate degradation/transport, antibiotic resistance biosynthesis, defense mechanisms, cell division/chromosome partitioning, functionally uncharacterized proteins, replication/recombination/repair, and cofactor metabolism; conversely, upregulation was observed for cell motility, transcription, inorganic ion transport, RNA processing/modification, and transcriptional misregulation. Parallel KEGG pathway analysis (Fig. 6d) confirmed the downregulation of metabolic processes, including transcription, lipid metabolism, immune regulation, digestive/endocrine functions, xenobiotic degradation, and cellular maintenance, in the CP group, in contrast with the upregulation of vitamin/cofactor metabolism, membrane transport, transcriptional misregulation, and bacterial pathogenesis pathways. Notably, the xenobiotic biodegradation capacity decreased by 62% (\u003cem\u003eP\u003c/em\u003e = 0.003), whereas bacterial chemotaxis increased 3.8-fold (\u003cem\u003eP\u003c/em\u003e = 0.017) in the CP group compared with the NC group. Notably, butyrate metabolism was not significantly greater in the CP group than in the NC group (P \u0026gt; 0.05; KEGG L3; Fig. 1S.).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the increasing prevalence of pet ownership, the incidence of clinical diseases in animals, particularly those affecting the reproductive system, has risen accordingly. This study investigated female canine patients admitted to multiple animal hospitals in Hefei city and analyzed the microbial composition of the canine pyometra group.\u003c/p\u003e\n\u003cp\u003eAlpha and beta diversity analyses revealed significant differences in bacterial diversity between the groups. The disease group exhibited a clear predominance of specific bacterial populations, indicating microbial dysbiosis where bacterial homeostasis was disrupted. At the phylum level, \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e were the dominant phyla in the NC group. However, in the CP group, the abundance of \u003cem\u003eFirmicutes\u003c/em\u003e decreased substantially, whereas the abundance of \u003cem\u003eProteobacteria\u003c/em\u003e increased markedly. This study hypothesizes that the onset of pyometra may be associated with the depletion of \u003cem\u003eFirmicutes\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eNetwork analysis revealed a negative correlation (suggesting competition) between \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eProteobacteria\u003c/em\u003e at the phylum level under healthy conditions. However, this competitive relationship appeared weakened in the diseased state.\u003c/p\u003e\n\u003cp\u003eWithin the \u003cem\u003eProteobacteria\u003c/em\u003e phylum, increased abundances of the genera \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e, and \u003cem\u003eHaemophilus were observed\u003c/em\u003e. The elevated abundance of \u003cem\u003eEscherichia\u003c/em\u003e is consistent with previously published findings\u0026nbsp;[22]. \u003cem\u003eEscherichia coli\u003c/em\u003e, a common gram-negative bacillus, is a member of the intestinal microbiota and a significant component of the gut microbiome in both humans and animals. While typically beneficial under normal conditions, pathogenic strains such as intestinal pathogenic \u003cem\u003eE. coli\u003c/em\u003e (IPEC) can cause intestinal infections, leading to diarrhea and abdominal pain when food or water is contaminated. Furthermore, extraintestinal pathogenic \u003cem\u003eE. coli\u003c/em\u003e (ExPEC) strains can cause urinary tract infections, respiratory infections, and other infectious diseases [23]. Research also indicates that uterine \u003cem\u003eE. coli\u003c/em\u003e infection can alter the expression of uterine hormone receptors, thereby enhancing factors that promote bacterial growth [24].\u003c/p\u003e\n\u003cp\u003eSimilarly, \u003cem\u003eSalmonella\u003c/em\u003e infection typically causes salmonellosis, which is characterized by diarrhea, abdominal pain, fever, nausea, and vomiting [25]. Overgrowth of the genus \u003cem\u003eHaemophilus\u003c/em\u003e has been associated with certain inflammatory bowel diseases, intestinal tumors, and metabolic disorders. Their presence can trigger inflammatory responses in the gut, leading to mucosal damage and disease progression [26]. Additionally, interactions exist between \u003cem\u003eHaemophilus\u003c/em\u003e and other disease-associated microbial communities.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eFirmicutes\u003c/em\u003e phylum includes genera identified in this study, such as \u003cem\u003eBlautia wexlerae\u003c/em\u003e, \u003cem\u003eGemmiger formicilis\u003c/em\u003e, \u003cem\u003eMegamonas funiformis\u003c/em\u003e, \u003cem\u003eEubacterium coprostanoligenes\u003c/em\u003e, \u003cem\u003eBlautia schinkii\u003c/em\u003e, and \u003cem\u003eAnaerostipes hadrus\u003c/em\u003e. These genera are implicated in anti-inflammatory processes and butyrate metabolism. This hypothesis, however, contradicts the functional prediction results, which indicated a nonsignificant increase (P \u0026gt; 0.05) in butyrate metabolism within the CP group[27,28]. It is important to acknowledge the inherent limitations of the 16S rRNA methodology employed. For example, OTU-based clustering typically achieves resolution only at the genus level, rendering functional difference analyses speculative at this stage and necessitating experimental validation.\u003c/p\u003e\n\u003cp\u003eGiven the growing issue of antibiotic dependence, this study proposes a novel therapeutic approach: the early administration of probiotics as a primary intervention. Probiotic microorganisms, which are naturally present in fermented foods, possess antioxidant, antimicrobial, and anti-inflammatory properties. They can improve metabolic parameters, modulate the gut microbiota, and regulate the immune system[29]. The bacterial genera most commonly used as probiotics include \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e [30].