Journal of Food and Nutrition

Abstract

Material and methods. 4 groups of 15 white rats: 1st - intact, 2nd - model of nephrosis-nephritis, 3rd - model of intra-abdominal infection, 4th - model of nephrosis-nephritis and intra-abdominal infection. Intra-abdominal infection was simulated by intraabdominal injection of autofaeces mixture. Nephrosis-nephritis was simulated by subcutaneous introduction of 5% sodium nitrite solution. Intra-abdominal infection was simulated 3 months after diabetes simulation. The small and large intestines and peritoneal exudate microflora were studied. The material for examination was taken before the simulation of intra-abdominal infection, in 6, 12, 24, 48 h after.

Results. Dysbiosis was found in the 2nd group. In the 3rd group, in 6 h, dysbiosis was found, in the 4th group dysbiosis progressed, with not only quantitative but also qualitative changes in the intestinal microbiocenosis were found, which further progressed. During the development of intra-abdominal infection, permanent changes in the intestinal microbiocenosis and peritoneal exudate microflora were detected. In the 4th group, the changes were more severe. In the 3rd group, in 48 h, microflora’s changes in both intestines and the peritoneal exudate indicate slight regression of pathological processes. In the 4th group, in 48 h, progression of pathological processes was detected.

Conclusion. 1. Intestinal dysbiosis was detected in rats 12 h since acute nephrosis-nephritis simulating.

2. Simulating acute intra-abdominal infection in intact rats causes intestinal dysbiosis, a syndrome of excess bacterial colonization of small intestines.

3. Simulation of acute intra-abdominal infection in rats with models of acute nephrosis-nephritis increases intestinal dysbiosis, in 6 h not only quantitative but also qualitative changes in the intestinal microbiocenosis were detected, which further progress.

4. In intact rats, 48 h since acute intra-abdominal infection modeling, signs of dysbiosis regression and a decrease in the number of microorganisms in peritoneal exudate were found, instead in rats with models of acute intra-abdominal infection with underlying acute nephrosis-nephritis, signs of dysbiosis progression and an increase in the number of microorganisms in peritoneal exudate were found.

Introduction

The intestinal microflora (MF) is an important factor in homeostasis maintenance [1-4]. The gut microorganisms (MO) produce a variety of nutrients including short-chain fatty acids, B vitamins, and vitamin K etc [4]. Because of their ability to interact with receptors on epithelial cells and subepithelial cells, MO also release several cellular factors that influence human metabolism [5,6]. Thus, they have potential roles in the pathogenesis of many diseases, in particular, kidney diseases [7-10]. At the same time, intestinal microflora is the main cause and promoter of intra-abdominal infections [11-14].

The global burden of kidney disease, in particular, different types of nephritis, is increasing worldwide [15-18]. Pathological disorders of the kidneys negatively affect the condition of all organs and systems [19-22].

At the same time, the prevalence of intraabdominal infections (IAI) is constant [11-14,23]. The number of patients with acute IAI associated with kidney disease is constantly increasing. In patients with kidney disease, clinical changes and an increase in the number of postoperative complications are observed [11-14,23]. Kidney disease is one of the risk factors for mortality in IAI [24]. So, such comorbidity requires changes in diagnostic tools and management [21,22]. Controversial data about the changes in pathophysiological mechanisms if IAI and kidney diseases are associated with the reasons for the unsatisfactory management results of patients with this comorbidity [21,22,25]. However, changes in MF have not yet been studied comprehensively.

The importance of such studies is determined by the main role of endogenous intestinal MF in IAI development and progression [11-14,23]. Nutrition is one of the main means for regulating intestinal microflora [26-30]. Therefore, such studies are also needed to understand how to correct the condition of the gut MF.

