Acinetobacter sp. IrC1 to copper and dyes. The effects of copper concentration on growth were determined using a spectrophotometer, while accumulation was analysed using an atomic absorption spectrophotometer. Bacteria-mediated dye decolourisation dyes were observed based on clear zone formation around bacterial colonies, while decolourisation percentage was calculated using a spectrophotometer. Results demonstrate that Acinetobacter sp. IrC1 resisted up to 8 mM CuSO4 and accumulated up to 292.93 mg/g dry weight of copper cells. Acinetobacter sp. IrC1 isolates were also resistant to 500 ppm Methylene Blue, Malachite Green, Congo Red, Mordant Orange, Reactive Black, Direct Yellow, Reactive Orange, Remazol, Wantex Red and Wantex Yellow dye, successfully removing up to 68.35% and 79.50% Methylene Blue and Basic Fuchsine in a medium containing 3 mM CuSO4, respectively. Further investigations are required to analyse the genetic composition of multi-resistant bacteria to optimise the effectiveness of indigenous bacterial isolates as bioremediation agents.Improper disposal of waste containing copper and dye is an environmental issue that must be resolved immediately due to its harmful, non-degradable and toxic properties. Bioremediation efficiency can improve by cultivating copper and dye multi-resistant bacteria to remove various pollutant types simultaneously. This study aims at establishing the multi-resistance of  Acinetobacter sp. IrC1 resisted up to 8 mM of CuSO4 and accumulated up to 292.93 mg/g dry weight of copper cells based on spectrophotometric analysis.Indigenous bacterial strain IrC1 isolates resisted up to 500 ppm 10 types of textile dye, namely Methylene Blue, Malachite Green, Congo Red, Mordant Orange, Reactive Black, Direct Yellow, Reactive Orange, Remazol, Wantex Red and Wantex Yellow dye.Acinetobacter sp. IrC1 is a multi-resistant strain that performs copper resistance and dye decolourisation simultaneously, successfully removing up to 68.35% of Methylene Blue and 79.50% of Basic Fuchsine dye in a medium containing 3 mM CuSO4.3 , and the generation of large quantities of sludge and other toxic products that require careful disposal , Citrobacteria freundii . Approximately 250 μL of the starter culture was inoculated into 25 mL of liquid LB medium containing various CuSO4 concentrations, incubated at 37°C and shaken at 200 rpm. Copper resistance was observed through cell turbidity measurement  using a Labo Med spectrophotometer at 600 nm.Copper stock with a concentration of 1000 mM CuSO2O then destructed by heating at 110°C and adding HNO3 to break the Cu bonds from the organic matter present in the medium. Destruction was carried out until the sample became clear. The concentration of Cu in the cell dry weight samples was analysed using an Atomic Absorption Spectrophotometer to obtain Cu bioaccumulation values  of the culture reaches 0.6. About 1% of each starter culture was inoculated to 10 mL of liquid LB medium, each containing 12 different dyes with concentrations of 200 ppm or 500 ppm. The same procedures and conditions were used to inoculate the culture to 10 mL liquid LB containing a dye and 3 mM CuSO4. Bacterial isolates were incubated for 24 h in an incubator shaker with a speed of 150 rpm and a temperature of 37°C. After 24 h and 48 h, as much as 1,000 uL of each liquid culture were then transferred to sterile microtubes and centrifuged for one minute at 15,000 rpm. The supernatant formed was analysed using a spectrophotometer at 300 nm–900 nm. The medium containing no dye was used as a control . Despite the absence of an “adaptation,” otherwise known as the lag phase when exposed to different levels of CuSO4, IrC1 survived well, albeit at a slower growth rate. However, differences in the speed bacterial growth rate on enriched media were observed. The higher the CuSO4 concentration, the slower the bacterial growth. Growth of Acinetobacter sp. IrC1 on 4 mM and 5 mM medium showed a higher rate than on 6 mM and 7 mM. 4 led to a 6-hour lag phase, while the absence of bacterial growth was observed on 9 mM-supplemented with the medium. This observation indicates that 8 mM is the threshold concentration for copper to exert growth-hindering toxicity on bacterial cells.Acinetobacter sp. IrC1 in media containing 4 mM to 8 mM of CuSO4  ions play a significant role in optimising the decolourisation process of Basic Fuchsine.Decolourisation ability of ervation . Adding Acinetobacter sp. IrC1 has a minimum inhibitory concentration of 9 mM CuSO4. Results of the copper-resistance test suggest that Acinetobacter sp. IrC1 was able to adapt to copper toxicity by developing a resistance mechanism upon entering a lag phase. Acinetobacter sp. IrC1 underwent a second lag phase from hours 12 to 36. A second lag phase allowed Acinetobacter sp. IrC1 to produce secondary metabolites in efforts to further equip themselves with the necessary properties needed to survive and thrive. The occurrence of the second lag phase suggests that, during the first lag phase, Acinetobacter sp. IrC1 initiated a novel transcriptional program to synthesise the components required for cellular multiplication during the exponential phase. Meanwhile, the second phase occurred due to the necessary synthesis of cellular components required to reparate macromolecular damage sustained during the stationary phase  ion coordination.Acinetobacter sp. IrC1 was successfully cultivated in media containing various dyes due to the development of an enzyme-mediated resistance mechanism. S. aureus and E. coli growth on direct yellow 12, direct red 23, direct red 31, and direct black 38 dye. 4 toxicity to bacterial cells also affected dye-resistance as observed through reduced malachite green decolourisation activity by Ochrobactrum pseudogrignonense strain CGUPV1 in the presence of CuSO4.Copper toxicity, interaction and coordination with the chemical compounds found in dye compositions are the factors that affect bacterial dye resistance. Acinetobacter sp. IrC1 in decolourisation methylene blue and basic fuchsine dye in the presence of copper is attributable to its intrinsic nature. The success of Acinetobacter sp. that harbor azo-reductases, laccases and peroxidases are capable of decolourising dyes by altering specific enzymatic activities involved (3)N functional group from the chemical structure of Methylene Blue dye. Based on previous research by Bacteria such as Decolourisation is a process highly dependent on the Fenton reaction cycle as hydroxyl radicals’ rapid production and effective longevity help improve decolourisation efficiency (Acinetobacter sp. IrC1. (88.49%) demonstrated a relatively high Methylene Blue decolourisation ability compared to Pseudomonas aeruginosa (21.31%), Acinetobacter sp. CN5 (57.64%), R. mannolytica (60.3%), and C. Aquatica (67.9%), albeit lower than Desmodesmus sp. (98.6%) (Acinetobacter sp. IrC1 (79.50%) also produced results higher than Saccharomyces ceresvisiae isolated from salt water (60.39%) and pine wine (72.61%), but lower than Acinetobacter sp. CN5 (91.37%) and Aeromonas hydrophila (93%) , direct, thiazin, pthalocyanin and heterocyclic (methylene blue) dye, but not resistant to copper (Bacillus, Pseudomonas and Zooglea isolated from batik textile wastewater in Yogyakarta, Indonesia, removed 74.63% copper and resisted napthol red dye. In contrast,Aspergillus versicolor isolated from Batman, Turkey, can accumulate 16.28% copper and at least 94% of 150 mg/L remazol blue dye.To date, research on copper and dye multi-resistant bacteria is still limited, while the potential of local marine microbes remains largely unexplored. For instance, Acinetobacter sp. IrC1 is a copper and dyes-multi-resistant bacterium. Acinetobacter sp. IrC1 exhibited resistance to 8 mM CuSO4 and accumulated up to 292.93 mg/g of cells dry weight of copper. Acinetobacter sp. IrC1 was also resistant to 500 ppm of 10 different dyes, including Methylene Blue, Malachite Green, Congo Red, Mordant Orange, Reactive Black, Direct Yellow, Reactive Orange, Remazol, Wantex Red and Wantex Yellow. Acinetobacter sp. IrC1 was found capable of decolourising Methylene Blue and Basic Fuchsine up to 68.35% and 79.50% in a medium containing 3 mM CuSO4, respectively. It is, therefore, of importance to explore Acinetobacter sp. IrC1 is further an eco-friendly, cost-efficient, and effective bioremediation agent for future manufacturing or discharging processes. The exploration of dye and copper multi-resistance bacteria would enhance the possibilities of applying bacteria in solving the problem of water pollution due to copper and dyes.The results of this work demonstrated that


Allium mongolicum on bacterial communities in rumen and feces of claves. Sixteen 7-month-old male calves were randomly divided into four groups (n = 4). All calves were fed a basal ration containing roughage  and mixed concentrate in a ratio of 60:40 on dry matter basis. In each group, the basal ration was supplemented with Allium mongolicum 0 (SL0), 200 (SL200), 400 (SL400), and 800 (SL800) mg/kg BW. The experiment lasted for 58 days. Rumen fluid and feces in rectum were collected, Rumen fluid and hindgut fecal were collected for analyzing bacterial community. In the rumen, Compared with SL0, there was a greater relative abundance of phylum Proteobacteria (p < 0.05) and genera Rikenellaceae_RC9_gut_group (p < 0.01) in SL800 treatment. In hindgut, compared with SL0, supplementation of A. mongolicum  decreased in the relative abundances of Ruminococcaceae_UCG-014 (p < 0.01), Ruminiclostridium_5 (p < 0.01), Eubacterium_coprostanoligenes_group (p < 0.05), and Alistipes (p < 0.05) in feces; Whereas, the relative abundances of Christensenellaceae_R-7_group (p < 0.05), and Prevotella_1 (p < 0.01) in SL800 were higher in feces, to maintain hindgut stability. This study provided evidence that A. mongolicum affects the gastrointestinal of calves, by influencing microbiota in their rumen and feces.Compared to traditional herbage, functional native herbage is playing more important role in ruminant agriculture through improving digestion, metabolism and health of livestock; however, their effects on rumen microbial communities and hindgut fermentation are still not well understood. The objective of present study was to evaluate the effects of dietary addition of  Gentiana straminea, Ligularia virgaurea, and Cistanche deserticola) are often not favored by herbivores because of their unique taste or poor palatability  of FNHs, , have been proved to improved the intestinal absorption of nutrients  from July to September 2017. The area is classified as a temperate arid, with an average annual temperature and precipitation of 7.7°C and 117.2 mm, respectively. The dominant agricultural systems here are intensively and extensively specialized crop and livestock production systems .A. mongolicum 0 (SL0), 200 (SL200), 400 (SL400), or 800 mg/kg BW (SL800). The experiment lasted for 58 days. The nutrients of fresh A. mongolicum and basal diets  were determined  and had free access to drinking water around the clock. A detailed description of the animal digestion