\u003c/p\u003e\n\u003cp\u003eMicrobiota transplantation (MT), such as rumen microbiota transplantation (transferring the rumen microbiota from healthy animals to those with related gastrointestinal issues) or fecal microbiota transplantation (FMT), aims to restore intestinal health and function by reconstructing or adjusting the gut microbial composition. FMT has been demonstrated to alleviate experimental colitis in mice by modulating the gut microbiota [31,32]. Similarly, it remains uncertain whether the uterine microbial state in dogs with pyometra can be modulated by inoculation with specific bacterial consortia. Some studies suggest that butyrate can induce changes in the gut microbial composition; for example, dietary butyrate significantly increases the relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e in mice. Conversely, Lu et al. reported that butyrate did not affect the diversity or abundance (e.g., the \u003cem\u003eFirmicutes\u003c/em\u003e-to-\u003cem\u003eBacteroidetes\u003c/em\u003e ratio) of the mouse gut microbiota. Building on disease treatment concepts, exploring adjunctive therapy with dietary butyrate warrants consideration. This approach could offer dual potential benefits: enhancing the animal\u0026apos;s anti-inflammatory response, boosting immunity, and regulating glucose levels while simultaneously inducing changes in the gut microbial composition (potentially adjusting the relative abundance of \u003cem\u003eFirmicutes\u003c/em\u003e). Understanding the mechanisms by which butyrate modulates the microbiota to regulate host metabolism could be highly valuable.\u003c/p\u003e\n\u003cp\u003eWhen investigating different microbial communities, regional variations and dietary differences may contribute to disparities in the uterine microbiota of affected dogs. The uterine mucosal microbial communities observed in the CP group in this study presented slight variations at the class and species levels and in the abundances of \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e compared with the results reported by Zheng et al. [10]. Possible reasons include regional climatic differences (e.g., temperature and sunlight) between Jilin Province and Hefei city, which may significantly impact the microbial communities of dogs with pyometra. Furthermore, the surveyed breeds differed, with border collies, golden retrievers, and poodles predominating in this study. Additionally, significant individual variation in microbial carriage exists even within the same habitat. Finally, differences in sequencing samples could also lead to inconsistencies in results.\u003c/p\u003e\n\u003cp\u003eThis study concludes that disruption of the intrauterine microbiota can lead to uterine disease and proposes the use of probiotics to mitigate pyometra. Further experiments are essential to refine this approach and provide a reference for pyometra treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent was obtained from the owner or legal guardian of all animals involved in the study. The study protocol was reviewed and approved by the Animal Ethics Committee of Laboratory Animals of Anhui Agricultural University. All methods were performed in accordance with the relevant guidelines and regulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors hereby consent to the publication of this article and agree to provide formal written consent and transfer of copyright prior to publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials in this study are available from the corresponding author upon reasonable request. The data supporting the findings of this study are available in the Bioject repository with the accession number SUB15052484 as of January, 2026. To review BioProject accession PRJNA1218027: https://dataview.ncbi.nlm.nih.gov/object/PRJNA1218027?reviewer=hj30fidgol2kmgt7o35hsi6fu2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Science and Technology Major Project of Anhui Province（202103b06020023）；Anhui Key Research and Development Program (2023z04020003). The funder had no role in the study design, data collection/interpretation, or the decision to publish. Author contributions Wanzhen Fu performed the experiments. Jiankun Cui, Yuke Jia, Yanqiuhong Li, Meng Cao assisted with the lab experiments. Hongyan Liao contributed the reagents/materials/ analysis tools, and proofread the manuscript. Fugui Fang, Yunsheng Li, Ya Liu designed the experiment and wrote the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYa Liu and FGF designed the experiments. Hongyan Liao, Weina Li and Ziyi Zheng analyzed the data and drafted the manuscript. Wanzhen Fu and Jiankun Cui collected the samples and Yuke Jia, Yanqiuhong Li, Meng Cao performed the experiments. Chunyan Yuan and Ruonan Yuan prepared figures and performed part of the data analysis. Fugui Fang, Yunsheng Li, Hongwei Duan and Ya Liu reviewed the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors are very grateful to the research technicians for supporting our experiments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeaudu-Lange C, Larrat S, Lange E, Lecoq KNguyen F A-O. Prevalence of Reproductive Disorders including Mammary Tumors and Associated Mortality in Female Dogs. LID - 10.3390/vetsci8090184 [doi] LID - 184. （2306-7381 (Electronic)）.\u003c/li\u003e\n\u003cli\u003eXavier R G C, Santana C H, de Castro Y G, de Souza T G V, do Amarante V S, Santos R LSilva R O S. Canine Pyometra: A Short Review of Current Advances. Animals (Basel). 2023; 13（21）, doi:10.3390/ani13213310.