Material And Methods

150 albino non-pedigree female rats. All rats were sexually mature (age 6 months). The rats’ mass was from 180 to 200 g. The rats were in a vivarium before the start of the experiment. The conditions of stay and food were the same for all rats. After the nephrosis-nephritis (NN) and IAI simulation, the rats were in the same stay conditions and had the same drink.
The rats were divided into 4 groups: 1st - intact (15), 2nd - NN model (15), 3rd - IAI model (60), 4th - NN model and IAI model (60).
NN was simulated by subcutaneous introduction of 5% sodium nitrite solution on distilled water in the dose of 0,5 mg per 100 g of mass. IAI was simulated by intraabdominal injection of 10% autofaeces mixture in the dose of 10 ml per 100 g of mass. IAI was induced 12 h since NN was simulated.
The small (SI) and large intestines (LI), and peritoneal exudate (PE) microflora were studied. Before modeling of IAI, as well as in 6, 12, 24, and 48 h since its inducement, laparotomy was performed and the material was taken for examination. 5 g of the intestinal contents for microbiological examination were taken in the middle part of SI and the middle part of LI. 5 ml of the exudate was taken from the area of greatest accumulation. 5 ml of sterile 0.9% NaCl solution was poured into the abdominal cavity, in animals of the 1st and 2nd groups after laparotomy, and in 10 min the solution was taken for microbiological examination.
Histological studies were performed to confirm the changes in the peritoneum and kidneys.
All manipulations were performed under the sevorane anesthesia. The animals were taken out of the experiment by an overdose of sevorane.
The microbiological examination included the study of the quantitative and species composition of the peritoneal exudate MF, as well as small and large intestines MF. The microbiological research was carried out with bacteriological methods with the isolation and identification of pure cultures of the pathogen to the genus and species. Selective nutrient media were used to isolate microorganisms (MO). The number of aerobic MO was counted after 1-2 days. The number of anaerobic MO was counted after 5-7 days of cultivation on nutrient media in an anaerostat. The concentration of MO was expressed in logarithms (lg) of colony-forming units (CFU) in 1 g or 1 ml of the collected material - lg CFU/g or lg CFU/ml.
The isolated microorganisms’ groups and types frequency of occurrence (FO) was determined by the formula:
FO = Ni x 100%/Nt,
where: FO is the frequency of occurrence; Ni is the number of objects in which the corresponding microorganism was isolated; Nt is the number of objects taken for examination.
After the identification of microorganisms’ strains, the dominance coefficient (DC) was determined by the formula:
DC = Nn/Nt,
where: DC is the dominance coefficient; Nn is the number of this species (genus) microorganisms’ isolated strains; Nt is the total number of isolated microorganisms’ strains.
While carrying out the study, the researchers followed the basic guidelines of the Vancouver Conventions (1979, 1994) concerning biomedical experiments and the Council of Europe Convention on the Protection of Vertebrate Animals Used in Experiments and for Other Scientific Purposes (1986).
The hypothesis of normal data distribution (Gaussian distribution) was tested in samples by Shapiro-Wilk criterion. Verification of the hypothesis of average data equality was carried out by Wilcoxon and Mann-Whitney-Wilcoxon criterion. To discover the strength of a link between sets of data the Spearman's Rank Correlation Coefficient was used. The significance level (alpha) 0.05 was set in the study. The study results were statistically processed by the Microsoft® Office Excel (build 11.5612.5703) tables. We have set the level of significance 0.05.
While performing the work, the norms of conducting research in the field of biology and medicine were observed: the Vancouver Conventions on Biomedical Research (1979, 1994), the Council of Europe Convention on the Protection of Vertebrate Animals Used in Experiments and for Other Scientific Purposes (1986).

Results

In the 1st group, E. coli (FO=100%) and B. fragilis (FO=100%), which are saprophytic MO, were found in SI (Table 1). Anaerobic MF dominated slightly (DC=0.53). In the 2nd group Proteus spp. (FO=50%) and P. niger (FO=60%) were found, in addition to those MO. Anaerobic MF dominated (D=0.71). The total number of MO, the number of aerobic and anaerobic MO in the 2nd group were significantly higher.