and metabolism trial, laboratory analyses and calculation of DMI, body weight (BW), nutrient digestibility, fecal nutrient content, methane (CH4), volatile fatty acids  can be found in a complementary paper on Simmental claves , were randomly divided into four groups with four calves in each group. All calves were fed a basal ration containing roughage  and mixed concentrate in a ratio of 60:40 on dry matter basis. The basal diet fulfilled the maintenance and growth requirements of calves to support 1 kg daily gain for fattening cattle (175 kg BW) according to the Chinese recommended beef cattle breeding standards (NY/T815-2004). In each group, the basal ration was supplemented with termined . Roughagl claves .Samples from rumen fluid and hindgut feces were taken on the last day of the experiment for analyze bacterial community. Fecal samples were collected and divided into three parts: one portion was dried at 65°C for 96 h to determine the neutral detergent fiber (NDF), acid detergent fiber (ADF), organic matter (OM) and ether extract (EE) content; other portion of fresh feces (100 g) was combined with 10 mL of 10% (v/v) sulfuric acid for N fixation and frozen at −20°C; and the final portion of the feces was placed in a vacuum tube and immediately stored in liquid nitrogen for bacterial determination.Fecal collection, for subsequent analysis of its microbiota, was done following the method described by Rumen fluid samples of 16 calves were taken via the orogastric tube technique on day 58. Two hours after morning feeding, a ruminal fluid collection tube  was inserted into the rumen and the rumen fluid was aspirated using a 150 mL syringe. After cleaning the orogastric tube with clean water, 100 mL of rumen fluid was discarded, preventing saliva contamination . The rum® DNA PCR-Free Sample Preparation Kit was used for library construction. The constructed library was quantified using a Qubit and q-PCR. After qualification of the library, HiSeq2500 PE250 was used for on-machine sequencing. The 16-rumen fluid and 16 fecal samples were sequenced. Paired-end reads were assigned to samples based on their unique barcodes. The reads of each sample were spliced using FLASH . Treatment means were compared using LSD test and differences were considered statistically significant at 4, bacterial genus, and ruminal fermentation parameters was performed using R software (version 4.0.2).Spearman correlation coefficients between chemical composition of herbage, nutrient digestibility, CHp = 0.036). The Goods_coverage value of the rumen and feces were 99.3 ± 0.2 and 99.5 ± 0.1%, respectively (p = 0.031). The Shannon and Simpson diversity indices of rumen and feces were not significantly different among the four treatment groups. In the rumen, each of the four treatments had a cluster of microorganisms (Chao 1 index in SL200 was significantly higher than that in SL0 (ectively . In fecerganisms . Howeverrganisms .p < 0.05) in the SL800 treatment  were higher in the SL400 and SL800 than those in the SL0 treatment. The relative abundances of Spirochaetes (p < 0.01) were lower in the SL200 and SL800 than those in the SL0 and SL400 treatment groups (p < 0.05) was lower in the groups receiving Allium mongolicum than SL0 in feces. The relative abundances of Actinobacteria (p < 0.05) were higher in the SL400 than those in the SL0 treatment group. The ratio of Firmicutes to Bacteroidetes (p > 0.05) was not significantly different among the four treatment groups.In feces, Firmicutes (57.7% ± 4.4), Proteobacteria (22.5% ± 5.8), and Bacteroidetes (16. 6% ± 2.1) were the predominant bacteria, accounting for more than 96.7% of the total bacteria . The relt groups  in fecesPrevotella_1 was the most dominant in ruminal fluid, followed by the Christensenellaceae_R-7_group, Rikenellaceae_RC9_gut_group, and Ruminococcaceae_NK4A214_group (Prevotella_1 was lower (p < 0.05), representing 16.8 and 14.7% of the populations in the SL200 and SL800 groups, respectively (Rikenellaceae_RC9_gut_group of the SL800 treatment was higher (p < 0.01) than in the SL0 and SL200 group, while SL400 was not significantly different than SL0, SL200, and SL800.At the genus level, the relative abundance of 14_group . Compareectively . The relAcinetobacter, Solibacillus, and Ruminococcaceae_UCG-005 were the dominant genera (Prevotella_1 (p < 0.01), Stenotrophomonas (p < 0.01), and Christensenellaceae_R-7_group (p < 0.05) were significantly higher in the SL800 group than the SL0 group (. Ruminococcaceae_UCG-005 (p < 0.05) had the greatest relative abundance in the SL400 group. The relative abundances of Ruminococcaceae_UCG-014 (p < 0.01), Ruminiclostridium_5 (p < 0.01), Alistipes (p < 0.05), Eubacterium_coprostanoligenes_group (p < 0.05), and dgA-11_gut_group (p < 0.01) were lower in feces of claves supplemented with Allium Mongolicum (at any rate) than observed in SL0. The highest relative abundance of Trichococcus (p < 0.01) was greatest in animals receiving SL200, while Acinetobacter (p < 0.05) was in the lowest abundance in feces of claves receiving SL400 and SL800 treatments.In feces, t genera . The relL0 group . RuminocRuminiclostridium_5, Alistipes, and dgA-11_gut_group had highest variation in the regions (p < 0.001) in feces of calves given different supplementation of A. mongolicum (p < 0.05) and supplementary level and regions interaction , Christensenellaceae_R-7_group (p < 0.001), Solibacillus (p < 0.05), Ruminococcaceae_UCG-010 (p < 0.01), Prevotellaceae_NK3B31_group (p < 0.01), Saccharofermentans (p < 0.001), Prevotellaceae_UCG-001 (p < 0.001), Fibrobacter (p < 0.001), Lachnospiraceae_NK3A20_group (p < 0.001), Ruminococcaceae_UCG-013 (p < 0.001), Ruminococcaceae_NK4A214_group (p < 0.001), Bacteroides (p < 0.001), and Prevotellaceae_UCG-004 (p < 0.001) were affected by the gastrointestinal region.The relative abundances of 4  in the rumen, while the total VFA and acetate were positively correlated with bacterial community structure in feces (p < 0.05). In the rumen bacteria community structure, the DMI, nutrient digestibility, total VFA, and CH4 of RAD 1 and RDA 2 represented 20.34 and 10.79%  (Roseburia (r = 0.699) and negatively correlated with Lachnospiraceae_XPB1014_group (r = −0.663), Ruminococcus_1 (r = −0.669), and Lachnospiraceae_NK3A20_group (r = −0.652). Daily CH4 emission was positively correlated with genera Fusobacterium (r = 0.563) and negatively correlated with Lachnospiraceae_ND3007_group (r = 0.536). The CH4/DMI ratio was positively correlated with the genera Butyrivibrio_2 (r = 0.529) and negatively correlated with Methanobrevibacter (r = −0.638) and Fibrobacter (r = −0.540). The CH4/BW0.75 was positively correlated with genera Roseburia (r = 0.527) and Lachnospiraceae_ND3007_group (r = 0.536) and negatively correlated with Ruminococcus_1 (r = −0.517) and Lachnospiraceae_NK3A20_group (r = −0.607). Nitrogen digestibility was positively correlated with genera Saccharofermentans (r = 0.551), Ruminococcus_2 (r = 0.540), Trichococcus (r = 0.539), Fusobacterium (r = 0.611) and Lachnospiraceae_AC2044_group (r = 0.598) and negatively correlated with Butyrivibrio_2 (r = −0.556) and Ruminiclostridium_5 (r = −0.563). The NDF digestibility was positively correlated with the genera Saccharofermentans (r = 0.538), Ruminococcus_2 (r = 0.565), Trichococcus (r = 0.532), Fusobacterium (r = 0.644) and Lachnospiraceae_AC2044_group (r = 0.627). Whereas OM digestibility was positively correlated with genera Ruminococcus_1 (r = 0.570). The total VFA concentration was positively correlated with genera Solibacillus (r = 0.577), Ruminococcus_1 (r = 0.566), and Ruminococcaceae_UCG-010 (r = 0.540) and negatively correlated with Trichococcus (r = −0.566). Acetate was positively correlated with genera Prevotella_1 (r = 0.667) and negatively correlated with Eubacterium_coprostanoligenes_group (r = −0.626). Butyrate was negatively correlated with genus Roseburia (r = −0.547). Valerate was positively correlated with genera Solibacillus (r = 0.584) and Ruminococcaceae_UCG-010 (r = 0.515) and negatively correlated with Prevotella_1 (r = −0.523) and Trichococcus (r = −0.580). The acetate to propionate ratio was positively correlated with Roseburia (r = 0.582) and negatively correlated with Christensenellaceae_R-7_group (r = −0.553), Prevotellaceae_UCG-003 (r = −0.609), Lachnospiraceae_XPB1014_group (r = 0.603), and Lachnospiraceae_NK3A20_group (r = 0.568) genera.In the rumen, DMI was positively correlated with the genera = 0.620) . Dry matAcinetobacter (r = 0.400), Ruminococcaceae_UCG-014 (r = 0.393), Ruminococcaceae_UCG-013 (r = 0.494), Bacteroides (r = 0.464), and Saccharofermentans (r = 0.410), and negatively correlated with Ruminococcaceae_UCG-002 , Rikenellaceae_RC9_gut_group (r = 0.417), Eubacterium_coprostanoligenes_group (r = 0.417), Comamonas (r = 0.467), Treponema_2 (r = 0.400), Ruminococcaceae_NK4A214_group (r = 0.367), Alistipes (r = 0.583), Ruminiclostridium_5 (r = 0.383), dgA-11_gut_group (r = 0.400), Psychrobacillus (r = 0.383), and Brevundimonas (r = 0.367). The N content was positively correlated with the genus Christensenellaceae_R-7_group (r = 0.407).In feces, the EE content was positively correlated with genera Prevotella_1 and Prevotellaceae_UCG-003 in rumen and Ruminococcaceae_NK4A214_group, Christensenellaceae_R-7_group and Ruminococcaceae_UCG-010 in feces could be considered as keystone taxa in Simmental calves.Microbial interactions between rumen and fecal bacterial communities were analyzed using microbial networks . PositivA. mongolicum have the potential to improve average daily gain, feed efficiency, and body antioxidant properties in ruminant species  relationship of microbe functions between rumen and hindgut; (2) the integrated effects to gastrointestinal, and (3) exploring the “gray box” of the intestine through analysis of the microbe in rumen and feces  of A. mongolicum supplementation did not affect Bacteroidetes significantly; however, SL800 significantly decreased the expansion of Bacteroidetes. This suggested that a critical dose of A. mongolicum must be achieved to affect the relative abundance of Bacteroidetes in rumen. It is possible that elevated levels of A. mongolicum containing higher concentrations of unsaturated fatty acids may be harmful to the Bacteroidetes  to exert such effects. A. mongolicum supplementation maintains the stability of the intestinal flora .FH: conceptualization, supervision, and experimental design. KX: data analysis and original draft writing. KX, YG, and CZ: animal feeding experiment. JN and CZ: laboratory sample analyses. FH, SC, and TY: review and editing. All authors contributed to the article and approved the submitted version.FH acknowledges the scholarship by the National Natural Science Foundation of China (32161143028); the Program of National Science and Technology Assistance (KY202002011); the Lanzhou City’s Scientifc Research Funding Subsidy to Lanzhou University; and the Program for Innovative Research Team of Ministry of Education (IRT17R50).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1174740/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.