\u003c/li\u003e\n\u003cli\u003eHagman R. Pyometra in Small Animals. Vet Clin North Am Small Anim Pract. 2018; 48（4）, 639-661, doi:10.1016/j.cvsm.2018.03.001.\u003c/li\u003e\n\u003cli\u003eEgenvall A, Hagman R, Bonnett B N, Hedhammar A, Olson PLagerstedt A S. Breed risk of pyometra in insured dogs in Sweden. J Vet Intern Med. 2001; 15（6）, 530-538, doi:10.1892/0891-6640(2001)015\u0026lt;0530:bropii\u0026gt;2.3.co;2.\u003c/li\u003e\n\u003cli\u003eDąbrowski R, Kocki T, Szczubiał M, Dąbrowski WParada-Turska J. Kynurenic acid in plasma and endometrium in bitches with pyometra. Inflammation. 2013; 36（1）, 131-135, doi:10.1007/s10753-012-9527-5.\u003c/li\u003e\n\u003cli\u003eNiskanen MThrusfield M V. Associations between age, parity, hormonal therapy and breed, and pyometra in Finnish dogs. Vet Rec. 1998; 143（18）, 493-498, doi:10.1136/vr.143.18.493.\u003c/li\u003e\n\u003cli\u003eNoakes D E, Dhaliwal G KEngland G C. Cystic endometrial hyperplasia/pyometra in dogs: a review of the causes and pathogenesis. J Reprod Fertil Suppl. 2001; 57, 395-406.\u003c/li\u003e\n\u003cli\u003eWijewardana V, Sugiura K, Wijesekera D P, Hatoya S, Nishimura T, Kanegi R, Ushigusa TInaba T. Effect of ovarian hormones on maturation of dendritic cells from peripheral blood monocytes in dogs. J Vet Med Sci. 2015; 77（7）, 771-775, doi:10.1292/jvms.14-0558.\u003c/li\u003e\n\u003cli\u003eLiang S, Wu X, Hu X, Wang TJin F. Recognizing Depression from the Microbiota⁻Gut⁻Brain Axis. Int J Mol Sci. 2018; 19（6）, doi:10.3390/ijms19061592.\u003c/li\u003e\n\u003cli\u003eZheng H H, Du C T, Zhang Y Z, Yu C, Huang R L, Tang X YXie G H. A study on the correlation between intrauterine microbiota and uterine pyogenesis in dogs. Theriogenology. 2023; 196, 97-105, doi:10.1016/j.theriogenology.2022.11.003.\u003c/li\u003e\n\u003cli\u003eLopes C E, De Carli S, Riboldi C I, De Lorenzo C, Panziera W, Driemeier DSiqueira F M. Pet Pyometra: Correlating Bacteria Pathogenicity to Endometrial Histological Changes. Pathogens. 2021; 10（7）, doi:10.3390/pathogens10070833.\u003c/li\u003e\n\u003cli\u003eLiao A-T, Huang W-hWang S-L. BACTERIAL ISOLATION AND ANTIBIOTIC SELECTION AFTER OVARIOHYSTERECTOMY OF CANINE PYOMETRA: A RETROSPECTIVE STUDY OF 55 CASES. Taiwan Veterinary Journal. 2020; 46, 1-8, doi:10.1142/S1682648520500067.\u003c/li\u003e\n\u003cli\u003eWareth G, Melzer F, El-Diasty M, Schmoock G, Elbauomy E, Abdel-Hamid N, Sayour ANeubauer H. Isolation of \u003cem\u003eBrucella abortus\u003c/em\u003e from a Dog and a Cat Confirms their Biological Role in Re-emergence and Dissemination of Bovine Brucellosis on Dairy Farms. Transbound Emerg Dis. 2017; 64（5）, e27-e30, doi:10.1111/tbed.12535.\u003c/li\u003e\n\u003cli\u003eRocha M F G, Paiva D D Q, Amando B R, Melgarejo C M A, Freitas A S, Gomes F I F, Ocadaque C J, Costa C L, Guedes G M M, Lima-Neto R G, Cordeiro R A, Sidrim J J CCastelo-Branco D. Antimicrobial susceptibility and production of virulence factors by bacteria recovered from bitches with pyometra. Reprod Domest Anim. 2022; 57（9）, 1063-1073, doi:10.1111/rda.14181.\u003c/li\u003e\n\u003cli\u003eVerstegen J, Dhaliwal GVerstegen-Onclin K. Mucometra, cystic endometrial hyperplasia, and pyometra in the bitch: advances in treatment and assessment of future reproductive success. Theriogenology. 2008; 70（3）, 364-374, doi:10.1016/j.theriogenology.2008.04.036.\u003c/li\u003e\n\u003cli\u003eSmith F O. Canine pyometra. Theriogenology. 2006; 66（3）, 610-612, doi:10.1016/j.theriogenology.2006.04.023.\u003c/li\u003e\n\u003cli\u003eKass\u0026eacute; F N, Fairbrother J MDubuc J. Relationship between \u003cem\u003eEscherichia coli\u003c/em\u003e virulence factors and postpartum metritis in dairy cows. J Dairy Sci. 2016; 99（6）, 4656-4667, doi:10.3168/jds.2015-10094.\u003c/li\u003e\n\u003cli\u003eYarza P, Yilmaz P, Pruesse E, Gl\u0026ouml;ckner F O, Ludwig W, Schleifer K H, Whitman W B, Euz\u0026eacute;by J, Amann RRossell\u0026oacute;-M\u0026oacute;ra R. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014; 12（9）, 635-645, doi:10.1038/nrmicro3330.\u003c/li\u003e\n\u003cli\u003eTindall B J, Rossell\u0026oacute;-M\u0026oacute;ra R, Busse H J, Ludwig WK\u0026auml;mpfer P. Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol Microbiol. 2010; 60（Pt 1）, 249-266, doi:10.1099/ijs.0.016949-0.\u003c/li\u003e\n\u003cli\u003eYarza P, Richter M, Peplies J, Euzeby J, Amann R, Schleifer K H, Ludwig W, Gl\u0026ouml;ckner F ORossell\u0026oacute;-M\u0026oacute;ra R. The All-Species Living Tree project: a 16S rRNA-based phylogenetic tree of all sequenced type strains. Syst Appl Microbiol. 2008; 31（4）, 241-250, doi:10.1016/j.syapm.2008.07.001.\u003c/li\u003e\n\u003cli\u003eZheng H H, Du C T, Zhang Y Z, Yu C, Huang R L, Tang X YXie G H. Identification of Canine Pyometra-Associated Metabolites Using Untargeted Metabolomics. Int J Mol Sci. 2022; 23（22）, doi:10.3390/ijms232214161.\u003c/li\u003e\n\u003cli\u003eHui N, Hariadi M, uPrimarizky H. A Retrospective Study of Canine Pyometra in Segar Veterinary Hospital, Kuala Lumpur, Malaysia Year 2012-2016. KnE Life Sciences. 2017; 3, 153, doi:10.18502/kls.v3i6.1124.\u003c/li\u003e\n\u003cli\u003eKong H, Hong XLi X. Current perspectivesin pathogenesis and antimicrobial resistance of enteroaggregative \u003cem\u003eEscherichia coli\u003c/em\u003e. Microb Pathog. 2015; 85, 44-49, doi:10.1016/j.micpath.2015.06.002.\u003c/li\u003e\n\u003cli\u003eRiley L W. Distinguishing Pathovars from Nonpathovars: \u003cem\u003eEscherichia coli\u003c/em\u003e. Microbiol Spectr. 2020; 8（4）, doi:10.1128/microbiolspec.AME-0014-2020.\u003c/li\u003e\n\u003cli\u003eQian CHou J. \u003cem\u003eEscherichia coli\u003c/em\u003e virulence influences the roles of sex hormone receptors in female dogs with simulated pyometra. Exp Ther Med. 2017; 14（4）, 3013-3021, doi:10.3892/etm.2017.4894.