Table 1: Microflora of the rat’s small intestine

In the 1st group, E. coli (FO=100%), Proteus spp. (FO=100%), B. fragilis (FO=100%), P. niger (FO=100%), and gram-positive diplococci (GPD) (FO=100%) were found in LI (Table 2). Aerobic MF dominated (DC=0.64). In the 2nd group, the number of GPD (FO=70%) was significantly lower. Instead, the number of P. niger (FO=100%) was significantly higher. At the same time, gram-positive spore-forming anaerobes (GPSFA) were found (FO=100%) in addition to those MO. Anaerobic MF dominated (DC=0.61). The total number of aerobic MO was significantly lower than in the 1st group, the total number of aerobic MO was significantly higher.

Table 2: Microflora of the rat’s large intestine (lg КУО/мл)

Abdominal lavages were sterile in all rats.

In 6 h since IAI simulation in the 3rd group, the number of E. coli (FO=100%) and B. fragilis (FO=100%) in the SI significantly increased (Table 3). At the same time, Proteus spp. (FO=80%) and P. niger (FO=46.67%) were found. The number of aerobes and the total number of MO increased significantly. Aerobes dominated (DC was 0.64). In the 4th group, the number of E. coli (FO=100%) and P. niger (FO=100%) significantly increased. The number of P. niger (FO=100%) significantly decreased. The number of anaerobes significantly decreased, the number of aerobes and the total number of MO increased significantly. Aerobes dominated (DC=0.53). The number of anaerobes and the total number of MO in the 4th group was significantly higher.

Table 3: Microflora of the rat’s small intestine in 6 h since IAI simulation

Note: *- validity coefficient between the output data < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the number of Proteus spp. (FO=100%) and B. fragilis (FO=100%) in LI significantly increased (Table 4). At the same time, GPSFA were found (FO=53.33%). The number of aerobes, anaerobes, and the total number of MO did not change significantly. DC of aerobes decreased to 0.56. In the 4th group, the number of E. coli (FO=80%) significantly decreased, and the number of B. fragilis (FO=100%) significantly increased. GPSFA was not found. At the same time, lactose-negative enterobacteria (LNE), which is a gram-negative conditionally pathogenic MF were found (FO=60%). The number of anaerobes decreased slightly. The number of anaerobes and the total number of MO decreased significantly. Aerobic MF slightly dominated (DC was 0.52). The number of aerobes in the 4th group was significantly lower. The number of anaerobes was slightly higher. The total number of MO in the 4th group was slightly lower.

Table 4: Microflora of the rat’s large intestine in 6 h since IAI simulation

Note: *- validity coefficient between the output data < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, E. coli (FO=100%) and B. fragilis (FO=80%) were found in PE (Table 5). Anaerobic MF slightly dominated (DC was 0.54).  In the 4th group, E. coli (FO=100%) and B. fragilis (FO=86.67%) were found in PE. The number of E. coli was significantly higher, and the number of B. fragilis was slightly lower. Anaerobic MF dominated (DC=0.73). The number of aerobes was significantly higher, and the number of aerobes was slightly lower. The total number of MO was slightly higher.

Table 5: Microflora of peritoneal exudate in 6 h since IAI simulation

The total number of MO in PE in both groups was directly and closely correlated with the total number of MO in both the small and large intestines (Table 6). The number of aerobic MO in PE in both groups was directly correlated with the number of MO in both the small and large intestines (Table 7). However, in the 3rd group, the correlation coefficients were less. The number of anaerobic MO in PE in both groups was directly correlated with the number of MO in both the small and large intestines (Table 8). However, in the 4th group, the correlation coefficient for LI was less.