Conversely, two baseline variables were selected into the Cox model indicating a significantly lower hazard rate of developing graft loss-due-to CHR: Received Donor Liver (LI or MV) (p = 0.002) and Received Induction (p = 0.007). In summary, while MMV/MV transplants  offered protection against graft loss-due-to AR, liver-containing grafts appeared to offer protection against graft loss-due-to CHR, supporting the results of other studies.In intestinal transplantation, while other centers have shown that liver-including allografts have significantly more favorable graft survival and graft loss-due-to chronic rejection (CHR) rates, our center has consistently shown that modified multivisceral (MMV) and full multivisceral (MV) allografts have significantly more favorable acute cellular rejection (ACR) and severe ACR rates compared with isolated intestine (I) and liver-intestine (LI) allografts. In the attempt to resolve this apparent discrepancy, we performed stepwise Cox multivariable analyses of the hazard rates of developing graft loss-due-to acute rejection (AR) vs. CHR among 350 consecutive intestinal transplants at our center with long-term follow-up (median: 13.5 years post-transplant). Observed percentages developing graft loss-due-to AR and CHR were 14.3% (50/350) and 6.6% (23/350), respectively. Only one baseline variable was selected into the Cox model indicating a significantly lower hazard rate of developing graft loss-due-to AR: Transplant Type MMV or MV (  In intestinal transplantation controversial results with differing interpretations on the protective effects of various transplant types have been reported, with liver-including grafts being shown in some studies to have significantly more favorable graft survival –4 and loIn our recent report of 445 consecutive intestinal transplant cases , 76.8%  during the first 60 months post-transplant ; however, separate analyses of predictors of the hazard rates of graft loss-due-to AR vs. CHR had not been performed . In the Our historical cohort of 350 consecutive intestinal transplant cases (308 primary recipients and 42 retransplants) at the Miami Transplant Institute during 1994–2012 were followed prospectively through 15 March 2019—the same last follow-up date as in our recent reports . In ordeen bloc the donor stomach, donor pancreaticoduodenal complex, and donor intestine into the recipient . Once this variable was controlled, none of other baseline variables offered additional prognostic value (p > 0.05). For instance, while p = 0.000002, 0.0006, and 0.003, respectively), due to their significant positive associations with Receiving Transplant Type MMV or MV, once this latter variable was controlled in the Cox model, multivariable tests to include Received Native Splenectomy, Received Donor Spleen, and Received Donor Liver (LI or MV) were non-significant .Using stepwise Cox regression, only one baseline variable was selected into the Cox model indicating a significantly lower hazard rate of developing graft loss-due-to AR : Transplp = 0.002) and Received Induction Other than No/Old (p = 0.007). Estimated HRs and 95% CIs for the effects of Received Donor Liver (LI or MV) and Received No/Old Induction were 0.280 [0.119–0.661] and 3.379 [1.316–8.674], respectively. Once these two variables were controlled, none of the other baseline variables offered additional prognostic value (p > 0.05). p = 0.02), once the two selected variables were controlled, the multivariable test to include this variable yielded p = 0.94. Thus, the stepwise Cox model results in Using stepwise Cox regression, two baseline variables were selected into the Cox model indicating a significantly lower hazard rate of developing graft loss-due-to CHR   Transplant Type MMV or MV is the single factor that clearly protects against graft loss-due-to AR, whereas this combination of transplant types did not independently protect against graft loss-due-to CHR, 2) Once the favorable influence of Transplant Type MMV or MV on the hazard rate of graft loss-due-to AR was controlled, Liver Inclusion (Transplant Type LI or MV) showed no protective effect against graft loss-due-to AR, and 3) Liver Inclusion appears to independently and significantly protect against graft loss-due-to CHR. These results are consistent with our most recent report  . Other previous studies have reported an association between the presence of de novo DSAs and a higher incidence of ACR development, but without a clear separation of acute AMR presence vs. strictly ACR occurrence being made [The vast vascular  endothelial surface of the liver uniquely enables it to absorb circulating DSAs, thereby offering protection against potential acute and chronic damage caused by their presence . This tyovo DSAs , 33–35. ovo DSAs  also shoansplant . Kubal eing made , 37. ThuSince DSA and humoral rejection data were not available in most of our patients transplanted prior to 2013, no attempt to analyze such results was made here, which is a clear study limitation. In addition, while Wu et al  showed tAnother clear study limitation was the fact that this study spans over several years, and variables such as immunosuppression, indications, and surgical techniques have changed. This makes interpretation of the results rather difficult. However, multivariable analysis of predictors of the hazard rate of graft loss-due-to AR found no significant effects of induction type, and multivariable analysis of predictors of the hazard rate of graft loss-due-to CHR found that only our earliest approaches (“no/old induction” during 1994–1997) were significantly less favorable. In addition, while this cohort of 350 consecutive intestinal transplant cases were prospectively followed and represents one of the largest experiences with intestinal transplantation ever reported, the liver-intestine and modified multivisceral subgroups were relatively small. Thus, generalization of our results to other centers could be limited by these relatively small subgroup sample sizes. Nonetheless, we believe that we are reporting statistically sound results regarding the significant multivariable predictors of the hazard rates of developing graft loss-due-to AR vs. CHR.Other observed differences in clinical outcomes between two of the historically largest intestinal transplant centers are worth noting. As reported here, among the transplanted cases who experienced graft loss-due-to rejection, 68.5% (50/73) and 31.5% (23/73) were due to AR and CHR, respectively. This is in stark contrast to the University of Pittsburgh results (with similarly long patient follow-up) , is it pWe also recently reported more favorable graft survival outcomes using our newer, more intensive induction strategy (since 2013) of combining a larger total dose (post-reperfusion) of rATG  with 1 standard rituximab dose given during the first 8 days post-transplant (with longer prophylactic care as well) . In our In summary, while the results reported here are based on an historical cohort of intestinal transplant cases who were transplanted at our center between 1994–2012 and received varying older induction immunosuppression protocols, we believe this study has helped to clarify some of the previously reported discrepancies in results that have existed between our center and the University of Pittsburgh regarding predictors of graft loss-due-to AR vs. graft loss due to CHR. It is our hope that some additional clarity has been provided here in terms of distinguishing between these two important clinical outcomes following intestinal transplantation. In addition, while direct comparison of two high volume intestinal transplant programs is relevant, it begs the question of a collaborative investigation using a multi-center approach rather than independent reports.


The general linear model revealed significant differences (p < 0.05) in mean failure loads when comparing the different materials for the discs, i.e., Trilor (5456 ± 1748 N) > Juvora (4274 ± 869 N) > Pekkton (3771 ± 294 N) > Katana (2859 ± 527 N) > Cercon (2319 ± 342 N) = BuxZir (2250 ± 515 N) = e.max (2303 ± 721 N) = Lisi (2333 ± 535 N) > Celtra (1965 ± 659 N). EDS showed that the zirconia materials contained yttria at different concentrations . The yttria concentration had a significant effect on the failure load of the Katana (3Y-PSZ) crowns, which revealed lower failure loads than the Cercon (4Y-PSZ) and BruxZir (5Y-PSZ) crowns, whose failure loads were comparable or higher than e.max glass ceramic. The failure load of the trilayer disc specimens did not correlate with the failure load of the respective crown specimens for the zirconia, glass-ceramic and polymeric materials.It is unclear how zirconia dental crowns with different yttria compositions will perform clinically, and how they will compare with crowns made of glass-ceramics and polymers. The present objective was to determine failure loads of crowns and discs made of glass ceramics or polymers as compared to yttria-partially stabilized zirconia (Y-PSZ) crowns and discs with varying yttria concentrations. Crowns of zirconia , glass ceramic , and polymeric materials  were fabricated and cemented to epoxy abutments. The total number of specimens was 135 for crowns and 135 for discs . A universal testing machine was used to perform compressive loading of crowns/discs to failure with a steel piston along the longitudinal axis of the abutments. Energy dispersive spectroscopy (EDS) was used to identify the yttria concentration for each zirconia brand. The data were analyzed using generalized linear models and regression analyses. The results revealed significant differences ( Yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) materials are a possible replacement for metal ceramic restorations because of their good mechanical properties ,2. Howev®CAD crowns. The strengths of high-performance PEEK and polyetherketoneketone (PEKK) depend on their functional groups and molecular bonds, while newly developed fiber-reinforced composite resins are strengthened by nanotubes and fibers of various types [In comparison, the mechanical properties of glass ceramic restorations are affected by the material type ,24, cemeus types .“Crunch the crown” (CTC) testing, where the crown is attached to an abutment with resin cement and loaded with a steel piston, has been used to simulate clinical conditions and to evaluate the strength of the restoring materials ,34. It hAlternative in vitro tests have been proposed in an effort better simulate the clinical behavior of dental restorations ,35, inclIt is unclear how the mechanical properties of zirconia dental crowns with different yttria compositions will vary, and how they will compare with crowns made of glass-ceramics and polymers. The objectives of the present study were to determine the failure loads of crowns/discs made of glass ceramics or polymers and to compare them with yttria-partially stabilized zirconia (Y-PSZ) crowns/discs with varying yttria concentrations. Author hypothesized that the failure loads of zirconia with a lower yttria concentration would be higher than those for the glass-ceramics and polymers, and the failure load of crown specimens would correlate with those of disc-shaped specimens.Crown specimens were created from a monolithic crown-on-abutment design containing the various test materials: zirconia ; glass ceramic ; and polymeric dental materials . The identifier, manufacturer, color, class, modulus of elasticity, and Poisson’s ratio of the test materials are listed in The zirconia and glass ceramic crowns were designed with a cement gap of 35 µm and an extra cement gap of 65 µm (the cement gap is the amount of offset in the area of the margin line while the extra cement gap is the amount of offset in the upper part of the interface). In contrast, the polymeric crowns were designed with a cement gap of 75 µm and an extra cement gap of 120 µm (according to the manufacturer’s instructions). The zirconia and polymeric crowns were fabricated