\u003c/li\u003e\n\u003cli\u003eTakaya A, Yamamoto TTokoyoda K. Humoral Immunity vs. Salmonella. Front Immunol. 2019; 10, 3155, doi:10.3389/fimmu.2019.03155.\u003c/li\u003e\n\u003cli\u003eZhang L, Liu C, Jiang QYin Y. Butyrate in Energy Metabolism: There Is Still More to Learn. （1879-3061 (Electronic)）.\u003c/li\u003e\n\u003cli\u003eLi Z, Yi C X, Katiraei S, Kooijman S, Zhou E, Chung C K, Gao Y, van den Heuvel J K, Meijer O C, Berb\u0026eacute;e J F P, Heijink M, Giera M, Willems van Dijk K, Groen A K, Rensen P C NWang Y. Butyrate reduces appetite and activates brown adipose tissue via the gut-brain neural circuit. （1468-3288 (Electronic)）.\u003c/li\u003e\n\u003cli\u003eSingh S A-O, Pal N, Shubham S, Sarma D A-O, Verma V, Marotta FKumar M A-O. Polycystic Ovary Syndrome: Etiology, Current Management, and Future Therapeutics. LID - 10.3390/jcm12041454 [doi] LID - 1454. 2023（2077-0383 (Print)）.\u003c/li\u003e\n\u003cli\u003eBhalla P, Rengaswamy R, Karunagaran D, Suraishkumar G KSahoo S. Metabolic modeling of host\u0026ndash;microbe interactions for therapeutics in colorectal cancer. npj Systems Biology and Applications. 2022; 8（1）, 1, doi:10.1038/s41540-021-00210-9.\u003c/li\u003e\n\u003cli\u003eWen X, Xie R, Wang H G, Zhang M N, He L, Zhang M HYang X Z. Fecal microbiota transplantation alleviates experimental colitis through the Toll-like receptor 4 signaling pathway. World J Gastroenterol. 2023; 29（30）, 4657-4670, doi:10.3748/wjg.v29.i30.4657.\u003c/li\u003e\n\u003cli\u003eZhang W, Zou G, Li B, Du X, Sun Z, Sun YJiang X. Fecal Microbiota Transplantation (FMT) Alleviates Experimental Colitis in Mice by Gut Microbiota Regulation. J Microbiol Biotechnol. 2020; 30（8）, 1132-1141, doi:10.4014/jmb.2002.02044.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 The Blood Routine Test Results of Diseased Dogs with Pyometra and Healthy Dogs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThe diseased dogs with pyometra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eThe healthy dogs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eReference value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eMini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eWhite blood cell (WBC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e38.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e8.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.0~17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eLymphocyte (Lym)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.8~5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eMonocyte (Mon)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.0~1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eGranulocyte (Gran)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e29.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.0~12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eRed blood cell (RBC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e7.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.50~8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10\u003csup\u003e12\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ehemoglobin (HGB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e112.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e145.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e110~190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eRed blood cell specific volume (HCT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e52.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e35.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e39.0~56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eMean corpusular volume (MCV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e61.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e69.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e69.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e81.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e75.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e62.0~72.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003efL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eMean corpuscular hemoglobin (MCH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e35.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e26.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20.0~25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003ePg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eMean corpusular hemoglobin concerntration (MCHC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e314.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e355.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e300~380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eRed blood cell distribution width~standard deviation (RDW~CV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.0~15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePlatelet count (PLT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e367.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e252.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e117~460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eMean platelet volume (MPV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e9.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e7.0~12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003efL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003ePlatelet distribution width (PDW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e20.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e16.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eProcalcitonin (PCT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e22.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0. 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 206px;\"\u003e\n \u003cp\u003eEosinophil (Eos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e5.