Table 6: Correlations between the total number of peritoneal exudate microflora and gut microflora in 6 h since IAI simulation

Table 7: Correlations between the number of peritoneal exudate aerobic microflora and gut aerobic microflora in 6 h since IAI simulation

Table 8: Correlations between the number of peritoneal exudate anaerobic microflora and gut anaerobic microflora in 6 h since IAI simulation

In 12 h since IAI simulation, in the 3rd group, the number of E. coli in the SI significantly increased, and the number of B. fragilis significantly decreased (Table 9). The number of aerobes, anaerobes, and the total number of MO has hardly changed. Aerobes dominated (DC=0.67). In the 4th group, the number of E. coli and P. niger increased significantly. The anaerobes dominated slightly (DC=0.53). The number of all MO increased significantly. The total number of MO and the number of anaerobic MO was significantly higher.

Table 9: Microflora of the rat’s small intestine in 12 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the number of E. coli in LI has hardly changed (Table 10). Hemolytic strains of E. coli were found (FO=40%). The number of B. fragilis and Proteus spp. significantly decreased. The number of P. niger has increased significantly. At the same time, LNE (FO=80%) and Staphylococcus spp. were found. (FO=60%). The number of aerobic MO increased significantly and aerobes dominated (DC=0.61). The total number of MO has slightly increased. In the 4th group E. coli was not found. The number of Proteus spp. significantly increased. The number of P. niger decreased significantly. At the same time, GPSFA (FO=100%) were found. The number of all MO increased significantly. Aerobic MF dominated, but DC (0.57) was less than in the 3rd group. The total number of MO, and the number of aerobes and anaerobes was higher.

Table 10: Microflora of the rat’s large intestine in 12 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the number of E. coli and B. fragilis significantly increased in PE (Table 11). In addition, Proteus spp. were detected. (FO=100%) and P. niger (FO=60%). Microbial contamination of PE has increased significantly. Aerobes dominated (DC=0.79). In the 4th group, E. coli and P. niger were not found. The number of B. fragilis increased slightly. Proteus spp. were also detected (FO=100%), and their number was significantly higher. In addition, GPSFAs were found (FO=70%). Aerobes dominated (DC=0.91). The number of aerobes and the total number of MO was significantly higher at the same time the number of anaerobes was less slightly.

Table 11: Microflora of peritoneal exudate in 12 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

The number of MO in the PE did not correlate with the number of MO in SI in both groups. There was a direct close correlation with the number of MO in LI in both groups (Table 12), but in the 4th group, it was insignificant. In both groups, the number of aerobic MO in PE was directly correlated with the number of MO in SI and LI (Table 13). In the 4th group, the closest correlation was with MO of LI. The number of anaerobic MO in PE in the 3rd group was directly correlated with the number of MO in SI and LI (Table 14). The number of anaerobic MO in the 4th group was directly correlated with the number of MO in SI, but the correlation coefficient was less, and inverse correlated with the number of MO in LI.

Table 12: Correlations between the total number of peritoneal exudate microflora and large intestine microflora in 12 h since IAI simulation

Table 13: Correlations between the number of peritoneal exudate aerobic microflora and gut aerobic microflora in 12 h since IAI simulation

Table 14: Correlations between the number of peritoneal exudate anaerobic microflora and gut anaerobic microflora in 12 h since IAI simulation

In 24 h since IAI simulation, in the 3rd group, the number of all MO in SI significantly increased (Table 15). However, the number of anaerobes increased more significantly. DC of aerobes decreased to 0.62. In the 4th group, the number of E. coli and Proteus spp. significantly decreased. P. niger was not found. At the same time, GPSFA were found (FO=100%). The number of all MO decreased. Aerobes dominated (DC=0.56). The total number of MO, the number of aerobes and anaerobes was less than in the 3rd group.

Table 15: Microflora of the rat’s small intestine in 24 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the number of MO in LI increased significantly, except Staphylococcus spp. (Table 16). DC of aerobes decreased to 0.57. The total number of all MO increased significantly. In the 4th group E. coli was not found. The number of Proteus spp. has hardly changed and remained significantly higher. The number of Staphylococcus spp. decreased significantly. The number of B. fragilis has hardly changed. Instead, the number of P. niger and GPSFA increased significantly. So, DC of the aerobes decreased to 0.53. The total number of all MO increased significantly.