using a Wieland Mini milling machine  from prefabricated discs and the zirconia specimens were sintered according to the manufacturer instructions, in which the multiple layers of different colors were retained the same for all specimens. Glass-ceramic specimens  were fabricated by digital designation of wax patterns, printed , and then heat pressed with glass-ceramic ingots of different materials . All crowns were fabricated with the occlusal thickness of 0.8 mm at the central fossa. The supporting epoxy resin abutments for the crowns were fabricated by duplicating the molar tooth and pouring it into epoxy resin to form a resin abutment with a height of 8 mm which represents the height from the highest point of the prepared molar tooth to lowest point to the roots, as described by Schriwer et al. .TM ceramic primer plus  was applied and air dried, and then the crowns were bonded to the corresponding resin abutments using Panavia V5 . A load of 50 N was applied to the specimens during the cementation process to obtain an even cement thickness. This was followed by the removal of excess cement and subsequent light curing using a VALO LED curing-light  for 30 s in 5 directions [The “intaglio” surface treatments of the zirconia, glass-ceramic, and polymeric crowns are described in rections . The cemIn the CTC tests, the crowns were compressed with an axial load applied through a steel piston (diameter of 11.37 mm), as shown in Trilayer disc specimens  were creThe fracture surfaces of the zirconia specimens were sputter coated with gold palladium, and scanning electron micrographs  were obtained using high-vacuum mode with an accelerating voltage of 30 kV. Energy dispersive spectroscopy (EDS) was then used in a blind matter to identify the concentration of yttria for each zirconia brand over multiple areas, for which an average was calculated as documented in the literature .p values associated with each regression were determined; p < 0.05 was considered significant.Comparative analyses for failure loads were performed for the crown and disc specimens among the 9 materials and 3 classes . Homogeneity of variance was tested using Levene’s test, and normality was tested with the Kolmogorov–Smirnov test. The rank transformation was used to improve the distribution characteristics. Analysis was subsequently carried out with SPSS v23  using a generalized linear model with a robust estimator on both crown and disc designs. Comparisons between the materials were carried out with the sequential Bonferroni method for multiple comparisons. Linear regression analysis was used to test for correlation between the mean failure load values of the crowns and discs for each class of material . Pearson’s correlation coefficient (R-squared) revealed the goodness of fit, while The fractured zirconia surfaces were fairly heterogeneous, with grains at different surface levels and diffuse boundaries. Backscatter SEM showed that all zirconia materials had an yttria stabilizing oxide with different concentrations , as illustrated in p = 0.072) between the mean failure loads (TRI and JUV (polymer) crowns yielded the highest mean failure loads, followed by CER and BRU (zirconia). There were no significant differences (re loads  comparinp < 0.05) in mean failure loads when comparing the different materials for the discs, i.e., TRI (5456 ± 1748 N) > JUV (4274 ± 869 N) > PEK (3771 ± 294 N) > KAT (2859 ± 527 N) > CER (2319 ± 342 N) = BRU (2250 ± 515 N) = EMA (2303 ± 721 N) = LIS (2333 ± 535 N) > CEL (1965 ± 659 N).The layered disc results showed similar trends as the crowns , but witThe probability of failure for the different material groups, calculated as the value of the failure load divided by the number of specimens (15), is shown in p > 0.05) between the failure loads for the crown and disc specimens, which was determined by linear regression for the material groups. As shown in As shown in The variations in the composition (yttria concentration) of zirconia were not discernable in the data provided by the manufacturers. Therefore, EDS was performed in the present study to quantify yttria concentration for each zirconia brand. The CER (4Y-PSZ) and BRU (5Y-PSZ) produced similar mean failure loads , statistically higher than those for KAT (3Y-PSZ = 3195 N). Thus, the hypothesis that yttria concentration would correlate with crown failure load was only partially supported.Mayinger et al.  reportedThe results were contradictory for the failure loads of the zirconia specimens comparing the two different specimen geometries. The 3Y-PSZ presented much higher strengths than 4Y-PSZ and 5Y-PSZ for layered disc geometry, as documented in the literature . SurprisIn the present study, EMA (lithium disilicate) yielded an average crown failure load similar to those of CER (4Y-PSZ) and BRU (5Y-PSZ), but higher than KAT (3Y-PSZ). Choo et al.  reportedThe EMA glass ceramic specimens revealed a higher average failure load than CEL and LIS. This result was likely due to the different crystalline types . This result is consistent with the results of Garoushi et al. , in whic2, and StarCeram Z-Smile) had higher 4-point flexural strength than a lithium disilicate (IPS e.max Press LT A2). Holman et al. [Our results differ from what has been presented in the literature using ASTM standardized tests. Nassary Zadeh et al.  reportedn et al.  reportedn et al.  reportedIn the present study, an occlusal thickness of 0.8 mm at the central fossae of zirconia crowns was fabricated without thermocycling, producing a range of failure loads between 3186–4260 N. Al Mortadi et al.  reported®CAD crowns by an average of 1381 N, which is similar to our findings in JUV crowns were higher than e.max®Press crowns by an average of 1234 N.The TRI and JUV crowns displayed the highest failure loads. TRI crowns had a higher average failure load than JUV and PEK crowns, likely due to the presence of glass fibers as dictated by manufacturer data. Abhay et al.  reportedThe present study used the CTC test specifically to determine which cubic containing zirconia, with different yttria concentrations , are closer to the mechanical properties of different glass-ceramics and polymers. We recognize, however, that the CTC test may be considered unreliable because, as a quasi-static test, it does not replicate the repetitive and multi-directional clinical loading of real restorations. Furthermore, higher failure loads can be attained for thicker restorations ,16, and Previous studies ,35 preseIn the present study, artificial epoxy resin abutments were selected for securing the crowns to have a standardized abutment shape for all the specimens. We admit that the die spacer thicknesses were different between the ceramics (zirconia and glass-ceramics) and polymers to comply with the different manufacturer instructions. The crown specimens were not polished because there is no standardized polishing protocol for the occlusal surface of crowns, and the standard polishing materials are different between the different material groups . This may have affected the measured failure loads. Other limitations of the present study include the lack of aging and fatigue testing. Lastly, the glass-materials were fabricated through a press procedure while the remaining were CAD-CAM milled, which may affect the resultant material behaviors and influence test outcomes.The crunch the crown tests revealed that the Katana UTML (3Y-PSZ) crowns had significantly lower failure loads than Cercon XT (4Y-PSZ) and BruxZir Anterior (5Y-PSZ) crowns. The IPS e.max Press specimens produced similar failure loads as zirconia crowns with Cercon XT (4Y-PSZ), and BruxZir Anterior (5Y-PSZ) but were stronger than the Katana UTML (3Y-PSZ) specimens.Two polymers (Trilor and Juvora) produced the highest failure loads in both the crunch the crown and layered disc tests.The failure load of the trilayer disc specimens did not correlate with the failure load of the respective crown specimens for the zirconia, glass-ceramic and polymeric materials.Within the limitations of this in vitro study, the following conclusions may be drawn:


Limosilactobacillus fermentum MG4294 and Lactiplantibacillus plantarum MG5289 on NAFLD. The MG4294 and MG5289 reduced lipid accumulation in FFA-induced HepG2 by suppressing the adipogenic proteins through the regulation of AMP-activated protein kinase (AMPK). The administration of these strains in the HFD-induced mice model lowered body weight, serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and cholesterol levels. In particular, MG4294 and MG5289 restored liver TG and TC to normal levels by lowering lipid and cholesterol-related proteins via the modulation of AMPK in the liver tissue. In addition, the administration of MG4294 and MG5289 reduced pro-inflammatory cytokines (tumor necrosis factor (TNF)-α and interleukin (IL)-1β-, and IL6) in the intestinal tissues of the HFD-induced mouse model. In conclusion, MG4294 and MG5289 can be presented as probiotics with the potential to prevent NAFLD.Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease and the leading cause of liver-related deaths worldwide. It has been established that microorganisms are involved in the interaction between the intestinal lumen and the liver; therefore, studies on probiotics as potential candidates are increasing. This study evaluated the effects of  Non-alcoholic fatty liver disease (NAFLD) is a common liver disease worldwide and is closely related to metabolic syndromes. In addition, it was recently known as metabolic (dysfunction) associated fatty liver disease (MAFLD) ,2. NALFDProbiotics, defined as living microorganisms that benefit human health, are emerging as a new strategy for NAFLDs . In patiLimosilactobacillus fermentum MG4294 and Lactiplantibacillus plantarum MG5289 on FFA-induced HepG2 cells and mice with NAFLD induced by a high-fat diet. In addition, to confirm whether L. fermentum MG4294 and L. plantarum MG5289 improve NAFLD based on the gut –liver axis, pro-inflammatory cytokines in the intestinal tissue were measured, and the possibility of safe probiotics was proven.This study investigated the mechanism underlying the ameliorating effect of L. fermentum MG4294 (human origin) and L. plantarum MG5289 (food origin), have been stored and provided by MEDIOGEN Co., Ltd. . L. fermentum MG4294 and L. plantarum MG5289 were analyzed by 16S rRNA sequencing  for identification, and cultured in De Man, Rogosa, and Sharpe (MRS) broth . For the in vitro study, probiotic pellets were adjusted at 1 × 109 CFU/mL in PBS and then homogenized using a homogenizer . For the animal study, probiotic pellets were harvested by centrifugation, freeze-dried, and mixed with maltodextrin for adjustment at 5 × 1010 CFU/g [Probiotics, 2-humidified air at 37 °C.HepG2, human-derived hepatocytes  were incubated in Dulbecco’s modified Eagle medium  containing 100 U/mL penicillin and 100 μg/mL streptomycin , and 10% heat-inactivated fetal bovine serum  in 5% CO2O and maintained at 55 °C. Thereafter, 10% BSA was mixed with the FFA solution and combined at 55 °C for 30 min to form 10 mM FFA/1% BSA solution [The FFA mixture was prepared by mixing palmitate and oleate (ratio of 1:2) in a sodium hydroxide , respectively, and maintained at 70 ℃ for 30 min to prepare a suspension. The 10% bovine serum albumin (BSA) was mixed with tertiary distilled Hsolution .5 cells/well), overnight. The FFA mixtures (500 μM) with L. fermentum MG4294 and L. plantarum MG5289 were treated for 24 h in each well. The 3--2,5-diphenyl-tetrazolium bromide  solution was added to each well and incubated for 2 h. Formazan crystals produced from viable cells were re-dissolved in dimethyl sulfoxide (DMSO). The absorbance at 550 nm was evaluated using a microplate reader .To evaluate cell viability, cells were dispensed in a 96-well plate (2 × 105 cells/well for 24 h. The FFA mixture (500 μM) with cell extract of L. fermentum MG4294 and L. plantarum MG5289 in DMEM was treated in each well, overnight. Thereafter, the