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 The Blood Biochemistry Test Results of Diseased Dogs with Pyometra and Healthy Dogs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThe diseased dogs with pyometra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003eThe healthy dogs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003cp\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eUnit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAlanine transaminase (ALT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e31.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e55.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e55.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.0~100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAspartate aminotransferase\u003c/p\u003e\n \u003cp\u003e(AST)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e42.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026le;50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAlkaline phosphatase (ALP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e128.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e54.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e23.0~212.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAlbumin (ALB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e22.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e34.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e27.0-38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003er~GGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e59.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e103.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e79.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026le;7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTotal Protein (TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e20.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e58.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e68.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e52.0~82.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eGLB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e85.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e52.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e33.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e41.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e24.0~45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eA/G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTotal bilirubin (TBIL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e4.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026le;15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eBlood Urea Nitrogen (BUN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e58.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e15.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.50~9.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCreatinine (CRE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e54.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e65.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e44.0~159.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eAmylase (AMY)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e67.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1278.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e784.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e291.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e864.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e500.0~1500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eLipase (LIP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e84.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e100.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e44.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20.0-180.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eGlutamic acid (GLU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e12.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.28~6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCalcium (Ca)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.98~3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003ePhosphorus (P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.81~2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e9.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.84~8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.11~1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e36.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e154.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e27.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e247.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e114.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e40-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eFUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e357.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e200.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e266.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e121.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e245.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e207.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026mu;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 177px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e44.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e526.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e177.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e68.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e198.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e127.