Table 16: Microflora of the rat’s large intestine in 24 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the number of E. coli, B. fragilis, and P. niger significantly increased in PE (Table 17). In addition, Staphylococcus spp. (FO=80%) and hemolytic strains of E. coli (FO=30%) were found. The total number of MO, the number of aerobes and anaerobes increased significantly. DC of aerobes decreased to 0.66. In the 4th group E. coli was found (FO=40%). The number of Proteus spp. has hardly changed and remained significantly higher. The number of B. fragilis decreased significantly. The number of GPSFA increased slightly. In addition, LNE were found (FO=60%). Staphylococcus spp. and P. niger were not found. The number of all MO has hardly changed. Aerobes dominated (DC=0.91).

Table 17: Microflora of peritoneal exudate in 24 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the total number of MO in the PE was directly correlated with the number of MO in SI and LI (Table 18). In the 4th group, there was a correlation only with the number of MO in SI. In the 3rd group, the number of aerobic MO in PE was directly correlated with the number of MO in SI and inversely correlated with the number of MO in LI (Table 19). Instead, in the 4th group, the number of aerobic MO in PE was directly correlated with the number of MO in SI and LI. In both groups, the number of anaerobic MO in PE was directly correlated with the number of MO in the SI and LI (Table 20).

Table 18: Correlations between the total number of peritoneal exudate microflora and gut microflora in 24 h since IAI simulation

Table 19: Correlations between the number of peritoneal exudate aerobic microflora and gut aerobic microflora in 24 h since IAI simulation

Table 20: Correlations between the number of peritoneal exudate anaerobic microflora and gut anaerobic microflora in 24 h since IAI simulation

In 48 h since IAI simulation, in the 3rd group, the number of E. coli, Proteus spp., and B. fragilis in SI decreased significantly (Table 21). P. niger was not found. GPD were found (FO=80%). P. niger was not found. The number of aerobes, anaerobes, and the total number of MO significantly decreased. DC of aerobes has hardly changed (0.65). In the 4th group, the number of E. coli increased significantly. Also were found GPD (FO=100%), and their number was higher significantly. The number of B. fragilis and GPSFA has hardly changed. P. niger was found (FO=100%). The number of aerobes, anaerobes, and the total number of MO increased and was significantly higher than in the 3rd group. DC of aerobes increased slightly (DC=0.58).

Table 21: Microflora of the rat’s small intestine in 48 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the number of E. coli and Proteus spp. in LI significantly decreased (Table 22). Staphylococci and LNE were not found. GPD were found (FO=100%). The number of aerobes and anaerobes, and the total number of MO decreased significantly. DC of aerobes did not change (0.57). In the 4th group E. coli was found (FO=100%). The number of Proteus spp. significantly decreased. The number of LNE and B. fragilis significantly increased. GPD were found (FO=100%). The number of P. niger and GPSFA increased significantly. The total number of MO, and the number of aerobes and anaerobes increased significantly. DC of the aerobes increased to 0.56.

Table 22: Microflora of the rat’s large intestine in 48 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 4th group the number of Proteus spp. decreased significantly. The number of LNE, B. fragilis, and GPSFA increased significantly. P. niger was found. DC of aerobes decreased to 0.87. The total number of MO and the number of aerobes increased significantly. The number of all MO was significantly higher than in the 3rd group.

Table 23: Microflora of peritoneal exudate in 48 h since IAI simulation

Note: *- validity coefficient between the previous observation term < 0,05, ** - < 0,01 (only statistically significant differences are given).

In the 3rd group, the total number of MO in PE was inversely correlated with the number of MO in SI and directly correlated with the number of MO in LI (Table 24). In the 4th group, in contrast, the correlation coefficients were opposite. In the 3rd group, the number of aerobic MO in PE was inversely correlated with the number of MO in SI and directly correlated with the number of MO in LI (Table 25). In the 4th group, in contrast, the correlation coefficients were opposite. In both groups, the number of anaerobic MO in PE was inversely correlated with the number of MO in SI and directly correlated with the number of MO in LI (Table 26). In the 4th group, the correlation coefficient with the number of MO in LI was significantly higher.