medium was removed, washed by phosphate-buffered saline (PBS), and cells were fixed using 10% formaldehyde at 25 °C. The fixed cells were washed with distilled water and stained with Oil Red O staining reagent for 15 min. The stained cells were washed with tertiary distilled water, and the stained lipids were eluted from the cells with isopropanol (200 μL). The absorbance (510 nm) was measured using a microplate reader (BioTek).For Oil Red O staining, HepG2 cells were cultured in a 12-well plate at 2 × 10C57BL/6 male mice (5 weeks old) were used in this study . All mice were housed in an environment with a controlled temperature of 21 ± 2 °C and humidity of 50 ± 20% under a 12 h light/dark cycle. During the experimental period, diet and drinking water were fed by free intake. All animals were approved by the Institutional Animal Care and Use Committee of the NDIC .n = 6 per group) as follows: (1) normal (ND) group, (2) HFD group, (3) HFD + MG4294 (1 × 109 CFU/head/day) group, and (4) HFD + MG5289 (1 × 109 CFU/head/day) group. The mice were administered probiotics via oral gavage and were provided with 60% HFD feed for 12 weeks. Their body weights were checked once a week to observe the condition of the experimental animals. After 12 weeks, all animals were sacrificed by inhalational anesthesia with isoflurane, and their blood was drawn through the abdominal vena cava.The experimental schedule is shown in The blood collected from the abdominal vena cava was divided into 0.6 mL SST microtainer tubes , and the blood contained in the SST tubes was completely solidified. Each tube was centrifuged at 4 °C at 12,000 rpm for 2 min, and serum was collected. The liver was immediately harvested, rinsed with physiological saline solution, weighted, and stored at −70 °C until analysis. The triglyceride (TG), total cholesterol (TC), HDL—cholesterol (HDL—C), LDL—cholesterol (LDL—C), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and glucose of the serum were analyzed by blood chemistry analyzer . Hepatic TG  and TC  were evaluated by commercial kits. The absorbance was measured at 450 nm .The liver tissue was fixed with 10% neutral buffered formalin and embedded in paraffin. Prepared paraffin blocks were cut into sections using a microtome  to a thickness of 4 μM. After removing the paraffin with xylene and 100–70% ethanol, the sections were stained with hematoxylin and eosin . Sections were examined under a light microscope  and photographed at 50 × magnification. The non-alcoholic fatty liver disease activity score (NAS) was performed using the previous report .g for 10 min at 4 °C), and a supernatant was used. Tumor necrosis factor (TNF)-α and interleukin (IL)-1β, IL-6 in colon tissue were analyzed by enzyme-linked immunosorbent assay kits . The absorbance was measured at 450 nm (BioTek).The colon tissue in Tissue Extraction Reagent I  was homogenized using BeadBug™ 6 . The homogenates were centrifuged . The liver tissues were homogenized and prepared in Tissue Extraction Reagent I (Invitrogen) containing phosphatase and protease inhibitors . Cell and tissue lysates containing equal amounts of total proteins were determined using the Take3™ Multi-Volume Plate (Biotek) and Bradford assay (Gendepot).Western blot was performed as previously reported . The proL. fermentum MG4294 and L. plantarum MG5289 was measured using ETEST , which is mainly used for gradient strip tests [Antibiotic susceptibility test to determine the minimum inhibitory concentrations (MIC) of ip tests . ETEST wL. fermentum MG4294 and L. plantarum MG5289 were plated on tryptic soy agar  containing 5% sheep blood  and cultured at 37 °C. After 48 h, hemolysis was reported according to the color of the ring around the bacterial colony [The l colony .L. fermentum MG4294 and L. plantarum MG5289 were inoculated on MRS agar containing 0.5% (w/v) sodium glycodeoxycholate and 0.5% (w/v) taurodeoxycholate and incubate for 48 h, at 37 °C. BSH activity was judged by the formation of precipitates around the colonies. [olonies. p < 0.05 using the Statistical Package for the Social Sciences (SPSS) software .All data were indicated by the mean ± standard error of the mean (SEM). The significant difference was performed through a one-way analysis of variance (ANOVA) followed by Fisher’s Least Significant Difference (LSD) test with L. fermentum MG4294 and L. plantarum MG5289 treated with FFA in HepG2 cells, there was no significant change  and FAS (p < 0.01) was increased, and p-AMPK (p < 0.05) was significantly reduced by the FFA induction in HepG2 cells. The L. plantarum MG5289 significantly decreased the expression of SREPB1c (p < 0.05) and FAS (p < 0.01) and increased the p-AMPK; however, L. fermentum MG4294 significantly reduced only the expression of FAS (p < 0.05).To confirm the effect of measured . ExpressL. fermentum MG4294 and L. plantarum MG5289 treated groups was less than that of the HFD group, respectively (L. fermentum MG4294 and L. plantarum MG5289 treated groups showed a significant decrease in weight gain by 22.93% (p < 0.01) and 18.91% (p < 0.05) compared to the HFD group . In serum TG levels, L. fermentum MG4294 and L. plantarum MG5289 treated groups slightly decreased by 7.27% and 2.64%, compared to the HFD group  but had no effect on HDL-C levels  compared to the HFD group  and hepatic TC (p < 0.001 and p < 0.01).The liver weight of the FD group b. As a rL. fermentum MG4294 and L. plantarum MG5289 is involved in the expression of proteins-related lipogenesis and cholesterol regulation in liver tissue in HFD-induced mice . In contrast, L. fermentum MG4294 and L. plantarum MG5289 treated groups had significantly decreased levels of TNF-α (p < 0.01 and p < 0.001), IL-1β (p < 0.05 and p < 0.01), and IL-6 (p < 0.001) in intestinal tissues of HFD-induced mice.The effect of treatments of analysis . In the As a result of antibiotic resistance confirmation of the selected strains, both strains had an antibiotic resistance lower than the antibiotic resistance limit of the European Food and Drug Administration (EFSA), confirming that they are safe strains that do not have antibiotic resistance .L. fermentum MG4294 and L. plantarum MG5289, both strains have no hemolytic activity (γ-hemolysis) and no BSH precipitation, proving that they are safe for the host when ingested (As a result of confirming the hemolytic activity of ingested .L. plantarum LC27, Bi. longum LC67, and Lc. lactis inhibit NAFLD by regulating the intestinal microflora, and L. rhamnosus GG and L. paracasei N1115 showed efficacy against NAFLD by regulating intestinal inflammatory cytokines and tight junctions [L. acidophilus, L fermentum, L. paracasei, and L. plantarum significantly improved the disease state in patients with NAFLD [L. fermentum MG4294 and L. plantarum MG5289 have on NAFLD on the FFA-induced HepG2 cells and HFD-induced mice model.NAFLD is the most common liver disease, with a very high prevalence estimated to affect more than 25% of the world population, including 1/3 of the population in the United States and Asia ,20. NAFLunctions ,31,32,33th NAFLD . In addith NAFLD . TherefoL. fermentum MG4294 and L. plantarum MG5289 markedly improved weight gain and lowered AST, ALT, and LDL/HDL in serum, demonstrating the lipid improvement effect in serum. In addition, the consumption of HFD exhibits hepatic steatosis, a typical histopathological feature of NAFLD [L. fermentum MG4294 and L. plantarum MG5289 on the degree of adipogenesis in liver tissue, it was visually observed that lipid droplets were reduced. These results were confirmed to be consistent with the results of suppressing the lipid accumulation of L. fermentum MG4294 and L. plantarum MG5289 in HepG2 cells in which the formation of lipid droplets was induced by FFA. In addition, hepatic TG, and TC in mice, which act as major indicators for NAFLD, are increased by HFD [L. fermentum MG4294 and L. plantarum MG5289 were fed to mice with NAFLD induced by HFD, significant decreases in hepatic TG and TC were observed.The HFD-induced mouse can be used as a preclinical model mimicking the metabolic and histological features of human NAFLD . Intake of NAFLD . As a red by HFD . As a reL. fermentum MG4294 and L. plantarum MG5289. SREBP1 is involved in lipid homeostasis in the liver and increases the protein expression of FAS [L. fermentum MG4294 and L. plantarum MG5289 significantly decreased the expression of SREBP1 and FAS in the liver of HFD-induced mice. Particularly, L. plantarum MG5289 reduced the expression of SREBP1 in FFA-induced HepG2 cells. In the liver, AMPK is activated by phosphorylation, which plays the role of a metabolic key in the regulation of both adipogenesis and lipogenesis [L. fermentum MG4294 and L. plantarum MG5289 reversed the level lowered by HFD. The increased expression of p-AMPK by L. fermentum MG4294 and L. plantarum MG5289 was similarly confirmed in FFA-induced HepG2 cells. Therefore, it is found that administration of L. fermentum MG4294 and L. plantarum MG5289 reduced the expression of SREBP1 and FAS related to TG formation and suppressed the expression of PPARγ and C/EBPα, which are adipogenesis-related proteins, through phosphorylation of AMPK in the liver of HFD-induced mice, thereby affecting NAFLD. In addition, as previously reported, HFD induces dysbiosis and increased intestinal epithelial permeability, leading to intestinal inflammatory cytokines, which enter the liver through the hepatic portal vein and cause liver damage [L. fermentum MG4294 and L. plantarum MG5289. These results suggest that L. fermentum MG4294 and L. plantarum MG5289 can alleviate NAFLD by regulating the gut–liver axis.The major proteins involved in adipogenesis in the liver are PPARγ and C/EBPα . Overexpn of FAS . Overexpn of FAS . Adminisr damage ,28. InteL. fermentum MG4294 and L. plantarum MG5289 were resistant to 16 commonly used antibiotics, it was confirmed that they met all MICs according to the EFSA guidelines. Because some pathogens cause hemolysis and lysis of red blood cells in humans, avoiding hemolysis is crucial for safety [L. fermentum MG4294 and L. plantarum MG5289 had no activity, confirming safety. Precipitation of BSH is caused by hydrolase activity, which can be potentially harmful to the human host by causing DNA damage, promoting colon cancer, and forming gallstones [L. fermentum MG4294 and L. plantarum MG5289 did not form a white ring, confirming that they were inactive.Probiotics have traditionally been used as food and recognized as safe . Howeverr safety . As a rellstones . L. fermL. fermentum MG4294 and L. plantarum MG5289 improve liver steatosis via AMPK signaling pathway in cell and animal models. However, it is unclear whether it has the same efficacy in humans. Therefore, further studies, such as clinical trials, are needed.In summary, we confirmed that L. fermentum MG4294 and L. plantarum MG5289 on NAFLD in vitro and in vivo. L. fermentum MG4294 and L. plantarum lowered the possibility of NAFLD exacerbation by suppressing intestinal inflammatory cytokines and activated AMPK phosphorylation in liver tissue to regulate the expression of proteins involved in lipid and cholesterol synthesis (L. fermentum MG4294 and L. plantarum MG5289 were safe. Based on the results of this study, L. fermentum MG4294 and L. plantarum MG5289 are safe probiotics and can be used as functional food or pharmaceutical materials to improve NAFLD.This study demonstrated the effect of ynthesis . In addi


Correction to: Japanese Journal of Radiology (2023) 10.1007/s11604-023-01400-7In the original publication, part b in caption of Fig. 5 should read as:Sagittal T1WI shows hyperintensity anteriorly and mildly hypointensity posteriorly.Reference 2 should read as:WHO Classification of Tumours Editorial Board. Endocrine and Neuroendocrine tumours [Internet].https://tumourclassification.iarc.who.int/chapters/53.Lyon (France): International Agency for Research on Cancer; 2022 [cited 2022 Nov 1]. . The original publication has been corrected.