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 68px;\"\u003e\n \u003cp\u003eU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 The C-Reactive Protein (CRP) Test of Diseased Dogs with Pyometra\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eresults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eunits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 160px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e55.75\u0026plusmn;4.3 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.0~10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003emg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 The Comparison of Alpha Diversity of Pyometra Dogs and Health Dogs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Values are expressed as mean counts \u0026plusmn;standard error. Statistically significant differences (P \u0026lt; 0.05) is indicated by *，Statistically dramatically significant differences (P \u0026lt; 0.01) is indicated by **.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 568px;\"\u003e\n \u003cp\u003eAlpha diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eSobs\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eChao1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eAce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eShannon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eSimpson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eNC group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e16.970\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e22.872\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e25.890\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.382\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.808\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eCP group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e18.667\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e24.507\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e27.232\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.386\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e0.801\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e＜0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e＜0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e＜0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e＜0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e＜0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e＜0.01**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Canine pyometra, Uterine microbiome, 16S rRNA sequencing, Microbial dysbiosis, Butyrate metabolism","lastPublishedDoi":"10.21203/rs.3.rs-7605933/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7605933/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCanine pyometra is a prevalent reproductive disorder predominantly affecting older, nulliparous bitches, with peak incidence during the diestrus phase. This condition is frequently complicated by systemic infections and carries high mortality risks. Using 16S rRNA sequencing, this study systematically compared the endometrial microbiota between pyometra-affected dogs (n\u0026thinsp;=\u0026thinsp;11) and healthy controls (n\u0026thinsp;=\u0026thinsp;12), revealing significant microbial dysbiosis characterized by pathogenic enrichment and metabolic pathway disruption. Alpha diversity indices (Shannon index: 3.21 vs. 1.89, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) confirmed reduced microbial richness in pyometra cases. Taxonomic analysis revealed that \u003cem\u003ePasteurellaceae, Leptotrichiaceae\u003c/em\u003e, and \u003cem\u003eLactobacillaceae\u003c/em\u003e were significantly enriched at the family level, whereas \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eFusobacterium\u003c/em\u003e predominated at the genus level. LDA effect size (LEfSe) analysis identified \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e as microbial biomarkers for pyometra. Functional prediction revealed significant downregulation of metabolic pathways related to nutrient absorption, immune modulation, and xenobiotic degradation, alongside upregulation of membrane transport systems and cellular signaling pathways. Network analysis revealed potential disease associations with butyrate metabolism. These findings provide critical insights for the development of microbiota-targeted therapeutic strategies against canine pyometra.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of the Uterine Microbiome in Canine Pyometra: A 16S rRNA Sequencing Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 16:11:11","doi":"10.21203/rs.3.rs-7605933/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-08T03:20:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T05:38:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22569878272458380647548335574628316078","date":"2025-12-05T03:39:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T14:42:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76346095651974031225976458447949479547","date":"2025-10-19T00:25:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238820942280235586286440260102453154069","date":"2025-10-14T05:26:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-02T01:22:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-02T01:01:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T11:06:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T09:51:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-09-18T07:16:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c4fd0cb-8622-40b1-871e-051cf703280b","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T08:39:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 16:11:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7605933","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7605933","identity":"rs-7605933","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}