Table 24: Correlations between the total number of peritoneal exudate microflora and gut microflora in 48 h since IAI simulation

Table 25: Correlations between the number of peritoneal exudate aerobic microflora and gut aerobic microflora in 48 h since IAI simulation

Table 26: Correlations between the number of peritoneal exudate anaerobic microflora and gut anaerobic microflora in 48 h since IAI simulation

Discussion

The data show that there are dysbiosis changes in 2nd group, which confirms known data [7-10]. The spectrum of MO differs quantitatively and qualitatively. In SI were found MO, which in intact animals were found only in LI. In addition, anaerobes substantially dominate. In LI, where anaerobes also dominated, were found GPSFA that were not found in the 1st group. Among the reasons for such changes, we can mention immune disorders characteristic of NN [17-20], a decrease of intestinal mucosa colonization resistance, which is one of the important mechanisms of immune dysfunction development [16,27,28].

In 6 hours since IAI simulation, signs of dysbiosis were detected in the 3rd group. It was the appearance of Proteus spp. and P. niger in SI, and the appearance of GPSFA in LI. At the same time, the total number of MO increased against the background of some saprophytic MO disappearance. In the 4th group, intestinal microbiocenosis disorders are ongoing. In SI, these are quantitative changes, and in LI, these are qualitative changes - there were no GPSFA, instead, LNE were found. This indicates more significant violations of the colonization resistance of the intestinal mucosa. Dysbiosis is a well-known phenomenon that occurs, in particular, with IAI [30-33]. At the same time, in the 4th group, the signs of dysbiosis are more severe. The dysbiosis caused by NN apparently is the basis of these changes. Bacterial contamination of the exudate in the 4th group is somewhat higher. The correlation coefficients indicate that in this period the microbial promoters of IAI are the MF of different intestine parts. But in the 4th group, the sources of aerobic MF are both intestines, and the main source of anaerobic MF is SI.

In 12 h since IAI simulation, in the 3rd group, the composition of the microbiocenosis did not change significantly in SI. The ratio between MO changed. In the 4th group, the microbiocenosis also changed only quantitatively. However, a large number of MO indicates the development of the syndrome of excess bacterial colonization of SI with dominance of anaerobic MF. In LI the differences are not only quantitative. No E. coli was found in the 4th group, and the number of MO, especially anaerobes, was higher. In both groups, PE is contaminated with microbial associations that differ in their composition. The number of MO, primarily aerobes, is significantly higher. It is important that there were GPSFA in PE. GPSFA produce exotoxins that have a severe proteolytic and hemolytic effect [15,27,29]. The result of such action is the destruction of the tissues. This burdens the development of IAI. The correlation coefficients indicate that MF of LI is more important for the progression of IAI during this period. In the 4th group, the aerobic MO in PE are more influenced by the MO of SI. In the 3rd group, the anaerobic MO in PE are more influenced by the MO of LI.

In 24 h since IAI simulation, in the 3rd group, the microbial biotope of SI was transformed only quantitatively. At the same time, in the 4th group P. niger was not found and GPSFA were found. This indicates severe dysbiosis. In the 3rd group, the microbial biotope of LI was transformed, mostly quantitatively, although GPD disappeared. In the 4th group, the microbial biotope differed quantitatively and qualitatively. The quantitative and species composition of MO in PE in both groups changed. Aerobes dominated in both groups. But in the 4th group in PE, there were GPSFA, which are very aggressive MO. The correlation coefficients indicate that in the 3rd group, changes in the MO in PE are associated with changes in the MO in SI. In the 4th group, changes in the MO in PE are associated with changes in the MO in both intestines.