The intensity of eddy diffusivity and the spatial average of water velocity at the depths of the water column in oceans and lakes play a fundamental role in phytoplankton production and phytoplankton and zooplankton biomass, and community composition. The critical depth and intensity of turbulent mixing within the water column profoundly affect phytoplankton biomass, which depends on the sinking characteristic of planktonic algal species. We propose an Nutrient-Phytoplankton-Zooplankton (NPZ) model in 3D space with light and nutrient-limited growth in a micro-scale ecological study. To incorporate micro-scale observation of phytoplankton intermittency in bloom mechanism in stationary as well as oceanic turbulent flows, a moment closure method has been applied in this study. Experimental observations imply that an increase in turbulence is sometimes ecologically advantageous for non-motile planktonic algae. How do we ensure whether there will be a bloom cycle or whether there can be any bloom at all when the existing phytoplankton group is buoyant, heavier, motile, or non-motile? To address these questions, we have explored the effects of critical depth, the intensity of eddy diffusivity, spatial average of water velocity, on the concentration as well as horizontal and vertical distribution of phytoplankton and zooplankton biomass using a mathematical model and moment closure technique. We quantify a critical threshold value of eddy diffusivity and the spatial average of water velocity and observe the corresponding changes in the phytoplankton bloom dynamics. Our results highlight the importance of eddy diffusivity and the spatial average of water velocity on seasonal bloom dynamics and also mimic different real-life bloom scenarios in Mikawa Bay (Japan), Tokyo Bay (Japan), Arakawa River (Japan), the Baltic Sea, the North Atlantic Ocean, Gulf Alaska, the North Arabian Sea, the Cantabrian Sea, Lake Nieuwe Meer (Netherlands) and several shallower lakes. The occurrence of phytoplankton bloom gets triggered by a rapid increment in the spatial distribution of phytoplankton density. Fundamentally speaking, regional and seasonal changes influence environmental factors like solar irradiance and water salinity, which cause irregularities in phytoplankton growth. The nature of dominating or existing plankton classes changes with regional and seasonal changes. Different phytoplankton classes possess different traits, most notably size, shape, specific rate of growth, and behavior, such as the nature of floating or sinking, which all together determine their dynamical behavior in an aquatic ecosystem.The impact of the physical mixing process on the interaction of phytoplankton species is immense4. Changes in turbulent mixing cause a significant shift in the existing phytoplankton community. Increase in the oceanic mixed layer depth negatively affects the leading light available to planktonic algae and the sedimentation loss rates of sinking phytoplankton, resulting in an overall decrease in phytoplankton growth with increasing mixing depth5. While buoyant species mostly float upwards during weak mixing, intense mixing causes species to dive into a deeper zone. However, significant change in the intensity of vertical mixing triggers the transportation of nutrients and other essential ingredients required for photosynthesis from the surface layer to a higher depth. Nutrient enrichment positively affects nutrient availability and phytoplankton growth, resulting in persistence of sinking species into deeper zones, which initiates the chances of bloom development at higher depths. The nature of bloom gets dominated by the nature of mixing, which varies inversely with surface water density and stability. When it comes to the density of surface water, variation in temperature, humidity, and salinity play a role, and these components vary with seasonal and regional changes, as a result of which, intensity of turbulent mixing varies along with the nature of bloom gets decided. So, the role of vertical eddy diffusivity is quite important in bloom development at higher depths.Eddy diffusion exerts an intriguing impact on the spatial distribution of plankton species along horizontal and vertical directions for any turbulent aquatic environment4.The other two factors that play essential roles in bloom initiation, either on the surface layer or at higher depths, are the swimming and sinking velocity of existing species, which get decided by the nature of the existing class. Generally, in the presence of weak mixing, if the swimming velocity of the existing community dominates over sinking velocity, then the existing community floats away instead of diving into a deeper zone; such circumstances trigger the chances of surface bloom. On the other hand, if existing species have a sinking tendency, then generally, bloom occurs at higher depth, which has been observed for several species on a global scale6. Measurements of phytoplankton distributions have improved with the advancement of modern technologies. Considering recent observations at finer resolution, the in situ spatial patterns become increasingly important for understanding the mechanisms that create and support the micro-scale structure and ecology of marine ecosystems7.The biological interactions between oceanic populations in turbulent flows drive plankton dynamics and cycling of nutrients, with impacts expected to be accumulative across multiple scales8 are capable of measuring high-resolution data of micro-scale planktonic ecosystem. Such advancement in technologies capture acute dynamical phenomena and in fact, this measurement has exhibited that spatial variability becomes rapidly irregular as we move from meso-scale to micro-scale distribution. Under such circumstances, inclusion of moment closure methodology on ecological modeling dynamics helps in a better understanding of phytoplankton bloom mechanism, since it captures dynamical nonlinearity acting on spatial variability of micro-scale observation.The influence of both physical and biological processes on phytoplankton dynamics from mesoscale down to microscale, as revealed by the high-resolution instruments, must be considered for a mechanistic understanding of plankton ecosystems. Therefore, further theoretical developments and new modelling tools are required to understand the observed small-scale vertical structure and its relationship to ecosystem behaviour. Intense intermittency in plankton distributional dynamics is observed pervasively in marine ecology, mostly when high-resolution instruments such as microstructure profiling fluorometers, water samplings, FIDO-A conventional study with closure approach can provide some dynamical understanding of phytoplankton bloom dynamics, still certain unresolved queries sustain, for example, how the NPZ closure model behaves with physics  in a turbulent medium in comparison with the conventional NPZ model. To understand the impact of physics on dynamical behaviour of NPZ ecosystem, we have explored the effects of critical depth, the intensity of eddy diffusivity, spatial average of water velocity, on the concentration as well as horizontal and vertical distribution of phytoplankton and zooplankton biomass using a 3D biophysical NPZ closure model to visualize the impact of sub-scale variability coupled with physical transport.16.Our study is designed to exhibit mathematical facts to illustrate the bloom cycle, surface bloom, bloom in higher depth, and no bloom at all, for micro-scale dynamics in both turbulent and stationary aquatic environments. The goal of this study is to provide a mathematical understanding behind all possible patterns of seasonal bloom dynamics based on a newly developed mathematical understanding in the micro-scale analysis, for example, why the surface bloom of any phytoplankton species, whether buoyant or heavier, occurs during the wind mixing period in the warmer season without any artificial mixing; why bloom in higher depths is most common in winter, without wind mixing; what triggers red-tide formation in the windy zone and, what is the impact of artificial mixing in bloom dynamics for low grazing zones? Our results highlight the importance of eddy diffusivity and the spatial average of water velocity on seasonal bloom dynamics and also mimic different real-life bloom scenarios in Mikawa Bay (Japan), Tokyo Bay (Japan), Arakawa river (Japan), the Baltic sea, the North Atlantic Ocean, Gulf Alaska, the North Arabian Sea, the Cantabrian Sea, Lake Nieuwe Meer (Netherlands) and several shallower lakesAccording to several observations regarding phytoplankton bloom occurrences in a global set of aquatic environments Table , we initN, P, Z describing a three dimensional aquatic ecosystem, given by(i)H is the depth of the layer within the water column at which irradiance I is measured17,light limited growth indicated by term (ii)nutrient limited growth, indicated by Holling type II functional response, In this model, u, v are the horizontal water velocities, w is the vertical water velocity, s) and cross thermocline exchange . Hence, the statistics of the fluctuating components is independent of the direction of sampling (isotropic). Therefore, the spatial average of each fluctuating component is zero at any particular time, that is, u, v, w are fluctuating components in space and time, whereas During the studies of the distribution of microscale phytoplankton using high-resolution profiling fluorometers, it has been observed that the local fluorescence values are highly fluctuating in space by Eqs.  does notx, y, z.Substituting –5 in 1)1), and aThree terms on the left hand side of the above equation namely, Considering 20 and first derive transport equations for N and accordingly for P and Z. Once these equations are derived, we note thatTo construct a closure for these quantities, we follow the principle of second order turbulence closure modelling21, and obtainThe consequent application of this derivation would result in vertical turbulent flux divergence formulations for the second order moments Using , and ass5. We have ignored the third and higher order fluctuating terms to obtain simple closure.We have assumed that the random variables Equations –12 repreB is the variance of the sum of all fluctuating components. Therefore, V provided in supplementary information (SI). All dimensionless parameters and variables are defined in Table Summing up first three equations of the above system providesine Eqs. –18 can bentclass1pt{minimaLet, S is matrix of order S and the Jacobian (J) of the linearized closure system about the point We now substitute, We now obtain the characteristic equation of the linearized closure system as We now consider a particular set of parameter values (to be estimated) representing a particular circumstantial scenario and a suitable set for amplitudes S. Thus, Let us assume that, for a considered set, We now proceed with Routh Hurwitz stability criteria to find a domain of mentclass2pt{minimh is the length scale along x, y directions and l is the depth scale along z direction, in particular, we have considered a particular cubic part of an aquatic system having volume To conserve the property of closure model, zero flux boundary condition has been introduced to the system. While non-dimensionalizing the system we have considered According to zero-flux boundary condition, rate of change of model variables along vertical and horizontal directions remains zero at boundary points implyingBesides, in this model it is assumed that only phytoplankton community has swimming and sinking velocities, hence 22, namely,The phytoplankton species existing in higher depth should have access to essential components like solar irradiance for the continuation of photosynthesis, and the availability of such ingredients depends on the value of the depth System 1: spatial average of water velocity is zero ((ii)System 2: spatial average of water velocity along with turbulent diffusion is non-zero  are heterogeneously distributed39. The majority of the phytoplankton classes are flagellated and, therefore, motile. Marine pelagic plankton such as ciliates, flagellates, and copepods exhibit a wide variety of motility patterns such as straight line and helical swimming, darting motion, etc. Cells move to new locations to seek more favorable environments or to interact with other organisms. Along with other variety of cues, including their encounter with other individuals , or environmental conditions such as light40, temperature, etc., these complex motility patterns are behavioral responses of plankton, which is controlled by turbulence41, which vary with regional and seasonal changes. Water turbulence significantly affects the distribution of phytoplankton42, and this leads to the concept that the formation of phytoplankton patches depends on the balance between phytoplankton growth rate and nature of eddy diffusivity, which controls the diffusion of the water43.Phytoplankton experience turbulence as an instantaneous, linearly varying fluid velocity across the cell body(i)nature of dominating phytoplankton community,(ii)horizontal and vertical eddy diffusivity,(iii)spatial average of water velocity,(iv)critical depth ((v)grazing impact.We now consider the case of occurrence of bloom at a higher depth (i)when vertical eddy diffusivity ((ii)when the vertical component of the spatial average of water velocity dominates over the corresponding horizontal component , in the presence of high vertical mixing caused by higher vertical eddy diffusivity mentclass2pt{minimOn the other hand, in the absence of a spatial average of water velocity, the chance of bloom formation at a higher depth  to high (yellow) observed in horizontal distribution , and the formation of algal blooms in low-nutrient surface waters is therefore possible48. Our study captures the dinoflagellates blooming on surface water in the spring season (Feb/March) in the southern Baltic Sea caused by water warming in the absence of extreme wind events10. The Baltic Sea is calm primarily during spring and summer, falling under system 1. As spring or summer arrives, water temperature increases due to an increment in irradiation, which reduces vertical eddy diffusivity The study suggests that stratification of the water column is generally a prerequisite for most dinoflagellate blooms to develop in temperate areasOur third query, why is the surface bloom of the phytoplankton community so common in nature during summer, spring, or autumn?50, which is possible when strong wind mixing period drives surface current to produce high horizontal eddy diffusivity  if the vertical eddy diffusivity is weak enough in the well-lit surface water for phytoplankton to receive sufficient light before being properly mixed beneath the critical depth or (ii) an intensely mixed surface layer shoals exposes phytoplankton to favorable light conditions54. This result also mimics the fact that the buoyant phytoplankton community “Microcystis” developed a surface bloom during July 1992 in Lake Nieuwe Meer15.As far as system 1  is concerned, the absence of surface winds cannot trigger internal waves to inherit any horizontal turbulence. Hence, vertical eddy diffusivity is the only controlling factor for such a system, which acts on turbulent mixing and determines the nature of the planktonic distribution. When externally induced artificial mixing does not cause an imbalance in the nature of stationary flow, variation of vertical eddy diffusivity depends mostly on water density and temperature. During late spring, summer, and early autumn, high incident solar irradiance causes extreme thermal stratification to subjugate the density of water among water layers, resulting in reduced vertical eddy diffusivity Microcystis” at higher depth in Lake Nieuwe Meer during initial days in the presence of artificial mixing15. In fact, the hypothesis stated in49 is supported by our numerical findings and the corresponding real-life occurrence regarding phytoplankton spring bloom initiation in an arbitrarily deep layer.Nevertheless, for stationary flow, phytoplankton bloom of buoyant class can also be initiated in an arbitrarily deeper layer Fig. d above c<Dc Fig. d. This eWe now explain how controls the nature of the bloom of existing phytoplankton species if its sinking tendency dominates its buoyancy in system 1?Cladocerans dominated by the Daphnia hyalina-galeata complex) as mentioned in16.Fundamentally speaking, the heavier phytoplankton community sinks deeper, accelerating in the presence of higher vertical eddy diffusivity during the cold season. In general, strong vertical eddy diffusivity facilitates mixing, intensifying the possibility of bloom development of the heavier community at higher depth during that period Fig. g. Thoughnts Fig. e and higmentclass2pt{minimOur next analysis answers the question what drives surface bloom formation of buoyant phytoplankton community in a turbulent marine environment?Wind-induced horizontal turbulence is one of those critical factors for developing surface bloom in system 2. In a turbulent environment, wind-driven horizontal currents deliver deep nutrient-rich water to the surface photic layers. Also, in the presence of weak vertical eddy diffusivity during the warmer season, wind-induced strong horizontal eddy diffusivity , “Ciliate”, “Mesodinium Rubrum” (November 1991: Autumn), etc. in Mikawa Bay, Japan during several periods from April 1989 to Dec 19919.Generally, the effect of turbulent current is different for different phytoplankton species. In particular, the conditions entclass1pt{minimades Fig. h  in the absence of any intense exchange of oceanic currents  in Mikawa Bay, Japan9.This bloom cycle has been numerically captured in Fig. ain Fig. c, and waWe conclude the discussion by answering our final question, how does grazing control surface bloom dynamics of phytoplankton on two opposite sides of critical vertical eddy diffusivity for the almost calm aquatic environment?12.Grazing by herbivorous zooplankton plays a vital role in both scenarios, whether the bloom occurs on the surface layer or at a higher depth. For a fixed region with seasonal changes, the breeding and reproduction of herbivorous zooplankton generate differences in the overall population density of the grazing community, which controls the overall density of the phytoplankton community. In fact, at a particular time, this situation might arise with regional differences. As discussed in case SB3, whenever critical horizontal eddy diffusivity gets dominated by existing wind-induced horizontal eddy diffusivity in a turbulent flow, where the spatial average of intense horizontal velocity dominates its critical value, the chance of surface bloom development increases. Surface bloom under such circumstances can only be developed if and only if grazing impact remains negligible surface bloom of buoyant species will mostly occur during a warmer season without any artificial or wind-induced mixing. Even in the presence of artificial mixing, a surface bloom of buoyant species can occur when vertical eddy diffusion mentclass2pt{minim(ii)But, if artificial mixing or cold weather influences the aquatic environment of the lake, pond, or almost stationary aquatic flow and affects the stability of the water column, then most bloom will occur at higher depths in the presence of low grazing depending on two facts (a) dominance of mentclass2pt{minimAs far as turbulent flow is concerned (system 2), we obtain the following facts regarding bloom formation in the presence of low grazing influence, (i)when wind-induced horizontal turbulent diffusion mentclass2pt{minim(ii)as winter season passes by and spring approaches during no wind mixing period, if thermal stratification results in entclass1pt{minimaentclass1pt{minima(iii)if in the presence of artificial mixing or during cold weather, when existing vertical turbulent diffusivity and the spatial average of water velocity dominate corresponding critical values in system 2 , we may conclude the following facts regarding phytoplankton bloom dynamics in the presence of low grazing impact, Supplementary Information.


Due to COVID-19, the spread of diseases through air transport has become an important issue for public health in countries globally. Moreover, mass transportation (such as air travel) was a fundamental reason why infections spread to all countries within weeks. In the last 2 years in this research area, many studies have applied machine learning methods to predict the spread of COVID-19 in different environments with optimal results. These studies have implemented algorithms, methods, techniques, and other statistical models to analyze the information in accuracy form. Accordingly, this study focuses on analyzing the spread of COVID-19 in the international airport network. Initially, we conducted a review of the technical literature on algorithms, techniques, and theorems for generating routes between two points, comprising an analysis of 80 scientific papers that were published in indexed journals between 2017 and 2021. Subsequently, we analyzed the international airport database and information on the spread of COVID-19 from 2020 to 2022 to develop an algorithm for determining airport routes and the prevention of disease spread (DetARPDS). The main objective of this computational algorithm is to generate the routes taken by people infected with COVID-19 who transited the international airport network. The DetARPDS algorithm uses graph theory to map the international airport network using geographic allocations to position each terminal (vertex), while the distance between terminals was calculated with the Euclidian distance. Additionally, the proposed algorithm employs the Dijkstra algorithm to generate route simulations from a starting point to a destination air terminal. The generated routes are then compared with chronological contagion information to determine whether they meet the temporality in the spread of the virus. Finally, the obtained results are presented achieving a high probability of 93.46% accuracy for determining the entire route of how the disease spreads. Above all, the results of the algorithm proposed improved different computational aspects, such as time processing and detection of airports with a high rate of infection concentration, in comparison with other similar studies shown in the literature review. One of the well-known cases was the 2014 Ebola outbreak, which started in West Africa and caused more than 11,000 deaths worldwide .The main objectives of this research are as follows:This type of study is essential for helping the decision-making of governments and public health organizations to reduce the transmission of highly contagious diseases in the population. In addition, this article proposes an essential tool for preventing future pandemics in an automatized way. Finally, this study opens a crucial issue: the application of data science, biology, medicine, and government management to prevent the spread of infectious diseases.The rest of this document is organized as follows “Related work” describes related work, which serves as a fundamental basis for developing the proposal. In “Materials”, the study materials are presented, wherein the databases used for developing the proposal are detailed. “COVID-19 spread analysis” presents the proposed algorithm, with its structure and operation, while the results obtained and comparisons with other relevant works are presented in “Results and discussion”. Finally, the conclusions and future work identified in the research are presented in “Conclusions and future work”.We conducted, a bibliographic study on the most commonly used algorithms for the generation of routes in air terminals, with a special emphasis on the spread of diseases. The search was restricted to articles published in the last 5 years (2017–2021), generating 80 articles from the following publishers: Springer (18 articles), Wiley Online Library (one), Elsevier (24 articles), IEEE (26 articles), Nature (one article), Acm (four articles), and Mdpi (six articles). The articles were then classified according to the type of contribution: conferences (60 articles) and journals (20 articles).The application of evolutionary algorithms (EAs) has been widespread in the specialized literature on the generation of routes. The study by A study carried out by The algorithm used real data from the Recently, In the latest study conducted in this field by Dijkstra’s algorithm (DJK) uses graphs to determine the shortest path based on the weight of each edge. An example application of this algorithm was presented by An interesting article in the same area was presented by Finally, In another work on neural networks by Similarly, Finally, As an example of the traveling salesman problem (TSP), Similarly, In addition, Finally, Along similar lines, Finally, Similarly, i.e., speed, direction, and angular movement). The results demonstrated that the model could generate excellent solutions for predicting trajectories with high accuracy. Similarly, Fuzzy logic (FL) is widely applied to solve problems involving shortest routes, as exemplified by Similarly, In the same area of fuzzy logic, Finally, The main objective of these works was to find the shortest route through some methodologies and then