In 48 h since IAI simulation, changes in intestinal microbiocenosis continued in both groups. During this period, species changes of MF in SI were first detected in the 3rd group. At the same time, dysbiosis intensified in the 4th group. The number of MO and the number of microorganism genera in this group are higher significantly.

In 48 h since IAI simulation, changes in intestinal microbiocenosis continued in both groups. During this period, species changes of MF in SI were first detected in the 3rd group. At the same time, dysbiosis intensified in the 4th group. The number of MO and the number of microorganism genera in this group are higher significantly. In the 4th group, the number of MO and the number of microorganism genera in PE also are higher significantly. In the 3rd group, the number of MO in PE decreased, and some strains of MO disappeared. It is possible that this was a consequence of the activation of immune defense mechanisms, which is also indicated by changes in the intestinal MF [31,34]. At the same time, in the 4th group, negative changes of MF in PE progressed. New MO were detected in PE, and the number of highly aggressive GPSFA increased. The correlation coefficients between the number of MO in PE and different intestines, for the most part, have different values and different directions.

Therefore, it can be summarized that the simulation of NN causes dysbiosis in SI and LI. After modeling of IAI, dysbiosis occurs in intact rats, dysbiosis progresses in rats with NN models. During the development of IAI, permanent changes in the intestinal microbiocenosis and microbial associations in PE are observed. Changes are more substantial in rats with NN models.

Changes of MF in SI, LI and PE in intact rats 48 h after IAI simulation indicate the activation of protective immune mechanisms. Instead, changes of MF in SI, LI and PE in rats with NN models indicate the progression of pathological processes Later experiments were not performed because all rats with IAI and underlying NN models died.

It should be noted that we found similar regularities of microbial contamination changes in the study of IAI with underlying diabetes mellitus [32]. This indicates that the comorbidity in IAI has common pathogenetic mechanisms, independent of the type of underlying disease.

Limitations

The presented study has a number of limitations. The study used small samples. It is necessary to conduct additional experiments on a larger number of animals to confirm the data. Female rats were used in the previous study. Therefore, male rats should be used to confirm the data in future studies. Material for microbiological examination was taken in the middle part of SI and the middle part of LI. It is advisable to study other parts of the intestines for better results. The previous study used rats. To confirm the data, it is advisable to conduct experiments with other species of animals. Specific data from other studies regarding the types of microorganisms, their number, etc., may differ. Such data are affected by various factors: the type of animals, feeding characteristics, living conditions, characteristics of applied simulating ways, etc. But, taking into account the known physiological and pathological regularities common to all warm-blooded mammals, these limitations will not have a significant effect on the generalizability of the findings. It can be expected that the basic regularities will be confirmed, but some indicators may change slightly.

Conclusion

1. Intestinal dysbiosis was detected in rats 12 h since acute nephrosis-nephritis simulating.
2. Simulating acute intra-abdominal infection in intact rats causes intestinal dysbiosis, a syndrome of excess bacterial colonization of small intestines.
3. Simulation of acute intra-abdominal infection in rats with models of acute nephrosis-nephritis increases intestinal dysbiosis, in 6 h not only quantitative but also qualitative changes in the intestinal microbiocenosis were detected, which further progress.
4. In intact rats, 48 h since acute intra-abdominal infection modeling, signs of dysbiosis regression and a decrease in the number of microorganisms in peritoneal exudate were found, instead in rats with models of acute intra-abdominal infection with underlying acute nephrosis-nephritis, signs of dysbiosis progression and an increase in the number of microorganisms in peritoneal exudate were found.

Acknowledgments

The authors acknowledge the employees of the microbiological laboratory for their help.

The presented research is part of the scientific work of the Department of Surgery No 1, Bukovinian State Medical University. One of the scientific work objectives is the study of comorbidity in urgent abdominal surgery. The presented study is one of the scientific work series on the peculiarities of the pathogenesis of comorbidity in IAI.

The authors confirm that they do not have any financial or personal relationships that could bias the content of this work.

The authors declare that they have no any conflict of interest related to the work.

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