compare them (or establish rules) to improve the air transport system in a pandemic situation. Many of these studies ended in the analysis phase and focused mainly on improving the air transport system. However, they did not generate routes for the prevention and spread of diseases. The main difference between the studies reported in the literature analysis and the research presented here is that their objective was to determine different optimal routes between airports.In In this section, details of the materials used for the development of this study are presented . In addition, we used the database of COVID-19 infections since the beginning of the pandemic in 2020, and its space-time (geographic location and date the infection was reported) spread until 2022.The airport database includes information from all international air terminals worldwide. This dataset was compiled by the International Air Transport Association (IATA) . The datThe distribution of existing international airports worldwide, classified by continent, where Asia (26.75%), Europe (34.85%), America (21.90%), Oceania (3.90%), and Africa (12.57%). The most significant number of international airport terminals are concentrated in Asia and Europe, which constitute 61.60% of the total number of international airports.The top 10 countries with the most significant number of international airports are China, which has the highest number of international airports (75), followed by the United States (71), Russia (65), France (44), Italy (32), United Kingdom (32), Germany (31), Japan (31), Mexico (30) and Spain (30).The top 10 countries with the highest number of international air routes are: The United Kingdom has 1,776 routes, followed by Germany with 1,503 routes, the United States with 1,417 routes, Spain with 1,410 routes, Italy with 1,266 routes, France with 1,231 routes, China with 990 routes, Greece 848 routes and Turkey 670 routes.With this information, we could describe the connections between each of the international terminals, as well as their distance and exact position.This data repository collects information on the global spread of COVID-19 and was collected by the Center for Systems Science and Engineering at Johns Hopkins University . The datThe number of COVID-19 infections is categorized by continent, where America is the one with the highest number of positive cases , followed by Europe , Asia , Africa , Oceania .The top 10 countries with the highest infection rates are The United States , followed by India , Brazil , United Kingdom , France , Russia , Turkey , Germany , Italy , and Spain .This section, presents an analysis of the spread of the COVID-19 virus, where the network of air terminals was designed by applying graph theory. Subsequently, multiple route simulations were carried out by applying Dijkstra’s algorithm to identify the terminals where the routes of spread of the virus were concentrated. These were compared with chronological data of the infections.This section describes the creation of an interconnected network of international air terminals (airports) by applying graph theory . The airHere G has 1,297 vertices  and 26,914 distances between the vertices .Graph G from WHU to eight different airports around the world.G with international air terminals, where each line represents a distance V, from WHU to eight different airports in the continents of America, Europe, Asia, and Oceania.In this section, the generation of routes by applying Dijkstra’s algorithm  is descrorithm 1  describeThese simulations were performed from the terminal of the city with the first reported COVID-19 case in WUH  algorithm, as defined by Algorithm 2 , was devorithm 1  from theTo perform the simulations, we worked with 26.915 records from the IATA database and 49.069 records from the COVID-19 dataset. Testing was conducted using MATLAB R2020b version 9.0 on an INTEL Core i7 (6th Gen) CPU with 12.0 GB of RAM running on Windows 10 64-bit OS and a 2.0 GB NVIDIA GPU (GTX-950).The simulation results are presented in As shown in the The busiest international air terminals for each continent were also determined, as shown in WHU to Amsterdam (Netherlands) and Frankfurt (Germany). In each of the simulations, two routes based on the chronology of the temporal spread of COVID-19 are presented in (WUH) WHU-Tianhe International Airport (Group 1)(DBX) Dubai International Airport (Group 1)(ADD) Bole Addis Ababa International Airport (Group 4)(CAI) Cairo International Airport (Group 2)(AMS) Amsterdam Airport (Group 3)Route 1: WUH–DXB–ADD–CAI–AMS(WUH) WHU-Tianhe International Airport (Group 1)(IST) Istanbul Airport (Group 4)(AMS) Amsterdam Airport (Group 3)Route 2: WUH–IST–AMS(WUH) WHU-Tianhe International Airport (Group 1)(DXB) Dubai International Airport (Group 1)(KGL) Kigali International Airport (Group 4)(DLA) Douala International Airport (Group 4)(LOS) Murtala Muhammad International Airport (Group 3)(FRA) Frankfurt Airport (Grupo 1)Route 3: WUH–DXB–KGL–DLA–LOS–FRA(WUH) WHU-Tianhe International Airport (Group 1)(IST) Istanbul Airport (Group 4)(FRA) Frankfurt Airport (Group 1)Route 4: WUH–IST–FRA(WUH) WHU-Tianhe International Airport (Group 1)(DXB) Dubai International Airport (Group 1)(CAI) Cairo International Airport (Group 2)(IST) Istanbul Airport (Group 4)Route 5: WUH–DXB–CAI–IST(WUH) WHU-Tianhe International Airport (Group 1)(DXB) Dubai International Airport (Group 1)(IST) Istanbul Airport (Group 4)Route 6: WUH–DXB–IST(WUH) WHU-Tianhe International Airport (Group 1)(IST) Istanbul Airport (Group 4)(CAI) Cairo International Airport (Group 2)(DXB) Dubai International Airport (Group 1)Route 7: WUH–IST–CAI–DXB(WUH) WHU-Tianhe International Airport (Group 1)(DXB) Dubai International Airport (Group 1)Route 8: WUH–DXB(WUH) WHU-Tianhe International Airport (Group 1)(DXB) Dubai International Airport (Group 1)(CAI) Cairo International Airport (Group 2)(CMN) Mohammed V International Airport (Group 4)Route 9: WUH–DXB–CAI–CMN(WUH) WHU-Tianhe International Airport (Group 1)(DXB) Dubai International Airport (Group 1)(CMN) Mohammed V International Airport (Group 4)Route 10: WUH–DXB–CMN(WUH) WHU-Tianhe International Airport (Group 1)(IST) Istanbul Airport (Group 4)(CAI) Cairo International Airport (Group 2)(ADD) Bole Addis Ababa International Airport (Group 4)Route 11: WUH–IST–CAI–ADD(WUH) WHU-Tianhe International Airport (Group 1)(DXB) Dubai International Airport (Group 1)(ADD) Bole Addis Ababa International Airport (Group 4)Route 12: WUH–DBX–ADD(WUH) WHU-Tianhe International Airport (Group 1) p 1)(FCO) Rome Fiumicino Airport (Group 1)(MAD) Adolfo Suarez Madrid-Barajas Airport (Group 2)(YYZ) Toronto Pearson International Airport (Group 1)Route 13: WUH–FCO–MAD–YYZ(WUH) WHU-Tianhe International Airport (Group 1) (Group 1)(DXB) Dubai International Airport (Group 1)(CAI) Cairo International Airport (Group 2)(YYZ) Toronto Pearson International Airport (Group 1)Route 14: WUH–DXB–CAI–YYZ(WUH) WHU-Tianhe International Airport (Group 1) (Group 1)(DXB) Dubai International Airport (Group 1)(CAI) Cairo International Airport (Group 2)(MAD) Adolfo Suarez Madrid-Barajas Airport (Group 2)(GYE) Jose Joaquin de Olmedo International Airport (Group 4)Route 15: WUH–DXB–MAD–AMS–GYE(WUH) WHU-Tianhe International Airport (Group 1) (Group 1)(FCO) Rome Fiumicino Airport (Group 1)(MAD) Adolfo Suarez Madrid-Barajas Airport (Group 2)(GYE) Jose Joaquin de Olmedo International Airport (Group 4)Route 16: WUH–FCO–MAD–GYE(WUH) WHU-Tianhe International Airport (Group 1)(HKG) Hong Kong International Airport (Group 1)(CMB) Bandaranaike International Airport (Group 1)(SYD) Sydney Airport (Group 1)Route 17: WUH–HKG–CMB–SYD(WUH) WHU-Tianhe International Airport (Group 1)(TPE)Taiwan Taoyuan International Airport (Group 1)(SYD) Sydney Airport (Group 1)Route 18: WUH–TPE–SYD(WUH) WHU-Tianhe International Airport (Group 1)(BKK) Suvarnabhumi International Airport (Group 1)(SYD) Sydney Airport (Group 1)(AKL) Auckland International Airport (Group 3)Route 19: WUH–BKK–SYD–AKL(WUH) WHU-Tianhe International Airport (Group 1)(TPE) Taiwan Taoyuan International Airport (Group 1)(AKL) Auckland International Airport (Group 3)Route 20: WUH–TPE–AKLThe simulation results showed that the length of connections of the routes ranged from two (two simulated routes) to six terminals (one simulated route), with an average of four connections (11 simulated routes). These simulations present the most likely routes  for the spread of COVID-19 since the chronology of the spread is evidenced , as presThe results obtained from the simulations carried out with the DetARPDS algorithm identified probable routes of spread to 10 international air terminals in the five continents around the world, as shown above. These routes contain one or more air terminals with a high audience, where the terminals were detected, as shown in The terminal that appeared in the simulations in greater proportion was Wuhan–Tianhe Dubai International Airport (WHU), for being the point of origin of the propagation, followed by Istanbul Airport—(IST) , which cSome interesting results presented in The results obtained with the DetARPDS algorithm are very similar to the reality of the spread of COVID-19 in the network of airport terminals with 93.46% accuracy (67.534 simulations) in the tests. The 6.53% rest are similar in 47.60% of the routes, where each route had one or more connexions with airports with high infections rate. The literature review found no comparable works where Dijkstra’s algorithm has been applied to COVID-19 in airport networks. The most similar were those presented by The results of the techniques were compared based on the processing time, where the DetARPDS algorithm consumed the maximum time (727.36 s) followed by the MALO algorithm In this research, an extensive literature review was conducted to determine the most widely used algorithms for identifying routes more efficiently. As a result of this scientific review, we could present some algorithms, such as EA, DP, Dijkstra’s algorithm, neural networks, TSP, graph theory, Markov chain, and fuzzy logic. Subsequently, we analyzed the database of the network of international airports and the information on COVID-19 infections in detail. One of the main obstacles in the study was collecting information from each air terminal and its routes since this constitutes considerable information that must be collected manually. The graph theory was eventually selected to graph the airport network, and Dijkstra’s algorithm was employed for the generation of routes to develop DetARPDS.Thanks to the literature review, we could know the framework of reference for the most important algorithms and techniques used in the research area. The first main contribution of the study was the development of a real graph of international airports, which describes the localization, distance, and connexions between all airports around the world. This graph allows knowing all possible routes that the virus will take. The DetARPDS algorithm generates spread routes in a short time near 300 s, where the distance between the terminals is calculated by applying the Euclidean distance between each airport using latitude and longitude.The second contribution was a discrete analysis of the generated routes of the spread of COVID-19. It was an essential tool for determining the possible behavior of virus propagation. With the graph of international airports, all possible routes are generated from the origin of the virus (the city where the first case of COVID-19 was reported) to 10 cities on different continents, applying the Dijkstra algorithm. The processing of this phase of the DetARPDS algorithm was optimal, with results between 120 to 220 s.The third contribution was the creation of a chronology of propagation based on historical data on COVID-19. It allowed identifying the routes with a high probability of infection and the airports with high-risk infection. With high accuracy of 93.46%, the DetARPDS algorithm can also determine all possible routes from departure airport to destination airport terminal. With this information, countries, states, and security institutions will define health safety policies to prevent future diseases of dangerous variants of COVID-19.For future research, it is proposed to incorporate information from other media  to improve the model so that it becomes increasingly closer to reality. Moreover, the application of platforms for the simultaneous execution of tasks would reduce the processing time. Finally, the application of deep neural networks can be used to determine the profiles of travelers infected with the virus.10.7717/peerj-cs.1228/supp-1Supplemental Information 1Click here for additional data file.